{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import rioxarray\n",
    "import json, os\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n",
    "from sklearn.metrics import f1_score, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "verbose = False\n",
    "details = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
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       "y        x                                                          ...   \n",
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       "                     Glyphosate      Mancozeb    Mecoprop-P    Metamitron  \\\n",
       "y        x                                                                  \n",
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       "\n",
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       "y        x                                                                 \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
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       "y        x                                                                      \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "y         x                                                \n",
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       "y         x                                                              \n",
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       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y         x                                                     \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y        x                                                                      \n",
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       "y        x                                                                  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "\n",
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       "y         x                                                \n",
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       "\n",
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       "y         x                                                              \n",
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       "\n",
       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
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       "\n",
       "                    PropamocarbHydrochloride  Prosulfocarb       Sulphur  \\\n",
       "y         x                                                                \n",
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       "\n",
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       "y         x                                   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y         x                                                \n",
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       "375500.0  469500.0                 16                  0   \n",
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       "\n",
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       "y         x                                                              \n",
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       "y         x                                                                \n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
       "      <th>Improve grassland</th>\n",
       "      <th>Neutral grassland</th>\n",
       "      <th>Calcareous grassland</th>\n",
       "      <th>Acid grassland</th>\n",
       "      <th>Fen</th>\n",
       "      <th>Heather</th>\n",
       "      <th>Heather grassland</th>\n",
       "      <th>...</th>\n",
       "      <th>Glyphosate</th>\n",
       "      <th>Mancozeb</th>\n",
       "      <th>Mecoprop-P</th>\n",
       "      <th>Metamitron</th>\n",
       "      <th>Pendimethalin</th>\n",
       "      <th>PropamocarbHydrochloride</th>\n",
       "      <th>Prosulfocarb</th>\n",
       "      <th>Sulphur</th>\n",
       "      <th>Tri-allate</th>\n",
       "      <th>Occurrence</th>\n",
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       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>637500.0</th>\n",
       "      <th>286500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.183865e-01</td>\n",
       "      <td>7.056607e-02</td>\n",
       "      <td>2.228176e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.624027e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784500.0</th>\n",
       "      <th>646500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103500.0</th>\n",
       "      <th>384500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.581972e+01</td>\n",
       "      <td>1.260713e+00</td>\n",
       "      <td>6.174690e+00</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.836491e+01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.129929e+01</td>\n",
       "      <td>4.079481e+00</td>\n",
       "      <td>8.705502e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207500.0</th>\n",
       "      <th>217500.0</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.724084e-01</td>\n",
       "      <td>3.704135e-02</td>\n",
       "      <td>2.476914e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.167010e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129500.0</th>\n",
       "      <th>318500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "637500.0  286500.0                   0                    0       0   \n",
       "784500.0  646500.0                   0                    0       0   \n",
       "103500.0  384500.0                   0                    0      96   \n",
       "207500.0  217500.0                  27                    0      16   \n",
       "1129500.0 318500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "637500.0  286500.0                 61                  0   \n",
       "784500.0  646500.0                  0                  0   \n",
       "103500.0  384500.0                  3                  0   \n",
       "207500.0  217500.0                 21                  0   \n",
       "1129500.0 318500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "637500.0  286500.0                     0               6    0        0   \n",
       "784500.0  646500.0                     0               0    0        0   \n",
       "103500.0  384500.0                     0               0    0        0   \n",
       "207500.0  217500.0                     0               0    0        0   \n",
       "1129500.0 318500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...    Glyphosate      Mancozeb  \\\n",
       "y         x                            ...                               \n",
       "637500.0  286500.0                  0  ...  5.183865e-01  7.056607e-02   \n",
       "784500.0  646500.0                  0  ... -3.400000e+38 -3.400000e+38   \n",
       "103500.0  384500.0                  0  ...  5.581972e+01  1.260713e+00   \n",
       "207500.0  217500.0                  0  ...  1.724084e-01  3.704135e-02   \n",
       "1129500.0 318500.0                  0  ... -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
       "637500.0  286500.0  2.228176e-01 -3.400000e+38   1.624027e-01   \n",
       "784500.0  646500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "103500.0  384500.0  6.174690e+00 -3.400000e+38   1.836491e+01   \n",
       "207500.0  217500.0  2.476914e-01 -3.400000e+38   1.167010e-01   \n",
       "1129500.0 318500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride  Prosulfocarb       Sulphur  \\\n",
       "y         x                                                                \n",
       "637500.0  286500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "784500.0  646500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "103500.0  384500.0             -3.400000e+38  1.129929e+01  4.079481e+00   \n",
       "207500.0  217500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "1129500.0 318500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                      Tri-allate  Occurrence  \n",
       "y         x                                   \n",
       "637500.0  286500.0 -3.400000e+38           0  \n",
       "784500.0  646500.0 -3.400000e+38           0  \n",
       "103500.0  384500.0  8.705502e+00           0  \n",
       "207500.0  217500.0 -3.400000e+38           0  \n",
       "1129500.0 318500.0 -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "281500.0  383500.0                   5                    6      58   \n",
       "757500.0  517500.0                   0                    0       0   \n",
       "1030500.0 136500.0                   0                    0       0   \n",
       "1108500.0 69500.0                    0                    0       0   \n",
       "175500.0  410500.0                   0                    0      94   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "281500.0  383500.0                 19                  0   \n",
       "757500.0  517500.0                  0                  0   \n",
       "1030500.0 136500.0                  0                  0   \n",
       "1108500.0 69500.0                   0                  0   \n",
       "175500.0  410500.0                  3                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "281500.0  383500.0                     0               0    0        0   \n",
       "757500.0  517500.0                     0               0    0        0   \n",
       "1030500.0 136500.0                     0               0    0        0   \n",
       "1108500.0 69500.0                      0               0    0        0   \n",
       "175500.0  410500.0                     0               0    0        0   \n",
       "\n",
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       "y         x                            ...                               \n",
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       "\n",
       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
       "281500.0  383500.0  5.357363e+00  2.858755e+00   8.222045e+00   \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y        x                                                                      \n",
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       "y        x                                                                  \n",
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       "\n",
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       "y        x                                                                 \n",
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       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.728302e-02</td>\n",
       "      <td>1.611009e-02</td>\n",
       "      <td>6.161585e-02</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>3.239392e-02</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055500.0</th>\n",
       "      <th>69500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88500.0</th>\n",
       "      <th>439500.0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.206366e+01</td>\n",
       "      <td>7.950239e+00</td>\n",
       "      <td>2.559340e+00</td>\n",
       "      <td>3.304436e+00</td>\n",
       "      <td>1.104135e+01</td>\n",
       "      <td>3.432077e+00</td>\n",
       "      <td>6.372918e+00</td>\n",
       "      <td>1.880297e+00</td>\n",
       "      <td>4.242424e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "659500.0  356500.0                   0                    0       0   \n",
       "682500.0  186500.0                   0                    0       0   \n",
       "840500.0  247500.0                  61                    0       0   \n",
       "1055500.0 69500.0                    0                    0       0   \n",
       "88500.0   439500.0                   2                    0      54   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "659500.0  356500.0                  0                  0   \n",
       "682500.0  186500.0                  0                  0   \n",
       "840500.0  247500.0                 39                  0   \n",
       "1055500.0 69500.0                   0                  0   \n",
       "88500.0   439500.0                 41                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "659500.0  356500.0                     0               0    0      100   \n",
       "682500.0  186500.0                     0               0    0        0   \n",
       "840500.0  247500.0                     0               0    0        0   \n",
       "1055500.0 69500.0                      0               0    0        0   \n",
       "88500.0   439500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...    Glyphosate      Mancozeb  \\\n",
       "y         x                            ...                               \n",
       "659500.0  356500.0                  0  ... -3.400000e+38 -3.400000e+38   \n",
       "682500.0  186500.0                  0  ... -3.400000e+38 -3.400000e+38   \n",
       "840500.0  247500.0                  0  ...  7.728302e-02  1.611009e-02   \n",
       "1055500.0 69500.0                   0  ... -3.400000e+38 -3.400000e+38   \n",
       "88500.0   439500.0                  0  ...  2.206366e+01  7.950239e+00   \n",
       "\n",
       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
       "659500.0  356500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "682500.0  186500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "840500.0  247500.0  6.161585e-02 -3.400000e+38   3.239392e-02   \n",
       "1055500.0 69500.0  -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "88500.0   439500.0  2.559340e+00  3.304436e+00   1.104135e+01   \n",
       "\n",
       "                    PropamocarbHydrochloride  Prosulfocarb       Sulphur  \\\n",
       "y         x                                                                \n",
       "659500.0  356500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "682500.0  186500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "840500.0  247500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "1055500.0 69500.0              -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "88500.0   439500.0              3.432077e+00  6.372918e+00  1.880297e+00   \n",
       "\n",
       "                      Tri-allate  Occurrence  \n",
       "y         x                                   \n",
       "659500.0  356500.0 -3.400000e+38           0  \n",
       "682500.0  186500.0 -3.400000e+38           0  \n",
       "840500.0  247500.0 -3.400000e+38           0  \n",
       "1055500.0 69500.0  -3.400000e+38           0  \n",
       "88500.0   439500.0  4.242424e+00           0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
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       "      <th>Deciduous woodland</th>\n",
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       "      <th>Arable</th>\n",
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       "      <th>Sulphur</th>\n",
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       "      <td>-3.400000e+38</td>\n",
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      "text/plain": [
       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "202500.0 235500.0                   0                    0       0   \n",
       "857500.0 471500.0                   0                    0       0   \n",
       "963500.0 192500.0                   0                    0       0   \n",
       "49500.0  143500.0                   0                    0       0   \n",
       "884500.0 21500.0                    0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "202500.0 235500.0                  0                  0                     0   \n",
       "857500.0 471500.0                  0                  0                     0   \n",
       "963500.0 192500.0                  0                  0                     0   \n",
       "49500.0  143500.0                  0                  0                     0   \n",
       "884500.0 21500.0                   0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "202500.0 235500.0               0    0        0                  0  ...   \n",
       "857500.0 471500.0               0    0        0                  0  ...   \n",
       "963500.0 192500.0               0    0        0                  0  ...   \n",
       "49500.0  143500.0               0    0        0                  0  ...   \n",
       "884500.0 21500.0                0    0        0                  0  ...   \n",
       "\n",
       "                     Glyphosate      Mancozeb    Mecoprop-P    Metamitron  \\\n",
       "y        x                                                                  \n",
       "202500.0 235500.0 -3.400000e+38 -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "857500.0 471500.0 -3.400000e+38 -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
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       "884500.0 21500.0  -3.400000e+38 -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                   Pendimethalin  PropamocarbHydrochloride  Prosulfocarb  \\\n",
       "y        x                                                                 \n",
       "202500.0 235500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "857500.0 471500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "963500.0 192500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "49500.0  143500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "884500.0 21500.0   -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                        Sulphur    Tri-allate  Occurrence  \n",
       "y        x                                                 \n",
       "202500.0 235500.0 -3.400000e+38 -3.400000e+38           0  \n",
       "857500.0 471500.0 -3.400000e+38 -3.400000e+38           0  \n",
       "963500.0 192500.0 -3.400000e+38 -3.400000e+38           0  \n",
       "49500.0  143500.0 -3.400000e+38 -3.400000e+38           0  \n",
       "884500.0 21500.0  -3.400000e+38 -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "INVASIVE_BIRDS_PATH = 'Datasets/Machine Learning/1km Rasters/Birds'\n",
    "# Use this if using coordinates as separate columns\n",
    "# df_1km = pd.read_csv('Datasets/Machine Learning/Dataframes/1km_All_Birds_DF.csv')\n",
    "\n",
    "# Use this if using coordinates as indices\n",
    "df_1km = pd.read_csv('Datasets/Machine Learning/Dataframes/1km_All_Birds_DF.csv', index_col=[0,1])\n",
    "\n",
    "total_birds = (df_1km['Occurrence']==1).sum()\n",
    "df_dicts = []\n",
    "\n",
    "for file in os.listdir(INVASIVE_BIRDS_PATH):\n",
    "    filename = os.fsdecode(file)\n",
    "    if not filename.endswith('.tif') or filename.endswith('All_Invasive_Birds_1km.tif') :\n",
    "         continue\n",
    "\n",
    "\n",
    "\n",
    "    bird_name = filename[:-4].replace('_', ' ')\n",
    "\n",
    "    bird_dataset = rioxarray.open_rasterio(f'{INVASIVE_BIRDS_PATH}/{file}')\n",
    "    bird_dataset.name = 'data'\n",
    "    bird_df = bird_dataset.squeeze().drop(\"spatial_ref\").drop(\"band\").to_dataframe()\n",
    "\n",
    "    # Check if index matches\n",
    "    if not df_1km.index.equals(bird_df.index):\n",
    "        print('Warning: Index does not match')\n",
    "        continue\n",
    "\n",
    "    bird_df['Occurrence'] = [0 if x == -1 else 1 for x in bird_df['data']]\n",
    "    bird_df = df_1km.drop(columns='Occurrence').join(bird_df.drop(columns='data'))\n",
    "    \n",
    "    bird_dict = {'name' : bird_name, 'dataframe' : bird_df }\n",
    "    df_dicts.append(bird_dict)\n",
    "    display(bird_df.sample(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 1km data before drop: \n",
      " Occurrence\n",
      "0    909231\n",
      "1       769\n",
      "dtype: int64 \n",
      "\n",
      "Barnacle Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    32315\n",
      "1      769\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 1km data before drop: \n",
      " Occurrence\n",
      "0    899853\n",
      "1     10147\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    22937\n",
      "1    10147\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 1km data before drop: \n",
      " Occurrence\n",
      "0    909137\n",
      "1       863\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    32221\n",
      "1      863\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 1km data before drop: \n",
      " Occurrence\n",
      "0    907795\n",
      "1      2205\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 1km data after drop: \n",
      " Occurrence\n",
      "0    30879\n",
      "1     2205\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 1km data before drop: \n",
      " Occurrence\n",
      "0    909554\n",
      "1       446\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 1km data after drop: \n",
      " Occurrence\n",
      "0    32638\n",
      "1      446\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 1km data before drop: \n",
      " Occurrence\n",
      "0    907877\n",
      "1      2123\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 1km data after drop: \n",
      " Occurrence\n",
      "0    30961\n",
      "1     2123\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 1km data before drop: \n",
      " Occurrence\n",
      "0    909706\n",
      "1       294\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 1km data after drop: \n",
      " Occurrence\n",
      "0    32790\n",
      "1      294\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 1km data before drop: \n",
      " Occurrence\n",
      "0    906452\n",
      "1      3548\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 1km data after drop: \n",
      " Occurrence\n",
      "0    29536\n",
      "1     3548\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 1km data before drop: \n",
      " Occurrence\n",
      "0    908990\n",
      "1      1010\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 1km data after drop: \n",
      " Occurrence\n",
      "0    32074\n",
      "1     1010\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 1km data before drop: \n",
      " Occurrence\n",
      "0    890876\n",
      "1     19124\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 1km data after drop: \n",
      " Occurrence\n",
      "1    19124\n",
      "0    13960\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 1km data before drop: \n",
      " Occurrence\n",
      "0    904145\n",
      "1      5855\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 1km data after drop: \n",
      " Occurrence\n",
      "0    27229\n",
      "1     5855\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 1km data before drop: \n",
      " Occurrence\n",
      "0    907354\n",
      "1      2646\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    30438\n",
      "1     2646\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 1km data before drop: \n",
      " Occurrence\n",
      "0    909303\n",
      "1       697\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 1km data after drop: \n",
      " Occurrence\n",
      "0    32387\n",
      "1      697\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 1km data before drop: \n",
      " Occurrence\n",
      "0    908943\n",
      "1      1057\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 1km data after drop: \n",
      " Occurrence\n",
      "0    32027\n",
      "1     1057\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 1km data before drop: \n",
      " Occurrence\n",
      "0    907047\n",
      "1      2953\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 1km data after drop: \n",
      " Occurrence\n",
      "0    30131\n",
      "1     2953\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 1km data before drop: \n",
      " Occurrence\n",
      "0    909496\n",
      "1       504\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 1km data after drop: \n",
      " Occurrence\n",
      "0    32580\n",
      "1      504\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 1km data before drop: \n",
      " Occurrence\n",
      "0    906081\n",
      "1      3919\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 1km data after drop: \n",
      " Occurrence\n",
      "0    29165\n",
      "1     3919\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 1km data before drop: \n",
      " Occurrence\n",
      "0    909876\n",
      "1       124\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 1km data after drop: \n",
      " Occurrence\n",
      "0    32960\n",
      "1      124\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 1km data before drop: \n",
      " Occurrence\n",
      "0    909045\n",
      "1       955\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 1km data after drop: \n",
      " Occurrence\n",
      "0    32129\n",
      "1      955\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 1km data before drop: \n",
      " Occurrence\n",
      "0    907683\n",
      "1      2317\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 1km data after drop: \n",
      " Occurrence\n",
      "0    30767\n",
      "1     2317\n",
      "dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data Cleaning\n",
    "np.random.seed(seed=seed)\n",
    "\n",
    "for dict in df_dicts:\n",
    "    cur_df = dict[\"dataframe\"]\n",
    "    cur_df_name = dict[\"name\"]\n",
    "\n",
    "    print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "    no_occurences = cur_df[cur_df['Occurrence']==0].index \n",
    "    sample_size = sum(cur_df['Occurrence']==0) - total_birds + sum(cur_df['Occurrence']==1)\n",
    "    random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "    dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "    print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')\n",
    "\n",
    "\n",
    "# for dict in df_dicts:\n",
    "#     cur_df = dict[\"dataframe\"]\n",
    "#     cur_df_name = dict[\"name\"]\n",
    "\n",
    "#     print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "#     no_occurences = cur_df[cur_df['Occurrence']==0].index\n",
    "#     sample_size = sum(cur_df['Occurrence']==0) - sum(cur_df['Occurrence']==1)\n",
    "#     random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "#     dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "#     print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardisation\n",
    "def standardise(X):\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "    # Add headers back\n",
    "    X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "\n",
    "    # Revert 'Surface type' back to non-standardised column as it is a categorical feature\n",
    "    X_scaled_df['Surface type'] = X['Surface type'].values\n",
    "    return X_scaled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Selection\n",
    "\n",
    "# Check if any columns have NaN in them\n",
    "# nan_columns = []\n",
    "# for column in X_scaled_df:\n",
    "#     if X_scaled_df[column].isnull().values.any():\n",
    "#         nan_columns.append(column)\n",
    "# print(nan_columns if len(nan_columns)!= 0 else 'None')\n",
    "\n",
    "\n",
    "# Using ANOVA F-Score as a feature selection method\n",
    "def feature_select(X, y):\n",
    "    k_nums = [10, 15, 20, 25, 30, 35]\n",
    "    kbest_dict = {}\n",
    "    for num in k_nums:\n",
    "        # Needs to be 1d array, y.values.ravel() converts y into a 1d array\n",
    "        best_X = SelectKBest(f_classif, k=num).fit(X, y.values.ravel())\n",
    "        # kbest_dict[str(num)] = best_X.get_feature_names_out().tolist()\n",
    "        kbest_dict[str(num)] = best_X\n",
    "    # kbest_dict['40'] = list(X.columns)\n",
    "\n",
    "    best_X = SelectKBest(f_classif, k='all').fit(X, y.values.ravel())\n",
    "\n",
    "    feat_scores = pd.DataFrame()\n",
    "    feat_scores[\"F Score\"] = best_X.scores_\n",
    "    feat_scores[\"P Value\"] = best_X.pvalues_\n",
    "    feat_scores[\"Attribute\"] = X.columns\n",
    "    kbest_dict['Dataframe'] = feat_scores.sort_values([\"F Score\", \"P Value\"], ascending=[False, False])\n",
    "\n",
    "\n",
    "    if details:\n",
    "        print(f'K-Best Features Dataframe: \\n{kbest_dict[\"Dataframe\"]} \\n')\n",
    "    # print(json.dumps(kbest_dict, indent=4))\n",
    "    return kbest_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample (upsample) minority data\n",
    "# for dict in df_dicts:\n",
    "#     if sum(dict['dataframe']['Occurence']==1) > sum(dict['dataframe']['Occurence']==0):\n",
    "#         continue\n",
    "\n",
    "# from sklearn.utils import resample\n",
    "\n",
    "# def upsample(X, y):\n",
    "#     X_1 = X[y['Occurrence'] == 1] # Getting positive occurrences (minority)\n",
    "#     X_0 = X[y['Occurrence'] == 0] # Getting negative occurrences (majority)\n",
    "    \n",
    "#     X_1_upsampled = resample(X_1 ,random_state=seed,n_samples=total_birds/2,replace=True)\n",
    "\n",
    "\n",
    "#     print(f'Resampling: \\n {y.value_counts()} \\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oversample(X_train, y_train):\n",
    "    over = RandomOverSampler(sampling_strategy='minority', random_state=seed)\n",
    "    smote = SMOTE(random_state=seed, sampling_strategy='minority')\n",
    "    X_smote, y_smote = smote.fit_resample(X_train, y_train)\n",
    "    \n",
    "    if details:\n",
    "        print(f'Resampled Value Counts: \\n {y_smote.value_counts()} \\n')\n",
    "\n",
    "    return X_smote, y_smote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Occurrence Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mute Swan 1km</td>\n",
       "      <td>19124</td>\n",
       "      <td>0.578044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 1km</td>\n",
       "      <td>10147</td>\n",
       "      <td>0.306704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 1km</td>\n",
       "      <td>5855</td>\n",
       "      <td>0.176974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 1km</td>\n",
       "      <td>3919</td>\n",
       "      <td>0.118456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 1km</td>\n",
       "      <td>3548</td>\n",
       "      <td>0.107242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 1km</td>\n",
       "      <td>2953</td>\n",
       "      <td>0.089258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 1km</td>\n",
       "      <td>2646</td>\n",
       "      <td>0.079978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 1km</td>\n",
       "      <td>2317</td>\n",
       "      <td>0.070034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 1km</td>\n",
       "      <td>2205</td>\n",
       "      <td>0.066649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 1km</td>\n",
       "      <td>2123</td>\n",
       "      <td>0.064170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 1km</td>\n",
       "      <td>1057</td>\n",
       "      <td>0.031949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 1km</td>\n",
       "      <td>1010</td>\n",
       "      <td>0.030528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 1km</td>\n",
       "      <td>955</td>\n",
       "      <td>0.028866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 1km</td>\n",
       "      <td>863</td>\n",
       "      <td>0.026085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 1km</td>\n",
       "      <td>769</td>\n",
       "      <td>0.023244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 1km</td>\n",
       "      <td>697</td>\n",
       "      <td>0.021068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 1km</td>\n",
       "      <td>504</td>\n",
       "      <td>0.015234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 1km</td>\n",
       "      <td>446</td>\n",
       "      <td>0.013481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 1km</td>\n",
       "      <td>294</td>\n",
       "      <td>0.008886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 1km</td>\n",
       "      <td>124</td>\n",
       "      <td>0.003748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  Occurrence Count  Percentage\n",
       "9              Mute Swan 1km             19124    0.578044\n",
       "1           Canada Goose 1km             10147    0.306704\n",
       "10              Pheasant 1km              5855    0.176974\n",
       "16             Rock Dove 1km              3919    0.118456\n",
       "7             Little Owl 1km              3548    0.107242\n",
       "14  Red-legged Partridge 1km              2953    0.089258\n",
       "11     Pink-footed Goose 1km              2646    0.079978\n",
       "19                Wigeon 1km              2317    0.070034\n",
       "3                Gadwall 1km              2205    0.066649\n",
       "5         Grey Partridge 1km              2123    0.064170\n",
       "13               Pochard 1km              1057    0.031949\n",
       "8          Mandarin Duck 1km              1010    0.030528\n",
       "18          Whooper Swan 1km               955    0.028866\n",
       "2         Egyptian Goose 1km               863    0.026085\n",
       "0         Barnacle Goose 1km               769    0.023244\n",
       "12               Pintail 1km               697    0.021068\n",
       "15  Ring-necked Parakeet 1km               504    0.015234\n",
       "4                Goshawk 1km               446    0.013481\n",
       "6         Indian Peafowl 1km               294    0.008886\n",
       "17            Ruddy Duck 1km               124    0.003748"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_bird_occurrences = pd.DataFrame([(dict['name'],sum(dict['dataframe']['Occurrence'] == 1)) for dict in df_dicts], columns=['Name', 'Occurrence Count'])\n",
    "All_bird_occurrences['Percentage'] = All_bird_occurrences['Occurrence Count']/total_birds\n",
    "\n",
    "All_bird_occurrences.sort_values('Occurrence Count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training with Barnacle Goose 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "25  1586.660696   0.000000e+00   Inflowing drainage direction\n",
      "29  1579.799646   0.000000e+00                 Chlorothalonil\n",
      "30  1579.799646   0.000000e+00                     Glyphosate\n",
      "31  1579.799646   0.000000e+00                       Mancozeb\n",
      "32  1579.799646   0.000000e+00                     Mecoprop-P\n",
      "34  1579.799646   0.000000e+00                  Pendimethalin\n",
      "18  1440.939379  1.143607e-308                      Saltmarsh\n",
      "23  1417.879628  7.271005e-304                   Surface type\n",
      "22  1269.611405  6.667737e-273      Cumulative catchment area\n",
      "24  1223.198053  3.502336e-263  Outflowing drainage direction\n",
      "21  1203.742558  4.196966e-259                      Elevation\n",
      "17  1078.608288  8.174358e-233              Littoral sediment\n",
      "13   978.472706  1.050650e-211                     Freshwater\n",
      "15   853.811730  2.457887e-185         Supralittoral sediment\n",
      "3    816.914586  1.639367e-177              Improve grassland\n",
      "38   682.011193  7.904841e-149                     Tri-allate\n",
      "37   676.149377  1.402808e-147                        Sulphur\n",
      "36   673.575429  4.960887e-147                   Prosulfocarb\n",
      "26   605.329133  1.806036e-132                   Fertiliser K\n",
      "27   605.329133  1.806036e-132                   Fertiliser N\n",
      "28   605.329133  1.806036e-132                   Fertiliser P\n",
      "35   504.754899  5.949135e-111       PropamocarbHydrochloride\n",
      "33   472.954811  3.918041e-104                     Metamitron\n",
      "16   416.369272   5.554940e-92                  Littoral rock\n",
      "7    360.657263   5.403022e-80                            Fen\n",
      "0    243.378276   1.130191e-54             Deciduous woodland\n",
      "2    223.631247   2.134039e-50                         Arable\n",
      "19   168.960021   1.551633e-38                          Urban\n",
      "20   156.098247   9.703696e-36                       Suburban\n",
      "14    88.036607   6.821306e-21             Supralittoral rock\n",
      "9     70.504247   4.773365e-17              Heather grassland\n",
      "4     52.091677   5.410742e-13              Neutral grassland\n",
      "12    28.131255   1.140877e-07                      Saltwater\n",
      "10    10.593482   1.136010e-03                            Bog\n",
      "6      3.881727   4.882264e-02                 Acid grassland\n",
