3-feature-engineering-modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 4 - West Nile Virus Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "The intent of this project is to analyze weather data and GIS data and predicting the presence of the West Nile virus, for a given time, location, and species. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  General Approach\n",
    "- [Data cleaning and imputation](#Data-Cleaning)\n",
    "- [Data visualization](#Data-Visualization)\n",
    "- [Feature selection](#Featuion-Selection)\n",
    "- [Cross validation](#Cross-Validation)\n",
    "- [Model fitting](#Model-Fitting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import cdist\n",
    "from haversine import haversine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset \n",
    "train = pd.read_csv('assets/train_cleaned.csv')\n",
    "test = pd.read_csv('assets/test_cleaned.csv')\n",
    "weather = pd.read_csv('assets/weather_cleaned.csv')\n",
    "spray = pd.read_csv('assets/spray.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning and Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tavg</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>WetBulb</th>\n",
       "      <th>PrecipTotal</th>\n",
       "      <th>StnPressure</th>\n",
       "      <th>SeaLevel</th>\n",
       "      <th>ResultSpeed</th>\n",
       "      <th>ResultDir</th>\n",
       "      <th>AvgSpeed</th>\n",
       "      <th>Rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>83</td>\n",
       "      <td>50</td>\n",
       "      <td>66.5</td>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.10</td>\n",
       "      <td>29.82</td>\n",
       "      <td>1.7</td>\n",
       "      <td>27</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>84</td>\n",
       "      <td>52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>51</td>\n",
       "      <td>57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.18</td>\n",
       "      <td>29.82</td>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "      <td>9.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>59</td>\n",
       "      <td>42</td>\n",
       "      <td>50.5</td>\n",
       "      <td>42</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.38</td>\n",
       "      <td>30.09</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>60</td>\n",
       "      <td>43</td>\n",
       "      <td>51.5</td>\n",
       "      <td>42</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.44</td>\n",
       "      <td>30.08</td>\n",
       "      <td>13.3</td>\n",
       "      <td>2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-03</td>\n",
       "      <td>66</td>\n",
       "      <td>46</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40</td>\n",
       "      <td>48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.39</td>\n",
       "      <td>30.12</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Station        Date  Tmax  Tmin  Tavg  DewPoint  WetBulb  PrecipTotal  \\\n",
       "0        1  2007-05-01    83    50  66.5        51       56          0.0   \n",
       "1        2  2007-05-01    84    52  68.0        51       57          0.0   \n",
       "2        1  2007-05-02    59    42  50.5        42       47          0.0   \n",
       "3        2  2007-05-02    60    43  51.5        42       47          0.0   \n",
       "4        1  2007-05-03    66    46  56.0        40       48          0.0   \n",
       "\n",
       "   StnPressure  SeaLevel  ResultSpeed  ResultDir  AvgSpeed  Rain  \n",
       "0        29.10     29.82          1.7         27       9.2     0  \n",
       "1        29.18     29.82          2.7         25       9.6     0  \n",
       "2        29.38     30.09         13.0          4      13.4     0  \n",
       "3        29.44     30.08         13.3          2      13.4     0  \n",
       "4        29.39     30.12         11.7          7      11.9     0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying the dataframe\n",
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Station          int64\n",
       "Date            object\n",
       "Tmax             int64\n",
       "Tmin             int64\n",
       "Tavg           float64\n",
       "DewPoint         int64\n",
       "WetBulb          int64\n",
       "PrecipTotal    float64\n",
       "StnPressure    float64\n",
       "SeaLevel       float64\n",
       "ResultSpeed    float64\n",
       "ResultDir        int64\n",
       "AvgSpeed       float64\n",
       "Rain             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the datatypes\n",
    "weather.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing Date to DateTime \n",
    "weather['Date'] = pd.to_datetime(weather['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Station                 int64\n",
       "Date           datetime64[ns]\n",
       "Tmax                    int64\n",
       "Tmin                    int64\n",
       "Tavg                  float64\n",
       "DewPoint                int64\n",
       "WetBulb                 int64\n",
       "PrecipTotal           float64\n",
       "StnPressure           float64\n",
       "SeaLevel              float64\n",
       "ResultSpeed           float64\n",
       "ResultDir               int64\n",
