Random Forest Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from mlxtend.evaluate import paired_ttest_5x2cv\n",
    "from sklearn.tree import export_graphviz\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>025-01</td>\n",
       "      <td>NORRISH</td>\n",
       "      <td>2000</td>\n",
       "      <td>945.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5/31/2000</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>87.4</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>-115.2919</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>142359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>025-02</td>\n",
       "      <td>GUN</td>\n",
       "      <td>2001</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5/20/2001</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>-116.1486</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>4.920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>143295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>025-03</td>\n",
       "      <td>ONEAL</td>\n",
       "      <td>2001</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>Campfire</td>\n",
       "      <td>7/17/2001</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>-116.0892</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>10.405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>143295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>025-04</td>\n",
       "      <td>EXPEDITION</td>\n",
       "      <td>2002</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>12/28/2002</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>-115.2050</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>3.580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>144818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>025-05</td>\n",
       "      <td>M L K</td>\n",
       "      <td>2002</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>Lightning</td>\n",
       "      <td>1/20/2002</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>-115.1186</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>6.040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>144818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 FIPS_ID   FIRE_NAME  FIRE_YEAR  DISCOVERY_TIME STAT_CAUSE_DESCR  \\\n",
       "0           0  025-01     NORRISH       2000           945.0    Miscellaneous   \n",
       "1           1  025-02         GUN       2001          1245.0    Miscellaneous   \n",
       "2           2  025-03       ONEAL       2001          1600.0         Campfire   \n",
       "3           3  025-04  EXPEDITION       2002          1934.0        Lightning   \n",
       "4           4  025-05       M L K       2002          1345.0        Lightning   \n",
       "\n",
       "    CONT_DATE  CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  ...  LONGITUDE  \\\n",
       "0   5/31/2000     1800.0       87.4               C  ...  -115.2919   \n",
       "1   5/20/2001     1345.0        0.1               A  ...  -116.1486   \n",
       "2   7/17/2001     2200.0       18.0               C  ...  -116.0892   \n",
       "3  12/28/2002     2000.0        0.1               A  ...  -115.2050   \n",
       "4   1/20/2002     1430.0        0.1               A  ...  -115.1186   \n",
       "\n",
       "   FIPS_NAME DISCOVERY_MONTH  DISCOVERY_DAY    AWND  PRCP  TAVG  \\\n",
       "0   Imperial               5             30     NaN   0.0  93.5   \n",
       "1   Imperial               5             20   4.920   0.0  88.4   \n",
       "2   Imperial               7             17  10.405   0.0  91.0   \n",
       "3   Imperial              12             28   3.580   0.0  56.4   \n",
       "4   Imperial               1             20   6.040   0.0  52.6   \n",
       "\n",
       "   DURING_A_DROUGHT  WATERSHD  POP_BY_COUNTY  \n",
       "0               0.0         0         142359  \n",
       "1               0.0         0         143295  \n",
       "2               0.0         0         143295  \n",
       "3               1.0         0         144818  \n",
       "4               0.0         0         144818  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load database\n",
    "wdf_df = pd.read_csv('V4_WDF_OFFICIAL.csv')\n",
    "wdf_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17245, 21)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1**: Add FIRE_SIZE_CLASS to 2016-Sep 2020 data based on FIRE_SIZE. Remove data points that don't have a FIRE_SIZE listed.\n",
    "\n",
    "Definition of the following columns:\n",
    "- FIRE_SIZE = Estimate of acres within the final perimeter of the fire.\n",
    "- FIRE_SIZE_CLASS = Code for fire size based on the number of acres within the final fire perimeter expenditures (A=greater than 0 but less than or equal to 0.25 acres, B=0.26-9.9 acres, C=10.0-99.9 acres, D=100-299 acres, E=300 to 999 acres, F=1000 to 4999 acres, and G=5000+ acres)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17234, 21)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove for null values in FIRE_SIZE from the dataframe\n",
