{
 "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 = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>Sulphur_5km</th>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1237500.0 447500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "712500.0  267500.0                  0                  0   \n",
       "912500.0  617500.0                  0                  0   \n",
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       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "912500.0  617500.0                     0               0    0        0   \n",
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       "1237500.0 447500.0                     0               0    0        0   \n",
       "\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",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
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       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \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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       "                    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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       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
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       "y         x                                                          \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "667500.0  492500.0                   0                    0       0   \n",
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       "1142500.0 162500.0                   0                    0       0   \n",
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       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1192500.0 62500.0                   0                  0   \n",
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       "337500.0  37500.0                   0                  0   \n",
       "1142500.0 162500.0                  0                  0   \n",
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       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "1192500.0 62500.0                      0               0    0        0   \n",
       "667500.0  492500.0                     0               0    0        0   \n",
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       "1142500.0 162500.0                     0               0    0        0   \n",
       "297500.0  232500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "1192500.0 62500.0                   0  ...   -3.400000e+38 -3.400000e+38   \n",
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       "1142500.0 162500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "297500.0  232500.0                  0  ...    4.729528e-03  1.255404e-03   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
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       "297500.0  232500.0    4.127780e-03   -3.400000e+38       2.866850e-03   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
       "1192500.0 62500.0                  -3.400000e+38     -3.400000e+38   \n",
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       "297500.0  232500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "307500.0  42500.0                    0                    0       0   \n",
       "622500.0  97500.0                    0                    0       0   \n",
       "1212500.0 462500.0                   0                    0       0   \n",
       "962500.0  102500.0                   0                    0       0   \n",
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       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
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       "1212500.0 462500.0                 95                  4   \n",
       "962500.0  102500.0                  0                  0   \n",
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       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
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       "622500.0  97500.0                      0               0    0        0   \n",
       "1212500.0 462500.0                     0               0    0        0   \n",
       "962500.0  102500.0                     0               0    0        0   \n",
       "702500.0  617500.0                     0               0    0        0   \n",
       "\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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       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
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       "      <th>y</th>\n",
       "      <th>x</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>157500.0</th>\n",
       "      <th>317500.0</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>1.039940e+00</td>\n",
       "      <td>9.736983e-01</td>\n",
       "      <td>4.649050e-01</td>\n",
       "      <td>1.416382e+01</td>\n",
       "      <td>5.824835e-01</td>\n",
       "      <td>7.660726e+00</td>\n",
       "      <td>1.317793e+01</td>\n",
       "      <td>4.763658e-01</td>\n",
       "      <td>2.666079e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102500.0</th>\n",
       "      <th>497500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>20</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>2.453516e-01</td>\n",
       "      <td>2.766496e-02</td>\n",
       "      <td>9.993535e-02</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>8.190275e-02</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.525854e+00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982500.0</th>\n",
       "      <th>657500.0</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>277500.0</th>\n",
       "      <th>357500.0</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>56</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>3.466839e-01</td>\n",
       "      <td>8.026547e-02</td>\n",
       "      <td>2.348484e-01</td>\n",
       "      <td>6.775991e+00</td>\n",
       "      <td>1.820343e-01</td>\n",
       "      <td>3.088803e+00</td>\n",
       "      <td>1.260376e+01</td>\n",
       "      <td>9.621822e+00</td>\n",
       "      <td>3.166261e-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837500.0</th>\n",
       "      <th>442500.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "157500.0 317500.0                   0                    0       0   \n",
       "102500.0 497500.0                   0                    0      60   \n",
       "982500.0 657500.0                   0                    0       0   \n",
       "277500.0 357500.0                  18                    0      24   \n",
       "837500.0 442500.0                   0                    0       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "157500.0 317500.0                  0                  0                     0   \n",
       "102500.0 497500.0                 20                  0                     0   \n",
       "982500.0 657500.0                  0                  0                     0   \n",
       "277500.0 357500.0                 56                  0                     0   \n",
       "837500.0 442500.0                  0                  0                     0   \n",
       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "157500.0 317500.0               0    0        0                  0  ...   \n",
       "102500.0 497500.0               0    0        0                  0  ...   \n",
       "982500.0 657500.0               0    0        0                  0  ...   \n",
       "277500.0 357500.0               0    0        0                  0  ...   \n",
       "837500.0 442500.0               0    0        0                  0  ...   \n",
       "\n",
       "                   Glyphosate_5km  Mancozeb_5km  Mecoprop-P_5km  \\\n",
       "y        x                                                        \n",
       "157500.0 317500.0    1.039940e+00  9.736983e-01    4.649050e-01   \n",
       "102500.0 497500.0    2.453516e-01  2.766496e-02    9.993535e-02   \n",
       "982500.0 657500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "277500.0 357500.0    3.466839e-01  8.026547e-02    2.348484e-01   \n",
       "837500.0 442500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "\n",
       "                   Metamitron_5km  Pendimethalin_5km  \\\n",
       "y        x                                             \n",
       "157500.0 317500.0    1.416382e+01       5.824835e-01   \n",
       "102500.0 497500.0   -3.400000e+38       8.190275e-02   \n",
       "982500.0 657500.0   -3.400000e+38      -3.400000e+38   \n",
       "277500.0 357500.0    6.775991e+00       1.820343e-01   \n",
       "837500.0 442500.0   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                   PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y        x                                                          \n",
       "157500.0 317500.0                  7.660726e+00      1.317793e+01   \n",
       "102500.0 497500.0                 -3.400000e+38     -3.400000e+38   \n",
       "982500.0 657500.0                 -3.400000e+38     -3.400000e+38   \n",
       "277500.0 357500.0                  3.088803e+00      1.260376e+01   \n",
       "837500.0 442500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                    Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y        x                                                   \n",
       "157500.0 317500.0  4.763658e-01    2.666079e+00           0  \n",
       "102500.0 497500.0 -3.400000e+38    1.525854e+00           0  \n",
       "982500.0 657500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "277500.0 357500.0  9.621822e+00    3.166261e-01           0  \n",
       "837500.0 442500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
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       "      <th>Arable</th>\n",
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       "      <th>382500.0</th>\n",
       "      <th>287500.0</th>\n",
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       "      <th>872500.0</th>\n",
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       "      <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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       "    <tr>\n",
       "      <th>207500.0</th>\n",
       "      <th>277500.0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>1.120048e+00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>492500.0</th>\n",
       "      <th>552500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</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",
       "382500.0  287500.0                   0                    0       0   \n",
       "872500.0  532500.0                   0                    0       0   \n",
       "207500.0  277500.0                   1                   24       0   \n",
       "492500.0  552500.0                   0                    0       0   \n",
       "1227500.0 597500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "382500.0  287500.0                  0                  0   \n",
       "872500.0  532500.0                  0                  0   \n",
       "207500.0  277500.0                  0                  0   \n",
       "492500.0  552500.0                  0                  0   \n",
       "1227500.0 597500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "382500.0  287500.0                     0               0    0        0   \n",
       "872500.0  532500.0                     0               0    0        0   \n",
       "207500.0  277500.0                     0              75    0        0   \n",
       "492500.0  552500.0                     0               0    0        0   \n",
       "1227500.0 597500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "382500.0  287500.0                  0  ...    2.014876e+01  1.258746e+01   \n",
       "872500.0  532500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "207500.0  277500.0                  0  ...    1.179403e+01  5.411642e+00   \n",
       "492500.0  552500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "1227500.0 597500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "382500.0  287500.0    8.709696e+00    1.525128e+00       1.512782e+01   \n",
       "872500.0  532500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "207500.0  277500.0    3.950109e+00   -3.400000e+38       1.388328e+01   \n",
       "492500.0  552500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "1227500.0 597500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
       "382500.0  287500.0                  8.309065e-01      1.292195e+00   \n",
       "872500.0  532500.0                 -3.400000e+38     -3.400000e+38   \n",
       "207500.0  277500.0                 -3.400000e+38     -3.400000e+38   \n",
       "492500.0  552500.0                 -3.400000e+38     -3.400000e+38   \n",
       "1227500.0 597500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "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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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
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       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
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       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
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       "\n",
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       "y        x                                                        \n",
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       "832500.0 602500.0   -3.400000e+38 -3.400000e+38   -3.400000e+38   \n",
       "\n",
       "                   Metamitron_5km  Pendimethalin_5km  \\\n",
       "y        x                                             \n",
       "682500.0 607500.0   -3.400000e+38      -3.400000e+38   \n",
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       "\n",
       "                   PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y        x                                                          \n",
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       "                    Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "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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       "y        x                                                        \n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>0</td>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "542500.0  552500.0                   0                    0       0   \n",
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       "182500.0  567500.0                   0                    0      53   \n",
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       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "172500.0  362500.0                  0                  0   \n",
       "542500.0  552500.0                  0                  0   \n",
       "1202500.0 72500.0                   0                  0   \n",
