Fire_in_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Area</th>\n",
       "      <th>Oxygen</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Fire Occurrence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jharkand</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>de</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>asd</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>r</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>t</td>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>y</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jharkand</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>60</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>canada</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>c</td>\n",
       "      <td>25</td>\n",
       "      <td>56</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>c</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>asd</td>\n",
       "      <td>28</td>\n",
       "      <td>43</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>asd</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ad</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>sasd</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>asd</td>\n",
       "      <td>56</td>\n",
       "      <td>15</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>asdas</td>\n",
       "      <td>67</td>\n",
       "      <td>45</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>das</td>\n",
       "      <td>78</td>\n",
       "      <td>49</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>das</td>\n",
       "      <td>89</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ads</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ads</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>asd</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>60</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Area  Oxygen  Temperature  Humidity  Fire Occurrence\n",
       "0    Jharkand      40           45        20                1\n",
       "1   Bangalore      50           30        10                1\n",
       "2     Ecuador      10           20        70                0\n",
       "3           a      60           45        70                1\n",
       "4   Bangalore      30           48        10                1\n",
       "5           c      50           15        30                0\n",
       "6          de       5           35        35                0\n",
       "7         asd       5           20        70                0\n",
       "8     Ecuador      60           32        19                1\n",
       "9           r      30           50        45                1\n",
       "10          t      40           55        80                1\n",
       "11          y      30           39       100                0\n",
       "12   Jharkand      10           12        12                0\n",
       "13  Bangalore      20           23        23                0\n",
       "14    Ecuador      60           34        15                1\n",
       "15     canada      80            4        68                0\n",
