{
 "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</th>\n",
       "      <th>Tri-allate</th>\n",
       "      <th>Occurrence</th>\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",
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       "y         x                                                     \n",
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       "\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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       "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    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
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       "y         x                                                                \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "                      Tri-allate  Occurrence  \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",
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       "      <td>-3.400000e+38</td>\n",
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       "      <td>-3.400000e+38</td>\n",
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       "      <th>630500.0</th>\n",
       "      <th>193500.0</th>\n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
       "60500.0  535500.0                   0                    0       0   \n",
       "426500.0 635500.0                   0                    0       0   \n",
       "412500.0 637500.0                   0                    0       0   \n",
       "249500.0 697500.0                   0                    0       0   \n",
       "630500.0 193500.0                   8                   88       0   \n",
       "\n",
       "                   Improve grassland  Neutral grassland  Calcareous grassland  \\\n",
       "y        x                                                                      \n",
       "60500.0  535500.0                  0                  0                     0   \n",
       "426500.0 635500.0                  0                  0                     0   \n",
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       "\n",
       "                   Acid grassland  Fen  Heather  Heather grassland  ...  \\\n",
       "y        x                                                          ...   \n",
       "60500.0  535500.0               0    0        0                  0  ...   \n",
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       "249500.0 697500.0               0    0        0                  0  ...   \n",
       "630500.0 193500.0               0    0        0                  4  ...   \n",
       "\n",
       "                     Glyphosate      Mancozeb    Mecoprop-P    Metamitron  \\\n",
       "y        x                                                                  \n",
       "60500.0  535500.0 -3.400000e+38 -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
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       "\n",
       "                   Pendimethalin  PropamocarbHydrochloride  Prosulfocarb  \\\n",
       "y        x                                                                 \n",
       "60500.0  535500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "426500.0 635500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "412500.0 637500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
       "249500.0 697500.0  -3.400000e+38             -3.400000e+38 -3.400000e+38   \n",
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       "\n",
       "                        Sulphur    Tri-allate  Occurrence  \n",
       "y        x                                                 \n",
       "60500.0  535500.0 -3.400000e+38 -3.400000e+38           0  \n",
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       "630500.0 193500.0 -3.400000e+38 -3.400000e+38           0  \n",
       "\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "394500.0  189500.0                   0                    0       0   \n",
       "259500.0  124500.0                   0                    0       0   \n",
       "1007500.0 128500.0                   0                    0       0   \n",
       "982500.0  569500.0                   0                    0       0   \n",
       "845500.0  319500.0                   0                   66       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "394500.0  189500.0                  0                  0   \n",
       "259500.0  124500.0                  0                  0   \n",
       "1007500.0 128500.0                  0                  0   \n",
       "982500.0  569500.0                  0                  0   \n",
       "845500.0  319500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "394500.0  189500.0                     0               0    0        0   \n",
       "259500.0  124500.0                     0               0    0        0   \n",
       "1007500.0 128500.0                     0               0    0        0   \n",
       "982500.0  569500.0                     0               0    0        0   \n",
       "845500.0  319500.0                     0               0    0       33   \n",
       "\n",
       "                    Heather grassland  ...    Glyphosate      Mancozeb  \\\n",
       "y         x                            ...                               \n",
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       "\n",
       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
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       "\n",
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       "y         x                                                                \n",
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       "\n",
       "                      Tri-allate  Occurrence  \n",
       "y         x                                   \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y         x                                                \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "y        x                                                                  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
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       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.966089e-01</td>\n",
       "      <td>2.871251e-01</td>\n",
       "      <td>3.092201e-01</td>\n",
       "      <td>1.212641e-01</td>\n",
       "      <td>3.717977e-01</td>\n",
       "      <td>7.553086e-02</td>\n",
       "      <td>2.008362e-01</td>\n",
       "      <td>4.586757e-02</td>\n",
       "      <td>2.622473e-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705500.0</th>\n",
       "      <th>221500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</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>170500.0</th>\n",
       "      <th>603500.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.301957e+00</td>\n",
       "      <td>6.618243e-02</td>\n",
       "      <td>5.164155e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>9.030058e-01</td>\n",
       "      <td>-3.400000e+38</td>\n",
       "      <td>5.312213e-01</td>\n",
       "      <td>2.615392e-01</td>\n",
       "      <td>3.595334e-01</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1253500.0</th>\n",
       "      <th>388500.0</th>\n",
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       "      <td>...</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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      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "636500.0  313500.0                   0                    0       0   \n",
       "258500.0  547500.0                   9                    0       0   \n",
       "705500.0  221500.0                   0                   17       0   \n",
       "170500.0  603500.0                   0                    0      10   \n",
       "1253500.0 388500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "636500.0  313500.0                  5                  0   \n",
       "258500.0  547500.0                 41                  0   \n",
       "705500.0  221500.0                  0                  0   \n",
       "170500.0  603500.0                 14                  0   \n",
       "1253500.0 388500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "636500.0  313500.0                     0              95    0        0   \n",
       "258500.0  547500.0                     0               0    0        0   \n",
       "705500.0  221500.0                     0              83    0        0   \n",
       "170500.0  603500.0                     0               0    0        0   \n",
       "1253500.0 388500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...    Glyphosate      Mancozeb  \\\n",
       "y         x                            ...                               \n",
       "636500.0  313500.0                  0  ...  1.450863e-01  2.292839e-02   \n",
       "258500.0  547500.0                  0  ...  9.966089e-01  2.871251e-01   \n",
       "705500.0  221500.0                  0  ... -3.400000e+38 -3.400000e+38   \n",
       "170500.0  603500.0                  0  ...  2.301957e+00  6.618243e-02   \n",
       "1253500.0 388500.0                  0  ... -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
       "636500.0  313500.0  7.954387e-02 -3.400000e+38   5.052060e-02   \n",
       "258500.0  547500.0  3.092201e-01  1.212641e-01   3.717977e-01   \n",
       "705500.0  221500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "170500.0  603500.0  5.164155e-01 -3.400000e+38   9.030058e-01   \n",
       "1253500.0 388500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride  Prosulfocarb       Sulphur  \\\n",
       "y         x                                                                \n",
       "636500.0  313500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "258500.0  547500.0              7.553086e-02  2.008362e-01  4.586757e-02   \n",
       "705500.0  221500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "170500.0  603500.0             -3.400000e+38  5.312213e-01  2.615392e-01   \n",
       "1253500.0 388500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                      Tri-allate  Occurrence  \n",
       "y         x                                   \n",
       "636500.0  313500.0 -3.400000e+38           0  \n",
       "258500.0  547500.0  2.622473e-01           0  \n",
       "705500.0  221500.0 -3.400000e+38           0  \n",
       "170500.0  603500.0  3.595334e-01           0  \n",
       "1253500.0 388500.0 -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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     "output_type": "display_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",
       "305500.0  357500.0                   3                    0      78   \n",
       "280500.0  367500.0                   4                    0      51   \n",
       "765500.0  188500.0                   0                    0       0   \n",
       "1003500.0 159500.0                   0                    0       0   \n",
       "1119500.0 518500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "305500.0  357500.0                 13                  0   \n",
       "280500.0  367500.0                 45                  0   \n",
       "765500.0  188500.0                  0                  0   \n",
       "1003500.0 159500.0                  0                  0   \n",
       "1119500.0 518500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "305500.0  357500.0                     0               0    0        0   \n",
       "280500.0  367500.0                     0               0    0        0   \n",
       "765500.0  188500.0                     0              94    0        0   \n",
       "1003500.0 159500.0                     0               0    0        0   \n",
       "1119500.0 518500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...    Glyphosate      Mancozeb  \\\n",
       "y         x                            ...                               \n",
       "305500.0  357500.0                  0  ...  8.528832e+00  1.420794e+01   \n",
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       "1119500.0 518500.0                  0  ... -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                      Mecoprop-P    Metamitron  Pendimethalin  \\\n",
       "y         x                                                     \n",
       "305500.0  357500.0  2.603747e+00  8.924331e+00   7.079559e+00   \n",
       "280500.0  367500.0  3.212259e+00  1.002133e+01   9.966151e+00   \n",
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       "1003500.0 159500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "1119500.0 518500.0 -3.400000e+38 -3.400000e+38  -3.400000e+38   \n",
       "\n",
       "                    PropamocarbHydrochloride  Prosulfocarb       Sulphur  \\\n",
       "y         x                                                                \n",
       "305500.0  357500.0              4.522233e+00  5.074559e+00  3.921629e+00   \n",
       "280500.0  367500.0              4.481364e+00  6.024839e+00  5.549138e+00   \n",
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       "1119500.0 518500.0             -3.400000e+38 -3.400000e+38 -3.400000e+38   \n",
       "\n",
       "                      Tri-allate  Occurrence  \n",
       "y         x                                   \n",
       "305500.0  357500.0  8.035636e+00           0  \n",
       "280500.0  367500.0  9.275659e+00           0  \n",
       "765500.0  188500.0 -3.400000e+38           0  \n",
       "1003500.0 159500.0 -3.400000e+38           0  \n",
       "1119500.0 518500.0 -3.400000e+38           0  \n",
       "\n",
       "[5 rows x 40 columns]"
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     },
     "metadata": {},
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   ],
   "source": [
    "INVASIVE_BIRDS_PATH = 'Datasets/Machine Learning/1km Rasters/Birds'\n",
