train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the modules\n",
    "from glob import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the list of csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset/dengue_features_train.csv',\n",
       " 'dataset/dengue_features_test.csv',\n",
       " 'dataset/dengue_labels_train.csv']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob('dataset/*.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Rows     Columns\n",
      "Dengue Features       1456       24\n",
      "Dengue Labels         1456        4\n"
     ]
    }
   ],
   "source": [
    "dengue_features = pd.read_csv('dataset/dengue_features_train.csv')\n",
    "dengue_labels = pd.read_csv('dataset/dengue_labels_train.csv')\n",
    "\n",
    "print('{:>26} {:>11}'.format('Rows', 'Columns'))\n",
    "\n",
    "print('Dengue Features' + '{:>11} {:>8}'.format(dengue_features.shape[0], dengue_features.shape[1]))\n",
    "\n",
    "print('Dengue Labels' + '{:>13} {:>8}'.format(dengue_labels.shape[0], dengue_labels.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>week_start_date</th>\n",
       "      <th>ndvi_ne</th>\n",
       "      <th>ndvi_nw</th>\n",
       "      <th>ndvi_se</th>\n",
       "      <th>ndvi_sw</th>\n",
       "      <th>precipitation_amt_mm</th>\n",
       "      <th>reanalysis_air_temp_k</th>\n",
       "      <th>...</th>\n",
       "      <th>reanalysis_precip_amt_kg_per_m2</th>\n",
       "      <th>reanalysis_relative_humidity_percent</th>\n",
       "      <th>reanalysis_sat_precip_amt_mm</th>\n",
       "      <th>reanalysis_specific_humidity_g_per_kg</th>\n",
       "      <th>reanalysis_tdtr_k</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_diur_temp_rng_c</th>\n",
       "      <th>station_max_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>station_precip_mm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>18</td>\n",
       "      <td>1990-04-30</td>\n",
       "      <td>0.122600</td>\n",
       "      <td>0.103725</td>\n",
       "      <td>0.198483</td>\n",
       "      <td>0.177617</td>\n",
       "      <td>12.42</td>\n",
       "      <td>297.572857</td>\n",
       "      <td>...</td>\n",
       "      <td>32.00</td>\n",
       "      <td>73.365714</td>\n",
       "      <td>12.42</td>\n",
       "      <td>14.012857</td>\n",
       "      <td>2.628571</td>\n",
       "      <td>25.442857</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>29.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>19</td>\n",
       "      <td>1990-05-07</td>\n",
       "      <td>0.169900</td>\n",
       "      <td>0.142175</td>\n",
       "      <td>0.162357</td>\n",
       "      <td>0.155486</td>\n",
       "      <td>22.82</td>\n",
       "      <td>298.211429</td>\n",
       "      <td>...</td>\n",
       "      <td>17.94</td>\n",
       "      <td>77.368571</td>\n",
       "      <td>22.82</td>\n",
       "      <td>15.372857</td>\n",
       "      <td>2.371429</td>\n",
       "      <td>26.714286</td>\n",
       "      <td>6.371429</td>\n",
       "      <td>31.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>20</td>\n",
       "      <td>1990-05-14</td>\n",
       "      <td>0.032250</td>\n",
       "      <td>0.172967</td>\n",
       "      <td>0.157200</td>\n",
       "      <td>0.170843</td>\n",
       "      <td>34.54</td>\n",
       "      <td>298.781429</td>\n",
       "      <td>...</td>\n",
       "      <td>26.10</td>\n",
       "      <td>82.052857</td>\n",
       "      <td>34.54</td>\n",
       "      <td>16.848571</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>26.714286</td>\n",
       "      <td>6.485714</td>\n",
       "      <td>32.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>21</td>\n",
       "      <td>1990-05-21</td>\n",
       "      <td>0.128633</td>\n",
       "      <td>0.245067</td>\n",
       "      <td>0.227557</td>\n",
       "      <td>0.235886</td>\n",
       "      <td>15.36</td>\n",
       "      <td>298.987143</td>\n",
       "      <td>...</td>\n",
       "      <td>13.90</td>\n",
       "      <td>80.337143</td>\n",
       "      <td>15.36</td>\n",
       "      <td>16.672857</td>\n",
       "      <td>2.428571</td>\n",
       "      <td>27.471429</td>\n",
       "      <td>6.771429</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>22</td>\n",
       "      <td>1990-05-28</td>\n",
       "      <td>0.196200</td>\n",
       "      <td>0.262200</td>\n",
       "      <td>0.251200</td>\n",
       "      <td>0.247340</td>\n",
       "      <td>7.52</td>\n",
       "      <td>299.518571</td>\n",
       "      <td>...</td>\n",
       "      <td>12.20</td>\n",
       "      <td>80.460000</td>\n",
       "      <td>7.52</td>\n",
       "      <td>17.210000</td>\n",
       "      <td>3.014286</td>\n",
       "      <td>28.942857</td>\n",
       "      <td>9.371429</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city  year  weekofyear week_start_date   ndvi_ne   ndvi_nw   ndvi_se  \\\n",