       "182500.0  567500.0                 18                  0   \n",
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       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "172500.0  362500.0                     0               0    0        0   \n",
       "542500.0  552500.0                     0               0    0        0   \n",
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       "182500.0  567500.0                     0               0    0        0   \n",
       "777500.0  162500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
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       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "172500.0  362500.0    5.398479e+00   -3.400000e+38       1.433207e+01   \n",
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       "777500.0  162500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
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       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
       "172500.0  362500.0  1.031152e+01    1.028668e+00           0  \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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       "1167500.0 492500.0                  0                  0   \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></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>1097500.0</th>\n",
       "      <th>367500.0</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>472500.0</th>\n",
       "      <th>367500.0</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>89</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>1.437559e+01</td>\n",
       "      <td>3.220966e+00</td>\n",
       "      <td>2.944471e+00</td>\n",
       "      <td>1.022916e+00</td>\n",
       "      <td>1.498519e+01</td>\n",
       "      <td>6.039355e-01</td>\n",
       "      <td>1.314591e+00</td>\n",
       "      <td>2.093061e-01</td>\n",
       "      <td>1.596312e+01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782500.0</th>\n",
       "      <th>547500.0</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>842500.0</th>\n",
       "      <th>327500.0</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.968980e+00</td>\n",
       "      <td>1.555213e-01</td>\n",
       "      <td>2.060352e+00</td>\n",
       "      <td>3.130774e+00</td>\n",
       "      <td>9.114452e-01</td>\n",
       "      <td>1.572151e+00</td>\n",
       "      <td>5.525377e-01</td>\n",
       "      <td>3.950021e+00</td>\n",
       "      <td>1.179231e+01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062500.0</th>\n",
       "      <th>202500.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "1097500.0 367500.0                   0                    0       0   \n",
       "472500.0  367500.0                   6                    0       3   \n",
       "782500.0  547500.0                   0                    0       0   \n",
       "842500.0  327500.0                  11                    8       7   \n",
       "1062500.0 202500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1097500.0 367500.0                  0                  0   \n",
       "472500.0  367500.0                 89                  0   \n",
       "782500.0  547500.0                  0                  0   \n",
       "842500.0  327500.0                 59                  0   \n",
       "1062500.0 202500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "1097500.0 367500.0                     0               0    0        0   \n",
       "472500.0  367500.0                     0               0    0        0   \n",
       "782500.0  547500.0                     0               0    0        0   \n",
       "842500.0  327500.0                     0              10    0        0   \n",
       "1062500.0 202500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "1097500.0 367500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "472500.0  367500.0                  0  ...    1.437559e+01  3.220966e+00   \n",
       "782500.0  547500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "842500.0  327500.0                  0  ...    1.968980e+00  1.555213e-01   \n",
       "1062500.0 202500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "1097500.0 367500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "472500.0  367500.0    2.944471e+00    1.022916e+00       1.498519e+01   \n",
       "782500.0  547500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "842500.0  327500.0    2.060352e+00    3.130774e+00       9.114452e-01   \n",
       "1062500.0 202500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
       "1097500.0 367500.0                 -3.400000e+38     -3.400000e+38   \n",
       "472500.0  367500.0                  6.039355e-01      1.314591e+00   \n",
       "782500.0  547500.0                 -3.400000e+38     -3.400000e+38   \n",
       "842500.0  327500.0                  1.572151e+00      5.525377e-01   \n",
       "1062500.0 202500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
       "1097500.0 367500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "472500.0  367500.0  2.093061e-01    1.596312e+01           0  \n",
       "782500.0  547500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "842500.0  327500.0  3.950021e+00    1.179231e+01           0  \n",
       "1062500.0 202500.0 -3.400000e+38   -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
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       "      <th>272500.0</th>\n",
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       "      <th>457500.0</th>\n",
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       "      <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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       "    <tr>\n",
       "      <th>792500.0</th>\n",
       "      <th>272500.0</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>-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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       "    <tr>\n",
       "      <th>1057500.0</th>\n",
       "      <th>12500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>-3.400000e+38</td>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "272500.0  142500.0                   0                    0       0   \n",
       "457500.0  532500.0                   0                    0       0   \n",
       "792500.0  272500.0                   0                    0       0   \n",
       "1057500.0 12500.0                    0                    0       0   \n",
       "1257500.0 562500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "272500.0  142500.0                  0                  0   \n",
       "457500.0  532500.0                  0                  0   \n",
       "792500.0  272500.0                  0                  0   \n",
       "1057500.0 12500.0                   0                  0   \n",
       "1257500.0 562500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "272500.0  142500.0                     0               0    0        0   \n",
       "457500.0  532500.0                     0               0    0        0   \n",
       "792500.0  272500.0                     0               0    0      100   \n",
       "1057500.0 12500.0                      0               0    0        0   \n",
       "1257500.0 562500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Glyphosate_5km  Mancozeb_5km  \\\n",
       "y         x                            ...                                 \n",
       "272500.0  142500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "457500.0  532500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "792500.0  272500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "1057500.0 12500.0                   0  ...   -3.400000e+38 -3.400000e+38   \n",
       "1257500.0 562500.0                  0  ...   -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                    Mecoprop-P_5km  Metamitron_5km  Pendimethalin_5km  \\\n",
       "y         x                                                             \n",
       "272500.0  142500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "457500.0  532500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "792500.0  272500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "1057500.0 12500.0    -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "1257500.0 562500.0   -3.400000e+38   -3.400000e+38      -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y         x                                                          \n",
       "272500.0  142500.0                 -3.400000e+38     -3.400000e+38   \n",
       "457500.0  532500.0                 -3.400000e+38     -3.400000e+38   \n",
       "792500.0  272500.0                 -3.400000e+38     -3.400000e+38   \n",
       "1057500.0 12500.0                  -3.400000e+38     -3.400000e+38   \n",
       "1257500.0 562500.0                 -3.400000e+38     -3.400000e+38   \n",
       "\n",
       "                     Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y         x                                                   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "\n",
       "                   Glyphosate_5km  Mancozeb_5km  Mecoprop-P_5km  \\\n",
       "y        x                                                        \n",
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       "y        x                                             \n",
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       "\n",
       "                   PropamocarbHydrochloride_5km  Prosulfocarb_5km  \\\n",
       "y        x                                                          \n",
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       "\n",
       "                    Sulphur_5km  Tri-allate_5km  Occurrence  \n",
       "y        x                                                   \n",
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   "source": [
    "INVASIVE_BIRDS_PATH = 'Datasets/Machine Learning/5km Rasters/Birds'\n",
    "# Use this if using coordinates as separate columns\n",
    "# df_5km = pd.read_csv('Datasets/Machine Learning/Dataframes/5km_All_Birds_DF.csv')\n",
    "\n",
    "# Use this if using coordinates as indices\n",
    "df_5km = pd.read_csv('Datasets/Machine Learning/Dataframes/5km_All_Birds_DF.csv', index_col=[0,1])\n",
    "\n",
    "total_birds = (df_5km['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_5km.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_5km.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_5km.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": {},
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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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       "y         x                                                                 \n",
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       "                    Surface type  Outflowing drainage direction  \\\n",
       "y         x                                                       \n",
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       "942500.0  192500.0            -1                             -1   \n",
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       "1237500.0 172500.0            -1                             -1   \n",
       "107500.0  687500.0            -1                             -1   \n",
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       "y         x                                                   \n",
       "1082500.0 462500.0                           255           0  \n",
       "942500.0  192500.0                           255           0  \n",
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       "1237500.0 172500.0                           255           0  \n",
       "107500.0  687500.0                           255           0  \n",
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       "y        x                                                                    \n",
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       "\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "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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       "y         x                                                \n",
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       "802500.0  322500.0      0         0       4998                          4   \n",
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       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557500.0</th>\n",
       "      <th>52500.0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187500.0</th>\n",
       "      <th>212500.0</th>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "102500.0  257500.0                  30                   11       0   \n",
       "1232500.0 332500.0                   0                    0       0   \n",
       "1182500.0 37500.0                    0                    0       0   \n",
       "557500.0  52500.0                    0                    0       0   \n",
       "187500.0  212500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "102500.0  257500.0                 60                  0   \n",
       "1232500.0 332500.0                  0                  0   \n",
       "1182500.0 37500.0                   0                  0   \n",
       "557500.0  52500.0                   0                  0   \n",
       "187500.0  212500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "102500.0  257500.0                     0               0    0        0   \n",
       "1232500.0 332500.0                     0               0    0        0   \n",
       "1182500.0 37500.0                      0               0    0        0   \n",
       "557500.0  52500.0                      0               0    0        0   \n",
       "187500.0  212500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Littoral sediment  Saltmarsh  \\\n",
       "y         x                            ...                                 \n",
       "102500.0  257500.0                  0  ...                  0          0   \n",
       "1232500.0 332500.0                  0  ...                  0          0   \n",
       "1182500.0 37500.0                   0  ...                  0          0   \n",
       "557500.0  52500.0                   0  ...                  0          0   \n",
       "187500.0  212500.0                  0  ...                  0          0   \n",
       "\n",
       "                    Urban  Suburban  Elevation  Cumulative catchment area  \\\n",
       "y         x                                                                 \n",
       "102500.0  257500.0      0         0        982                        202   \n",
       "1232500.0 332500.0      0         0      -9999                      -9999   \n",
       "1182500.0 37500.0       0         0      -9999                      -9999   \n",
       "557500.0  52500.0       0         0      -9999                      -9999   \n",