    "# Use this if using coordinates as separate columns\n",
    "# df_1km = pd.read_csv('Datasets/Machine Learning/Dataframes/1km_All_Birds_DF.csv')\n",
    "\n",
    "# Use this if using coordinates as indices\n",
    "df_1km = pd.read_csv('Datasets/Machine Learning/Dataframes/1km_All_Birds_DF.csv', index_col=[0,1])\n",
    "\n",
    "total_birds = (df_1km['Occurrence']==1).sum()\n",
    "df_dicts = []\n",
    "\n",
    "for file in os.listdir(INVASIVE_BIRDS_PATH):\n",
    "    filename = os.fsdecode(file)\n",
    "    if not filename.endswith('.tif') or filename.endswith('All_Invasive_Birds_1km.tif') :\n",
    "         continue\n",
    "\n",
    "\n",
    "\n",
    "    bird_name = filename[:-4].replace('_', ' ')\n",
    "\n",
    "    bird_dataset = rioxarray.open_rasterio(f'{INVASIVE_BIRDS_PATH}/{file}')\n",
    "    bird_dataset.name = 'data'\n",
    "    bird_df = bird_dataset.squeeze().drop(\"spatial_ref\").drop(\"band\").to_dataframe()\n",
    "\n",
    "    # Check if index matches\n",
    "    if not df_1km.index.equals(bird_df.index):\n",
    "        print('Warning: Index does not match')\n",
    "        continue\n",
    "\n",
    "    bird_df['Occurrence'] = [0 if x == -1 else 1 for x in bird_df['data']]\n",
    "    bird_df = df_1km.drop(columns='Occurrence').join(bird_df.drop(columns='data'))\n",
    "    \n",
    "    bird_dict = {'name' : bird_name, 'dataframe' : bird_df }\n",
    "    df_dicts.append(bird_dict)\n",
    "    display(bird_df.sample(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Deciduous woodland</th>\n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "660500.0  28500.0                    0                    0       0   \n",
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       "\n",
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       "y         x                                                \n",
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       "1124500.0 453500.0                  0                  0   \n",
       "85500.0   233500.0                 69                  0   \n",
       "\n",
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       "y         x                                                              \n",
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       "1124500.0 453500.0                     0               0    0        0   \n",
       "85500.0   233500.0                     0               0    0        0   \n",
       "\n",
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       "y         x                            ...                                 \n",
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       "660500.0  28500.0                   0  ...                  0          0   \n",
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       "1124500.0 453500.0                  0  ...                  0          0   \n",
       "85500.0   233500.0                  0  ...                  0          0   \n",
       "\n",
       "                    Urban  Suburban  Elevation  Cumulative catchment area  \\\n",
       "y         x                                                                 \n",
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       "660500.0  28500.0       0         0      -9999                      -9999   \n",
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       "1124500.0 453500.0      0         0      -9999                      -9999   \n",
       "85500.0   233500.0      2        23       1271                        367   \n",
       "\n",
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       "1124500.0 453500.0            -1                             -1   \n",
       "85500.0   233500.0             2                              6   \n",
       "\n",
       "                    Inflowing drainage direction  Occurrence  \n",
       "y         x                                                   \n",
       "376500.0  337500.0                            14           0  \n",
       "660500.0  28500.0                            255           0  \n",
       "304500.0  547500.0                            17           0  \n",
       "1124500.0 453500.0                           255           0  \n",
       "85500.0   233500.0                            41           0  \n",
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       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
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       "1050500.0 29500.0                    0                    0       0   \n",
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       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
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       "1050500.0 29500.0                   0                  0   \n",
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       "136500.0  477500.0                 43                  0   \n",
       "864500.0  170500.0                  0                  0   \n",
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       "y         x                                                              \n",
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       "1050500.0 29500.0                      0               0    0        0   \n",
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       "136500.0  477500.0                     0               0    0        4   \n",
       "864500.0  170500.0                     0               0    0        0   \n",
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       "y         x                            ...                                 \n",
       "194500.0  430500.0                  0  ...                  0          0   \n",
       "1050500.0 29500.0                   0  ...                  0          0   \n",
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       "                    Urban  Suburban  Elevation  Cumulative catchment area  \\\n",
       "y         x                                                                 \n",
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       "136500.0  477500.0      0        16        758                        259   \n",
       "864500.0  170500.0      0         0      -1000                      -9999   \n",
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       "                   Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y        x                                                           \n",
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       "                   Cumulative catchment area  Surface type  \\\n",
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       "                   Outflowing drainage direction  \\\n",
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       "122500.0 86500.0                              -1   \n",
       "806500.0 245500.0                              9   \n",
       "486500.0 372500.0                              7   \n",
       "522500.0 82500.0                              -1   \n",
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       "                   Inflowing drainage direction  Occurrence  \n",
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       "486500.0 372500.0                            59           0  \n",
       "522500.0 82500.0                            255           0  \n",
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       "\n",
       "                   Littoral sediment  Saltmarsh  Urban  Suburban  Elevation  \\\n",
       "y        x                                                                    \n",
       "901500.0 533500.0                  0          0      0         0      -9999   \n",
       "775500.0 220500.0                  0          0      0         0       6065   \n",
       "771500.0 458500.0                  0          0      0         0      -9999   \n",
       "867500.0 245500.0                  0          0      0         0       9718   \n",
       "101500.0 653500.0                  0          0      0         0      -9999   \n",
       "\n",
       "                   Cumulative catchment area  Surface type  \\\n",
       "y        x                                                   \n",
       "901500.0 533500.0                      -9999            -1   \n",
       "775500.0 220500.0                         68             2   \n",
       "771500.0 458500.0                      -9999            -1   \n",
       "867500.0 245500.0                          6             2   \n",
       "101500.0 653500.0                      -9999            -1   \n",
       "\n",
       "                   Outflowing drainage direction  \\\n",
       "y        x                                         \n",
       "901500.0 533500.0                             -1   \n",
       "775500.0 220500.0                             10   \n",
       "771500.0 458500.0                             -1   \n",
       "867500.0 245500.0                              6   \n",
       "101500.0 653500.0                             -1   \n",
       "\n",
       "                   Inflowing drainage direction  Occurrence  \n",
       "y        x                                                   \n",
       "901500.0 533500.0                           255           0  \n",
       "775500.0 220500.0                             5           0  \n",
       "771500.0 458500.0                           255           0  \n",
       "867500.0 245500.0                            23           0  \n",
       "101500.0 653500.0                           255           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>Deciduous woodland</th>\n",
       "      <th>Coniferous woodland</th>\n",
       "      <th>Arable</th>\n",
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       "      <th>Inflowing drainage direction</th>\n",
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      ],
      "text/plain": [
       "                    Deciduous woodland  Coniferous woodland  Arable  \\\n",
       "y         x                                                           \n",
       "862500.0  105500.0                   0                    0       0   \n",
       "324500.0  685500.0                   0                    0       0   \n",
       "759500.0  552500.0                   0                    0       0   \n",
       "516500.0  674500.0                   0                    0       0   \n",
       "1189500.0 583500.0                   0                    0       0   \n",
       "\n",
       "                    Improve grassland  Neutral grassland  \\\n",
       "y         x                                                \n",
       "862500.0  105500.0                  0                  0   \n",
       "324500.0  685500.0                  0                  0   \n",
       "759500.0  552500.0                  0                  0   \n",
       "516500.0  674500.0                  0                  0   \n",
       "1189500.0 583500.0                  0                  0   \n",
       "\n",
       "                    Calcareous grassland  Acid grassland  Fen  Heather  \\\n",
       "y         x                                                              \n",
       "862500.0  105500.0                     0               0    0        0   \n",
       "324500.0  685500.0                     0               0    0        0   \n",
       "759500.0  552500.0                     0               0    0        0   \n",
       "516500.0  674500.0                     0               0    0        0   \n",
       "1189500.0 583500.0                     0               0    0        0   \n",
       "\n",
       "                    Heather grassland  ...  Littoral sediment  Saltmarsh  \\\n",
       "y         x                            ...                                 \n",
       "862500.0  105500.0                  0  ...                  0          0   \n",
       "324500.0  685500.0                  0  ...                  0          0   \n",
       "759500.0  552500.0                  0  ...                  0          0   \n",
       "516500.0  674500.0                  0  ...                  0          0   \n",
       "1189500.0 583500.0                  0  ...                  0          0   \n",
       "\n",
       "                    Urban  Suburban  Elevation  Cumulative catchment area  \\\n",
       "y         x                                                                 \n",
       "862500.0  105500.0      0         0      -9999                      -9999   \n",
       "324500.0  685500.0      0         0      -9999                      -9999   \n",
       "759500.0  552500.0      0         0      -9999                      -9999   \n",
       "516500.0  674500.0      0         0      -9999                      -9999   \n",
       "1189500.0 583500.0      0         0      -9999                      -9999   \n",
       "\n",
       "                    Surface type  Outflowing drainage direction  \\\n",
       "y         x                                                       \n",
       "862500.0  105500.0            -1                             -1   \n",
       "324500.0  685500.0            -1                             -1   \n",
       "759500.0  552500.0            -1                             -1   \n",
       "516500.0  674500.0            -1                             -1   \n",
       "1189500.0 583500.0            -1                             -1   \n",
       "\n",
       "                    Inflowing drainage direction  Occurrence  \n",
       "y         x                                                   \n",
       "862500.0  105500.0                           255           0  \n",
       "324500.0  685500.0                           255           0  \n",
       "759500.0  552500.0                           255           0  \n",
       "516500.0  674500.0                           255           0  \n",