       "0   sj  1990          18      1990-04-30  0.122600  0.103725  0.198483   \n",
       "1   sj  1990          19      1990-05-07  0.169900  0.142175  0.162357   \n",
       "2   sj  1990          20      1990-05-14  0.032250  0.172967  0.157200   \n",
       "3   sj  1990          21      1990-05-21  0.128633  0.245067  0.227557   \n",
       "4   sj  1990          22      1990-05-28  0.196200  0.262200  0.251200   \n",
       "\n",
       "    ndvi_sw  precipitation_amt_mm  reanalysis_air_temp_k        ...          \\\n",
       "0  0.177617                 12.42             297.572857        ...           \n",
       "1  0.155486                 22.82             298.211429        ...           \n",
       "2  0.170843                 34.54             298.781429        ...           \n",
       "3  0.235886                 15.36             298.987143        ...           \n",
       "4  0.247340                  7.52             299.518571        ...           \n",
       "\n",
       "   reanalysis_precip_amt_kg_per_m2  reanalysis_relative_humidity_percent  \\\n",
       "0                            32.00                             73.365714   \n",
       "1                            17.94                             77.368571   \n",
       "2                            26.10                             82.052857   \n",
       "3                            13.90                             80.337143   \n",
       "4                            12.20                             80.460000   \n",
       "\n",
       "   reanalysis_sat_precip_amt_mm  reanalysis_specific_humidity_g_per_kg  \\\n",
       "0                         12.42                              14.012857   \n",
       "1                         22.82                              15.372857   \n",
       "2                         34.54                              16.848571   \n",
       "3                         15.36                              16.672857   \n",
       "4                          7.52                              17.210000   \n",
       "\n",
       "   reanalysis_tdtr_k  station_avg_temp_c  station_diur_temp_rng_c  \\\n",
       "0           2.628571           25.442857                 6.900000   \n",
       "1           2.371429           26.714286                 6.371429   \n",
       "2           2.300000           26.714286                 6.485714   \n",
       "3           2.428571           27.471429                 6.771429   \n",
       "4           3.014286           28.942857                 9.371429   \n",
       "\n",
       "   station_max_temp_c  station_min_temp_c  station_precip_mm  \n",
       "0                29.4                20.0               16.0  \n",
       "1                31.7                22.2                8.6  \n",
       "2                32.2                22.8               41.4  \n",
       "3                33.3                23.3                4.0  \n",
       "4                35.0                23.9                5.8  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city  year  weekofyear  total_cases\n",
       "0   sj  1990          18            4\n",
       "1   sj  1990          19            5\n",
       "2   sj  1990          20            4\n",
       "3   sj  1990          21            3\n",
       "4   sj  1990          22            6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_labels.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custimizing the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn') # Setting plot style\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 8] # Setting plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the year that has maximum cases of dengue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_max_cases = dengue_labels.groupby(['year'])['total_cases'].agg('sum')\n",
    "\n",
    "#Visualizing it\n",
    "year_max_cases.plot(kind='bar')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Cases of Dengue')\n",
    "plt.yticks(np.arange(0,7500, step=500))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, Year 1994 faced maximum cases of dengue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### City that has maximum cases of dengue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_affected = dengue_labels.groupby(['city'])['total_cases'].agg('sum')\n",
    "\n",
    "city_affected.plot(kind='bar')\n",
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Total Cases of Dengue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**sj -> SanJuan** has maximum cases of dengue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Checking null entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1456 entries, 0 to 1455\n",
      "Data columns (total 24 columns):\n",
      "city                                     1456 non-null object\n",
      "year                                     1456 non-null int64\n",
      "weekofyear                               1456 non-null int64\n",
