{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "564dfa7f-55ae-423a-ae59-22ac4f00364f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import StratifiedShuffleSplit, cross_val_score, train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import os\n",
    "import subprocess\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0867074-62b1-4bff-9188-0c6e73f814d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSV Loaded, Dataset shape: (154, 6)\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "df = pd.read_csv(\"csv/ems_data.csv\")\n",
    "print(\"CSV Loaded, Dataset shape:\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e2ebde8-90f1-483a-a3cb-4119878459d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>emsConcentration</th>\n",
       "      <th>soakDuration</th>\n",
       "      <th>lowestTemp</th>\n",
       "      <th>highestTemp</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cotton - TM-1</td>\n",
       "      <td>1.50</td>\n",
       "      <td>180</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hemp - Lembang A</td>\n",
       "      <td>0.25</td>\n",
       "      <td>360</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cotton - TM-1</td>\n",
       "      <td>3.50</td>\n",
       "      <td>360</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hyoscyamus niger - Black Henbane</td>\n",
       "      <td>0.02</td>\n",
       "      <td>60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunflower - Helianthus annuus (BBS-1)</td>\n",
       "      <td>1.00</td>\n",
       "      <td>480</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Maize - Zea mays (B73)</td>\n",
       "      <td>1.20</td>\n",
       "      <td>720</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Hyoscyamus niger - Black Henbane</td>\n",
       "      <td>0.09</td>\n",
       "      <td>60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Hemp - Kumamoto</td>\n",
       "      <td>0.25</td>\n",
       "      <td>360</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Chinese cabbage - Brassica rapa ssp. pekinensis</td>\n",
       "      <td>0.30</td>\n",
       "      <td>480</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Marigold - Tagetes erecta</td>\n",
       "      <td>0.08</td>\n",
       "      <td>360</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             species  emsConcentration  \\\n",
       "0                                      Cotton - TM-1              1.50   \n",
       "1                                   Hemp - Lembang A              0.25   \n",
       "2                                      Cotton - TM-1              3.50   \n",
       "3                   Hyoscyamus niger - Black Henbane              0.02   \n",
       "4              Sunflower - Helianthus annuus (BBS-1)              1.00   \n",
       "..                                               ...               ...   \n",
       "149                           Maize - Zea mays (B73)              1.20   \n",
       "150                 Hyoscyamus niger - Black Henbane              0.09   \n",
       "151                                  Hemp - Kumamoto              0.25   \n",
       "152  Chinese cabbage - Brassica rapa ssp. pekinensis              0.30   \n",
       "153                        Marigold - Tagetes erecta              0.08   \n",
       "\n",
       "     soakDuration  lowestTemp  highestTemp  result  \n",
       "0             180        20.0         32.0       1  \n",
       "1             360        25.0         30.0       0  \n",
       "2             360        20.0         32.0       0  \n",
       "3              60        25.0         28.0       1  \n",
       "4             480        22.0         25.0       0  \n",
       "..            ...         ...          ...     ...  \n",
       "149           720        25.0         30.0       0  \n",
       "150            60        25.0         28.0       0  \n",
       "151           360        25.0         30.0       0  \n",
       "152           480         4.0         20.0       0  \n",
       "153           360        25.0         30.0       0  \n",
       "\n",
       "[154 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9e0823c-72bc-4c8f-959a-bcbd973f2a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chinese cabbage - Brassica rapa ssp. pekinensis</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cotton - Luyanmian21</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cotton - TM-1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fenugreek - Trigonella foenum-graecum</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fodder Barley - Hordeum vulgare</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hemp - Bandung A</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hemp - Indochina</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hemp - Kumamoto</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hemp - Lembang A</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hemp - Seiki Selskin</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hyoscyamus niger - Black Henbane</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jasmine - Jasminum grandiflorum</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Maize - Zea mays (B73)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Marigold - Tagetes erecta</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Marigold - Tagetes patula</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sesame - Tilottama</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sunflower - Helianthus annuus (BBS-1)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Watermelon - G42</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            species  sum\n",
       "0   Chinese cabbage - Brassica rapa ssp. pekinensis    5\n",
       "1                              Cotton - Luyanmian21    8\n",
       "2                                     Cotton - TM-1    8\n",
       "3             Fenugreek - Trigonella foenum-graecum    4\n",
       "4                   Fodder Barley - Hordeum vulgare   25\n",
       "5                                  Hemp - Bandung A    8\n",
       "6                                  Hemp - Indochina    8\n",
       "7                                   Hemp - Kumamoto    8\n",
       "8                                  Hemp - Lembang A    8\n",
       "9                              Hemp - Seiki Selskin    8\n",
       "10                 Hyoscyamus niger - Black Henbane   10\n",