       "187500.0  212500.0      0         0      -1000                      -9999   \n",
       "\n",
       "                    Surface type  Outflowing drainage direction  \\\n",
       "y         x                                                       \n",
       "102500.0  257500.0             2                              9   \n",
       "1232500.0 332500.0            -1                             -1   \n",
       "1182500.0 37500.0             -1                             -1   \n",
       "557500.0  52500.0             -1                             -1   \n",
       "187500.0  212500.0             0                             -2   \n",
       "\n",
       "                    Inflowing drainage direction  Occurrence  \n",
       "y         x                                                   \n",
       "102500.0  257500.0                            15           0  \n",
       "1232500.0 332500.0                           255           0  \n",
       "1182500.0 37500.0                            255           0  \n",
       "557500.0  52500.0                            255           0  \n",
       "187500.0  212500.0                             0           0  \n",
       "\n",
       "[5 rows x 27 columns]"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "      <th>Deciduous woodland</th>\n",
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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "1147500.0 547500.0                   0                    0       0   \n",
       "1027500.0 172500.0                   0                    0       0   \n",
       "1247500.0 612500.0                   0                    0       0   \n",
       "62500.0   372500.0                   0                    0       0   \n",
       "752500.0  272500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "1147500.0 547500.0                  0                  0   \n",
       "1027500.0 172500.0                  0                  0   \n",
       "1247500.0 612500.0                  0                  0   \n",
       "62500.0   372500.0                  0                  0   \n",
       "752500.0  272500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "1147500.0 547500.0                     0               0    0        0   \n",
       "1027500.0 172500.0                     0               0    0        0   \n",
       "1247500.0 612500.0                     0               0    0        0   \n",
       "62500.0   372500.0                     0               0    0        0   \n",
       "752500.0  272500.0                     0               0    0      100   \n",
       "\n",
       "                    Heather grassland  ...  Littoral sediment  Saltmarsh  \\\n",
       "y         x                            ...                                 \n",
       "1147500.0 547500.0                  0  ...                  0          0   \n",
       "1027500.0 172500.0                  0  ...                  0          0   \n",
       "1247500.0 612500.0                  0  ...                  0          0   \n",
       "62500.0   372500.0                  0  ...                  0          0   \n",
       "752500.0  272500.0                  0  ...                  0          0   \n",
       "\n",
       "                    Urban  Suburban  Elevation  Cumulative catchment area  \\\n",
       "y         x                                                                 \n",
       "1147500.0 547500.0      0         0      -9999                      -9999   \n",
       "1027500.0 172500.0      0         0      -9999                      -9999   \n",
       "1247500.0 612500.0      0         0      -9999                      -9999   \n",
       "62500.0   372500.0      0         0      -1000                      -9999   \n",
       "752500.0  272500.0      0         0       6182                          4   \n",
       "\n",
       "                    Surface type  Outflowing drainage direction  \\\n",
       "y         x                                                       \n",
       "1147500.0 547500.0            -1                             -1   \n",
       "1027500.0 172500.0            -1                             -1   \n",
       "1247500.0 612500.0            -1                             -1   \n",
       "62500.0   372500.0             0                             -2   \n",
       "752500.0  272500.0             2                              7   \n",
       "\n",
       "                    Inflowing drainage direction  Occurrence  \n",
       "y         x                                                   \n",
       "1147500.0 547500.0                           255           0  \n",
       "1027500.0 172500.0                           255           0  \n",
       "1247500.0 612500.0                           255           0  \n",
       "62500.0   372500.0                             0           0  \n",
       "752500.0  272500.0                             4           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop Fertiliser and Pesticide\n",
    "for dict in df_dicts:\n",
    "    cur_df = dict[\"dataframe\"]\n",
    "    dict[\"dataframe\"].drop(cur_df.iloc[:, 26:-1], inplace=True, axis=1)\n",
    "    display(cur_df.sample(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    35813\n",
      "1      587\n",
      "dtype: int64 \n",
      "\n",
      "Barnacle Goose 5km data after drop: \n",
      " Occurrence\n",
      "0    7378\n",
      "1     587\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    32095\n",
      "1     4305\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 5km data after drop: \n",
      " Occurrence\n",
      "1    4305\n",
      "0    3660\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    35915\n",
      "1      485\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 5km data after drop: \n",
      " Occurrence\n",
      "0    7480\n",
      "1     485\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 5km data before drop: \n",
      " Occurrence\n",
      "0    35001\n",
      "1     1399\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 5km data after drop: \n",
      " Occurrence\n",
      "0    6566\n",
      "1    1399\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 5km data before drop: \n",
      " Occurrence\n",
      "0    35954\n",
      "1      446\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 5km data after drop: \n",
      " Occurrence\n",
      "0    7519\n",
      "1     446\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 5km data before drop: \n",
      " Occurrence\n",
      "0    34771\n",
      "1     1629\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 5km data after drop: \n",
      " Occurrence\n",
      "0    6336\n",
      "1    1629\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 5km data before drop: \n",
      " Occurrence\n",
      "0    36116\n",
      "1      284\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 5km data after drop: \n",
      " Occurrence\n",
      "0    7681\n",
      "1     284\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 5km data before drop: \n",
      " Occurrence\n",
      "0    34242\n",
      "1     2158\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 5km data after drop: \n",
      " Occurrence\n",
      "0    5807\n",
      "1    2158\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 5km data before drop: \n",
      " Occurrence\n",
      "0    35686\n",
      "1      714\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 5km data after drop: \n",
      " Occurrence\n",
      "0    7251\n",
      "1     714\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 5km data before drop: \n",
      " Occurrence\n",
      "0    31133\n",
      "1     5267\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 5km data after drop: \n",
      " Occurrence\n",
      "1    5267\n",
      "0    2698\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 5km data before drop: \n",
      " Occurrence\n",
      "0    32552\n",
      "1     3848\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 5km data after drop: \n",
      " Occurrence\n",
      "0    4117\n",
      "1    3848\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 5km data before drop: \n",
      " Occurrence\n",
      "0    35087\n",
      "1     1313\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 5km data after drop: \n",
      " Occurrence\n",
      "0    6652\n",
      "1    1313\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 5km data before drop: \n",
      " Occurrence\n",
      "0    35751\n",
      "1      649\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 5km data after drop: \n",
      " Occurrence\n",
      "0    7316\n",
      "1     649\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 5km data before drop: \n",
      " Occurrence\n",
      "0    35458\n",
      "1      942\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 5km data after drop: \n",
      " Occurrence\n",
      "0    7023\n",
      "1     942\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 5km data before drop: \n",
      " Occurrence\n",
      "0    34250\n",
      "1     2150\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 5km data after drop: \n",
      " Occurrence\n",
      "0    5815\n",
      "1    2150\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 5km data before drop: \n",
      " Occurrence\n",
      "0    36194\n",
      "1      206\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 5km data after drop: \n",
      " Occurrence\n",
      "0    7759\n",
      "1     206\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 5km data before drop: \n",
      " Occurrence\n",
      "0    33570\n",
      "1     2830\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 5km data after drop: \n",
      " Occurrence\n",
      "0    5135\n",
      "1    2830\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 5km data before drop: \n",
      " Occurrence\n",
      "0    36291\n",
      "1      109\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 5km data after drop: \n",
      " Occurrence\n",
      "0    7856\n",
      "1     109\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 5km data before drop: \n",
      " Occurrence\n",
      "0    35558\n",
      "1      842\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 5km data after drop: \n",
      " Occurrence\n",
      "0    7123\n",
      "1     842\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 5km data before drop: \n",
      " Occurrence\n",
      "0    34543\n",
      "1     1857\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 5km data after drop: \n",
      " Occurrence\n",
      "0    6108\n",
      "1    1857\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": 6,
   "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": 7,
   "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 = [5, 10, 15, 20]\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": 8,
   "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": 9,
   "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": 10,
   "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 5km</td>\n",
       "      <td>5267</td>\n",
       "      <td>0.661268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 5km</td>\n",
       "      <td>4305</td>\n",
       "      <td>0.540490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 5km</td>\n",
       "      <td>3848</td>\n",
       "      <td>0.483114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 5km</td>\n",
       "      <td>2830</td>\n",
       "      <td>0.355304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 5km</td>\n",
       "      <td>2158</td>\n",
       "      <td>0.270935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 5km</td>\n",
       "      <td>2150</td>\n",
       "      <td>0.269931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 5km</td>\n",
       "      <td>1857</td>\n",
       "      <td>0.233145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 5km</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.204520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 5km</td>\n",
       "      <td>1399</td>\n",
       "      <td>0.175643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 5km</td>\n",
       "      <td>1313</td>\n",
       "      <td>0.164846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 5km</td>\n",
       "      <td>942</td>\n",
       "      <td>0.118267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 5km</td>\n",
       "      <td>842</td>\n",
       "      <td>0.105712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 5km</td>\n",
       "      <td>714</td>\n",
       "      <td>0.089642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 5km</td>\n",
       "      <td>649</td>\n",
       "      <td>0.081481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 5km</td>\n",
       "      <td>587</td>\n",
       "      <td>0.073697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 5km</td>\n",
       "      <td>485</td>\n",
       "      <td>0.060891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 5km</td>\n",
       "      <td>446</td>\n",
       "      <td>0.055995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 5km</td>\n",
       "      <td>284</td>\n",
       "      <td>0.035656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 5km</td>\n",
       "      <td>206</td>\n",
       "      <td>0.025863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 5km</td>\n",
       "      <td>109</td>\n",
       "      <td>0.013685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  Occurrence Count  Percentage\n",
       "9              Mute Swan 5km              5267    0.661268\n",
       "1           Canada Goose 5km              4305    0.540490\n",
       "10              Pheasant 5km              3848    0.483114\n",
       "16             Rock Dove 5km              2830    0.355304\n",
       "7             Little Owl 5km              2158    0.270935\n",
       "14  Red-legged Partridge 5km              2150    0.269931\n",
       "19                Wigeon 5km              1857    0.233145\n",
       "5         Grey Partridge 5km              1629    0.204520\n",
       "3                Gadwall 5km              1399    0.175643\n",
       "11     Pink-footed Goose 5km              1313    0.164846\n",
       "13               Pochard 5km               942    0.118267\n",
       "18          Whooper Swan 5km               842    0.105712\n",
       "8          Mandarin Duck 5km               714    0.089642\n",
       "12               Pintail 5km               649    0.081481\n",
       "0         Barnacle Goose 5km               587    0.073697\n",
       "2         Egyptian Goose 5km               485    0.060891\n",
       "4                Goshawk 5km               446    0.055995\n",
       "6         Indian Peafowl 5km               284    0.035656\n",
       "15  Ring-necked Parakeet 5km               206    0.025863\n",
       "17            Ruddy Duck 5km               109    0.013685"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training with Barnacle Goose 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.934793683138054,\n",