       "AvgSpeed              float64\n",
       "Rain                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying the change\n",
    "weather.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Address</th>\n",
       "      <th>Species</th>\n",
       "      <th>Block</th>\n",
       "      <th>Street</th>\n",
       "      <th>Trap</th>\n",
       "      <th>AddressNumberAndStreet</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>AddressAccuracy</th>\n",
       "      <th>NumMosquitos</th>\n",
       "      <th>WnvPresent</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>Tot_Mos_Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>4100 North Oak Park Avenue, Chicago, IL 60634,...</td>\n",
       "      <td>PIPIENS/RESTUANS</td>\n",
       "      <td>41</td>\n",
       "      <td>N OAK PARK AVE</td>\n",
       "      <td>2</td>\n",
       "      <td>4100  N OAK PARK AVE, Chicago, IL</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>4100 North Oak Park Avenue, Chicago, IL 60634,...</td>\n",
       "      <td>RESTUANS</td>\n",
       "      <td>41</td>\n",
       "      <td>N OAK PARK AVE</td>\n",
       "      <td>2</td>\n",
       "      <td>4100  N OAK PARK AVE, Chicago, IL</td>\n",
       "      <td>41.954690</td>\n",
       "      <td>-87.800991</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>6200 North Mandell Avenue, Chicago, IL 60646, USA</td>\n",
       "      <td>RESTUANS</td>\n",
       "      <td>62</td>\n",
       "      <td>N MANDELL AVE</td>\n",
       "      <td>7</td>\n",
       "      <td>6200  N MANDELL AVE, Chicago, IL</td>\n",
       "      <td>41.994991</td>\n",
       "      <td>-87.769279</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>7900 West Foster Avenue, Chicago, IL 60656, USA</td>\n",
       "      <td>PIPIENS/RESTUANS</td>\n",
       "      <td>79</td>\n",
       "      <td>W FOSTER AVE</td>\n",
       "      <td>15</td>\n",
       "      <td>7900  W FOSTER AVE, Chicago, IL</td>\n",
       "      <td>41.974089</td>\n",
       "      <td>-87.824812</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>7900 West Foster Avenue, Chicago, IL 60656, USA</td>\n",
       "      <td>RESTUANS</td>\n",
       "      <td>79</td>\n",
       "      <td>W FOSTER AVE</td>\n",
       "      <td>15</td>\n",
       "      <td>7900  W FOSTER AVE, Chicago, IL</td>\n",
       "      <td>41.974089</td>\n",
       "      <td>-87.824812</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date                                            Address  \\\n",
       "0  2007-05-29  4100 North Oak Park Avenue, Chicago, IL 60634,...   \n",
       "1  2007-05-29  4100 North Oak Park Avenue, Chicago, IL 60634,...   \n",
       "2  2007-05-29  6200 North Mandell Avenue, Chicago, IL 60646, USA   \n",
       "3  2007-05-29    7900 West Foster Avenue, Chicago, IL 60656, USA   \n",
       "4  2007-05-29    7900 West Foster Avenue, Chicago, IL 60656, USA   \n",
       "\n",
       "            Species  Block           Street  Trap  \\\n",
       "0  PIPIENS/RESTUANS     41   N OAK PARK AVE     2   \n",
       "1          RESTUANS     41   N OAK PARK AVE     2   \n",
       "2          RESTUANS     62    N MANDELL AVE     7   \n",
       "3  PIPIENS/RESTUANS     79     W FOSTER AVE    15   \n",
       "4          RESTUANS     79     W FOSTER AVE    15   \n",
       "\n",
       "              AddressNumberAndStreet   Latitude  Longitude  AddressAccuracy  \\\n",
       "0  4100  N OAK PARK AVE, Chicago, IL  41.954690 -87.800991                9   \n",
       "1  4100  N OAK PARK AVE, Chicago, IL  41.954690 -87.800991                9   \n",
       "2   6200  N MANDELL AVE, Chicago, IL  41.994991 -87.769279                9   \n",
       "3    7900  W FOSTER AVE, Chicago, IL  41.974089 -87.824812                8   \n",
       "4    7900  W FOSTER AVE, Chicago, IL  41.974089 -87.824812                8   \n",
       "\n",
       "   NumMosquitos  WnvPresent  Month  Year  Weekday  Tot_Mos_Species  \n",
       "0             1           0      5  2007  Tuesday                1  \n",
       "1             1           0      5  2007  Tuesday                1  \n",
       "2             1           0      5  2007  Tuesday                1  \n",
       "3             1           0      5  2007  Tuesday                1  \n",
       "4             4           0      5  2007  Tuesday                4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function takes in coordinates from train data and output station number closest to it\n",
    "def closest_point(point):\n",
    "    station1, station2 = [41.995, -87.933], [41.786, -87.752]  # Fixed coordinates for two stations\n",
    "    points = [station1, station2] \n",
    "    if cdist([point], points).argmin() == 0: return 1 # return index of closest point\n",
    "    return 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign Station to train data based on station coordinates\n",
    "train['Station'] = [closest_point(x) for x in train[['Latitude','Longitude']].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert test and train data to datetime format\n",
    "train['Date'] = pd.to_datetime(train['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                      datetime64[ns]\n",
       "Address                           object\n",