      "week_start_date                          1456 non-null object\n",
      "ndvi_ne                                  1262 non-null float64\n",
      "ndvi_nw                                  1404 non-null float64\n",
      "ndvi_se                                  1434 non-null float64\n",
      "ndvi_sw                                  1434 non-null float64\n",
      "precipitation_amt_mm                     1443 non-null float64\n",
      "reanalysis_air_temp_k                    1446 non-null float64\n",
      "reanalysis_avg_temp_k                    1446 non-null float64\n",
      "reanalysis_dew_point_temp_k              1446 non-null float64\n",
      "reanalysis_max_air_temp_k                1446 non-null float64\n",
      "reanalysis_min_air_temp_k                1446 non-null float64\n",
      "reanalysis_precip_amt_kg_per_m2          1446 non-null float64\n",
      "reanalysis_relative_humidity_percent     1446 non-null float64\n",
      "reanalysis_sat_precip_amt_mm             1443 non-null float64\n",
      "reanalysis_specific_humidity_g_per_kg    1446 non-null float64\n",
      "reanalysis_tdtr_k                        1446 non-null float64\n",
      "station_avg_temp_c                       1413 non-null float64\n",
      "station_diur_temp_rng_c                  1413 non-null float64\n",
      "station_max_temp_c                       1436 non-null float64\n",
      "station_min_temp_c                       1442 non-null float64\n",
      "station_precip_mm                        1434 non-null float64\n",
      "dtypes: float64(20), int64(2), object(2)\n",
      "memory usage: 273.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dengue_features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                                       0\n",
       "year                                       0\n",
       "weekofyear                                 0\n",
       "week_start_date                            0\n",
       "ndvi_ne                                  194\n",
       "ndvi_nw                                   52\n",
       "ndvi_se                                   22\n",
       "ndvi_sw                                   22\n",
       "precipitation_amt_mm                      13\n",
       "reanalysis_air_temp_k                     10\n",
       "reanalysis_avg_temp_k                     10\n",
       "reanalysis_dew_point_temp_k               10\n",
       "reanalysis_max_air_temp_k                 10\n",
       "reanalysis_min_air_temp_k                 10\n",
       "reanalysis_precip_amt_kg_per_m2           10\n",
       "reanalysis_relative_humidity_percent      10\n",
       "reanalysis_sat_precip_amt_mm              13\n",
       "reanalysis_specific_humidity_g_per_kg     10\n",
       "reanalysis_tdtr_k                         10\n",
       "station_avg_temp_c                        43\n",
       "station_diur_temp_rng_c                   43\n",
       "station_max_temp_c                        20\n",
       "station_min_temp_c                        14\n",
       "station_precip_mm                         22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking columns having null values\n",
    "dengue_features.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ndvi_ne',\n",
       " 'ndvi_nw',\n",
       " 'ndvi_se',\n",
       " 'ndvi_sw',\n",
       " 'precipitation_amt_mm',\n",
       " 'reanalysis_air_temp_k',\n",
       " 'reanalysis_avg_temp_k',\n",
       " 'reanalysis_dew_point_temp_k',\n",
       " 'reanalysis_max_air_temp_k',\n",
       " 'reanalysis_min_air_temp_k',\n",
       " 'reanalysis_precip_amt_kg_per_m2',\n",
       " 'reanalysis_relative_humidity_percent',\n",
       " 'reanalysis_sat_precip_amt_mm',\n",
       " 'reanalysis_specific_humidity_g_per_kg',\n",
       " 'reanalysis_tdtr_k',\n",
       " 'station_avg_temp_c',\n",
       " 'station_diur_temp_rng_c',\n",
       " 'station_max_temp_c',\n",
       " 'station_min_temp_c',\n",
       " 'station_precip_mm']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the list of columns having missing values\n",
    "x = dengue_features.columns[dengue_features.isna().sum() > 0].tolist()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling the missing value with mean\n",
    "for i in x:\n",
    "    dengue_features[i] = dengue_features[i].fillna(dengue_features[i].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting all the temperatures from Kelvin to Centigrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reanalysis_air_temp_k',\n",
       " 'reanalysis_avg_temp_k',\n",
       " 'reanalysis_dew_point_temp_k',\n",
       " 'reanalysis_max_air_temp_k',\n",
       " 'reanalysis_min_air_temp_k']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature_kelvin_columns = [i for i in dengue_features.columns if i.find('_temp_k') > -1]\n",
    "temperature_kelvin_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in temperature_kelvin_columns:\n",