      "        \"recall\": 0.995119305856833,\n",
      "        \"f1-score\": 0.9640136590491201,\n",
      "        \"support\": 1844\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6896551724137931,\n",
      "        \"recall\": 0.13513513513513514,\n",
      "        \"f1-score\": 0.22598870056497178,\n",
      "        \"support\": 148\n",
      "    },\n",
      "    \"accuracy\": 0.9312248995983936,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8122244277759236,\n",
      "        \"recall\": 0.5651272204959841,\n",
      "        \"f1-score\": 0.5950011798070459,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9165805809356491,\n",
      "        \"recall\": 0.9312248995983936,\n",
      "        \"f1-score\": 0.9091804794027075,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Barnacle Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9457964601769911,\n",
      "        \"recall\": 0.9273318872017353,\n",
      "        \"f1-score\": 0.936473165388828,\n",
      "        \"support\": 1844\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2717391304347826,\n",
      "        \"recall\": 0.33783783783783783,\n",
      "        \"f1-score\": 0.3012048192771084,\n",
      "        \"support\": 148\n",
      "    },\n",
      "    \"accuracy\": 0.8835341365461847,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6087677953058869,\n",
      "        \"recall\": 0.6325848625197865,\n",
      "        \"f1-score\": 0.6188389923329682,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8957158955174294,\n",
      "        \"recall\": 0.8835341365461847,\n",
      "        \"f1-score\": 0.8892745131676761,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Canada Goose 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9504830917874396,\n",
      "        \"recall\": 0.8582333696837514,\n",
      "        \"f1-score\": 0.9020057306590258,\n",
      "        \"support\": 917\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8883161512027491,\n",
      "        \"recall\": 0.9618604651162791,\n",
      "        \"f1-score\": 0.923626619026351,\n",
      "        \"support\": 1075\n",
      "    },\n",
      "    \"accuracy\": 0.9141566265060241,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9193996214950944,\n",
      "        \"recall\": 0.9100469174000152,\n",
      "        \"f1-score\": 0.9128161748426884,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.916934165518091,\n",
      "        \"recall\": 0.9141566265060241,\n",
      "        \"f1-score\": 0.9136736297528384,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Canada Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9319492502883506,\n",
      "        \"recall\": 0.88113413304253,\n",
      "        \"f1-score\": 0.905829596412556,\n",
      "        \"support\": 917\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9031111111111111,\n",
      "        \"recall\": 0.9451162790697675,\n",
      "        \"f1-score\": 0.9236363636363636,\n",
      "        \"support\": 1075\n",
      "    },\n",
      "    \"accuracy\": 0.9156626506024096,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9175301806997309,\n",
      "        \"recall\": 0.9131252060561488,\n",
      "        \"f1-score\": 0.9147329800244598,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9163864994773404,\n",
      "        \"recall\": 0.9156626506024096,\n",
      "        \"f1-score\": 0.9154391720980948,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Egyptian Goose 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9474768280123584,\n",
      "        \"recall\": 0.9881847475832438,\n",
      "        \"f1-score\": 0.9674027339642481,\n",
      "        \"support\": 1862\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.56,\n",
      "        \"recall\": 0.2153846153846154,\n",
      "        \"f1-score\": 0.3111111111111111,\n",
      "        \"support\": 130\n",
      "    },\n",
      "    \"accuracy\": 0.9377510040160643,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7537384140061792,\n",
      "        \"recall\": 0.6017846814839296,\n",
      "        \"f1-score\": 0.6392569225376796,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9221896856219937,\n",
      "        \"recall\": 0.9377510040160643,\n",
      "        \"f1-score\": 0.924572457372427,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Egyptian Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9553571428571429,\n",
      "        \"recall\": 0.976906552094522,\n",
      "        \"f1-score\": 0.9660116834838025,\n",
      "        \"support\": 1862\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5113636363636364,\n",
      "        \"recall\": 0.34615384615384615,\n",
      "        \"f1-score\": 0.41284403669724773,\n",
      "        \"support\": 130\n",
      "    },\n",
      "    \"accuracy\": 0.9357429718875502,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7333603896103896,\n",
      "        \"recall\": 0.6615301991241841,\n",
      "        \"f1-score\": 0.6894278600905251,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9263816630156991,\n",
      "        \"recall\": 0.9357429718875502,\n",
      "        \"f1-score\": 0.9299113852497402,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Gadwall 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9283965728274174,\n",
      "        \"recall\": 0.9266951740989615,\n",
      "        \"f1-score\": 0.9275450932436564,\n",
      "        \"support\": 1637\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.664804469273743,\n",
      "        \"recall\": 0.6704225352112676,\n",
      "        \"f1-score\": 0.667601683029453,\n",
      "        \"support\": 355\n",
      "    },\n",
      "    \"accuracy\": 0.8810240963855421,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7966005210505802,\n",
      "        \"recall\": 0.7985588546551146,\n",
      "        \"f1-score\": 0.7975733881365548,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.881421072445111,\n",
      "        \"recall\": 0.8810240963855421,\n",
      "        \"f1-score\": 0.8812198369052818,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Gadwall 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.905727923627685,\n",
      "        \"recall\": 0.9273060476481368,\n",
      "        \"f1-score\": 0.9163899788711137,\n",
      "        \"support\": 1637\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6234177215189873,\n",
      "        \"recall\": 0.5549295774647888,\n",
      "        \"f1-score\": 0.587183308494784,\n",
      "        \"support\": 355\n",
      "    },\n",
      "    \"accuracy\": 0.8609437751004017,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7645728225733361,\n",
      "        \"recall\": 0.7411178125564628,\n",
      "        \"f1-score\": 0.7517866436829488,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8554166175289963,\n",
      "        \"recall\": 0.8609437751004017,\n",
      "        \"f1-score\": 0.8577211194415972,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Goshawk 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9532994923857868,\n",
      "        \"recall\": 0.9910290237467019,\n",
      "        \"f1-score\": 0.9717981888745149,\n",
      "        \"support\": 1895\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.22727272727272727,\n",
      "        \"recall\": 0.05154639175257732,\n",
      "        \"f1-score\": 0.08403361344537814,\n",
      "        \"support\": 97\n",
      "    },\n",
      "    \"accuracy\": 0.945281124497992,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.590286109829257,\n",
      "        \"recall\": 0.5212877077496396,\n",
      "        \"f1-score\": 0.5279159011599465,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9179457794259641,\n",
      "        \"recall\": 0.945281124497992,\n",
      "        \"f1-score\": 0.92856868896657,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Goshawk 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9608967674661105,\n",
      "        \"recall\": 0.9725593667546174,\n",
      "        \"f1-score\": 0.966692892735379,\n",
      "        \"support\": 1895\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2972972972972973,\n",
      "        \"recall\": 0.2268041237113402,\n",
      "        \"f1-score\": 0.2573099415204678,\n",
      "        \"support\": 97\n",
      "    },\n",
      "    \"accuracy\": 0.9362449799196787,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6290970323817039,\n",
      "        \"recall\": 0.5996817452329788,\n",
      "        \"f1-score\": 0.6120014171279233,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9285829378444365,\n",
      "        \"recall\": 0.9362449799196787,\n",
      "        \"f1-score\": 0.9321496466169821,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Grey Partridge 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9430628272251309,\n",
      "        \"recall\": 0.902316844082655,\n",
      "        \"f1-score\": 0.92224,\n",
      "        \"support\": 1597\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6637931034482759,\n",
      "        \"recall\": 0.779746835443038,\n",
      "        \"f1-score\": 0.7171129220023283,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"accuracy\": 0.8780120481927711,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8034279653367034,\n",
      "        \"recall\": 0.8410318397628465,\n",
      "        \"f1-score\": 0.8196764610011642,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8876855476609453,\n",
      "        \"recall\": 0.8780120481927711,\n",
      "        \"f1-score\": 0.881564700899056,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Grey Partridge 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8988278840222085,\n",
      "        \"recall\": 0.9123356293049468,\n",
      "        \"f1-score\": 0.9055313859540087,\n",
      "        \"support\": 1597\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6226415094339622,\n",
      "        \"recall\": 0.5848101265822785,\n",
      "        \"f1-score\": 0.6031331592689295,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"accuracy\": 0.8473895582329317,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7607346967280854,\n",
      "        \"recall\": 0.7485728779436127,\n",
      "        \"f1-score\": 0.7543322726114692,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8440620115511456,\n",
      "        \"recall\": 0.8473895582329317,\n",
      "        \"f1-score\": 0.8455678821685638,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Indian Peafowl 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9617321248741189,\n",
      "        \"recall\": 0.9968684759916493,\n",
      "        \"f1-score\": 0.9789851358277807,\n",
      "        \"support\": 1916\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9588353413654619,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.4808660624370594,\n",
      "        \"recall\": 0.49843423799582465,\n",
      "        \"f1-score\": 0.48949256791389034,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9250395337644637,\n",
      "        \"recall\": 0.9588353413654619,\n",
      "        \"f1-score\": 0.941634297312263,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Indian Peafowl 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9655172413793104,\n",
      "        \"recall\": 0.9791231732776617,\n",
      "        \"f1-score\": 0.9722726094843224,\n",
      "        \"support\": 1916\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.1836734693877551,\n",
      "        \"recall\": 0.11842105263157894,\n",
      "        \"f1-score\": 0.144,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.946285140562249,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5745953553835328,\n",
      "        \"recall\": 0.5487721129546204,\n",
      "        \"f1-score\": 0.5581363047421611,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9356878605201948,\n",
      "        \"recall\": 0.946285140562249,\n",
      "        \"f1-score\": 0.9406718472750811,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Little Owl 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9757174392935982,\n",
      "        \"recall\": 0.8941335131490222,\n",
      "        \"f1-score\": 0.9331456720619283,\n",
      "        \"support\": 1483\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7519747235387045,\n",
      "        \"recall\": 0.9351669941060904,\n",
      "        \"f1-score\": 0.8336252189141856,\n",
      "        \"support\": 509\n",
      "    },\n",
      "    \"accuracy\": 0.9046184738955824,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8638460814161514,\n",
      "        \"recall\": 0.9146502536275563,\n",
      "        \"f1-score\": 0.883385445488057,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9185462333100436,\n",
      "        \"recall\": 0.9046184738955824,\n",
      "        \"f1-score\": 0.9077159980397391,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Little Owl 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9462897526501767,\n",
      "        \"recall\": 0.9028995279838166,\n",
      "        \"f1-score\": 0.9240855762594893,\n",
      "        \"support\": 1483\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7504332755632582,\n",
      "        \"recall\": 0.8506876227897839,\n",
      "        \"f1-score\": 0.7974217311233885,\n",
      "        \"support\": 509\n",
      "    },\n",
      "    \"accuracy\": 0.8895582329317269,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8483615141067175,\n",
      "        \"recall\": 0.8767935753868003,\n",
      "        \"f1-score\": 0.8607536536914389,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8962440966073847,\n",
      "        \"recall\": 0.8895582329317269,\n",
      "        \"f1-score\": 0.8917201660314393,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mandarin Duck 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9205194805194805,\n",
      "        \"recall\": 0.9800884955752213,\n",
      "        \"f1-score\": 0.9493704795070987,\n",
      "        \"support\": 1808\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4626865671641791,\n",
      "        \"recall\": 0.16847826086956522,\n",
      "        \"f1-score\": 0.24701195219123506,\n",
      "        \"support\": 184\n",
      "    },\n",
      "    \"accuracy\": 0.9051204819277109,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6916030238418298,\n",
      "        \"recall\": 0.5742833782223933,\n",
      "        \"f1-score\": 0.5981912158491669,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8782296933420832,\n",
      "        \"recall\": 0.9051204819277109,\n",
      "        \"f1-score\": 0.8844939890321394,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Mandarin Duck 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9320282762370854,\n",
      "        \"recall\": 0.9480088495575221,\n",