      "8      1.726979   1.888063e-01                        Heather\n",
      "11     1.309928   2.524160e-01                    Inland rock\n",
      "1      1.193636   2.746053e-01            Coniferous woodland\n",
      "5      0.275406   5.997316e-01           Calcareous grassland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24236\n",
      "1             24236\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9857919446503582,\n",
      "        \"recall\": 0.9876222304740686,\n",
      "        \"f1-score\": 0.9867062387930501,\n",
      "        \"support\": 8079\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4350282485875706,\n",
      "        \"recall\": 0.4010416666666667,\n",
      "        \"f1-score\": 0.41734417344173447,\n",
      "        \"support\": 192\n",
      "    },\n",
      "    \"accuracy\": 0.9740055616007738,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7104100966189644,\n",
      "        \"recall\": 0.6943319485703676,\n",
      "        \"f1-score\": 0.7020252061173923,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9730067155796226,\n",
      "        \"recall\": 0.9740055616007738,\n",
      "        \"f1-score\": 0.9734892739100308,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Barnacle Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9841054706752095,\n",
      "        \"recall\": 0.9886124520361431,\n",
      "        \"f1-score\": 0.9863538129052178,\n",
      "        \"support\": 8079\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4064516129032258,\n",
      "        \"recall\": 0.328125,\n",
      "        \"f1-score\": 0.3631123919308357,\n",
      "        \"support\": 192\n",
      "    },\n",
      "    \"accuracy\": 0.9732801354128884,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6952785417892177,\n",
      "        \"recall\": 0.6583687260180715,\n",
      "        \"f1-score\": 0.6747331024180268,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9706960231244635,\n",
      "        \"recall\": 0.9732801354128884,\n",
      "        \"f1-score\": 0.971886112164427,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Canada Goose 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "         F Score        P Value                      Attribute\n",
      "29  31307.822741   0.000000e+00                 Chlorothalonil\n",
      "30  31307.822741   0.000000e+00                     Glyphosate\n",
      "31  31307.822741   0.000000e+00                       Mancozeb\n",
      "32  31307.822741   0.000000e+00                     Mecoprop-P\n",
      "34  31307.822741   0.000000e+00                  Pendimethalin\n",
      "23  27980.651957   0.000000e+00                   Surface type\n",
      "26  27539.757586   0.000000e+00                   Fertiliser K\n",
      "27  27539.757586   0.000000e+00                   Fertiliser N\n",
      "28  27539.757586   0.000000e+00                   Fertiliser P\n",
      "24  22192.853532   0.000000e+00  Outflowing drainage direction\n",
      "25  21798.073867   0.000000e+00   Inflowing drainage direction\n",
      "21  20557.269520   0.000000e+00                      Elevation\n",
      "37  14416.271856   0.000000e+00                        Sulphur\n",
      "36  14379.008562   0.000000e+00                   Prosulfocarb\n",
      "38  14239.510204   0.000000e+00                     Tri-allate\n",
      "22  10467.973712   0.000000e+00      Cumulative catchment area\n",
      "35  10058.930524   0.000000e+00       PropamocarbHydrochloride\n",
      "33   9820.800013   0.000000e+00                     Metamitron\n",
      "3    9373.992389   0.000000e+00              Improve grassland\n",
      "20   6200.682674   0.000000e+00                       Suburban\n",
      "0    4606.437967   0.000000e+00             Deciduous woodland\n",
      "2    4435.557422   0.000000e+00                         Arable\n",
      "19   2407.892997   0.000000e+00                          Urban\n",
      "13   1994.218535   0.000000e+00                     Freshwater\n",
      "4     591.947491  1.305021e-129              Neutral grassland\n",
      "18    389.095029   4.077898e-86                      Saltmarsh\n",
      "7     240.538301   4.654537e-54                            Fen\n",
      "17    162.207343   4.555336e-37              Littoral sediment\n",
      "5      56.217105   6.651725e-14           Calcareous grassland\n",
      "15     54.988572   1.241339e-13         Supralittoral sediment\n",
      "12     49.209521   2.344481e-12                      Saltwater\n",
      "1      18.689323   1.542908e-05            Coniferous woodland\n",
      "10     16.182904   5.763851e-05                            Bog\n",
      "11     13.004010   3.112817e-04                    Inland rock\n",
      "9      10.057049   1.519046e-03              Heather grassland\n",
      "16      3.194359   7.390188e-02                  Littoral rock\n",
      "14      1.831655   1.759414e-01             Supralittoral rock\n",
      "8       1.564030   2.110850e-01                        Heather\n",
      "6       1.341571   2.467655e-01                 Acid grassland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             17232\n",
      "1             17232\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada Goose 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9475095785440613,\n",
      "        \"recall\": 0.8669588080631025,\n",
      "        \"f1-score\": 0.9054462242562928,\n",
      "        \"support\": 5705\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7512291052114061,\n",
      "        \"recall\": 0.8932190179267342,\n",
      "        \"f1-score\": 0.8160940003560618,\n",
      "        \"support\": 2566\n",
      "    },\n",
      "    \"accuracy\": 0.8751057913190666,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8493693418777337,\n",
      "        \"recall\": 0.8800889129949183,\n",
      "        \"f1-score\": 0.8607701123061773,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8866154067907553,\n",
      "        \"recall\": 0.8751057913190666,\n",
      "        \"f1-score\": 0.8777255367302389,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Canada Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8952180028129395,\n",
      "        \"recall\": 0.8925503943908852,\n",
      "        \"f1-score\": 0.8938822083735627,\n",
      "        \"support\": 5705\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7626790553619822,\n",
      "        \"recall\": 0.7677318784099766,\n",
      "        \"f1-score\": 0.7651971256554672,\n",
      "        \"support\": 2566\n",
      "    },\n",
      "    \"accuracy\": 0.8538266231410954,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8289485290874609,\n",
      "        \"recall\": 0.830141136400431,\n",
      "        \"f1-score\": 0.829539667014515,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8540990402740498,\n",
      "        \"recall\": 0.8538266231410954,\n",
      "        \"f1-score\": 0.8539588711405034,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Egyptian Goose 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "26  4833.620741   0.000000e+00                   Fertiliser K\n",
      "27  4833.620741   0.000000e+00                   Fertiliser N\n",
      "28  4833.620741   0.000000e+00                   Fertiliser P\n",
      "22  4398.345684   0.000000e+00      Cumulative catchment area\n",
      "13  3391.728526   0.000000e+00                     Freshwater\n",
      "29  3198.335134   0.000000e+00                 Chlorothalonil\n",
      "30  3198.335134   0.000000e+00                     Glyphosate\n",
      "31  3198.335134   0.000000e+00                       Mancozeb\n",
      "32  3198.335134   0.000000e+00                     Mecoprop-P\n",
      "34  3198.335134   0.000000e+00                  Pendimethalin\n",
      "24  2769.983626   0.000000e+00  Outflowing drainage direction\n",
      "19  2688.563744   0.000000e+00                          Urban\n",
      "23  2448.189595   0.000000e+00                   Surface type\n",
      "36  2166.345317   0.000000e+00                   Prosulfocarb\n",
      "37  2164.465187   0.000000e+00                        Sulphur\n",
      "38  2130.679135   0.000000e+00                     Tri-allate\n",
      "33  1913.836423   0.000000e+00                     Metamitron\n",
      "25  1867.181758   0.000000e+00   Inflowing drainage direction\n",
      "35  1851.637936   0.000000e+00       PropamocarbHydrochloride\n",
      "20  1608.119655   0.000000e+00                       Suburban\n",
      "21  1508.446202  1.037538e-322                      Elevation\n",
      "3   1160.254219  5.600729e-250              Improve grassland\n",
      "0   1025.549798  1.228685e-221             Deciduous woodland\n",
      "7    631.476180  4.722469e-138                            Fen\n",
      "2    600.148490  2.308798e-131                         Arable\n",
      "18   214.961650   1.613752e-48                      Saltmarsh\n",
      "4     66.795470   3.118157e-16              Neutral grassland\n",
      "6     24.532078   7.344270e-07                 Acid grassland\n",
      "9     16.103881   6.009295e-05              Heather grassland\n",
      "10    10.942250   9.409617e-04                            Bog\n",
      "8      6.460287   1.103570e-02                        Heather\n",
      "15     6.121062   1.336303e-02         Supralittoral sediment\n",
      "17     4.658208   3.091261e-02              Littoral sediment\n",
      "5      3.654048   5.594175e-02           Calcareous grassland\n",
      "11     3.305391   6.906195e-02                    Inland rock\n",
      "16     3.051750   8.065946e-02                  Littoral rock\n",
      "12     0.870474   3.508309e-01                      Saltwater\n",
      "14     0.756741   3.843565e-01             Supralittoral rock\n",
      "1      0.003547   9.525091e-01            Coniferous woodland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24140\n",
      "1             24140\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9874953571870744,\n",
      "        \"recall\": 0.9870065585942334,\n",
      "        \"f1-score\": 0.9872508973882905,\n",
      "        \"support\": 8081\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4587628865979381,\n",
      "        \"recall\": 0.46842105263157896,\n",
      "        \"f1-score\": 0.46354166666666663,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.9750937008826018,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7231291218925062,\n",
      "        \"recall\": 0.7277138056129062,\n",
      "        \"f1-score\": 0.7253962820274786,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9753494051363023,\n",
      "        \"recall\": 0.9750937008826018,\n",
      "        \"f1-score\": 0.9752203383462028,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Egyptian Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9858199753390875,\n",
      "        \"recall\": 0.9893577527533721,\n",
      "        \"f1-score\": 0.987585695756902,\n",
      "        \"support\": 8081\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4658385093167702,\n",
      "        \"recall\": 0.39473684210526316,\n",
      "        \"f1-score\": 0.4273504273504274,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.9756982227058397,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7258292423279289,\n",
      "        \"recall\": 0.6920472974293177,\n",
      "        \"f1-score\": 0.7074680615536647,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9738750498712794,\n",
      "        \"recall\": 0.9756982227058397,\n",
      "        \"f1-score\": 0.974716066812732,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Gadwall 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "26  8495.245130   0.000000e+00                   Fertiliser K\n",
      "27  8495.245130   0.000000e+00                   Fertiliser N\n",
      "28  8495.245130   0.000000e+00                   Fertiliser P\n",
      "29  7138.523124   0.000000e+00                 Chlorothalonil\n",
      "30  7138.523124   0.000000e+00                     Glyphosate\n",
      "31  7138.523124   0.000000e+00                       Mancozeb\n",
      "32  7138.523124   0.000000e+00                     Mecoprop-P\n",
      "34  7138.523124   0.000000e+00                  Pendimethalin\n",
      "37  6747.511680   0.000000e+00                        Sulphur\n",
      "36  6716.673071   0.000000e+00                   Prosulfocarb\n",
      "38  6693.525002   0.000000e+00                     Tri-allate\n",
      "35  5956.455748   0.000000e+00       PropamocarbHydrochloride\n",
      "33  5952.405449   0.000000e+00                     Metamitron\n",
      "24  5605.884628   0.000000e+00  Outflowing drainage direction\n",
      "22  5421.221412   0.000000e+00      Cumulative catchment area\n",
      "23  5337.742415   0.000000e+00                   Surface type\n",
      "25  4250.868883   0.000000e+00   Inflowing drainage direction\n",
      "13  3657.950458   0.000000e+00                     Freshwater\n",
      "21  3513.520144   0.000000e+00                      Elevation\n",
      "2   2555.774940   0.000000e+00                         Arable\n",
      "3   2162.540101   0.000000e+00              Improve grassland\n",
      "20  1871.462238   0.000000e+00                       Suburban\n",
      "0   1487.487019  2.357158e-318             Deciduous woodland\n",
      "19  1271.177886  3.134320e-273                          Urban\n",
      "18   947.217815  4.200123e-205                      Saltmarsh\n",
      "7    892.320809  1.714432e-193                            Fen\n",
      "4    739.334890  4.953129e-161              Neutral grassland\n",
      "17   160.826029   9.095729e-37              Littoral sediment\n",
      "15   130.604141   3.443944e-30         Supralittoral sediment\n",
      "6     45.799690   1.331664e-11                 Acid grassland\n",
      "12    32.280187   1.345866e-08                      Saltwater\n",
      "9     29.120347   6.848560e-08              Heather grassland\n",
      "10    21.703699   3.194143e-06                            Bog\n",
      "8     17.560026   2.791009e-05                        Heather\n",
      "11     7.808204   5.203950e-03                    Inland rock\n",
      "1      7.188161   7.342254e-03            Coniferous woodland\n",
      "14     1.781952   1.819190e-01             Supralittoral rock\n",
      "5      0.735834   3.910048e-01           Calcareous grassland\n",
      "16     0.097151   7.552781e-01                  Littoral rock \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             23153\n",
      "1             23153\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gadwall 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.961431812452735,\n",
      "        \"recall\": 0.9873155578565881,\n",
      "        \"f1-score\": 0.9742017879948913,\n",
      "        \"support\": 7726\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7091988130563798,\n",
      "        \"recall\": 0.43853211009174314,\n",
      "        \"f1-score\": 0.5419501133786848,\n",
      "        \"support\": 545\n",
      "    },\n",
      "    \"accuracy\": 0.9511546366823842,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8353153127545574,\n",
      "        \"recall\": 0.7129238339741656,\n",
      "        \"f1-score\": 0.758075950686788,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9448114540110697,\n",
      "        \"recall\": 0.9511546366823842,\n",
      "        \"f1-score\": 0.945719480817303,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Gadwall 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9636247606892151,\n",
      "        \"recall\": 0.977219777375097,\n",
      "        \"f1-score\": 0.9703746545851809,\n",
      "        \"support\": 7726\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5963302752293578,\n",
      "        \"recall\": 0.47706422018348627,\n",
      "        \"f1-score\": 0.5300713557594292,\n",
      "        \"support\": 545\n",
      "    },\n",
      "    \"accuracy\": 0.9442630878974732,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7799775179592865,\n",
      "        \"recall\": 0.7271419987792916,\n",
      "        \"f1-score\": 0.7502230051723051,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9394226696995377,\n",
      "        \"recall\": 0.9442630878974732,\n",
      "        \"f1-score\": 0.94136180270995,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Goshawk 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "23  1228.008833  3.437230e-264                   Surface type\n",
      "21  1131.658994  5.687958e-244                      Elevation\n",
      "29  1110.016642  2.016393e-239                 Chlorothalonil\n",
      "30  1110.016642  2.016393e-239                     Glyphosate\n",
      "31  1110.016642  2.016393e-239                       Mancozeb\n",
      "32  1110.016642  2.016393e-239                     Mecoprop-P\n",
      "34  1110.016642  2.016393e-239                  Pendimethalin\n",
      "24  1068.037277  1.373575e-230  Outflowing drainage direction\n",
      "22  1044.933301  1.009602e-225      Cumulative catchment area\n",
      "25   919.889067  2.517246e-199   Inflowing drainage direction\n",
      "0    809.818171  5.254241e-176             Deciduous woodland\n",
      "3    684.780108  2.032079e-149              Improve grassland\n",
      "1    397.809147   5.441281e-88            Coniferous woodland\n",
      "38   378.743353   6.890413e-84                     Tri-allate\n",
      "37   375.216071   3.958707e-83                        Sulphur\n",
      "36   366.309524   3.275528e-81                   Prosulfocarb\n",
      "6    362.459808   2.209967e-80                 Acid grassland\n",
      "35   245.243731   4.460776e-55       PropamocarbHydrochloride\n",
      "26   234.255491   1.066866e-52                   Fertiliser K\n",
      "27   234.255491   1.066866e-52                   Fertiliser N\n",
      "28   234.255491   1.066866e-52                   Fertiliser P\n",
      "33   226.160901   6.042257e-51                     Metamitron\n",
      "2     87.757391   7.852555e-21                         Arable\n",
      "20    79.803707   4.343909e-19                       Suburban\n",
      "8     45.558282   1.506066e-11                        Heather\n",
      "5     16.053837   6.170139e-05           Calcareous grassland\n",
      "13    15.723043   7.347960e-05                     Freshwater\n",
      "18     8.693071   3.196452e-03                      Saltmarsh\n",
      "7      4.213295   4.011621e-02                            Fen\n",
      "10     3.293395   6.956812e-02                            Bog\n",
      "14     1.492644   2.218154e-01             Supralittoral rock\n",
      "19     1.235707   2.663082e-01                          Urban\n",
      "9      0.653081   4.190191e-01              Heather grassland\n",
      "11     0.416397   5.187449e-01                    Inland rock\n",
      "12     0.222689   6.370020e-01                      Saltwater\n",
      "15     0.046972   8.284207e-01         Supralittoral sediment\n",
      "4      0.041436   8.386999e-01              Neutral grassland\n",
      "17     0.032689   8.565239e-01              Littoral sediment\n",
      "16     0.002739   9.582594e-01                  Littoral rock \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24473\n",
      "1             24473\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goshawk 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9871717293961031,\n",
      "        \"recall\": 0.9990202082057563,\n",
      "        \"f1-score\": 0.9930606281957634,\n",
      "        \"support\": 8165\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 106\n",
      "    },\n",
      "    \"accuracy\": 0.9862169024301777,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49358586469805155,\n",
      "        \"recall\": 0.49951010410287816,\n",
      "        \"f1-score\": 0.4965303140978817,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9745202720975918,\n",
      "        \"recall\": 0.9862169024301777,\n",
      "        \"f1-score\": 0.9803336995790604,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Goshawk 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9879444715051144,\n",
      "        \"recall\": 0.9936313533374158,\n",
      "        \"f1-score\": 0.9907797520913476,\n",
      "        \"support\": 8165\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.11864406779661017,\n",
      "        \"recall\": 0.0660377358490566,\n",
      "        \"f1-score\": 0.08484848484848484,\n",
      "        \"support\": 106\n",
      "    },\n",
      "    \"accuracy\": 0.9817434409382179,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5532942696508623,\n",
      "        \"recall\": 0.5298345445932362,\n",
      "        \"f1-score\": 0.5378141184699162,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9768036369273002,\n",
      "        \"recall\": 0.9817434409382179,\n",
      "        \"f1-score\": 0.9791694613976294,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Grey Partridge 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "26  8765.050569   0.000000e+00                   Fertiliser K\n",
      "27  8765.050569   0.000000e+00                   Fertiliser N\n",
      "28  8765.050569   0.000000e+00                   Fertiliser P\n",
      "37  8712.410120   0.000000e+00                        Sulphur\n",
      "36  8711.451485   0.000000e+00                   Prosulfocarb\n",
      "38  8703.792647   0.000000e+00                     Tri-allate\n",
      "2   8141.700589   0.000000e+00                         Arable\n",
      "32  7660.609011   0.000000e+00                     Mecoprop-P\n",
      "29  7659.048939   0.000000e+00                 Chlorothalonil\n",
      "30  7659.048939   0.000000e+00                     Glyphosate\n",
      "31  7659.048939   0.000000e+00                       Mancozeb\n",
      "34  7659.048939   0.000000e+00                  Pendimethalin\n",
      "35  7405.435155   0.000000e+00       PropamocarbHydrochloride\n",
      "33  7361.522264   0.000000e+00                     Metamitron\n",
      "23  5901.470556   0.000000e+00                   Surface type\n",
      "24  4800.409421   0.000000e+00  Outflowing drainage direction\n",
      "22  4601.906150   0.000000e+00      Cumulative catchment area\n",
      "25  4463.973740   0.000000e+00   Inflowing drainage direction\n",
      "21  4389.262340   0.000000e+00                      Elevation\n",
      "3   2126.495399   0.000000e+00              Improve grassland\n",
      "0    901.238763  2.221379e-195             Deciduous woodland\n",
      "20   518.009891  8.599298e-114                       Suburban\n",
      "5    327.045266   9.488789e-73           Calcareous grassland\n",
      "4    319.849011   3.384407e-71              Neutral grassland\n",
      "19    98.781469   3.039441e-23                          Urban\n",
      "15    42.304272   7.923464e-11         Supralittoral sediment\n",
      "18    42.065170   8.952540e-11                      Saltmarsh\n",
      "13    27.868120   1.306903e-07                     Freshwater\n",
      "7     18.302932   1.889456e-05                            Fen\n",
      "17     6.233018   1.254381e-02              Littoral sediment\n",
      "1      5.215300   2.239529e-02            Coniferous woodland\n",
      "11     4.740881   2.946103e-02                    Inland rock\n",
      "14     4.021946   4.491999e-02             Supralittoral rock\n",
      "8      3.136113   7.658531e-02                        Heather\n",
      "6      2.392910   1.218961e-01                 Acid grassland\n",
      "12     0.249330   6.175506e-01                      Saltwater\n",
      "10     0.120851   7.281160e-01                            Bog\n",
      "9      0.012848   9.097560e-01              Heather grassland\n",
      "16     0.000036   9.952257e-01                  Littoral rock \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             23218\n",
      "1             23218\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grey Partridge 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9373253977893842,\n",
      "        \"recall\": 0.9966421283740152,\n",
      "        \"f1-score\": 0.9660741111667501,\n",
      "        \"support\": 7743\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3157894736842105,\n",
      "        \"recall\": 0.022727272727272728,\n",
      "        \"f1-score\": 0.04240282685512368,\n",
      "        \"support\": 528\n",
      "    },\n",
      "    \"accuracy\": 0.9344698343610204,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6265574357367973,\n",
      "        \"recall\": 0.509684700550644,\n",
      "        \"f1-score\": 0.5042384690109369,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.89764809541633,\n",
      "        \"recall\": 0.9344698343610204,\n",
      "        \"f1-score\": 0.9071092413666608,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Grey Partridge 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9493670886075949,\n",
      "        \"recall\": 0.9686168151879117,\n",
      "        \"f1-score\": 0.9588953525538579,\n",
      "        \"support\": 7743\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3450134770889488,\n",
      "        \"recall\": 0.24242424242424243,\n",
      "        \"f1-score\": 0.28476084538375973,\n",
      "        \"support\": 528\n",
      "    },\n",
      "    \"accuracy\": 0.9222584935316165,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6471902828482718,\n",
      "        \"recall\": 0.605520528806077,\n",
      "        \"f1-score\": 0.6218280989688089,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9107866621921862,\n",
      "        \"recall\": 0.9222584935316165,\n",
      "        \"f1-score\": 0.9158602878959191,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Indian Peafowl 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "26  1472.657485  2.862598e-315                   Fertiliser K\n",
      "27  1472.657485  2.862598e-315                   Fertiliser N\n",
      "28  1472.657485  2.862598e-315                   Fertiliser P\n",
      "36  1175.153234  4.174421e-253                   Prosulfocarb\n",
      "37  1172.615821  1.422851e-252                        Sulphur\n",
      "38  1166.318796  2.985253e-251                     Tri-allate\n",
      "29  1150.513144  6.221230e-248                 Chlorothalonil\n",
      "30  1150.513144  6.221230e-248                     Glyphosate\n",
      "31  1150.513144  6.221230e-248                       Mancozeb\n",
      "32  1150.513144  6.221230e-248                     Mecoprop-P\n",
      "34  1150.513144  6.221230e-248                  Pendimethalin\n",
      "35   993.549316  6.904766e-215       PropamocarbHydrochloride\n",
      "33   989.712520  4.455187e-214                     Metamitron\n",
      "23   826.341713  1.639780e-179                   Surface type\n",
      "22   773.116357  3.264405e-168      Cumulative catchment area\n",
      "24   694.651908  1.603430e-151  Outflowing drainage direction\n",
      "2    636.396656  4.208646e-139                         Arable\n",
      "25   618.098698  3.386540e-135   Inflowing drainage direction\n",
      "21   581.270648  2.497700e-127                      Elevation\n",
      "3    579.289817  6.621914e-127              Improve grassland\n",
      "0    557.985251  2.382356e-122             Deciduous woodland\n",
      "20   341.361921   7.768283e-76                       Suburban\n",
      "4     31.037453   2.550632e-08              Neutral grassland\n",
      "19    28.479423   9.532196e-08                          Urban\n",
      "5     19.591241   9.621430e-06           Calcareous grassland\n",
      "7     11.127497   8.515085e-04                            Fen\n",
      "13     9.872829   1.678853e-03                     Freshwater\n",
      "6      4.975747   2.571178e-02                 Acid grassland\n",
      "10     3.521124   6.060014e-02                            Bog\n",
      "1      2.674323   1.019882e-01            Coniferous woodland\n",
      "9      2.267492   1.321231e-01              Heather grassland\n",
      "8      1.470891   2.252138e-01                        Heather\n",
      "11     0.730498   3.927281e-01                    Inland rock\n",
      "12     0.717725   3.968973e-01                      Saltwater\n",
      "15     0.509339   4.754303e-01         Supralittoral sediment\n",
      "18     0.335080   5.626872e-01                      Saltmarsh\n",
      "14     0.132251   7.161121e-01             Supralittoral rock\n",
      "17     0.099303   7.526692e-01              Littoral sediment\n",
      "16     0.034266   8.531431e-01                  Littoral rock \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24594\n",
      "1             24594\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indian Peafowl 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.990921195981116,\n",
      "        \"recall\": 0.9987798926305514,\n",
      "        \"f1-score\": 0.9948350246095886,\n",
      "        \"support\": 8196\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 75\n",
      "    },\n",
      "    \"accuracy\": 0.9897231290049571,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.495460597990558,\n",
      "        \"recall\": 0.4993899463152757,\n",
      "        \"f1-score\": 0.4974175123047943,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9819356936599235,\n",
      "        \"recall\": 0.9897231290049571,\n",
      "        \"f1-score\": 0.9858140323661212,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Indian Peafowl 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9912684938151831,\n",
      "        \"recall\": 0.9973157637872133,\n",
      "        \"f1-score\": 0.9942829339496412,\n",
      "        \"support\": 8196\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.12,\n",
      "        \"recall\": 0.04,\n",
      "        \"f1-score\": 0.05999999999999999,\n",
      "        \"support\": 75\n",
      "    },\n",
      "    \"accuracy\": 0.988634989723129,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5556342469075916,\n",
      "        \"recall\": 0.5186578818936066,\n",
      "        \"f1-score\": 0.5271414669748206,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9833679815390207,\n",
      "        \"recall\": 0.988634989723129,\n",
      "        \"f1-score\": 0.9858110176098729,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Little Owl 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "         F Score        P Value                      Attribute\n",
      "26  19475.045822   0.000000e+00                   Fertiliser K\n",
      "27  19475.045822   0.000000e+00                   Fertiliser N\n",
      "28  19475.045822   0.000000e+00                   Fertiliser P\n",
      "36  14464.783437   0.000000e+00                   Prosulfocarb\n",
      "37  14463.764574   0.000000e+00                        Sulphur\n",
      "38  14420.985632   0.000000e+00                     Tri-allate\n",
      "32  13365.596356   0.000000e+00                     Mecoprop-P\n",
      "29  13362.750579   0.000000e+00                 Chlorothalonil\n",
      "30  13362.750579   0.000000e+00                     Glyphosate\n",
      "31  13362.750579   0.000000e+00                       Mancozeb\n",
      "34  13362.750579   0.000000e+00                  Pendimethalin\n",
      "33  11175.998989   0.000000e+00                     Metamitron\n",
      "35  11122.139446   0.000000e+00       PropamocarbHydrochloride\n",
      "23   9941.994917   0.000000e+00                   Surface type\n",
      "2    9462.794504   0.000000e+00                         Arable\n",
      "24   8442.661824   0.000000e+00  Outflowing drainage direction\n",
      "22   7788.899641   0.000000e+00      Cumulative catchment area\n",
      "25   7508.970331   0.000000e+00   Inflowing drainage direction\n",
      "21   6742.553493   0.000000e+00                      Elevation\n",
      "3    4944.473615   0.000000e+00              Improve grassland\n",
      "20   2250.993774   0.000000e+00                       Suburban\n",
      "0    1224.086080  2.281664e-263             Deciduous woodland\n",
      "4     630.020519  9.656906e-138              Neutral grassland\n",
      "5     439.830706   5.052213e-97           Calcareous grassland\n",
      "19    344.014425   2.082536e-76                          Urban\n",
      "13    191.220766   2.273330e-43                     Freshwater\n",
      "7     143.222650   6.146287e-33                            Fen\n",
      "18     59.796551   1.081619e-14                      Saltmarsh\n",
      "6      42.200831   8.353273e-11                 Acid grassland\n",
      "1      34.080188   5.338007e-09            Coniferous woodland\n",
      "10     30.463089   3.428286e-08                            Bog\n",
      "8      26.647155   2.456208e-07                        Heather\n",
      "9      19.056362   1.272986e-05              Heather grassland\n",
      "15     16.754547   4.264108e-05         Supralittoral sediment\n",
      "16      7.372175   6.627513e-03                  Littoral rock\n",
      "11      7.137556   7.552295e-03                    Inland rock\n",
      "14      5.890295   1.522985e-02             Supralittoral rock\n",
      "12      0.157614   6.913651e-01                      Saltwater\n",
      "17      0.095097   7.577967e-01              Littoral sediment \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             22143\n",
      "1             22143\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Little Owl 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9410580021482277,\n",
      "        \"recall\": 0.9480589747058028,\n",
      "        \"f1-score\": 0.944545515800822,\n",
      "        \"support\": 7393\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5334143377885784,\n",
      "        \"recall\": 0.5,\n",
      "        \"f1-score\": 0.516166960611405,\n",
      "        \"support\": 878\n",
      "    },\n",
      "    \"accuracy\": 0.9004957078950551,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.737236169968403,\n",
      "        \"recall\": 0.7240294873529014,\n",
      "        \"f1-score\": 0.7303562382061135,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8977849834917445,\n",
      "        \"recall\": 0.9004957078950551,\n",
      "        \"f1-score\": 0.89907140487635,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Little Owl 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.939565627950897,\n",
      "        \"recall\": 0.9421073988908427,\n",
      "        \"f1-score\": 0.9408347967040389,\n",
      "        \"support\": 7393\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5011655011655012,\n",
      "        \"recall\": 0.489749430523918,\n",
      "        \"f1-score\": 0.4953917050691244,\n",
      "        \"support\": 878\n",
      "    },\n",
      "    \"accuracy\": 0.8940877765687342,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7203655645581991,\n",
      "        \"recall\": 0.7159284147073803,\n",
      "        \"f1-score\": 0.7181132508865816,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8930276867929261,\n",
      "        \"recall\": 0.8940877765687342,\n",
      "        \"f1-score\": 0.8935492164289265,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mandarin Duck 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "26  4982.270656   0.000000e+00                   Fertiliser K\n",
      "27  4982.270656   0.000000e+00                   Fertiliser N\n",
      "28  4982.270656   0.000000e+00                   Fertiliser P\n",
      "29  3746.947882   0.000000e+00                 Chlorothalonil\n",
      "30  3746.947882   0.000000e+00                     Glyphosate\n",
      "31  3746.947882   0.000000e+00                       Mancozeb\n",
      "32  3746.947882   0.000000e+00                     Mecoprop-P\n",
      "34  3746.947882   0.000000e+00                  Pendimethalin\n",
      "22  3559.780952   0.000000e+00      Cumulative catchment area\n",
      "0   3525.975295   0.000000e+00             Deciduous woodland\n",
      "24  2973.634105   0.000000e+00  Outflowing drainage direction\n",
      "23  2900.268149   0.000000e+00                   Surface type\n",
      "3   2556.207049   0.000000e+00              Improve grassland\n",
      "37  2345.273531   0.000000e+00                        Sulphur\n",
