California_environment.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",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.options.display.max_columns=999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('all_conditions.csv', parse_dates=['Date'])"
   ]
  },
  {
   "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>Stn Id</th>\n",
       "      <th>Stn Name</th>\n",
       "      <th>CIMIS Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>ETo (in)</th>\n",
       "      <th>Precip (in)</th>\n",
       "      <th>Sol Rad (Ly/day)</th>\n",
       "      <th>Avg Vap Pres (mBars)</th>\n",
       "      <th>Max Air Temp (F)</th>\n",
       "      <th>Min Air Temp (F)</th>\n",
       "      <th>Avg Air Temp (F)</th>\n",
       "      <th>Max Rel Hum (%)</th>\n",
       "      <th>Min Rel Hum (%)</th>\n",
       "      <th>Avg Rel Hum (%)</th>\n",
       "      <th>Dew Point (F)</th>\n",
       "      <th>Avg Wind Speed (mph)</th>\n",
       "      <th>Wind Run (miles)</th>\n",
       "      <th>Avg Soil Temp (F)</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>63.4</td>\n",
       "      <td>35.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>82.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>78.3</td>\n",
       "      <td>51.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>59.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>47.2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>74.5</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>37.3</td>\n",
       "      <td>49.9</td>\n",
       "      <td>79.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>107.5</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.01</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>69.2</td>\n",
       "      <td>48.7</td>\n",
       "      <td>56.8</td>\n",
       "      <td>94.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>140.2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>239.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>73.8</td>\n",
       "      <td>47.5</td>\n",
       "      <td>59.8</td>\n",
       "      <td>94.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>50.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>101.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Stn Id    Stn Name        CIMIS Region       Date  ETo (in)  Precip (in)  \\\n",
       "0       2  FivePoints  San Joaquin Valley 2018-01-01      0.06         0.00   \n",
       "1       2  FivePoints  San Joaquin Valley 2018-01-02      0.04         0.00   \n",
       "2       2  FivePoints  San Joaquin Valley 2018-01-03      0.04         0.00   \n",
       "3       2  FivePoints  San Joaquin Valley 2018-01-04      0.07         0.01   \n",
       "4       2  FivePoints  San Joaquin Valley 2018-01-05      0.07         0.00   \n",
       "\n",
       "   Sol Rad (Ly/day)  Avg Vap Pres (mBars)  Max Air Temp (F)  Min Air Temp (F)  \\\n",
       "0             219.0                   7.3              63.4              35.3   \n",
       "1             127.0                   7.4              59.8              37.7   \n",
       "2             125.0                   8.4              61.1              37.3   \n",
       "3             219.0                  11.6              69.2              48.7   \n",
       "4             239.0                  12.7              73.8              47.5   \n",
       "\n",
       "   Avg Air Temp (F)  Max Rel Hum (%)  Min Rel Hum (%)  Avg Rel Hum (%)  \\\n",
       "0              47.8             82.0             46.0             65.0   \n",
       "1              47.2             80.0             52.0             67.0   \n",
       "2              49.9             79.0             49.0             68.0   \n",
       "3              56.8             94.0             52.0             74.0   \n",
       "4              59.8             94.0             49.0             72.0   \n",
       "\n",
       "   Dew Point (F)  Avg Wind Speed (mph)  Wind Run (miles)  Avg Soil Temp (F)  \\\n",
       "0           36.6                   3.3              78.3               51.1   \n",
       "1           36.7                   3.1              74.5               51.3   \n",
       "2           39.9                   4.5             107.5               51.3   \n",
       "3           48.5                   5.8             140.2               53.0   \n",
       "4           50.8                   4.2             101.4               54.4   \n",
       "\n",
       "   Target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 128125 entries, 0 to 128124\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count   Dtype         \n",
      "---  ------                --------------   -----         \n",
      " 0   Stn Id                128125 non-null  int64         \n",
      " 1   Stn Name              128125 non-null  object        \n",