       "1189500.0 583500.0                           255           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 1km data before drop: \n",
      " Occurrence\n",
      "0    909231\n",
      "1       769\n",
      "dtype: int64 \n",
      "\n",
      "Barnacle Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    32315\n",
      "1      769\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 1km data before drop: \n",
      " Occurrence\n",
      "0    899853\n",
      "1     10147\n",
      "dtype: int64 \n",
      "\n",
      "Canada Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    22937\n",
      "1    10147\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 1km data before drop: \n",
      " Occurrence\n",
      "0    909137\n",
      "1       863\n",
      "dtype: int64 \n",
      "\n",
      "Egyptian Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    32221\n",
      "1      863\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 1km data before drop: \n",
      " Occurrence\n",
      "0    907795\n",
      "1      2205\n",
      "dtype: int64 \n",
      "\n",
      "Gadwall 1km data after drop: \n",
      " Occurrence\n",
      "0    30879\n",
      "1     2205\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 1km data before drop: \n",
      " Occurrence\n",
      "0    909554\n",
      "1       446\n",
      "dtype: int64 \n",
      "\n",
      "Goshawk 1km data after drop: \n",
      " Occurrence\n",
      "0    32638\n",
      "1      446\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 1km data before drop: \n",
      " Occurrence\n",
      "0    907877\n",
      "1      2123\n",
      "dtype: int64 \n",
      "\n",
      "Grey Partridge 1km data after drop: \n",
      " Occurrence\n",
      "0    30961\n",
      "1     2123\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 1km data before drop: \n",
      " Occurrence\n",
      "0    909706\n",
      "1       294\n",
      "dtype: int64 \n",
      "\n",
      "Indian Peafowl 1km data after drop: \n",
      " Occurrence\n",
      "0    32790\n",
      "1      294\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 1km data before drop: \n",
      " Occurrence\n",
      "0    906452\n",
      "1      3548\n",
      "dtype: int64 \n",
      "\n",
      "Little Owl 1km data after drop: \n",
      " Occurrence\n",
      "0    29536\n",
      "1     3548\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 1km data before drop: \n",
      " Occurrence\n",
      "0    908990\n",
      "1      1010\n",
      "dtype: int64 \n",
      "\n",
      "Mandarin Duck 1km data after drop: \n",
      " Occurrence\n",
      "0    32074\n",
      "1     1010\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 1km data before drop: \n",
      " Occurrence\n",
      "0    890876\n",
      "1     19124\n",
      "dtype: int64 \n",
      "\n",
      "Mute Swan 1km data after drop: \n",
      " Occurrence\n",
      "1    19124\n",
      "0    13960\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 1km data before drop: \n",
      " Occurrence\n",
      "0    904145\n",
      "1      5855\n",
      "dtype: int64 \n",
      "\n",
      "Pheasant 1km data after drop: \n",
      " Occurrence\n",
      "0    27229\n",
      "1     5855\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 1km data before drop: \n",
      " Occurrence\n",
      "0    907354\n",
      "1      2646\n",
      "dtype: int64 \n",
      "\n",
      "Pink-footed Goose 1km data after drop: \n",
      " Occurrence\n",
      "0    30438\n",
      "1     2646\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 1km data before drop: \n",
      " Occurrence\n",
      "0    909303\n",
      "1       697\n",
      "dtype: int64 \n",
      "\n",
      "Pintail 1km data after drop: \n",
      " Occurrence\n",
      "0    32387\n",
      "1      697\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 1km data before drop: \n",
      " Occurrence\n",
      "0    908943\n",
      "1      1057\n",
      "dtype: int64 \n",
      "\n",
      "Pochard 1km data after drop: \n",
      " Occurrence\n",
      "0    32027\n",
      "1     1057\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 1km data before drop: \n",
      " Occurrence\n",
      "0    907047\n",
      "1      2953\n",
      "dtype: int64 \n",
      "\n",
      "Red-legged Partridge 1km data after drop: \n",
      " Occurrence\n",
      "0    30131\n",
      "1     2953\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 1km data before drop: \n",
      " Occurrence\n",
      "0    909496\n",
      "1       504\n",
      "dtype: int64 \n",
      "\n",
      "Ring-necked Parakeet 1km data after drop: \n",
      " Occurrence\n",
      "0    32580\n",
      "1      504\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 1km data before drop: \n",
      " Occurrence\n",
      "0    906081\n",
      "1      3919\n",
      "dtype: int64 \n",
      "\n",
      "Rock Dove 1km data after drop: \n",
      " Occurrence\n",
      "0    29165\n",
      "1     3919\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 1km data before drop: \n",
      " Occurrence\n",
      "0    909876\n",
      "1       124\n",
      "dtype: int64 \n",
      "\n",
      "Ruddy Duck 1km data after drop: \n",
      " Occurrence\n",
      "0    32960\n",
      "1      124\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 1km data before drop: \n",
      " Occurrence\n",
      "0    909045\n",
      "1       955\n",
      "dtype: int64 \n",
      "\n",
      "Whooper Swan 1km data after drop: \n",
      " Occurrence\n",
      "0    32129\n",
      "1      955\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 1km data before drop: \n",
      " Occurrence\n",
      "0    907683\n",
      "1      2317\n",
      "dtype: int64 \n",
      "\n",
      "Wigeon 1km data after drop: \n",
      " Occurrence\n",
      "0    30767\n",
      "1     2317\n",
      "dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data Cleaning\n",
    "np.random.seed(seed=seed)\n",
    "\n",
    "for dict in df_dicts:\n",
    "    cur_df = dict[\"dataframe\"]\n",
    "    cur_df_name = dict[\"name\"]\n",
    "\n",
    "    print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "    no_occurences = cur_df[cur_df['Occurrence']==0].index \n",
    "    sample_size = sum(cur_df['Occurrence']==0) - total_birds + sum(cur_df['Occurrence']==1)\n",
    "    random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "    dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "    print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')\n",
    "\n",
    "\n",
    "# for dict in df_dicts:\n",
    "#     cur_df = dict[\"dataframe\"]\n",
    "#     cur_df_name = dict[\"name\"]\n",
    "\n",
    "#     print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n",
    "    \n",
    "#     no_occurences = cur_df[cur_df['Occurrence']==0].index\n",
    "#     sample_size = sum(cur_df['Occurrence']==0) - sum(cur_df['Occurrence']==1)\n",
    "#     random_indices = np.random.choice(no_occurences, sample_size, replace=False)\n",
    "#     dict[\"dataframe\"] =  cur_df.drop(random_indices)\n",
    "    \n",
    "#     print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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 1km</td>\n",
       "      <td>19124</td>\n",
       "      <td>0.578044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 1km</td>\n",
       "      <td>10147</td>\n",
       "      <td>0.306704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 1km</td>\n",
       "      <td>5855</td>\n",
       "      <td>0.176974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 1km</td>\n",
       "      <td>3919</td>\n",
       "      <td>0.118456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 1km</td>\n",
       "      <td>3548</td>\n",
       "      <td>0.107242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 1km</td>\n",
       "      <td>2953</td>\n",
       "      <td>0.089258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 1km</td>\n",
       "      <td>2646</td>\n",
       "      <td>0.079978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 1km</td>\n",
       "      <td>2317</td>\n",
       "      <td>0.070034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 1km</td>\n",
       "      <td>2205</td>\n",
       "      <td>0.066649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 1km</td>\n",
       "      <td>2123</td>\n",
       "      <td>0.064170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 1km</td>\n",
       "      <td>1057</td>\n",
       "      <td>0.031949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 1km</td>\n",
       "      <td>1010</td>\n",
       "      <td>0.030528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 1km</td>\n",
       "      <td>955</td>\n",
       "      <td>0.028866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 1km</td>\n",
       "      <td>863</td>\n",
       "      <td>0.026085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 1km</td>\n",
       "      <td>769</td>\n",
       "      <td>0.023244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 1km</td>\n",
       "      <td>697</td>\n",
       "      <td>0.021068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 1km</td>\n",
       "      <td>504</td>\n",
       "      <td>0.015234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 1km</td>\n",
       "      <td>446</td>\n",
       "      <td>0.013481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 1km</td>\n",
       "      <td>294</td>\n",
       "      <td>0.008886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 1km</td>\n",
       "      <td>124</td>\n",
       "      <td>0.003748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  Occurrence Count  Percentage\n",
       "9              Mute Swan 1km             19124    0.578044\n",
       "1           Canada Goose 1km             10147    0.306704\n",
       "10              Pheasant 1km              5855    0.176974\n",
       "16             Rock Dove 1km              3919    0.118456\n",
       "7             Little Owl 1km              3548    0.107242\n",
       "14  Red-legged Partridge 1km              2953    0.089258\n",
       "11     Pink-footed Goose 1km              2646    0.079978\n",
       "19                Wigeon 1km              2317    0.070034\n",
       "3                Gadwall 1km              2205    0.066649\n",
       "5         Grey Partridge 1km              2123    0.064170\n",
       "13               Pochard 1km              1057    0.031949\n",
       "8          Mandarin Duck 1km              1010    0.030528\n",
       "18          Whooper Swan 1km               955    0.028866\n",
       "2         Egyptian Goose 1km               863    0.026085\n",
       "0         Barnacle Goose 1km               769    0.023244\n",
       "12               Pintail 1km               697    0.021068\n",
       "15  Ring-necked Parakeet 1km               504    0.015234\n",
       "4                Goshawk 1km               446    0.013481\n",
       "6         Indian Peafowl 1km               294    0.008886\n",
       "17            Ruddy Duck 1km               124    0.003748"
      ]
     },
     "execution_count": 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 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9829886182841757,\n",
      "        \"recall\": 0.9941824483228122,\n",
      "        \"f1-score\": 0.9885538461538461,\n",
      "        \"support\": 8079\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.53,\n",
      "        \"recall\": 0.2760416666666667,\n",
      "        \"f1-score\": 0.363013698630137,\n",
      "        \"support\": 192\n",
      "    },\n",
      "    \"accuracy\": 0.9775117881755532,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7564943091420879,\n",
      "        \"recall\": 0.6351120574947394,\n",
      "        \"f1-score\": 0.6757837723919915,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9724731044756204,\n",
      "        \"recall\": 0.9775117881755532,\n",
      "        \"f1-score\": 0.9740327836070498,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Barnacle Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9830174747723357,\n",
      "        \"recall\": 0.9887362297314024,\n",
      "        \"f1-score\": 0.9858685590867017,\n",
      "        \"support\": 8079\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3724137931034483,\n",
      "        \"recall\": 0.28125,\n",
      "        \"f1-score\": 0.3204747774480712,\n",
      "        \"support\": 192\n",
      "    },\n",
      "    \"accuracy\": 0.9723129004957078,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.677715633937892,\n",
      "        \"recall\": 0.6349931148657012,\n",
      "        \"f1-score\": 0.6531716682673865,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9688431419370768,\n",
      "        \"recall\": 0.9723129004957078,\n",
      "        \"f1-score\": 0.9704223487040856,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Canada Goose 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.935126884182408,\n",
      "        \"recall\": 0.8590709903593339,\n",
      "        \"f1-score\": 0.8954869358669834,\n",
      "        \"support\": 5705\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7346534653465346,\n",
      "        \"recall\": 0.8674980514419329,\n",
      "        \"f1-score\": 0.7955682630450321,\n",
      "        \"support\": 2566\n",
      "    },\n",
      "    \"accuracy\": 0.8616854068431871,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8348901747644712,\n",
      "        \"recall\": 0.8632845209006335,\n",
      "        \"f1-score\": 0.8455275994560078,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8729318905017344,\n",
      "        \"recall\": 0.8616854068431871,\n",