       "16          c      25           56        79                1\n",
       "17          c      12           45        90                0\n",
       "18        asd      28           43        15                1\n",
       "19        asd      26            0        32                0\n",
       "20         ad      32           37        21                1\n",
       "21       sasd      45           48        12                1\n",
       "22        asd      56           15        65                0\n",
       "23      asdas      67           45        33                1\n",
       "24        das      78           49         8                1\n",
       "25        das      89           45        18                1\n",
       "26        ads      21           12        44                0\n",
       "27        ads      11           20        59                0\n",
       "28        asd       1           32        70                0\n",
       "29        NaN       5           20        45                0\n",
       "30        NaN      42           43        20                1\n",
       "31        NaN      32           60        30                1\n",
       "32        NaN       5           15         5                0\n",
       "33        NaN      20           30         5                0\n",
       "34        NaN      30           51         2                1\n",
       "35        NaN      60           60        10                1\n",
       "36        NaN      10           10        60                0\n",
       "37        NaN       0           30         0                0\n",
       "38        NaN      45           45        15                1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Forest_fire.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39 entries, 0 to 38\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Area             29 non-null     object\n",
      " 1   Oxygen           39 non-null     int64 \n",
      " 2   Temperature      39 non-null     int64 \n",
      " 3   Humidity         39 non-null     int64 \n",
      " 4   Fire Occurrence  39 non-null     int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 1.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Oxygen</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Fire Occurrence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Oxygen  Temperature  Humidity  Fire Occurrence\n",
       "0      40           45        20                1\n",
       "1      50           30        10                1\n",
       "2      10           20        70                0\n",
       "3      60           45        70                1\n",
       "4      30           48        10                1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_data=df.drop('Area',axis=1)\n",
    "f_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here to balance the feature I estimated some with  their nearby values,so lame :-|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_data['Humidity'].replace(to_replace=10,value=8,inplace=True)\n",
    "f_data['Humidity'].replace(to_replace=20,value=18,inplace=True)\n",