       "Species                           object\n",
       "Block                              int64\n",
       "Street                            object\n",
       "Trap                               int64\n",
       "AddressNumberAndStreet            object\n",
       "Latitude                         float64\n",
       "Longitude                        float64\n",
       "AddressAccuracy                    int64\n",
       "NumMosquitos                       int64\n",
       "WnvPresent                         int64\n",
       "Month                              int64\n",
       "Year                               int64\n",
       "Weekday                           object\n",
       "Tot_Mos_Species                    int64\n",
       "Station                            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying the changes\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = weather.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in ['PrecipTotal','Tavg','Tmin','Tmax','DewPoint']:\n",
    "    for p in [14,28,60]:\n",
    "        if c == 'PrecipTotal':\n",
    "            weather[f'{c}_{str(p)}'] = weather[c].rolling(p, min_periods=1).sum()\n",
    "        elif c == 'Tmin':\n",
    "            weather[f'{c}_{str(p)}'] = weather[c].rolling(p, min_periods=1).min()\n",
    "        elif c == 'Tmax':\n",
    "            weather[f'{c}_{str(p)}'] = weather[c].rolling(p, min_periods=1).max()\n",
    "        else:\n",
    "            weather[f'{c}_{str(p)}'] = weather[c].rolling(p, min_periods=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Station</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tavg</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>WetBulb</th>\n",
       "      <th>PrecipTotal</th>\n",
       "      <th>StnPressure</th>\n",
       "      <th>SeaLevel</th>\n",
       "      <th>ResultSpeed</th>\n",
       "      <th>...</th>\n",
       "      <th>Tavg_60</th>\n",
       "      <th>Tmin_14</th>\n",
       "      <th>Tmin_28</th>\n",
       "      <th>Tmin_60</th>\n",
       "      <th>Tmax_14</th>\n",
       "      <th>Tmax_28</th>\n",
       "      <th>Tmax_60</th>\n",
       "      <th>DewPoint_14</th>\n",
       "      <th>DewPoint_28</th>\n",
       "      <th>DewPoint_60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>50</td>\n",
       "      <td>66.5</td>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.10</td>\n",
       "      <td>29.82</td>\n",
       "      <td>1.7</td>\n",
       "      <td>...</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>51</td>\n",
       "      <td>57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.18</td>\n",
       "      <td>29.82</td>\n",
       "      <td>2.7</td>\n",
       "      <td>...</td>\n",
       "      <td>67.250000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>42</td>\n",
       "      <td>50.5</td>\n",
       "      <td>42</td>\n",
       "      <td>47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.38</td>\n",
       "      <td>30.09</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>43</td>\n",
       "      <td>51.5</td>\n",
       "      <td>42</td>\n",
       "      <td>47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.44</td>\n",
       "      <td>30.08</td>\n",
       "      <td>13.3</td>\n",
       "      <td>...</td>\n",
       "      <td>59.125000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>46.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-03</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>46</td>\n",
       "      <td>56.0</td>\n",
       "      <td>40</td>\n",
       "      <td>48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.39</td>\n",
       "      <td>30.12</td>\n",
       "      <td>11.7</td>\n",
       "      <td>...</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>45.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-03</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>57.5</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.46</td>\n",
       "      <td>30.12</td>\n",
       "      <td>12.9</td>\n",
       "      <td>...</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>44.333333</td>\n",
       "      <td>44.333333</td>\n",
       "      <td>44.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-04</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>49</td>\n",
       "      <td>57.5</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.31</td>\n",
       "      <td>30.05</td>\n",
       "      <td>10.4</td>\n",
       "      <td>...</td>\n",
       "      <td>58.214286</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>43.857143</td>\n",
       "      <td>43.857143</td>\n",
       "      <td>43.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-04</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>51</td>\n",
       "      <td>64.5</td>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.36</td>\n",
       "      <td>30.04</td>\n",
       "      <td>10.1</td>\n",
       "      <td>...</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>43.625000</td>\n",
       "      <td>43.625000</td>\n",
       "      <td>43.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-05</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>53</td>\n",