      "        \"f1-score\": 0.864488106890907,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Canada Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9002157497303128,\n",
      "        \"recall\": 0.8776511831726556,\n",
      "        \"f1-score\": 0.8887902724771456,\n",
      "        \"support\": 5705\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7423403469915097,\n",
      "        \"recall\": 0.7837100545596258,\n",
      "        \"f1-score\": 0.7624644549763032,\n",
      "        \"support\": 2566\n",
      "    },\n",
      "    \"accuracy\": 0.8485068310966026,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8212780483609112,\n",
      "        \"recall\": 0.8306806188661406,\n",
      "        \"f1-score\": 0.8256273637267244,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8512363901090133,\n",
      "        \"recall\": 0.8485068310966026,\n",
      "        \"f1-score\": 0.8495988750999044,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Egyptian Goose 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9795518550263255,\n",
      "        \"recall\": 0.9899764880584087,\n",
      "        \"f1-score\": 0.9847365829640572,\n",
      "        \"support\": 8081\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.22115384615384615,\n",
      "        \"recall\": 0.12105263157894737,\n",
      "        \"f1-score\": 0.1564625850340136,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.9700157175674042,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6003528505900858,\n",
      "        \"recall\": 0.555514559818678,\n",
      "        \"f1-score\": 0.5705995839990354,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9621300654379116,\n",
      "        \"recall\": 0.9700157175674042,\n",
      "        \"f1-score\": 0.9657096140840296,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Egyptian Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9849993852207057,\n",
      "        \"recall\": 0.9913377057294889,\n",
      "        \"f1-score\": 0.9881583816454916,\n",
      "        \"support\": 8081\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4927536231884058,\n",
      "        \"recall\": 0.35789473684210527,\n",
      "        \"f1-score\": 0.41463414634146345,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.9767863619876678,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7388765042045558,\n",
      "        \"recall\": 0.6746162212857971,\n",
      "        \"f1-score\": 0.7013962639934775,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9736915996100012,\n",
      "        \"recall\": 0.9767863619876678,\n",
      "        \"f1-score\": 0.9749834808224032,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Gadwall 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9579474342928661,\n",
      "        \"recall\": 0.9906808180170852,\n",
      "        \"f1-score\": 0.9740391957241028,\n",
      "        \"support\": 7726\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7437722419928826,\n",
      "        \"recall\": 0.3834862385321101,\n",
      "        \"f1-score\": 0.5060532687651331,\n",
      "        \"support\": 545\n",
      "    },\n",
      "    \"accuracy\": 0.9506710192237939,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8508598381428744,\n",
      "        \"recall\": 0.6870835282745976,\n",
      "        \"f1-score\": 0.740046232244618,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.943834814319043,\n",
      "        \"recall\": 0.9506710192237939,\n",
      "        \"f1-score\": 0.9432022557902813,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Gadwall 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9636246786632391,\n",
      "        \"recall\": 0.970359823971007,\n",
      "        \"f1-score\": 0.9669805236682575,\n",
      "        \"support\": 7726\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5336048879837068,\n",
      "        \"recall\": 0.48073394495412847,\n",
      "        \"f1-score\": 0.5057915057915059,\n",
      "        \"support\": 545\n",
      "    },\n",
      "    \"accuracy\": 0.9380969653004474,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7486147833234729,\n",
      "        \"recall\": 0.7255468844625677,\n",
      "        \"f1-score\": 0.7363860147298817,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9352894367432361,\n",
      "        \"recall\": 0.9380969653004474,\n",
      "        \"f1-score\": 0.9365914516403493,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Goshawk 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9871732817037754,\n",
      "        \"recall\": 0.9991426821800368,\n",
      "        \"f1-score\": 0.9931219185586463,\n",
      "        \"support\": 8165\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 106\n",
      "    },\n",
      "    \"accuracy\": 0.9863378067948253,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.4935866408518877,\n",
      "        \"recall\": 0.4995713410900184,\n",
      "        \"f1-score\": 0.49656095927932314,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9745218045111022,\n",
      "        \"recall\": 0.9863378067948253,\n",
      "        \"f1-score\": 0.9803942044530706,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Goshawk 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9871327992231124,\n",
      "        \"recall\": 0.9959583588487446,\n",
      "        \"f1-score\": 0.9915259403767603,\n",
      "        \"support\": 8165\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 106\n",
      "    },\n",
      "    \"accuracy\": 0.9831942933139887,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.4935663996115562,\n",
      "        \"recall\": 0.4979791794243723,\n",
      "        \"f1-score\": 0.49576297018838017,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9744818408483511,\n",
      "        \"recall\": 0.9831942933139887,\n",
      "        \"f1-score\": 0.9788186801083604,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Grey Partridge 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9372121212121212,\n",
      "        \"recall\": 0.998579362004391,\n",
      "        \"f1-score\": 0.9669230288251109,\n",
      "        \"support\": 7743\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.47619047619047616,\n",
      "        \"recall\": 0.01893939393939394,\n",
      "        \"f1-score\": 0.03642987249544627,\n",
      "        \"support\": 528\n",
      "    },\n",
      "    \"accuracy\": 0.9360415911014388,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7067012987012986,\n",
      "        \"recall\": 0.5087593779718925,\n",
      "        \"f1-score\": 0.5016764506602787,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9077816498578196,\n",
      "        \"recall\": 0.9360415911014388,\n",
      "        \"f1-score\": 0.9075226677391404,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Grey Partridge 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9471826547333921,\n",
      "        \"recall\": 0.9704248999095958,\n",
      "        \"f1-score\": 0.958662924215361,\n",
      "        \"support\": 7743\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3224852071005917,\n",
      "        \"recall\": 0.20643939393939395,\n",
      "        \"f1-score\": 0.2517321016166282,\n",
      "        \"support\": 528\n",
      "    },\n",
      "    \"accuracy\": 0.9216539717083787,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6348339309169919,\n",
      "        \"recall\": 0.5884321469244949,\n",
      "        \"f1-score\": 0.6051975129159946,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9073035285878089,\n",
      "        \"recall\": 0.9216539717083787,\n",
      "        \"f1-score\": 0.9135342246225511,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Indian Peafowl 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9909222948438635,\n",
      "        \"recall\": 0.9989019033674963,\n",
      "        \"f1-score\": 0.994896099161502,\n",
      "        \"support\": 8196\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 75\n",
      "    },\n",
      "    \"accuracy\": 0.9898440333696047,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49546114742193176,\n",
      "        \"recall\": 0.49945095168374815,\n",
      "        \"f1-score\": 0.497448049580751,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9819367825583732,\n",
      "        \"recall\": 0.9898440333696047,\n",
      "        \"f1-score\": 0.9858745531045424,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Indian Peafowl 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9913908087789499,\n",
      "        \"recall\": 0.997559785261103,\n",
      "        \"f1-score\": 0.9944657300979141,\n",
      "        \"support\": 8196\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.16666666666666666,\n",
      "        \"recall\": 0.05333333333333334,\n",
      "        \"f1-score\": 0.0808080808080808,\n",
      "        \"support\": 75\n",
      "    },\n",
      "    \"accuracy\": 0.9889977028170717,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5790287377228083,\n",
      "        \"recall\": 0.5254465592972182,\n",
      "        \"f1-score\": 0.5376369054529975,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9839123526480805,\n",
      "        \"recall\": 0.9889977028170717,\n",
      "        \"f1-score\": 0.9861808402784561,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Little Owl 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.935244161358811,\n",
      "        \"recall\": 0.9533342350872447,\n",
      "        \"f1-score\": 0.9442025587782168,\n",
      "        \"support\": 7393\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5306122448979592,\n",
      "        \"recall\": 0.44419134396355353,\n",
      "        \"f1-score\": 0.48357098574085555,\n",
      "        \"support\": 878\n",
      "    },\n",
      "    \"accuracy\": 0.8992866642485794,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7329282031283851,\n",
      "        \"recall\": 0.6987627895253992,\n",
      "        \"f1-score\": 0.7138867722595361,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8922908518856365,\n",
      "        \"recall\": 0.8992866642485794,\n",
      "        \"f1-score\": 0.8953046599598389,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Little Owl 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9363194819212088,\n",
      "        \"recall\": 0.9387258217232517,\n",
      "        \"f1-score\": 0.9375211077338738,\n",
      "        \"support\": 7393\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.47264260768335276,\n",
      "        \"recall\": 0.4624145785876993,\n",
      "        \"f1-score\": 0.4674726540011514,\n",
      "        \"support\": 878\n",
      "    },\n",
      "    \"accuracy\": 0.8881634627010035,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7044810448022808,\n",
      "        \"recall\": 0.7005702001554754,\n",
      "        \"f1-score\": 0.7024968808675126,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.887098312101255,\n",
      "        \"recall\": 0.8881634627010035,\n",
      "        \"f1-score\": 0.8876235690593084,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mandarin Duck 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9761353951053209,\n",
      "        \"recall\": 0.9965195773772529,\n",
      "        \"f1-score\": 0.9862221675482838,\n",
      "        \"support\": 8045\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5172413793103449,\n",
      "        \"recall\": 0.13274336283185842,\n",
      "        \"f1-score\": 0.21126760563380284,\n",
      "        \"support\": 226\n",
      "    },\n",
      "    \"accuracy\": 0.9729174223189457,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7466883872078329,\n",
      "        \"recall\": 0.5646314701045557,\n",
      "        \"f1-score\": 0.5987448865910433,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.963596397696342,\n",
      "        \"recall\": 0.9729174223189457,\n",
      "        \"f1-score\": 0.9650470096480696,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Mandarin Duck 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9784694881889764,\n",
      "        \"recall\": 0.9885643256681168,\n",
      "        \"f1-score\": 0.9834910035243926,\n",
      "        \"support\": 8045\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.35664335664335667,\n",
      "        \"recall\": 0.22566371681415928,\n",
      "        \"f1-score\": 0.2764227642276423,\n",
      "        \"support\": 226\n",
      "    },\n",
      "    \"accuracy\": 0.9677185346391005,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6675564224161665,\n",
      "        \"recall\": 0.607114021241138,\n",
      "        \"f1-score\": 0.6299568838760174,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9614784706905711,\n",
      "        \"recall\": 0.9677185346391005,\n",
      "        \"f1-score\": 0.9641707977353627,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Mute Swan 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9216121866074262,\n",
      "        \"recall\": 0.8446771378708552,\n",
      "        \"f1-score\": 0.8814691151919867,\n",
      "        \"support\": 3438\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.895703125,\n",
      "        \"recall\": 0.9488930271053176,\n",
      "        \"f1-score\": 0.9215311966241334,\n",
      "        \"support\": 4833\n",
      "    },\n",
      "    \"accuracy\": 0.9055736912102527,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9086576558037132,\n",
      "        \"recall\": 0.8967850824880864,\n",
      "        \"f1-score\": 0.90150015590806,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9064727240577115,\n",
      "        \"recall\": 0.9055736912102527,\n",
      "        \"f1-score\": 0.9048786230582139,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Mute Swan 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8850950118764845,\n",