      "36  2339.229953   0.000000e+00                   Prosulfocarb\n",
      "38  2322.533150   0.000000e+00                     Tri-allate\n",
      "25  2139.256963   0.000000e+00   Inflowing drainage direction\n",
      "21  2040.599063   0.000000e+00                      Elevation\n",
      "20  1659.981195   0.000000e+00                       Suburban\n",
      "35  1618.553741   0.000000e+00       PropamocarbHydrochloride\n",
      "33  1589.076229   0.000000e+00                     Metamitron\n",
      "13   792.705495  2.254877e-172                     Freshwater\n",
      "19   434.160164   8.347169e-96                          Urban\n",
      "2    322.317750   9.929157e-72                         Arable\n",
      "4    289.027410   1.523840e-64              Neutral grassland\n",
      "5     86.986582   1.158292e-20           Calcareous grassland\n",
      "1     43.617236   4.053481e-11            Coniferous woodland\n",
      "7     30.454378   3.443701e-08                            Fen\n",
      "9     12.680495   3.700080e-04              Heather grassland\n",
      "10    11.170154   8.321583e-04                            Bog\n",
      "6      4.774378   2.889326e-02                 Acid grassland\n",
      "16     3.468317   6.256379e-02                  Littoral rock\n",
      "14     3.191997   7.400873e-02             Supralittoral rock\n",
      "17     3.080693   7.923601e-02              Littoral sediment\n",
      "12     2.603302   1.066509e-01                      Saltwater\n",
      "11     2.290784   1.301538e-01                    Inland rock\n",
      "15     1.408976   2.352351e-01         Supralittoral sediment\n",
      "8      0.023329   8.786052e-01                        Heather\n",
      "18     0.004613   9.458479e-01                      Saltmarsh \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24029\n",
      "1             24029\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9780353874313605,\n",
      "        \"recall\": 0.9962709757613425,\n",
      "        \"f1-score\": 0.9870689655172414,\n",
      "        \"support\": 8045\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6052631578947368,\n",
      "        \"recall\": 0.20353982300884957,\n",
      "        \"f1-score\": 0.30463576158940403,\n",
      "        \"support\": 226\n",
      "    },\n",
      "    \"accuracy\": 0.9746100834240116,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7916492726630486,\n",
      "        \"recall\": 0.599905399385096,\n",
      "        \"f1-score\": 0.6458523635533228,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9678496149884545,\n",
      "        \"recall\": 0.9746100834240116,\n",
      "        \"f1-score\": 0.9684218969538644,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Mandarin Duck 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9797247480953551,\n",
      "        \"recall\": 0.9910503418272218,\n",
      "        \"f1-score\": 0.9853550021627634,\n",
      "        \"support\": 8045\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.45864661654135336,\n",
      "        \"recall\": 0.26991150442477874,\n",
      "        \"f1-score\": 0.3398328690807799,\n",
      "        \"support\": 226\n",
      "    },\n",
      "    \"accuracy\": 0.9713456655785274,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7191856823183542,\n",
      "        \"recall\": 0.6304809231260002,\n",
      "        \"f1-score\": 0.6625939356217716,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9654866078787907,\n",
      "        \"recall\": 0.9713456655785274,\n",
      "        \"f1-score\": 0.9677165059619983,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mute Swan 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "         F Score        P Value                      Attribute\n",
      "30  43676.953726   0.000000e+00                     Glyphosate\n",
      "34  43676.953726   0.000000e+00                  Pendimethalin\n",
      "29  43659.642747   0.000000e+00                 Chlorothalonil\n",
      "31  43642.342398   0.000000e+00                       Mancozeb\n",
      "32  43625.052671   0.000000e+00                     Mecoprop-P\n",
      "23  41778.683149   0.000000e+00                   Surface type\n",
      "25  37707.864005   0.000000e+00   Inflowing drainage direction\n",
      "21  32149.803935   0.000000e+00                      Elevation\n",
      "24  24629.730532   0.000000e+00  Outflowing drainage direction\n",
      "26  19640.235987   0.000000e+00                   Fertiliser K\n",
      "27  19640.235987   0.000000e+00                   Fertiliser N\n",
      "28  19640.235987   0.000000e+00                   Fertiliser P\n",
      "37  14704.076106   0.000000e+00                        Sulphur\n",
      "36  14617.037623   0.000000e+00                   Prosulfocarb\n",
      "38  14559.360173   0.000000e+00                     Tri-allate\n",
      "35   9242.027887   0.000000e+00       PropamocarbHydrochloride\n",
      "33   8916.083195   0.000000e+00                     Metamitron\n",
      "22   7714.991172   0.000000e+00      Cumulative catchment area\n",
      "3    6922.429433   0.000000e+00              Improve grassland\n",
      "20   5556.769597   0.000000e+00                       Suburban\n",
      "2    4548.309043   0.000000e+00                         Arable\n",
      "0    3188.833827   0.000000e+00             Deciduous woodland\n",
      "19   2126.916337   0.000000e+00                          Urban\n",
      "13   1261.094423  4.042871e-271                     Freshwater\n",
      "4     710.989883  5.323266e-155              Neutral grassland\n",
      "17    350.947774   6.674534e-78              Littoral sediment\n",
      "6     274.300533   2.317521e-61                 Acid grassland\n",
      "18    258.697255   5.478710e-58                      Saltmarsh\n",
      "7     204.439981   3.082649e-46                            Fen\n",
      "15    138.876035   5.432921e-32         Supralittoral sediment\n",
      "10    122.310053   2.214177e-28                            Bog\n",
      "12    113.202400   2.149655e-26                      Saltwater\n",
      "8     103.816162   2.411588e-24                        Heather\n",
      "9      88.379538   5.738193e-21              Heather grassland\n",
      "16     66.697722   3.276343e-16                  Littoral rock\n",
      "11     49.312998   2.224191e-12                    Inland rock\n",
      "1      44.669892   2.369145e-11            Coniferous woodland\n",
      "14     29.331059   6.143449e-08             Supralittoral rock\n",
      "5       8.698519   3.186914e-03           Calcareous grassland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             14291\n",
      "1             14291\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9197844007609385,\n",
      "        \"recall\": 0.8438045375218151,\n",
      "        \"f1-score\": 0.8801577669902911,\n",
      "        \"support\": 3438\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8950556966972836,\n",
      "        \"recall\": 0.9476515621767019,\n",
      "        \"f1-score\": 0.9206030150753769,\n",
      "        \"support\": 4833\n",
      "    },\n",
      "    \"accuracy\": 0.9044855519284246,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9074200487291111,\n",
      "        \"recall\": 0.8957280498492585,\n",
      "        \"f1-score\": 0.9003803910328341,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9053346574723827,\n",
      "        \"recall\": 0.9044855519284246,\n",
      "        \"f1-score\": 0.9037911709311953,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Mute Swan 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8887548990051252,\n",
      "        \"recall\": 0.8574752763234439,\n",
      "        \"f1-score\": 0.8728349370836418,\n",
      "        \"support\": 3438\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.901090028259992,\n",
      "        \"recall\": 0.9236499068901304,\n",
      "        \"f1-score\": 0.9122305098600184,\n",
      "        \"support\": 4833\n",
      "    },\n",
      "    \"accuracy\": 0.8961431507677428,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8949224636325586,\n",
      "        \"recall\": 0.8905625916067872,\n",
      "        \"f1-score\": 0.8925327234718301,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8959626948809286,\n",
      "        \"recall\": 0.8961431507677428,\n",
      "        \"f1-score\": 0.8958549834176073,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pheasant 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "         F Score       P Value                      Attribute\n",
      "31  16151.612969  0.000000e+00                       Mancozeb\n",
      "32  16151.612969  0.000000e+00                     Mecoprop-P\n",
      "29  16147.891932  0.000000e+00                 Chlorothalonil\n",
      "30  16147.891932  0.000000e+00                     Glyphosate\n",
      "34  16147.891932  0.000000e+00                  Pendimethalin\n",
      "23  14828.439300  0.000000e+00                   Surface type\n",
      "26  13583.366928  0.000000e+00                   Fertiliser K\n",
      "27  13583.366928  0.000000e+00                   Fertiliser N\n",
      "28  13583.366928  0.000000e+00                   Fertiliser P\n",
      "24  12411.664397  0.000000e+00  Outflowing drainage direction\n",
      "21  11511.436305  0.000000e+00                      Elevation\n",
      "25  11025.748971  0.000000e+00   Inflowing drainage direction\n",
      "37  10813.051867  0.000000e+00                        Sulphur\n",
      "36  10803.090695  0.000000e+00                   Prosulfocarb\n",
      "38  10762.210355  0.000000e+00                     Tri-allate\n",
      "22   9363.861895  0.000000e+00      Cumulative catchment area\n",
      "35   7504.726180  0.000000e+00       PropamocarbHydrochloride\n",
      "33   7356.605139  0.000000e+00                     Metamitron\n",
      "3    6889.758402  0.000000e+00              Improve grassland\n",
      "2    5020.115469  0.000000e+00                         Arable\n",
      "0    3497.652887  0.000000e+00             Deciduous woodland\n",
      "20   1872.691498  0.000000e+00                       Suburban\n",
      "5     415.858475  7.152889e-92           Calcareous grassland\n",
      "4     355.069398  8.637276e-79              Neutral grassland\n",
      "1     235.243263  6.519726e-53            Coniferous woodland\n",
      "19    219.109454  2.036749e-49                          Urban\n",
      "6     141.740399  1.292165e-32                 Acid grassland\n",
      "13    137.784819  9.390379e-32                     Freshwater\n",
      "8      88.397878  5.685377e-21                        Heather\n",
      "7      57.673169  3.176391e-14                            Fen\n",
      "10     33.154847  8.585587e-09                            Bog\n",
      "18     21.413945  3.714720e-06                      Saltmarsh\n",
      "15     13.123056  2.921201e-04         Supralittoral sediment\n",
      "11      5.451389  1.955872e-02                    Inland rock\n",
      "9       4.362221  3.675199e-02              Heather grassland\n",
      "16      2.751696  9.716078e-02                  Littoral rock\n",
      "12      0.202376  6.528125e-01                      Saltwater\n",
      "17      0.146886  7.015312e-01              Littoral sediment\n",
      "14      0.077339  7.809384e-01             Supralittoral rock \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             20410\n",
      "1             20410\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pheasant 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9195280592951142,\n",
      "        \"recall\": 0.8914796891039742,\n",
      "        \"f1-score\": 0.9052866716306776,\n",
      "        \"support\": 6819\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5542168674698795,\n",
      "        \"recall\": 0.6336088154269972,\n",
      "        \"f1-score\": 0.5912596401028278,\n",
      "        \"support\": 1452\n",
      "    },\n",
      "    \"accuracy\": 0.8462096481682989,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7368724633824968,\n",
      "        \"recall\": 0.7625442522654857,\n",
      "        \"f1-score\": 0.7482731558667527,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8553965334179239,\n",
      "        \"recall\": 0.8462096481682989,\n",
      "        \"f1-score\": 0.8501582409961185,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pheasant 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8843537414965986,\n",
      "        \"recall\": 0.9150901891772987,\n",
      "        \"f1-score\": 0.8994594594594594,\n",
      "        \"support\": 6819\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5234567901234568,\n",
      "        \"recall\": 0.4380165289256198,\n",
      "        \"f1-score\": 0.47694038245219345,\n",
      "        \"support\": 1452\n",
      "    },\n",
      "    \"accuracy\": 0.8313384113166485,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7039052658100278,\n",
      "        \"recall\": 0.6765533590514592,\n",
      "        \"f1-score\": 0.6881999209558264,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8209971493803125,\n",
      "        \"recall\": 0.8313384113166485,\n",
      "        \"f1-score\": 0.8252849098506394,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pink-footed Goose 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "29  7036.747030   0.000000e+00                 Chlorothalonil\n",
      "30  7036.747030   0.000000e+00                     Glyphosate\n",
      "31  7036.747030   0.000000e+00                       Mancozeb\n",
      "32  7036.747030   0.000000e+00                     Mecoprop-P\n",
      "34  7036.747030   0.000000e+00                  Pendimethalin\n",
      "25  5823.246006   0.000000e+00   Inflowing drainage direction\n",
      "23  5796.329675   0.000000e+00                   Surface type\n",
      "37  5571.993565   0.000000e+00                        Sulphur\n",
      "36  5543.722242   0.000000e+00                   Prosulfocarb\n",
      "38  5512.222341   0.000000e+00                     Tri-allate\n",
      "21  4750.062797   0.000000e+00                      Elevation\n",
      "35  4557.628130   0.000000e+00       PropamocarbHydrochloride\n",
      "24  4416.411546   0.000000e+00  Outflowing drainage direction\n",
      "33  4315.390457   0.000000e+00                     Metamitron\n",
      "22  4111.240745   0.000000e+00      Cumulative catchment area\n",
      "2   4027.385455   0.000000e+00                         Arable\n",
      "17  1680.756257   0.000000e+00              Littoral sediment\n",
      "3   1578.759585   0.000000e+00              Improve grassland\n",
      "18  1503.642898  1.032597e-321                      Saltmarsh\n",
      "20  1109.115857  3.118895e-239                       Suburban\n",
      "0   1064.014160  9.660098e-230             Deciduous woodland\n",
      "26   924.852644  2.244918e-200                   Fertiliser K\n",
      "27   924.852644  2.244918e-200                   Fertiliser N\n",
      "28   924.852644  2.244918e-200                   Fertiliser P\n",
      "15   852.335728  5.051033e-185         Supralittoral sediment\n",
      "19   449.450750   4.341152e-99                          Urban\n",
      "13   441.183451   2.587866e-97                     Freshwater\n",
      "16   329.374467   2.984579e-73                  Littoral rock\n",
      "7    117.207897   2.872142e-27                            Fen\n",
      "12    55.275652   1.072913e-13                      Saltwater\n",
      "4     50.484241   1.225518e-12              Neutral grassland\n",
      "1      7.481350   6.237451e-03            Coniferous woodland\n",
      "6      3.024752   8.201214e-02                 Acid grassland\n",
      "14     2.001253   1.571786e-01             Supralittoral rock\n",
      "8      1.379174   2.402504e-01                        Heather\n",
      "11     1.198463   2.736371e-01                    Inland rock\n",
      "10     0.051515   8.204487e-01                            Bog\n",
      "5      0.022436   8.809347e-01           Calcareous grassland\n",
      "9      0.002302   9.617297e-01              Heather grassland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             22823\n",
      "1             22823\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pink-footed Goose 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9526175009552923,\n",
      "        \"recall\": 0.9821405121470781,\n",
      "        \"f1-score\": 0.9671537566274409,\n",
      "        \"support\": 7615\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6761904761904762,\n",
      "        \"recall\": 0.4329268292682927,\n",
      "        \"f1-score\": 0.5278810408921933,\n",
      "        \"support\": 656\n",
      "    },\n",
      "    \"accuracy\": 0.9385805827590376,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8144039885728842,\n",
      "        \"recall\": 0.7075336707076854,\n",
      "        \"f1-score\": 0.7475173987598172,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9306931715820944,\n",
      "        \"recall\": 0.9385805827590376,\n",
      "        \"f1-score\": 0.932313604103886,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pink-footed Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9577043206663196,\n",
      "        \"recall\": 0.9663821405121471,\n",
      "        \"f1-score\": 0.9620236616772339,\n",
      "        \"support\": 7615\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5638841567291312,\n",
      "        \"recall\": 0.5045731707317073,\n",
      "        \"f1-score\": 0.5325824617860015,\n",
      "        \"support\": 656\n",
      "    },\n",
      "    \"accuracy\": 0.9297545641397654,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7607942386977253,\n",
      "        \"recall\": 0.7354776556219271,\n",
      "        \"f1-score\": 0.7473030617316176,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9264691583470359,\n",
      "        \"recall\": 0.9297545641397654,\n",
      "        \"f1-score\": 0.9279632787575569,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pintail 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "29  1342.972285  3.070953e-288                 Chlorothalonil\n",
      "30  1342.972285  3.070953e-288                     Glyphosate\n",
      "31  1342.972285  3.070953e-288                       Mancozeb\n",
      "32  1342.972285  3.070953e-288                     Mecoprop-P\n",
      "34  1342.972285  3.070953e-288                  Pendimethalin\n",
      "25  1325.168183  1.608067e-284   Inflowing drainage direction\n",
      "23  1169.840995  5.439990e-252                   Surface type\n",
      "36  1152.405110  2.491814e-248                   Prosulfocarb\n",
      "37  1150.598093  5.970826e-248                        Sulphur\n",
      "38  1144.636386  1.067303e-246                     Tri-allate\n",
      "26  1092.499220  9.754959e-236                   Fertiliser K\n",
      "27  1092.499220  9.754959e-236                   Fertiliser N\n",
      "28  1092.499220  9.754959e-236                   Fertiliser P\n",
      "21   987.248175  1.475675e-213                      Elevation\n",
      "33   964.658121  8.682894e-209                     Metamitron\n",
      "35   958.762685  1.527456e-207       PropamocarbHydrochloride\n",
      "24   832.260391  9.109264e-181  Outflowing drainage direction\n",
      "18   829.398253  3.685447e-180                      Saltmarsh\n",
      "22   760.526243  1.546155e-165      Cumulative catchment area\n",
      "2    723.168873  1.361610e-157                         Arable\n",
      "17   700.432781  9.417029e-153              Littoral sediment\n",
      "3    435.362823   4.604501e-96              Improve grassland\n",
      "20   333.610377   3.643021e-74                       Suburban\n",
      "19   306.416036   2.680625e-68                          Urban\n",
      "0    244.336169   7.011810e-55             Deciduous woodland\n",
      "4    241.540306   2.824742e-54              Neutral grassland\n",
      "7    156.617233   7.482770e-36                            Fen\n",
      "15   129.030277   7.586888e-30         Supralittoral sediment\n",
      "12    78.133376   1.009712e-18                      Saltwater\n",
      "13    49.612911   1.909316e-12                     Freshwater\n",
      "6      4.766818   2.902040e-02                 Acid grassland\n",
      "8      1.605596   2.051210e-01                        Heather\n",
      "11     1.411725   2.347787e-01                    Inland rock\n",
      "9      1.135354   2.866439e-01              Heather grassland\n",
      "1      0.735527   3.911038e-01            Coniferous woodland\n",
      "14     0.389845   5.323851e-01             Supralittoral rock\n",
      "5      0.173820   6.767413e-01           Calcareous grassland\n",
      "16     0.049150   8.245503e-01                  Littoral rock\n",
      "10     0.034271   8.531325e-01                            Bog \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24298\n",
      "1             24298\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9795918367346939,\n",
      "        \"recall\": 0.9969093831128694,\n",
      "        \"f1-score\": 0.9881747441945959,\n",
      "        \"support\": 8089\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.358974358974359,\n",
      "        \"recall\": 0.07692307692307693,\n",
      "        \"f1-score\": 0.12669683257918554,\n",
      "        \"support\": 182\n",
      "    },\n",
      "    \"accuracy\": 0.9766654576230202,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6692830978545264,\n",
      "        \"recall\": 0.5369162300179732,\n",
      "        \"f1-score\": 0.5574357883868908,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9659354008802167,\n",
      "        \"recall\": 0.9766654576230202,\n",
      "        \"f1-score\": 0.9692182721943535,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pintail 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.979526958290946,\n",
      "        \"recall\": 0.9522808752627024,\n",
      "        \"f1-score\": 0.9657117783489,\n",
      "        \"support\": 8089\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.051597051597051594,\n",
      "        \"recall\": 0.11538461538461539,\n",
      "        \"f1-score\": 0.07130730050933785,\n",
      "        \"support\": 182\n",
      "    },\n",
      "    \"accuracy\": 0.9338653125377826,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5155620049439988,\n",
      "        \"recall\": 0.5338327453236589,\n",
      "        \"f1-score\": 0.5185095394291189,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9591082370941998,\n",
      "        \"recall\": 0.9338653125377826,\n",
      "        \"f1-score\": 0.9460307706150346,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pochard 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "26  2536.936766   0.000000e+00                   Fertiliser K\n",
      "27  2536.936766   0.000000e+00                   Fertiliser N\n",
      "28  2536.936766   0.000000e+00                   Fertiliser P\n",
      "29  2457.352155   0.000000e+00                 Chlorothalonil\n",
      "30  2457.352155   0.000000e+00                     Glyphosate\n",
      "31  2457.352155   0.000000e+00                       Mancozeb\n",
      "32  2457.352155   0.000000e+00                     Mecoprop-P\n",
      "34  2457.352155   0.000000e+00                  Pendimethalin\n",
      "37  2207.208744   0.000000e+00                        Sulphur\n",
      "36  2202.231304   0.000000e+00                   Prosulfocarb\n",
      "38  2198.961775   0.000000e+00                     Tri-allate\n",
      "33  2022.083842   0.000000e+00                     Metamitron\n",
      "35  1989.817324   0.000000e+00       PropamocarbHydrochloride\n",
      "23  1842.761284   0.000000e+00                   Surface type\n",
      "2   1555.536108   0.000000e+00                         Arable\n",
      "25  1549.051565   0.000000e+00   Inflowing drainage direction\n",
      "24  1485.853766  5.152527e-318  Outflowing drainage direction\n",
      "22  1392.732967  1.268634e-298      Cumulative catchment area\n",
      "21  1309.187405  3.516732e-281                      Elevation\n",
      "20   894.234195  6.746925e-194                       Suburban\n",
      "3    745.160492  2.857352e-162              Improve grassland\n",
      "19   743.681811  5.893993e-162                          Urban\n",
      "0    574.470893  7.099727e-126             Deciduous woodland\n",
      "13   271.809180   8.008227e-61                     Freshwater\n",
      "4    149.345332   2.857674e-34              Neutral grassland\n",
      "7    131.344663   2.375107e-30                            Fen\n",
      "18    99.865736   1.760901e-23                      Saltmarsh\n",
      "15    46.675600   8.521422e-12         Supralittoral sediment\n",
      "17    38.959990   4.378224e-10              Littoral sediment\n",
      "12    13.290046   2.672276e-04                      Saltwater\n",
      "6     12.015067   5.283914e-04                 Acid grassland\n",
      "5     10.402339   1.259780e-03           Calcareous grassland\n",
      "10    10.105514   1.479624e-03                            Bog\n",
      "9      4.772190   2.893001e-02              Heather grassland\n",
      "8      4.024274   4.485804e-02                        Heather\n",
      "11     2.159090   1.417381e-01                    Inland rock\n",
      "14     0.851691   3.560812e-01             Supralittoral rock\n",
      "1      0.632046   4.266115e-01            Coniferous woodland\n",
      "16     0.240235   6.240394e-01                  Littoral rock \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24018\n",
      "1             24018\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9687764734357981,\n",
      "        \"recall\": 0.9995005618678986,\n",
      "        \"f1-score\": 0.98389872173058,\n",
      "        \"support\": 8009\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5,\n",
      "        \"recall\": 0.015267175572519083,\n",
      "        \"f1-score\": 0.029629629629629627,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"accuracy\": 0.9683230564623383,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7343882367178991,\n",
      "        \"recall\": 0.5073838687202088,\n",
      "        \"f1-score\": 0.5067641756801048,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9539270675549881,\n",
      "        \"recall\": 0.9683230564623383,\n",
      "        \"f1-score\": 0.9536703935803624,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pochard 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9704837721984079,\n",
      "        \"recall\": 0.9893869396928455,\n",
      "        \"f1-score\": 0.9798441943860516,\n",
      "        \"support\": 8009\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.19811320754716982,\n",
      "        \"recall\": 0.08015267175572519,\n",
      "        \"f1-score\": 0.11413043478260869,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"accuracy\": 0.9605851771248942,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5842984898727889,\n",
      "        \"recall\": 0.5347698057242853,\n",
      "        \"f1-score\": 0.5469873145843301,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9460174334317987,\n",
      "        \"recall\": 0.9605851771248942,\n",
      "        \"f1-score\": 0.9524210285033164,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Red-legged Partridge 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "         F Score        P Value                      Attribute\n",
      "26  13523.622232   0.000000e+00                   Fertiliser K\n",
      "27  13523.622232   0.000000e+00                   Fertiliser N\n",
      "28  13523.622232   0.000000e+00                   Fertiliser P\n",
      "37  11673.899484   0.000000e+00                        Sulphur\n",
      "36  11667.108199   0.000000e+00                   Prosulfocarb\n",
      "38  11666.574791   0.000000e+00                     Tri-allate\n",
      "29  10659.554341   0.000000e+00                 Chlorothalonil\n",
      "30  10659.554341   0.000000e+00                     Glyphosate\n",
      "31  10659.554341   0.000000e+00                       Mancozeb\n",
      "32  10659.554341   0.000000e+00                     Mecoprop-P\n",
      "34  10659.554341   0.000000e+00                  Pendimethalin\n",
      "2    9262.561980   0.000000e+00                         Arable\n",
      "35   9224.344758   0.000000e+00       PropamocarbHydrochloride\n",
      "33   9063.157294   0.000000e+00                     Metamitron\n",
      "23   8310.347324   0.000000e+00                   Surface type\n",
      "24   6580.468060   0.000000e+00  Outflowing drainage direction\n",
      "25   6132.393216   0.000000e+00   Inflowing drainage direction\n",
      "21   6122.856668   0.000000e+00                      Elevation\n",
      "22   6094.072173   0.000000e+00      Cumulative catchment area\n",
      "3    3761.382336   0.000000e+00              Improve grassland\n",
      "0    1821.322033   0.000000e+00             Deciduous woodland\n",
      "20    591.420483  1.691353e-129                       Suburban\n",
      "5     325.757650   1.798558e-72           Calcareous grassland\n",
      "4     108.694533   2.073050e-25              Neutral grassland\n",
      "19     58.130510   2.518487e-14                          Urban\n",
      "7      54.477132   1.609616e-13                            Fen\n",
      "13     38.514509   5.499196e-10                     Freshwater\n",
      "18     19.620936   9.473139e-06                      Saltmarsh\n",
      "9       7.224620   7.194636e-03              Heather grassland\n",
      "11      7.125487   7.603285e-03                    Inland rock\n",
      "15      4.532290   3.326844e-02         Supralittoral sediment\n",
      "8       4.459810   3.470883e-02                        Heather\n",
      "16      3.979559   4.606380e-02                  Littoral rock\n",
      "10      3.505345   6.117991e-02                            Bog\n",
      "1       2.115325   1.458406e-01            Coniferous woodland\n",
      "14      0.747430   3.872974e-01             Supralittoral rock\n",
      "6       0.289120   5.907888e-01                 Acid grassland\n",
      "17      0.153603   6.951180e-01              Littoral sediment\n",
      "12      0.139898   7.083844e-01                      Saltwater \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             22563\n",
      "1             22563\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red-legged Partridge 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9202310717797444,\n",
      "        \"recall\": 0.9892970401691332,\n",
      "        \"f1-score\": 0.9535150280183393,\n",
      "        \"support\": 7568\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4,\n",
      "        \"recall\": 0.07681365576102418,\n",
      "        \"f1-score\": 0.1288782816229117,\n",
      "        \"support\": 703\n",
      "    },\n",
      "    \"accuracy\": 0.9117398138072784,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6601155358898723,\n",
      "        \"recall\": 0.5330553479650787,\n",
      "        \"f1-score\": 0.5411966548206255,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8760136321157183,\n",
      "        \"recall\": 0.9117398138072784,\n",
      "        \"f1-score\": 0.8834243941510941,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Red-legged Partridge 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9294176959919335,\n",
      "        \"recall\": 0.9743657505285412,\n",
      "        \"f1-score\": 0.9513611146948782,\n",
      "        \"support\": 7568\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.42433234421364985,\n",
      "        \"recall\": 0.2034139402560455,\n",
      "        \"f1-score\": 0.27499999999999997,\n",
      "        \"support\": 703\n",
      "    },\n",
      "    \"accuracy\": 0.9088381090557369,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6768750201027917,\n",
      "        \"recall\": 0.5888898453922934,\n",
      "        \"f1-score\": 0.613180557347439,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8864875784366035,\n",
      "        \"recall\": 0.9088381090557369,\n",
      "        \"f1-score\": 0.8938732820711931,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ring-necked Parakeet 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "20  5174.029480   0.000000e+00                       Suburban\n",
      "19  4816.293327   0.000000e+00                          Urban\n",
      "26  2496.791010   0.000000e+00                   Fertiliser K\n",
      "27  2496.791010   0.000000e+00                   Fertiliser N\n",
      "28  2496.791010   0.000000e+00                   Fertiliser P\n",
      "22  2110.055997   0.000000e+00      Cumulative catchment area\n",
      "29  1731.345378   0.000000e+00                 Chlorothalonil\n",
      "31  1731.345378   0.000000e+00                       Mancozeb\n",
      "32  1731.345378   0.000000e+00                     Mecoprop-P\n",
      "30  1731.004980   0.000000e+00                     Glyphosate\n",
      "34  1731.004980   0.000000e+00                  Pendimethalin\n",
      "23  1451.182004  8.429407e-311                   Surface type\n",
      "24  1239.102083  1.629495e-266  Outflowing drainage direction\n",
      "25  1101.552188  1.215523e-237   Inflowing drainage direction\n",
      "21   934.465809  2.082605e-202                      Elevation\n",
      "0    644.695315  7.138206e-141             Deciduous woodland\n",
      "3    590.530935  2.620156e-129              Improve grassland\n",
      "36   550.735617  8.473886e-121                   Prosulfocarb\n",
      "37   549.972053  1.234441e-120                        Sulphur\n",
      "38   531.829928  9.435110e-117                     Tri-allate\n",
      "35   454.966109  2.841247e-100       PropamocarbHydrochloride\n",
      "33   447.760495   1.001226e-98                     Metamitron\n",
      "13   266.908247   9.184502e-60                     Freshwater\n",
      "2     33.737881   6.363711e-09                         Arable\n",
      "6     16.142590   5.887779e-05                 Acid grassland\n",
      "9      8.350427   3.858472e-03              Heather grassland\n",
      "10     6.304742   1.204628e-02                            Bog\n",
      "4      6.018766   1.415966e-02              Neutral grassland\n",
      "8      5.354666   2.067299e-02                        Heather\n",
      "1      4.630696   3.141203e-02            Coniferous woodland\n",
      "16     2.075782   1.496627e-01                  Littoral rock\n",
      "11     2.028360   1.543966e-01                    Inland rock\n",
      "14     1.773163   1.830003e-01             Supralittoral rock\n",
      "17     1.343003   2.465134e-01              Littoral sediment\n",
      "18     1.007935   3.154055e-01                      Saltmarsh\n",
      "12     0.767993   3.808449e-01                      Saltwater\n",
      "15     0.170880   6.793333e-01         Supralittoral sediment\n",
      "7      0.122766   7.260555e-01                            Fen\n",
      "5      0.061965   8.034183e-01           Calcareous grassland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24433\n",
      "1             24433\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring-necked Parakeet 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.989615527259241,\n",
      "        \"recall\": 0.9825702712654965,\n",
      "        \"f1-score\": 0.986080315348608,\n",
      "        \"support\": 8147\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.21978021978021978,\n",
      "        \"recall\": 0.3225806451612903,\n",
      "        \"f1-score\": 0.261437908496732,\n",
      "        \"support\": 124\n",
      "    },\n",
      "    \"accuracy\": 0.9726756135896506,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6046978735197304,\n",
      "        \"recall\": 0.6525754582133934,\n",
      "        \"f1-score\": 0.62375911192267,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9780740476162233,\n",
      "        \"recall\": 0.9726756135896506,\n",
      "        \"f1-score\": 0.9752163740537666,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Ring-necked Parakeet 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9896062606994375,\n",