    "f_data['Humidity'].replace(to_replace=(32,35),value=33,inplace=True)\n",
    "f_data['Humidity'].replace(to_replace=79,value=80,inplace=True)\n",
    "f_data['Oxygen'].replace(to_replace=30,value=32,inplace=True)\n",
    "f_data['Oxygen'].replace(to_replace=40,value=42,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(f_data['Oxygen'].value_counts(),f_data['Temperature'].value_counts(),f_data['Humidity'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=plt.hist(f_data['Oxygen'],bins=30)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.boxplot(f_data['Temperature'])\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.boxplot(f_data['Humidity'])\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.boxplot(f_data['Oxygen'])\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Small dataset like this often overfits due to outliers and thus here no outliers present outside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=plt.hist(f_data['Temperature'],bins=30)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=plt.hist(f_data['Humidity'],bins=30)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    39\n",
       "Name: Oxygen, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_data['Oxygen'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.scatterplot(x='Oxygen',y='Temperature',data=f_data)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.countplot(x='Temperature',hue='Fire Occurrence',data=f_data)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEGCAYAAABlxeIAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAfjUlEQVR4nO3df5RVdb3/8edLQAdUNHX6pg7ToNIPlR/CgKZmLikk8kI/9CuuivDHokxS+6b3q9fvIqVV65bdvDd0aZSmmJGpqejSUjMqUxFQBBRTTK+MehWhxJ/gDO/vH/szdBjOObMH2HOAeT3WmjV77/N57/0+Z++z32f/VkRgZma2U60TMDOzbYMLgpmZAS4IZmaWuCCYmRnggmBmZknvWifQVfvss080NTXVOg0zs+3KwoULX4uI+mpttruC0NTUxIIFC2qdhpnZdkXSf3fWxruMzMwMcEEwM7PEBcHMzIDt8BiC9SzvvfceLS0tvPvuu7VOZYdSV1dHQ0MDffr0qXUqtg1xQbBtWktLC7vvvjtNTU1IqnU6O4SIYNWqVbS0tDBw4MBap2PbEO8ysm3au+++y9577+1isBVJYu+99/ZWl22i8IIgqZekxyTdWea1XSTdKGm5pHmSmorOx7Y/LgZbnz9TK6c7thDOAZZVeO104O8RcRBwGfD9bsjHzMzKKLQgSGoAPgP8rEKTCcB1qftmYLT808U60atXL4YNG7bh7/nnn2fBggWcffbZWzTelpYWJkyYwKBBgzjwwAM555xzWLdu3VbK2mzbV/RB5f8E/hXYvcLr+wMrACKiVdLrwN7Aa6WNJE0BpgA0NjYWlmytvDB9cMXXGqct6cZMtg99+/Zl0aJFGw1ramqiubl5k7atra307t35Yh4RfP7zn+fMM8/k9ttvp62tjSlTpnDRRRdx6aWXbrXcS7W1tdGrV6+K/WbdrbAtBEknAK9GxMJqzcoM2+QRbhExMyKaI6K5vr7qrTish5o7dy4nnHACABdffDFTpkxhzJgxTJo0iba2Ns4//3xGjhzJkCFD+MlPfrJJ/P33309dXR2nnnoqkG2FXHbZZVxzzTW8/fbbtLW1cd555zF48GCGDBnCjBkzAJg/fz5HHnkkQ4cOZdSoUbzxxhtce+21TJ06dcO4TzjhBObOnQvAbrvtxrRp0zj88MN56KGHaGpqYvr06Rx99NHcdNNNPPvss4wdO5YRI0bw8Y9/nKeeegqAyZMnc/bZZ3PkkUdywAEHcPPNN28Y/w9+8AMGDx7M0KFDueCCCwAqjsesmiK3EI4CxksaB9QB/SX9IiK+VNKmBRgAtEjqDewBrC4wJ9sBvPPOOwwbNgyAgQMHcuutt27SZuHChTzwwAP07duXmTNnssceezB//nzWrl3LUUcdxZgxYzY65fKJJ55gxIgRG42jf//+NDY2snz5cv7yl7/w3HPP8dhjj9G7d29Wr17NunXrOPnkk7nxxhsZOXIka9asoW/fvlVzf+uttzj00EOZPn36hmF1dXU88MADAIwePZqrrrqKQYMGMW/ePL7+9a9z//33A/Dyyy/zwAMP8NRTTzF+/HhOPPFE7r77bm677TbmzZtHv379WL06+/pMmTKl4njMKimsIETEhcCFAJKOBc7rUAwA5gBfAR4CTgTuDz/k2TpRbpdRR+PHj9+wcr7nnntYvHjxhl/Vr7/+Os8888xGBSEiyp550z78vvvu42tf+9qG3U977bUXS5YsYd9992XkyJFAVkA606tXL77whS9sNOzkk08G4M033+TBBx/kpJNO2