    "    dengue_features[i] = dengue_features[i] - 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the column names from _temp_k to _temp_c\n",
    "dengue_features.columns = [i.replace('_temp_k', '_temp_c') \n",
    "                           for i in dengue_features.columns if i.find('_temp_k')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Rounding every value upto 3 decimal places"
   ]
  },
  {
   "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>city</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>week_start_date</th>\n",
       "      <th>ndvi_ne</th>\n",
       "      <th>ndvi_nw</th>\n",
       "      <th>ndvi_se</th>\n",
       "      <th>ndvi_sw</th>\n",
       "      <th>precipitation_amt_mm</th>\n",
       "      <th>reanalysis_air_temp_c</th>\n",
       "      <th>...</th>\n",
       "      <th>reanalysis_precip_amt_kg_per_m2</th>\n",
       "      <th>reanalysis_relative_humidity_percent</th>\n",
       "      <th>reanalysis_sat_precip_amt_mm</th>\n",
       "      <th>reanalysis_specific_humidity_g_per_kg</th>\n",
       "      <th>reanalysis_tdtr_k</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_diur_temp_rng_c</th>\n",
       "      <th>station_max_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>station_precip_mm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>18</td>\n",
       "      <td>1990-04-30</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.178</td>\n",
       "      <td>12.42</td>\n",
       "      <td>24.423</td>\n",
       "      <td>...</td>\n",
       "      <td>32.00</td>\n",
       "      <td>73.366</td>\n",
       "      <td>12.42</td>\n",
       "      <td>14.013</td>\n",
       "      <td>2.629</td>\n",
       "      <td>25.443</td>\n",
       "      <td>6.900</td>\n",
       "      <td>29.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>19</td>\n",
       "      <td>1990-05-07</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.155</td>\n",
       "      <td>22.82</td>\n",
       "      <td>25.061</td>\n",
       "      <td>...</td>\n",
       "      <td>17.94</td>\n",
       "      <td>77.369</td>\n",
       "      <td>22.82</td>\n",
       "      <td>15.373</td>\n",
       "      <td>2.371</td>\n",
       "      <td>26.714</td>\n",
       "      <td>6.371</td>\n",
       "      <td>31.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>20</td>\n",
       "      <td>1990-05-14</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.171</td>\n",
       "      <td>34.54</td>\n",
       "      <td>25.631</td>\n",
       "      <td>...</td>\n",
       "      <td>26.10</td>\n",
       "      <td>82.053</td>\n",
       "      <td>34.54</td>\n",
       "      <td>16.849</td>\n",
       "      <td>2.300</td>\n",
       "      <td>26.714</td>\n",
       "      <td>6.486</td>\n",
       "      <td>32.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>21</td>\n",
       "      <td>1990-05-21</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.236</td>\n",
       "      <td>15.36</td>\n",
       "      <td>25.837</td>\n",
       "      <td>...</td>\n",
       "      <td>13.90</td>\n",
       "      <td>80.337</td>\n",
       "      <td>15.36</td>\n",
       "      <td>16.673</td>\n",
       "      <td>2.429</td>\n",
       "      <td>27.471</td>\n",
       "      <td>6.771</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>1990</td>\n",
       "      <td>22</td>\n",
       "      <td>1990-05-28</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.247</td>\n",
       "      <td>7.52</td>\n",
       "      <td>26.369</td>\n",
       "      <td>...</td>\n",
       "      <td>12.20</td>\n",
       "      <td>80.460</td>\n",
       "      <td>7.52</td>\n",
       "      <td>17.210</td>\n",
       "      <td>3.014</td>\n",
       "      <td>28.943</td>\n",
       "      <td>9.371</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  city  year  weekofyear week_start_date  ndvi_ne  ndvi_nw  ndvi_se  ndvi_sw  \\\n",
       "0   sj  1990          18      1990-04-30    0.123    0.104    0.198    0.178   \n",
       "1   sj  1990          19      1990-05-07    0.170    0.142    0.162    0.155   \n",
       "2   sj  1990          20      1990-05-14    0.032    0.173    0.157    0.171   \n",
       "3   sj  1990          21      1990-05-21    0.129    0.245    0.228    0.236   \n",
       "4   sj  1990          22      1990-05-28    0.196    0.262    0.251    0.247   \n",
       "\n",
       "   precipitation_amt_mm  reanalysis_air_temp_c        ...          \\\n",
       "0                 12.42                 24.423        ...           \n",
       "1                 22.82                 25.061        ...           \n",
       "2                 34.54                 25.631        ...           \n",
       "3                 15.36                 25.837        ...           \n",
       "4                  7.52                 26.369        ...           \n",
       "\n",