      "        \"recall\": 0.9933717932981465,\n",
      "        \"f1-score\": 0.9914854517611026,\n",
      "        \"support\": 8147\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.41935483870967744,\n",
      "        \"recall\": 0.31451612903225806,\n",
      "        \"f1-score\": 0.35944700460829493,\n",
      "        \"support\": 124\n",
      "    },\n",
      "    \"accuracy\": 0.9831942933139887,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7044805497045574,\n",
      "        \"recall\": 0.6539439611652023,\n",
      "        \"f1-score\": 0.6754662281846988,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9810569708521724,\n",
      "        \"recall\": 0.9831942933139887,\n",
      "        \"f1-score\": 0.9820098421072581,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Rock Dove 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "         F Score       P Value                      Attribute\n",
      "29  11974.716865  0.000000e+00                 Chlorothalonil\n",
      "30  11974.716865  0.000000e+00                     Glyphosate\n",
      "31  11974.716865  0.000000e+00                       Mancozeb\n",
      "32  11974.716865  0.000000e+00                     Mecoprop-P\n",
      "34  11974.716865  0.000000e+00                  Pendimethalin\n",
      "23   9715.208896  0.000000e+00                   Surface type\n",
      "26   9320.817826  0.000000e+00                   Fertiliser K\n",
      "27   9320.817826  0.000000e+00                   Fertiliser N\n",
      "28   9320.817826  0.000000e+00                   Fertiliser P\n",
      "24   7716.649425  0.000000e+00  Outflowing drainage direction\n",
      "25   7489.623513  0.000000e+00   Inflowing drainage direction\n",
      "37   7222.092735  0.000000e+00                        Sulphur\n",
      "36   7220.986108  0.000000e+00                   Prosulfocarb\n",
      "21   7209.182719  0.000000e+00                      Elevation\n",
      "38   7185.198282  0.000000e+00                     Tri-allate\n",
      "22   6950.626400  0.000000e+00      Cumulative catchment area\n",
      "35   5320.538837  0.000000e+00       PropamocarbHydrochloride\n",
      "33   5174.072845  0.000000e+00                     Metamitron\n",
      "3    4736.971375  0.000000e+00              Improve grassland\n",
      "20   4361.914241  0.000000e+00                       Suburban\n",
      "2    3272.566853  0.000000e+00                         Arable\n",
      "0    1947.276915  0.000000e+00             Deciduous woodland\n",
      "19   1698.722674  0.000000e+00                          Urban\n",
      "4     327.460980  7.718845e-73              Neutral grassland\n",
      "5     172.810554  2.260169e-39           Calcareous grassland\n",
      "13    158.476505  2.949316e-36                     Freshwater\n",
      "16     63.399064  1.741628e-15                  Littoral rock\n",
      "14     38.512231  5.505612e-10             Supralittoral rock\n",
      "15     35.360040  2.767982e-09         Supralittoral sediment\n",
      "7      32.305738  1.328301e-08                            Fen\n",
      "18     15.733289  7.308286e-05                      Saltmarsh\n",
      "17     13.808123  2.027922e-04              Littoral sediment\n",
      "11      5.939593  1.480967e-02                    Inland rock\n",
      "8       1.467724  2.257138e-01                        Heather\n",
      "6       1.183074  2.767391e-01                 Acid grassland\n",
      "10      1.068542  3.012825e-01                            Bog\n",
      "9       0.520829  4.704932e-01              Heather grassland\n",
      "12      0.491905  4.830835e-01                      Saltwater\n",
      "1       0.363769  5.464245e-01            Coniferous woodland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             21951\n",
      "1             21951\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock Dove 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9031088082901555,\n",
      "        \"recall\": 0.9664541169947325,\n",
      "        \"f1-score\": 0.9337083165930092,\n",
      "        \"support\": 7214\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5607985480943739,\n",
      "        \"recall\": 0.2923368022705771,\n",
      "        \"f1-score\": 0.3843283582089552,\n",
      "        \"support\": 1057\n",
      "    },\n",
      "    \"accuracy\": 0.8803046789989118,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7319536781922646,\n",
      "        \"recall\": 0.6293954596326548,\n",
      "        \"f1-score\": 0.6590183374009823,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8593629559111274,\n",
      "        \"recall\": 0.8803046789989118,\n",
      "        \"f1-score\": 0.863499802989824,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Rock Dove 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8975622406639004,\n",
      "        \"recall\": 0.9595231494316606,\n",
      "        \"f1-score\": 0.927509044620126,\n",
      "        \"support\": 7214\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4776386404293381,\n",
      "        \"recall\": 0.25260170293282874,\n",
      "        \"f1-score\": 0.3304455445544554,\n",
      "        \"support\": 1057\n",
      "    },\n",
      "    \"accuracy\": 0.869181477451336,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6876004405466193,\n",
      "        \"recall\": 0.6060624261822447,\n",
      "        \"f1-score\": 0.6289772945872907,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8438977206000711,\n",
      "        \"recall\": 0.869181477451336,\n",
      "        \"f1-score\": 0.8512067692520431,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ruddy Duck 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "13  2084.023887   0.000000e+00                     Freshwater\n",
      "26   611.973820  6.880144e-134                   Fertiliser K\n",
      "27   611.973820  6.880144e-134                   Fertiliser N\n",
      "28   611.973820  6.880144e-134                   Fertiliser P\n",
      "24   574.800456  6.036397e-126  Outflowing drainage direction\n",
      "22   439.953763   4.753911e-97      Cumulative catchment area\n",
      "29   420.253312   8.124948e-93                 Chlorothalonil\n",
      "31   420.253312   8.124948e-93                       Mancozeb\n",
      "32   420.253312   8.124948e-93                     Mecoprop-P\n",
      "30   420.170729   8.463895e-93                     Glyphosate\n",
      "34   420.170729   8.463895e-93                  Pendimethalin\n",
      "37   334.799327   2.018860e-74                        Sulphur\n",
      "23   331.644948   9.666367e-74                   Surface type\n",
      "38   327.259937   8.529270e-73                     Tri-allate\n",
      "36   316.625993   1.678218e-70                   Prosulfocarb\n",
      "35   279.546885   1.702940e-62       PropamocarbHydrochloride\n",
      "33   275.651878   1.182909e-61                     Metamitron\n",
      "25   252.505585   1.196889e-56   Inflowing drainage direction\n",
      "19   209.791045   2.131385e-47                          Urban\n",
      "21   206.348108   1.188914e-46                      Elevation\n",
      "0    197.545829   9.644094e-45             Deciduous woodland\n",
      "4    178.469812   1.333079e-40              Neutral grassland\n",
      "3    159.658114   1.632226e-36              Improve grassland\n",
      "20   117.615266   2.340567e-27                       Suburban\n",
      "7    105.045117   1.299505e-24                            Fen\n",
      "2     54.679745   1.452179e-13                         Arable\n",
      "18    36.700240   1.392471e-09                      Saltmarsh\n",
      "15    18.578174   1.635478e-05         Supralittoral sediment\n",
      "12    17.716380   2.570854e-05                      Saltwater\n",
      "6      3.935350   4.728954e-02                 Acid grassland\n",
      "17     2.178205   1.399871e-01              Littoral sediment\n",
      "8      1.916130   1.662933e-01                        Heather\n",
      "10     1.383460   2.395211e-01                            Bog\n",
      "9      1.221097   2.691535e-01              Heather grassland\n",
      "1      1.008807   3.151962e-01            Coniferous woodland\n",
      "14     0.445443   5.045116e-01             Supralittoral rock\n",
      "16     0.433081   5.104857e-01                  Littoral rock\n",
      "11     0.247335   6.189611e-01                    Inland rock\n",
      "5      0.072280   7.880478e-01           Calcareous grassland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24719\n",
      "1             24719\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ruddy Duck 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9963728690605731,\n",
      "        \"recall\": 1.0,\n",
      "        \"f1-score\": 0.9981831395348837,\n",
      "        \"support\": 8241\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 30\n",
      "    },\n",
      "    \"accuracy\": 0.9963728690605731,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49818643453028655,\n",
      "        \"recall\": 0.5,\n",
      "        \"f1-score\": 0.49909156976744184,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9927588941999981,\n",
      "        \"recall\": 0.9963728690605731,\n",
      "        \"f1-score\": 0.9945625985862625,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Ruddy Duck 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9967312348668281,\n",
      "        \"recall\": 0.9990292440237836,\n",
      "        \"f1-score\": 0.9978789164293074,\n",
      "        \"support\": 8241\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2727272727272727,\n",
      "        \"recall\": 0.1,\n",
      "        \"f1-score\": 0.14634146341463417,\n",
      "        \"support\": 30\n",
      "    },\n",
      "    \"accuracy\": 0.9957683472373353,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6347292537970504,\n",
      "        \"recall\": 0.5495146220118918,\n",
      "        \"f1-score\": 0.5721101899219707,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9941051776954841,\n",
      "        \"recall\": 0.9957683472373353,\n",
      "        \"f1-score\": 0.994790278587397,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Whooper Swan 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "25  1676.560528   0.000000e+00   Inflowing drainage direction\n",
      "23  1555.884294   0.000000e+00                   Surface type\n",
      "21  1411.884325  1.291051e-302                      Elevation\n",
      "29  1368.299617  1.585328e-293                 Chlorothalonil\n",
      "31  1368.299617  1.585328e-293                       Mancozeb\n",
      "32  1368.299617  1.585328e-293                     Mecoprop-P\n",
      "30  1367.972943  1.854762e-293                     Glyphosate\n",
      "34  1367.972943  1.854762e-293                  Pendimethalin\n",
      "24  1295.050837  3.175931e-278  Outflowing drainage direction\n",
      "22  1252.169594  2.987125e-269      Cumulative catchment area\n",
      "37   822.832470  9.102816e-179                        Sulphur\n",
      "36   818.843021  6.390379e-178                   Prosulfocarb\n",
      "38   809.297607  6.776178e-176                     Tri-allate\n",
      "3    560.870223  5.752589e-123              Improve grassland\n",
      "35   537.803158  4.965172e-118       PropamocarbHydrochloride\n",
      "2    518.099739  8.226528e-114                         Arable\n",
      "33   491.320567  4.509861e-108                     Metamitron\n",
      "26   428.892315   1.130612e-94                   Fertiliser K\n",
      "27   428.892315   1.130612e-94                   Fertiliser N\n",
      "28   428.892315   1.130612e-94                   Fertiliser P\n",
      "13   302.457075   1.918026e-67                     Freshwater\n",
      "17   294.097064   1.224555e-65              Littoral sediment\n",
      "0    236.168141   4.111276e-53             Deciduous woodland\n",
      "18   210.131628   1.798147e-47                      Saltmarsh\n",
      "7    171.006773   5.572412e-39                            Fen\n",
      "20   166.933497   4.277570e-38                       Suburban\n",
      "4    152.889945   4.839547e-35              Neutral grassland\n",
      "19   129.204755   6.950815e-30                          Urban\n",
      "9     88.659782   4.982099e-21              Heather grassland\n",
      "15    87.366558   9.563148e-21         Supralittoral sediment\n",
      "14    65.810060   5.135459e-16             Supralittoral rock\n",
      "16    62.367071   2.938167e-15                  Littoral rock\n",
      "10    36.089602   1.904150e-09                            Bog\n",
      "1     25.502277   4.442055e-07            Coniferous woodland\n",
      "8     13.925116   1.905575e-04                        Heather\n",
      "12     7.753771   5.363096e-03                      Saltwater\n",
      "6      7.525727   6.085674e-03                 Acid grassland\n",
      "11     0.360808   5.480621e-01                    Inland rock\n",
      "5      0.028754   8.653489e-01           Calcareous grassland \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             24119\n",
      "1             24119\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whooper Swan 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9686592449177154,\n",
      "        \"recall\": 0.9993757802746567,\n",
      "        \"f1-score\": 0.9837778050878702,\n",
      "        \"support\": 8010\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2857142857142857,\n",
      "        \"recall\": 0.007662835249042145,\n",
      "        \"f1-score\": 0.014925373134328356,\n",
      "        \"support\": 261\n",
      "    },\n",
      "    \"accuracy\": 0.9680812477330432,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6271867653160006,\n",
      "        \"recall\": 0.5035193077618494,\n",
      "        \"f1-score\": 0.4993515891110993,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9471082070320794,\n",
      "        \"recall\": 0.9680812477330432,\n",
      "        \"f1-score\": 0.9532046597922741,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Whooper Swan 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9717665013353681,\n",
      "        \"recall\": 0.9539325842696629,\n",
      "        \"f1-score\": 0.9627669627669627,\n",
      "        \"support\": 8010\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.09558823529411764,\n",
      "        \"recall\": 0.14942528735632185,\n",
      "        \"f1-score\": 0.11659192825112107,\n",
      "        \"support\": 261\n",
      "    },\n",
      "    \"accuracy\": 0.9285455204932898,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5336773683147429,\n",
      "        \"recall\": 0.5516789358129923,\n",
      "        \"f1-score\": 0.5396794455090419,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9441177856496268,\n",
      "        \"recall\": 0.9285455204932898,\n",
      "        \"f1-score\": 0.9360650302305542,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Wigeon 1km cells... \n",
      "\n",
      "K-Best Features Dataframe: \n",
      "        F Score        P Value                      Attribute\n",
      "29  4819.105469   0.000000e+00                 Chlorothalonil\n",
      "30  4819.105469   0.000000e+00                     Glyphosate\n",
      "31  4819.105469   0.000000e+00                       Mancozeb\n",
      "32  4819.105469   0.000000e+00                     Mecoprop-P\n",
      "34  4819.105469   0.000000e+00                  Pendimethalin\n",
      "25  4543.858943   0.000000e+00   Inflowing drainage direction\n",
      "23  4385.352401   0.000000e+00                   Surface type\n",
      "21  3772.569210   0.000000e+00                      Elevation\n",
      "37  3368.272204   0.000000e+00                        Sulphur\n",
      "36  3344.881952   0.000000e+00                   Prosulfocarb\n",
      "38  3316.664763   0.000000e+00                     Tri-allate\n",
      "24  3296.213107   0.000000e+00  Outflowing drainage direction\n",
      "22  3018.447019   0.000000e+00      Cumulative catchment area\n",
      "35  2471.153767   0.000000e+00       PropamocarbHydrochloride\n",
      "33  2444.415822   0.000000e+00                     Metamitron\n",
      "3   1984.684092   0.000000e+00              Improve grassland\n",
      "2   1640.404091   0.000000e+00                         Arable\n",
      "26  1577.395456   0.000000e+00                   Fertiliser K\n",
      "27  1577.395456   0.000000e+00                   Fertiliser N\n",
      "28  1577.395456   0.000000e+00                   Fertiliser P\n",
      "0   1016.533733  9.783489e-220             Deciduous woodland\n",
      "20   871.988879  3.462062e-189                       Suburban\n",
      "17   839.439277  2.736586e-182              Littoral sediment\n",
      "13   570.717851  4.505394e-125                     Freshwater\n",
      "18   551.354132  6.247919e-121                      Saltmarsh\n",
      "19   535.113644  1.869402e-117                          Urban\n",
      "15   329.558717   2.723634e-73         Supralittoral sediment\n",
      "16   261.381261   1.439437e-58                  Littoral rock\n",
      "7    176.243055   4.059697e-40                            Fen\n",
      "4    150.239640   1.825651e-34              Neutral grassland\n",
      "12   104.357556   1.836557e-24                      Saltwater\n",
      "14    37.244870   1.053444e-09             Supralittoral rock\n",
      "8     14.097058   1.739122e-04                        Heather\n",
      "1     12.680417   3.700235e-04            Coniferous woodland\n",
      "9      9.281786   2.316259e-03              Heather grassland\n",
      "5      7.220527   7.211058e-03           Calcareous grassland\n",
      "10     1.418478   2.336626e-01                            Bog\n",
      "6      0.976582   3.230512e-01                 Acid grassland\n",
      "11     0.096023   7.566574e-01                    Inland rock \n",
      "\n",
      "Resampled Value Counts: \n",
      " Occurrence\n",
      "0             23081\n",
      "1             23081\n",
      "dtype: int64 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wigeon 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9378988708885616,\n",
      "        \"recall\": 0.9942753057507155,\n",
      "        \"f1-score\": 0.9652646204370341,\n",
      "        \"support\": 7686\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6422764227642277,\n",
      "        \"recall\": 0.13504273504273503,\n",
      "        \"f1-score\": 0.2231638418079096,\n",
      "        \"support\": 585\n",
      "    },\n",
      "    \"accuracy\": 0.93350259944384,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7900876468263947,\n",
      "        \"recall\": 0.5646590203967253,\n",
      "        \"f1-score\": 0.5942142311224718,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9169897749929341,\n",
      "        \"recall\": 0.93350259944384,\n",
      "        \"f1-score\": 0.9127765348974334,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Wigeon 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9475020037403152,\n",
      "        \"recall\": 0.9228467343221441,\n",
      "        \"f1-score\": 0.9350118639599261,\n",
      "        \"support\": 7686\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2445859872611465,\n",
      "        \"recall\": 0.3282051282051282,\n",
      "        \"f1-score\": 0.28029197080291973,\n",
      "        \"support\": 585\n",
      "    },\n",
      "    \"accuracy\": 0.8807882964575021,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5960439955007308,\n",
      "        \"recall\": 0.6255259312636362,\n",
      "        \"f1-score\": 0.6076519173814229,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8977854193321041,\n",
      "        \"recall\": 0.8807882964575021,\n",
      "        \"f1-score\": 0.8887041457279289,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add model pipeline\n",
    "estimators = [\n",
    "    ('lr', LogisticRegression(max_iter=10000, solver='saga', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('knn', KNeighborsClassifier(n_neighbors=5, weights='distance')),\n",
    "    ('sgd', SGDClassifier( max_iter=10000, loss='modified_huber', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('rf', RandomForestClassifier(n_estimators=20,max_features=None, random_state=seed, verbose=verbose))\n",
    "]\n",
    "\n",
    "\n",
    "for dict in df_dicts:\n",
    "    print(f'Training with {dict[\"name\"]} cells... \\n')\n",
    "    # Use this if using coordinates as separate columns\n",
    "    # coords, X, y = data['dataframe'].iloc[:, :2], data['dataframe'].iloc[:, 2:-1], data['dataframe'].iloc[:, [-1]]\n",
    "    # data['coords'] = coords\n",
    "    \n",
    "    # Use this if using coordinates as indices\n",
    "    X, y = dict['dataframe'].iloc[:, 0:-1], dict['dataframe'].iloc[:, [-1]], \n",
    "\n",
    "    dict['X'] = standardise(X)\n",
    "    dict['y'] = y\n",
    "    dict['kbest'] = feature_select(dict['X'], dict['y'])\n",
    "\n",
    "    # dict['X'] = dict['kbest']['10'].transform(dict['X'])\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(dict['X'], dict['y'], random_state=seed)\n",
    "    dict['X_train'], dict['X_test'], dict['y_train'], dict['y_test'] = X_train, X_test, y_train, y_test # for debugging purposes\n",
    "\n",
    "    dict['X_smote'], dict['y_smote'] = oversample(X_train, y_train)\n",
    "\n",
    "    stack_clf = StackingClassifier(\n",
    "        estimators=estimators, \n",
    "        final_estimator=GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=None, max_depth=2, random_state=seed)\n",
    "    )\n",
    "\n",
    "    # Classifier without SMOTE\n",
    "    stack_clf.fit(dict['X_train'], dict['y_train'])\n",
    "    y_pred = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions'] = y_pred\n",
    "    dict['report'] = classification_report(y_test, y_pred, output_dict=True)\n",
    "    \n",
    "\n",
    "    # Classifier with SMOTE\n",
    "    stack_clf.fit(dict['X_smote'], dict['y_smote'])\n",
    "    y_pred_smote = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions_smote'] = y_pred_smote\n",
    "    dict['report_smote'] = classification_report(y_test, y_pred_smote, output_dict=True)\n",
    "    \n",
    "    print(f'{dict[\"name\"]} Classification Report: \\n {json.dumps(dict[\"report\"], indent=4)} \\n')\n",
    "    print(f'{dict[\"name\"]} SMOTE Classification Report: \\n {json.dumps(dict[\"report_smote\"], indent=4)} \\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9857919446503582,\n",
      "        \"recall\": 0.9876222304740686,\n",
      "        \"f1-score\": 0.9867062387930501,\n",
      "        \"support\": 8079\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4350282485875706,\n",
      "        \"recall\": 0.4010416666666667,\n",
      "        \"f1-score\": 0.41734417344173447,\n",
      "        \"support\": 192\n",
      "    },\n",
      "    \"accuracy\": 0.9740055616007738,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7104100966189644,\n",
      "        \"recall\": 0.6943319485703676,\n",
      "        \"f1-score\": 0.7020252061173923,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9730067155796226,\n",
      "        \"recall\": 0.9740055616007738,\n",
      "        \"f1-score\": 0.9734892739100308,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Canada Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9475095785440613,\n",
      "        \"recall\": 0.8669588080631025,\n",
      "        \"f1-score\": 0.9054462242562928,\n",
      "        \"support\": 5705\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7512291052114061,\n",
      "        \"recall\": 0.8932190179267342,\n",
      "        \"f1-score\": 0.8160940003560618,\n",
      "        \"support\": 2566\n",
      "    },\n",
      "    \"accuracy\": 0.8751057913190666,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8493693418777337,\n",
      "        \"recall\": 0.8800889129949183,\n",
      "        \"f1-score\": 0.8607701123061773,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8866154067907553,\n",
      "        \"recall\": 0.8751057913190666,\n",
      "        \"f1-score\": 0.8777255367302389,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Egyptian Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9874953571870744,\n",
      "        \"recall\": 0.9870065585942334,\n",
      "        \"f1-score\": 0.9872508973882905,\n",
      "        \"support\": 8081\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4587628865979381,\n",
      "        \"recall\": 0.46842105263157896,\n",
      "        \"f1-score\": 0.46354166666666663,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.9750937008826018,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7231291218925062,\n",
      "        \"recall\": 0.7277138056129062,\n",
      "        \"f1-score\": 0.7253962820274786,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9753494051363023,\n",
      "        \"recall\": 0.9750937008826018,\n",
      "        \"f1-score\": 0.9752203383462028,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Gadwall 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.961431812452735,\n",
      "        \"recall\": 0.9873155578565881,\n",
      "        \"f1-score\": 0.9742017879948913,\n",
      "        \"support\": 7726\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7091988130563798,\n",
      "        \"recall\": 0.43853211009174314,\n",
      "        \"f1-score\": 0.5419501133786848,\n",
      "        \"support\": 545\n",
      "    },\n",
      "    \"accuracy\": 0.9511546366823842,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8353153127545574,\n",
      "        \"recall\": 0.7129238339741656,\n",
      "        \"f1-score\": 0.758075950686788,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9448114540110697,\n",
      "        \"recall\": 0.9511546366823842,\n",
      "        \"f1-score\": 0.945719480817303,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Goshawk 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9871717293961031,\n",
      "        \"recall\": 0.9990202082057563,\n",
      "        \"f1-score\": 0.9930606281957634,\n",
      "        \"support\": 8165\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 106\n",
      "    },\n",
      "    \"accuracy\": 0.9862169024301777,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49358586469805155,\n",
      "        \"recall\": 0.49951010410287816,\n",
      "        \"f1-score\": 0.4965303140978817,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9745202720975918,\n",
      "        \"recall\": 0.9862169024301777,\n",
      "        \"f1-score\": 0.9803336995790604,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Grey Partridge 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9373253977893842,\n",
      "        \"recall\": 0.9966421283740152,\n",
      "        \"f1-score\": 0.9660741111667501,\n",
      "        \"support\": 7743\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3157894736842105,\n",
      "        \"recall\": 0.022727272727272728,\n",
      "        \"f1-score\": 0.04240282685512368,\n",
      "        \"support\": 528\n",
      "    },\n",
      "    \"accuracy\": 0.9344698343610204,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6265574357367973,\n",
      "        \"recall\": 0.509684700550644,\n",
      "        \"f1-score\": 0.5042384690109369,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.89764809541633,\n",
      "        \"recall\": 0.9344698343610204,\n",
      "        \"f1-score\": 0.9071092413666608,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Indian Peafowl 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.990921195981116,\n",
      "        \"recall\": 0.9987798926305514,\n",
      "        \"f1-score\": 0.9948350246095886,\n",
      "        \"support\": 8196\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 75\n",
      "    },\n",
      "    \"accuracy\": 0.9897231290049571,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.495460597990558,\n",
      "        \"recall\": 0.4993899463152757,\n",
      "        \"f1-score\": 0.4974175123047943,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9819356936599235,\n",
      "        \"recall\": 0.9897231290049571,\n",
      "        \"f1-score\": 0.9858140323661212,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Little Owl 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9410580021482277,\n",
      "        \"recall\": 0.9480589747058028,\n",
      "        \"f1-score\": 0.944545515800822,\n",
      "        \"support\": 7393\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5334143377885784,\n",
      "        \"recall\": 0.5,\n",
      "        \"f1-score\": 0.516166960611405,\n",
      "        \"support\": 878\n",
      "    },\n",
      "    \"accuracy\": 0.9004957078950551,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.737236169968403,\n",
      "        \"recall\": 0.7240294873529014,\n",
      "        \"f1-score\": 0.7303562382061135,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8977849834917445,\n",
      "        \"recall\": 0.9004957078950551,\n",
      "        \"f1-score\": 0.89907140487635,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Mandarin Duck 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9780353874313605,\n",
      "        \"recall\": 0.9962709757613425,\n",
      "        \"f1-score\": 0.9870689655172414,\n",
      "        \"support\": 8045\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6052631578947368,\n",
      "        \"recall\": 0.20353982300884957,\n",
      "        \"f1-score\": 0.30463576158940403,\n",
      "        \"support\": 226\n",
      "    },\n",
      "    \"accuracy\": 0.9746100834240116,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7916492726630486,\n",
      "        \"recall\": 0.599905399385096,\n",
      "        \"f1-score\": 0.6458523635533228,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9678496149884545,\n",
      "        \"recall\": 0.9746100834240116,\n",
      "        \"f1-score\": 0.9684218969538644,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Mute Swan 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9197844007609385,\n",
      "        \"recall\": 0.8438045375218151,\n",
      "        \"f1-score\": 0.8801577669902911,\n",
      "        \"support\": 3438\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8950556966972836,\n",
      "        \"recall\": 0.9476515621767019,\n",
      "        \"f1-score\": 0.9206030150753769,\n",
      "        \"support\": 4833\n",
      "    },\n",
      "    \"accuracy\": 0.9044855519284246,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9074200487291111,\n",
      "        \"recall\": 0.8957280498492585,\n",
      "        \"f1-score\": 0.9003803910328341,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9053346574723827,\n",
      "        \"recall\": 0.9044855519284246,\n",
      "        \"f1-score\": 0.9037911709311953,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pheasant 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9195280592951142,\n",
      "        \"recall\": 0.8914796891039742,\n",
      "        \"f1-score\": 0.9052866716306776,\n",
      "        \"support\": 6819\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5542168674698795,\n",
      "        \"recall\": 0.6336088154269972,\n",
      "        \"f1-score\": 0.5912596401028278,\n",
      "        \"support\": 1452\n",
      "    },\n",
      "    \"accuracy\": 0.8462096481682989,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7368724633824968,\n",
      "        \"recall\": 0.7625442522654857,\n",
      "        \"f1-score\": 0.7482731558667527,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8553965334179239,\n",
      "        \"recall\": 0.8462096481682989,\n",
      "        \"f1-score\": 0.8501582409961185,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pink-footed Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9526175009552923,\n",
      "        \"recall\": 0.9821405121470781,\n",
      "        \"f1-score\": 0.9671537566274409,\n",
      "        \"support\": 7615\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6761904761904762,\n",
      "        \"recall\": 0.4329268292682927,\n",
      "        \"f1-score\": 0.5278810408921933,\n",
      "        \"support\": 656\n",
      "    },\n",
      "    \"accuracy\": 0.9385805827590376,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8144039885728842,\n",
      "        \"recall\": 0.7075336707076854,\n",
      "        \"f1-score\": 0.7475173987598172,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9306931715820944,\n",
      "        \"recall\": 0.9385805827590376,\n",
      "        \"f1-score\": 0.932313604103886,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pintail 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9795918367346939,\n",
      "        \"recall\": 0.9969093831128694,\n",
      "        \"f1-score\": 0.9881747441945959,\n",
      "        \"support\": 8089\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.358974358974359,\n",
      "        \"recall\": 0.07692307692307693,\n",
      "        \"f1-score\": 0.12669683257918554,\n",
      "        \"support\": 182\n",
      "    },\n",
      "    \"accuracy\": 0.9766654576230202,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6692830978545264,\n",
      "        \"recall\": 0.5369162300179732,\n",
      "        \"f1-score\": 0.5574357883868908,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9659354008802167,\n",
      "        \"recall\": 0.9766654576230202,\n",
      "        \"f1-score\": 0.9692182721943535,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pochard 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9687764734357981,\n",
      "        \"recall\": 0.9995005618678986,\n",
      "        \"f1-score\": 0.98389872173058,\n",
      "        \"support\": 8009\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5,\n",
      "        \"recall\": 0.015267175572519083,\n",
      "        \"f1-score\": 0.029629629629629627,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"accuracy\": 0.9683230564623383,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7343882367178991,\n",
      "        \"recall\": 0.5073838687202088,\n",
      "        \"f1-score\": 0.5067641756801048,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9539270675549881,\n",
      "        \"recall\": 0.9683230564623383,\n",
      "        \"f1-score\": 0.9536703935803624,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Red-legged Partridge 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9202310717797444,\n",
      "        \"recall\": 0.9892970401691332,\n",
      "        \"f1-score\": 0.9535150280183393,\n",
      "        \"support\": 7568\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4,\n",
      "        \"recall\": 0.07681365576102418,\n",
      "        \"f1-score\": 0.1288782816229117,\n",
      "        \"support\": 703\n",
      "    },\n",
      "    \"accuracy\": 0.9117398138072784,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6601155358898723,\n",
      "        \"recall\": 0.5330553479650787,\n",
      "        \"f1-score\": 0.5411966548206255,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8760136321157183,\n",
      "        \"recall\": 0.9117398138072784,\n",
      "        \"f1-score\": 0.8834243941510941,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Ring-necked Parakeet 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.989615527259241,\n",
      "        \"recall\": 0.9825702712654965,\n",
      "        \"f1-score\": 0.986080315348608,\n",
      "        \"support\": 8147\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.21978021978021978,\n",
      "        \"recall\": 0.3225806451612903,\n",
      "        \"f1-score\": 0.261437908496732,\n",
      "        \"support\": 124\n",
      "    },\n",
      "    \"accuracy\": 0.9726756135896506,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6046978735197304,\n",
      "        \"recall\": 0.6525754582133934,\n",
      "        \"f1-score\": 0.62375911192267,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9780740476162233,\n",
      "        \"recall\": 0.9726756135896506,\n",