       "11                  Jasmine - Jasminum grandiflorum    4\n",
       "12                           Maize - Zea mays (B73)    4\n",
       "13                        Marigold - Tagetes erecta    9\n",
       "14                        Marigold - Tagetes patula   13\n",
       "15                               Sesame - Tilottama    9\n",
       "16            Sunflower - Helianthus annuus (BBS-1)    6\n",
       "17                                 Watermelon - G42    9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('species').size().reset_index(name='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd25961d-14b9-4818-bd2e-cdd090729839",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"pickles\"):\n",
    "    os.makedirs(\"pickles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebed42cc-3fc6-49c0-9512-f1b40a878ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                        Cotton - TM-1\n",
       "1                                     Hemp - Lembang A\n",
       "2                                        Cotton - TM-1\n",
       "3                     Hyoscyamus niger - Black Henbane\n",
       "4                Sunflower - Helianthus annuus (BBS-1)\n",
       "                            ...                       \n",
       "149                             Maize - Zea mays (B73)\n",
       "150                   Hyoscyamus niger - Black Henbane\n",
       "151                                    Hemp - Kumamoto\n",
       "152    Chinese cabbage - Brassica rapa ssp. pekinensis\n",
       "153                          Marigold - Tagetes erecta\n",
       "Name: species, Length: 154, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d80c362c-21c3-43ae-b3e1-699ce68fba2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label encode the species column\n",
    "le = LabelEncoder()\n",
    "df['species'] = le.fit_transform(df['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef7b15fa-cbf8-4501-88d4-983c3f336555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1       8\n",
       "2       2\n",
       "3      10\n",
       "4      16\n",
       "       ..\n",
       "149    12\n",
       "150    10\n",
       "151     7\n",
       "152     0\n",
       "153    13\n",
       "Name: species, Length: 154, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d9432cb-1b76-4257-a11a-aeda39dc9de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save label encoder\n",
    "with open('pickles/label_encoding.pkl', 'wb') as f:\n",
    "    pickle.dump(le, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23b88731-5bb4-4944-9bef-582147039330",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>soakDuration</th>\n",
       "      <th>lowestTemp</th>\n",
       "      <th>highestTemp</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>360</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
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       "      <th>149</th>\n",
       "      <td>720</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>360</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>480</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>360</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     soakDuration  lowestTemp  highestTemp\n",
       "0             180        20.0         32.0\n",
       "1             360        25.0         30.0\n",
       "2             360        20.0         32.0\n",
       "3              60        25.0         28.0\n",
       "4             480        22.0         25.0\n",
       "..            ...         ...          ...\n",
       "149           720        25.0         30.0\n",
       "150            60        25.0         28.0\n",
       "151           360        25.0         30.0\n",
       "152           480         4.0         20.0\n",
       "153           360        25.0         30.0\n",
       "\n",
       "[154 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_columns = ['soakDuration', 'lowestTemp', 'highestTemp']\n",
    "df[numerical_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "998ed801-8791-46e6-86ef-8652439ea797",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>lowestTemp</th>\n",
       "      <th>highestTemp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.060543</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.164927</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.248434</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.164927</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     soakDuration  lowestTemp  highestTemp\n",
       "0        0.060543        0.68     1.000000\n",
       "1        0.123173        0.88     0.857143\n",
       "2        0.123173        0.68     1.000000\n",
       "3        0.018789        0.88     0.714286\n",
       "4        0.164927        0.76     0.500000\n",
       "..            ...         ...          ...\n",
       "149      0.248434        0.88     0.857143\n",
       "150      0.018789        0.88     0.714286\n",
       "151      0.123173        0.88     0.857143\n",
       "152      0.164927        0.04     0.142857\n",
       "153      0.123173        0.88     0.857143\n",
       "\n",
       "[154 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale numerical features\n",
    "scaler = MinMaxScaler()\n",
    "df[numerical_columns] = scaler.fit_transform(df[numerical_columns])\n",
    "df[numerical_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "750d729d-fcb6-4ac6-b12e-c072656f53c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save scaler\n",
    "with open('pickles/scaler_encoding.pkl', 'wb') as f:\n",
    "    pickle.dump(scaler, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14bc165d-f845-45bf-bc70-19f0e8620d66",
   "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>species</th>\n",
       "      <th>emsConcentration</th>\n",
       "      <th>soakDuration</th>\n",
       "      <th>lowestTemp</th>\n",
       "      <th>highestTemp</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.060543</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.164927</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>12</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.248434</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.018789</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>7</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.164927</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.123173</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     species  emsConcentration  soakDuration  lowestTemp  highestTemp  result\n",