       "   reanalysis_precip_amt_kg_per_m2  reanalysis_relative_humidity_percent  \\\n",
       "0                            32.00                                73.366   \n",
       "1                            17.94                                77.369   \n",
       "2                            26.10                                82.053   \n",
       "3                            13.90                                80.337   \n",
       "4                            12.20                                80.460   \n",
       "\n",
       "   reanalysis_sat_precip_amt_mm  reanalysis_specific_humidity_g_per_kg  \\\n",
       "0                         12.42                                 14.013   \n",
       "1                         22.82                                 15.373   \n",
       "2                         34.54                                 16.849   \n",
       "3                         15.36                                 16.673   \n",
       "4                          7.52                                 17.210   \n",
       "\n",
       "   reanalysis_tdtr_k  station_avg_temp_c  station_diur_temp_rng_c  \\\n",
       "0              2.629              25.443                    6.900   \n",
       "1              2.371              26.714                    6.371   \n",
       "2              2.300              26.714                    6.486   \n",
       "3              2.429              27.471                    6.771   \n",
       "4              3.014              28.943                    9.371   \n",
       "\n",
       "   station_max_temp_c  station_min_temp_c  station_precip_mm  \n",
       "0                29.4                20.0               16.0  \n",
       "1                31.7                22.2                8.6  \n",
       "2                32.2                22.8               41.4  \n",
       "3                33.3                23.3                4.0  \n",
       "4                35.0                23.9                5.8  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features = dengue_features.round(3)\n",
    "\n",
    "dengue_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combining station_max_temp_c and station_min_temp_c as `avg_station_max_min`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.70\n",
       "1    26.95\n",
       "2    27.50\n",
       "3    28.30\n",
       "4    29.45\n",
       "Name: avg_station_max_min, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features['avg_station_max_min'] = (dengue_features['station_max_temp_c'] + \n",
    "                                          dengue_features['station_min_temp_c']) / 2\n",
    "\n",
    "dengue_features['avg_station_max_min'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combining reanalysis_max_air_temp_c and reanalysis_min_air_temp_c as `avg_analysis_max_min`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.70\n",
       "1    25.50\n",
       "2    25.75\n",
       "3    26.05\n",
       "4    26.55\n",
       "Name: avg_analysis_max_min, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features['avg_analysis_max_min'] = (dengue_features['reanalysis_max_air_temp_c'] + \n",
    "                                           dengue_features['reanalysis_min_air_temp_c']) / 2\n",
    "\n",
    "dengue_features['avg_analysis_max_min'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding total cases\n",
    "dengue_features['total_cases'] = dengue_labels['total_cases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>ndvi_ne</th>\n",
       "      <th>ndvi_nw</th>\n",
       "      <th>ndvi_se</th>\n",
       "      <th>ndvi_sw</th>\n",
       "      <th>precipitation_amt_mm</th>\n",
       "      <th>reanalysis_air_temp_c</th>\n",
       "      <th>reanalysis_avg_temp_c</th>\n",
       "      <th>reanalysis_dew_point_temp_c</th>\n",
       "      <th>...</th>\n",
       "      <th>reanalysis_tdtr_k</th>\n",
       "      <th>station_avg_temp_c</th>\n",
       "      <th>station_diur_temp_rng_c</th>\n",
       "      <th>station_max_temp_c</th>\n",
       "      <th>station_min_temp_c</th>\n",
       "      <th>station_precip_mm</th>\n",
       "      <th>avg_max_min_temp</th>\n",
       "      <th>avg_station_max_min</th>\n",
       "      <th>avg_analysis_max_min</th>\n",
       "      <th>total_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "      <td>1456.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2001.031593</td>\n",
       "      <td>26.503434</td>\n",
       "      <td>0.142249</td>\n",
       "      <td>0.130568</td>\n",
       "      <td>0.203781</td>\n",
       "      <td>0.202295</td>\n",
       "      <td>45.760385</td>\n",
       "      <td>25.551865</td>\n",
       "      <td>26.075588</td>\n",
       "      <td>22.096353</td>\n",
       "      <td>...</td>\n",
       "      <td>4.903755</td>\n",
       "      <td>27.185795</td>\n",
       "      <td>8.059324</td>\n",
       "      <td>32.452431</td>\n",
       "      <td>22.102148</td>\n",