      "        \"recall\": 0.8670738801628854,\n",
      "        \"f1-score\": 0.8759917719659124,\n",
      "        \"support\": 3438\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.9067917601468488,\n",
      "        \"recall\": 0.9199255121042831,\n",
      "        \"f1-score\": 0.9133114215283485,\n",
      "        \"support\": 4833\n",
      "    },\n",
      "    \"accuracy\": 0.8979567162374562,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8959433860116667,\n",
      "        \"recall\": 0.8934996961335843,\n",
      "        \"f1-score\": 0.8946515967471305,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8977730900279379,\n",
      "        \"recall\": 0.8979567162374562,\n",
      "        \"f1-score\": 0.8977987924392837,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pheasant 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8981844589687727,\n",
      "        \"recall\": 0.9068778413257076,\n",
      "        \"f1-score\": 0.9025102159953299,\n",
      "        \"support\": 6819\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5418470418470418,\n",
      "        \"recall\": 0.5172176308539945,\n",
      "        \"f1-score\": 0.5292459478505989,\n",
      "        \"support\": 1452\n",
      "    },\n",
      "    \"accuracy\": 0.8384717688308548,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7200157504079072,\n",
      "        \"recall\": 0.7120477360898511,\n",
      "        \"f1-score\": 0.7158780819229644,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8356283073957159,\n",
      "        \"recall\": 0.8384717688308548,\n",
      "        \"f1-score\": 0.8369825026177274,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pheasant 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8786086466694831,\n",
      "        \"recall\": 0.9149435401085203,\n",
      "        \"f1-score\": 0.8964080459770115,\n",
      "        \"support\": 6819\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5042735042735043,\n",
      "        \"recall\": 0.40633608815426997,\n",
      "        \"f1-score\": 0.45003813882532423,\n",
      "        \"support\": 1452\n",
      "    },\n",
      "    \"accuracy\": 0.825655906178213,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6914410754714937,\n",
      "        \"recall\": 0.6606398141313952,\n",
      "        \"f1-score\": 0.6732230924011678,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8128929379572402,\n",
      "        \"recall\": 0.825655906178213,\n",
      "        \"f1-score\": 0.8180464083051157,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pink-footed Goose 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9385822847888377,\n",
      "        \"recall\": 0.9893630991464215,\n",
      "        \"f1-score\": 0.9633039253292418,\n",
      "        \"support\": 7615\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6680327868852459,\n",
      "        \"recall\": 0.24847560975609756,\n",
      "        \"f1-score\": 0.3622222222222223,\n",
      "        \"support\": 656\n",
      "    },\n",
      "    \"accuracy\": 0.9306008946922983,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8033075358370418,\n",
      "        \"recall\": 0.6189193544512596,\n",
      "        \"f1-score\": 0.6627630737757321,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9171241212506008,\n",
      "        \"recall\": 0.9306008946922983,\n",
      "        \"f1-score\": 0.9156301740007199,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pink-footed Goose 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.948260250937783,\n",
      "        \"recall\": 0.9627051871306632,\n",
      "        \"f1-score\": 0.9554281245927279,\n",
      "        \"support\": 7615\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4740740740740741,\n",
      "        \"recall\": 0.3902439024390244,\n",
      "        \"f1-score\": 0.4280936454849498,\n",
      "        \"support\": 656\n",
      "    },\n",
      "    \"accuracy\": 0.9173014145810664,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7111671625059286,\n",
      "        \"recall\": 0.6764745447848438,\n",
      "        \"f1-score\": 0.6917608850388388,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9106509978822162,\n",
      "        \"recall\": 0.9173014145810664,\n",
      "        \"f1-score\": 0.9136035062521763,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pintail 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9794728531519494,\n",
      "        \"recall\": 0.9969093831128694,\n",
      "        \"f1-score\": 0.9881142016909692,\n",
      "        \"support\": 8089\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.34210526315789475,\n",
      "        \"recall\": 0.07142857142857142,\n",
      "        \"f1-score\": 0.11818181818181817,\n",
      "        \"support\": 182\n",
      "    },\n",
      "    \"accuracy\": 0.9765445532583726,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6607890581549221,\n",
      "        \"recall\": 0.5341689772707204,\n",
      "        \"f1-score\": 0.5531480099363937,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9654478378721866,\n",
      "        \"recall\": 0.9765445532583726,\n",
      "        \"f1-score\": 0.9689716924661276,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pintail 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9800152749490835,\n",
      "        \"recall\": 0.9517863765607615,\n",
      "        \"f1-score\": 0.9656945751019128,\n",
      "        \"support\": 8089\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.060240963855421686,\n",
      "        \"recall\": 0.13736263736263737,\n",
      "        \"f1-score\": 0.08375209380234507,\n",
      "        \"support\": 182\n",
      "    },\n",
      "    \"accuracy\": 0.9338653125377826,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5201281194022526,\n",
      "        \"recall\": 0.5445745069616994,\n",
      "        \"f1-score\": 0.5247233344521289,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9597760143253323,\n",
      "        \"recall\": 0.9338653125377826,\n",
      "        \"f1-score\": 0.9462877885468988,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Pochard 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.968296224588577,\n",
      "        \"recall\": 0.9991259832688226,\n",
      "        \"f1-score\": 0.9834695507896516,\n",
      "        \"support\": 8009\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"accuracy\": 0.9674767259098054,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.4841481122942885,\n",
      "        \"recall\": 0.4995629916344113,\n",
      "        \"f1-score\": 0.4917347753948258,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9376235597545536,\n",
      "        \"recall\": 0.9674767259098054,\n",
      "        \"f1-score\": 0.9523162413582782,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Pochard 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9701437523037229,\n",
      "        \"recall\": 0.9858908727681358,\n",
      "        \"f1-score\": 0.9779539261828091,\n",
      "        \"support\": 8009\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.14393939393939395,\n",
      "        \"recall\": 0.07251908396946564,\n",
      "        \"f1-score\": 0.09644670050761421,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"accuracy\": 0.9569580461854673,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5570415731215584,\n",
      "        \"recall\": 0.5292049783688008,\n",
      "        \"f1-score\": 0.5372003133452117,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.943972123493246,\n",
      "        \"recall\": 0.9569580461854673,\n",
      "        \"f1-score\": 0.9500304715670551,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Red-legged Partridge 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9180307842658197,\n",
      "        \"recall\": 0.9929968287526427,\n",
      "        \"f1-score\": 0.9540434175447505,\n",
      "        \"support\": 7568\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3764705882352941,\n",
      "        \"recall\": 0.04551920341394026,\n",
      "        \"f1-score\": 0.08121827411167512,\n",
      "        \"support\": 703\n",
      "    },\n",
      "    \"accuracy\": 0.9124652399951638,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6472506862505569,\n",
      "        \"recall\": 0.5192580160832915,\n",
      "        \"f1-score\": 0.5176308458282128,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8720004592979248,\n",
      "        \"recall\": 0.9124652399951638,\n",
      "        \"f1-score\": 0.8798569738458686,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Red-legged Partridge 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9304915514592934,\n",
      "        \"recall\": 0.9604915433403806,\n",
      "        \"f1-score\": 0.9452535760728219,\n",
      "        \"support\": 7568\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3485838779956427,\n",
      "        \"recall\": 0.22759601706970128,\n",
      "        \"f1-score\": 0.27538726333907054,\n",
      "        \"support\": 703\n",
      "    },\n",
      "    \"accuracy\": 0.8981985249667513,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6395377147274681,\n",
      "        \"recall\": 0.5940437802050409,\n",
      "        \"f1-score\": 0.6103204197059462,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.881031861646097,\n",
      "        \"recall\": 0.8981985249667513,\n",
      "        \"f1-score\": 0.8883177741320859,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ring-necked Parakeet 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9898652824125571,\n",
      "        \"recall\": 0.9830612495397079,\n",
      "        \"f1-score\": 0.9864515334400787,\n",
      "        \"support\": 8147\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.23333333333333334,\n",
      "        \"recall\": 0.3387096774193548,\n",
      "        \"f1-score\": 0.2763157894736842,\n",
      "        \"support\": 124\n",
      "    },\n",
      "    \"accuracy\": 0.973401039777536,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6115993078729453,\n",
      "        \"recall\": 0.6608854634795314,\n",
      "        \"f1-score\": 0.6313836614568815,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9785232485973203,\n",
      "        \"recall\": 0.973401039777536,\n",
      "        \"f1-score\": 0.9758050780837938,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Ring-necked Parakeet 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.989613880742913,\n",
      "        \"recall\": 0.9941082607094636,\n",
      "        \"f1-score\": 0.9918559794256322,\n",
      "        \"support\": 8147\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.4482758620689655,\n",
      "        \"recall\": 0.31451612903225806,\n",
      "        \"f1-score\": 0.3696682464454976,\n",
      "        \"support\": 124\n",
      "    },\n",
      "    \"accuracy\": 0.9839197195018741,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7189448714059392,\n",
      "        \"recall\": 0.6543121948708608,\n",
      "        \"f1-score\": 0.6807621129355649,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9814980647212023,\n",
      "        \"recall\": 0.9839197195018741,\n",
      "        \"f1-score\": 0.9825280530697457,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Rock Dove 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8963236229487342,\n",
      "        \"recall\": 0.9767119489880788,\n",
      "        \"f1-score\": 0.9347927031509121,\n",
      "        \"support\": 7214\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5902439024390244,\n",
      "        \"recall\": 0.22894985808893092,\n",
      "        \"f1-score\": 0.3299250170415814,\n",
      "        \"support\": 1057\n",
      "    },\n",
      "    \"accuracy\": 0.8811510095514448,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7432837626938793,\n",
      "        \"recall\": 0.6028309035385049,\n",
      "        \"f1-score\": 0.6323588600962468,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8572078854830393,\n",
      "        \"recall\": 0.8811510095514448,\n",
      "        \"f1-score\": 0.8574930846987826,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Rock Dove 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9075641542388821,\n",
      "        \"recall\": 0.9363737177710009,\n",
      "        \"f1-score\": 0.9217438766459712,\n",
      "        \"support\": 7214\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.44565217391304346,\n",
      "        \"recall\": 0.3491012298959319,\n",
      "        \"f1-score\": 0.3915119363395225,\n",
      "        \"support\": 1057\n",
      "    },\n",
      "    \"accuracy\": 0.8613226937492443,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6766081640759628,\n",
      "        \"recall\": 0.6427374738334664,\n",
      "        \"f1-score\": 0.6566279064927469,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8485336907877382,\n",
      "        \"recall\": 0.8613226937492443,\n",
      "        \"f1-score\": 0.8539824015034351,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Ruddy Duck 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9976944545564859,\n",
      "        \"recall\": 0.9976944545564859,\n",
      "        \"f1-score\": 0.9976944545564859,\n",
      "        \"support\": 8241\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.36666666666666664,\n",
      "        \"recall\": 0.36666666666666664,\n",
      "        \"f1-score\": 0.36666666666666664,\n",
      "        \"support\": 30\n",
      "    },\n",
      "    \"accuracy\": 0.9954056341433926,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6821805606115763,\n",
      "        \"recall\": 0.6821805606115763,\n",
      "        \"f1-score\": 0.6821805606115763,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9954056341433926,\n",