      "        \"f1-score\": 0.9752163740537666,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Rock Dove 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9031088082901555,\n",
      "        \"recall\": 0.9664541169947325,\n",
      "        \"f1-score\": 0.9337083165930092,\n",
      "        \"support\": 7214\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5607985480943739,\n",
      "        \"recall\": 0.2923368022705771,\n",
      "        \"f1-score\": 0.3843283582089552,\n",
      "        \"support\": 1057\n",
      "    },\n",
      "    \"accuracy\": 0.8803046789989118,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7319536781922646,\n",
      "        \"recall\": 0.6293954596326548,\n",
      "        \"f1-score\": 0.6590183374009823,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8593629559111274,\n",
      "        \"recall\": 0.8803046789989118,\n",
      "        \"f1-score\": 0.863499802989824,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Ruddy Duck 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9963728690605731,\n",
      "        \"recall\": 1.0,\n",
      "        \"f1-score\": 0.9981831395348837,\n",
      "        \"support\": 8241\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 30\n",
      "    },\n",
      "    \"accuracy\": 0.9963728690605731,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49818643453028655,\n",
      "        \"recall\": 0.5,\n",
      "        \"f1-score\": 0.49909156976744184,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9927588941999981,\n",
      "        \"recall\": 0.9963728690605731,\n",
      "        \"f1-score\": 0.9945625985862625,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Whooper Swan 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9686592449177154,\n",
      "        \"recall\": 0.9993757802746567,\n",
      "        \"f1-score\": 0.9837778050878702,\n",
      "        \"support\": 8010\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2857142857142857,\n",
      "        \"recall\": 0.007662835249042145,\n",
      "        \"f1-score\": 0.014925373134328356,\n",
      "        \"support\": 261\n",
      "    },\n",
      "    \"accuracy\": 0.9680812477330432,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6271867653160006,\n",
      "        \"recall\": 0.5035193077618494,\n",
      "        \"f1-score\": 0.4993515891110993,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9471082070320794,\n",
      "        \"recall\": 0.9680812477330432,\n",
      "        \"f1-score\": 0.9532046597922741,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Wigeon 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9378988708885616,\n",
      "        \"recall\": 0.9942753057507155,\n",
      "        \"f1-score\": 0.9652646204370341,\n",
      "        \"support\": 7686\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6422764227642277,\n",
      "        \"recall\": 0.13504273504273503,\n",
      "        \"f1-score\": 0.2231638418079096,\n",
      "        \"support\": 585\n",
      "    },\n",
      "    \"accuracy\": 0.93350259944384,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7900876468263947,\n",
      "        \"recall\": 0.5646590203967253,\n",
      "        \"f1-score\": 0.5942142311224718,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9169897749929341,\n",
      "        \"recall\": 0.93350259944384,\n",
      "        \"f1-score\": 0.9127765348974334,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'], '\\n', json.dumps(dict['report'], indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Labels</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Precision (Smote)</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Recall (Smote)</th>\n",
       "      <th>F1</th>\n",
       "      <th>F1 (Smote)</th>\n",
       "      <th>Occurrence Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mute Swan 1km</td>\n",
       "      <td>0.895056</td>\n",
       "      <td>0.901090</td>\n",
       "      <td>0.947652</td>\n",
       "      <td>0.923650</td>\n",
       "      <td>0.920603</td>\n",
       "      <td>0.912231</td>\n",
       "      <td>19124</td>\n",
       "      <td>0.578044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 1km</td>\n",
       "      <td>0.751229</td>\n",
       "      <td>0.762679</td>\n",
       "      <td>0.893219</td>\n",
       "      <td>0.767732</td>\n",
       "      <td>0.816094</td>\n",
       "      <td>0.765197</td>\n",
       "      <td>10147</td>\n",
       "      <td>0.306704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 1km</td>\n",
       "      <td>0.554217</td>\n",
       "      <td>0.523457</td>\n",
       "      <td>0.633609</td>\n",
       "      <td>0.438017</td>\n",
       "      <td>0.591260</td>\n",
       "      <td>0.476940</td>\n",
       "      <td>5855</td>\n",
       "      <td>0.176974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 1km</td>\n",
       "      <td>0.560799</td>\n",
       "      <td>0.477639</td>\n",
       "      <td>0.292337</td>\n",
       "      <td>0.252602</td>\n",
       "      <td>0.384328</td>\n",
       "      <td>0.330446</td>\n",
       "      <td>3919</td>\n",
       "      <td>0.118456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 1km</td>\n",
       "      <td>0.533414</td>\n",
       "      <td>0.501166</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.489749</td>\n",
       "      <td>0.516167</td>\n",
       "      <td>0.495392</td>\n",
       "      <td>3548</td>\n",
       "      <td>0.107242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 1km</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.424332</td>\n",
       "      <td>0.076814</td>\n",
       "      <td>0.203414</td>\n",
       "      <td>0.128878</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>2953</td>\n",
       "      <td>0.089258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 1km</td>\n",
       "      <td>0.676190</td>\n",
       "      <td>0.563884</td>\n",
       "      <td>0.432927</td>\n",
       "      <td>0.504573</td>\n",
       "      <td>0.527881</td>\n",
       "      <td>0.532582</td>\n",
       "      <td>2646</td>\n",
       "      <td>0.079978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 1km</td>\n",
       "      <td>0.642276</td>\n",
       "      <td>0.244586</td>\n",
       "      <td>0.135043</td>\n",
       "      <td>0.328205</td>\n",
       "      <td>0.223164</td>\n",
       "      <td>0.280292</td>\n",
       "      <td>2317</td>\n",
       "      <td>0.070034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 1km</td>\n",
       "      <td>0.709199</td>\n",
       "      <td>0.596330</td>\n",
       "      <td>0.438532</td>\n",
       "      <td>0.477064</td>\n",
       "      <td>0.541950</td>\n",
       "      <td>0.530071</td>\n",
       "      <td>2205</td>\n",
       "      <td>0.066649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 1km</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.345013</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.242424</td>\n",
       "      <td>0.042403</td>\n",
       "      <td>0.284761</td>\n",
       "      <td>2123</td>\n",
       "      <td>0.064170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 1km</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.198113</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.080153</td>\n",
       "      <td>0.029630</td>\n",
       "      <td>0.114130</td>\n",
       "      <td>1057</td>\n",
       "      <td>0.031949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 1km</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.458647</td>\n",
       "      <td>0.203540</td>\n",
       "      <td>0.269912</td>\n",
       "      <td>0.304636</td>\n",
       "      <td>0.339833</td>\n",
       "      <td>1010</td>\n",
       "      <td>0.030528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 1km</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.095588</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.149425</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.116592</td>\n",
       "      <td>955</td>\n",
       "      <td>0.028866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 1km</td>\n",
       "      <td>0.458763</td>\n",
       "      <td>0.465839</td>\n",
       "      <td>0.468421</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.463542</td>\n",
       "      <td>0.427350</td>\n",
       "      <td>863</td>\n",
       "      <td>0.026085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 1km</td>\n",
       "      <td>0.435028</td>\n",
       "      <td>0.406452</td>\n",
       "      <td>0.401042</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.417344</td>\n",
       "      <td>0.363112</td>\n",
       "      <td>769</td>\n",
       "      <td>0.023244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 1km</td>\n",
       "      <td>0.358974</td>\n",
       "      <td>0.051597</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.126697</td>\n",
       "      <td>0.071307</td>\n",
       "      <td>697</td>\n",
       "      <td>0.021068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 1km</td>\n",
       "      <td>0.219780</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.314516</td>\n",
       "      <td>0.261438</td>\n",
       "      <td>0.359447</td>\n",
       "      <td>504</td>\n",
       "      <td>0.015234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 1km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066038</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084848</td>\n",
       "      <td>446</td>\n",
       "      <td>0.013481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 1km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>294</td>\n",
       "      <td>0.008886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 1km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>124</td>\n",
       "      <td>0.003748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Labels  Precision  Precision (Smote)    Recall  \\\n",
       "9              Mute Swan 1km   0.895056           0.901090  0.947652   \n",
       "1           Canada Goose 1km   0.751229           0.762679  0.893219   \n",
       "10              Pheasant 1km   0.554217           0.523457  0.633609   \n",
       "16             Rock Dove 1km   0.560799           0.477639  0.292337   \n",
       "7             Little Owl 1km   0.533414           0.501166  0.500000   \n",
       "14  Red-legged Partridge 1km   0.400000           0.424332  0.076814   \n",
       "11     Pink-footed Goose 1km   0.676190           0.563884  0.432927   \n",
       "19                Wigeon 1km   0.642276           0.244586  0.135043   \n",
       "3                Gadwall 1km   0.709199           0.596330  0.438532   \n",
       "5         Grey Partridge 1km   0.315789           0.345013  0.022727   \n",
       "13               Pochard 1km   0.500000           0.198113  0.015267   \n",
       "8          Mandarin Duck 1km   0.605263           0.458647  0.203540   \n",
       "18          Whooper Swan 1km   0.285714           0.095588  0.007663   \n",
       "2         Egyptian Goose 1km   0.458763           0.465839  0.468421   \n",
       "0         Barnacle Goose 1km   0.435028           0.406452  0.401042   \n",
       "12               Pintail 1km   0.358974           0.051597  0.076923   \n",
       "15  Ring-necked Parakeet 1km   0.219780           0.419355  0.322581   \n",
       "4                Goshawk 1km   0.000000           0.118644  0.000000   \n",
       "6         Indian Peafowl 1km   0.000000           0.120000  0.000000   \n",
       "17            Ruddy Duck 1km   0.000000           0.272727  0.000000   \n",
       "\n",
       "    Recall (Smote)        F1  F1 (Smote)  Occurrence Count  Percentage  \n",
       "9         0.923650  0.920603    0.912231             19124    0.578044  \n",
       "1         0.767732  0.816094    0.765197             10147    0.306704  \n",
       "10        0.438017  0.591260    0.476940              5855    0.176974  \n",
       "16        0.252602  0.384328    0.330446              3919    0.118456  \n",
       "7         0.489749  0.516167    0.495392              3548    0.107242  \n",
       "14        0.203414  0.128878    0.275000              2953    0.089258  \n",
       "11        0.504573  0.527881    0.532582              2646    0.079978  \n",
       "19        0.328205  0.223164    0.280292              2317    0.070034  \n",
       "3         0.477064  0.541950    0.530071              2205    0.066649  \n",
       "5         0.242424  0.042403    0.284761              2123    0.064170  \n",
       "13        0.080153  0.029630    0.114130              1057    0.031949  \n",
       "8         0.269912  0.304636    0.339833              1010    0.030528  \n",
       "18        0.149425  0.014925    0.116592               955    0.028866  \n",
       "2         0.394737  0.463542    0.427350               863    0.026085  \n",
       "0         0.328125  0.417344    0.363112               769    0.023244  \n",
       "12        0.115385  0.126697    0.071307               697    0.021068  \n",
       "15        0.314516  0.261438    0.359447               504    0.015234  \n",
       "4         0.066038  0.000000    0.084848               446    0.013481  \n",
       "6         0.040000  0.000000    0.060000               294    0.008886  \n",
       "17        0.100000  0.000000    0.146341               124    0.003748  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create graphs to show off data\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [9, 12]\n",
    "\n",
    "occurrence_count, occurrence_percentage = All_bird_occurrences['Occurrence Count'], All_bird_occurrences['Percentage']\n",
    "precision = []\n",
    "precision_smote = []\n",
    "recall = []\n",
    "recall_smote = []\n",
    "f1 = []\n",
    "f1_smote = []\n",
    "labels = []\n",
    "for dict in df_dicts:\n",
    "    precision.append(dict['report']['1']['precision'])\n",
    "    precision_smote.append(dict['report_smote']['1']['precision'])\n",
    "    recall.append(dict['report']['1']['recall'])\n",
    "    recall_smote.append(dict['report_smote']['1']['recall'])\n",
    "    f1.append(dict['report']['1']['f1-score'])\n",
    "    f1_smote.append(dict['report_smote']['1']['f1-score'])\n",
    "    labels.append(dict['name'])\n",
    "\n",
    "\n",
    "\n",
    "scores = pd.DataFrame({'Labels' : labels, \n",
    "                        'Precision': precision, 'Precision (Smote)': precision_smote, \n",
    "                        'Recall': recall, 'Recall (Smote)': recall_smote, \n",
    "                        'F1': f1, 'F1 (Smote)': f1_smote,\n",
    "                        'Occurrence Count' : occurrence_count, 'Percentage' : occurrence_percentage} )\n",
    "                        \n",
    "scores.sort_values('Occurrence Count', inplace=True)\n",
    "\n",
    "n=20\n",
    "r = np.arange(n)\n",
    "height = 0.25\n",
    "\n",
    "plt.barh(r, 'Percentage', data=scores, label='Occurrence Percentage', height = height, color='g')\n",
    "plt.barh(r+height, 'F1', data=scores, label='F1-Score',  height= height, color='b')\n",
    "plt.barh(r+height*2, 'F1 (Smote)', data=scores, label='F1-Score (Smote)',  height = height, color='r')\n",
    "plt.legend(framealpha=1, frameon=True)\n",
    "plt.yticks(r+height*2, scores['Labels'])\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "scores.sort_values('Occurrence Count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'df_dicts_1km' (list)\n"
     ]
    }
   ],
   "source": [
    "# Store dictionaries for later use\n",
    "df_dicts_1km = df_dicts\n",
    "%store df_dicts_1km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# Export predictions to CSV for QGIS\n",
    "RESULTS_PATH = 'Datasets/Machine Learning/Results/1km/'\n",
    "for dict in df_dicts:\n",
    "    # Join with y_test datafram\n",
    "    result_df = dict['y_test'] \n",
    "    result_df['Predictions'] = dict['predictions_smote']\n",
    "    display(result_df)\n",
    "    result_df.to_csv(RESULTS_PATH + dict['name'] + '.csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 1km\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>F Score</th>\n",
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       "      <th>25</th>\n",
       "      <td>1586.660696</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
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       "      <td>1579.799646</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <th>30</th>\n",
       "      <td>1579.799646</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <th>18</th>\n",
       "      <td>1440.939379</td>\n",
       "      <td>1.143607e-308</td>\n",
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       "      <th>23</th>\n",
       "      <td>1417.879628</td>\n",
       "      <td>7.271005e-304</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1269.611405</td>\n",
       "      <td>6.667737e-273</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1223.198053</td>\n",
       "      <td>3.502336e-263</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1203.742558</td>\n",
       "      <td>4.196966e-259</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1078.608288</td>\n",
       "      <td>8.174358e-233</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>978.472706</td>\n",
       "      <td>1.050650e-211</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>853.811730</td>\n",
       "      <td>2.457887e-185</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>816.914586</td>\n",
       "      <td>1.639367e-177</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>682.011193</td>\n",
       "      <td>7.904841e-149</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>676.149377</td>\n",
       "      <td>1.402808e-147</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>673.575429</td>\n",
       "      <td>4.960887e-147</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>605.329133</td>\n",
       "      <td>1.806036e-132</td>\n",
       "      <td>Fertiliser K</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>605.329133</td>\n",
       "      <td>1.806036e-132</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>605.329133</td>\n",
       "      <td>1.806036e-132</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>504.754899</td>\n",
       "      <td>5.949135e-111</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>472.954811</td>\n",
       "      <td>3.918041e-104</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>416.369272</td>\n",
       "      <td>5.554940e-92</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>360.657263</td>\n",
       "      <td>5.403022e-80</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>243.378276</td>\n",
       "      <td>1.130191e-54</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223.631247</td>\n",
       "      <td>2.134039e-50</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>168.960021</td>\n",
       "      <td>1.551633e-38</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>156.098247</td>\n",
       "      <td>9.703696e-36</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>88.036607</td>\n",
       "      <td>6.821306e-21</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70.504247</td>\n",
       "      <td>4.773365e-17</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.091677</td>\n",
       "      <td>5.410742e-13</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28.131255</td>\n",
       "      <td>1.140877e-07</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.593482</td>\n",
       "      <td>1.136010e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.881727</td>\n",
       "      <td>4.882264e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.726979</td>\n",
       "      <td>1.888063e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.309928</td>\n",
       "      <td>2.524160e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.193636</td>\n",
       "      <td>2.746053e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.275406</td>\n",
       "      <td>5.997316e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1586.660696   0.000000e+00   Inflowing drainage direction\n",
       "29  1579.799646   0.000000e+00                 Chlorothalonil\n",
       "30  1579.799646   0.000000e+00                     Glyphosate\n",
       "31  1579.799646   0.000000e+00                       Mancozeb\n",
       "32  1579.799646   0.000000e+00                     Mecoprop-P\n",
       "34  1579.799646   0.000000e+00                  Pendimethalin\n",
       "18  1440.939379  1.143607e-308                      Saltmarsh\n",
       "23  1417.879628  7.271005e-304                   Surface type\n",
       "22  1269.611405  6.667737e-273      Cumulative catchment area\n",
       "24  1223.198053  3.502336e-263  Outflowing drainage direction\n",
       "21  1203.742558  4.196966e-259                      Elevation\n",
       "17  1078.608288  8.174358e-233              Littoral sediment\n",
       "13   978.472706  1.050650e-211                     Freshwater\n",
       "15   853.811730  2.457887e-185         Supralittoral sediment\n",
       "3    816.914586  1.639367e-177              Improve grassland\n",
       "38   682.011193  7.904841e-149                     Tri-allate\n",
       "37   676.149377  1.402808e-147                        Sulphur\n",
       "36   673.575429  4.960887e-147                   Prosulfocarb\n",
       "26   605.329133  1.806036e-132                   Fertiliser K\n",
       "27   605.329133  1.806036e-132                   Fertiliser N\n",
       "28   605.329133  1.806036e-132                   Fertiliser P\n",
       "35   504.754899  5.949135e-111       PropamocarbHydrochloride\n",
       "33   472.954811  3.918041e-104                     Metamitron\n",
       "16   416.369272   5.554940e-92                  Littoral rock\n",
       "7    360.657263   5.403022e-80                            Fen\n",
       "0    243.378276   1.130191e-54             Deciduous woodland\n",
       "2    223.631247   2.134039e-50                         Arable\n",
       "19   168.960021   1.551633e-38                          Urban\n",
       "20   156.098247   9.703696e-36                       Suburban\n",
       "14    88.036607   6.821306e-21             Supralittoral rock\n",
       "9     70.504247   4.773365e-17              Heather grassland\n",
       "4     52.091677   5.410742e-13              Neutral grassland\n",
       "12    28.131255   1.140877e-07                      Saltwater\n",
       "10    10.593482   1.136010e-03                            Bog\n",
       "6      3.881727   4.882264e-02                 Acid grassland\n",
       "8      1.726979   1.888063e-01                        Heather\n",
       "11     1.309928   2.524160e-01                    Inland rock\n",
       "1      1.193636   2.746053e-01            Coniferous woodland\n",
       "5      0.275406   5.997316e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada Goose 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31307.822741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31307.822741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31307.822741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>31307.822741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>31307.822741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27980.651957</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27539.757586</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27539.757586</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>27539.757586</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22192.853532</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>21798.073867</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20557.269520</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>14416.271856</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>14379.008562</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>14239.510204</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10467.973712</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>10058.930524</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9820.800013</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9373.992389</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6200.682674</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4606.437967</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4435.557422</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2407.892997</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1994.218535</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>591.947491</td>\n",
       "      <td>1.305021e-129</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>389.095029</td>\n",
       "      <td>4.077898e-86</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>240.538301</td>\n",
       "      <td>4.654537e-54</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>162.207343</td>\n",
       "      <td>4.555336e-37</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56.217105</td>\n",
       "      <td>6.651725e-14</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>54.988572</td>\n",
       "      <td>1.241339e-13</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>49.209521</td>\n",
       "      <td>2.344481e-12</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.689323</td>\n",
       "      <td>1.542908e-05</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16.182904</td>\n",
       "      <td>5.763851e-05</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13.004010</td>\n",
       "      <td>3.112817e-04</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.057049</td>\n",
       "      <td>1.519046e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.194359</td>\n",
       "      <td>7.390188e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.831655</td>\n",
       "      <td>1.759414e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.564030</td>\n",
       "      <td>2.110850e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.341571</td>\n",
       "      <td>2.467655e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "29  31307.822741   0.000000e+00                 Chlorothalonil\n",
       "30  31307.822741   0.000000e+00                     Glyphosate\n",
       "31  31307.822741   0.000000e+00                       Mancozeb\n",
       "32  31307.822741   0.000000e+00                     Mecoprop-P\n",
       "34  31307.822741   0.000000e+00                  Pendimethalin\n",
       "23  27980.651957   0.000000e+00                   Surface type\n",
       "26  27539.757586   0.000000e+00                   Fertiliser K\n",
       "27  27539.757586   0.000000e+00                   Fertiliser N\n",
       "28  27539.757586   0.000000e+00                   Fertiliser P\n",
       "24  22192.853532   0.000000e+00  Outflowing drainage direction\n",
       "25  21798.073867   0.000000e+00   Inflowing drainage direction\n",
       "21  20557.269520   0.000000e+00                      Elevation\n",
       "37  14416.271856   0.000000e+00                        Sulphur\n",
       "36  14379.008562   0.000000e+00                   Prosulfocarb\n",
       "38  14239.510204   0.000000e+00                     Tri-allate\n",
       "22  10467.973712   0.000000e+00      Cumulative catchment area\n",
       "35  10058.930524   0.000000e+00       PropamocarbHydrochloride\n",
       "33   9820.800013   0.000000e+00                     Metamitron\n",
       "3    9373.992389   0.000000e+00              Improve grassland\n",
       "20   6200.682674   0.000000e+00                       Suburban\n",
       "0    4606.437967   0.000000e+00             Deciduous woodland\n",
       "2    4435.557422   0.000000e+00                         Arable\n",
       "19   2407.892997   0.000000e+00                          Urban\n",
       "13   1994.218535   0.000000e+00                     Freshwater\n",
       "4     591.947491  1.305021e-129              Neutral grassland\n",
       "18    389.095029   4.077898e-86                      Saltmarsh\n",
       "7     240.538301   4.654537e-54                            Fen\n",
       "17    162.207343   4.555336e-37              Littoral sediment\n",
       "5      56.217105   6.651725e-14           Calcareous grassland\n",
       "15     54.988572   1.241339e-13         Supralittoral sediment\n",
       "12     49.209521   2.344481e-12                      Saltwater\n",
       "1      18.689323   1.542908e-05            Coniferous woodland\n",
       "10     16.182904   5.763851e-05                            Bog\n",
       "11     13.004010   3.112817e-04                    Inland rock\n",
       "9      10.057049   1.519046e-03              Heather grassland\n",
       "16      3.194359   7.390188e-02                  Littoral rock\n",
       "14      1.831655   1.759414e-01             Supralittoral rock\n",
       "8       1.564030   2.110850e-01                        Heather\n",
       "6       1.341571   2.467655e-01                 Acid grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 1km\n"
     ]
    },
    {
     "data": {
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       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4833.620741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4833.620741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4833.620741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4398.345684</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3391.728526</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3198.335134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3198.335134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3198.335134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3198.335134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3198.335134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2769.983626</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2688.563744</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2448.189595</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2166.345317</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2164.465187</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2130.679135</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1913.836423</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1867.181758</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1851.637936</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1608.119655</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1508.446202</td>\n",
       "      <td>1.037538e-322</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1160.254219</td>\n",
       "      <td>5.600729e-250</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1025.549798</td>\n",
       "      <td>1.228685e-221</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>631.476180</td>\n",
       "      <td>4.722469e-138</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600.148490</td>\n",
       "      <td>2.308798e-131</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>214.961650</td>\n",
       "      <td>1.613752e-48</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.795470</td>\n",
       "      <td>3.118157e-16</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24.532078</td>\n",
       "      <td>7.344270e-07</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.103881</td>\n",
       "      <td>6.009295e-05</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.942250</td>\n",
       "      <td>9.409617e-04</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.460287</td>\n",
       "      <td>1.103570e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.121062</td>\n",
       "      <td>1.336303e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.658208</td>\n",
       "      <td>3.091261e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.654048</td>\n",
       "      <td>5.594175e-02</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.305391</td>\n",
       "      <td>6.906195e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.051750</td>\n",
       "      <td>8.065946e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.870474</td>\n",
       "      <td>3.508309e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.756741</td>\n",
       "      <td>3.843565e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003547</td>\n",
       "      <td>9.525091e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  4833.620741   0.000000e+00                   Fertiliser K\n",
       "27  4833.620741   0.000000e+00                   Fertiliser N\n",
       "28  4833.620741   0.000000e+00                   Fertiliser P\n",
       "22  4398.345684   0.000000e+00      Cumulative catchment area\n",
       "13  3391.728526   0.000000e+00                     Freshwater\n",
       "29  3198.335134   0.000000e+00                 Chlorothalonil\n",
       "30  3198.335134   0.000000e+00                     Glyphosate\n",
       "31  3198.335134   0.000000e+00                       Mancozeb\n",
       "32  3198.335134   0.000000e+00                     Mecoprop-P\n",
       "34  3198.335134   0.000000e+00                  Pendimethalin\n",
       "24  2769.983626   0.000000e+00  Outflowing drainage direction\n",
       "19  2688.563744   0.000000e+00                          Urban\n",
       "23  2448.189595   0.000000e+00                   Surface type\n",
       "36  2166.345317   0.000000e+00                   Prosulfocarb\n",
       "37  2164.465187   0.000000e+00                        Sulphur\n",
       "38  2130.679135   0.000000e+00                     Tri-allate\n",
       "33  1913.836423   0.000000e+00                     Metamitron\n",
       "25  1867.181758   0.000000e+00   Inflowing drainage direction\n",
       "35  1851.637936   0.000000e+00       PropamocarbHydrochloride\n",
       "20  1608.119655   0.000000e+00                       Suburban\n",
       "21  1508.446202  1.037538e-322                      Elevation\n",
       "3   1160.254219  5.600729e-250              Improve grassland\n",
       "0   1025.549798  1.228685e-221             Deciduous woodland\n",
       "7    631.476180  4.722469e-138                            Fen\n",
       "2    600.148490  2.308798e-131                         Arable\n",
       "18   214.961650   1.613752e-48                      Saltmarsh\n",
       "4     66.795470   3.118157e-16              Neutral grassland\n",
       "6     24.532078   7.344270e-07                 Acid grassland\n",
       "9     16.103881   6.009295e-05              Heather grassland\n",
       "10    10.942250   9.409617e-04                            Bog\n",
       "8      6.460287   1.103570e-02                        Heather\n",
       "15     6.121062   1.336303e-02         Supralittoral sediment\n",
       "17     4.658208   3.091261e-02              Littoral sediment\n",
       "5      3.654048   5.594175e-02           Calcareous grassland\n",
       "11     3.305391   6.906195e-02                    Inland rock\n",
       "16     3.051750   8.065946e-02                  Littoral rock\n",
       "12     0.870474   3.508309e-01                      Saltwater\n",
       "14     0.756741   3.843565e-01             Supralittoral rock\n",
       "1      0.003547   9.525091e-01            Coniferous woodland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gadwall 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8495.245130</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8495.245130</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8495.245130</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7138.523124</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7138.523124</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7138.523124</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>7138.523124</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7138.523124</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>6747.511680</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6716.673071</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>6693.525002</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5956.455748</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5952.405449</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5605.884628</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5421.221412</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5337.742415</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4250.868883</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3657.950458</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3513.520144</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2555.774940</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2162.540101</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1871.462238</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1487.487019</td>\n",