      "        \"f1-score\": 0.9399506443652318,\n",
      "        \"support\": 1808\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.38562091503267976,\n",
      "        \"recall\": 0.32065217391304346,\n",
      "        \"f1-score\": 0.3501483679525223,\n",
      "        \"support\": 184\n",
      "    },\n",
      "    \"accuracy\": 0.8900602409638554,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6588245956348826,\n",
      "        \"recall\": 0.6343305117352828,\n",
      "        \"f1-score\": 0.645049506158877,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8815569135555539,\n",
      "        \"recall\": 0.8900602409638554,\n",
      "        \"f1-score\": 0.8854709160218891,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mute Swan 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9235668789808917,\n",
      "        \"recall\": 0.8357348703170029,\n",
      "        \"f1-score\": 0.8774583963691377,\n",
      "        \"support\": 694\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9164222873900293,\n",
      "        \"recall\": 0.963020030816641,\n",
      "        \"f1-score\": 0.9391435011269722,\n",
      "        \"support\": 1298\n",
      "    },\n",
      "    \"accuracy\": 0.9186746987951807,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9199945831854606,\n",
      "        \"recall\": 0.899377450566822,\n",
      "        \"f1-score\": 0.9083009487480549,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9189114171912635,\n",
      "        \"recall\": 0.9186746987951807,\n",
      "        \"f1-score\": 0.9176528069994937,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Mute Swan 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8382749326145552,\n",
      "        \"recall\": 0.8962536023054755,\n",
      "        \"f1-score\": 0.8662952646239555,\n",
      "        \"support\": 694\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9424,\n",
      "        \"recall\": 0.9075500770416025,\n",
      "        \"f1-score\": 0.9246467817896389,\n",
      "        \"support\": 1298\n",
      "    },\n",
      "    \"accuracy\": 0.9036144578313253,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8903374663072776,\n",
      "        \"recall\": 0.901901839673539,\n",
      "        \"f1-score\": 0.8954710232067972,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9061234955996493,\n",
      "        \"recall\": 0.9036144578313253,\n",
      "        \"f1-score\": 0.9043174881586227,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pheasant 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.931321540062435,\n",
      "        \"recall\": 0.8655705996131529,\n",
      "        \"f1-score\": 0.8972431077694236,\n",
      "        \"support\": 1034\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8651794374393792,\n",
      "        \"recall\": 0.9311064718162839,\n",
      "        \"f1-score\": 0.8969331322272499,\n",
      "        \"support\": 958\n",
      "    },\n",
      "    \"accuracy\": 0.8970883534136547,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8982504887509071,\n",
      "        \"recall\": 0.8983385357147184,\n",
      "        \"f1-score\": 0.8970881199983367,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8995122356884955,\n",
      "        \"recall\": 0.8970883534136547,\n",
      "        \"f1-score\": 0.8970940331863902,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pheasant 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9376321353065539,\n",
      "        \"recall\": 0.8578336557059961,\n",
      "        \"f1-score\": 0.895959595959596,\n",
      "        \"support\": 1034\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8594646271510517,\n",
      "        \"recall\": 0.9384133611691023,\n",
      "        \"f1-score\": 0.8972055888223555,\n",
      "        \"support\": 958\n",
      "    },\n",
      "    \"accuracy\": 0.8965863453815262,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8985483812288028,\n",
      "        \"recall\": 0.8981235084375492,\n",
      "        \"f1-score\": 0.8965825923909757,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.900039528472733,\n",
      "        \"recall\": 0.8965863453815262,\n",
      "        \"f1-score\": 0.8965588234508226,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pink-footed Goose 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9038018433179723,\n",
      "        \"recall\": 0.9378362223550508,\n",
      "        \"f1-score\": 0.9205045467879144,\n",
      "        \"support\": 1673\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.59375,\n",
      "        \"recall\": 0.47648902821316613,\n",
      "        \"f1-score\": 0.528695652173913,\n",
      "        \"support\": 319\n",
      "    },\n",
      "    \"accuracy\": 0.8639558232931727,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7487759216589862,\n",
      "        \"recall\": 0.7071626252841084,\n",
      "        \"f1-score\": 0.7246000994809136,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8541499668026946,\n",
      "        \"recall\": 0.8639558232931727,\n",
      "        \"f1-score\": 0.8577600501102707,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pink-footed Goose 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9159925326695706,\n",
      "        \"recall\": 0.8798565451285116,\n",
      "        \"f1-score\": 0.8975609756097561,\n",
      "        \"support\": 1673\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4779220779220779,\n",
      "        \"recall\": 0.5768025078369906,\n",
      "        \"f1-score\": 0.5227272727272727,\n",
      "        \"support\": 319\n",
      "    },\n",
      "    \"accuracy\": 0.8313253012048193,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6969573052958242,\n",
      "        \"recall\": 0.7283295264827512,\n",
      "        \"f1-score\": 0.7101441241685145,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8458396837416337,\n",
      "        \"recall\": 0.8313253012048193,\n",
      "        \"f1-score\": 0.8375348956802822,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pintail 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.937856033143449,\n",
      "        \"recall\": 0.9831704668838219,\n",
      "        \"f1-score\": 0.9599787967134905,\n",
      "        \"support\": 1842\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4918032786885246,\n",
      "        \"recall\": 0.2,\n",
      "        \"f1-score\": 0.2843601895734597,\n",
      "        \"support\": 150\n",
      "    },\n",
      "    \"accuracy\": 0.9241967871485943,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7148296559159868,\n",
      "        \"recall\": 0.5915852334419109,\n",
      "        \"f1-score\": 0.6221694931434751,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.904267723320036,\n",
      "        \"recall\": 0.9241967871485943,\n",
      "        \"f1-score\": 0.9091039015975243,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pintail 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.945693911135491,\n",
      "        \"recall\": 0.9359391965255157,\n",
      "        \"f1-score\": 0.9407912687585266,\n",
      "        \"support\": 1842\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.30177514792899407,\n",
      "        \"recall\": 0.34,\n",
      "        \"f1-score\": 0.31974921630094044,\n",
      "        \"support\": 150\n",
      "    },\n",
      "    \"accuracy\": 0.8910642570281124,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6237345295322425,\n",
      "        \"recall\": 0.6379695982627579,\n",
      "        \"f1-score\": 0.6302702425297335,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8972060524603029,\n",
      "        \"recall\": 0.8910642570281124,\n",
      "        \"f1-score\": 0.8940260539650337,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pochard 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9050188882892607,\n",
      "        \"recall\": 0.9555555555555556,\n",
      "        \"f1-score\": 0.9296008869179603,\n",
      "        \"support\": 1755\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.43884892086330934,\n",
      "        \"recall\": 0.25738396624472576,\n",
      "        \"f1-score\": 0.32446808510638303,\n",
      "        \"support\": 237\n",
      "    },\n",
      "    \"accuracy\": 0.8724899598393574,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.671933904576285,\n",
      "        \"recall\": 0.6064697609001407,\n",
      "        \"f1-score\": 0.6270344860121717,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8495558951768358,\n",
      "        \"recall\": 0.8724899598393574,\n",
      "        \"f1-score\": 0.8576046650156792,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Pochard 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9162946428571429,\n",
      "        \"recall\": 0.9356125356125357,\n",
      "        \"f1-score\": 0.9258528333803214,\n",
      "        \"support\": 1755\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.435,\n",
      "        \"recall\": 0.3670886075949367,\n",
      "        \"f1-score\": 0.39816933638443935,\n",
      "        \"support\": 237\n",
      "    },\n",
      "    \"accuracy\": 0.8679718875502008,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6756473214285714,\n",
      "        \"recall\": 0.6513505716037362,\n",
      "        \"f1-score\": 0.6620110848823804,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8590321778184166,\n",
      "        \"recall\": 0.8679718875502008,\n",
      "        \"f1-score\": 0.8630712125027993,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Red-legged Partridge 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9553314121037464,\n",
      "        \"recall\": 0.8953409858203917,\n",
      "        \"f1-score\": 0.9243638898570932,\n",
      "        \"support\": 1481\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7433774834437086,\n",
      "        \"recall\": 0.8786692759295499,\n",
      "        \"f1-score\": 0.8053811659192824,\n",
      "        \"support\": 511\n",
      "    },\n",
      "    \"accuracy\": 0.8910642570281124,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8493544477737275,\n",
      "        \"recall\": 0.8870051308749708,\n",
      "        \"f1-score\": 0.8648725278881878,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.900959696468566,\n",
      "        \"recall\": 0.8910642570281124,\n",
      "        \"f1-score\": 0.8938417151923235,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Red-legged Partridge 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9372355430183357,\n",
      "        \"recall\": 0.8973666441593517,\n",
      "        \"f1-score\": 0.9168678854777509,\n",
      "        \"support\": 1481\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.735191637630662,\n",
      "        \"recall\": 0.8258317025440313,\n",
      "        \"f1-score\": 0.7778801843317972,\n",
      "        \"support\": 511\n",
      "    },\n",
      "    \"accuracy\": 0.8790160642570282,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8362135903244988,\n",
      "        \"recall\": 0.8615991733516914,\n",
      "        \"f1-score\": 0.847374034904774,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8854060070479034,\n",
      "        \"recall\": 0.8790160642570282,\n",
      "        \"f1-score\": 0.8812139119408119,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ring-necked Parakeet 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9845599588265569,\n",
      "        \"recall\": 0.9886304909560724,\n",
      "        \"f1-score\": 0.9865910263022177,\n",
      "        \"support\": 1935\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5510204081632653,\n",
      "        \"recall\": 0.47368421052631576,\n",
      "        \"f1-score\": 0.5094339622641509,\n",
      "        \"support\": 57\n",
      "    },\n",
      "    \"accuracy\": 0.9738955823293173,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.767790183494911,\n",
      "        \"recall\": 0.731157350741194,\n",
      "        \"f1-score\": 0.7480124942831843,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9721544596358903,\n",
      "        \"recall\": 0.9738955823293173,\n",
      "        \"f1-score\": 0.9729374356143815,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Ring-necked Parakeet 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9816044966785897,\n",
      "        \"recall\": 0.992764857881137,\n",
      "        \"f1-score\": 0.9871531346351491,\n",
      "        \"support\": 1935\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6,\n",
      "        \"recall\": 0.3684210526315789,\n",
      "        \"f1-score\": 0.4565217391304348,\n",
      "        \"support\": 57\n",
      "    },\n",
      "    \"accuracy\": 0.9748995983935743,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7908022483392949,\n",
      "        \"recall\": 0.680592955256358,\n",
      "        \"f1-score\": 0.721837436882792,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9706850909001362,\n",
      "        \"recall\": 0.9748995983935743,\n",
      "        \"f1-score\": 0.9719694049445021,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Rock Dove 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9191176470588235,\n",
      "        \"recall\": 0.8581235697940504,\n",
      "        \"f1-score\": 0.8875739644970413,\n",
      "        \"support\": 1311\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7578125,\n",
      "        \"recall\": 0.8546255506607929,\n",
      "        \"f1-score\": 0.8033126293995859,\n",
      "        \"support\": 681\n",
      "    },\n",
      "    \"accuracy\": 0.8569277108433735,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8384650735294117,\n",
      "        \"recall\": 0.8563745602274216,\n",
      "        \"f1-score\": 0.8454432969483137,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8639726645552799,\n",
      "        \"recall\": 0.8569277108433735,\n",
      "        \"f1-score\": 0.8587677550586039,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Rock Dove 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9181669394435352,\n",
      "        \"recall\": 0.8558352402745996,\n",
      "        \"f1-score\": 0.8859060402684564,\n",
      "        \"support\": 1311\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7545454545454545,\n",
      "        \"recall\": 0.8531571218795888,\n",
      "        \"f1-score\": 0.800827015851137,\n",
      "        \"support\": 681\n",
      "    },\n",
      "    \"accuracy\": 0.8549196787148594,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8363561969944948,\n",
      "        \"recall\": 0.8544961810770941,\n",
      "        \"f1-score\": 0.8433665280597967,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8622300763834986,\n",
      "        \"recall\": 0.8549196787148594,\n",
      "        \"f1-score\": 0.8568202894510897,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ruddy Duck 5km cells... \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": [