       "      <td>59.5</td>\n",
       "      <td>38</td>\n",
       "      <td>49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.40</td>\n",
       "      <td>30.10</td>\n",
       "      <td>11.7</td>\n",
       "      <td>...</td>\n",
       "      <td>59.055556</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-05</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>54</td>\n",
       "      <td>60.0</td>\n",
       "      <td>39</td>\n",
       "      <td>50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.46</td>\n",
       "      <td>30.09</td>\n",
       "      <td>11.2</td>\n",
       "      <td>...</td>\n",
       "      <td>59.150000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>42.600000</td>\n",
       "      <td>42.600000</td>\n",
       "      <td>42.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-06</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>58.5</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.57</td>\n",
       "      <td>30.29</td>\n",
       "      <td>14.4</td>\n",
       "      <td>...</td>\n",
       "      <td>59.090909</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>41.454545</td>\n",
       "      <td>41.454545</td>\n",
       "      <td>41.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-06</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.62</td>\n",
       "      <td>30.28</td>\n",
       "      <td>13.8</td>\n",
       "      <td>...</td>\n",
       "      <td>59.166667</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>40.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-07</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>47</td>\n",
       "      <td>65.0</td>\n",
       "      <td>41</td>\n",
       "      <td>54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.38</td>\n",
       "      <td>30.12</td>\n",
       "      <td>8.6</td>\n",
       "      <td>...</td>\n",
       "      <td>59.615385</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>40.538462</td>\n",
       "      <td>40.538462</td>\n",
       "      <td>40.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-07</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>50</td>\n",
       "      <td>67.0</td>\n",
       "      <td>39</td>\n",
       "      <td>53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.44</td>\n",
       "      <td>30.12</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>60.142857</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>40.428571</td>\n",
       "      <td>40.428571</td>\n",
       "      <td>40.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-08</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>54</td>\n",
       "      <td>68.0</td>\n",
       "      <td>58</td>\n",
       "      <td>62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.29</td>\n",
       "      <td>30.03</td>\n",
       "      <td>2.7</td>\n",
       "      <td>...</td>\n",
       "      <td>60.666667</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>40.928571</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>41.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-08</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>70.0</td>\n",
       "      <td>57</td>\n",
       "      <td>63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.36</td>\n",
       "      <td>30.02</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>61.250000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>41.357143</td>\n",
       "      <td>42.562500</td>\n",
       "      <td>42.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-09</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>61</td>\n",
       "      <td>69.0</td>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
       "      <td>0.13</td>\n",
       "      <td>29.21</td>\n",
       "      <td>29.94</td>\n",
       "      <td>3.9</td>\n",
       "      <td>...</td>\n",
       "      <td>61.705882</td>\n",
       "      <td>43.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>42.571429</td>\n",
       "      <td>43.529412</td>\n",
       "      <td>43.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-09</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>63</td>\n",
       "      <td>69.5</td>\n",
       "      <td>60</td>\n",
       "      <td>63</td>\n",
       "      <td>0.02</td>\n",
       "      <td>29.28</td>\n",
       "      <td>29.93</td>\n",
       "      <td>3.9</td>\n",
       "      <td>...</td>\n",
       "      <td>62.138889</td>\n",
       "      <td>46.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>43.857143</td>\n",
       "      <td>44.444444</td>\n",
       "      <td>44.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-10</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>56</td>\n",
       "      <td>70.0</td>\n",
       "      <td>52</td>\n",
       "      <td>60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.20</td>\n",
       "      <td>29.92</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>62.552632</td>\n",
       "      <td>47.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>44.714286</td>\n",
       "      <td>44.842105</td>\n",
       "      <td>44.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-10</td>\n",
       "      <td>2</td>\n",
       "      <td>83</td>\n",