       "      <td>2.357158e-318</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1271.177886</td>\n",
       "      <td>3.134320e-273</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>947.217815</td>\n",
       "      <td>4.200123e-205</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>892.320809</td>\n",
       "      <td>1.714432e-193</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>739.334890</td>\n",
       "      <td>4.953129e-161</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>160.826029</td>\n",
       "      <td>9.095729e-37</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>130.604141</td>\n",
       "      <td>3.443944e-30</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45.799690</td>\n",
       "      <td>1.331664e-11</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>32.280187</td>\n",
       "      <td>1.345866e-08</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29.120347</td>\n",
       "      <td>6.848560e-08</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21.703699</td>\n",
       "      <td>3.194143e-06</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.560026</td>\n",
       "      <td>2.791009e-05</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.808204</td>\n",
       "      <td>5.203950e-03</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.188161</td>\n",
       "      <td>7.342254e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.781952</td>\n",
       "      <td>1.819190e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.735834</td>\n",
       "      <td>3.910048e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.097151</td>\n",
       "      <td>7.552781e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  8495.245130   0.000000e+00                   Fertiliser K\n",
       "27  8495.245130   0.000000e+00                   Fertiliser N\n",
       "28  8495.245130   0.000000e+00                   Fertiliser P\n",
       "29  7138.523124   0.000000e+00                 Chlorothalonil\n",
       "30  7138.523124   0.000000e+00                     Glyphosate\n",
       "31  7138.523124   0.000000e+00                       Mancozeb\n",
       "32  7138.523124   0.000000e+00                     Mecoprop-P\n",
       "34  7138.523124   0.000000e+00                  Pendimethalin\n",
       "37  6747.511680   0.000000e+00                        Sulphur\n",
       "36  6716.673071   0.000000e+00                   Prosulfocarb\n",
       "38  6693.525002   0.000000e+00                     Tri-allate\n",
       "35  5956.455748   0.000000e+00       PropamocarbHydrochloride\n",
       "33  5952.405449   0.000000e+00                     Metamitron\n",
       "24  5605.884628   0.000000e+00  Outflowing drainage direction\n",
       "22  5421.221412   0.000000e+00      Cumulative catchment area\n",
       "23  5337.742415   0.000000e+00                   Surface type\n",
       "25  4250.868883   0.000000e+00   Inflowing drainage direction\n",
       "13  3657.950458   0.000000e+00                     Freshwater\n",
       "21  3513.520144   0.000000e+00                      Elevation\n",
       "2   2555.774940   0.000000e+00                         Arable\n",
       "3   2162.540101   0.000000e+00              Improve grassland\n",
       "20  1871.462238   0.000000e+00                       Suburban\n",
       "0   1487.487019  2.357158e-318             Deciduous woodland\n",
       "19  1271.177886  3.134320e-273                          Urban\n",
       "18   947.217815  4.200123e-205                      Saltmarsh\n",
       "7    892.320809  1.714432e-193                            Fen\n",
       "4    739.334890  4.953129e-161              Neutral grassland\n",
       "17   160.826029   9.095729e-37              Littoral sediment\n",
       "15   130.604141   3.443944e-30         Supralittoral sediment\n",
       "6     45.799690   1.331664e-11                 Acid grassland\n",
       "12    32.280187   1.345866e-08                      Saltwater\n",
       "9     29.120347   6.848560e-08              Heather grassland\n",
       "10    21.703699   3.194143e-06                            Bog\n",
       "8     17.560026   2.791009e-05                        Heather\n",
       "11     7.808204   5.203950e-03                    Inland rock\n",
       "1      7.188161   7.342254e-03            Coniferous woodland\n",
       "14     1.781952   1.819190e-01             Supralittoral rock\n",
       "5      0.735834   3.910048e-01           Calcareous grassland\n",
       "16     0.097151   7.552781e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goshawk 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1228.008833</td>\n",
       "      <td>3.437230e-264</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1131.658994</td>\n",
       "      <td>5.687958e-244</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1110.016642</td>\n",
       "      <td>2.016393e-239</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1110.016642</td>\n",
       "      <td>2.016393e-239</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1110.016642</td>\n",
       "      <td>2.016393e-239</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1110.016642</td>\n",
       "      <td>2.016393e-239</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1110.016642</td>\n",
       "      <td>2.016393e-239</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1068.037277</td>\n",
       "      <td>1.373575e-230</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1044.933301</td>\n",
       "      <td>1.009602e-225</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>919.889067</td>\n",
       "      <td>2.517246e-199</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>809.818171</td>\n",
       "      <td>5.254241e-176</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>684.780108</td>\n",
       "      <td>2.032079e-149</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>397.809147</td>\n",
       "      <td>5.441281e-88</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>378.743353</td>\n",
       "      <td>6.890413e-84</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>375.216071</td>\n",
       "      <td>3.958707e-83</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>366.309524</td>\n",
       "      <td>3.275528e-81</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>362.459808</td>\n",
       "      <td>2.209967e-80</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>245.243731</td>\n",
       "      <td>4.460776e-55</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>234.255491</td>\n",
       "      <td>1.066866e-52</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>234.255491</td>\n",
       "      <td>1.066866e-52</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>234.255491</td>\n",
       "      <td>1.066866e-52</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>226.160901</td>\n",
       "      <td>6.042257e-51</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87.757391</td>\n",
       "      <td>7.852555e-21</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>79.803707</td>\n",
       "      <td>4.343909e-19</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.558282</td>\n",
       "      <td>1.506066e-11</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.053837</td>\n",
       "      <td>6.170139e-05</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15.723043</td>\n",
       "      <td>7.347960e-05</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.693071</td>\n",
       "      <td>3.196452e-03</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.213295</td>\n",
       "      <td>4.011621e-02</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.293395</td>\n",
       "      <td>6.956812e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.492644</td>\n",
       "      <td>2.218154e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.235707</td>\n",
       "      <td>2.663082e-01</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.653081</td>\n",
       "      <td>4.190191e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.416397</td>\n",
       "      <td>5.187449e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.222689</td>\n",
       "      <td>6.370020e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.046972</td>\n",
       "      <td>8.284207e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.041436</td>\n",
       "      <td>8.386999e-01</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.032689</td>\n",
       "      <td>8.565239e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.002739</td>\n",
       "      <td>9.582594e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "23  1228.008833  3.437230e-264                   Surface type\n",
       "21  1131.658994  5.687958e-244                      Elevation\n",
       "29  1110.016642  2.016393e-239                 Chlorothalonil\n",
       "30  1110.016642  2.016393e-239                     Glyphosate\n",
       "31  1110.016642  2.016393e-239                       Mancozeb\n",
       "32  1110.016642  2.016393e-239                     Mecoprop-P\n",
       "34  1110.016642  2.016393e-239                  Pendimethalin\n",
       "24  1068.037277  1.373575e-230  Outflowing drainage direction\n",
       "22  1044.933301  1.009602e-225      Cumulative catchment area\n",
       "25   919.889067  2.517246e-199   Inflowing drainage direction\n",
       "0    809.818171  5.254241e-176             Deciduous woodland\n",
       "3    684.780108  2.032079e-149              Improve grassland\n",
       "1    397.809147   5.441281e-88            Coniferous woodland\n",
       "38   378.743353   6.890413e-84                     Tri-allate\n",
       "37   375.216071   3.958707e-83                        Sulphur\n",
       "36   366.309524   3.275528e-81                   Prosulfocarb\n",
       "6    362.459808   2.209967e-80                 Acid grassland\n",
       "35   245.243731   4.460776e-55       PropamocarbHydrochloride\n",
       "26   234.255491   1.066866e-52                   Fertiliser K\n",
       "27   234.255491   1.066866e-52                   Fertiliser N\n",
       "28   234.255491   1.066866e-52                   Fertiliser P\n",
       "33   226.160901   6.042257e-51                     Metamitron\n",
       "2     87.757391   7.852555e-21                         Arable\n",
       "20    79.803707   4.343909e-19                       Suburban\n",
       "8     45.558282   1.506066e-11                        Heather\n",
       "5     16.053837   6.170139e-05           Calcareous grassland\n",
       "13    15.723043   7.347960e-05                     Freshwater\n",
       "18     8.693071   3.196452e-03                      Saltmarsh\n",
       "7      4.213295   4.011621e-02                            Fen\n",
       "10     3.293395   6.956812e-02                            Bog\n",
       "14     1.492644   2.218154e-01             Supralittoral rock\n",
       "19     1.235707   2.663082e-01                          Urban\n",
       "9      0.653081   4.190191e-01              Heather grassland\n",
       "11     0.416397   5.187449e-01                    Inland rock\n",
       "12     0.222689   6.370020e-01                      Saltwater\n",
       "15     0.046972   8.284207e-01         Supralittoral sediment\n",
       "4      0.041436   8.386999e-01              Neutral grassland\n",
       "17     0.032689   8.565239e-01              Littoral sediment\n",
       "16     0.002739   9.582594e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grey Partridge 1km\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8765.050569</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8765.050569</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8765.050569</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8712.410120</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8711.451485</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8703.792647</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8141.700589</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>7660.609011</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7659.048939</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7659.048939</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7659.048939</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7659.048939</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>7405.435155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7361.522264</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5901.470556</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4800.409421</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4601.906150</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4463.973740</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4389.262340</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2126.495399</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>901.238763</td>\n",
       "      <td>2.221379e-195</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>518.009891</td>\n",
       "      <td>8.599298e-114</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>327.045266</td>\n",
       "      <td>9.488789e-73</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>319.849011</td>\n",
       "      <td>3.384407e-71</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>98.781469</td>\n",
       "      <td>3.039441e-23</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42.304272</td>\n",
       "      <td>7.923464e-11</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>42.065170</td>\n",
       "      <td>8.952540e-11</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27.868120</td>\n",
       "      <td>1.306903e-07</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.302932</td>\n",
       "      <td>1.889456e-05</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.233018</td>\n",
       "      <td>1.254381e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.215300</td>\n",
       "      <td>2.239529e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.740881</td>\n",
       "      <td>2.946103e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.021946</td>\n",
       "      <td>4.491999e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.136113</td>\n",
       "      <td>7.658531e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.392910</td>\n",
       "      <td>1.218961e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.249330</td>\n",
       "      <td>6.175506e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.120851</td>\n",
       "      <td>7.281160e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.012848</td>\n",
       "      <td>9.097560e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000036</td>\n",
       "      <td>9.952257e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  8765.050569   0.000000e+00                   Fertiliser K\n",
       "27  8765.050569   0.000000e+00                   Fertiliser N\n",
       "28  8765.050569   0.000000e+00                   Fertiliser P\n",
       "37  8712.410120   0.000000e+00                        Sulphur\n",
       "36  8711.451485   0.000000e+00                   Prosulfocarb\n",
       "38  8703.792647   0.000000e+00                     Tri-allate\n",
       "2   8141.700589   0.000000e+00                         Arable\n",
       "32  7660.609011   0.000000e+00                     Mecoprop-P\n",
       "29  7659.048939   0.000000e+00                 Chlorothalonil\n",
       "30  7659.048939   0.000000e+00                     Glyphosate\n",
       "31  7659.048939   0.000000e+00                       Mancozeb\n",
       "34  7659.048939   0.000000e+00                  Pendimethalin\n",
       "35  7405.435155   0.000000e+00       PropamocarbHydrochloride\n",
       "33  7361.522264   0.000000e+00                     Metamitron\n",
       "23  5901.470556   0.000000e+00                   Surface type\n",
       "24  4800.409421   0.000000e+00  Outflowing drainage direction\n",
       "22  4601.906150   0.000000e+00      Cumulative catchment area\n",
       "25  4463.973740   0.000000e+00   Inflowing drainage direction\n",
       "21  4389.262340   0.000000e+00                      Elevation\n",
       "3   2126.495399   0.000000e+00              Improve grassland\n",
       "0    901.238763  2.221379e-195             Deciduous woodland\n",
       "20   518.009891  8.599298e-114                       Suburban\n",
       "5    327.045266   9.488789e-73           Calcareous grassland\n",
       "4    319.849011   3.384407e-71              Neutral grassland\n",
       "19    98.781469   3.039441e-23                          Urban\n",
       "15    42.304272   7.923464e-11         Supralittoral sediment\n",
       "18    42.065170   8.952540e-11                      Saltmarsh\n",
       "13    27.868120   1.306903e-07                     Freshwater\n",
       "7     18.302932   1.889456e-05                            Fen\n",
       "17     6.233018   1.254381e-02              Littoral sediment\n",
       "1      5.215300   2.239529e-02            Coniferous woodland\n",
       "11     4.740881   2.946103e-02                    Inland rock\n",
       "14     4.021946   4.491999e-02             Supralittoral rock\n",
       "8      3.136113   7.658531e-02                        Heather\n",
       "6      2.392910   1.218961e-01                 Acid grassland\n",
       "12     0.249330   6.175506e-01                      Saltwater\n",
       "10     0.120851   7.281160e-01                            Bog\n",
       "9      0.012848   9.097560e-01              Heather grassland\n",
       "16     0.000036   9.952257e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indian Peafowl 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1472.657485</td>\n",
       "      <td>2.862598e-315</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1472.657485</td>\n",
       "      <td>2.862598e-315</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1472.657485</td>\n",
       "      <td>2.862598e-315</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1175.153234</td>\n",
       "      <td>4.174421e-253</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1172.615821</td>\n",
       "      <td>1.422851e-252</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1166.318796</td>\n",
       "      <td>2.985253e-251</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1150.513144</td>\n",
       "      <td>6.221230e-248</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1150.513144</td>\n",
       "      <td>6.221230e-248</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1150.513144</td>\n",
       "      <td>6.221230e-248</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1150.513144</td>\n",
       "      <td>6.221230e-248</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1150.513144</td>\n",
       "      <td>6.221230e-248</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>993.549316</td>\n",
       "      <td>6.904766e-215</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>989.712520</td>\n",
       "      <td>4.455187e-214</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>826.341713</td>\n",
       "      <td>1.639780e-179</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>773.116357</td>\n",
       "      <td>3.264405e-168</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>694.651908</td>\n",
       "      <td>1.603430e-151</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>636.396656</td>\n",
       "      <td>4.208646e-139</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>618.098698</td>\n",
       "      <td>3.386540e-135</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>581.270648</td>\n",
       "      <td>2.497700e-127</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>579.289817</td>\n",
       "      <td>6.621914e-127</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>557.985251</td>\n",
       "      <td>2.382356e-122</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>341.361921</td>\n",
       "      <td>7.768283e-76</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.037453</td>\n",
       "      <td>2.550632e-08</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28.479423</td>\n",
       "      <td>9.532196e-08</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.591241</td>\n",
       "      <td>9.621430e-06</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.127497</td>\n",
       "      <td>8.515085e-04</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.872829</td>\n",
       "      <td>1.678853e-03</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.975747</td>\n",
       "      <td>2.571178e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.521124</td>\n",
       "      <td>6.060014e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.674323</td>\n",
       "      <td>1.019882e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.267492</td>\n",
       "      <td>1.321231e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.470891</td>\n",
       "      <td>2.252138e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.730498</td>\n",
       "      <td>3.927281e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.717725</td>\n",
       "      <td>3.968973e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.509339</td>\n",
       "      <td>4.754303e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.335080</td>\n",
       "      <td>5.626872e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.132251</td>\n",
       "      <td>7.161121e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.099303</td>\n",
       "      <td>7.526692e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.034266</td>\n",
       "      <td>8.531431e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  1472.657485  2.862598e-315                   Fertiliser K\n",
       "27  1472.657485  2.862598e-315                   Fertiliser N\n",
       "28  1472.657485  2.862598e-315                   Fertiliser P\n",
       "36  1175.153234  4.174421e-253                   Prosulfocarb\n",
       "37  1172.615821  1.422851e-252                        Sulphur\n",
       "38  1166.318796  2.985253e-251                     Tri-allate\n",
       "29  1150.513144  6.221230e-248                 Chlorothalonil\n",
       "30  1150.513144  6.221230e-248                     Glyphosate\n",
       "31  1150.513144  6.221230e-248                       Mancozeb\n",
       "32  1150.513144  6.221230e-248                     Mecoprop-P\n",
       "34  1150.513144  6.221230e-248                  Pendimethalin\n",
       "35   993.549316  6.904766e-215       PropamocarbHydrochloride\n",
       "33   989.712520  4.455187e-214                     Metamitron\n",
       "23   826.341713  1.639780e-179                   Surface type\n",
       "22   773.116357  3.264405e-168      Cumulative catchment area\n",
       "24   694.651908  1.603430e-151  Outflowing drainage direction\n",
       "2    636.396656  4.208646e-139                         Arable\n",
       "25   618.098698  3.386540e-135   Inflowing drainage direction\n",
       "21   581.270648  2.497700e-127                      Elevation\n",
       "3    579.289817  6.621914e-127              Improve grassland\n",
       "0    557.985251  2.382356e-122             Deciduous woodland\n",
       "20   341.361921   7.768283e-76                       Suburban\n",
       "4     31.037453   2.550632e-08              Neutral grassland\n",
       "19    28.479423   9.532196e-08                          Urban\n",
       "5     19.591241   9.621430e-06           Calcareous grassland\n",
       "7     11.127497   8.515085e-04                            Fen\n",
       "13     9.872829   1.678853e-03                     Freshwater\n",
       "6      4.975747   2.571178e-02                 Acid grassland\n",
       "10     3.521124   6.060014e-02                            Bog\n",
       "1      2.674323   1.019882e-01            Coniferous woodland\n",
       "9      2.267492   1.321231e-01              Heather grassland\n",
       "8      1.470891   2.252138e-01                        Heather\n",
       "11     0.730498   3.927281e-01                    Inland rock\n",
       "12     0.717725   3.968973e-01                      Saltwater\n",
       "15     0.509339   4.754303e-01         Supralittoral sediment\n",
       "18     0.335080   5.626872e-01                      Saltmarsh\n",
       "14     0.132251   7.161121e-01             Supralittoral rock\n",
       "17     0.099303   7.526692e-01              Littoral sediment\n",
       "16     0.034266   8.531431e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Little Owl 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19475.045822</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19475.045822</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19475.045822</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>14464.783437</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>14463.764574</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>14420.985632</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13365.596356</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>13362.750579</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>13362.750579</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13362.750579</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>13362.750579</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>11175.998989</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>11122.139446</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9941.994917</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9462.794504</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8442.661824</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7788.899641</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7508.970331</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6742.553493</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4944.473615</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2250.993774</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1224.086080</td>\n",
       "      <td>2.281664e-263</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>630.020519</td>\n",
       "      <td>9.656906e-138</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>439.830706</td>\n",
       "      <td>5.052213e-97</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>344.014425</td>\n",
       "      <td>2.082536e-76</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>191.220766</td>\n",
       "      <td>2.273330e-43</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>143.222650</td>\n",
       "      <td>6.146287e-33</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>59.796551</td>\n",
       "      <td>1.081619e-14</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42.200831</td>\n",
       "      <td>8.353273e-11</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.080188</td>\n",
       "      <td>5.338007e-09</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.463089</td>\n",
       "      <td>3.428286e-08</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26.647155</td>\n",
       "      <td>2.456208e-07</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.056362</td>\n",
       "      <td>1.272986e-05</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.754547</td>\n",
       "      <td>4.264108e-05</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.372175</td>\n",
       "      <td>6.627513e-03</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.137556</td>\n",
       "      <td>7.552295e-03</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.890295</td>\n",
       "      <td>1.522985e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.157614</td>\n",
       "      <td>6.913651e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.095097</td>\n",
       "      <td>7.577967e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "26  19475.045822   0.000000e+00                   Fertiliser K\n",
       "27  19475.045822   0.000000e+00                   Fertiliser N\n",
       "28  19475.045822   0.000000e+00                   Fertiliser P\n",
       "36  14464.783437   0.000000e+00                   Prosulfocarb\n",
       "37  14463.764574   0.000000e+00                        Sulphur\n",
       "38  14420.985632   0.000000e+00                     Tri-allate\n",
       "32  13365.596356   0.000000e+00                     Mecoprop-P\n",
       "29  13362.750579   0.000000e+00                 Chlorothalonil\n",
       "30  13362.750579   0.000000e+00                     Glyphosate\n",
       "31  13362.750579   0.000000e+00                       Mancozeb\n",
       "34  13362.750579   0.000000e+00                  Pendimethalin\n",
       "33  11175.998989   0.000000e+00                     Metamitron\n",
       "35  11122.139446   0.000000e+00       PropamocarbHydrochloride\n",
       "23   9941.994917   0.000000e+00                   Surface type\n",
       "2    9462.794504   0.000000e+00                         Arable\n",
       "24   8442.661824   0.000000e+00  Outflowing drainage direction\n",
       "22   7788.899641   0.000000e+00      Cumulative catchment area\n",
       "25   7508.970331   0.000000e+00   Inflowing drainage direction\n",
       "21   6742.553493   0.000000e+00                      Elevation\n",
       "3    4944.473615   0.000000e+00              Improve grassland\n",
       "20   2250.993774   0.000000e+00                       Suburban\n",
       "0    1224.086080  2.281664e-263             Deciduous woodland\n",
       "4     630.020519  9.656906e-138              Neutral grassland\n",
       "5     439.830706   5.052213e-97           Calcareous grassland\n",
       "19    344.014425   2.082536e-76                          Urban\n",
       "13    191.220766   2.273330e-43                     Freshwater\n",
       "7     143.222650   6.146287e-33                            Fen\n",
       "18     59.796551   1.081619e-14                      Saltmarsh\n",
       "6      42.200831   8.353273e-11                 Acid grassland\n",
       "1      34.080188   5.338007e-09            Coniferous woodland\n",
       "10     30.463089   3.428286e-08                            Bog\n",
       "8      26.647155   2.456208e-07                        Heather\n",
       "9      19.056362   1.272986e-05              Heather grassland\n",
       "15     16.754547   4.264108e-05         Supralittoral sediment\n",
       "16      7.372175   6.627513e-03                  Littoral rock\n",
       "11      7.137556   7.552295e-03                    Inland rock\n",
       "14      5.890295   1.522985e-02             Supralittoral rock\n",
       "12      0.157614   6.913651e-01                      Saltwater\n",
       "17      0.095097   7.577967e-01              Littoral sediment"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 1km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4982.270656</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4982.270656</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4982.270656</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3746.947882</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3746.947882</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3746.947882</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3746.947882</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3746.947882</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3559.780952</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3525.975295</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2973.634105</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2900.268149</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2556.207049</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2345.273531</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2339.229953</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2322.533150</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2139.256963</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2040.599063</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1659.981195</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1618.553741</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1589.076229</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>792.705495</td>\n",
       "      <td>2.254877e-172</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>434.160164</td>\n",
       "      <td>8.347169e-96</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>322.317750</td>\n",
       "      <td>9.929157e-72</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>289.027410</td>\n",
       "      <td>1.523840e-64</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>86.986582</td>\n",
       "      <td>1.158292e-20</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.617236</td>\n",
       "      <td>4.053481e-11</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30.454378</td>\n",
       "      <td>3.443701e-08</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.680495</td>\n",
       "      <td>3.700080e-04</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.170154</td>\n",
       "      <td>8.321583e-04</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.774378</td>\n",
       "      <td>2.889326e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.468317</td>\n",
       "      <td>6.256379e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.191997</td>\n",
       "      <td>7.400873e-02</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.080693</td>\n",
       "      <td>7.923601e-02</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.603302</td>\n",
       "      <td>1.066509e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.290784</td>\n",
       "      <td>1.301538e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.408976</td>\n",
       "      <td>2.352351e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.023329</td>\n",
       "      <td>8.786052e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.004613</td>\n",
       "      <td>9.458479e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  4982.270656   0.000000e+00                   Fertiliser K\n",
       "27  4982.270656   0.000000e+00                   Fertiliser N\n",
       "28  4982.270656   0.000000e+00                   Fertiliser P\n",
       "29  3746.947882   0.000000e+00                 Chlorothalonil\n",
       "30  3746.947882   0.000000e+00                     Glyphosate\n",
       "31  3746.947882   0.000000e+00                       Mancozeb\n",
       "32  3746.947882   0.000000e+00                     Mecoprop-P\n",
       "34  3746.947882   0.000000e+00                  Pendimethalin\n",
       "22  3559.780952   0.000000e+00      Cumulative catchment area\n",
       "0   3525.975295   0.000000e+00             Deciduous woodland\n",
       "24  2973.634105   0.000000e+00  Outflowing drainage direction\n",
       "23  2900.268149   0.000000e+00                   Surface type\n",
       "3   2556.207049   0.000000e+00              Improve grassland\n",
       "37  2345.273531   0.000000e+00                        Sulphur\n",
       "36  2339.229953   0.000000e+00                   Prosulfocarb\n",
       "38  2322.533150   0.000000e+00                     Tri-allate\n",
       "25  2139.256963   0.000000e+00   Inflowing drainage direction\n",
       "21  2040.599063   0.000000e+00                      Elevation\n",
       "20  1659.981195   0.000000e+00                       Suburban\n",
       "35  1618.553741   0.000000e+00       PropamocarbHydrochloride\n",
       "33  1589.076229   0.000000e+00                     Metamitron\n",
       "13   792.705495  2.254877e-172                     Freshwater\n",
       "19   434.160164   8.347169e-96                          Urban\n",
       "2    322.317750   9.929157e-72                         Arable\n",
       "4    289.027410   1.523840e-64              Neutral grassland\n",
       "5     86.986582   1.158292e-20           Calcareous grassland\n",
       "1     43.617236   4.053481e-11            Coniferous woodland\n",
       "7     30.454378   3.443701e-08                            Fen\n",
       "9     12.680495   3.700080e-04              Heather grassland\n",
       "10    11.170154   8.321583e-04                            Bog\n",
       "6      4.774378   2.889326e-02                 Acid grassland\n",