      " 2   CIMIS Region          128125 non-null  object        \n",
      " 3   Date                  128125 non-null  datetime64[ns]\n",
      " 4   ETo (in)              128042 non-null  float64       \n",
      " 5   Precip (in)           128125 non-null  float64       \n",
      " 6   Sol Rad (Ly/day)      128125 non-null  float64       \n",
      " 7   Avg Vap Pres (mBars)  128125 non-null  float64       \n",
      " 8   Max Air Temp (F)      128122 non-null  float64       \n",
      " 9   Min Air Temp (F)      128124 non-null  float64       \n",
      " 10  Avg Air Temp (F)      128120 non-null  float64       \n",
      " 11  Max Rel Hum (%)       128125 non-null  float64       \n",
      " 12  Min Rel Hum (%)       128125 non-null  float64       \n",
      " 13  Avg Rel Hum (%)       128112 non-null  float64       \n",
      " 14  Dew Point (F)         128112 non-null  float64       \n",
      " 15  Avg Wind Speed (mph)  128125 non-null  float64       \n",
      " 16  Wind Run (miles)      128125 non-null  float64       \n",
      " 17  Avg Soil Temp (F)     128105 non-null  float64       \n",
      " 18  Target                128125 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(14), int64(2), object(2)\n",
      "memory usage: 18.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stn Id                   0\n",
       "Stn Name                 0\n",
       "CIMIS Region             0\n",
       "Date                     0\n",
       "ETo (in)                83\n",
       "Precip (in)              0\n",
       "Sol Rad (Ly/day)         0\n",
       "Avg Vap Pres (mBars)     0\n",
       "Max Air Temp (F)         3\n",
       "Min Air Temp (F)         1\n",
       "Avg Air Temp (F)         5\n",
       "Max Rel Hum (%)          0\n",
       "Min Rel Hum (%)          0\n",
       "Avg Rel Hum (%)         13\n",
       "Dew Point (F)           13\n",
       "Avg Wind Speed (mph)     0\n",
       "Wind Run (miles)         0\n",
       "Avg Soil Temp (F)       20\n",
       "Target                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null percentage of Average Temperature feature: 0.06%\n"
     ]
    }
   ],
   "source": [
    "print(f\"Null percentage of Average Temperature feature: {((df['ETo (in)'].isnull().sum()) / (len(df)) * 100).round(2)}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 128009 entries, 0 to 128124\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count   Dtype         \n",
      "---  ------                --------------   -----         \n",
      " 0   Stn Id                128009 non-null  int64         \n",
      " 1   Stn Name              128009 non-null  object        \n",
      " 2   CIMIS Region          128009 non-null  object        \n",
      " 3   Date                  128009 non-null  datetime64[ns]\n",
      " 4   ETo (in)              128009 non-null  float64       \n",
      " 5   Precip (in)           128009 non-null  float64       \n",
      " 6   Sol Rad (Ly/day)      128009 non-null  float64       \n",
      " 7   Avg Vap Pres (mBars)  128009 non-null  float64       \n",
      " 8   Max Air Temp (F)      128009 non-null  float64       \n",
      " 9   Min Air Temp (F)      128009 non-null  float64       \n",
      " 10  Avg Air Temp (F)      128009 non-null  float64       \n",
      " 11  Max Rel Hum (%)       128009 non-null  float64       \n",
      " 12  Min Rel Hum (%)       128009 non-null  float64       \n",
      " 13  Avg Rel Hum (%)       128009 non-null  float64       \n",
      " 14  Dew Point (F)         128009 non-null  float64       \n",
      " 15  Avg Wind Speed (mph)  128009 non-null  float64       \n",
      " 16  Wind Run (miles)      128009 non-null  float64       \n",
      " 17  Avg Soil Temp (F)     128009 non-null  float64       \n",
      " 18  Target                128009 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(14), int64(2), object(2)\n",
      "memory usage: 19.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Dataset\n",
    "\n",
    "Data source: https://www.kaggle.com/chelseazaloumis/cimis-dataset-with-fire-target\n",
    "\n",
    "Context\n",
    "Explore an environmental conditions dataframe scraped from CIMIS (California Irrigation Management Information System) weather stations using a selenium chromedriver. With California’s wildfires setting records in 2020, it is worthwhile to explore factors that may contribute to creating at risk environments.\n",
    "\n",
    "Acknowledgements\n",
    "CIMIS: https://cimis.water.ca.gov/Default.aspx\n",
    "\n",
    "Column on dataset:\n",
    "1. Station Id: California weather station IDs\n",
    "2. Station Name: California weather station names\n",
    "3. CIMIS region: Regions in California State\n",
    "4. Date: recorded date\n",