    "wdf_df = wdf_df[wdf_df['FIRE_SIZE'].notnull()]\n",
    "wdf_df.reset_index(inplace=True, drop=True)\n",
    "wdf_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>025-174</td>\n",
       "      <td>Carter Fire</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/4/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-115.586500</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>12.750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>182333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>584</td>\n",
       "      <td>029-1360</td>\n",
       "      <td>Cedar Fire</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29322.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.567800</td>\n",
       "      <td>Kern</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>6.825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>880856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>585</td>\n",
       "      <td>029-1361</td>\n",
       "      <td>Cotton Fire</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-120.251000</td>\n",
       "      <td>Kern</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>9.840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.800000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>880856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>586</td>\n",
       "      <td>029-1362</td>\n",
       "      <td>Deer Fire</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.722720</td>\n",
       "      <td>Kern</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>10.065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>880856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>587</td>\n",
       "      <td>029-1363</td>\n",
       "      <td>Erskine Fire</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.456280</td>\n",
       "      <td>Kern</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>7.605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>880856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17144</th>\n",
       "      <td>17221</td>\n",
       "      <td>111-148</td>\n",
       "      <td>South Fire</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.461363</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>5.220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>846006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17145</th>\n",
       "      <td>17222</td>\n",
       "      <td>111-149</td>\n",
       "      <td>Wendy Fire</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.950833</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>14.425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>846006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17147</th>\n",
       "      <td>17224</td>\n",
       "      <td>111-150</td>\n",
       "      <td>Elizabeth Fire</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>289.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-119.178080</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6.410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>851297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17148</th>\n",
       "      <td>17225</td>\n",
       "      <td>111-151</td>\n",
       "      <td>Holser Fire</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.758965</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>5.520</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>851297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17149</th>\n",
       "      <td>17226</td>\n",
       "      <td>111-152</td>\n",
       "      <td>Lime Fire</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>803.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.773470</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>6.410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>851297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   FIPS_ID       FIRE_NAME  FIRE_YEAR  DISCOVERY_TIME  \\\n",
       "91             91   025-174     Carter Fire       2020             NaN   \n",
       "576           584  029-1360      Cedar Fire       2016             NaN   \n",
       "577           585  029-1361     Cotton Fire       2016             NaN   \n",
       "578           586  029-1362       Deer Fire       2016             NaN   \n",
       "579           587  029-1363    Erskine Fire       2016             NaN   \n",
       "...           ...       ...             ...        ...             ...   \n",
       "17144       17221   111-148      South Fire       2019             NaN   \n",
       "17145       17222   111-149      Wendy Fire       2019             NaN   \n",
       "17147       17224   111-150  Elizabeth Fire       2020             NaN   \n",
       "17148       17225   111-151     Holser Fire       2020             NaN   \n",
       "17149       17226   111-152       Lime Fire       2020             NaN   \n",
       "\n",
       "      STAT_CAUSE_DESCR CONT_DATE  CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  ...  \\\n",