       "0          2              1.50      0.060543        0.68     1.000000       1\n",
       "1          8              0.25      0.123173        0.88     0.857143       0\n",
       "2          2              3.50      0.123173        0.68     1.000000       0\n",
       "3         10              0.02      0.018789        0.88     0.714286       1\n",
       "4         16              1.00      0.164927        0.76     0.500000       0\n",
       "..       ...               ...           ...         ...          ...     ...\n",
       "149       12              1.20      0.248434        0.88     0.857143       0\n",
       "150       10              0.09      0.018789        0.88     0.714286       0\n",
       "151        7              0.25      0.123173        0.88     0.857143       0\n",
       "152        0              0.30      0.164927        0.04     0.142857       0\n",
       "153       13              0.08      0.123173        0.88     0.857143       0\n",
       "\n",
       "[154 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3a563bd-e60d-46aa-9ad9-6ba0e72a82bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare features and target\n",
    "X = df[['species', 'emsConcentration', 'soakDuration', 'lowestTemp', 'highestTemp']]\n",
    "y = df['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2bb98509-dce7-4e3a-8fed-c1a76287036d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dd3a818-c206-4993-be54-f3933c0a3276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize and train the model\n",
    "rf_model = RandomForestClassifier(n_estimators=100, criterion='gini', random_state=42)\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5177b39-502a-4adb-ac59-f0b78ea07759",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "with open('pickles/ems_model.pkl', 'wb') as f:\n",
    "    pickle.dump(rf_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "86ca9871-2dc2-43a0-a9c9-d0fd18604209",
   "metadata": {},
   "outputs": [],
   "source": [
    "sss = StratifiedShuffleSplit(n_splits=10, test_size=0.3, random_state=42)\n",
    "scores = cross_val_score(rf_model, X, y, cv=sss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "214e50eb-8a4a-44b5-98cd-0177318bf51c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82978723, 0.63829787, 0.68085106, 0.70212766, 0.72340426,\n",
       "       0.68085106, 0.74468085, 0.74468085, 0.74468085, 0.68085106])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7089a59e-061a-48e4-913e-9962da369721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.7170212765957447)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83a91ebc-5ccf-4ac6-9ceb-dceb1d0a139b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.050394550137561736)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "664e4879-ce66-4459-b306-874f134942d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy Score: 80.9%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Model Accuracy Score: {round(rf_model.score(X_test, y_test) * 100,1)}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "314a66f8-1d8d-441b-9fcd-c876b43410b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.90      0.85        29\n",
      "           1       0.80      0.67      0.73        18\n",
      "\n",
      "    accuracy                           0.81        47\n",
      "   macro avg       0.81      0.78      0.79        47\n",
      "weighted avg       0.81      0.81      0.80        47\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf_model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "14162b15-6bd1-4ee4-8f32-f9186146b6d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('True Label')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36cb958d-c438-44e0-a25d-dfeb22b500ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Fail rate  Success rate\n",
      "Fail rate            26             3\n",
      "Success rate          6            12\n"
     ]
    }
   ],
   "source": [
    "confusion_df = pd.crosstab(y_test, y_pred, rownames=['Actual'], colnames=['Predicted'])\\\n",
    "    .rename_axis(index={'Actual': 'Actual'}, columns={'Predicted': 'Predicted'})\\\n",
    "    .set_axis(['Fail rate', 'Success rate'], axis=0)\\\n",
    "    .set_axis(['Fail rate', 'Success rate'], axis=1)\n",
    "print(confusion_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27d8e70d-9331-49b1-a51f-a06e66777e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and sort feature importance DataFrame\n",
    "feature_names = ['Species', 'EMS Concentration', 'Soak Duration', 'Lowest Temperature', 'Highest Temperature']\n",
    "feature_importance = pd.DataFrame({\n",
    "    'feature': feature_names,\n",
    "    'importance': rf_model.feature_importances_\n",
    "})\n",
    "feature_importance = feature_importance.sort_values('importance', ascending=False)\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.barplot(\n",
    "    x='importance', \n",
    "    y='feature', \n",
    "    hue='feature',  # Add hue parameter\n",
    "    data=feature_importance, \n",
    "    legend=False    # Hide the legend\n",
    ")\n",
    "\n",
    "# Add value labels on the bars\n",
    "for i, v in enumerate(feature_importance['importance']):\n",
    "    # Format the value to show only 3 decimal places\n",
    "    percentage = f'{v:.3f}'\n",
    "    ax.text(v, i, f' {percentage}', va='center')\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Feature Importance', pad=20, fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Importance Score', fontsize=10)\n",
    "plt.ylabel('Features', fontsize=10)\n",
    "\n",
    "# Adjust layout to prevent text cutoff\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e85837c0-29a2-46d0-abda-d18e735f22d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature Importance Scores:\n",
      "            feature  importance\n",
      "  EMS Concentration    0.458966\n",
      "            Species    0.229847\n",
      "      Soak Duration    0.205221\n",
      " Lowest Temperature    0.053902\n",
      "Highest Temperature    0.052065\n"
     ]
    }
   ],
   "source": [
    "# Print tabular format for reference\n",
    "print(\"\\nFeature Importance Scores:\")\n",
    "print(feature_importance.to_string(index=False))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}