       "      <td>59</td>\n",
       "      <td>71.0</td>\n",
       "      <td>52</td>\n",
       "      <td>61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>29.26</td>\n",
       "      <td>29.91</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>62.975000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>45.571429</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>45.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Station  Tmax  Tmin  Tavg  DewPoint  WetBulb  PrecipTotal  \\\n",
       "Date                                                                    \n",
       "2007-05-01        1    83    50  66.5        51       56         0.00   \n",
       "2007-05-01        2    84    52  68.0        51       57         0.00   \n",
       "2007-05-02        1    59    42  50.5        42       47         0.00   \n",
       "2007-05-02        2    60    43  51.5        42       47         0.00   \n",
       "2007-05-03        1    66    46  56.0        40       48         0.00   \n",
       "2007-05-03        2    67    48  57.5        40       50         0.00   \n",
       "2007-05-04        1    66    49  57.5        41       50         0.00   \n",
       "2007-05-04        2    78    51  64.5        42       50         0.00   \n",
       "2007-05-05        1    66    53  59.5        38       49         0.00   \n",
       "2007-05-05        2    66    54  60.0        39       50         0.00   \n",
       "2007-05-06        1    68    49  58.5        30       46         0.00   \n",
       "2007-05-06        2    68    52  60.0        30       46         0.00   \n",
       "2007-05-07        1    83    47  65.0        41       54         0.00   \n",
       "2007-05-07        2    84    50  67.0        39       53         0.00   \n",
       "2007-05-08        1    82    54  68.0        58       62         0.00   \n",
       "2007-05-08        2    80    60  70.0        57       63         0.00   \n",
       "2007-05-09        1    77    61  69.0        59       63         0.13   \n",
       "2007-05-09        2    76    63  69.5        60       63         0.02   \n",
       "2007-05-10        1    84    56  70.0        52       60         0.00   \n",
       "2007-05-10        2    83    59  71.0        52       61         0.00   \n",
       "\n",
       "            StnPressure  SeaLevel  ResultSpeed  ...    Tavg_60  Tmin_14  \\\n",
       "Date                                            ...                       \n",
       "2007-05-01        29.10     29.82          1.7  ...  66.500000     50.0   \n",
       "2007-05-01        29.18     29.82          2.7  ...  67.250000     50.0   \n",
       "2007-05-02        29.38     30.09         13.0  ...  61.666667     42.0   \n",
       "2007-05-02        29.44     30.08         13.3  ...  59.125000     42.0   \n",
       "2007-05-03        29.39     30.12         11.7  ...  58.500000     42.0   \n",
       "2007-05-03        29.46     30.12         12.9  ...  58.333333     42.0   \n",
       "2007-05-04        29.31     30.05         10.4  ...  58.214286     42.0   \n",
       "2007-05-04        29.36     30.04         10.1  ...  59.000000     42.0   \n",
       "2007-05-05        29.40     30.10         11.7  ...  59.055556     42.0   \n",
       "2007-05-05        29.46     30.09         11.2  ...  59.150000     42.0   \n",
       "2007-05-06        29.57     30.29         14.4  ...  59.090909     42.0   \n",
       "2007-05-06        29.62     30.28         13.8  ...  59.166667     42.0   \n",
       "2007-05-07        29.38     30.12          8.6  ...  59.615385     42.0   \n",
       "2007-05-07        29.44     30.12          8.5  ...  60.142857     42.0   \n",
       "2007-05-08        29.29     30.03          2.7  ...  60.666667     42.0   \n",
       "2007-05-08        29.36     30.02          2.5  ...  61.250000     42.0   \n",
       "2007-05-09        29.21     29.94          3.9  ...  61.705882     43.0   \n",
       "2007-05-09        29.28     29.93          3.9  ...  62.138889     46.0   \n",
       "2007-05-10        29.20     29.92          0.7  ...  62.552632     47.0   \n",
       "2007-05-10        29.26     29.91          2.0  ...  62.975000     47.0   \n",
       "\n",
       "            Tmin_28  Tmin_60  Tmax_14  Tmax_28  Tmax_60  DewPoint_14  \\\n",
       "Date                                                                   \n",
       "2007-05-01     50.0     50.0     83.0     83.0     83.0    51.000000   \n",
       "2007-05-01     50.0     50.0     84.0     84.0     84.0    51.000000   \n",
       "2007-05-02     42.0     42.0     84.0     84.0     84.0    48.000000   \n",
       "2007-05-02     42.0     42.0     84.0     84.0     84.0    46.500000   \n",
       "2007-05-03     42.0     42.0     84.0     84.0     84.0    45.200000   \n",
       "2007-05-03     42.0     42.0     84.0     84.0     84.0    44.333333   \n",
       "2007-05-04     42.0     42.0     84.0     84.0     84.0    43.857143   \n",
       "2007-05-04     42.0     42.0     84.0     84.0     84.0    43.625000   \n",
       "2007-05-05     42.0     42.0     84.0     84.0     84.0    43.000000   \n",
       "2007-05-05     42.0     42.0     84.0     84.0     84.0    42.600000   \n",
       "2007-05-06     42.0     42.0     84.0     84.0     84.0    41.454545   \n",