       "91                 NaN  4/4/2020        NaN      275.0             NaN  ...   \n",
       "576                NaN       NaN        NaN    29322.0             NaN  ...   \n",
       "577                NaN       NaN        NaN       61.0             NaN  ...   \n",
       "578                NaN       NaN        NaN     1785.0             NaN  ...   \n",
       "579                NaN       NaN        NaN    48019.0             NaN  ...   \n",
       "...                ...       ...        ...        ...             ...  ...   \n",
       "17144              NaN       NaN        NaN      131.0             NaN  ...   \n",
       "17145              NaN       NaN        NaN       91.0             NaN  ...   \n",
       "17147              NaN       NaN        NaN      289.0             NaN  ...   \n",
       "17148              NaN       NaN        NaN     3000.0             NaN  ...   \n",
       "17149              NaN       NaN        NaN      803.0             NaN  ...   \n",
       "\n",
       "        LONGITUDE  FIPS_NAME DISCOVERY_MONTH  DISCOVERY_DAY    AWND  PRCP  \\\n",
       "91    -115.586500   Imperial               4              4  12.750   0.0   \n",
       "576   -118.567800       Kern               8             16   6.825   0.0   \n",
       "577   -120.251000       Kern               6             11   9.840   0.0   \n",
       "578   -118.722720       Kern               7              1  10.065   0.0   \n",
       "579   -118.456280       Kern               6             23   7.605   0.0   \n",
       "...           ...        ...             ...            ...     ...   ...   \n",
       "17144 -117.461363    Ventura               7             14   5.220   0.0   \n",
       "17145 -118.950833    Ventura              10             10  14.425   0.0   \n",
       "17147 -119.178080    Ventura               6             10   6.410   0.0   \n",
       "17148 -118.758965    Ventura               8             17   5.520   0.0   \n",
       "17149 -118.773470    Ventura               6             10   6.410   0.0   \n",
       "\n",
       "            TAVG  DURING_A_DROUGHT  WATERSHD  POP_BY_COUNTY  \n",
       "91     70.666667               0.0         0         182333  \n",
       "576    81.400000               1.0         0         880856  \n",
       "577    72.800000               1.0         1         880856  \n",
       "578    84.400000               1.0         1         880856  \n",
       "579    81.400000               1.0         1         880856  \n",
       "...          ...               ...       ...            ...  \n",
       "17144  76.400000               0.0         1         846006  \n",
       "17145  57.600000               0.0         0         846006  \n",
       "17147  74.166667               0.0         1         851297  \n",
       "17148  82.666667               0.0         0         851297  \n",
       "17149  74.166667               0.0         0         851297  \n",
       "\n",
       "[427 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check columns that have null vales in FIRE_SIZE_CLASS\n",
    "# This should be from FIRE_YEAR 2016-Sept 2020\n",
    "wdf_df[wdf_df['FIRE_SIZE_CLASS'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "17229    False\n",
       "17230    False\n",
       "17231    False\n",
       "17232    False\n",
       "17233    False\n",
       "Name: FIRE_SIZE_CLASS, Length: 17234, dtype: bool"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_test = wdf_df['FIRE_SIZE_CLASS'].isnull()\n",
    "null_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-b02a991e9b4d>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wdf_df.FIRE_SIZE_CLASS[i] = 'G'\n",
      "<ipython-input-34-b02a991e9b4d>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wdf_df.FIRE_SIZE_CLASS[i] = 'C'\n",
      "<ipython-input-34-b02a991e9b4d>:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wdf_df.FIRE_SIZE_CLASS[i] = 'F'\n",
      "<ipython-input-34-b02a991e9b4d>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wdf_df.FIRE_SIZE_CLASS[i] = 'D'\n",
      "<ipython-input-34-b02a991e9b4d>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wdf_df.FIRE_SIZE_CLASS[i] = 'E'\n",
      "<ipython-input-34-b02a991e9b4d>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  wdf_df.FIRE_SIZE_CLASS[i] = 'B'\n"
     ]
    }
   ],
   "source": [
    "# Apply FIRE_SIZE_CLASS to 2016-2020 rows\n",
    "for i in range(len(wdf_df)):\n",
    "    if null_test[i] == True:\n",
    "        if wdf_df.FIRE_SIZE[i] > 0 and wdf_df.FIRE_SIZE[i] <= 0.25:\n",
    "            wdf_df.FIRE_SIZE_CLASS[i] = 'A'\n",
    "        if wdf_df.FIRE_SIZE[i] >= 0.26 and wdf_df.FIRE_SIZE[i] <= 9.9:\n",
    "            wdf_df.FIRE_SIZE_CLASS[i] = 'B'\n",
    "        if wdf_df.FIRE_SIZE[i] >= 10.0 and wdf_df.FIRE_SIZE[i] <= 99.9:\n",
    "            wdf_df.FIRE_SIZE_CLASS[i] = 'C'\n",
    "        if wdf_df.FIRE_SIZE[i] >= 100 and wdf_df.FIRE_SIZE[i] <= 299:\n",