       "16     3.468317   6.256379e-02                  Littoral rock\n",
       "14     3.191997   7.400873e-02             Supralittoral rock\n",
       "17     3.080693   7.923601e-02              Littoral sediment\n",
       "12     2.603302   1.066509e-01                      Saltwater\n",
       "11     2.290784   1.301538e-01                    Inland rock\n",
       "15     1.408976   2.352351e-01         Supralittoral sediment\n",
       "8      0.023329   8.786052e-01                        Heather\n",
       "18     0.004613   9.458479e-01                      Saltmarsh"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>43676.953726</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>43676.953726</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>43659.642747</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>43642.342398</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>43625.052671</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>41778.683149</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>37707.864005</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>32149.803935</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24629.730532</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19640.235987</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19640.235987</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19640.235987</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>14704.076106</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>14617.037623</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>14559.360173</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>9242.027887</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8916.083195</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7714.991172</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6922.429433</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5556.769597</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4548.309043</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3188.833827</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2126.916337</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1261.094423</td>\n",
       "      <td>4.042871e-271</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>710.989883</td>\n",
       "      <td>5.323266e-155</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>350.947774</td>\n",
       "      <td>6.674534e-78</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>274.300533</td>\n",
       "      <td>2.317521e-61</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>258.697255</td>\n",
       "      <td>5.478710e-58</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>204.439981</td>\n",
       "      <td>3.082649e-46</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>138.876035</td>\n",
       "      <td>5.432921e-32</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>122.310053</td>\n",
       "      <td>2.214177e-28</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>113.202400</td>\n",
       "      <td>2.149655e-26</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>103.816162</td>\n",
       "      <td>2.411588e-24</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>88.379538</td>\n",
       "      <td>5.738193e-21</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>66.697722</td>\n",
       "      <td>3.276343e-16</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>49.312998</td>\n",
       "      <td>2.224191e-12</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.669892</td>\n",
       "      <td>2.369145e-11</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29.331059</td>\n",
       "      <td>6.143449e-08</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.698519</td>\n",
       "      <td>3.186914e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "30  43676.953726   0.000000e+00                     Glyphosate\n",
       "34  43676.953726   0.000000e+00                  Pendimethalin\n",
       "29  43659.642747   0.000000e+00                 Chlorothalonil\n",
       "31  43642.342398   0.000000e+00                       Mancozeb\n",
       "32  43625.052671   0.000000e+00                     Mecoprop-P\n",
       "23  41778.683149   0.000000e+00                   Surface type\n",
       "25  37707.864005   0.000000e+00   Inflowing drainage direction\n",
       "21  32149.803935   0.000000e+00                      Elevation\n",
       "24  24629.730532   0.000000e+00  Outflowing drainage direction\n",
       "26  19640.235987   0.000000e+00                   Fertiliser K\n",
       "27  19640.235987   0.000000e+00                   Fertiliser N\n",
       "28  19640.235987   0.000000e+00                   Fertiliser P\n",
       "37  14704.076106   0.000000e+00                        Sulphur\n",
       "36  14617.037623   0.000000e+00                   Prosulfocarb\n",
       "38  14559.360173   0.000000e+00                     Tri-allate\n",
       "35   9242.027887   0.000000e+00       PropamocarbHydrochloride\n",
       "33   8916.083195   0.000000e+00                     Metamitron\n",
       "22   7714.991172   0.000000e+00      Cumulative catchment area\n",
       "3    6922.429433   0.000000e+00              Improve grassland\n",
       "20   5556.769597   0.000000e+00                       Suburban\n",
       "2    4548.309043   0.000000e+00                         Arable\n",
       "0    3188.833827   0.000000e+00             Deciduous woodland\n",
       "19   2126.916337   0.000000e+00                          Urban\n",
       "13   1261.094423  4.042871e-271                     Freshwater\n",
       "4     710.989883  5.323266e-155              Neutral grassland\n",
       "17    350.947774   6.674534e-78              Littoral sediment\n",
       "6     274.300533   2.317521e-61                 Acid grassland\n",
       "18    258.697255   5.478710e-58                      Saltmarsh\n",
       "7     204.439981   3.082649e-46                            Fen\n",
       "15    138.876035   5.432921e-32         Supralittoral sediment\n",
       "10    122.310053   2.214177e-28                            Bog\n",
       "12    113.202400   2.149655e-26                      Saltwater\n",
       "8     103.816162   2.411588e-24                        Heather\n",
       "9      88.379538   5.738193e-21              Heather grassland\n",
       "16     66.697722   3.276343e-16                  Littoral rock\n",
       "11     49.312998   2.224191e-12                    Inland rock\n",
       "1      44.669892   2.369145e-11            Coniferous woodland\n",
       "14     29.331059   6.143449e-08             Supralittoral rock\n",
       "5       8.698519   3.186914e-03           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pheasant 1km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>16151.612969</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16151.612969</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16147.891932</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>16147.891932</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16147.891932</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14828.439300</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13583.366928</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13583.366928</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13583.366928</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12411.664397</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11511.436305</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11025.748971</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10813.051867</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10803.090695</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10762.210355</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9363.861895</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>7504.726180</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7356.605139</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6889.758402</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5020.115469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3497.652887</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1872.691498</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>415.858475</td>\n",
       "      <td>7.152889e-92</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>355.069398</td>\n",
       "      <td>8.637276e-79</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>235.243263</td>\n",
       "      <td>6.519726e-53</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>219.109454</td>\n",
       "      <td>2.036749e-49</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>141.740399</td>\n",
       "      <td>1.292165e-32</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>137.784819</td>\n",
       "      <td>9.390379e-32</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>88.397878</td>\n",
       "      <td>5.685377e-21</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.673169</td>\n",
       "      <td>3.176391e-14</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33.154847</td>\n",
       "      <td>8.585587e-09</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21.413945</td>\n",
       "      <td>3.714720e-06</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.123056</td>\n",
       "      <td>2.921201e-04</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.451389</td>\n",
       "      <td>1.955872e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.362221</td>\n",
       "      <td>3.675199e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.751696</td>\n",
       "      <td>9.716078e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.202376</td>\n",
       "      <td>6.528125e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.146886</td>\n",
       "      <td>7.015312e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.077339</td>\n",
       "      <td>7.809384e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score       P Value                      Attribute\n",
       "31  16151.612969  0.000000e+00                       Mancozeb\n",
       "32  16151.612969  0.000000e+00                     Mecoprop-P\n",
       "29  16147.891932  0.000000e+00                 Chlorothalonil\n",
       "30  16147.891932  0.000000e+00                     Glyphosate\n",
       "34  16147.891932  0.000000e+00                  Pendimethalin\n",
       "23  14828.439300  0.000000e+00                   Surface type\n",
       "26  13583.366928  0.000000e+00                   Fertiliser K\n",
       "27  13583.366928  0.000000e+00                   Fertiliser N\n",
       "28  13583.366928  0.000000e+00                   Fertiliser P\n",
       "24  12411.664397  0.000000e+00  Outflowing drainage direction\n",
       "21  11511.436305  0.000000e+00                      Elevation\n",
       "25  11025.748971  0.000000e+00   Inflowing drainage direction\n",
       "37  10813.051867  0.000000e+00                        Sulphur\n",
       "36  10803.090695  0.000000e+00                   Prosulfocarb\n",
       "38  10762.210355  0.000000e+00                     Tri-allate\n",
       "22   9363.861895  0.000000e+00      Cumulative catchment area\n",
       "35   7504.726180  0.000000e+00       PropamocarbHydrochloride\n",
       "33   7356.605139  0.000000e+00                     Metamitron\n",
       "3    6889.758402  0.000000e+00              Improve grassland\n",
       "2    5020.115469  0.000000e+00                         Arable\n",
       "0    3497.652887  0.000000e+00             Deciduous woodland\n",
       "20   1872.691498  0.000000e+00                       Suburban\n",
       "5     415.858475  7.152889e-92           Calcareous grassland\n",
       "4     355.069398  8.637276e-79              Neutral grassland\n",
       "1     235.243263  6.519726e-53            Coniferous woodland\n",
       "19    219.109454  2.036749e-49                          Urban\n",
       "6     141.740399  1.292165e-32                 Acid grassland\n",
       "13    137.784819  9.390379e-32                     Freshwater\n",
       "8      88.397878  5.685377e-21                        Heather\n",
       "7      57.673169  3.176391e-14                            Fen\n",
       "10     33.154847  8.585587e-09                            Bog\n",
       "18     21.413945  3.714720e-06                      Saltmarsh\n",
       "15     13.123056  2.921201e-04         Supralittoral sediment\n",
       "11      5.451389  1.955872e-02                    Inland rock\n",
       "9       4.362221  3.675199e-02              Heather grassland\n",
       "16      2.751696  9.716078e-02                  Littoral rock\n",
       "12      0.202376  6.528125e-01                      Saltwater\n",
       "17      0.146886  7.015312e-01              Littoral sediment\n",
       "14      0.077339  7.809384e-01             Supralittoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pink-footed Goose 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7036.747030</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7036.747030</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7036.747030</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>7036.747030</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7036.747030</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5823.246006</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5796.329675</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5571.993565</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5543.722242</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5512.222341</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4750.062797</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>4557.628130</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4416.411546</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4315.390457</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4111.240745</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4027.385455</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1680.756257</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1578.759585</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1503.642898</td>\n",
       "      <td>1.032597e-321</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1109.115857</td>\n",
       "      <td>3.118895e-239</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1064.014160</td>\n",
       "      <td>9.660098e-230</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>924.852644</td>\n",
       "      <td>2.244918e-200</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>924.852644</td>\n",
       "      <td>2.244918e-200</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>924.852644</td>\n",
       "      <td>2.244918e-200</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>852.335728</td>\n",
       "      <td>5.051033e-185</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>449.450750</td>\n",
       "      <td>4.341152e-99</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>441.183451</td>\n",
       "      <td>2.587866e-97</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>329.374467</td>\n",
       "      <td>2.984579e-73</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>117.207897</td>\n",
       "      <td>2.872142e-27</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>55.275652</td>\n",
       "      <td>1.072913e-13</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.484241</td>\n",
       "      <td>1.225518e-12</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.481350</td>\n",
       "      <td>6.237451e-03</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.024752</td>\n",
       "      <td>8.201214e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.001253</td>\n",
       "      <td>1.571786e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.379174</td>\n",
       "      <td>2.402504e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.198463</td>\n",
       "      <td>2.736371e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.051515</td>\n",
       "      <td>8.204487e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.022436</td>\n",
       "      <td>8.809347e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.002302</td>\n",
       "      <td>9.617297e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "29  7036.747030   0.000000e+00                 Chlorothalonil\n",
       "30  7036.747030   0.000000e+00                     Glyphosate\n",
       "31  7036.747030   0.000000e+00                       Mancozeb\n",
       "32  7036.747030   0.000000e+00                     Mecoprop-P\n",
       "34  7036.747030   0.000000e+00                  Pendimethalin\n",
       "25  5823.246006   0.000000e+00   Inflowing drainage direction\n",
       "23  5796.329675   0.000000e+00                   Surface type\n",
       "37  5571.993565   0.000000e+00                        Sulphur\n",
       "36  5543.722242   0.000000e+00                   Prosulfocarb\n",
       "38  5512.222341   0.000000e+00                     Tri-allate\n",
       "21  4750.062797   0.000000e+00                      Elevation\n",
       "35  4557.628130   0.000000e+00       PropamocarbHydrochloride\n",
       "24  4416.411546   0.000000e+00  Outflowing drainage direction\n",
       "33  4315.390457   0.000000e+00                     Metamitron\n",
       "22  4111.240745   0.000000e+00      Cumulative catchment area\n",
       "2   4027.385455   0.000000e+00                         Arable\n",
       "17  1680.756257   0.000000e+00              Littoral sediment\n",
       "3   1578.759585   0.000000e+00              Improve grassland\n",
       "18  1503.642898  1.032597e-321                      Saltmarsh\n",
       "20  1109.115857  3.118895e-239                       Suburban\n",
       "0   1064.014160  9.660098e-230             Deciduous woodland\n",
       "26   924.852644  2.244918e-200                   Fertiliser K\n",
       "27   924.852644  2.244918e-200                   Fertiliser N\n",
       "28   924.852644  2.244918e-200                   Fertiliser P\n",
       "15   852.335728  5.051033e-185         Supralittoral sediment\n",
       "19   449.450750   4.341152e-99                          Urban\n",
       "13   441.183451   2.587866e-97                     Freshwater\n",
       "16   329.374467   2.984579e-73                  Littoral rock\n",
       "7    117.207897   2.872142e-27                            Fen\n",
       "12    55.275652   1.072913e-13                      Saltwater\n",
       "4     50.484241   1.225518e-12              Neutral grassland\n",
       "1      7.481350   6.237451e-03            Coniferous woodland\n",
       "6      3.024752   8.201214e-02                 Acid grassland\n",
       "14     2.001253   1.571786e-01             Supralittoral rock\n",
       "8      1.379174   2.402504e-01                        Heather\n",
       "11     1.198463   2.736371e-01                    Inland rock\n",
       "10     0.051515   8.204487e-01                            Bog\n",
       "5      0.022436   8.809347e-01           Calcareous grassland\n",
       "9      0.002302   9.617297e-01              Heather grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1342.972285</td>\n",
       "      <td>3.070953e-288</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1342.972285</td>\n",
       "      <td>3.070953e-288</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1342.972285</td>\n",
       "      <td>3.070953e-288</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1342.972285</td>\n",
       "      <td>3.070953e-288</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1342.972285</td>\n",
       "      <td>3.070953e-288</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1325.168183</td>\n",
       "      <td>1.608067e-284</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1169.840995</td>\n",
       "      <td>5.439990e-252</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1152.405110</td>\n",
       "      <td>2.491814e-248</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1150.598093</td>\n",
       "      <td>5.970826e-248</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1144.636386</td>\n",
       "      <td>1.067303e-246</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1092.499220</td>\n",
       "      <td>9.754959e-236</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1092.499220</td>\n",
       "      <td>9.754959e-236</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1092.499220</td>\n",
       "      <td>9.754959e-236</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>987.248175</td>\n",
       "      <td>1.475675e-213</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>964.658121</td>\n",
       "      <td>8.682894e-209</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>958.762685</td>\n",
       "      <td>1.527456e-207</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>832.260391</td>\n",
       "      <td>9.109264e-181</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>829.398253</td>\n",
       "      <td>3.685447e-180</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>760.526243</td>\n",
       "      <td>1.546155e-165</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>723.168873</td>\n",
       "      <td>1.361610e-157</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>700.432781</td>\n",
       "      <td>9.417029e-153</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>435.362823</td>\n",
       "      <td>4.604501e-96</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>333.610377</td>\n",
       "      <td>3.643021e-74</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>306.416036</td>\n",
       "      <td>2.680625e-68</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>244.336169</td>\n",
       "      <td>7.011810e-55</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>241.540306</td>\n",
       "      <td>2.824742e-54</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>156.617233</td>\n",
       "      <td>7.482770e-36</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>129.030277</td>\n",
       "      <td>7.586888e-30</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>78.133376</td>\n",
       "      <td>1.009712e-18</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49.612911</td>\n",
       "      <td>1.909316e-12</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.766818</td>\n",
       "      <td>2.902040e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.605596</td>\n",
       "      <td>2.051210e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.411725</td>\n",
       "      <td>2.347787e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.135354</td>\n",
       "      <td>2.866439e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.735527</td>\n",
       "      <td>3.911038e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.389845</td>\n",
       "      <td>5.323851e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.173820</td>\n",
       "      <td>6.767413e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.049150</td>\n",
       "      <td>8.245503e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.034271</td>\n",
       "      <td>8.531325e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "29  1342.972285  3.070953e-288                 Chlorothalonil\n",
       "30  1342.972285  3.070953e-288                     Glyphosate\n",
       "31  1342.972285  3.070953e-288                       Mancozeb\n",
       "32  1342.972285  3.070953e-288                     Mecoprop-P\n",
       "34  1342.972285  3.070953e-288                  Pendimethalin\n",
       "25  1325.168183  1.608067e-284   Inflowing drainage direction\n",
       "23  1169.840995  5.439990e-252                   Surface type\n",
       "36  1152.405110  2.491814e-248                   Prosulfocarb\n",
       "37  1150.598093  5.970826e-248                        Sulphur\n",
       "38  1144.636386  1.067303e-246                     Tri-allate\n",
       "26  1092.499220  9.754959e-236                   Fertiliser K\n",
       "27  1092.499220  9.754959e-236                   Fertiliser N\n",
       "28  1092.499220  9.754959e-236                   Fertiliser P\n",
       "21   987.248175  1.475675e-213                      Elevation\n",
       "33   964.658121  8.682894e-209                     Metamitron\n",
       "35   958.762685  1.527456e-207       PropamocarbHydrochloride\n",
       "24   832.260391  9.109264e-181  Outflowing drainage direction\n",
       "18   829.398253  3.685447e-180                      Saltmarsh\n",
       "22   760.526243  1.546155e-165      Cumulative catchment area\n",
       "2    723.168873  1.361610e-157                         Arable\n",
       "17   700.432781  9.417029e-153              Littoral sediment\n",
       "3    435.362823   4.604501e-96              Improve grassland\n",
       "20   333.610377   3.643021e-74                       Suburban\n",
       "19   306.416036   2.680625e-68                          Urban\n",
       "0    244.336169   7.011810e-55             Deciduous woodland\n",
       "4    241.540306   2.824742e-54              Neutral grassland\n",
       "7    156.617233   7.482770e-36                            Fen\n",
       "15   129.030277   7.586888e-30         Supralittoral sediment\n",
       "12    78.133376   1.009712e-18                      Saltwater\n",
       "13    49.612911   1.909316e-12                     Freshwater\n",
       "6      4.766818   2.902040e-02                 Acid grassland\n",
       "8      1.605596   2.051210e-01                        Heather\n",
       "11     1.411725   2.347787e-01                    Inland rock\n",
       "9      1.135354   2.866439e-01              Heather grassland\n",
       "1      0.735527   3.911038e-01            Coniferous woodland\n",
       "14     0.389845   5.323851e-01             Supralittoral rock\n",
       "5      0.173820   6.767413e-01           Calcareous grassland\n",
       "16     0.049150   8.245503e-01                  Littoral rock\n",
       "10     0.034271   8.531325e-01                            Bog"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 1km\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2536.936766</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2536.936766</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2536.936766</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2457.352155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2457.352155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2457.352155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2457.352155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2457.352155</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2207.208744</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2202.231304</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2198.961775</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2022.083842</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1989.817324</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1842.761284</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1555.536108</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1549.051565</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1485.853766</td>\n",
       "      <td>5.152527e-318</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1392.732967</td>\n",
       "      <td>1.268634e-298</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1309.187405</td>\n",
       "      <td>3.516732e-281</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>894.234195</td>\n",
       "      <td>6.746925e-194</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>745.160492</td>\n",
       "      <td>2.857352e-162</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>743.681811</td>\n",
       "      <td>5.893993e-162</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>574.470893</td>\n",
       "      <td>7.099727e-126</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>271.809180</td>\n",
       "      <td>8.008227e-61</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149.345332</td>\n",
       "      <td>2.857674e-34</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>131.344663</td>\n",
       "      <td>2.375107e-30</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>99.865736</td>\n",
       "      <td>1.760901e-23</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>46.675600</td>\n",
       "      <td>8.521422e-12</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>38.959990</td>\n",
       "      <td>4.378224e-10</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.290046</td>\n",
       "      <td>2.672276e-04</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.015067</td>\n",
       "      <td>5.283914e-04</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.402339</td>\n",
       "      <td>1.259780e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.105514</td>\n",
       "      <td>1.479624e-03</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.772190</td>\n",
       "      <td>2.893001e-02</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.024274</td>\n",
       "      <td>4.485804e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.159090</td>\n",
       "      <td>1.417381e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.851691</td>\n",
       "      <td>3.560812e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.632046</td>\n",
       "      <td>4.266115e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.240235</td>\n",
       "      <td>6.240394e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "26  2536.936766   0.000000e+00                   Fertiliser K\n",
       "27  2536.936766   0.000000e+00                   Fertiliser N\n",
       "28  2536.936766   0.000000e+00                   Fertiliser P\n",
       "29  2457.352155   0.000000e+00                 Chlorothalonil\n",
       "30  2457.352155   0.000000e+00                     Glyphosate\n",
       "31  2457.352155   0.000000e+00                       Mancozeb\n",
       "32  2457.352155   0.000000e+00                     Mecoprop-P\n",
       "34  2457.352155   0.000000e+00                  Pendimethalin\n",
       "37  2207.208744   0.000000e+00                        Sulphur\n",
       "36  2202.231304   0.000000e+00                   Prosulfocarb\n",
       "38  2198.961775   0.000000e+00                     Tri-allate\n",
       "33  2022.083842   0.000000e+00                     Metamitron\n",
       "35  1989.817324   0.000000e+00       PropamocarbHydrochloride\n",
       "23  1842.761284   0.000000e+00                   Surface type\n",
       "2   1555.536108   0.000000e+00                         Arable\n",
       "25  1549.051565   0.000000e+00   Inflowing drainage direction\n",
       "24  1485.853766  5.152527e-318  Outflowing drainage direction\n",
       "22  1392.732967  1.268634e-298      Cumulative catchment area\n",
       "21  1309.187405  3.516732e-281                      Elevation\n",
       "20   894.234195  6.746925e-194                       Suburban\n",
       "3    745.160492  2.857352e-162              Improve grassland\n",
       "19   743.681811  5.893993e-162                          Urban\n",
       "0    574.470893  7.099727e-126             Deciduous woodland\n",
       "13   271.809180   8.008227e-61                     Freshwater\n",
       "4    149.345332   2.857674e-34              Neutral grassland\n",
       "7    131.344663   2.375107e-30                            Fen\n",
       "18    99.865736   1.760901e-23                      Saltmarsh\n",
       "15    46.675600   8.521422e-12         Supralittoral sediment\n",
       "17    38.959990   4.378224e-10              Littoral sediment\n",
       "12    13.290046   2.672276e-04                      Saltwater\n",
       "6     12.015067   5.283914e-04                 Acid grassland\n",
       "5     10.402339   1.259780e-03           Calcareous grassland\n",
       "10    10.105514   1.479624e-03                            Bog\n",
       "9      4.772190   2.893001e-02              Heather grassland\n",
       "8      4.024274   4.485804e-02                        Heather\n",
       "11     2.159090   1.417381e-01                    Inland rock\n",
       "14     0.851691   3.560812e-01             Supralittoral rock\n",
       "1      0.632046   4.266115e-01            Coniferous woodland\n",
       "16     0.240235   6.240394e-01                  Littoral rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red-legged Partridge 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13523.622232</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13523.622232</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13523.622232</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>11673.899484</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>11667.108199</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>11666.574791</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10659.554341</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>10659.554341</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10659.554341</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>10659.554341</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>10659.554341</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9262.561980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>9224.344758</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9063.157294</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8310.347324</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6580.468060</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6132.393216</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6122.856668</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6094.072173</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3761.382336</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1821.322033</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>591.420483</td>\n",
       "      <td>1.691353e-129</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>325.757650</td>\n",
       "      <td>1.798558e-72</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108.694533</td>\n",
       "      <td>2.073050e-25</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>58.130510</td>\n",
       "      <td>2.518487e-14</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54.477132</td>\n",
       "      <td>1.609616e-13</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>38.514509</td>\n",
       "      <td>5.499196e-10</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.620936</td>\n",
       "      <td>9.473139e-06</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.224620</td>\n",
       "      <td>7.194636e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.125487</td>\n",
       "      <td>7.603285e-03</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.532290</td>\n",
       "      <td>3.326844e-02</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.459810</td>\n",
       "      <td>3.470883e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.979559</td>\n",
       "      <td>4.606380e-02</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.505345</td>\n",
       "      <td>6.117991e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.115325</td>\n",
       "      <td>1.458406e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.747430</td>\n",
       "      <td>3.872974e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.289120</td>\n",
       "      <td>5.907888e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.153603</td>\n",
       "      <td>6.951180e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.139898</td>\n",
       "      <td>7.083844e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score        P Value                      Attribute\n",
       "26  13523.622232   0.000000e+00                   Fertiliser K\n",
       "27  13523.622232   0.000000e+00                   Fertiliser N\n",
       "28  13523.622232   0.000000e+00                   Fertiliser P\n",
       "37  11673.899484   0.000000e+00                        Sulphur\n",
       "36  11667.108199   0.000000e+00                   Prosulfocarb\n",
       "38  11666.574791   0.000000e+00                     Tri-allate\n",
       "29  10659.554341   0.000000e+00                 Chlorothalonil\n",
       "30  10659.554341   0.000000e+00                     Glyphosate\n",
       "31  10659.554341   0.000000e+00                       Mancozeb\n",
       "32  10659.554341   0.000000e+00                     Mecoprop-P\n",