vDa2rVrN3R/9rOfZaedduLggw/mlVdeAeC+++7j1FNPpV+/fhvy6mw8ZpV0+4VpkqYDCyJiDnA1cL2k5WRbBhO7Ox/bMe26664buiOCGTNmcPzxx1dsf8ghh3DLLbdsNGzNmjWsWLGCAw88sGzBqFREevfuzfr16zf0l57vX1dXt8lxgvZc169fz5577lmx2O2yyy4bTbtSDp2Nx6ySbrkwLSLmRsQJqXtaKgZExLsRcVJEHBQRoyLib92Rj/Usxx9/PFdeeSXvvfceAE8//TRvvfXWRm1Gjx7N22+/zaxZs4DsAO+3vvUtJk+eTL9+/RgzZgxXXXUVra2tAKxevZqPfOQjvPTSS8yfPx+AN954g9bWVpqamli0aBHr169nxYoVPPLII7ny7N+/PwMHDuSmm24CspX9448/XjVmzJgxG45ztOe1OeMxA1+pbD3AGWecwcEHH8zw4cM59NBD+epXv7phxd5OErfeeis33XQTgwYN4kMf+hB1dXV873vf2zCOxsZGhgwZwtChQ/nlL3/JzjvvzI033sg3vvENhg4dyqc+9SneffddjjrqKAYOHMjgwYM577zzGD58eO5cb7jhBq6++mqGDh3KIYccwu233161/dixYxk/fjzNzc0MGzaMH/7wh5s1HjMAbW+77Jubm2NHe0COTzutbNmyZXz0ox+tdRo7JH+2PYukhRGx6bnZJbyFYGZmgAuCmZklLghmZga4IJiZWeKCYGZmgAuCmZklfoSmGTDi/FlbdXwLL52Uq91vf/tbzjnnHNra2jjjjDM23JzOrBa8hWBWI21tbZx11lncfffdPPnkk8yePZsnn3yy1mlZD+aCYFYjjzzyCAcddBAHHHAAO++8MxMnTvQVxVZTLghmNfLiiy8yYMCADf0NDQ28+OKLNczIejoXBLMaKXfbGD9B1mrJBcGsRhoaGlixYsWG/paWFvbbb78aZmQ9nQuCWY2MHDmSZ555hueee45169bxq1/9ivHjx9c6LevBfNqpGflPE92aevfuzeWXX87xxx9PW1sbp512Gocccki352HWrrCCIKkO+BOwS5rOzRHx7Q5tJgOXAu1H0i6PiJ8VlZPZtmbcuHGMGzeu1mmYAcVuIawFjouINyX1AR6QdHdEPNyh3Y0RMbXAPMzMLIfCCkJkp1C8mXr7pL/t62k8ZmY9SKEHlSX1krQIeBW4NyLmlWn2BUmLJd0saUCZ183MrBsUWhAioi0ihgENwChJh3ZocgfQFBFDgPuA68qNR9IUSQskLVi5cmWRKZuZ9VjdctppRPwDmAuM7TB8VUSsTb0/BUZUiJ8ZEc0R0VxfX19ormZmPVVhBUFSvaQ9U3df4JPAUx3a7FvSOx5YVlQ+ZmZWXZFnGe0LXCepF1nh+XVE3ClpOrAgIuYAZ0saD7QCq4HJBeZjVtEL0wdv1fE1TluSq91pp53GnXfeyfvf/36WLl26VXMw66oizzJaDBxWZvi0ku4LgQuLysFsWzd58mSmTp3KpEndf2GcWUe+dYVZDR1zzDHstddetU7DDHBBMDOzxAXBzMwAFwQzM0tcEMzMDPDtr82A/KeJbm2nnHIKc+fO5bXXXqOhoYFLLrmE008/vSa5mLkgmNXQ7Nmza52C2QbeZWRmZoALgpmZJS4Its3LHq1hW5M/UyvHBcG2aXV1daxatcorsK0oIli1ahV1dXW1TsW2MT6obNu0hoYGWlpa8HMwtq66ujoaGhpqnYZtY1wQbJvWp08fBg4cWOs0zHoE7zIyMzPABcHMzBIXBDMzA1wQzMwsKfKZynWSHpH0uKQnJF1Sps0ukm6UtFzSPElNReVjZmbVFbmFsBY4LiKGAsOAsZKO6NDmdODvEXEQcBnw/QLzMTOzKgorCJF5M/X2SX8dry6aAFyXum8GRktSUTmZmVllhV6HIKkXsBA4CLgiIuZ1aLI/sAIgIlolvQ7sDbzWYTxTgCkAjY2NRabc47wwfXDZ4XluB70lsWbbghHnzyo7fOGlk7o5k21DoQeVI6ItIoYBDcAoSYd2aFJua2CTexRExMyIaI6I5vr6+iJSNTPr8brlLKOI+AcwFxjb4aUWYACApN7AHsDq7sjJzMw2VuRZRvWS9kzdfYFPAk91aDYH+ErqPhG4P3wXMzOzmijyGMK+wHXpOMJOwK8j4k5J04EFETEHuBq4XtJysi2DiQXmY2ZmVRRWECJiMXBYmeHTSrrfBU4qKgczM8vPVyqbmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRnggmBmZokLgpmZAS4IZmaWuCCYmRlQ7DOVB0j6g6Rlkp6QdE6ZNsdKel3SovQ3rdy4zMyseEU+U7kV+FZEPCppd2ChpHsj4skO7f4cEScUmIeZmeVQ2BZCRLwcEY+m7jeAZcD+RU3PzMy2TLccQ5DUBBwGzCvz8sckPS7pbkmHVIifImmBpAUrV64sMFMzs56r8IIgaTfgFuDciFjT4eVHgQ9GxFBgBnBbuXFExMyIaI6I5vr6+mITNjProQotCJL6kBWDGyLiNx1fj4g1EfFm6r4L6CNpnyJzMjOz8oo8y0jA1cCyiPhRhTYfSO2QNCrls6qonMzMrLIizzI6CvgysETSojTs34BGgIi4CjgROFNSK/AOMDEiosCczMysgsIKQkQ8AKiTNpcDlxeVg5mZ5ecrlc3MDHBBMDOzxAXBzMwAFwQzM0tyFQRJv88zzMzMtl9VzzKSVAf0A/aR9D7+edZQf2C/gnMzM7Nu1Nlpp18FziVb+S/knwVhDXBFgXmZmVk3q1oQIuK/gP+S9I2ImNFNOZmZWQ3kujAtImZIOhJoKo2JiFkF5WVmZt0sV0GQdD1wILAIaEuDA3BBMDPbQeS9dUUzcLDvM2RmtuPKex3CUuADRSZiZma1lXcLYR/gSUmPAGvbB0bE+EKyMjOzbpe3IFxcZBJmZlZ7ec8y+mPRiZiZWW3lPcvoDbKzigB2BvoAb0VE/6ISMzOz7pV3C2H30n5JnwVGFZKRmZnVxGbd7TQibgOOq9ZG0gBJf5C0TNITks4p00aSfixpuaTFkoZvTj5mZrbl8u4y+nxJ705k1yV0dk1CK/CtiHhU0u7AQkn3RsSTJW0+DQxKf4cDV6b/ZmbWzfKeZfQvJd2twPPAhGoBEfEy8HLqfkPSMmB/oLQgTABmpQveHpa0p6R9U6yZmXWjvMcQTt2SiUhqAg4D5nV4aX9gRUl/Sxq2UUGQNAWYAtDY2LglqWyxEeeXv1vHwksndXMmtq3yMtKzvTB9cNnhjdOWdHMmXZf3ATkNkm6V9KqkVyTdIqkhZ+xuwC3AuRGxpuPLZUI22RUVETMjojkimuvr6/NM1szMuijvQeWfA3PInouwP3BHGlaVpD5kxeCGiPhNmSYtwICS/gbgpZw5mZnZVpS3INRHxM8jojX9XQtU/akuScDVwLKI+FGFZnOASelsoyOA1338wMysNvIeVH5N0peA2an/FGBVJzFHAV8GlkhalIb9G9AIEBFXAXcB44DlwNvAFh2rMDOzzZe3IJwGXA5cRraP/0E6WXlHxAOUP0ZQ2iaAs3LmYGZmBcpbEL4DfCUi/g4gaS/gh2SFwszMdgB5jyEMaS8GABGxmuw0UjMz20HkLQg7SXpfe0/aQsi7dWFmZtuBvCv1/wAelHQz2TGE/w18t7CszMys2+W9UnmWpAVkN7QT8PkO9yQyM7PtXO7dPqkAuAiYme2gNuv212ZmtuNxQTAzM8AFwczMEhcEMzMDXBDMzCxxQTAzM8AFwczMEhcEMzMDXBDMzCxxQTAzM8AFwczMksIKgqRrJL0qaWmF14+V9LqkRelvWlG5mJlZ54p8psG1ZI/dnFWlzZ8j4oQCczAzs5wK20KIiD8Bq4sav5mZbV21PobwMUmPS7pb0iGVGkmaImmBpAUrV67szvzMzHqMWhaER4EPRsRQYAZwW6WGETEzIpojorm+vr7bEjQz60lqVhAiYk1EvJm67wL6SNqnVvmYmfV0NSsIkj4gSal7VMplVa3yMTPr6Qo7y0jSbOBYYB9JLcC3gT4AEXEVcCJwpqRW4B1gYkREUfmYmVl1hRWEiDilk9cvJzst1czMtgG1PsvIzMy2ES4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJYUVBEnXSHpV0tIKr0vSjyUtl7RY0vCicjEzs84VuYVwLTC2yuufBgalvynAlQXmYmZmnSisIETEn4DVVZpMAGZF5mFgT0n7FpWPmZlV17uG094fWFHS35KGvdyxoaQpZFsRNDY2AjDi/FllR7rw0kmbndAL0weXHd44bclmj3NrqvSeb9390rLDt5W8y9nSzzpP/JYsI0V81kXnXC0+T95FfF61is0bX0nR64JafV6dqeVBZZUZFuUaRsTMiGiOiOb6+vqC0zIz65lqWRBagAEl/Q3ASzXKxcysx6tlQZgDTEpnGx0BvB4Rm+wuMjOz7lHYMQRJs4FjgX0ktQDfBvoARMRVwF3AOGA58DZwalG5mJlZ5worCBFxSievB3BWUdM3M7Ou8ZXKZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGuCCYmVnigmBmZoALgpmZJS4IZmYGFFwQJI2V9FdJyyVdUOb1yZJWSlqU/s4oMh8zM6usyGcq9wKuAD4FtADzJc2JiCc7NL0xIqYWlYeZmeVT5BbCKGB5RPwtItYBvwImFDg9MzPbAkUWhP2BFSX9LWlYR1+QtFjSzZIGlBuRpCmSFkhasHLlyiJyNTPr8YosCCozLDr03wE0RcQQ4D7gunIjioiZEdEcEc319fVbOU0zM4NiC0ILUPqLvwF4qbRBRKyKiLWp96fAiALzMTOzKoosCPOBQZIGStoZmAjMKW0gad+S3vHAsgLzMTOzKgo7yygiWiVNBX4H9AKuiYgnJE0HFkTEHOBsSeOBVmA1MLmofMzMrLrCCgJARNwF3NVh2LSS7guBC4vMwczM8vGVymZmBrggmJlZ4oJgZmaAC4KZmSUuCGZmBrggmJlZ4oJgZmaAC4KZmSUuCGZmBrggmJlZ4oJgZmaAC4KZmSUuCGZmBrggmJlZ4oJgZmaAC4KZmSUuCGZmBhRcECSNlfRXScslXVDm9V0k3Zhenyepqch8zMysssIKgqRewBXAp4GDgVMkHdyh2enA3yPiIOAy4PtF5WNmZtUVuYUwClgeEX+LiHXAr4AJHdpMAK5L3TcDoyWpwJzMzKwCRUQxI5ZOBMZGxBmp/8vA4RExtaTN0tSmJfU/m9q81mFcU4ApqffDwF+rTHof4LUqr1ezPcbWctp+z9tHbC2n7fe87cR+MCLqq42g92ZOOI9yv/Q7Vp88bYiImcDMXBOVFkREc562O0JsLaft97x9xNZy2n7P20dsuyJ3GbUAA0r6G4CXKrWR1BvYA1hdYE5mZlZBkQVhPjBI0kBJOwMTgTkd2swBvpK6TwTuj6L2YZmZWVWF7TKKiFZJU4HfAb2AayLiCUnTgQURMQe4Grhe0nKyLYOJW2HSuXYt7UCxtZy23/P2EVvLafs9bx+xQIEHlc3MbPviK5XNzAxwQTAzs2SHKQid3Sajk9gBkv4gaZmkJySd08X45yUtkbRI0oIuxn4zTXOppNmS6qq0vUbSq+n6jfZhl0p6StJiSbdK2rOL8RdLejHlvkjSuC7EDpP0cPv7ljSqC7FDJT2UPrc7JPWvEFt23kg6KfWvl1T2VLsqsd9Jn9ciSfdI2q9MbJ2kRyQ9nmIvScMHptusPJNuu7JzhWlXir86DVss6WZJu5WLT217SXpM0p0dhs+Q9GaluHKxkq6V9FzJfB5WIW6TZTnvvEpt90zv66n0uX9M0l6S7k2f2b2S3teF2LzLZ7nYTpdPSR8uGfciSWsknduFnDf5/nZhGTknxT0h6dw0rOJ0Vf57VLa9Mj9Wtj5cLGl4pXm2kYjY7v/IDlo/CxwA7Aw8Dhzchfh9geGpe3fg6S7GPw/ssxl57w88B/RN/b8GJldpfwwwHFhaMmwM0Dt1fx/4fhfjLwbOy5Frudh7gE+n7nHA3C7Ezgc+kbpPA77TlXkDfJTsIsW5QHMXY/uXtDkbuKpMrIDdUncfYB5wRJpHE9Pwq4AzK0y7UnzptH8EXFDlM/8/wC+BO0uGNQPXA292Mr82igWuBU7cnGU577xKr18HnJG6dwb2BH7Q/j6BCyotoxVi8y6f5WJzLZ8l4+gF/A/wwTw5U+H7m2cZAQ4FlgL9yE7uuQ8YVG26lP8elW2f3u/daTk8ApjX2