    "            wdf_df.FIRE_SIZE_CLASS[i] = 'D'\n",
    "        if wdf_df.FIRE_SIZE[i] >= 300 and wdf_df.FIRE_SIZE[i] <= 999:\n",
    "            wdf_df.FIRE_SIZE_CLASS[i] = 'E'\n",
    "        if wdf_df.FIRE_SIZE[i] >= 1000 and wdf_df.FIRE_SIZE[i] <= 4999:\n",
    "            wdf_df.FIRE_SIZE_CLASS[i] = 'F'\n",
    "        if wdf_df.FIRE_SIZE[i] >= 5000:\n",
    "            wdf_df.FIRE_SIZE_CLASS[i] = 'G'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, FIPS_ID, FIRE_NAME, FIRE_YEAR, DISCOVERY_TIME, STAT_CAUSE_DESCR, CONT_DATE, CONT_TIME, FIRE_SIZE, FIRE_SIZE_CLASS, LATITUDE, LONGITUDE, FIPS_NAME, DISCOVERY_MONTH, DISCOVERY_DAY, AWND, PRCP, TAVG, DURING_A_DROUGHT, WATERSHD, POP_BY_COUNTY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check FIRE_SIZE_CLASS was added from 2016-2020\n",
    "wdf_df[wdf_df['FIRE_SIZE_CLASS'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2**: Check null values in the following columns: AWND, PRCP, TAVG, DURING_A_DROUGHT, WATERSHD, POP_BY_COUNTY. Remove rows if there are null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17234, 21)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdf_df.shape"
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>025-01</td>\n",
       "      <td>NORRISH</td>\n",
       "      <td>2000</td>\n",
       "      <td>945.0</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>5/31/2000</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>87.4</td>\n",
       "      <td>C</td>\n",
       "      <td>...</td>\n",
       "      <td>-115.291900</td>\n",
       "      <td>Imperial</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>142359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3494</th>\n",
       "      <td>3527</td>\n",
       "      <td>037-2741</td>\n",
       "      <td>Martindale Fire</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.0</td>\n",
       "      <td>D</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.400000</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10079000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3500</th>\n",
       "      <td>3534</td>\n",
       "      <td>037-2748</td>\n",
       "      <td>Woolsey Fire</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96949.0</td>\n",
       "      <td>G</td>\n",
       "      <td>...</td>\n",
       "      <td>-118.701280</td>\n",
       "      <td>Los Angeles and Ventura</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10913</th>\n",
       "      <td>10953</td>\n",
       "      <td>065-6576</td>\n",
       "      <td>Candy Fire</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.0</td>\n",
       "      <td>D</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.374419</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2517830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17012</th>\n",
       "      <td>17084</td>\n",
       "      <td>083-250</td>\n",
       "      <td>Thomas Fire</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>281893.0</td>\n",
       "      <td>G</td>\n",
       "      <td>...</td>\n",
       "      <td>-119.091240</td>\n",
       "      <td>Santa Barbara and Ventura</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>91574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   FIPS_ID        FIRE_NAME  FIRE_YEAR  DISCOVERY_TIME  \\\n",
       "0               0    025-01          NORRISH       2000           945.0   \n",
       "3494         3527  037-2741  Martindale Fire       2020             NaN   \n",
       "3500         3534  037-2748     Woolsey Fire       2018             NaN   \n",
       "10913       10953  065-6576       Candy Fire       2020             NaN   \n",
       "17012       17084   083-250      Thomas Fire       2017             NaN   \n",
       "\n",
       "      STAT_CAUSE_DESCR  CONT_DATE  CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  ...  \\\n",
       "0        Miscellaneous  5/31/2000     1800.0       87.4               C  ...   \n",
       "3494               NaN        NaN        NaN      230.0               D  ...   \n",
       "3500               NaN        NaN        NaN    96949.0               G  ...   \n",
       "10913              NaN        NaN        NaN      227.0               D  ...   \n",
       "17012              NaN        NaN        NaN   281893.0               G  ...   \n",
       "\n",
       "        LONGITUDE                  FIPS_NAME DISCOVERY_MONTH  DISCOVERY_DAY  \\\n",
       "0     -115.291900                   Imperial               5             30   \n",
       "3494  -118.400000                Los Angeles               9             28   \n",
       "3500  -118.701280    Los Angeles and Ventura              11              8   \n",
       "10913 -117.374419                  Riverside               9             29   \n",
       "17012 -119.091240  Santa Barbara and Ventura              12              4   \n",
       "\n",
       "       AWND  PRCP       TAVG  DURING_A_DROUGHT  WATERSHD  POP_BY_COUNTY  \n",