       "34  10659.554341   0.000000e+00                  Pendimethalin\n",
       "2    9262.561980   0.000000e+00                         Arable\n",
       "35   9224.344758   0.000000e+00       PropamocarbHydrochloride\n",
       "33   9063.157294   0.000000e+00                     Metamitron\n",
       "23   8310.347324   0.000000e+00                   Surface type\n",
       "24   6580.468060   0.000000e+00  Outflowing drainage direction\n",
       "25   6132.393216   0.000000e+00   Inflowing drainage direction\n",
       "21   6122.856668   0.000000e+00                      Elevation\n",
       "22   6094.072173   0.000000e+00      Cumulative catchment area\n",
       "3    3761.382336   0.000000e+00              Improve grassland\n",
       "0    1821.322033   0.000000e+00             Deciduous woodland\n",
       "20    591.420483  1.691353e-129                       Suburban\n",
       "5     325.757650   1.798558e-72           Calcareous grassland\n",
       "4     108.694533   2.073050e-25              Neutral grassland\n",
       "19     58.130510   2.518487e-14                          Urban\n",
       "7      54.477132   1.609616e-13                            Fen\n",
       "13     38.514509   5.499196e-10                     Freshwater\n",
       "18     19.620936   9.473139e-06                      Saltmarsh\n",
       "9       7.224620   7.194636e-03              Heather grassland\n",
       "11      7.125487   7.603285e-03                    Inland rock\n",
       "15      4.532290   3.326844e-02         Supralittoral sediment\n",
       "8       4.459810   3.470883e-02                        Heather\n",
       "16      3.979559   4.606380e-02                  Littoral rock\n",
       "10      3.505345   6.117991e-02                            Bog\n",
       "1       2.115325   1.458406e-01            Coniferous woodland\n",
       "14      0.747430   3.872974e-01             Supralittoral rock\n",
       "6       0.289120   5.907888e-01                 Acid grassland\n",
       "17      0.153603   6.951180e-01              Littoral sediment\n",
       "12      0.139898   7.083844e-01                      Saltwater"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ring-necked Parakeet 1km\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5174.029480</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4816.293327</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2496.791010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2496.791010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2496.791010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2110.055997</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1731.345378</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1731.345378</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1731.345378</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1731.004980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1731.004980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1451.182004</td>\n",
       "      <td>8.429407e-311</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1239.102083</td>\n",
       "      <td>1.629495e-266</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1101.552188</td>\n",
       "      <td>1.215523e-237</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>934.465809</td>\n",
       "      <td>2.082605e-202</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>644.695315</td>\n",
       "      <td>7.138206e-141</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>590.530935</td>\n",
       "      <td>2.620156e-129</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>550.735617</td>\n",
       "      <td>8.473886e-121</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>549.972053</td>\n",
       "      <td>1.234441e-120</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>531.829928</td>\n",
       "      <td>9.435110e-117</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>454.966109</td>\n",
       "      <td>2.841247e-100</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>447.760495</td>\n",
       "      <td>1.001226e-98</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>266.908247</td>\n",
       "      <td>9.184502e-60</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.737881</td>\n",
       "      <td>6.363711e-09</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.142590</td>\n",
       "      <td>5.887779e-05</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.350427</td>\n",
       "      <td>3.858472e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.304742</td>\n",
       "      <td>1.204628e-02</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.018766</td>\n",
       "      <td>1.415966e-02</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.354666</td>\n",
       "      <td>2.067299e-02</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.630696</td>\n",
       "      <td>3.141203e-02</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.075782</td>\n",
       "      <td>1.496627e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.028360</td>\n",
       "      <td>1.543966e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.773163</td>\n",
       "      <td>1.830003e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.343003</td>\n",
       "      <td>2.465134e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.007935</td>\n",
       "      <td>3.154055e-01</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.767993</td>\n",
       "      <td>3.808449e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.170880</td>\n",
       "      <td>6.793333e-01</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.122766</td>\n",
       "      <td>7.260555e-01</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.061965</td>\n",
       "      <td>8.034183e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "20  5174.029480   0.000000e+00                       Suburban\n",
       "19  4816.293327   0.000000e+00                          Urban\n",
       "26  2496.791010   0.000000e+00                   Fertiliser K\n",
       "27  2496.791010   0.000000e+00                   Fertiliser N\n",
       "28  2496.791010   0.000000e+00                   Fertiliser P\n",
       "22  2110.055997   0.000000e+00      Cumulative catchment area\n",
       "29  1731.345378   0.000000e+00                 Chlorothalonil\n",
       "31  1731.345378   0.000000e+00                       Mancozeb\n",
       "32  1731.345378   0.000000e+00                     Mecoprop-P\n",
       "30  1731.004980   0.000000e+00                     Glyphosate\n",
       "34  1731.004980   0.000000e+00                  Pendimethalin\n",
       "23  1451.182004  8.429407e-311                   Surface type\n",
       "24  1239.102083  1.629495e-266  Outflowing drainage direction\n",
       "25  1101.552188  1.215523e-237   Inflowing drainage direction\n",
       "21   934.465809  2.082605e-202                      Elevation\n",
       "0    644.695315  7.138206e-141             Deciduous woodland\n",
       "3    590.530935  2.620156e-129              Improve grassland\n",
       "36   550.735617  8.473886e-121                   Prosulfocarb\n",
       "37   549.972053  1.234441e-120                        Sulphur\n",
       "38   531.829928  9.435110e-117                     Tri-allate\n",
       "35   454.966109  2.841247e-100       PropamocarbHydrochloride\n",
       "33   447.760495   1.001226e-98                     Metamitron\n",
       "13   266.908247   9.184502e-60                     Freshwater\n",
       "2     33.737881   6.363711e-09                         Arable\n",
       "6     16.142590   5.887779e-05                 Acid grassland\n",
       "9      8.350427   3.858472e-03              Heather grassland\n",
       "10     6.304742   1.204628e-02                            Bog\n",
       "4      6.018766   1.415966e-02              Neutral grassland\n",
       "8      5.354666   2.067299e-02                        Heather\n",
       "1      4.630696   3.141203e-02            Coniferous woodland\n",
       "16     2.075782   1.496627e-01                  Littoral rock\n",
       "11     2.028360   1.543966e-01                    Inland rock\n",
       "14     1.773163   1.830003e-01             Supralittoral rock\n",
       "17     1.343003   2.465134e-01              Littoral sediment\n",
       "18     1.007935   3.154055e-01                      Saltmarsh\n",
       "12     0.767993   3.808449e-01                      Saltwater\n",
       "15     0.170880   6.793333e-01         Supralittoral sediment\n",
       "7      0.122766   7.260555e-01                            Fen\n",
       "5      0.061965   8.034183e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rock Dove 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11974.716865</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11974.716865</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11974.716865</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>11974.716865</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>11974.716865</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9715.208896</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9320.817826</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9320.817826</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9320.817826</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7716.649425</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7489.623513</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7222.092735</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7220.986108</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7209.182719</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>7185.198282</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6950.626400</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>5320.538837</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5174.072845</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4736.971375</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4361.914241</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3272.566853</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1947.276915</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1698.722674</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>327.460980</td>\n",
       "      <td>7.718845e-73</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>172.810554</td>\n",
       "      <td>2.260169e-39</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>158.476505</td>\n",
       "      <td>2.949316e-36</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>63.399064</td>\n",
       "      <td>1.741628e-15</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>38.512231</td>\n",
       "      <td>5.505612e-10</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35.360040</td>\n",
       "      <td>2.767982e-09</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32.305738</td>\n",
       "      <td>1.328301e-08</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15.733289</td>\n",
       "      <td>7.308286e-05</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.808123</td>\n",
       "      <td>2.027922e-04</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.939593</td>\n",
       "      <td>1.480967e-02</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.467724</td>\n",
       "      <td>2.257138e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.183074</td>\n",
       "      <td>2.767391e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.068542</td>\n",
       "      <td>3.012825e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.520829</td>\n",
       "      <td>4.704932e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.491905</td>\n",
       "      <td>4.830835e-01</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.363769</td>\n",
       "      <td>5.464245e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         F Score       P Value                      Attribute\n",
       "29  11974.716865  0.000000e+00                 Chlorothalonil\n",
       "30  11974.716865  0.000000e+00                     Glyphosate\n",
       "31  11974.716865  0.000000e+00                       Mancozeb\n",
       "32  11974.716865  0.000000e+00                     Mecoprop-P\n",
       "34  11974.716865  0.000000e+00                  Pendimethalin\n",
       "23   9715.208896  0.000000e+00                   Surface type\n",
       "26   9320.817826  0.000000e+00                   Fertiliser K\n",
       "27   9320.817826  0.000000e+00                   Fertiliser N\n",
       "28   9320.817826  0.000000e+00                   Fertiliser P\n",
       "24   7716.649425  0.000000e+00  Outflowing drainage direction\n",
       "25   7489.623513  0.000000e+00   Inflowing drainage direction\n",
       "37   7222.092735  0.000000e+00                        Sulphur\n",
       "36   7220.986108  0.000000e+00                   Prosulfocarb\n",
       "21   7209.182719  0.000000e+00                      Elevation\n",
       "38   7185.198282  0.000000e+00                     Tri-allate\n",
       "22   6950.626400  0.000000e+00      Cumulative catchment area\n",
       "35   5320.538837  0.000000e+00       PropamocarbHydrochloride\n",
       "33   5174.072845  0.000000e+00                     Metamitron\n",
       "3    4736.971375  0.000000e+00              Improve grassland\n",
       "20   4361.914241  0.000000e+00                       Suburban\n",
       "2    3272.566853  0.000000e+00                         Arable\n",
       "0    1947.276915  0.000000e+00             Deciduous woodland\n",
       "19   1698.722674  0.000000e+00                          Urban\n",
       "4     327.460980  7.718845e-73              Neutral grassland\n",
       "5     172.810554  2.260169e-39           Calcareous grassland\n",
       "13    158.476505  2.949316e-36                     Freshwater\n",
       "16     63.399064  1.741628e-15                  Littoral rock\n",
       "14     38.512231  5.505612e-10             Supralittoral rock\n",
       "15     35.360040  2.767982e-09         Supralittoral sediment\n",
       "7      32.305738  1.328301e-08                            Fen\n",
       "18     15.733289  7.308286e-05                      Saltmarsh\n",
       "17     13.808123  2.027922e-04              Littoral sediment\n",
       "11      5.939593  1.480967e-02                    Inland rock\n",
       "8       1.467724  2.257138e-01                        Heather\n",
       "6       1.183074  2.767391e-01                 Acid grassland\n",
       "10      1.068542  3.012825e-01                            Bog\n",
       "9       0.520829  4.704932e-01              Heather grassland\n",
       "12      0.491905  4.830835e-01                      Saltwater\n",
       "1       0.363769  5.464245e-01            Coniferous woodland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ruddy Duck 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2084.023887</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>611.973820</td>\n",
       "      <td>6.880144e-134</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>611.973820</td>\n",
       "      <td>6.880144e-134</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>611.973820</td>\n",
       "      <td>6.880144e-134</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>574.800456</td>\n",
       "      <td>6.036397e-126</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>439.953763</td>\n",
       "      <td>4.753911e-97</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>420.253312</td>\n",
       "      <td>8.124948e-93</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>420.253312</td>\n",
       "      <td>8.124948e-93</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>420.253312</td>\n",
       "      <td>8.124948e-93</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>420.170729</td>\n",
       "      <td>8.463895e-93</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>420.170729</td>\n",
       "      <td>8.463895e-93</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>334.799327</td>\n",
       "      <td>2.018860e-74</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>331.644948</td>\n",
       "      <td>9.666367e-74</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>327.259937</td>\n",
       "      <td>8.529270e-73</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>316.625993</td>\n",
       "      <td>1.678218e-70</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>279.546885</td>\n",
       "      <td>1.702940e-62</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>275.651878</td>\n",
       "      <td>1.182909e-61</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>252.505585</td>\n",
       "      <td>1.196889e-56</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>209.791045</td>\n",
       "      <td>2.131385e-47</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>206.348108</td>\n",
       "      <td>1.188914e-46</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>197.545829</td>\n",
       "      <td>9.644094e-45</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>178.469812</td>\n",
       "      <td>1.333079e-40</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159.658114</td>\n",
       "      <td>1.632226e-36</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>117.615266</td>\n",
       "      <td>2.340567e-27</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>105.045117</td>\n",
       "      <td>1.299505e-24</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.679745</td>\n",
       "      <td>1.452179e-13</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36.700240</td>\n",
       "      <td>1.392471e-09</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18.578174</td>\n",
       "      <td>1.635478e-05</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17.716380</td>\n",
       "      <td>2.570854e-05</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.935350</td>\n",
       "      <td>4.728954e-02</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.178205</td>\n",
       "      <td>1.399871e-01</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.916130</td>\n",
       "      <td>1.662933e-01</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.383460</td>\n",
       "      <td>2.395211e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.221097</td>\n",
       "      <td>2.691535e-01</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.008807</td>\n",
       "      <td>3.151962e-01</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.445443</td>\n",
       "      <td>5.045116e-01</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.433081</td>\n",
       "      <td>5.104857e-01</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.247335</td>\n",
       "      <td>6.189611e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.072280</td>\n",
       "      <td>7.880478e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "13  2084.023887   0.000000e+00                     Freshwater\n",
       "26   611.973820  6.880144e-134                   Fertiliser K\n",
       "27   611.973820  6.880144e-134                   Fertiliser N\n",
       "28   611.973820  6.880144e-134                   Fertiliser P\n",
       "24   574.800456  6.036397e-126  Outflowing drainage direction\n",
       "22   439.953763   4.753911e-97      Cumulative catchment area\n",
       "29   420.253312   8.124948e-93                 Chlorothalonil\n",
       "31   420.253312   8.124948e-93                       Mancozeb\n",
       "32   420.253312   8.124948e-93                     Mecoprop-P\n",
       "30   420.170729   8.463895e-93                     Glyphosate\n",
       "34   420.170729   8.463895e-93                  Pendimethalin\n",
       "37   334.799327   2.018860e-74                        Sulphur\n",
       "23   331.644948   9.666367e-74                   Surface type\n",
       "38   327.259937   8.529270e-73                     Tri-allate\n",
       "36   316.625993   1.678218e-70                   Prosulfocarb\n",
       "35   279.546885   1.702940e-62       PropamocarbHydrochloride\n",
       "33   275.651878   1.182909e-61                     Metamitron\n",
       "25   252.505585   1.196889e-56   Inflowing drainage direction\n",
       "19   209.791045   2.131385e-47                          Urban\n",
       "21   206.348108   1.188914e-46                      Elevation\n",
       "0    197.545829   9.644094e-45             Deciduous woodland\n",
       "4    178.469812   1.333079e-40              Neutral grassland\n",
       "3    159.658114   1.632226e-36              Improve grassland\n",
       "20   117.615266   2.340567e-27                       Suburban\n",
       "7    105.045117   1.299505e-24                            Fen\n",
       "2     54.679745   1.452179e-13                         Arable\n",
       "18    36.700240   1.392471e-09                      Saltmarsh\n",
       "15    18.578174   1.635478e-05         Supralittoral sediment\n",
       "12    17.716380   2.570854e-05                      Saltwater\n",
       "6      3.935350   4.728954e-02                 Acid grassland\n",
       "17     2.178205   1.399871e-01              Littoral sediment\n",
       "8      1.916130   1.662933e-01                        Heather\n",
       "10     1.383460   2.395211e-01                            Bog\n",
       "9      1.221097   2.691535e-01              Heather grassland\n",
       "1      1.008807   3.151962e-01            Coniferous woodland\n",
       "14     0.445443   5.045116e-01             Supralittoral rock\n",
       "16     0.433081   5.104857e-01                  Littoral rock\n",
       "11     0.247335   6.189611e-01                    Inland rock\n",
       "5      0.072280   7.880478e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whooper Swan 1km\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1676.560528</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1555.884294</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1411.884325</td>\n",
       "      <td>1.291051e-302</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1368.299617</td>\n",
       "      <td>1.585328e-293</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1368.299617</td>\n",
       "      <td>1.585328e-293</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1368.299617</td>\n",
       "      <td>1.585328e-293</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1367.972943</td>\n",
       "      <td>1.854762e-293</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1367.972943</td>\n",
       "      <td>1.854762e-293</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1295.050837</td>\n",
       "      <td>3.175931e-278</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1252.169594</td>\n",
       "      <td>2.987125e-269</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>822.832470</td>\n",
       "      <td>9.102816e-179</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>818.843021</td>\n",
       "      <td>6.390379e-178</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>809.297607</td>\n",
       "      <td>6.776178e-176</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>560.870223</td>\n",
       "      <td>5.752589e-123</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>537.803158</td>\n",
       "      <td>4.965172e-118</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>518.099739</td>\n",
       "      <td>8.226528e-114</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>491.320567</td>\n",
       "      <td>4.509861e-108</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>428.892315</td>\n",
       "      <td>1.130612e-94</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>428.892315</td>\n",
       "      <td>1.130612e-94</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>428.892315</td>\n",
       "      <td>1.130612e-94</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>302.457075</td>\n",
       "      <td>1.918026e-67</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>294.097064</td>\n",
       "      <td>1.224555e-65</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>236.168141</td>\n",
       "      <td>4.111276e-53</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>210.131628</td>\n",
       "      <td>1.798147e-47</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>171.006773</td>\n",
       "      <td>5.572412e-39</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>166.933497</td>\n",
       "      <td>4.277570e-38</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152.889945</td>\n",
       "      <td>4.839547e-35</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>129.204755</td>\n",
       "      <td>6.950815e-30</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>88.659782</td>\n",
       "      <td>4.982099e-21</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>87.366558</td>\n",
       "      <td>9.563148e-21</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>65.810060</td>\n",
       "      <td>5.135459e-16</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>62.367071</td>\n",
       "      <td>2.938167e-15</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>36.089602</td>\n",
       "      <td>1.904150e-09</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.502277</td>\n",
       "      <td>4.442055e-07</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.925116</td>\n",
       "      <td>1.905575e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7.753771</td>\n",
       "      <td>5.363096e-03</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.525727</td>\n",
       "      <td>6.085674e-03</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.360808</td>\n",
       "      <td>5.480621e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.028754</td>\n",
       "      <td>8.653489e-01</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "25  1676.560528   0.000000e+00   Inflowing drainage direction\n",
       "23  1555.884294   0.000000e+00                   Surface type\n",
       "21  1411.884325  1.291051e-302                      Elevation\n",
       "29  1368.299617  1.585328e-293                 Chlorothalonil\n",
       "31  1368.299617  1.585328e-293                       Mancozeb\n",
       "32  1368.299617  1.585328e-293                     Mecoprop-P\n",
       "30  1367.972943  1.854762e-293                     Glyphosate\n",
       "34  1367.972943  1.854762e-293                  Pendimethalin\n",
       "24  1295.050837  3.175931e-278  Outflowing drainage direction\n",
       "22  1252.169594  2.987125e-269      Cumulative catchment area\n",
       "37   822.832470  9.102816e-179                        Sulphur\n",
       "36   818.843021  6.390379e-178                   Prosulfocarb\n",
       "38   809.297607  6.776178e-176                     Tri-allate\n",
       "3    560.870223  5.752589e-123              Improve grassland\n",
       "35   537.803158  4.965172e-118       PropamocarbHydrochloride\n",
       "2    518.099739  8.226528e-114                         Arable\n",
       "33   491.320567  4.509861e-108                     Metamitron\n",
       "26   428.892315   1.130612e-94                   Fertiliser K\n",
       "27   428.892315   1.130612e-94                   Fertiliser N\n",
       "28   428.892315   1.130612e-94                   Fertiliser P\n",
       "13   302.457075   1.918026e-67                     Freshwater\n",
       "17   294.097064   1.224555e-65              Littoral sediment\n",
       "0    236.168141   4.111276e-53             Deciduous woodland\n",
       "18   210.131628   1.798147e-47                      Saltmarsh\n",
       "7    171.006773   5.572412e-39                            Fen\n",
       "20   166.933497   4.277570e-38                       Suburban\n",
       "4    152.889945   4.839547e-35              Neutral grassland\n",
       "19   129.204755   6.950815e-30                          Urban\n",
       "9     88.659782   4.982099e-21              Heather grassland\n",
       "15    87.366558   9.563148e-21         Supralittoral sediment\n",
       "14    65.810060   5.135459e-16             Supralittoral rock\n",
       "16    62.367071   2.938167e-15                  Littoral rock\n",
       "10    36.089602   1.904150e-09                            Bog\n",
       "1     25.502277   4.442055e-07            Coniferous woodland\n",
       "8     13.925116   1.905575e-04                        Heather\n",
       "12     7.753771   5.363096e-03                      Saltwater\n",
       "6      7.525727   6.085674e-03                 Acid grassland\n",
       "11     0.360808   5.480621e-01                    Inland rock\n",
       "5      0.028754   8.653489e-01           Calcareous grassland"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wigeon 1km\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4819.105469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Chlorothalonil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4819.105469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Glyphosate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4819.105469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mancozeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4819.105469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Mecoprop-P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4819.105469</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Pendimethalin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4543.858943</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4385.352401</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3772.569210</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3368.272204</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sulphur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3344.881952</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Prosulfocarb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3316.664763</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Tri-allate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3296.213107</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3018.447019</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2471.153767</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>PropamocarbHydrochloride</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2444.415822</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Metamitron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984.684092</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Improve grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1640.404091</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1577.395456</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1577.395456</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1577.395456</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Fertiliser P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1016.533733</td>\n",
       "      <td>9.783489e-220</td>\n",
       "      <td>Deciduous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>871.988879</td>\n",
       "      <td>3.462062e-189</td>\n",
       "      <td>Suburban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>839.439277</td>\n",
       "      <td>2.736586e-182</td>\n",
       "      <td>Littoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>570.717851</td>\n",
       "      <td>4.505394e-125</td>\n",
       "      <td>Freshwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>551.354132</td>\n",
       "      <td>6.247919e-121</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>535.113644</td>\n",
       "      <td>1.869402e-117</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>329.558717</td>\n",
       "      <td>2.723634e-73</td>\n",
       "      <td>Supralittoral sediment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>261.381261</td>\n",
       "      <td>1.439437e-58</td>\n",
       "      <td>Littoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>176.243055</td>\n",
       "      <td>4.059697e-40</td>\n",
       "      <td>Fen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.239640</td>\n",
       "      <td>1.825651e-34</td>\n",
       "      <td>Neutral grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>104.357556</td>\n",
       "      <td>1.836557e-24</td>\n",
       "      <td>Saltwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>37.244870</td>\n",
       "      <td>1.053444e-09</td>\n",
       "      <td>Supralittoral rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.097058</td>\n",
       "      <td>1.739122e-04</td>\n",
       "      <td>Heather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.680417</td>\n",
       "      <td>3.700235e-04</td>\n",
       "      <td>Coniferous woodland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.281786</td>\n",
       "      <td>2.316259e-03</td>\n",
       "      <td>Heather grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.220527</td>\n",
       "      <td>7.211058e-03</td>\n",
       "      <td>Calcareous grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.418478</td>\n",
       "      <td>2.336626e-01</td>\n",
       "      <td>Bog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.976582</td>\n",
       "      <td>3.230512e-01</td>\n",
       "      <td>Acid grassland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.096023</td>\n",
       "      <td>7.566574e-01</td>\n",
       "      <td>Inland rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        F Score        P Value                      Attribute\n",
       "29  4819.105469   0.000000e+00                 Chlorothalonil\n",
       "30  4819.105469   0.000000e+00                     Glyphosate\n",
       "31  4819.105469   0.000000e+00                       Mancozeb\n",
       "32  4819.105469   0.000000e+00                     Mecoprop-P\n",
       "34  4819.105469   0.000000e+00                  Pendimethalin\n",
       "25  4543.858943   0.000000e+00   Inflowing drainage direction\n",
       "23  4385.352401   0.000000e+00                   Surface type\n",
       "21  3772.569210   0.000000e+00                      Elevation\n",
       "37  3368.272204   0.000000e+00                        Sulphur\n",
       "36  3344.881952   0.000000e+00                   Prosulfocarb\n",
       "38  3316.664763   0.000000e+00                     Tri-allate\n",
       "24  3296.213107   0.000000e+00  Outflowing drainage direction\n",
       "22  3018.447019   0.000000e+00      Cumulative catchment area\n",
       "35  2471.153767   0.000000e+00       PropamocarbHydrochloride\n",
       "33  2444.415822   0.000000e+00                     Metamitron\n",
       "3   1984.684092   0.000000e+00              Improve grassland\n",
       "2   1640.404091   0.000000e+00                         Arable\n",
       "26  1577.395456   0.000000e+00                   Fertiliser K\n",
       "27  1577.395456   0.000000e+00                   Fertiliser N\n",
       "28  1577.395456   0.000000e+00                   Fertiliser P\n",
       "0   1016.533733  9.783489e-220             Deciduous woodland\n",
       "20   871.988879  3.462062e-189                       Suburban\n",
       "17   839.439277  2.736586e-182              Littoral sediment\n",
       "13   570.717851  4.505394e-125                     Freshwater\n",
       "18   551.354132  6.247919e-121                      Saltmarsh\n",
       "19   535.113644  1.869402e-117                          Urban\n",
       "15   329.558717   2.723634e-73         Supralittoral sediment\n",
       "16   261.381261   1.439437e-58                  Littoral rock\n",
       "7    176.243055   4.059697e-40                            Fen\n",
       "4    150.239640   1.825651e-34              Neutral grassland\n",
       "12   104.357556   1.836557e-24                      Saltwater\n",
       "14    37.244870   1.053444e-09             Supralittoral rock\n",
       "8     14.097058   1.739122e-04                        Heather\n",
       "1     12.680417   3.700235e-04            Coniferous woodland\n",
       "9      9.281786   2.316259e-03              Heather grassland\n",
       "5      7.220527   7.211058e-03           Calcareous grassland\n",
       "10     1.418478   2.336626e-01                            Bog\n",
       "6      0.976582   3.230512e-01                 Acid grassland\n",
       "11     0.096023   7.566574e-01                    Inland rock"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'])\n",
    "    display(dict['kbest']['Dataframe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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