    "5. ETo (in): Evapotranspiration; is the process whereby liquid water is converted to water vapour (vaporization) and removed from the evaporating surface (vapour removal) (http://www.fao.org/3/x0490e/x0490e04.htm). Water evaporates from a variety of surfaces, such as lakes, rivers, pavements, soils, and wet vegetation. Evapotranspiration unit in inch\n",
    "6. Precip (m): Precipitation; is water released from clouds in the form of rain, freezing rain, sleet, snow, or hail. Most precipitation falls as rain (USGS gov: https://www.usgs.gov/special-topic/water-science-school/science/precipitation-and-water-cycle?qt-science_center_objects=0#qt-science_center_objects). Precipitation unit in meter.\n",
    "7. Sol Rad (Ly/day): Solar Radiation; is the heat and light and other radiation given off by the sun (Book, 2002. Source: https://www.sciencedirect.com/topics/chemistry/solar-radiation)\n",
    "8. Avg Vap Pres (mBars): Average Vapor Pressure; defined as the pressure exerted by a vapor that is in thermodynamic equilibrium with the condensed phase (solid or liquid) at a given temperature in a closed system (2019, Speight. Source: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/vapour-pressure.\n",
    "9. Max Air Temp, Min Air Tem, Avg Air Temperature: Maximum, minimum, and average air temperature, units in Fahrenheit.\n",
    "10. Max Rel Hum, Min Rel Hum, Avg Rel Hum: Maximum, minimum, and average relative humidity, units in %%.\n",
    "11. Dew Point (F); is the temperature to which a parcel of moist air must be cooled at constant atmospheric pressure and constant water vapor content in order for saturation to occur (2019, Camuffo. Source: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/dew-point).\n",
    "12. Avg Wind Speed (mph): Average wind speed, unit in miles per hour\n",
    "13. Wind Run (miles): a meteorological term used to categorize or determine the total distance (or amount) of the travelled wind over a period of time. The readings are collected using an anemometer. Wind run can help to determine the rate of evaporation of moisture over a particular area.\n",
    "14. Avg Soil Temp (F): Average Soil Temperature; the measurement of the warmth in soil.\n",
    "15. Target: Target column, whether there is wildfires happen (1) or not (0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Goals:\n",
    "\n",
    "1. Determine key factors that may contribute on California's wildfires causes.\n",
    "2. Create machine learning model to predict probability of Wildfire based on input factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "desc = []\n",
    "\n",
    "for i in df.columns:\n",
    "    desc.append([\n",
    "        i,\n",
    "        df[i].dtypes,\n",
    "        df[i].isnull().sum(),\n",
    "        round((((df[i].isnull().sum())/(len(df)))*100), 2),\n",
    "        df[i].nunique(),\n",
    "        df[i].drop_duplicates().sample(2).values\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>dataFeatures</th>\n",
       "      <th>dataType</th>\n",
       "      <th>null</th>\n",
       "      <th>nullPercentage</th>\n",
       "      <th>unique</th>\n",
       "      <th>uniqueSample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stn Id</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>[62, 86]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Stn Name</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143</td>\n",
       "      <td>[Linden, San Luis Obispo]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CIMIS Region</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14</td>\n",
       "      <td>[San Francisco Bay, Monterey Bay]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Date</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>991</td>\n",
       "      <td>[2020-04-12T00:00:00.000000000, 2019-02-17T00:...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ETo (in)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>[0.21, 0.4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Precip (in)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>312</td>\n",
       "      <td>[1.88, 2.49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sol Rad (Ly/day)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>976</td>\n",
       "      <td>[976.0, 254.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Avg Vap Pres (mBars)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>332</td>\n",
       "      <td>[24.1, 11.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Max Air Temp (F)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>921</td>\n",
       "      <td>[75.5, 120.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Min Air Temp (F)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>884</td>\n",
       "      <td>[52.7, 32.9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Avg Air Temp (F)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>850</td>\n",
       "      <td>[81.3, 81.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Max Rel Hum (%)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101</td>\n",