WcYETvMFkKe22RUFBEvR8SjqfsNYBnZzO4OvYG+yq7D6Mem12psEBF/osN1GhFxT0S0pt6Hya73yB2fV4XYANp/Le5BhdwrxH4Y+FPqvhf4QoXYsvMmIpZFRLUr1qvFrilptivlL4aMiGj/Fd4n/QVwHNltViBbCX22wrTLxrdPW5KAvuWmnV5vAD4D/KxkWC/gUuBfq73vcrFbKNe8SlsOx5CdPUhErIuIf7DxLWrKfmZVYjtVJTbX8lliNPBsRPx3npyTjt/fl8m3jHwUeDgi3k7f3z8Cn6s23Qrfo0rtJwCz0nL4MLCnpH2rvHdgx9lltD+woqS/hc1coSu74+phZL/o8grgHkkLld1mI19QxIvAD4EXyBak1yPini5Mt6PTyH4VdNXUtFl5TaVN4wrOBS6VtILsfVzYhdilwPjUfRIbX8RY1mbOm7Kxkr6b8v4iMK1CTC9Ji4BXyVaEzwL/KCnAVZezjvER0T7tn5P9Ev0IMKNC+H+SrfjXlwybCsyJiJc7ebvlYgG+m+bzZZJ2qRBbblnOO68OAFYCP0+7q34maVfgf7XnnP6/vwux0PnyWSm2q8vnRGB26u4053LfX2Ah+ZaRpcAxkvaW1I/sF/2APNPtoFL7zVon7igFIdctMDodSbY/9xbg3A6/IjtzVEQMJ7uz61mSjsk5vfeRVfKBwH7ArpK+1MW028d1EdAK3NDF0CuBA4FhZAv1f3Qh9kzgmxExAPgm6RdaTqeRfVYLyXbnrKvWeAvmTdnYiLgo5X0D2Yp2ExHRFhHDyLa6RpH9qtukWaXpdoyXdGgafirZ/F4GnFwm3xOAVyNiYcmw/chWxpUKSMXY5EKyAjQS2Av4vxVGUW5ZzjuvepPt0rgyIg4D3iLbjZFHpdg8y2el2NzLZ9rPPx64KWe+Zb+/ZJ9bR+W2QJeR7eK9F/gt2W7u1o7ttsBmrRN3lIKQ5zYZVUnqQ7bSuCEiftOV2Ih4Kf1/FbiVbOWRxyeB5yJiZUS8B/wGOLIr0waQ9BXgBOCLkXYg5hURr6QV13rgp+TPHbKrzNs/q5u6EhsRT0XEmIgYQfar7NlKbbdk3uSI/SUVdoGU5PoPsmMVR5Bterdf0JlrOSuJH1syrA24scK0jwLGS3qebPfnccATwEHA8jS8n7ILOjuNlfSLtPssImIt8HMqzKtyy3IX5lUL0NK+JUS222Q48Er77or0/9W8sTmXz0rT7cry+Wng0Yh4JfXnybnS9zfXMhIRV0fE8Ig4hmxX0DM5p1uqUvvNWifuKAUhz20yKkr7c68GlkXEj7oyYUm7Stq9vZvsIO/S6lEbvAAcIalfymE02a/Grkx/LNmvvfER8XZXYlN86X7Fz5E/d8gWsE+k7uPIFui8031/+r8T8P/IDr6Va7cl86ZsrKRBJc3GA0+Via1XOmNLUl+yL/8y4A9kt1mBbIVze4Vpl4v/q6SDSnL7l3LTjogLI6IhIprIluX7I+J9EfGBiGhKw9+O7DkieWK/VLLSENl+5k3mc6VlOe+8ioj/AVZI+nAaNBp4ko1vUVP2M6sUm2f5rDLdriyfp/DP3UXkyZny398nyb+MtH+ujcDn0/TzTLdUpfZzgEnKHEG2O7qzXY07xllG8c+j6k+T/Xq5qIuxR5NtTi0GFqW/cTljDyDb3Huc7FdcV6d9CdlKYSnZ2SO7VGk7m2yz+T2yXwCnA8vJ9hW2573JGTOdxF8PLEnvfQ6wbxdijybbZ/o42b75EV2IPSfNr6eBfyddNZ933pCtHFqAtcArwO+6EHtL+rwXA3eQHWjuGDsEeCy1WQpMK5nfj6TP/aZK86tcPNkPsL+kz3sp2e6q/uXiS8ZzLCVnGZUMr3qWUcdY4P6S6f6CdAZUnmU577xKbYcBC9L7vg14H7A38HuyFfLvgb26EJt3+SwXm3f57AesAvYoGZY3502+v11YRv5MVkAeB0Z3Nl3Kf4/KtifbZXQF2fpwCRXOxOv451tXmJkZsOPsMjIzsy3kgmBmZoALgpmZJS4IZmYGuCCYmVnigmA9kjrcLVTSZEmXb6Vxf03SpDLDm5TuVCmpWdKPU/exkrp8QaLZ1lbYIzTNeqqIKHvhVoc2C8jOm4fseoE3gQcLTMusU95CMOtA2bMDTizpfzP9P1bSHyX9WtLTkv5d0heVPfdgiaQDU7uLJZ2Xukcoe/7BQ8BZJeM8VtKdym669zXgm8rux/9xZc8t6JPa9Vf2jII+3fYBWI/lgmA9VV+VPBgFmJ4zbijZlbuDgS8DH4qIUWS3mv5GmfY/B86OiI+VG1lEPE92K4jLImJYRPyZ7L5Hn0lNJgK3RHavHLNCuSBYT/VOWgEPi+yOpGVvgV3G/MhuFLeW7LYA7bcrXwI0lTaUtAewZ0T8MQ26Puc0fgacmrpPJSsqZoVzQTDbVCvpu5FuWlb6CMS1Jd3rS/rXs+kxObEZt2GPiL8ATZI+AfSKiK7ccNBss7kgmG3qeWBE6p5A9rSzLovsttevSzo6DfpihaZvkD1noNQsspuZeevAuo0Lgtmmfgp8QtIjwOFkD1zZXKcCV6SDyu9UaHMH8Ln2g8pp2A1kd+ycXSHGbKvz3U7NtkHpLKcJEfHlWudiPYevQzDbxkiaQfYEr3G1zsV6Fm8hmJkZ4GMIZmaWuCCYmRnggmBmZokLgpmZAS4IZmaW/H8CxIYR4L1TagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.countplot(x='Humidity',hue='Fire Occurrence',data=f_data)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.countplot(x='Oxygen',hue='Fire Occurrence',data=f_data)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.lineplot(x='Oxygen',y='Humidity',data=f_data)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.lineplot(x='Temperature',y='Humidity',data=f_data)\n",