      "        \"recall\": 0.9954056341433926,\n",
      "        \"f1-score\": 0.9954056341433926,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Ruddy Duck 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9969715324046032,\n",
      "        \"recall\": 0.9986652105327023,\n",
      "        \"f1-score\": 0.9978176527643065,\n",
      "        \"support\": 8241\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3125,\n",
      "        \"recall\": 0.16666666666666666,\n",
      "        \"f1-score\": 0.21739130434782608,\n",
      "        \"support\": 30\n",
      "    },\n",
      "    \"accuracy\": 0.9956474428726877,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6547357662023017,\n",
      "        \"recall\": 0.5826659385996845,\n",
      "        \"f1-score\": 0.6076044785560663,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9944888645322615,\n",
      "        \"recall\": 0.9956474428726877,\n",
      "        \"f1-score\": 0.994986944210021,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Whooper Swan 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9686478634547876,\n",
      "        \"recall\": 0.9990012484394507,\n",
      "        \"f1-score\": 0.983590436973757,\n",
      "        \"support\": 8010\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2,\n",
      "        \"recall\": 0.007662835249042145,\n",
      "        \"f1-score\": 0.014760147601476014,\n",
      "        \"support\": 261\n",
      "    },\n",
      "    \"accuracy\": 0.9677185346391005,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5843239317273938,\n",
      "        \"recall\": 0.5033320418442464,\n",
      "        \"f1-score\": 0.49917529228761653,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9443923813653546,\n",
      "        \"recall\": 0.9677185346391005,\n",
      "        \"f1-score\": 0.9530179904103228,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Whooper Swan 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9716717479674797,\n",
      "        \"recall\": 0.9549313358302123,\n",
      "        \"f1-score\": 0.9632288124921294,\n",
      "        \"support\": 8010\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.09523809523809523,\n",
      "        \"recall\": 0.14559386973180077,\n",
      "        \"f1-score\": 0.11515151515151516,\n",
      "        \"support\": 261\n",
      "    },\n",
      "    \"accuracy\": 0.9293918510458228,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5334549216027875,\n",
      "        \"recall\": 0.5502626027810065,\n",
      "        \"f1-score\": 0.5391901638218223,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9440149732894033,\n",
      "        \"recall\": 0.9293918510458228,\n",
      "        \"f1-score\": 0.936466852075505,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Training with Wigeon 1km 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 1km Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9347108448465583,\n",
      "        \"recall\": 0.9946656258131668,\n",
      "        \"f1-score\": 0.9637566971320518,\n",
      "        \"support\": 7686\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5543478260869565,\n",
      "        \"recall\": 0.08717948717948718,\n",
      "        \"f1-score\": 0.15066469719350073,\n",
      "        \"support\": 585\n",
      "    },\n",
      "    \"accuracy\": 0.9304799903276508,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7445293354667575,\n",
      "        \"recall\": 0.5409225564963269,\n",
      "        \"f1-score\": 0.5572106971627763,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9078081286122014,\n",
      "        \"recall\": 0.9304799903276508,\n",
      "        \"f1-score\": 0.9062474697152881,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n",
      "Wigeon 1km SMOTE Classification Report: \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9417615495354011,\n",
      "        \"recall\": 0.936247723132969,\n",
      "        \"f1-score\": 0.9389965420499772,\n",
      "        \"support\": 7686\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2222222222222222,\n",
      "        \"recall\": 0.23931623931623933,\n",
      "        \"f1-score\": 0.23045267489711932,\n",
      "        \"support\": 585\n",
      "    },\n",
      "    \"accuracy\": 0.8869544190545279,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5819918858788117,\n",
      "        \"recall\": 0.5877819812246041,\n",
      "        \"f1-score\": 0.5847246084735482,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.890869214088876,\n",
      "        \"recall\": 0.8869544190545279,\n",
      "        \"f1-score\": 0.8888819050913964,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Add model pipeline\n",
    "estimators = [\n",
    "    ('lr', LogisticRegression(max_iter=10000, solver='saga', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('knn', KNeighborsClassifier(n_neighbors=5, weights='distance')),\n",
    "    ('sgd', SGDClassifier( max_iter=10000, loss='modified_huber', random_state=seed, penalty='l2', verbose=verbose)),\n",
    "    ('rf', RandomForestClassifier(n_estimators=20,max_features=None, random_state=seed, verbose=verbose))\n",
    "]\n",
    "\n",
    "\n",
    "for dict in df_dicts:\n",
    "    print(f'Training with {dict[\"name\"]} cells... \\n')\n",
    "    # Use this if using coordinates as separate columns\n",
    "    # coords, X, y = data['dataframe'].iloc[:, :2], data['dataframe'].iloc[:, 2:-1], data['dataframe'].iloc[:, [-1]]\n",
    "    # data['coords'] = coords\n",
    "    \n",
    "    # Use this if using coordinates as indices\n",
    "    X, y = dict['dataframe'].iloc[:, 0:-1], dict['dataframe'].iloc[:, [-1]], \n",
    "\n",
    "    dict['X'] = standardise(X)\n",
    "    dict['y'] = y\n",
    "    dict['kbest'] = feature_select(dict['X'], dict['y'])\n",
    "\n",
    "    # dict['X'] = dict['kbest']['10'].transform(dict['X'])\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(dict['X'], dict['y'], random_state=seed)\n",
    "    dict['X_train'], dict['X_test'], dict['y_train'], dict['y_test'] = X_train, X_test, y_train, y_test # for debugging purposes\n",
    "\n",
    "    dict['X_smote'], dict['y_smote'] = oversample(X_train, y_train)\n",
    "\n",
    "    stack_clf = StackingClassifier(\n",
    "        estimators=estimators, \n",
    "        final_estimator=GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=None, max_depth=2, random_state=seed)\n",
    "    )\n",
    "\n",
    "    # Classifier without SMOTE\n",
    "    stack_clf.fit(dict['X_train'], dict['y_train'])\n",
    "    y_pred = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions'] = y_pred\n",
    "    dict['report'] = classification_report(y_test, y_pred, output_dict=True)\n",
    "    \n",
    "\n",
    "    # Classifier with SMOTE\n",
    "    stack_clf.fit(dict['X_smote'], dict['y_smote'])\n",
    "    y_pred_smote = stack_clf.predict(X_test)\n",
    "    \n",
    "    dict['predictions_smote'] = y_pred_smote\n",
    "    dict['report_smote'] = classification_report(y_test, y_pred_smote, output_dict=True)\n",
    "    \n",
    "    print(f'{dict[\"name\"]} Classification Report: \\n {json.dumps(dict[\"report\"], indent=4)} \\n')\n",
    "    print(f'{dict[\"name\"]} SMOTE Classification Report: \\n {json.dumps(dict[\"report_smote\"], indent=4)} \\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9829886182841757,\n",
      "        \"recall\": 0.9941824483228122,\n",
      "        \"f1-score\": 0.9885538461538461,\n",
      "        \"support\": 8079\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.53,\n",
      "        \"recall\": 0.2760416666666667,\n",
      "        \"f1-score\": 0.363013698630137,\n",
      "        \"support\": 192\n",
      "    },\n",
      "    \"accuracy\": 0.9775117881755532,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7564943091420879,\n",
      "        \"recall\": 0.6351120574947394,\n",
      "        \"f1-score\": 0.6757837723919915,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9724731044756204,\n",
      "        \"recall\": 0.9775117881755532,\n",
      "        \"f1-score\": 0.9740327836070498,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Canada Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.935126884182408,\n",
      "        \"recall\": 0.8590709903593339,\n",
      "        \"f1-score\": 0.8954869358669834,\n",
      "        \"support\": 5705\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7346534653465346,\n",
      "        \"recall\": 0.8674980514419329,\n",
      "        \"f1-score\": 0.7955682630450321,\n",
      "        \"support\": 2566\n",
      "    },\n",
      "    \"accuracy\": 0.8616854068431871,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8348901747644712,\n",
      "        \"recall\": 0.8632845209006335,\n",
      "        \"f1-score\": 0.8455275994560078,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8729318905017344,\n",
      "        \"recall\": 0.8616854068431871,\n",
      "        \"f1-score\": 0.864488106890907,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Egyptian Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9795518550263255,\n",
      "        \"recall\": 0.9899764880584087,\n",
      "        \"f1-score\": 0.9847365829640572,\n",
      "        \"support\": 8081\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.22115384615384615,\n",
      "        \"recall\": 0.12105263157894737,\n",
      "        \"f1-score\": 0.1564625850340136,\n",
      "        \"support\": 190\n",
      "    },\n",
      "    \"accuracy\": 0.9700157175674042,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6003528505900858,\n",
      "        \"recall\": 0.555514559818678,\n",
      "        \"f1-score\": 0.5705995839990354,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9621300654379116,\n",
      "        \"recall\": 0.9700157175674042,\n",
      "        \"f1-score\": 0.9657096140840296,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Gadwall 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9579474342928661,\n",
      "        \"recall\": 0.9906808180170852,\n",
      "        \"f1-score\": 0.9740391957241028,\n",
      "        \"support\": 7726\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.7437722419928826,\n",
      "        \"recall\": 0.3834862385321101,\n",
      "        \"f1-score\": 0.5060532687651331,\n",
      "        \"support\": 545\n",
      "    },\n",
      "    \"accuracy\": 0.9506710192237939,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8508598381428744,\n",
      "        \"recall\": 0.6870835282745976,\n",
      "        \"f1-score\": 0.740046232244618,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.943834814319043,\n",
      "        \"recall\": 0.9506710192237939,\n",
      "        \"f1-score\": 0.9432022557902813,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Goshawk 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9871732817037754,\n",
      "        \"recall\": 0.9991426821800368,\n",
      "        \"f1-score\": 0.9931219185586463,\n",
      "        \"support\": 8165\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 106\n",
      "    },\n",
      "    \"accuracy\": 0.9863378067948253,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.4935866408518877,\n",
      "        \"recall\": 0.4995713410900184,\n",
      "        \"f1-score\": 0.49656095927932314,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9745218045111022,\n",
      "        \"recall\": 0.9863378067948253,\n",
      "        \"f1-score\": 0.9803942044530706,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Grey Partridge 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9372121212121212,\n",
      "        \"recall\": 0.998579362004391,\n",
      "        \"f1-score\": 0.9669230288251109,\n",
      "        \"support\": 7743\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.47619047619047616,\n",
      "        \"recall\": 0.01893939393939394,\n",
      "        \"f1-score\": 0.03642987249544627,\n",
      "        \"support\": 528\n",
      "    },\n",
      "    \"accuracy\": 0.9360415911014388,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7067012987012986,\n",
      "        \"recall\": 0.5087593779718925,\n",
      "        \"f1-score\": 0.5016764506602787,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9077816498578196,\n",
      "        \"recall\": 0.9360415911014388,\n",
      "        \"f1-score\": 0.9075226677391404,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Indian Peafowl 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9909222948438635,\n",
      "        \"recall\": 0.9989019033674963,\n",
      "        \"f1-score\": 0.994896099161502,\n",
      "        \"support\": 8196\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 75\n",
      "    },\n",
      "    \"accuracy\": 0.9898440333696047,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.49546114742193176,\n",
      "        \"recall\": 0.49945095168374815,\n",
      "        \"f1-score\": 0.497448049580751,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9819367825583732,\n",
      "        \"recall\": 0.9898440333696047,\n",
      "        \"f1-score\": 0.9858745531045424,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Little Owl 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.935244161358811,\n",
      "        \"recall\": 0.9533342350872447,\n",
      "        \"f1-score\": 0.9442025587782168,\n",
      "        \"support\": 7393\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5306122448979592,\n",
      "        \"recall\": 0.44419134396355353,\n",
      "        \"f1-score\": 0.48357098574085555,\n",
      "        \"support\": 878\n",
      "    },\n",
      "    \"accuracy\": 0.8992866642485794,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7329282031283851,\n",