       "      <td>39.326354</td>\n",
       "      <td>27.277290</td>\n",
       "      <td>27.277290</td>\n",
       "      <td>26.423132</td>\n",
       "      <td>24.675137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.408314</td>\n",
       "      <td>15.019437</td>\n",
       "      <td>0.130830</td>\n",
       "      <td>0.117844</td>\n",
       "      <td>0.073299</td>\n",
       "      <td>0.083268</td>\n",
       "      <td>43.519806</td>\n",
       "      <td>1.357720</td>\n",
       "      <td>1.257382</td>\n",
       "      <td>1.522545</td>\n",
       "      <td>...</td>\n",
       "      <td>3.534244</td>\n",
       "      <td>1.273107</td>\n",
       "      <td>2.096867</td>\n",
       "      <td>1.945806</td>\n",
       "      <td>1.566475</td>\n",
       "      <td>47.095179</td>\n",
       "      <td>1.331498</td>\n",
       "      <td>1.331498</td>\n",
       "      <td>1.325096</td>\n",
       "      <td>43.596000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1990.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.406000</td>\n",
       "      <td>-0.456000</td>\n",
       "      <td>-0.016000</td>\n",
       "      <td>-0.063000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.486000</td>\n",
       "      <td>21.743000</td>\n",
       "      <td>16.493000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.357000</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>4.529000</td>\n",
       "      <td>26.700000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1997.000000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>0.055750</td>\n",
       "      <td>0.051000</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>9.960000</td>\n",
       "      <td>24.515250</td>\n",
       "      <td>25.114000</td>\n",
       "      <td>20.980500</td>\n",
       "      <td>...</td>\n",
       "      <td>2.329000</td>\n",
       "      <td>26.329000</td>\n",
       "      <td>6.529000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>8.850000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>0.142000</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.197000</td>\n",
       "      <td>0.191000</td>\n",
       "      <td>38.710000</td>\n",
       "      <td>25.512000</td>\n",
       "      <td>26.117500</td>\n",
       "      <td>22.476000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.864000</td>\n",
       "      <td>27.386000</td>\n",
       "      <td>7.386000</td>\n",
       "      <td>32.800000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>24.450000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>39.250000</td>\n",
       "      <td>0.229250</td>\n",
       "      <td>0.212250</td>\n",
       "      <td>0.247000</td>\n",
       "      <td>0.246250</td>\n",
       "      <td>70.047500</td>\n",
       "      <td>26.677500</td>\n",
       "      <td>27.057000</td>\n",
       "      <td>23.306250</td>\n",
       "      <td>...</td>\n",
       "      <td>7.553750</td>\n",
       "      <td>28.129000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>53.600000</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.508000</td>\n",
       "      <td>0.454000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>0.546000</td>\n",
       "      <td>390.600000</td>\n",
       "      <td>29.050000</td>\n",
       "      <td>29.779000</td>\n",
       "      <td>25.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.029000</td>\n",
       "      <td>30.800000</td>\n",
       "      <td>15.800000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>543.300000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>461.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              year   weekofyear      ndvi_ne      ndvi_nw      ndvi_se  \\\n",
       "count  1456.000000  1456.000000  1456.000000  1456.000000  1456.000000   \n",
       "mean   2001.031593    26.503434     0.142249     0.130568     0.203781   \n",
       "std       5.408314    15.019437     0.130830     0.117844     0.073299   \n",
       "min    1990.000000     1.000000    -0.406000    -0.456000    -0.016000   \n",
       "25%    1997.000000    13.750000     0.055750     0.051000     0.156000   \n",
       "50%    2002.000000    26.500000     0.142000     0.128000     0.197000   \n",
       "75%    2005.000000    39.250000     0.229250     0.212250     0.247000   \n",
       "max    2010.000000    53.000000     0.508000     0.454000     0.538000   \n",
       "\n",
       "           ndvi_sw  precipitation_amt_mm  reanalysis_air_temp_c  \\\n",
       "count  1456.000000           1456.000000            1456.000000   \n",
       "mean      0.202295             45.760385              25.551865   \n",
       "std       0.083268             43.519806               1.357720   \n",
       "min      -0.063000              0.000000              21.486000   \n",
       "25%       0.145000              9.960000              24.515250   \n",