       "      <td>[6.0, 42.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Min Rel Hum (%)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101</td>\n",
       "      <td>[20.0, 91.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Avg Rel Hum (%)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101</td>\n",
       "      <td>[81.0, 44.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Dew Point (F)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>857</td>\n",
       "      <td>[47.1, 52.3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Avg Wind Speed (mph)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195</td>\n",
       "      <td>[13.9, 17.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wind Run (miles)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3307</td>\n",
       "      <td>[244.3, 40.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Avg Soil Temp (F)</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>648</td>\n",
       "      <td>[80.0, 62.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Target</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>[1, 0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dataFeatures        dataType  null  nullPercentage  unique  \\\n",
       "0                 Stn Id           int64     0             0.0     143   \n",
       "1               Stn Name          object     0             0.0     143   \n",
       "2           CIMIS Region          object     0             0.0      14   \n",
       "3                   Date  datetime64[ns]     0             0.0     991   \n",
       "4               ETo (in)         float64     0             0.0      50   \n",
       "5            Precip (in)         float64     0             0.0     312   \n",
       "6       Sol Rad (Ly/day)         float64     0             0.0     976   \n",
       "7   Avg Vap Pres (mBars)         float64     0             0.0     332   \n",
       "8       Max Air Temp (F)         float64     0             0.0     921   \n",
       "9       Min Air Temp (F)         float64     0             0.0     884   \n",
       "10      Avg Air Temp (F)         float64     0             0.0     850   \n",
       "11       Max Rel Hum (%)         float64     0             0.0     101   \n",
       "12       Min Rel Hum (%)         float64     0             0.0     101   \n",
       "13       Avg Rel Hum (%)         float64     0             0.0     101   \n",
       "14         Dew Point (F)         float64     0             0.0     857   \n",
       "15  Avg Wind Speed (mph)         float64     0             0.0     195   \n",
       "16      Wind Run (miles)         float64     0             0.0    3307   \n",
       "17     Avg Soil Temp (F)         float64     0             0.0     648   \n",
       "18                Target           int64     0             0.0       2   \n",
       "\n",
       "                                         uniqueSample  \n",
       "0                                            [62, 86]  \n",
       "1                           [Linden, San Luis Obispo]  \n",
       "2                   [San Francisco Bay, Monterey Bay]  \n",
       "3   [2020-04-12T00:00:00.000000000, 2019-02-17T00:...  \n",
       "4                                         [0.21, 0.4]  \n",
       "5                                        [1.88, 2.49]  \n",
       "6                                      [976.0, 254.0]  \n",
       "7                                        [24.1, 11.0]  \n",
       "8                                       [75.5, 120.5]  \n",
       "9                                        [52.7, 32.9]  \n",
       "10                                       [81.3, 81.5]  \n",
       "11                                        [6.0, 42.0]  \n",
       "12                                       [20.0, 91.0]  \n",
       "13                                       [81.0, 44.0]  \n",
       "14                                       [47.1, 52.3]  \n",
       "15                                       [13.9, 17.5]  \n",
       "16                                      [244.3, 40.5]  \n",
       "17                                       [80.0, 62.5]  \n",
       "18                                             [1, 0]  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDesc = pd.DataFrame(desc, columns=['dataFeatures', 'dataType', 'null', 'nullPercentage', 'unique', 'uniqueSample'])\n",
    "dfDesc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *There is no more null data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check whether data in column target is balanced or not\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "sns.countplot(x='Target', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data is imbalanced**.\n",
    "\n",