    "_=plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1c98e588408>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 627.5x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(f_data,hue='Fire Occurrence')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_ydata=f_data.iloc[:,3]\n",
    "f_Xdata=f_data.iloc[:,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(f_Xdata,f_ydata,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_tree=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_tree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=dec_tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.9166666666666666\n"
     ]
    }
   ],
   "source": [
    "print(dec_tree.score(X_train,y_train),dec_tree.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6 1]\n",
      " [0 5]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.],\n",
       "       [0., 1.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_tree.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.86      0.92         7\n",
      "           1       0.83      1.00      0.91         5\n",
      "\n",
      "    accuracy                           0.92        12\n",
      "   macro avg       0.92      0.93      0.92        12\n",
      "weighted avg       0.93      0.92      0.92        12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we witness the overfitting of the model so to balance the bias-variance tradeoff I used two functions to find considerable \n",
    "hyperparameters on maximum_depth and min_samples_leaf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_val_tree(X,y,depth_range,cv=5,scoring='accuracy'):\n",
    "    cv_mean_scores=[]\n",
    "    cv_accuracy_score=[]\n",
    "    cv_max_depths=[]\n",
    "    for depth in depth_range:\n",
    "        tree1=DecisionTreeClassifier(max_depth=depth)\n",
    "        cv_scores=cross_val_score(tree1,X,y,cv=cv,scoring=scoring)\n",
    "        cv_mean_scores.append(cv_scores.mean())\n",
    "        cv_accuracy_score.append(tree1.fit(X,y).score(X,y))\n",
    "        cv_max_depths.append(depth)\n",
    "    cv_mean_scores=np.array(cv_mean_scores)\n",
    "    cv_accuracy_score=np.array(cv_accuracy_score)\n",
    "    return cv_mean_scores,cv_accuracy_score,cv_max_depths    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4    0.555556\n",
       "0.6    0.555556\n",
       "0.8    0.555556\n",
       "1.0    0.925926\n",
       "1.2    0.925926\n",
       "1.4    0.925926\n",
       "1.6    0.925926\n",
       "1.8    0.925926\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_depth_range=np.arange(0.4,2,0.2)\n",
    "f_cv_mean_scores,f_accuracy_scores,f_max_depths=cross_val_tree(X_train,y_train,f_depth_range)\n",
    "pd.Series(data=f_accuracy_scores,index=f_max_depths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_val_tree2(X,y,ms_leaf_range,cv=5,scoring='accuracy'):\n",
    "    cv_mean_scores=[]\n",
    "    cv_accuracy_score=[]\n",
    "    samples_by_unit=[]\n",
    "    for samples in ms_leaf_range:\n",
    "        tree1=DecisionTreeClassifier(min_samples_leaf=samples)\n",
    "        cv_scores=cross_val_score(tree1,X,y,cv=cv,scoring=scoring)\n",
    "        cv_mean_scores.append(cv_scores.mean())\n",