      "Ruddy Duck 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9834254143646409,\n",
      "        \"recall\": 0.9994895354772844,\n",
      "        \"f1-score\": 0.9913924050632913,\n",
      "        \"support\": 1959\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 33\n",
      "    },\n",
      "    \"accuracy\": 0.9829317269076305,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49171270718232046,\n",
      "        \"recall\": 0.4997447677386422,\n",
      "        \"f1-score\": 0.49569620253164565,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.967133728283299,\n",
      "        \"recall\": 0.9829317269076305,\n",
      "        \"f1-score\": 0.9749687357023031,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Ruddy Duck 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9833501513622603,\n",
      "        \"recall\": 0.9948953547728433,\n",
      "        \"f1-score\": 0.989089063689419,\n",
      "        \"support\": 1959\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 33\n",
      "    },\n",
      "    \"accuracy\": 0.9784136546184738,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49167507568113017,\n",
      "        \"recall\": 0.49744767738642165,\n",
      "        \"f1-score\": 0.4945445318447095,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9670597121077651,\n",
      "        \"recall\": 0.9784136546184738,\n",
      "        \"f1-score\": 0.9727035520921545,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Whooper Swan 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8978658536585366,\n",
      "        \"recall\": 0.9904708520179372,\n",
      "        \"f1-score\": 0.9418976545842217,\n",
      "        \"support\": 1784\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2916666666666667,\n",
      "        \"recall\": 0.03365384615384615,\n",
      "        \"f1-score\": 0.06034482758620689,\n",
      "        \"support\": 208\n",
      "    },\n",
      "    \"accuracy\": 0.8905622489959839,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5947662601626016,\n",
      "        \"recall\": 0.5120623490858917,\n",
      "        \"f1-score\": 0.5011212410852143,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8345679465830803,\n",
      "        \"recall\": 0.8905622489959839,\n",
      "        \"f1-score\": 0.8498479618053124,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Whooper Swan 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9171332586786114,\n",
      "        \"recall\": 0.9181614349775785,\n",
      "        \"f1-score\": 0.9176470588235294,\n",
      "        \"support\": 1784\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2912621359223301,\n",
      "        \"recall\": 0.28846153846153844,\n",
      "        \"f1-score\": 0.28985507246376807,\n",
      "        \"support\": 208\n",
      "    },\n",
      "    \"accuracy\": 0.8524096385542169,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6041976973004708,\n",
      "        \"recall\": 0.6033114867195585,\n",
      "        \"f1-score\": 0.6037510656436487,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.851781253892815,\n",
      "        \"recall\": 0.8524096385542169,\n",
      "        \"f1-score\": 0.8520944819345583,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Wigeon 5km cells... \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 5km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8922470433639947,\n",
      "        \"recall\": 0.8846905537459283,\n",
      "        \"f1-score\": 0.8884527314360484,\n",
      "        \"support\": 1535\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6234042553191489,\n",
      "        \"recall\": 0.6411378555798687,\n",
      "        \"f1-score\": 0.6321467098166127,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.8288152610441767,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7578256493415718,\n",
      "        \"recall\": 0.7629142046628985,\n",
      "        \"f1-score\": 0.7602997206263306,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.830569757150895,\n",
      "        \"recall\": 0.8288152610441767,\n",
      "        \"f1-score\": 0.8296516009741597,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "} \n",
      "\n",
      "Wigeon 5km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8767213114754099,\n",
      "        \"recall\": 0.8710097719869707,\n",
      "        \"f1-score\": 0.8738562091503269,\n",
      "        \"support\": 1535\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.576017130620985,\n",
      "        \"recall\": 0.5886214442013129,\n",
      "        \"f1-score\": 0.5822510822510824,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.8062248995983936,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7263692210481975,\n",
      "        \"recall\": 0.7298156080941418,\n",
      "        \"f1-score\": 0.7280536457007046,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.807734458739229,\n",
      "        \"recall\": 0.8062248995983936,\n",
      "        \"f1-score\": 0.8069568401779601,\n",
      "        \"support\": 1992\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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.934793683138054,\n",
      "        \"recall\": 0.995119305856833,\n",
      "        \"f1-score\": 0.9640136590491201,\n",
      "        \"support\": 1844\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6896551724137931,\n",
      "        \"recall\": 0.13513513513513514,\n",
      "        \"f1-score\": 0.22598870056497178,\n",
      "        \"support\": 148\n",
      "    },\n",
      "    \"accuracy\": 0.9312248995983936,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8122244277759236,\n",
      "        \"recall\": 0.5651272204959841,\n",
      "        \"f1-score\": 0.5950011798070459,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9165805809356491,\n",
      "        \"recall\": 0.9312248995983936,\n",
      "        \"f1-score\": 0.9091804794027075,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Canada Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9504830917874396,\n",
      "        \"recall\": 0.8582333696837514,\n",
      "        \"f1-score\": 0.9020057306590258,\n",
      "        \"support\": 917\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8883161512027491,\n",
      "        \"recall\": 0.9618604651162791,\n",
      "        \"f1-score\": 0.923626619026351,\n",
      "        \"support\": 1075\n",
      "    },\n",
      "    \"accuracy\": 0.9141566265060241,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9193996214950944,\n",
      "        \"recall\": 0.9100469174000152,\n",
      "        \"f1-score\": 0.9128161748426884,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.916934165518091,\n",
      "        \"recall\": 0.9141566265060241,\n",
      "        \"f1-score\": 0.9136736297528384,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Egyptian Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9474768280123584,\n",
      "        \"recall\": 0.9881847475832438,\n",
      "        \"f1-score\": 0.9674027339642481,\n",
      "        \"support\": 1862\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.56,\n",
      "        \"recall\": 0.2153846153846154,\n",
      "        \"f1-score\": 0.3111111111111111,\n",
      "        \"support\": 130\n",
      "    },\n",
      "    \"accuracy\": 0.9377510040160643,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7537384140061792,\n",
      "        \"recall\": 0.6017846814839296,\n",
      "        \"f1-score\": 0.6392569225376796,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9221896856219937,\n",
      "        \"recall\": 0.9377510040160643,\n",
      "        \"f1-score\": 0.924572457372427,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Gadwall 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9283965728274174,\n",
      "        \"recall\": 0.9266951740989615,\n",
      "        \"f1-score\": 0.9275450932436564,\n",
      "        \"support\": 1637\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.664804469273743,\n",
      "        \"recall\": 0.6704225352112676,\n",
      "        \"f1-score\": 0.667601683029453,\n",
      "        \"support\": 355\n",
      "    },\n",
      "    \"accuracy\": 0.8810240963855421,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7966005210505802,\n",
      "        \"recall\": 0.7985588546551146,\n",
      "        \"f1-score\": 0.7975733881365548,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.881421072445111,\n",
      "        \"recall\": 0.8810240963855421,\n",
      "        \"f1-score\": 0.8812198369052818,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Goshawk 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9532994923857868,\n",
      "        \"recall\": 0.9910290237467019,\n",
      "        \"f1-score\": 0.9717981888745149,\n",
      "        \"support\": 1895\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.22727272727272727,\n",
      "        \"recall\": 0.05154639175257732,\n",
      "        \"f1-score\": 0.08403361344537814,\n",
      "        \"support\": 97\n",
      "    },\n",
      "    \"accuracy\": 0.945281124497992,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.590286109829257,\n",
      "        \"recall\": 0.5212877077496396,\n",
      "        \"f1-score\": 0.5279159011599465,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9179457794259641,\n",
      "        \"recall\": 0.945281124497992,\n",
      "        \"f1-score\": 0.92856868896657,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Grey Partridge 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9430628272251309,\n",
      "        \"recall\": 0.902316844082655,\n",
      "        \"f1-score\": 0.92224,\n",
      "        \"support\": 1597\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6637931034482759,\n",
      "        \"recall\": 0.779746835443038,\n",
      "        \"f1-score\": 0.7171129220023283,\n",
      "        \"support\": 395\n",
      "    },\n",
      "    \"accuracy\": 0.8780120481927711,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8034279653367034,\n",
      "        \"recall\": 0.8410318397628465,\n",
      "        \"f1-score\": 0.8196764610011642,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8876855476609453,\n",
      "        \"recall\": 0.8780120481927711,\n",
      "        \"f1-score\": 0.881564700899056,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Indian Peafowl 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9617321248741189,\n",
      "        \"recall\": 0.9968684759916493,\n",
      "        \"f1-score\": 0.9789851358277807,\n",
      "        \"support\": 1916\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 76\n",
      "    },\n",
      "    \"accuracy\": 0.9588353413654619,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.4808660624370594,\n",
      "        \"recall\": 0.49843423799582465,\n",
      "        \"f1-score\": 0.48949256791389034,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9250395337644637,\n",
      "        \"recall\": 0.9588353413654619,\n",
      "        \"f1-score\": 0.941634297312263,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Little Owl 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9757174392935982,\n",
      "        \"recall\": 0.8941335131490222,\n",
      "        \"f1-score\": 0.9331456720619283,\n",
      "        \"support\": 1483\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7519747235387045,\n",
      "        \"recall\": 0.9351669941060904,\n",
      "        \"f1-score\": 0.8336252189141856,\n",
      "        \"support\": 509\n",
      "    },\n",
      "    \"accuracy\": 0.9046184738955824,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8638460814161514,\n",
      "        \"recall\": 0.9146502536275563,\n",
      "        \"f1-score\": 0.883385445488057,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9185462333100436,\n",
      "        \"recall\": 0.9046184738955824,\n",
      "        \"f1-score\": 0.9077159980397391,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Mandarin Duck 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9205194805194805,\n",
      "        \"recall\": 0.9800884955752213,\n",
      "        \"f1-score\": 0.9493704795070987,\n",
      "        \"support\": 1808\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4626865671641791,\n",
      "        \"recall\": 0.16847826086956522,\n",
      "        \"f1-score\": 0.24701195219123506,\n",
      "        \"support\": 184\n",
      "    },\n",
      "    \"accuracy\": 0.9051204819277109,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6916030238418298,\n",
      "        \"recall\": 0.5742833782223933,\n",
      "        \"f1-score\": 0.5981912158491669,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8782296933420832,\n",
      "        \"recall\": 0.9051204819277109,\n",
      "        \"f1-score\": 0.8844939890321394,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Mute Swan 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9235668789808917,\n",
      "        \"recall\": 0.8357348703170029,\n",
      "        \"f1-score\": 0.8774583963691377,\n",
      "        \"support\": 694\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9164222873900293,\n",
      "        \"recall\": 0.963020030816641,\n",
      "        \"f1-score\": 0.9391435011269722,\n",
      "        \"support\": 1298\n",
      "    },\n",
      "    \"accuracy\": 0.9186746987951807,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9199945831854606,\n",
      "        \"recall\": 0.899377450566822,\n",
      "        \"f1-score\": 0.9083009487480549,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9189114171912635,\n",
      "        \"recall\": 0.9186746987951807,\n",
      "        \"f1-score\": 0.9176528069994937,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pheasant 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.931321540062435,\n",
      "        \"recall\": 0.8655705996131529,\n",
      "        \"f1-score\": 0.8972431077694236,\n",
      "        \"support\": 1034\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.8651794374393792,\n",
      "        \"recall\": 0.9311064718162839,\n",
      "        \"f1-score\": 0.8969331322272499,\n",
      "        \"support\": 958\n",
      "    },\n",
      "    \"accuracy\": 0.8970883534136547,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8982504887509071,\n",
      "        \"recall\": 0.8983385357147184,\n",
      "        \"f1-score\": 0.8970881199983367,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8995122356884955,\n",
      "        \"recall\": 0.8970883534136547,\n",
      "        \"f1-score\": 0.8970940331863902,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pink-footed Goose 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9038018433179723,\n",
      "        \"recall\": 0.9378362223550508,\n",
      "        \"f1-score\": 0.9205045467879144,\n",
      "        \"support\": 1673\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.59375,\n",