    "Handling imbalanced data will be treated with Oversampling method (Machine Learning model building)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    122681\n",
       "1      5328\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df['Date'].dt.month\n",
    "df['Year'] = df['Date'].dt.year\n",
    "df['date'] = df['Date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding out factors related to California's Wildfires\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "\n",
    "\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above, it can be seen that features having correlation score higher farthest from 0 (indicates having correlation with target column) are features:\n",
    "- Avg Vap Pres: 0.12\n",
    "- Air temperatures; min, max, and average: 0.17, 0.4, 0.16\n",
    "- Avg Soil temp: 0.16\n",
    "- Month: 0.14\n",
    "- Year: 0.17\n",
    "\n",
    "Suspected factors that are correlated with wildfire are some kind related to temperature, such as air and solid temperatures. That being said, wildfire occurances can be predicted related to Season/Month. Hot/Summer season might likely to have higher potential in wildfire occurrences than cold/wet season."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Stn Id</th>\n",
       "      <th>Stn Name</th>\n",
       "      <th>CIMIS Region</th>\n",
       "      <th>ETo (in)</th>\n",
       "      <th>Precip (in)</th>\n",
       "      <th>Sol Rad (Ly/day)</th>\n",
       "      <th>Avg Vap Pres (mBars)</th>\n",
       "      <th>Max Air Temp (F)</th>\n",
       "      <th>Min Air Temp (F)</th>\n",
       "      <th>Avg Air Temp (F)</th>\n",
       "      <th>Max Rel Hum (%)</th>\n",
       "      <th>Min Rel Hum (%)</th>\n",
       "      <th>Avg Rel Hum (%)</th>\n",
       "      <th>Dew Point (F)</th>\n",
       "      <th>Avg Wind Speed (mph)</th>\n",
       "      <th>Wind Run (miles)</th>\n",
       "      <th>Avg Soil Temp (F)</th>\n",
       "      <th>Target</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>63.4</td>\n",
       "      <td>35.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>82.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>78.3</td>\n",
       "      <td>51.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>59.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>47.2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>74.5</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>37.3</td>\n",
       "      <td>49.9</td>\n",
       "      <td>79.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>107.5</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.01</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>69.2</td>\n",
       "      <td>48.7</td>\n",
       "      <td>56.8</td>\n",
       "      <td>94.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>140.2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>239.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>73.8</td>\n",
       "      <td>47.5</td>\n",
       "      <td>59.8</td>\n",
       "      <td>94.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>50.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>101.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Stn Id    Stn Name        CIMIS Region  ETo (in)  Precip (in)  \\\n",
       "0       2  FivePoints  San Joaquin Valley      0.06         0.00   \n",
       "1       2  FivePoints  San Joaquin Valley      0.04         0.00   \n",
       "2       2  FivePoints  San Joaquin Valley      0.04         0.00   \n",
       "3       2  FivePoints  San Joaquin Valley      0.07         0.01   \n",
       "4       2  FivePoints  San Joaquin Valley      0.07         0.00   \n",
       "\n",
       "   Sol Rad (Ly/day)  Avg Vap Pres (mBars)  Max Air Temp (F)  Min Air Temp (F)  \\\n",
       "0             219.0                   7.3              63.4              35.3   \n",
       "1             127.0                   7.4              59.8              37.7   \n",
       "2             125.0                   8.4              61.1              37.3   \n",
       "3             219.0                  11.6              69.2              48.7   \n",
       "4             239.0                  12.7              73.8              47.5   \n",
       "\n",
       "   Avg Air Temp (F)  Max Rel Hum (%)  Min Rel Hum (%)  Avg Rel Hum (%)  \\\n",
       "0              47.8             82.0             46.0             65.0   \n",
       "1              47.2             80.0             52.0             67.0   \n",
       "2              49.9             79.0             49.0             68.0   \n",
       "3              56.8             94.0             52.0             74.0   \n",
       "4              59.8             94.0             49.0             72.0   \n",
       "\n",
       "   Dew Point (F)  Avg Wind Speed (mph)  Wind Run (miles)  Avg Soil Temp (F)  \\\n",
       "0           36.6                   3.3              78.3               51.1   \n",