      "        \"recall\": 0.6987627895253992,\n",
      "        \"f1-score\": 0.7138867722595361,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8922908518856365,\n",
      "        \"recall\": 0.8992866642485794,\n",
      "        \"f1-score\": 0.8953046599598389,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Mandarin Duck 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9761353951053209,\n",
      "        \"recall\": 0.9965195773772529,\n",
      "        \"f1-score\": 0.9862221675482838,\n",
      "        \"support\": 8045\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5172413793103449,\n",
      "        \"recall\": 0.13274336283185842,\n",
      "        \"f1-score\": 0.21126760563380284,\n",
      "        \"support\": 226\n",
      "    },\n",
      "    \"accuracy\": 0.9729174223189457,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7466883872078329,\n",
      "        \"recall\": 0.5646314701045557,\n",
      "        \"f1-score\": 0.5987448865910433,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.963596397696342,\n",
      "        \"recall\": 0.9729174223189457,\n",
      "        \"f1-score\": 0.9650470096480696,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Mute Swan 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9216121866074262,\n",
      "        \"recall\": 0.8446771378708552,\n",
      "        \"f1-score\": 0.8814691151919867,\n",
      "        \"support\": 3438\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.895703125,\n",
      "        \"recall\": 0.9488930271053176,\n",
      "        \"f1-score\": 0.9215311966241334,\n",
      "        \"support\": 4833\n",
      "    },\n",
      "    \"accuracy\": 0.9055736912102527,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.9086576558037132,\n",
      "        \"recall\": 0.8967850824880864,\n",
      "        \"f1-score\": 0.90150015590806,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9064727240577115,\n",
      "        \"recall\": 0.9055736912102527,\n",
      "        \"f1-score\": 0.9048786230582139,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pheasant 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8981844589687727,\n",
      "        \"recall\": 0.9068778413257076,\n",
      "        \"f1-score\": 0.9025102159953299,\n",
      "        \"support\": 6819\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5418470418470418,\n",
      "        \"recall\": 0.5172176308539945,\n",
      "        \"f1-score\": 0.5292459478505989,\n",
      "        \"support\": 1452\n",
      "    },\n",
      "    \"accuracy\": 0.8384717688308548,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7200157504079072,\n",
      "        \"recall\": 0.7120477360898511,\n",
      "        \"f1-score\": 0.7158780819229644,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8356283073957159,\n",
      "        \"recall\": 0.8384717688308548,\n",
      "        \"f1-score\": 0.8369825026177274,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pink-footed Goose 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9385822847888377,\n",
      "        \"recall\": 0.9893630991464215,\n",
      "        \"f1-score\": 0.9633039253292418,\n",
      "        \"support\": 7615\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.6680327868852459,\n",
      "        \"recall\": 0.24847560975609756,\n",
      "        \"f1-score\": 0.3622222222222223,\n",
      "        \"support\": 656\n",
      "    },\n",
      "    \"accuracy\": 0.9306008946922983,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.8033075358370418,\n",
      "        \"recall\": 0.6189193544512596,\n",
      "        \"f1-score\": 0.6627630737757321,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9171241212506008,\n",
      "        \"recall\": 0.9306008946922983,\n",
      "        \"f1-score\": 0.9156301740007199,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pintail 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9794728531519494,\n",
      "        \"recall\": 0.9969093831128694,\n",
      "        \"f1-score\": 0.9881142016909692,\n",
      "        \"support\": 8089\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.34210526315789475,\n",
      "        \"recall\": 0.07142857142857142,\n",
      "        \"f1-score\": 0.11818181818181817,\n",
      "        \"support\": 182\n",
      "    },\n",
      "    \"accuracy\": 0.9765445532583726,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6607890581549221,\n",
      "        \"recall\": 0.5341689772707204,\n",
      "        \"f1-score\": 0.5531480099363937,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9654478378721866,\n",
      "        \"recall\": 0.9765445532583726,\n",
      "        \"f1-score\": 0.9689716924661276,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Pochard 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.968296224588577,\n",
      "        \"recall\": 0.9991259832688226,\n",
      "        \"f1-score\": 0.9834695507896516,\n",
      "        \"support\": 8009\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.0,\n",
      "        \"recall\": 0.0,\n",
      "        \"f1-score\": 0.0,\n",
      "        \"support\": 262\n",
      "    },\n",
      "    \"accuracy\": 0.9674767259098054,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.4841481122942885,\n",
      "        \"recall\": 0.4995629916344113,\n",
      "        \"f1-score\": 0.4917347753948258,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9376235597545536,\n",
      "        \"recall\": 0.9674767259098054,\n",
      "        \"f1-score\": 0.9523162413582782,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Red-legged Partridge 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9180307842658197,\n",
      "        \"recall\": 0.9929968287526427,\n",
      "        \"f1-score\": 0.9540434175447505,\n",
      "        \"support\": 7568\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.3764705882352941,\n",
      "        \"recall\": 0.04551920341394026,\n",
      "        \"f1-score\": 0.08121827411167512,\n",
      "        \"support\": 703\n",
      "    },\n",
      "    \"accuracy\": 0.9124652399951638,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6472506862505569,\n",
      "        \"recall\": 0.5192580160832915,\n",
      "        \"f1-score\": 0.5176308458282128,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8720004592979248,\n",
      "        \"recall\": 0.9124652399951638,\n",
      "        \"f1-score\": 0.8798569738458686,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Ring-necked Parakeet 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9898652824125571,\n",
      "        \"recall\": 0.9830612495397079,\n",
      "        \"f1-score\": 0.9864515334400787,\n",
      "        \"support\": 8147\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.23333333333333334,\n",
      "        \"recall\": 0.3387096774193548,\n",
      "        \"f1-score\": 0.2763157894736842,\n",
      "        \"support\": 124\n",
      "    },\n",
      "    \"accuracy\": 0.973401039777536,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6115993078729453,\n",
      "        \"recall\": 0.6608854634795314,\n",
      "        \"f1-score\": 0.6313836614568815,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9785232485973203,\n",
      "        \"recall\": 0.973401039777536,\n",
      "        \"f1-score\": 0.9758050780837938,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Rock Dove 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.8963236229487342,\n",
      "        \"recall\": 0.9767119489880788,\n",
      "        \"f1-score\": 0.9347927031509121,\n",
      "        \"support\": 7214\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5902439024390244,\n",
      "        \"recall\": 0.22894985808893092,\n",
      "        \"f1-score\": 0.3299250170415814,\n",
      "        \"support\": 1057\n",
      "    },\n",
      "    \"accuracy\": 0.8811510095514448,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7432837626938793,\n",
      "        \"recall\": 0.6028309035385049,\n",
      "        \"f1-score\": 0.6323588600962468,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.8572078854830393,\n",
      "        \"recall\": 0.8811510095514448,\n",
      "        \"f1-score\": 0.8574930846987826,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Ruddy Duck 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9976944545564859,\n",
      "        \"recall\": 0.9976944545564859,\n",
      "        \"f1-score\": 0.9976944545564859,\n",
      "        \"support\": 8241\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.36666666666666664,\n",
      "        \"recall\": 0.36666666666666664,\n",
      "        \"f1-score\": 0.36666666666666664,\n",
      "        \"support\": 30\n",
      "    },\n",
      "    \"accuracy\": 0.9954056341433926,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.6821805606115763,\n",
      "        \"recall\": 0.6821805606115763,\n",
      "        \"f1-score\": 0.6821805606115763,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9954056341433926,\n",
      "        \"recall\": 0.9954056341433926,\n",
      "        \"f1-score\": 0.9954056341433926,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Whooper Swan 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9686478634547876,\n",
      "        \"recall\": 0.9990012484394507,\n",
      "        \"f1-score\": 0.983590436973757,\n",
      "        \"support\": 8010\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.2,\n",
      "        \"recall\": 0.007662835249042145,\n",
      "        \"f1-score\": 0.014760147601476014,\n",
      "        \"support\": 261\n",
      "    },\n",
      "    \"accuracy\": 0.9677185346391005,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.5843239317273938,\n",
      "        \"recall\": 0.5033320418442464,\n",
      "        \"f1-score\": 0.49917529228761653,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9443923813653546,\n",
      "        \"recall\": 0.9677185346391005,\n",
      "        \"f1-score\": 0.9530179904103228,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n",
      "Wigeon 1km \n",
      " {\n",
      "    \"0\": {\n",
      "        \"precision\": 0.9347108448465583,\n",
      "        \"recall\": 0.9946656258131668,\n",
      "        \"f1-score\": 0.9637566971320518,\n",
      "        \"support\": 7686\n",
      "    },\n",
      "    \"1\": {\n",
      "        \"precision\": 0.5543478260869565,\n",
      "        \"recall\": 0.08717948717948718,\n",
      "        \"f1-score\": 0.15066469719350073,\n",
      "        \"support\": 585\n",
      "    },\n",
      "    \"accuracy\": 0.9304799903276508,\n",
      "    \"macro avg\": {\n",
      "        \"precision\": 0.7445293354667575,\n",
      "        \"recall\": 0.5409225564963269,\n",
      "        \"f1-score\": 0.5572106971627763,\n",
      "        \"support\": 8271\n",
      "    },\n",
      "    \"weighted avg\": {\n",
      "        \"precision\": 0.9078081286122014,\n",
      "        \"recall\": 0.9304799903276508,\n",
      "        \"f1-score\": 0.9062474697152881,\n",
      "        \"support\": 8271\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "for dict in df_dicts:\n",
    "    print(dict['name'], '\\n', json.dumps(dict['report'], indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Labels</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Precision (Smote)</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Recall (Smote)</th>\n",
       "      <th>F1</th>\n",
       "      <th>F1 (Smote)</th>\n",
       "      <th>Occurrence Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mute Swan 1km</td>\n",
       "      <td>0.895703</td>\n",
       "      <td>0.906792</td>\n",
       "      <td>0.948893</td>\n",
       "      <td>0.919926</td>\n",
       "      <td>0.921531</td>\n",
       "      <td>0.913311</td>\n",
       "      <td>19124</td>\n",
       "      <td>0.578044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada Goose 1km</td>\n",
       "      <td>0.734653</td>\n",
       "      <td>0.742340</td>\n",
       "      <td>0.867498</td>\n",
       "      <td>0.783710</td>\n",
       "      <td>0.795568</td>\n",
       "      <td>0.762464</td>\n",
       "      <td>10147</td>\n",
       "      <td>0.306704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pheasant 1km</td>\n",
       "      <td>0.541847</td>\n",
       "      <td>0.504274</td>\n",
       "      <td>0.517218</td>\n",
       "      <td>0.406336</td>\n",
       "      <td>0.529246</td>\n",
       "      <td>0.450038</td>\n",
       "      <td>5855</td>\n",
       "      <td>0.176974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rock Dove 1km</td>\n",
       "      <td>0.590244</td>\n",
       "      <td>0.445652</td>\n",
       "      <td>0.228950</td>\n",
       "      <td>0.349101</td>\n",
       "      <td>0.329925</td>\n",
       "      <td>0.391512</td>\n",
       "      <td>3919</td>\n",
       "      <td>0.118456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Little Owl 1km</td>\n",
       "      <td>0.530612</td>\n",
       "      <td>0.472643</td>\n",
       "      <td>0.444191</td>\n",
       "      <td>0.462415</td>\n",
       "      <td>0.483571</td>\n",
       "      <td>0.467473</td>\n",
       "      <td>3548</td>\n",
       "      <td>0.107242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Red-legged Partridge 1km</td>\n",
       "      <td>0.376471</td>\n",
       "      <td>0.348584</td>\n",
       "      <td>0.045519</td>\n",
       "      <td>0.227596</td>\n",
       "      <td>0.081218</td>\n",
       "      <td>0.275387</td>\n",
       "      <td>2953</td>\n",
       "      <td>0.089258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pink-footed Goose 1km</td>\n",
       "      <td>0.668033</td>\n",
       "      <td>0.474074</td>\n",
       "      <td>0.248476</td>\n",