      "        \"recall\": 0.47648902821316613,\n",
      "        \"f1-score\": 0.528695652173913,\n",
      "        \"support\": 319\n",
      "    },\n",
      "    \"accuracy\": 0.8639558232931727,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7487759216589862,\n",
      "        \"recall\": 0.7071626252841084,\n",
      "        \"f1-score\": 0.7246000994809136,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8541499668026946,\n",
      "        \"recall\": 0.8639558232931727,\n",
      "        \"f1-score\": 0.8577600501102707,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pintail 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.937856033143449,\n",
      "        \"recall\": 0.9831704668838219,\n",
      "        \"f1-score\": 0.9599787967134905,\n",
      "        \"support\": 1842\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4918032786885246,\n",
      "        \"recall\": 0.2,\n",
      "        \"f1-score\": 0.2843601895734597,\n",
      "        \"support\": 150\n",
      "    },\n",
      "    \"accuracy\": 0.9241967871485943,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7148296559159868,\n",
      "        \"recall\": 0.5915852334419109,\n",
      "        \"f1-score\": 0.6221694931434751,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.904267723320036,\n",
      "        \"recall\": 0.9241967871485943,\n",
      "        \"f1-score\": 0.9091039015975243,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Pochard 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9050188882892607,\n",
      "        \"recall\": 0.9555555555555556,\n",
      "        \"f1-score\": 0.9296008869179603,\n",
      "        \"support\": 1755\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.43884892086330934,\n",
      "        \"recall\": 0.25738396624472576,\n",
      "        \"f1-score\": 0.32446808510638303,\n",
      "        \"support\": 237\n",
      "    },\n",
      "    \"accuracy\": 0.8724899598393574,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.671933904576285,\n",
      "        \"recall\": 0.6064697609001407,\n",
      "        \"f1-score\": 0.6270344860121717,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8495558951768358,\n",
      "        \"recall\": 0.8724899598393574,\n",
      "        \"f1-score\": 0.8576046650156792,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Red-legged Partridge 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9553314121037464,\n",
      "        \"recall\": 0.8953409858203917,\n",
      "        \"f1-score\": 0.9243638898570932,\n",
      "        \"support\": 1481\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7433774834437086,\n",
      "        \"recall\": 0.8786692759295499,\n",
      "        \"f1-score\": 0.8053811659192824,\n",
      "        \"support\": 511\n",
      "    },\n",
      "    \"accuracy\": 0.8910642570281124,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8493544477737275,\n",
      "        \"recall\": 0.8870051308749708,\n",
      "        \"f1-score\": 0.8648725278881878,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.900959696468566,\n",
      "        \"recall\": 0.8910642570281124,\n",
      "        \"f1-score\": 0.8938417151923235,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Ring-necked Parakeet 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9845599588265569,\n",
      "        \"recall\": 0.9886304909560724,\n",
      "        \"f1-score\": 0.9865910263022177,\n",
      "        \"support\": 1935\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5510204081632653,\n",
      "        \"recall\": 0.47368421052631576,\n",
      "        \"f1-score\": 0.5094339622641509,\n",
      "        \"support\": 57\n",
      "    },\n",
      "    \"accuracy\": 0.9738955823293173,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.767790183494911,\n",
      "        \"recall\": 0.731157350741194,\n",
      "        \"f1-score\": 0.7480124942831843,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9721544596358903,\n",
      "        \"recall\": 0.9738955823293173,\n",
      "        \"f1-score\": 0.9729374356143815,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Rock Dove 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9191176470588235,\n",
      "        \"recall\": 0.8581235697940504,\n",
      "        \"f1-score\": 0.8875739644970413,\n",
      "        \"support\": 1311\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7578125,\n",
      "        \"recall\": 0.8546255506607929,\n",
      "        \"f1-score\": 0.8033126293995859,\n",
      "        \"support\": 681\n",
      "    },\n",
      "    \"accuracy\": 0.8569277108433735,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8384650735294117,\n",
      "        \"recall\": 0.8563745602274216,\n",
      "        \"f1-score\": 0.8454432969483137,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8639726645552799,\n",
      "        \"recall\": 0.8569277108433735,\n",
      "        \"f1-score\": 0.8587677550586039,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Ruddy Duck 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9834254143646409,\n",
      "        \"recall\": 0.9994895354772844,\n",
      "        \"f1-score\": 0.9913924050632913,\n",
      "        \"support\": 1959\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 33\n",
      "    },\n",
      "    \"accuracy\": 0.9829317269076305,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49171270718232046,\n",
      "        \"recall\": 0.4997447677386422,\n",
      "        \"f1-score\": 0.49569620253164565,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.967133728283299,\n",
      "        \"recall\": 0.9829317269076305,\n",
      "        \"f1-score\": 0.9749687357023031,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Whooper Swan 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8978658536585366,\n",
      "        \"recall\": 0.9904708520179372,\n",
      "        \"f1-score\": 0.9418976545842217,\n",
      "        \"support\": 1784\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2916666666666667,\n",
      "        \"recall\": 0.03365384615384615,\n",
      "        \"f1-score\": 0.06034482758620689,\n",
      "        \"support\": 208\n",
      "    },\n",
      "    \"accuracy\": 0.8905622489959839,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5947662601626016,\n",
      "        \"recall\": 0.5120623490858917,\n",
      "        \"f1-score\": 0.5011212410852143,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8345679465830803,\n",
      "        \"recall\": 0.8905622489959839,\n",
      "        \"f1-score\": 0.8498479618053124,\n",
      "        \"support\": 1992\n",
      "    }\n",
      "}\n",
      "Wigeon 5km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8922470433639947,\n",
      "        \"recall\": 0.8846905537459283,\n",
      "        \"f1-score\": 0.8884527314360484,\n",
      "        \"support\": 1535\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6234042553191489,\n",
      "        \"recall\": 0.6411378555798687,\n",
      "        \"f1-score\": 0.6321467098166127,\n",
      "        \"support\": 457\n",
      "    },\n",
      "    \"accuracy\": 0.8288152610441767,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7578256493415718,\n",
      "        \"recall\": 0.7629142046628985,\n",
      "        \"f1-score\": 0.7602997206263306,\n",
      "        \"support\": 1992\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.830569757150895,\n",
      "        \"recall\": 0.8288152610441767,\n",
      "        \"f1-score\": 0.8296516009741597,\n",
      "        \"support\": 1992\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": 13,
   "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 5km</td>\n",
       "      <td>0.916422</td>\n",
       "      <td>0.942400</td>\n",
       "      <td>0.963020</td>\n",
       "      <td>0.907550</td>\n",
       "      <td>0.939144</td>\n",
       "      <td>0.924647</td>\n",
       "      <td>5267</td>\n",
       "      <td>0.661268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 5km</td>\n",
       "      <td>0.888316</td>\n",
       "      <td>0.903111</td>\n",
       "      <td>0.961860</td>\n",
       "      <td>0.945116</td>\n",
       "      <td>0.923627</td>\n",
       "      <td>0.923636</td>\n",
       "      <td>4305</td>\n",
       "      <td>0.540490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 5km</td>\n",
       "      <td>0.865179</td>\n",
       "      <td>0.859465</td>\n",
       "      <td>0.931106</td>\n",
       "      <td>0.938413</td>\n",
       "      <td>0.896933</td>\n",
       "      <td>0.897206</td>\n",
       "      <td>3848</td>\n",
       "      <td>0.483114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 5km</td>\n",
       "      <td>0.757812</td>\n",
       "      <td>0.754545</td>\n",
       "      <td>0.854626</td>\n",
       "      <td>0.853157</td>\n",
       "      <td>0.803313</td>\n",
       "      <td>0.800827</td>\n",
       "      <td>2830</td>\n",
       "      <td>0.355304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 5km</td>\n",
       "      <td>0.751975</td>\n",
       "      <td>0.750433</td>\n",
       "      <td>0.935167</td>\n",
       "      <td>0.850688</td>\n",
       "      <td>0.833625</td>\n",
       "      <td>0.797422</td>\n",
       "      <td>2158</td>\n",
       "      <td>0.270935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 5km</td>\n",
       "      <td>0.743377</td>\n",
       "      <td>0.735192</td>\n",
       "      <td>0.878669</td>\n",
       "      <td>0.825832</td>\n",
       "      <td>0.805381</td>\n",
       "      <td>0.777880</td>\n",
       "      <td>2150</td>\n",
       "      <td>0.269931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 5km</td>\n",
       "      <td>0.623404</td>\n",
       "      <td>0.576017</td>\n",
       "      <td>0.641138</td>\n",
       "      <td>0.588621</td>\n",
       "      <td>0.632147</td>\n",
       "      <td>0.582251</td>\n",
       "      <td>1857</td>\n",
       "      <td>0.233145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 5km</td>\n",
       "      <td>0.663793</td>\n",
       "      <td>0.622642</td>\n",
       "      <td>0.779747</td>\n",
       "      <td>0.584810</td>\n",
       "      <td>0.717113</td>\n",
       "      <td>0.603133</td>\n",
       "      <td>1629</td>\n",
       "      <td>0.204520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 5km</td>\n",
       "      <td>0.664804</td>\n",
       "      <td>0.623418</td>\n",
       "      <td>0.670423</td>\n",
       "      <td>0.554930</td>\n",
       "      <td>0.667602</td>\n",
       "      <td>0.587183</td>\n",
       "      <td>1399</td>\n",
       "      <td>0.175643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 5km</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.477922</td>\n",
       "      <td>0.476489</td>\n",
       "      <td>0.576803</td>\n",
       "      <td>0.528696</td>\n",
       "      <td>0.522727</td>\n",
       "      <td>1313</td>\n",
       "      <td>0.164846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 5km</td>\n",
       "      <td>0.438849</td>\n",
       "      <td>0.435000</td>\n",
       "      <td>0.257384</td>\n",
       "      <td>0.367089</td>\n",
       "      <td>0.324468</td>\n",
       "      <td>0.398169</td>\n",
       "      <td>942</td>\n",
       "      <td>0.118267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 5km</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.291262</td>\n",
       "      <td>0.033654</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.060345</td>\n",
       "      <td>0.289855</td>\n",
       "      <td>842</td>\n",
       "      <td>0.105712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 5km</td>\n",
       "      <td>0.462687</td>\n",
       "      <td>0.385621</td>\n",
       "      <td>0.168478</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>0.247012</td>\n",
       "      <td>0.350148</td>\n",
       "      <td>714</td>\n",
       "      <td>0.089642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 5km</td>\n",
       "      <td>0.491803</td>\n",
       "      <td>0.301775</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.284360</td>\n",
       "      <td>0.319749</td>\n",
       "      <td>649</td>\n",
       "      <td>0.081481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 5km</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>0.271739</td>\n",
       "      <td>0.135135</td>\n",
       "      <td>0.337838</td>\n",
       "      <td>0.225989</td>\n",
       "      <td>0.301205</td>\n",
       "      <td>587</td>\n",
       "      <td>0.073697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 5km</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.511364</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.311111</td>\n",
       "      <td>0.412844</td>\n",
       "      <td>485</td>\n",
       "      <td>0.060891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 5km</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.297297</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>0.226804</td>\n",
       "      <td>0.084034</td>\n",
       "      <td>0.257310</td>\n",
       "      <td>446</td>\n",
       "      <td>0.055995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 5km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.118421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>284</td>\n",
       "      <td>0.035656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 5km</td>\n",
       "      <td>0.551020</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.509434</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>206</td>\n",
       "      <td>0.025863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 5km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109</td>\n",
       "      <td>0.013685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Labels  Precision  Precision (Smote)    Recall  \\\n",
       "9              Mute Swan 5km   0.916422           0.942400  0.963020   \n",
       "1           Canada Goose 5km   0.888316           0.903111  0.961860   \n",
       "10              Pheasant 5km   0.865179           0.859465  0.931106   \n",
       "16             Rock Dove 5km   0.757812           0.754545  0.854626   \n",
       "7             Little Owl 5km   0.751975           0.750433  0.935167   \n",
       "14  Red-legged Partridge 5km   0.743377           0.735192  0.878669   \n",
       "19                Wigeon 5km   0.623404           0.576017  0.641138   \n",
       "5         Grey Partridge 5km   0.663793           0.622642  0.779747   \n",
       "3                Gadwall 5km   0.664804           0.623418  0.670423   \n",
       "11     Pink-footed Goose 5km   0.593750           0.477922  0.476489   \n",
       "13               Pochard 5km   0.438849           0.435000  0.257384   \n",
       "18          Whooper Swan 5km   0.291667           0.291262  0.033654   \n",
