wildfires_pred.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sqlite3\n",
    "# https://www.kaggle.com/edhirif/predict-the-causes-of-wildfires-using-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.connect('Data/FPA_FOD_20170508.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_DATE</th>\n",
       "      <th>DISCOVERY_DOY</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>2453403.5</td>\n",
       "      <td>33</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>40.036944</td>\n",
       "      <td>-121.005833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453137.5</td>\n",
       "      <td>133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.25</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.933056</td>\n",
       "      <td>-120.404444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453156.5</td>\n",
       "      <td>152</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Debris Burning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.984167</td>\n",
       "      <td>-120.735556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.913333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.933056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIRE_YEAR  DISCOVERY_DATE  DISCOVERY_DOY  STAT_CAUSE_CODE STAT_CAUSE_DESCR  \\\n",
       "0       2005       2453403.5             33              9.0    Miscellaneous   \n",
       "1       2004       2453137.5            133              1.0        Lightning   \n",
       "2       2004       2453156.5            152              5.0   Debris Burning   \n",
       "3       2004       2453184.5            180              1.0        Lightning   \n",
       "4       2004       2453184.5            180              1.0        Lightning   \n",
       "\n",
       "   FIRE_SIZE STATE   LATITUDE   LONGITUDE  \n",
       "0       0.10    CA  40.036944 -121.005833  \n",
       "1       0.25    CA  38.933056 -120.404444  \n",
       "2       0.10    CA  38.984167 -120.735556  \n",
       "3       0.10    CA  38.559167 -119.913333  \n",
       "4       0.10    CA  38.559167 -119.933056  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql_query(\n",
    "    \"\"\"SELECT \n",
    "        FIRE_YEAR,\n",
    "        DISCOVERY_DATE,\n",
    "        DISCOVERY_DOY,\n",
    "        STAT_CAUSE_CODE,\n",
    "        STAT_CAUSE_DESCR,\n",
    "        FIRE_SIZE,\n",
    "        STATE,\n",
    "        LATITUDE,\n",
    "        LONGITUDE\n",
    "        \n",
    "        FROM Fires\"\"\", conn)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1880465 entries, 0 to 1880464\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Dtype  \n",
      "---  ------            -----  \n",
      " 0   FIRE_YEAR         int64  \n",
      " 1   DISCOVERY_DATE    float64\n",
      " 2   DISCOVERY_DOY     int64  \n",
      " 3   STAT_CAUSE_CODE   float64\n",
      " 4   STAT_CAUSE_DESCR  object \n",
      " 5   FIRE_SIZE         float64\n",
      " 6   STATE             object \n",
      " 7   LATITUDE          float64\n",
      " 8   LONGITUDE         float64\n",
      "dtypes: float64(5), int64(2), object(2)\n",
      "memory usage: 129.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2005-02-02\n",
       "1   2004-05-12\n",
       "2   2004-05-31\n",
       "3   2004-06-28\n",
       "4   2004-06-28\n",
       "Name: DATE, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DATE'] = pd.to_datetime(df['DISCOVERY_DATE'] - pd.Timestamp(0).to_julian_date(), unit='D')\n",
    "df['DATE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    5\n",
       "2    5\n",
       "3    6\n",
       "4    6\n",
       "Name: FIRE_MONTH, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FIRE_MONTH'] = pd.DatetimeIndex(df['DATE']).month\n",
    "df['FIRE_MONTH'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: DAY_OF_WEEK, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DAY_OF_WEEK'] = df['DATE'].dt.weekday\n",
    "df['DAY_OF_WEEK'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    297986\n",
       "6    276779\n",
       "0    272441\n",
       "1    260778\n",
       "2    258490\n",
       "4    258106\n",
       "3    255885\n",
       "Name: DAY_OF_WEEK, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DAY_OF_WEEK'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Wednesday\n",
       "1    Wednesday\n",
       "2       Monday\n",
       "3       Monday\n",
       "4       Monday\n",
       "Name: DAY_NAME, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DAY_NAME'] = df['DATE'].dt.day_name()\n",
    "df['DAY_NAME'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>STAT_CAUSE_DESCR</th>\n",
       "      <th>Arson</th>\n",
       "      <th>Campfire</th>\n",
       "      <th>Children</th>\n",
       "      <th>Debris Burning</th>\n",
       "      <th>Equipment Use</th>\n",
       "      <th>Fireworks</th>\n",
       "      <th>Lightning</th>\n",
       "      <th>Miscellaneous</th>\n",