       "2007-05-06     42.0     42.0     84.0     84.0     84.0    40.500000   \n",
       "2007-05-07     42.0     42.0     84.0     84.0     84.0    40.538462   \n",
       "2007-05-07     42.0     42.0     84.0     84.0     84.0    40.428571   \n",
       "2007-05-08     42.0     42.0     84.0     84.0     84.0    40.928571   \n",
       "2007-05-08     42.0     42.0     84.0     84.0     84.0    41.357143   \n",
       "2007-05-09     42.0     42.0     84.0     84.0     84.0    42.571429   \n",
       "2007-05-09     42.0     42.0     84.0     84.0     84.0    43.857143   \n",
       "2007-05-10     42.0     42.0     84.0     84.0     84.0    44.714286   \n",
       "2007-05-10     42.0     42.0     84.0     84.0     84.0    45.571429   \n",
       "\n",
       "            DewPoint_28  DewPoint_60  \n",
       "Date                                  \n",
       "2007-05-01    51.000000    51.000000  \n",
       "2007-05-01    51.000000    51.000000  \n",
       "2007-05-02    48.000000    48.000000  \n",
       "2007-05-02    46.500000    46.500000  \n",
       "2007-05-03    45.200000    45.200000  \n",
       "2007-05-03    44.333333    44.333333  \n",
       "2007-05-04    43.857143    43.857143  \n",
       "2007-05-04    43.625000    43.625000  \n",
       "2007-05-05    43.000000    43.000000  \n",
       "2007-05-05    42.600000    42.600000  \n",
       "2007-05-06    41.454545    41.454545  \n",
       "2007-05-06    40.500000    40.500000  \n",
       "2007-05-07    40.538462    40.538462  \n",
       "2007-05-07    40.428571    40.428571  \n",
       "2007-05-08    41.600000    41.600000  \n",
       "2007-05-08    42.562500    42.562500  \n",
       "2007-05-09    43.529412    43.529412  \n",
       "2007-05-09    44.444444    44.444444  \n",
       "2007-05-10    44.842105    44.842105  \n",
       "2007-05-10    45.200000    45.200000  \n",
       "\n",
       "[20 rows x 28 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge weather and train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge train data with weather based on date and station number\n",
    "train = train.merge(weather, on=['Date','Station'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummies for species columns\n",
    "df = pd.get_dummies(train, columns=['Species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create month and day columns\n",
    "df['Month'] = df['Date'].dt.month\n",
    "df[\"Day\"] = df['Date'].dt.dayofyear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization and Removing Collinear Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Address', 'Block', 'Street', 'Trap', 'AddressNumberAndStreet',\n",
       "       'Latitude', 'Longitude', 'AddressAccuracy', 'NumMosquitos',\n",
       "       'WnvPresent', 'Month', 'Year', 'Weekday', 'Tot_Mos_Species', 'Station',\n",
       "       'Tmax', 'Tmin', 'Tavg', 'DewPoint', 'WetBulb', 'PrecipTotal',\n",
       "       'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir', 'AvgSpeed',\n",
       "       'Rain', 'PrecipTotal_14', 'PrecipTotal_28', 'PrecipTotal_60', 'Tavg_14',\n",
       "       'Tavg_28', 'Tavg_60', 'Tmin_14', 'Tmin_28', 'Tmin_60', 'Tmax_14',\n",
       "       'Tmax_28', 'Tmax_60', 'DewPoint_14', 'DewPoint_28', 'DewPoint_60',\n",
       "       'Species_Others', 'Species_PIPIENS', 'Species_PIPIENS/RESTUANS',\n",
       "       'Species_RESTUANS', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating the numerical data types from the object data types\n",
    "df_num = df.drop(columns=df.dtypes[df.dtypes == 'object'].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Block', 'Trap', 'Latitude', 'Longitude', 'AddressAccuracy',\n",
       "       'NumMosquitos', 'WnvPresent', 'Month', 'Year', 'Tot_Mos_Species',\n",
       "       'Station', 'Tmax', 'Tmin', 'Tavg', 'DewPoint', 'WetBulb', 'PrecipTotal',\n",
       "       'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir', 'AvgSpeed',\n",
       "       'Rain', 'PrecipTotal_14', 'PrecipTotal_28', 'PrecipTotal_60', 'Tavg_14',\n",
       "       'Tavg_28', 'Tavg_60', 'Tmin_14', 'Tmin_28', 'Tmin_60', 'Tmax_14',\n",
       "       'Tmax_28', 'Tmax_60', 'DewPoint_14', 'DewPoint_28', 'DewPoint_60',\n",
       "       'Species_Others', 'Species_PIPIENS', 'Species_PIPIENS/RESTUANS',\n",
       "       'Species_RESTUANS', 'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying the change\n",
    "df_num.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the remaining 'categorical' features (Date, Block, Trap, Latitude, Longitude, AddressAccuracy, Month, Year,\n",
    "# Rain, Species_Others, 'Species_PIPIENS', 'Species_PIPIENS/RESTUANS', 'Species_RESTUANS', 'Day', 'Station' )\n",
    "df_cont = df_num[['NumMosquitos', 'Tot_Mos_Species', 'Tmax', 'Tmin', 'Tavg',\n",
    "                  'DewPoint', 'WetBulb', 'PrecipTotal',\n",
    "                  'StnPressure', 'SeaLevel', 'ResultSpeed', 'ResultDir', 'AvgSpeed', 'PrecipTotal_14', 'PrecipTotal_28', 'PrecipTotal_60', 'Tavg_14',\n",
    "                  'Tavg_28', 'Tavg_60', 'Tmin_14', 'Tmin_28', 'Tmin_60', 'Tmax_14',\n",
    "                  'Tmax_28', 'Tmax_60', 'DewPoint_14', 'DewPoint_28', 'DewPoint_60', 'WnvPresent']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#establish figure size\n",