       "0       NaN   0.0  93.500000               0.0         0         142359  \n",
       "3494    NaN   0.0  77.285714               0.0         1       10079000  \n",
       "3500    NaN   NaN        NaN               1.0         0       10070000  \n",
       "10913   NaN   0.0  81.111111               0.0         0        2517830  \n",
       "17012   NaN   NaN        NaN               1.0         0          91574  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check AWND\n",
    "wdf_df[wdf_df['AWND'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17229, 21)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove null values from AWND\n",
    "wdf_df = wdf_df[wdf_df['AWND'].notnull()]\n",
    "wdf_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, FIPS_ID, FIRE_NAME, FIRE_YEAR, DISCOVERY_TIME, STAT_CAUSE_DESCR, CONT_DATE, CONT_TIME, FIRE_SIZE, FIRE_SIZE_CLASS, LATITUDE, LONGITUDE, FIPS_NAME, DISCOVERY_MONTH, DISCOVERY_DAY, AWND, PRCP, TAVG, DURING_A_DROUGHT, WATERSHD, POP_BY_COUNTY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check PRCP\n",
    "wdf_df[wdf_df['PRCP'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, FIPS_ID, FIRE_NAME, FIRE_YEAR, DISCOVERY_TIME, STAT_CAUSE_DESCR, CONT_DATE, CONT_TIME, FIRE_SIZE, FIRE_SIZE_CLASS, LATITUDE, LONGITUDE, FIPS_NAME, DISCOVERY_MONTH, DISCOVERY_DAY, AWND, PRCP, TAVG, DURING_A_DROUGHT, WATERSHD, POP_BY_COUNTY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check TAVG\n",
    "wdf_df[wdf_df['TAVG'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15387</th>\n",
       "      <td>15449</td>\n",
       "      <td>073-367</td>\n",
       "      <td>HAYBARN</td>\n",
       "      <td>2004</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>Missing/Undefined</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>D</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.315000</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>6.997143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1254000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15512</th>\n",
       "      <td>15574</td>\n",
       "      <td>073-480</td>\n",
       "      <td>SUNDEVIL</td>\n",
       "      <td>2005</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Missing/Undefined</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160.0</td>\n",
       "      <td>D</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.114444</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>72.764706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15894</th>\n",
       "      <td>15957</td>\n",
       "      <td>073-829</td>\n",
       "      <td>JULIETT</td>\n",
       "      <td>2008</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>Missing/Undefined</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4026.0</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>-117.296111</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>8.456000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.941176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2977000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  FIPS_ID FIRE_NAME  FIRE_YEAR  DISCOVERY_TIME  \\\n",
       "15387       15449  073-367   HAYBARN       2004          1630.0   \n",
       "15512       15574  073-480  SUNDEVIL       2005          1300.0   \n",
       "15894       15957  073-829   JULIETT       2008          1442.0   \n",
       "\n",
       "        STAT_CAUSE_DESCR CONT_DATE  CONT_TIME  FIRE_SIZE FIRE_SIZE_CLASS  ...  \\\n",
       "15387  Missing/Undefined       NaN        NaN      120.0               D  ...   \n",
       "15512  Missing/Undefined       NaN        NaN      160.0               D  ...   \n",
       "15894  Missing/Undefined       NaN        NaN     4026.0               F  ...   \n",
       "\n",
       "        LONGITUDE  FIPS_NAME DISCOVERY_MONTH  DISCOVERY_DAY      AWND  \\\n",
       "15387 -117.315000  San Diego               9             22  6.997143   \n",
       "15512 -117.114444  San Diego               9              5  4.570000   \n",
       "15894 -117.296111  San Diego              10             13  8.456000   \n",
       "\n",
       "           PRCP       TAVG  DURING_A_DROUGHT  WATERSHD  POP_BY_COUNTY  \n",
       "15387  0.000000  71.166667               NaN         0        1254000  \n",
       "15512  0.000741  72.764706               NaN         0        1248000  \n",
       "15894  0.000000  59.941176               NaN         0        2977000  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check DURING_A_DROUGHT\n",
    "wdf_df[wdf_df['DURING_A_DROUGHT'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17226, 21)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove null values from DURING_A_DROUGHT\n",
    "wdf_df = wdf_df[wdf_df['DURING_A_DROUGHT'].notnull()]\n",
    "wdf_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, FIPS_ID, FIRE_NAME, FIRE_YEAR, DISCOVERY_TIME, STAT_CAUSE_DESCR, CONT_DATE, CONT_TIME, FIRE_SIZE, FIRE_SIZE_CLASS, LATITUDE, LONGITUDE, FIPS_NAME, DISCOVERY_MONTH, DISCOVERY_DAY, AWND, PRCP, TAVG, DURING_A_DROUGHT, WATERSHD, POP_BY_COUNTY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check WATERSHD\n",