       "      <th>Missing/Undefined</th>\n",
       "      <th>Powerline</th>\n",
       "      <th>Railroad</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Structure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_NAME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>38116</td>\n",
       "      <td>8868</td>\n",
       "      <td>7910</td>\n",
       "      <td>58526</td>\n",
       "      <td>20962</td>\n",
       "      <td>1741</td>\n",
       "      <td>40598</td>\n",
       "      <td>44282</td>\n",
       "      <td>22617</td>\n",
       "      <td>1998</td>\n",
       "      <td>4557</td>\n",
       "      <td>7400</td>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>40603</td>\n",
       "      <td>11264</td>\n",
       "      <td>8581</td>\n",
       "      <td>62220</td>\n",
       "      <td>22194</td>\n",
       "      <td>1529</td>\n",
       "      <td>39331</td>\n",
       "      <td>46908</td>\n",
       "      <td>24017</td>\n",
       "      <td>2208</td>\n",
       "      <td>5208</td>\n",
       "      <td>7838</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>43477</td>\n",
       "      <td>14979</td>\n",
       "      <td>10724</td>\n",
       "      <td>74337</td>\n",
       "      <td>21185</td>\n",
       "      <td>1986</td>\n",
       "      <td>38772</td>\n",
       "      <td>50744</td>\n",
       "      <td>26410</td>\n",
       "      <td>1845</td>\n",
       "      <td>4761</td>\n",
       "      <td>8229</td>\n",
       "      <td>537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>44998</td>\n",
       "      <td>16157</td>\n",
       "      <td>10654</td>\n",
       "      <td>57903</td>\n",
       "      <td>18200</td>\n",
       "      <td>1753</td>\n",
       "      <td>38306</td>\n",
       "      <td>49692</td>\n",
       "      <td>24350</td>\n",
       "      <td>2003</td>\n",
       "      <td>4279</td>\n",
       "      <td>7944</td>\n",
       "      <td>540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>37589</td>\n",
       "      <td>7990</td>\n",
       "      <td>7647</td>\n",
       "      <td>57789</td>\n",
       "      <td>21408</td>\n",
       "      <td>1499</td>\n",
       "      <td>40557</td>\n",
       "      <td>43698</td>\n",
       "      <td>22841</td>\n",
       "      <td>2170</td>\n",
       "      <td>4958</td>\n",
       "      <td>7183</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>38421</td>\n",
       "      <td>8755</td>\n",
       "      <td>7980</td>\n",
       "      <td>59662</td>\n",
       "      <td>21881</td>\n",
       "      <td>1381</td>\n",
       "      <td>39522</td>\n",
       "      <td>44916</td>\n",
       "      <td>23493</td>\n",
       "      <td>2009</td>\n",
       "      <td>4989</td>\n",
       "      <td>7196</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>38251</td>\n",
       "      <td>8126</td>\n",
       "      <td>7671</td>\n",
       "      <td>58591</td>\n",
       "      <td>21782</td>\n",
       "      <td>1611</td>\n",
       "      <td>41382</td>\n",
       "      <td>43565</td>\n",
       "      <td>22995</td>\n",
       "      <td>2215</td>\n",
       "      <td>4703</td>\n",
       "      <td>7079</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "STAT_CAUSE_DESCR  Arson  Campfire  Children  Debris Burning  Equipment Use  \\\n",
       "DAY_NAME                                                                     \n",
       "Friday            38116      8868      7910           58526          20962   \n",
       "Monday            40603     11264      8581           62220          22194   \n",
       "Saturday          43477     14979     10724           74337          21185   \n",
       "Sunday            44998     16157     10654           57903          18200   \n",
       "Thursday          37589      7990      7647           57789          21408   \n",
       "Tuesday           38421      8755      7980           59662          21881   \n",
       "Wednesday         38251      8126      7671           58591          21782   \n",
       "\n",
       "STAT_CAUSE_DESCR  Fireworks  Lightning  Miscellaneous  Missing/Undefined  \\\n",
       "DAY_NAME                                                                   \n",
       "Friday                 1741      40598          44282              22617   \n",
       "Monday                 1529      39331          46908              24017   \n",
       "Saturday               1986      38772          50744              26410   \n",
       "Sunday                 1753      38306          49692              24350   \n",
       "Thursday               1499      40557          43698              22841   \n",
       "Tuesday                1381      39522          44916              23493   \n",
       "Wednesday              1611      41382          43565              22995   \n",
       "\n",
       "STAT_CAUSE_DESCR  Powerline  Railroad  Smoking  Structure  \n",
