DengAI/DengAI Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.style as style\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "style.use('fivethirtyeight')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training Data\n",
    "dengue_features_train = pd.read_csv('dengue_features_train.csv')\n",
    "dengue_labels_train = pd.read_csv('dengue_labels_train.csv')\n",
    "\n",
    "# Testing Dataset Features Only\n",
    "dengue_features_test = pd.read_csv('dengue_features_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1456, 24)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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>2008</td>\n",
       "      <td>18</td>\n",
       "      <td>2008-04-29</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>-0.018900</td>\n",
       "      <td>0.102729</td>\n",
       "      <td>0.091200</td>\n",
       "      <td>78.60</td>\n",
       "      <td>298.492857</td>\n",
       "      <td>...</td>\n",
       "      <td>25.37</td>\n",
       "      <td>78.781429</td>\n",
       "      <td>78.60</td>\n",
       "      <td>15.918571</td>\n",
       "      <td>3.128571</td>\n",
       "      <td>26.528571</td>\n",
       "      <td>7.057143</td>\n",
       "      <td>33.3</td>\n",
       "      <td>21.7</td>\n",
       "      <td>75.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>2008-05-06</td>\n",
       "      <td>-0.0180</td>\n",
       "      <td>-0.012400</td>\n",
       "      <td>0.082043</td>\n",
       "      <td>0.072314</td>\n",
       "      <td>12.56</td>\n",
       "      <td>298.475714</td>\n",
       "      <td>...</td>\n",
       "      <td>21.83</td>\n",
       "      <td>78.230000</td>\n",
       "      <td>12.56</td>\n",
       "      <td>15.791429</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>26.071429</td>\n",
       "      <td>5.557143</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>2008-05-13</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.151083</td>\n",
       "      <td>0.091529</td>\n",
       "      <td>3.66</td>\n",
       "      <td>299.455714</td>\n",
       "      <td>...</td>\n",
       "      <td>4.12</td>\n",
       "      <td>78.270000</td>\n",
       "      <td>3.66</td>\n",
       "      <td>16.674286</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>27.928571</td>\n",
       "      <td>7.785714</td>\n",
       "      <td>32.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>21</td>\n",
       "      <td>2008-05-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.019867</td>\n",
       "      <td>0.124329</td>\n",
       "      <td>0.125686</td>\n",
       "      <td>0.00</td>\n",
       "      <td>299.690000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.20</td>\n",
       "      <td>73.015714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.775714</td>\n",
       "      <td>4.342857</td>\n",
       "      <td>28.057143</td>\n",
       "      <td>6.271429</td>\n",
       "      <td>33.3</td>\n",
       "      <td>24.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sj</td>\n",
       "      <td>2008</td>\n",
       "      <td>22</td>\n",
       "      <td>2008-05-27</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.039833</td>\n",
       "      <td>0.062267</td>\n",
       "      <td>0.075914</td>\n",
       "      <td>0.76</td>\n",
       "      <td>299.780000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.36</td>\n",
       "      <td>74.084286</td>\n",
       "      <td>0.76</td>\n",
       "      <td>16.137143</td>\n",
       "      <td>3.542857</td>\n",
       "      <td>27.614286</td>\n",
       "      <td>7.085714</td>\n",
       "      <td>33.3</td>\n",
       "      <td>23.3</td>\n",
       "      <td>84.1</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  2008          18      2008-04-29  -0.0189 -0.018900  0.102729   \n",
       "1   sj  2008          19      2008-05-06  -0.0180 -0.012400  0.082043   \n",
       "2   sj  2008          20      2008-05-13  -0.0015       NaN  0.151083   \n",
       "3   sj  2008          21      2008-05-20      NaN -0.019867  0.124329   \n",
       "4   sj  2008          22      2008-05-27   0.0568  0.039833  0.062267   \n",
       "\n",
       "    ndvi_sw  precipitation_amt_mm  reanalysis_air_temp_k        ...          \\\n",
       "0  0.091200                 78.60             298.492857        ...           \n",
       "1  0.072314                 12.56             298.475714        ...           \n",
       "2  0.091529                  3.66             299.455714        ...           \n",
       "3  0.125686                  0.00             299.690000        ...           \n",
       "4  0.075914                  0.76             299.780000        ...           \n",
       "\n",
       "   reanalysis_precip_amt_kg_per_m2  reanalysis_relative_humidity_percent  \\\n",
       "0                            25.37                             78.781429   \n",
       "1                            21.83                             78.230000   \n",
       "2                             4.12                             78.270000   \n",
       "3                             2.20                             73.015714   \n",
       "4                             4.36                             74.084286   \n",
       "\n",
       "   reanalysis_sat_precip_amt_mm  reanalysis_specific_humidity_g_per_kg  \\\n",