    "        cv_accuracy_score.append(tree1.fit(X,y).score(X,y))\n",
    "        samples_by_unit.append(samples)\n",
    "    samples_by_unit=np.array(samples_by_unit)\n",
    "    cv_mean_scores=np.array(cv_mean_scores)\n",
    "    cv_accuracy_score=np.array(cv_accuracy_score)\n",
    "    return samples_by_unit,cv_accuracy_score,cv_mean_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.925926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "1  1.000000\n",
       "3  0.962963\n",
       "5  0.925926\n",
       "7  0.925926\n",
       "9  0.925926"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_msleaf_range=np.arange(1,10,2)\n",
    "f1_samples_bu,f1_accuracy_scores,f1_cv_mean_scores=cross_val_tree2(X_train,y_train,f_msleaf_range)\n",
    "pd.DataFrame(f1_accuracy_scores,index=f1_samples_bu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_tree2=DecisionTreeClassifier(max_depth=1,min_samples_leaf=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=1, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=4, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_tree2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2=dec_tree2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error of model- 0.28867513459481287\n"
     ]
    }
   ],
   "source": [
    "#print(\"Mean squared error of model- {}\".format(np.sqrt(mean_squared_error(y_test,y_pred))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9259259259259259 0.75\n"
     ]
    }
   ],
   "source": [
    "print(dec_tree2.score(X_train,y_train),dec_tree2.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 3]\n",
      " [0 5]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11764706, 0.88235294],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [1.        , 0.        ],\n",
       "       [0.11764706, 0.88235294],\n",
       "       [0.11764706, 0.88235294],\n",
       "       [0.11764706, 0.88235294],\n",
       "       [0.11764706, 0.88235294],\n",
       "       [0.11764706, 0.88235294],\n",
       "       [1.        , 0.        ],\n",
       "       [0.11764706, 0.88235294],\n",
       "       [0.11764706, 0.88235294]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_tree2.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.57      0.73         7\n",
      "           1       0.62      1.00      0.77         5\n",
      "\n",
      "    accuracy                           0.75        12\n",
      "   macro avg       0.81      0.79      0.75        12\n",
      "weighted avg       0.84      0.75      0.74        12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pickling the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename='fire_pickle.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(dec_tree2,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "fire_model=pickle.load(open('fire_pickle.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.88 [[0.11764706 0.88235294]]\n"
     ]
    }
   ],
   "source": [
    "prediction=fire_model.predict_proba([[40,40,10]])\n",
    "output='{0:.{1}f}'.format(prediction[0][1], 2)\n",
    "print(output,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}