       "DAY_NAME                                                   \n",
       "Friday                 1998      4557     7400        531  \n",
       "Monday                 2208      5208     7838        540  \n",
       "Saturday               1845      4761     8229        537  \n",
       "Sunday                 2003      4279     7944        540  \n",
       "Thursday               2170      4958     7183        556  \n",
       "Tuesday                2009      4989     7196        573  \n",
       "Wednesday              2215      4703     7079        519  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['count'] = 1\n",
    "pvweek = pd.pivot_table(data=df, values='count', index='DAY_NAME', columns='STAT_CAUSE_DESCR', aggfunc=np.sum )\n",
    "pvweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_NAME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Friday</th>\n",
       "      <th>Arson</th>\n",
       "      <td>38116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Campfire</th>\n",
       "      <td>8868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children</th>\n",
       "      <td>7910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Debris Burning</th>\n",
       "      <td>58526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equipment Use</th>\n",
       "      <td>20962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Wednesday</th>\n",
       "      <th>Missing/Undefined</th>\n",
       "      <td>22995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Powerline</th>\n",
       "      <td>2215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Railroad</th>\n",
       "      <td>4703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smoking</th>\n",
       "      <td>7079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Structure</th>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count\n",
       "DAY_NAME  STAT_CAUSE_DESCR        \n",
       "Friday    Arson              38116\n",
       "          Campfire            8868\n",
       "          Children            7910\n",
       "          Debris Burning     58526\n",
       "          Equipment Use      20962\n",
       "...                            ...\n",
       "Wednesday Missing/Undefined  22995\n",
       "          Powerline           2215\n",
       "          Railroad            4703\n",
       "          Smoking             7079\n",
       "          Structure            519\n",
       "\n",
       "[91 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires_day = df[['DAY_NAME','STAT_CAUSE_DESCR','count']]\n",
    "pvday = fires_day.groupby(by=['DAY_NAME', 'STAT_CAUSE_DESCR']).count()\n",
    "pvday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_order = ['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']\n",
    "sns.set()\n",
    "plt.figure(figsize=(17,8))\n",
    "\n",
    "sns.barplot(x='DAY_NAME', y='Arson', data=pvweek.reset_index(), order=days_order, color='red')\n",
    "campfire = sns.barplot(x='DAY_NAME', y='Campfire', data=pvweek.reset_index(), order=days_order, color='blue')\n",
    "children = sns.barplot(x='DAY_NAME', y='Children', data=pvweek.reset_index(), order=days_order, color='green')\n",
    "debris = sns.barplot(x='DAY_NAME', y='Debris Burning', data=pvweek.reset_index(), order=days_order, color='#CCFFCC')\n",
    "equipment = sns.barplot(x='DAY_NAME', y='Equipment Use', data=pvweek.reset_index(), order=days_order, color='black')\n",
    "fireworks = sns.barplot(x='DAY_NAME', y='Fireworks', data=pvweek.reset_index(), order=days_order, color='purple')\n",
    "lightning = sns.barplot(x='DAY_NAME', y='Lightning', data=pvweek.reset_index(), order=days_order, color='orange')\n",
    "random = sns.barplot(x='DAY_NAME', y='Miscellaneous', data=pvweek.reset_index(), order=days_order, color='yellow')\n",
    "missing = sns.barplot(x='DAY_NAME', y='Missing/Undefined', data=pvweek.reset_index(), order=days_order, color='pink')\n",
    "powerline = sns.barplot(x='DAY_NAME', y='Powerline', data=pvweek.reset_index(), order=days_order, color='grey')\n",
    "railroad = sns.barplot(x='DAY_NAME', y='Railroad', data=pvweek.reset_index(), order=days_order, color='brown')\n",
    "\n",
    "\n",
    "ars = plt.Rectangle((0,0),1,1,fc=\"red\", edgecolor = 'none')\n",
    "camp = plt.Rectangle((0,0),1,1,fc='#0000A3',  edgecolor = 'none')\n",
    "child = plt.Rectangle((0,0),1,1,fc='green',  edgecolor = 'none')\n",
    "deb = plt.Rectangle((0,0),1,1,fc='#CCFFCC',  edgecolor = 'none')\n",
    "equip = plt.Rectangle((0,0),1,1,fc='black',  edgecolor = 'none')\n",
    "firew = plt.Rectangle((0,0),1,1,fc='purple',  edgecolor = 'none')\n",
    "light = plt.Rectangle((0,0),1,1,fc='orange',  edgecolor = 'none')\n",
    "rand = plt.Rectangle((0,0),1,1,fc='yellow',  edgecolor = 'none')\n",
    "miss = plt.Rectangle((0,0),1,1,fc='pink',  edgecolor = 'none')\n",
    "power = plt.Rectangle((0,0),1,1,fc='grey',  edgecolor = 'none')\n",
    "rail = plt.Rectangle((0,0),1,1,fc='brown',  edgecolor = 'none')\n",