       "50%       0.191000             38.710000              25.512000   \n",
       "75%       0.246250             70.047500              26.677500   \n",
       "max       0.546000            390.600000              29.050000   \n",
       "\n",
       "       reanalysis_avg_temp_c  reanalysis_dew_point_temp_c     ...       \\\n",
       "count            1456.000000                  1456.000000     ...        \n",
       "mean               26.075588                    22.096353     ...        \n",
       "std                 1.257382                     1.522545     ...        \n",
       "min                21.743000                    16.493000     ...        \n",
       "25%                25.114000                    20.980500     ...        \n",
       "50%                26.117500                    22.476000     ...        \n",
       "75%                27.057000                    23.306250     ...        \n",
       "max                29.779000                    25.300000     ...        \n",
       "\n",
       "       reanalysis_tdtr_k  station_avg_temp_c  station_diur_temp_rng_c  \\\n",
       "count        1456.000000         1456.000000              1456.000000   \n",
       "mean            4.903755           27.185795                 8.059324   \n",
       "std             3.534244            1.273107                 2.096867   \n",
       "min             1.357000           21.400000                 4.529000   \n",
       "25%             2.329000           26.329000                 6.529000   \n",
       "50%             2.864000           27.386000                 7.386000   \n",
       "75%             7.553750           28.129000                 9.500000   \n",
       "max            16.029000           30.800000                15.800000   \n",
       "\n",
       "       station_max_temp_c  station_min_temp_c  station_precip_mm  \\\n",
       "count         1456.000000         1456.000000        1456.000000   \n",
       "mean            32.452431           22.102148          39.326354   \n",
       "std              1.945806            1.566475          47.095179   \n",
       "min             26.700000           14.700000           0.000000   \n",
       "25%             31.100000           21.100000           8.850000   \n",
       "50%             32.800000           22.200000          24.450000   \n",
       "75%             33.900000           23.300000          53.600000   \n",
       "max             42.200000           25.600000         543.300000   \n",
       "\n",
       "       avg_max_min_temp  avg_station_max_min  avg_analysis_max_min  \\\n",
       "count       1456.000000          1456.000000           1456.000000   \n",
       "mean          27.277290            27.277290             26.423132   \n",
       "std            1.331498             1.331498              1.325096   \n",
       "min           22.800000            22.800000             22.650000   \n",
       "25%           26.400000            26.400000             25.400000   \n",
       "50%           27.500000            27.500000             26.500000   \n",
       "75%           28.300000            28.300000             27.400000   \n",
       "max           32.000000            32.000000             31.250000   \n",
       "\n",
       "       total_cases  \n",
       "count  1456.000000  \n",
       "mean     24.675137  \n",
       "std      43.596000  \n",
       "min       0.000000  \n",
       "25%       5.000000  \n",
       "50%      12.000000  \n",
       "75%      28.000000  \n",
       "max     461.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "dengue_features.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting plot size\n",
    "plt.rcParams['figure.figsize'] = [12, 12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ndvi_ne\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(dengue_features[x[0]], dengue_features['total_cases'], \n",
    "         marker='.', linestyle='none', color='red')\n",
    "plt.xlabel(x[0])\n",
    "plt.ylabel('total cases')\n",
    "plt.title('total cases vs ' + x[0])\n",
    "\n",
    "# ndvi_nw\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(dengue_features[x[1]], dengue_features['total_cases'], \n",
    "         marker='.', linestyle='none', color='green')\n",
    "plt.xlabel(x[1])\n",
    "plt.ylabel('total cases')\n",
    "plt.title('total cases vs ' + x[1])\n",
    "\n",
    "# ndvi_se\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(dengue_features[x[2]], dengue_features['total_cases'], \n",
    "         marker='.', linestyle='none', color='blue')\n",
    "plt.xlabel(x[2])\n",
    "plt.ylabel('total cases')\n",
    "plt.title('total cases vs ' + x[2])\n",
    "\n",
    "# ndvi_sw\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(dengue_features[x[3]], dengue_features['total_cases'], \n",
    "         marker='.', linestyle='none', color='#B16150')\n",
    "plt.xlabel(x[3])\n",
    "plt.ylabel('total cases')\n",