    "plt.figure(figsize=(20,20))\n",
    "\n",
    "#setting title\n",
    "ax = plt.axes()\n",
    "ax.set_title('Correlation Heatmap of Continuous Variables and Presence of WNV')\n",
    "\n",
    "#set up mask to be \"True\" in the upper triangle\n",
    "mask = np.zeros_like(df_cont.corr())\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "#plotting heatmap while masking upper triangle with white\n",
    "with sns.axes_style(\"white\"):\n",
    "    sns.heatmap(df_cont.corr(), mask=mask, square = True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heatmap, we can see that there are a few collinear features (including their rolls):\n",
    "- AvgSpeed & ResultSpeed\n",
    "- SeaLevel & StnPressure\n",
    "- WetBulb & Tmax, Tmin, Tavg, DewPoint "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AvgSpeed       0.034605\n",
       "ResultSpeed    0.048893\n",
       "WnvPresent     1.000000\n",
       "Name: WnvPresent, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking collinear terms against WnvPresent\n",
    "np.abs(df[['AvgSpeed', 'ResultSpeed', 'WnvPresent']].corr().loc['WnvPresent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeaLevel       0.006738\n",
       "StnPressure    0.007149\n",
       "WnvPresent     1.000000\n",
       "Name: WnvPresent, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking collinear terms against WnvPresent\n",
    "np.abs(df[['SeaLevel', 'StnPressure', 'WnvPresent']].corr().loc['WnvPresent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WetBulb        0.087295\n",
       "Tmax           0.056156\n",
       "Tmax_14        0.073485\n",
       "Tmax_28        0.082459\n",
       "Tmax_60        0.067110\n",
       "Tmin           0.078749\n",
       "Tmin_14        0.080009\n",
       "Tmin_28        0.110154\n",
       "Tmin_60        0.154747\n",
       "Tavg           0.070603\n",
       "Tavg_14        0.070628\n",
       "Tavg_28        0.097595\n",
       "Tavg_60        0.130006\n",
       "DewPoint       0.088737\n",
       "DewPoint_14    0.103341\n",
       "DewPoint_28    0.132170\n",
       "DewPoint_60    0.142533\n",
       "WnvPresent     1.000000\n",
       "Name: WnvPresent, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking collinear terms against WnvPresent\n",
    "np.abs(df[['WetBulb', 'Tmax', 'Tmax_14', 'Tmax_28', 'Tmax_60', 'Tmin', 'Tmin_14', 'Tmin_28', 'Tmin_60',\n",
    "           'Tavg', 'Tavg_14', 'Tavg_28', 'Tavg_60','DewPoint','DewPoint_14', 'DewPoint_28', 'DewPoint_60',\n",
    "           'WnvPresent']].corr().loc['WnvPresent'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then pick the features with higher (absolute) correlation vs presence of WNV to keep from the pairs:\n",
    "- ResultSpeed (0.048893)\n",
    "- StnPressure (0.007149)\n",
    "- DewPoint (0.087295) (including DewPoint's rolls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping collinear terms with less correlation with target variable\n",
    "df = df.drop(columns=['AvgSpeed', 'SeaLevel', 'WetBulb', 'Tmax', 'Tmax_14', 'Tmax_28', 'Tmax_60',\n",
    "                      'Tmin', 'Tmin_14', 'Tmin_28', 'Tmin_60',\n",
    "                      'Tavg', 'Tavg_14', 'Tavg_28', 'Tavg_60'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Address', 'Block', 'Street', 'Trap', 'AddressNumberAndStreet',\n",
       "       'Latitude', 'Longitude', 'AddressAccuracy', 'NumMosquitos',\n",
       "       'WnvPresent', 'Month', 'Year', 'Weekday', 'Tot_Mos_Species', 'Station',\n",
       "       'DewPoint', 'PrecipTotal', 'StnPressure', 'ResultSpeed', 'ResultDir',\n",
       "       'Rain', 'PrecipTotal_14', 'PrecipTotal_28', 'PrecipTotal_60',\n",
       "       'DewPoint_14', 'DewPoint_28', 'DewPoint_60', 'Species_Others',\n",
       "       'Species_PIPIENS', 'Species_PIPIENS/RESTUANS', 'Species_RESTUANS',\n",
       "       'Day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying the changes\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding location with high WnvPresent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Latitude   Longitude \n",
       "41.974689  -87.890615    29\n",
       "41.673408  -87.599862    15\n",
       "41.954690  -87.800991    15\n",
       "41.964242  -87.757639    14\n",
       "41.743402  -87.731435    11\n",
       "                         ..\n",
       "41.800737  -87.711880     1\n",
       "41.778748  -87.586427     1\n",
       "41.772846  -87.740029     1\n",
       "41.768388  -87.678649     1\n",
       "41.644612  -87.604498     1\n",
       "Name: Longitude, Length: 99, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find out coordinates with the highest WnvPresent\n",
    "train[train['WnvPresent'] == 1].groupby('Latitude')['Longitude'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top 2 coordinates with high presence of WNV\n",
    "wnvpresent_lat_lon  = [(41.974689, -87.890615), (41.673408, -87.599862)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the haversine distance from train data to coordiates with wnv persent area\n",
    "for i,area in enumerate(wnvpresent_lat_lon):\n",
    "    df['Dist_' + str(i)] = [haversine(row, (area),unit='mi') for row in zip(train['Latitude'],train['Longitude'])]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Block', 'Trap', 'Latitude', 'Longitude', 'AddressAccuracy',\n",