       "1           36.7                   3.1              74.5               51.3   \n",
       "2           39.9                   4.5             107.5               51.3   \n",
       "3           48.5                   5.8             140.2               53.0   \n",
       "4           50.8                   4.2             101.4               54.4   \n",
       "\n",
       "   Target  Month  Year  date  \n",
       "0       0      1  2018     1  \n",
       "1       0      1  2018     2  \n",
       "2       0      1  2018     3  \n",
       "3       0      1  2018     4  \n",
       "4       0      1  2018     5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some features are redundant to each other, so it's possible to eliminate one.\n",
    "\n",
    "df.drop(columns=['Stn Id', 'Max Rel Hum (%)', 'Min Rel Hum (%)', 'date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data\n",
    "\n",
    "X = df.drop(columns=['Target'])\n",
    "y = df['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoostClassifier with Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, RobustScaler\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Stn Name</th>\n",
       "      <th>CIMIS Region</th>\n",
       "      <th>ETo (in)</th>\n",
       "      <th>Precip (in)</th>\n",
       "      <th>Sol Rad (Ly/day)</th>\n",
       "      <th>Avg Vap Pres (mBars)</th>\n",
       "      <th>Max Air Temp (F)</th>\n",
       "      <th>Min Air Temp (F)</th>\n",
       "      <th>Avg Air Temp (F)</th>\n",
       "      <th>Avg Rel Hum (%)</th>\n",
       "      <th>Dew Point (F)</th>\n",
       "      <th>Avg Wind Speed (mph)</th>\n",
       "      <th>Wind Run (miles)</th>\n",
       "      <th>Avg Soil Temp (F)</th>\n",
       "      <th>Target</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>63.4</td>\n",
       "      <td>35.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>65.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>78.3</td>\n",
       "      <td>51.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>59.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>47.2</td>\n",
       "      <td>67.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>74.5</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>61.1</td>\n",
       "      <td>37.3</td>\n",
       "      <td>49.9</td>\n",
       "      <td>68.0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>107.5</td>\n",
       "      <td>51.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.01</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>69.2</td>\n",
       "      <td>48.7</td>\n",
       "      <td>56.8</td>\n",
       "      <td>74.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>140.2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FivePoints</td>\n",
       "      <td>San Joaquin Valley</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>239.0</td>\n",
       "      <td>12.7</td>\n",
       "      <td>73.8</td>\n",
       "      <td>47.5</td>\n",
       "      <td>59.8</td>\n",
       "      <td>72.0</td>\n",
       "      <td>50.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>101.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Stn Name        CIMIS Region  ETo (in)  Precip (in)  Sol Rad (Ly/day)  \\\n",
       "0  FivePoints  San Joaquin Valley      0.06         0.00             219.0   \n",
       "1  FivePoints  San Joaquin Valley      0.04         0.00             127.0   \n",
       "2  FivePoints  San Joaquin Valley      0.04         0.00             125.0   \n",
       "3  FivePoints  San Joaquin Valley      0.07         0.01             219.0   \n",
       "4  FivePoints  San Joaquin Valley      0.07         0.00             239.0   \n",
       "\n",
       "   Avg Vap Pres (mBars)  Max Air Temp (F)  Min Air Temp (F)  Avg Air Temp (F)  \\\n",
       "0                   7.3              63.4              35.3              47.8   \n",
       "1                   7.4              59.8              37.7              47.2   \n",
       "2                   8.4              61.1              37.3              49.9   \n",
       "3                  11.6              69.2              48.7              56.8   \n",
       "4                  12.7              73.8              47.5              59.8   \n",
       "\n",
       "   Avg Rel Hum (%)  Dew Point (F)  Avg Wind Speed (mph)  Wind Run (miles)  \\\n",
       "0             65.0           36.6                   3.3              78.3   \n",
       "1             67.0           36.7                   3.1              74.5   \n",
       "2             68.0           39.9                   4.5             107.5   \n",
       "3             74.0           48.5                   5.8             140.2   \n",
       "4             72.0           50.8                   4.2             101.4   \n",
       "\n",