       "      <td>0.390244</td>\n",
       "      <td>0.362222</td>\n",
       "      <td>0.428094</td>\n",
       "      <td>2646</td>\n",
       "      <td>0.079978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wigeon 1km</td>\n",
       "      <td>0.554348</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.087179</td>\n",
       "      <td>0.239316</td>\n",
       "      <td>0.150665</td>\n",
       "      <td>0.230453</td>\n",
       "      <td>2317</td>\n",
       "      <td>0.070034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gadwall 1km</td>\n",
       "      <td>0.743772</td>\n",
       "      <td>0.533605</td>\n",
       "      <td>0.383486</td>\n",
       "      <td>0.480734</td>\n",
       "      <td>0.506053</td>\n",
       "      <td>0.505792</td>\n",
       "      <td>2205</td>\n",
       "      <td>0.066649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Grey Partridge 1km</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.322485</td>\n",
       "      <td>0.018939</td>\n",
       "      <td>0.206439</td>\n",
       "      <td>0.036430</td>\n",
       "      <td>0.251732</td>\n",
       "      <td>2123</td>\n",
       "      <td>0.064170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pochard 1km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143939</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.072519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096447</td>\n",
       "      <td>1057</td>\n",
       "      <td>0.031949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mandarin Duck 1km</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.356643</td>\n",
       "      <td>0.132743</td>\n",
       "      <td>0.225664</td>\n",
       "      <td>0.211268</td>\n",
       "      <td>0.276423</td>\n",
       "      <td>1010</td>\n",
       "      <td>0.030528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Whooper Swan 1km</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.145594</td>\n",
       "      <td>0.014760</td>\n",
       "      <td>0.115152</td>\n",
       "      <td>955</td>\n",
       "      <td>0.028866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Egyptian Goose 1km</td>\n",
       "      <td>0.221154</td>\n",
       "      <td>0.492754</td>\n",
       "      <td>0.121053</td>\n",
       "      <td>0.357895</td>\n",
       "      <td>0.156463</td>\n",
       "      <td>0.414634</td>\n",
       "      <td>863</td>\n",
       "      <td>0.026085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barnacle Goose 1km</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.372414</td>\n",
       "      <td>0.276042</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.363014</td>\n",
       "      <td>0.320475</td>\n",
       "      <td>769</td>\n",
       "      <td>0.023244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pintail 1km</td>\n",
       "      <td>0.342105</td>\n",
       "      <td>0.060241</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.137363</td>\n",
       "      <td>0.118182</td>\n",
       "      <td>0.083752</td>\n",
       "      <td>697</td>\n",
       "      <td>0.021068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ring-necked Parakeet 1km</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.448276</td>\n",
       "      <td>0.338710</td>\n",
       "      <td>0.314516</td>\n",
       "      <td>0.276316</td>\n",
       "      <td>0.369668</td>\n",
       "      <td>504</td>\n",
       "      <td>0.015234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goshawk 1km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>446</td>\n",
       "      <td>0.013481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Indian Peafowl 1km</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080808</td>\n",
       "      <td>294</td>\n",
       "      <td>0.008886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ruddy Duck 1km</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>124</td>\n",
       "      <td>0.003748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Labels  Precision  Precision (Smote)    Recall  \\\n",
       "9              Mute Swan 1km   0.895703           0.906792  0.948893   \n",
       "1           Canada Goose 1km   0.734653           0.742340  0.867498   \n",
       "10              Pheasant 1km   0.541847           0.504274  0.517218   \n",
       "16             Rock Dove 1km   0.590244           0.445652  0.228950   \n",
       "7             Little Owl 1km   0.530612           0.472643  0.444191   \n",
       "14  Red-legged Partridge 1km   0.376471           0.348584  0.045519   \n",
       "11     Pink-footed Goose 1km   0.668033           0.474074  0.248476   \n",
       "19                Wigeon 1km   0.554348           0.222222  0.087179   \n",
       "3                Gadwall 1km   0.743772           0.533605  0.383486   \n",
       "5         Grey Partridge 1km   0.476190           0.322485  0.018939   \n",
       "13               Pochard 1km   0.000000           0.143939  0.000000   \n",
       "8          Mandarin Duck 1km   0.517241           0.356643  0.132743   \n",
       "18          Whooper Swan 1km   0.200000           0.095238  0.007663   \n",
       "2         Egyptian Goose 1km   0.221154           0.492754  0.121053   \n",
       "0         Barnacle Goose 1km   0.530000           0.372414  0.276042   \n",
       "12               Pintail 1km   0.342105           0.060241  0.071429   \n",
       "15  Ring-necked Parakeet 1km   0.233333           0.448276  0.338710   \n",
       "4                Goshawk 1km   0.000000           0.000000  0.000000   \n",
       "6         Indian Peafowl 1km   0.000000           0.166667  0.000000   \n",
       "17            Ruddy Duck 1km   0.366667           0.312500  0.366667   \n",
       "\n",
       "    Recall (Smote)        F1  F1 (Smote)  Occurrence Count  Percentage  \n",
       "9         0.919926  0.921531    0.913311             19124    0.578044  \n",
       "1         0.783710  0.795568    0.762464             10147    0.306704  \n",
       "10        0.406336  0.529246    0.450038              5855    0.176974  \n",
       "16        0.349101  0.329925    0.391512              3919    0.118456  \n",
       "7         0.462415  0.483571    0.467473              3548    0.107242  \n",
       "14        0.227596  0.081218    0.275387              2953    0.089258  \n",
       "11        0.390244  0.362222    0.428094              2646    0.079978  \n",
       "19        0.239316  0.150665    0.230453              2317    0.070034  \n",
       "3         0.480734  0.506053    0.505792              2205    0.066649  \n",
       "5         0.206439  0.036430    0.251732              2123    0.064170  \n",
       "13        0.072519  0.000000    0.096447              1057    0.031949  \n",
       "8         0.225664  0.211268    0.276423              1010    0.030528  \n",
       "18        0.145594  0.014760    0.115152               955    0.028866  \n",
       "2         0.357895  0.156463    0.414634               863    0.026085  \n",
       "0         0.281250  0.363014    0.320475               769    0.023244  \n",
       "12        0.137363  0.118182    0.083752               697    0.021068  \n",
       "15        0.314516  0.276316    0.369668               504    0.015234  \n",
       "4         0.000000  0.000000    0.000000               446    0.013481  \n",
       "6         0.053333  0.000000    0.080808               294    0.008886  \n",
       "17        0.166667  0.366667    0.217391               124    0.003748  "
      ]
     },
     "execution_count": 15,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'df_dicts_1km_no_fp' (list)\n"
     ]
    }
   ],
   "source": [
    "# Store dictionaries for later use\n",
    "df_dicts_1km_no_fp = df_dicts\n",
    "%store df_dicts_1km_no_fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "                    Occurrence  Predictions\n",
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   ],
   "source": [
    "# Export predictions to CSV for QGIS\n",
    "RESULTS_PATH = 'Datasets/Machine Learning/Results/1km/'\n",
    "for dict in df_dicts:\n",
    "    # Join with y_test datafram\n",
    "    result_df = dict['y_test'] \n",
    "    result_df['Predictions'] = dict['predictions_smote']\n",
    "    display(result_df)\n",
    "    result_df.to_csv(RESULTS_PATH + dict['name'] + '(without Fertiliser+Pesticides).csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barnacle Goose 1km\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>F Score</th>\n",
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       "      <th>25</th>\n",
       "      <td>1586.660696</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
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       "      <th>18</th>\n",
       "      <td>1440.939379</td>\n",
       "      <td>1.143607e-308</td>\n",
       "      <td>Saltmarsh</td>\n",
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       "      <th>23</th>\n",
       "      <td>1417.879628</td>\n",
       "      <td>7.271005e-304</td>\n",
       "      <td>Surface type</td>\n",
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       "      <th>22</th>\n",
       "      <td>1269.611405</td>\n",
       "      <td>6.667737e-273</td>\n",
       "      <td>Cumulative catchment area</td>\n",
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       "      <td>1223.198053</td>\n",
       "      <td>3.502336e-263</td>\n",
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       "      <th>21</th>\n",
       "      <td>1203.742558</td>\n",
       "      <td>4.196966e-259</td>\n",
       "      <td>Elevation</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1078.608288</td>\n",
       "      <td>8.174358e-233</td>\n",
       "      <td>Littoral sediment</td>\n",
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       "    <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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27980.651957</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Surface type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22192.853532</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Outflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>21798.073867</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Inflowing drainage direction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20557.269520</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Elevation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10467.973712</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Cumulative catchment area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Egyptian Goose 1km\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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"
      ]
     },
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    },
    {
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     "text": [
      "Gadwall 1km\n"
     ]
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     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": {
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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>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": {
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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>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",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
       "      <th>Attribute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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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       "<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>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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mandarin Duck 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>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"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mute Swan 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>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"
      ]
     },
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    },
    {
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     "text": [
      "Pheasant 1km\n"
     ]
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       "      <th>F Score</th>\n",
       "      <th>P Value</th>\n",
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       "  <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": [
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       "<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"
      ]
     },
     "metadata": {},
     "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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pochard 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>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": {
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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": {
      "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>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": {
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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>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"
      ]
     },
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    },
    {
     "name": "stdout",
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     "text": [
      "Ruddy 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>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",
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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": {
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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>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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