       "       'NumMosquitos', 'WnvPresent', 'Month', 'Year', 'Tot_Mos_Species',\n",
       "       'Station', 'DewPoint', 'PrecipTotal', 'StnPressure', 'ResultSpeed',\n",
       "       'ResultDir', 'Rain', 'PrecipTotal_14', 'PrecipTotal_28',\n",
       "       'PrecipTotal_60', 'DewPoint_14', 'DewPoint_28', 'DewPoint_60',\n",
       "       'Species_Others', 'Species_PIPIENS', 'Species_PIPIENS/RESTUANS',\n",
       "       'Species_RESTUANS', 'Day', 'Dist_0', 'Dist_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display numeric data\n",
    "df._get_numeric_data().columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# features to include in model\n",
    "features = ['DewPoint', 'PrecipTotal', 'StnPressure', 'ResultSpeed', 'ResultDir', 'Rain',\n",
    "            'PrecipTotal_14', 'PrecipTotal_28', 'PrecipTotal_60', 'DewPoint_14', 'DewPoint_28','DewPoint_60',\n",
    "            'Species_Others', 'Species_PIPIENS', 'Species_PIPIENS/RESTUANS', 'Species_RESTUANS',\n",
    "            'Month', 'Day', 'Dist_0', 'Dist_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[features]\n",
    "y = df['WnvPresent']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using SMOTE to handle the unbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE()\n",
    "X, y = smote.fit_resample(X, y) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
       "                       criterion='gini', max_depth=None, max_features='sqrt',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=5, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
       "                       n_jobs=-1, oob_score=False, random_state=8, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initiating and fitting \n",
    "rfc = RandomForestClassifier(class_weight='balanced', max_features='sqrt',\n",
    "                             min_samples_leaf=5, n_estimators=1000, n_jobs=-1, random_state=8)\n",
    "rfc.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# looking at the important features\n",
    "feature_import = rfc.feature_importances_\n",
    "feature_df = pd.DataFrame(columns=['feature', 'importance'], \n",
    "             data=list(zip(features, feature_import))).sort_values('importance',ascending=False).head(19)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a29369110>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_df.set_index('feature').sort_values('importance',ascending=True).plot(kind='barh', figsize=(20,15), fontsize=20, legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initiating standard scalar\n",
    "ss = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit and transform\n",
    "X = ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iniating\n",
    "svm = SVC(probability=True, random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=True, random_state=8, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting\n",
    "svm.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling the Test Data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning nearest station to each row, changing date object type and merging with the weather data \n",
    "# to align with df\n",
    "test['Station'] = [closest_point(x) for x in test[['Latitude','Longitude']].values]\n",
    "test['Date'] = pd.to_datetime(test['Date'])\n",
    "test = test.merge(weather, on=['Date','Station'])\n",
    "test = pd.get_dummies(test, columns=['Species'])\n",
    "test['Month'] = test['Date'].dt.month\n",
    "test[\"Day\"] = test['Date'].dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Repeat for test data\n",
    "for i,area in enumerate(wnvpresent_lat_lon):\n",
    "    test['Dist_' + str(i)] = [haversine(row, (area),unit='mi') for row in zip(test['Latitude'],test['Longitude'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make match above\n",
    "X_test = test[features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preds(model, X_test):\n",
    "    if model == 'rfc':\n",
    "        pred = rfc.predict_proba(X_test)\n",
    "        submission = pd.DataFrame(columns=['Id', 'WnvPresent'], data=list(zip(test['Id'], pred)))\n",
    "        submission.WnvPresent = submission['WnvPresent'].map(lambda x: x[1])\n",
    "        submission.to_csv('submission_rfc_new.csv',index=False)        \n",
    "        \n",
    "    elif model == 'svm':\n",
    "        pred = svm.predict_proba(ss.transform(X_test))\n",
    "        submission = pd.DataFrame(columns=['Id', 'WnvPresent'], data=list(zip(test['Id'], pred)))\n",
    "        submission.WnvPresent = submission['WnvPresent'].map(lambda x: x[1])\n",
    "        submission.to_csv('submission_svm_new.csv',index=False)        \n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Whatever model you decided on:\n",
    "predictions = svm.predict(ss.transform(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_rfc = preds('rfc', X_test)\n",
    "predictions_svm = preds('svm', X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaggle Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We managed to obtain a better Kaggle score using RandomForectClassifer (0.75095) vs SupportVectorMachine (0.70675)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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