    "\n",
    "leg = plt.legend([ars, camp, child,deb,equip,firew,light,rand,miss,power,rail],['Arson','Campfire','Children','Debris Burning','Equipment Use','Fireworks','Lightning','Miscellaneous','Missing/Undefined','Powerline','Railroad'], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "leg.draw_frame(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0ab5e6cdd0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.figure(figsize=(17,8))\n",
    "\n",
    "sns.countplot(x='DAY_NAME', data=df, palette='autumn', order=['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday'])\n",
    "plt.xlabel('Week Day')\n",
    "plt.ylabel('Number of Fires')\n",
    "plt.title('Number of Fires per Week Day')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_DATE</th>\n",
       "      <th>DISCOVERY_DOY</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>FIRE_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>DAY_NAME</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>2453403.5</td>\n",
       "      <td>33</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>40.036944</td>\n",
       "      <td>-121.005833</td>\n",
       "      <td>2005-02-02</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453137.5</td>\n",
       "      <td>133</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.25</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.933056</td>\n",
       "      <td>-120.404444</td>\n",
       "      <td>2004-05-12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453156.5</td>\n",
       "      <td>152</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Debris Burning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.984167</td>\n",
       "      <td>-120.735556</td>\n",
       "      <td>2004-05-31</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.913333</td>\n",
       "      <td>2004-06-28</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.933056</td>\n",
       "      <td>2004-06-28</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIRE_YEAR  DISCOVERY_DATE  DISCOVERY_DOY  STAT_CAUSE_CODE STAT_CAUSE_DESCR  \\\n",
       "0       2005       2453403.5             33              9.0    Miscellaneous   \n",
       "1       2004       2453137.5            133              1.0        Lightning   \n",
       "2       2004       2453156.5            152              5.0   Debris Burning   \n",
       "3       2004       2453184.5            180              1.0        Lightning   \n",
       "4       2004       2453184.5            180              1.0        Lightning   \n",
       "\n",
       "   FIRE_SIZE STATE   LATITUDE   LONGITUDE       DATE  FIRE_MONTH  DAY_OF_WEEK  \\\n",
       "0       0.10    CA  40.036944 -121.005833 2005-02-02           2            2   \n",
       "1       0.25    CA  38.933056 -120.404444 2004-05-12           5            2   \n",
       "2       0.10    CA  38.984167 -120.735556 2004-05-31           5            0   \n",
       "3       0.10    CA  38.559167 -119.913333 2004-06-28           6            0   \n",
       "4       0.10    CA  38.559167 -119.933056 2004-06-28           6            0   \n",
       "\n",
       "    DAY_NAME  count  \n",
       "0  Wednesday      1  \n",
       "1  Wednesday      1  \n",
       "2     Monday      1  \n",
       "3     Monday      1  \n",
       "4     Monday      1  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIRE_YEAR', 'DISCOVERY_DATE', 'DISCOVERY_DOY', 'STAT_CAUSE_CODE',\n",
       "       'STAT_CAUSE_DESCR', 'FIRE_SIZE', 'STATE', 'LATITUDE', 'LONGITUDE',\n",
       "       'DATE', 'FIRE_MONTH', 'DAY_OF_WEEK', 'DAY_NAME', 'count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_DATE</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIRE_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>2453403.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>40.036944</td>\n",
       "      <td>-121.005833</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453137.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.25</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.933056</td>\n",
       "      <td>-120.404444</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453156.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Debris Burning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.984167</td>\n",
       "      <td>-120.735556</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.913333</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.933056</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIRE_YEAR  DISCOVERY_DATE  STAT_CAUSE_CODE STAT_CAUSE_DESCR  FIRE_SIZE  \\\n",
       "0       2005       2453403.5              9.0    Miscellaneous       0.10   \n",
       "1       2004       2453137.5              1.0        Lightning       0.25   \n",
       "2       2004       2453156.5              5.0   Debris Burning       0.10   \n",
       "3       2004       2453184.5              1.0        Lightning       0.10   \n",