    "plt.title('total cases vs ' + x[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(dengue_features[x[4]], dengue_features['total_cases'], \n",
    "         marker='.', linestyle='none', color='green')\n",
    "plt.xlabel(x[4])\n",
    "plt.ylabel('total cases')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. The dengue cases of San Juan `sj`  and Iquitos `iq` are not dependent. So splitting them into different DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sj = dengue_features[dengue_features['city'] == 'sj']\n",
    "X_iq = dengue_features[dengue_features['city'] == 'iq']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sj = X_sj.drop(['city', 'weekofyear', 'week_start_date'], axis=1)\n",
    "X_iq = X_iq.drop(['city', 'weekofyear', 'week_start_date'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_sj = pd.get_dummies(X_sj['year'])\n",
    "X_sj = X_sj.drop('year', axis=1)\n",
    "X_sj = X_sj.join(one_hot_sj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_iq = pd.get_dummies(X_iq['year'])\n",
    "X_iq = X_iq.drop('year', axis=1)\n",
    "X_iq = X_iq.join(one_hot_iq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Changing the order of columns in X_sj and X_iq DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = X_sj.columns[:23:-1].values.tolist()\n",
    "X_sj = X_sj[years[::-1]].join(X_sj[X_sj.columns[:24]])\n",
    "X_sj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "years = X_iq.columns[:23:-1].values.tolist()\n",
    "X_iq = X_iq[years[::-1]].join(X_iq[X_iq.columns[:24]])\n",
    "X_iq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing dummy variable trap\n",
    "X_sj = X_sj.drop(1990, axis=1)\n",
    "X_iq = X_iq.drop(2000, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_sj_arr = X_sj.iloc[:].values\n",
    "x_iq_arr = X_iq.iloc[:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.random.seed(23)\n",
    "np.random.shuffle(x_sj_arr)\n",
    "np.random.shuffle(x_iq_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Splitting the dataframes into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_sj_train, X_sj_test, y_sj_train, y_sj_test = train_test_split(x_sj_arr[:, :-1], x_sj_arr[:, -1], test_size=0.2)\n",
    "X_iq_train, X_iq_test, y_iq_train, y_iq_test = train_test_split(x_iq_arr[:, :-1], x_iq_arr[:, -1], test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Applying Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sj_scaler = StandardScaler()\n",
    "X_sj_train = sj_scaler.fit_transform(X_sj_train)\n",
    "X_sj_test = sj_scaler.transform(X_sj_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(X_sj_train, y_sj_train, marker='.', color='green', linestyle='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time for modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "sj_knn = KNeighborsRegressor(n_neighbors=5)\n",
    "\n",
    "sj_knn.fit(X_sj_train, y_sj_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_sj_pred = sj_knn.predict(X_sj_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "print(mean_absolute_error(y_sj_test, y_sj_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iq_scaler = StandardScaler()\n",
    "X_iq_train = iq_scaler.fit_transform(X_iq_train)\n",
    "X_iq_test = iq_scaler.transform(X_iq_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iq_knn = KNeighborsRegressor(n_neighbors=5)\n",
    "\n",
    "iq_knn.fit(X_iq_train, y_iq_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_iq_pred = iq_knn.predict(X_iq_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(mean_absolute_error(y_iq_test, y_iq_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "sj_ada = RandomForestRegressor(n_estimators=50, max_depth=15)\n",
    "sj_ada.fit(X_sj_train, y_sj_train)\n",
    "\n",
    "y_sj_pred = sj_ada.predict(X_sj_test)\n",
    "\n",
    "mean_absolute_error(y_sj_test, y_sj_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iq_ada = RandomForestRegressor(n_estimators=50, max_depth=15)\n",
    "iq_ada.fit(X_iq_train, y_iq_train)\n",
    "\n",
    "y_iq_pred = iq_ada.predict(X_iq_test)\n",
    "\n",
    "mean_absolute_error(y_iq_test, y_iq_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning the hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "knn_parameters = [{'algorithm':['ball_tree'], 'leaf_size':[25]},\n",
    "                  {'algorithm':['kd_tree'], 'leaf_size':[25]},\n",
    "                  {'algorithm':['brute']}]\n",
    "\n",
    "sj_knn_grid_cv = GridSearchCV(estimator=sj_knn, param_grid=knn_parameters,\n",
    "                              scoring='neg_mean_absolute_error', cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_knn_grid_cv = sj_knn_grid_cv.fit(X_sj_train, y_sj_train)\n",
    "\n",
    "# sj_knn_grid_cv.predict(X_sj_test)\n",
    "\n",
    "# mean_absolute_error(y_sj_test, sj_knn_grid_cv.predict(X_sj_test))\n",
    "print(sj_knn_grid_cv.best_score_)\n",
    "print(sj_knn_grid_cv.best_params_)"
   ]
  }
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