       "8          Mandarin Duck 5km   0.462687           0.385621  0.168478   \n",
       "12               Pintail 5km   0.491803           0.301775  0.200000   \n",
       "0         Barnacle Goose 5km   0.689655           0.271739  0.135135   \n",
       "2         Egyptian Goose 5km   0.560000           0.511364  0.215385   \n",
       "4                Goshawk 5km   0.227273           0.297297  0.051546   \n",
       "6         Indian Peafowl 5km   0.000000           0.183673  0.000000   \n",
       "15  Ring-necked Parakeet 5km   0.551020           0.600000  0.473684   \n",
       "17            Ruddy Duck 5km   0.000000           0.000000  0.000000   \n",
       "\n",
       "    Recall (Smote)        F1  F1 (Smote)  Occurrence Count  Percentage  \n",
       "9         0.907550  0.939144    0.924647              5267    0.661268  \n",
       "1         0.945116  0.923627    0.923636              4305    0.540490  \n",
       "10        0.938413  0.896933    0.897206              3848    0.483114  \n",
       "16        0.853157  0.803313    0.800827              2830    0.355304  \n",
       "7         0.850688  0.833625    0.797422              2158    0.270935  \n",
       "14        0.825832  0.805381    0.777880              2150    0.269931  \n",
       "19        0.588621  0.632147    0.582251              1857    0.233145  \n",
       "5         0.584810  0.717113    0.603133              1629    0.204520  \n",
       "3         0.554930  0.667602    0.587183              1399    0.175643  \n",
       "11        0.576803  0.528696    0.522727              1313    0.164846  \n",
       "13        0.367089  0.324468    0.398169               942    0.118267  \n",
       "18        0.288462  0.060345    0.289855               842    0.105712  \n",
       "8         0.320652  0.247012    0.350148               714    0.089642  \n",
       "12        0.340000  0.284360    0.319749               649    0.081481  \n",
       "0         0.337838  0.225989    0.301205               587    0.073697  \n",
       "2         0.346154  0.311111    0.412844               485    0.060891  \n",
       "4         0.226804  0.084034    0.257310               446    0.055995  \n",
       "6         0.118421  0.000000    0.144000               284    0.035656  \n",
       "15        0.368421  0.509434    0.456522               206    0.025863  \n",
       "17        0.000000  0.000000    0.000000               109    0.013685  "
      ]
     },
     "execution_count": 13,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'df_dicts_5km_no_fp' (list)\n"
     ]
    }
   ],
   "source": [
    "# Store dictionaries for later use\n",
    "df_dicts_5km_no_fp = df_dicts\n",
    "%store df_dicts_5km_no_fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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       "                    Occurrence  Predictions\n",
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       "...                        ...          ...\n",
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       "...                        ...          ...\n",
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       "                    Occurrence  Predictions\n",
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       "...                        ...          ...\n",
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       "182500.0  582500.0           1            0\n",
       "\n",
       "[1992 rows x 2 columns]"
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     "metadata": {},
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   ],
   "source": [
    "# Export predictions to CSV for QGIS\n",
    "RESULTS_PATH = 'Datasets/Machine Learning/Results/5km/'\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'] + '(without Fertiliser+Pesticides).csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 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>25</th>\n",
       "      <td>1586.660696</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1440.939379</td>\n",
       "      <td>1.143607e-308</td>\n",
       "      <td>Saltmarsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
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       "      <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>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",
       "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",
       "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"
     ]
    },
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       "      <th>23</th>\n",
       "      <td>27980.651957</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
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       "      <th>24</th>\n",
       "      <td>22192.853532</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
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       "      <th>25</th>\n",
       "      <td>21798.073867</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
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       "      <th>21</th>\n",
       "      <td>20557.269520</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
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       "      <th>22</th>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
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       "      <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",
       "23  27980.651957   0.000000e+00                   Surface type\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",
       "22  10467.973712   0.000000e+00      Cumulative catchment area\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": {
      "text/html": [
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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>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>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>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>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",
       "22  4398.345684   0.000000e+00      Cumulative catchment area\n",
       "13  3391.728526   0.000000e+00                     Freshwater\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",
       "25  1867.181758   0.000000e+00   Inflowing drainage direction\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": {
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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>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",
       "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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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>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>6</th>\n",
       "      <td>362.459808</td>\n",
       "      <td>2.209967e-80</td>\n",
       "      <td>Acid grassland</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",
       "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",
       "6    362.459808   2.209967e-80                 Acid grassland\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": {
      "text/html": [
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       "\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>2</th>\n",
       "      <td>8141.700589</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</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",
       "2   8141.700589   0.000000e+00                         Arable\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",
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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>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",
       "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": {
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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>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",
       "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"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 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>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>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>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",
       "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",
       "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",
       "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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       "    .dataframe tbody tr th:only-of-type {\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>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>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",
       "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",
       "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>23</th>\n",
       "      <td>14828.439300</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</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>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>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",
       "23  14828.439300  0.000000e+00                   Surface type\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",
       "22   9363.861895  0.000000e+00      Cumulative catchment area\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",
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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>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>21</th>\n",
       "      <td>4750.062797</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</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>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>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",
       "25  5823.246006   0.000000e+00   Inflowing drainage direction\n",
       "23  5796.329675   0.000000e+00                   Surface type\n",
       "21  4750.062797   0.000000e+00                      Elevation\n",
       "24  4416.411546   0.000000e+00  Outflowing drainage direction\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",
       "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"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pintail 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>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>21</th>\n",
       "      <td>987.248175</td>\n",
       "      <td>1.475675e-213</td>\n",
       "      <td>Elevation</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",
       "25  1325.168183  1.608067e-284   Inflowing drainage direction\n",
       "23  1169.840995  5.439990e-252                   Surface type\n",
       "21   987.248175  1.475675e-213                      Elevation\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"
      ]
     },
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    },
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     "text": [
      "Pochard 1km\n"
     ]
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       "      <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>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",
       "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>2</th>\n",
       "      <td>9262.561980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Arable</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",
       "2   9262.561980   0.000000e+00                         Arable\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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       "<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>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>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>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>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",
       "22  2110.055997   0.000000e+00      Cumulative catchment area\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",
       "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",
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       "\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>23</th>\n",
       "      <td>9715.208896</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</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>21</th>\n",
       "      <td>7209.182719</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</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>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",
       "23  9715.208896  0.000000e+00                   Surface type\n",
       "24  7716.649425  0.000000e+00  Outflowing drainage direction\n",
       "25  7489.623513  0.000000e+00   Inflowing drainage direction\n",
       "21  7209.182719  0.000000e+00                      Elevation\n",
       "22  6950.626400  0.000000e+00      Cumulative catchment area\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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       "    }\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>13</th>\n",
       "      <td>2084.023887</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Freshwater</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>23</th>\n",
       "      <td>331.644948</td>\n",
       "      <td>9.666367e-74</td>\n",
       "      <td>Surface type</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",
       "24   574.800456  6.036397e-126  Outflowing drainage direction\n",
       "22   439.953763   4.753911e-97      Cumulative catchment area\n",
       "23   331.644948   9.666367e-74                   Surface type\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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>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>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>3</th>\n",
       "      <td>560.870223</td>\n",
       "      <td>5.752589e-123</td>\n",
       "      <td>Improve grassland</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>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",
       "24  1295.050837  3.175931e-278  Outflowing drainage direction\n",
       "22  1252.169594  2.987125e-269      Cumulative catchment area\n",
       "3    560.870223  5.752589e-123              Improve grassland\n",
       "2    518.099739  8.226528e-114                         Arable\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": [
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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>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>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>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>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",
       "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",
       "24  3296.213107   0.000000e+00  Outflowing drainage direction\n",
       "22  3018.447019   0.000000e+00      Cumulative catchment area\n",
       "3   1984.684092   0.000000e+00              Improve grassland\n",
       "2   1640.404091   0.000000e+00                         Arable\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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