       "4       2004       2453184.5              1.0        Lightning       0.10   \n",
       "\n",
       "  STATE   LATITUDE   LONGITUDE  FIRE_MONTH  DAY_OF_WEEK  \n",
       "0    CA  40.036944 -121.005833           2            2  \n",
       "1    CA  38.933056 -120.404444           5            2  \n",
       "2    CA  38.984167 -120.735556           5            0  \n",
       "3    CA  38.559167 -119.913333           6            0  \n",
       "4    CA  38.559167 -119.933056           6            0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['FIRE_YEAR', 'DISCOVERY_DATE', 'STAT_CAUSE_CODE',\n",
    "       'STAT_CAUSE_DESCR', 'FIRE_SIZE', 'STATE', 'LATITUDE', 'LONGITUDE', 'FIRE_MONTH', 'DAY_OF_WEEK']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_DATE</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIRE_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>2453403.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>40.036944</td>\n",
       "      <td>-121.005833</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453137.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.25</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.933056</td>\n",
       "      <td>-120.404444</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453156.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Debris Burning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.984167</td>\n",
       "      <td>-120.735556</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.913333</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>CA</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.933056</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIRE_YEAR  DISCOVERY_DATE  STAT_CAUSE_CODE STAT_CAUSE_DESCR  FIRE_SIZE  \\\n",
       "0       2005       2453403.5              9.0    Miscellaneous       0.10   \n",
       "1       2004       2453137.5              1.0        Lightning       0.25   \n",
       "2       2004       2453156.5              5.0   Debris Burning       0.10   \n",
       "3       2004       2453184.5              1.0        Lightning       0.10   \n",
       "4       2004       2453184.5              1.0        Lightning       0.10   \n",
       "\n",
       "  STATE   LATITUDE   LONGITUDE  FIRE_MONTH  DAY_OF_WEEK  \n",
       "0    CA  40.036944 -121.005833           2            2  \n",
       "1    CA  38.933056 -120.404444           5            2  \n",
       "2    CA  38.984167 -120.735556           5            0  \n",
       "3    CA  38.559167 -119.913333           6            0  \n",
       "4    CA  38.559167 -119.933056           6            0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data['STAT_CAUSE_DESCR']!='Missing/Undefined']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_DATE</th>\n",
       "      <th>STAT_CAUSE_CODE</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>STATE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIRE_MONTH</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>2453403.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>40.036944</td>\n",
       "      <td>-121.005833</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453137.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.25</td>\n",
       "      <td>4</td>\n",
       "      <td>38.933056</td>\n",
       "      <td>-120.404444</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453156.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Debris Burning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>38.984167</td>\n",
       "      <td>-120.735556</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.913333</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>2453184.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>0.10</td>\n",
       "      <td>4</td>\n",
       "      <td>38.559167</td>\n",
       "      <td>-119.933056</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FIRE_YEAR  DISCOVERY_DATE  STAT_CAUSE_CODE STAT_CAUSE_DESCR  FIRE_SIZE  \\\n",
       "0       2005       2453403.5              9.0    Miscellaneous       0.10   \n",
       "1       2004       2453137.5              1.0        Lightning       0.25   \n",
       "2       2004       2453156.5              5.0   Debris Burning       0.10   \n",
       "3       2004       2453184.5              1.0        Lightning       0.10   \n",
       "4       2004       2453184.5              1.0        Lightning       0.10   \n",
       "\n",
       "   STATE   LATITUDE   LONGITUDE  FIRE_MONTH  DAY_OF_WEEK  \n",
       "0      4  40.036944 -121.005833           2            2  \n",
       "1      4  38.933056 -120.404444           5            2  \n",
       "2      4  38.984167 -120.735556           5            0  \n",
       "3      4  38.559167 -119.913333           6            0  \n",
       "4      4  38.559167 -119.933056           6            0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = preprocessing.LabelEncoder()\n",