       "   Avg Soil Temp (F)  Target  Month  Year  \n",
       "0               51.1       0      1  2018  \n",
       "1               51.3       0      1  2018  \n",
       "2               51.3       0      1  2018  \n",
       "3               53.0       0      1  2018  \n",
       "4               54.4       0      1  2018  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stn Name', 'CIMIS Region', 'ETo (in)', 'Precip (in)',\n",
       "       'Sol Rad (Ly/day)', 'Avg Vap Pres (mBars)', 'Max Air Temp (F)',\n",
       "       'Min Air Temp (F)', 'Avg Air Temp (F)', 'Avg Rel Hum (%)',\n",
       "       'Dew Point (F)', 'Avg Wind Speed (mph)', 'Wind Run (miles)',\n",
       "       'Avg Soil Temp (F)', 'Target', 'Month', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "prep = ColumnTransformer([\n",
    "    ('scaled', StandardScaler(), ['ETo (in)', 'Precip (in)', 'Sol Rad (Ly/day)', 'Avg Vap Pres (mBars)', 'Max Air Temp (F)',\n",
    "       'Min Air Temp (F)', 'Avg Air Temp (F)', 'Avg Rel Hum (%)',\n",
    "       'Dew Point (F)', 'Avg Wind Speed (mph)', 'Wind Run (miles)',\n",
    "       'Avg Soil Temp (F)', 'Month', 'Year']),\n",
    "    ('encode', OneHotEncoder(), ['Stn Name', 'CIMIS Region'])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Pipeline([\n",
    "    ('prep', prep),\n",
    "    ('algo', XGBClassifier())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('prep',\n",
       "                 ColumnTransformer(transformers=[('scaled', StandardScaler(),\n",
       "                                                  ['ETo (in)', 'Precip (in)',\n",
       "                                                   'Sol Rad (Ly/day)',\n",
       "                                                   'Avg Vap Pres (mBars)',\n",
       "                                                   'Max Air Temp (F)',\n",
       "                                                   'Min Air Temp (F)',\n",
       "                                                   'Avg Air Temp (F)',\n",
       "                                                   'Avg Rel Hum (%)',\n",
       "                                                   'Dew Point (F)',\n",
       "                                                   'Avg Wind Speed (mph)',\n",
       "                                                   'Wind Run (miles)',\n",
       "                                                   'Avg Soil Temp (F)', 'Month',\n",
       "                                                   'Year']),\n",
       "                                                 ('encode', OneHotEncoder(),\n",
       "                                                  ['Stn Name',\n",
       "                                                   'CIMIS Region'])])),\n",
       "                ('...\n",
       "                               colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "                               importance_type='gain',\n",
       "                               interaction_constraints='',\n",
       "                               learning_rate=0.300000012, max_delta_step=0,\n",
       "                               max_depth=6, min_child_weight=1, missing=nan,\n",
       "                               monotone_constraints='()', n_estimators=100,\n",
       "                               n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "                               subsample=1, tree_method='exact',\n",
       "                               validate_parameters=1, verbosity=None))])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, recall_score, f1_score, accuracy_score, precision_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99     36815\n",
      "           1       0.87      0.80      0.84      1588\n",
      "\n",
      "    accuracy                           0.99     38403\n",
      "   macro avg       0.93      0.90      0.91     38403\n",
      "weighted avg       0.99      0.99      0.99     38403\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred, labels=[1, 0])\n",
    "df = pd.DataFrame (cm,\n",
    "                  columns=['Pred 1', 'Pred 0'],\n",
    "                  index=['Act 1', 'Act 0'])\n",
    "plt.figure(figsize=(10, 6))\n",
    "# sns.heatmap(df, annot=True)\n",
    "sns.heatmap(cm/np.sum(cm), annot=True, \n",
    "            fmt='.2%')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Based on the data, the primary cause of California Wildfires are natural factors related to temperatures, such as air and soil temperatures.\n",
    "2. Machine Learning model using XGBoostClassifier algorithm gives good evaluation in parameters such precision, recall, f1 score, and overall accuracy. This model can reduce the prediction of false negatif, that there is actually high potential of wildfire that predicted not."
   ]
  },
  {
   "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
}