    "wdf_df[wdf_df['WATERSHD'].isnull()]"
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>FIPS_ID</th>\n",
       "      <th>FIRE_NAME</th>\n",
       "      <th>FIRE_YEAR</th>\n",
       "      <th>DISCOVERY_TIME</th>\n",
       "      <th>STAT_CAUSE_DESCR</th>\n",
       "      <th>CONT_DATE</th>\n",
       "      <th>CONT_TIME</th>\n",
       "      <th>FIRE_SIZE</th>\n",
       "      <th>FIRE_SIZE_CLASS</th>\n",
       "      <th>...</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>FIPS_NAME</th>\n",
       "      <th>DISCOVERY_MONTH</th>\n",
       "      <th>DISCOVERY_DAY</th>\n",
       "      <th>AWND</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>DURING_A_DROUGHT</th>\n",
       "      <th>WATERSHD</th>\n",
       "      <th>POP_BY_COUNTY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, FIPS_ID, FIRE_NAME, FIRE_YEAR, DISCOVERY_TIME, STAT_CAUSE_DESCR, CONT_DATE, CONT_TIME, FIRE_SIZE, FIRE_SIZE_CLASS, LATITUDE, LONGITUDE, FIPS_NAME, DISCOVERY_MONTH, DISCOVERY_DAY, AWND, PRCP, TAVG, DURING_A_DROUGHT, WATERSHD, POP_BY_COUNTY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check POP_BY_COUNTY\n",
    "wdf_df[wdf_df['POP_BY_COUNTY'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3**: Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=6, random_state=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create x and y Dataframes\n",
    "X = wdf_df[['AWND', 'PRCP', 'TAVG', 'DURING_A_DROUGHT', 'WATERSHD', 'POP_BY_COUNTY']]\n",
    "y = wdf_df['FIRE_SIZE_CLASS']\n",
    "\n",
    "# Create train and test sets\n",
    "# random_state as 42 is an arbitrary value\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "# Create DecisionTreeClassifer class\n",
    "# Apply max_depth=6 based on the decision tree model\n",
    "rf = RandomForestClassifier(random_state=0, max_depth=6)\n",
    "\n",
    "# Train the model on the data\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.627792436235708\n"
     ]
    }
   ],
   "source": [
    "# Measuring model performance\n",
    "print('Score:', rf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 62.34295 +/- 0.01999%\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation\n",
    "scores = cross_val_score(rf, X_train, y_train, cv=5, scoring='accuracy')\n",
    "print(\"Accuracy: {} +/- {}%\".format(round(scores.mean()*100, 5), round(scores.std()*100, 5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4**: Dummy Classifier\n",
    "The dummy classifier gives you a measure of “baseline” performance — i.e. the success rate one should expect to achieve even if simply guessing.\n",
    "\n",
    "**Link**: https://medium.com/@mamonu/what-is-the-scikit-learn-dummy-classifier-95549d9cd44<br>\n",
    "**Documentation**: https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Values: ['A' 'B' 'A' ... 'A' 'B' 'A']\n",
      "Score: 0.47521188900499245\n"
     ]
    }
   ],
   "source": [
    "# Dummy Classifier\n",
    "# Create DummyClassifier class\n",
    "dummy_clf = DummyClassifier(strategy=\"stratified\", random_state=0)\n",
    "\n",
    "# Train the model on the data\n",
    "dummy_clf.fit(X, y)\n",
    "\n",
    "# Train a dummy classifier to make predictions based on the class values\n",
    "print('Prediction Values:', dummy_clf.predict(X))\n",
    "\n",
    "# Measuring model performance\n",
    "print('Score:', dummy_clf.score(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best mean cross-validation accuracy 47.81723 +/- 0.47223% on training dataset\n"
     ]
    }
   ],
   "source": [
    "# Cross Validation\n",
    "# Gather the mean and standard deviation of the accuracy\n",
    "cv_scores = cross_val_score(dummy_clf, X, y, cv=5, scoring='accuracy')\n",
    "sm_best_tree_cv_score = cv_scores.mean()\n",
    "sm_best_tree_cv_score_std = cv_scores.std()\n",
    "print('The best mean cross-validation accuracy {} +/- {}% on training dataset'.format(\n",
    "      round(sm_best_tree_cv_score*100,5), round(sm_best_tree_cv_score_std*100, 5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 5**: 5x2cv Paired t Test\n",
    "\n",
    "The 5x2cv paired t test is a procedure for comparing the performance of two models (classifiers or regressors). For this case we will compare the decision tree model with the dummy classifier.\n",
    "\n",
    "**Link**: http://rasbt.github.io/mlxtend/user_guide/evaluate/paired_ttest_5x2cv/<br>\n",
    "**Documentation**: http://rasbt.github.io/mlxtend/#welcome-to-mlxtends-documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t statistic: 36.428\n",
      "p value: 0.000000293\n"
     ]
    }
   ],
   "source": [
    "# 5x2cv Paired t Test\n",
    "t, p = paired_ttest_5x2cv(estimator1=rf, estimator2=dummy_clf, X=X, y=y, random_seed=1)\n",
    "\n",
    "print('t statistic: %.3f' % t)\n",
    "print('p value: %.9f' % p)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}