    "data['STATE'] = le.fit_transform(data['STATE'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(labels=['STAT_CAUSE_CODE', 'STAT_CAUSE_DESCR',],axis=1)\n",
    "y = data['STAT_CAUSE_DESCR']\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=50)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[46427   699  1028 21995  2377   136  2023  8662    75   293   455     7]\n",
      " [ 1444  7427   252  4701   896    67  4315  3248    32   120   241    20]\n",
      " [ 2663   398  2731  6342  1319   149   954  3071    30   437   285    25]\n",
      " [17747  1775  1413 87985  3919   196  3225 11102   215   575   657    64]\n",
      " [ 3691   729   600 11670 12920   120  3459  9419   136  1160   406    31]\n",
      " [  309    54   110   433   148  1482   440   446    10     4    22     5]\n",
      " [ 1469  1163   177  3281  1191    61 71739  3914    68   242   123     5]\n",
      " [ 7604  2077  1190 19543  6618   287  6358 51616   219   808   808    37]\n",
      " [  315    75    35  1327   449    27   449  1205   427    16    33    10]\n",
      " [  577   212   124  1879   945    18   956   753    17  4580    61     0]\n",
      " [ 1783   677   459  5368  1141    56  1215  3964    32   212   905     5]\n",
      " [  124    33    63   477    90    25    81   231    15     2     4    52]]\n",
      "\n",
      "\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "         Arson       0.55      0.55      0.55     84177\n",
      "      Campfire       0.48      0.33      0.39     22763\n",
      "      Children       0.33      0.15      0.21     18404\n",
      "Debris Burning       0.53      0.68      0.60    128873\n",
      " Equipment Use       0.40      0.29      0.34     44341\n",
      "     Fireworks       0.56      0.43      0.49      3463\n",
      "     Lightning       0.75      0.86      0.80     83433\n",
      " Miscellaneous       0.53      0.53      0.53     97165\n",
      "     Powerline       0.33      0.10      0.15      4368\n",
      "      Railroad       0.54      0.45      0.49     10122\n",
      "       Smoking       0.23      0.06      0.09     15817\n",
      "     Structure       0.20      0.04      0.07      1197\n",
      "\n",
      "      accuracy                           0.56    514123\n",
      "     macro avg       0.45      0.37      0.39    514123\n",
      "  weighted avg       0.54      0.56      0.54    514123\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,rfc_pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_pred = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[46312   664   970 22134  2343   134  2068  8748    74   301   419    10]\n",
      " [ 1378  7379   247  4701   861    69  4347  3330    28   146   267    10]\n",
      " [ 2549   394  2704  6432  1310   145   946  3106    25   464   299    30]\n",
      " [17124  1674  1317 88988  3761   209  3228 11048   199   609   657    59]\n",
      " [ 3553   656   576 11759 12900   123  3486  9564   136  1157   399    32]\n",
      " [  323    65   113   411   152  1482   444   434    11     5    20     3]\n",
      " [ 1412  1096   160  3267  1129    74 71995  3915    58   197   126     4]\n",
      " [ 7328  2047  1108 19569  6389   295  6338 52268   206   828   754    35]\n",
      " [  292    74    36  1334   438    31   432  1228   446    10    38     9]\n",
      " [  584   204   109  1859   931    12   973   745    16  4628    60     1]\n",
      " [ 1713   649   440  5401  1100    60  1243  4037    32   212   927     3]\n",
      " [  121    37    66   472    87    28    79   235    14     1     7    50]]\n",
      "\n",
      "\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "         Arson       0.56      0.55      0.56     84177\n",
      "      Campfire       0.49      0.32      0.39     22763\n",
      "      Children       0.34      0.15      0.21     18404\n",
      "Debris Burning       0.54      0.69      0.60    128873\n",
      " Equipment Use       0.41      0.29      0.34     44341\n",
      "     Fireworks       0.56      0.43      0.48      3463\n",
      "     Lightning       0.75      0.86      0.80     83433\n",
      " Miscellaneous       0.53      0.54      0.53     97165\n",
      "     Powerline       0.36      0.10      0.16      4368\n",
      "      Railroad       0.54      0.46      0.50     10122\n",
      "       Smoking       0.23      0.06      0.09     15817\n",
      "     Structure       0.20      0.04      0.07      1197\n",
      "\n",
      "      accuracy                           0.56    514123\n",
      "     macro avg       0.46      0.37      0.39    514123\n",
      "  weighted avg       0.54      0.56      0.54    514123\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,forest_pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,forest_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Wildfires-Fj3VjvPf",
   "language": "python",
   "name": "wildfires-fj3vjvpf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}