       "0                         78.60                              15.918571   \n",
       "1                         12.56                              15.791429   \n",
       "2                          3.66                              16.674286   \n",
       "3                          0.00                              15.775714   \n",
       "4                          0.76                              16.137143   \n",
       "\n",
       "   reanalysis_tdtr_k  station_avg_temp_c  station_diur_temp_rng_c  \\\n",
       "0           3.128571           26.528571                 7.057143   \n",
       "1           2.571429           26.071429                 5.557143   \n",
       "2           4.428571           27.928571                 7.785714   \n",
       "3           4.342857           28.057143                 6.271429   \n",
       "4           3.542857           27.614286                 7.085714   \n",
       "\n",
       "   station_max_temp_c  station_min_temp_c  station_precip_mm  \n",
       "0                33.3                21.7               75.2  \n",
       "1                30.0                22.2               34.3  \n",
       "2                32.8                22.8                3.0  \n",
       "3                33.3                24.4                0.3  \n",
       "4                33.3                23.3               84.1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the labels and features of the training data into one data set\n",
    "#dengue_train = pd.merge(dengue_features_train,dengue_labels_train.iloc[:,:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to fill the missing values in each column with the mean in that column\n",
    "def fillNulls(nullcols,df):\n",
    "    for col in nullcols:\n",
    "        df[col].fillna(value=df[col].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_month(s):\n",
    "    return int(s[5:7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_cols = ['ndvi_ne', 'ndvi_nw',\n",
    "       'ndvi_se', 'ndvi_sw', 'precipitation_amt_mm', 'reanalysis_air_temp_k',\n",
    "       'reanalysis_avg_temp_k', 'reanalysis_dew_point_temp_k',\n",
    "       'reanalysis_max_air_temp_k', 'reanalysis_min_air_temp_k',\n",
    "       'reanalysis_precip_amt_kg_per_m2',\n",
    "       'reanalysis_relative_humidity_percent', 'reanalysis_sat_precip_amt_mm',\n",
    "       'reanalysis_specific_humidity_g_per_kg', 'reanalysis_tdtr_k',\n",
    "       'station_avg_temp_c', 'station_diur_temp_rng_c', 'station_max_temp_c',\n",
    "       'station_min_temp_c', 'station_precip_mm']\n",
    "numeric_cols = na_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process(X, train = True):\n",
    "    \"\"\"\n",
    "    Extracts the month out of date and converts it to a one hot\n",
    "    Standardizes the numerical features\n",
    "    Converts the city column to a boolean feature\n",
    "    \"\"\"\n",
    "    # Converting the city column into a boolean, since we just have two cities\n",
    "    is_sj = X.city == 'sj'\n",
    "\n",
    "    #Extracting month from the date\n",
    "    months = X.week_start_date.apply(extract_month)\n",
    "\n",
    "    # Removing the dates info for now\n",
    "    X.drop(['city', 'year', 'weekofyear', 'week_start_date'], axis=1, inplace=True)\n",
    "    \n",
    "    if train:\n",
    "        # Dropping the rows with null values for now\n",
    "        X.dropna(inplace=True)\n",
    "    else:\n",
    "        X.fillna(0,inplace=True)\n",
    "\n",
    "    # Standardizing the data\n",
    "    scaler = StandardScaler()\n",
    "    X[X.columns] = scaler.fit_transform(X)\n",
    "\n",
    "    # Adding the city boolean column\n",
    "    X['is_sj'] = is_sj.loc[X.index]\n",
    "\n",
    "    sliced_months = months.loc[X.index]\n",
    "    # Month one hot features\n",
    "    month_features = pd.get_dummies(sliced_months)\n",
    "    X = X.join(month_features)\n",
    "\n",
    "    # Alternatively use months as a discrete feature\n",
    "    #X = X.join(sliced_months)\n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dengue_features_testNumeric = pre_process(dengue_features_test, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dengue_features_testNumeric.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#months =dengue_train.week_start_date.apply(extract_month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sliced_months = months.loc[dengue_train.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.get_dummies(sliced_months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th></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",
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       "      <th>reanalysis_max_air_temp_k</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.122207</td>\n",
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       "      <td>-0.814598</td>\n",
       "      <td>-0.808198</td>\n",
       "      <td>-1.190146</td>\n",
       "      <td>-1.926304</td>\n",
       "      <td>-1.171085</td>\n",
       "      <td>0.122086</td>\n",
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       "      <td>-0.580536</td>\n",
       "      <td>-0.587307</td>\n",
       "      <td>-0.573640</td>\n",
       "      <td>-0.340090</td>\n",
       "      <td>-0.634981</td>\n",
       "      <td>-0.901462</td>\n",
       "      <td>-0.837468</td>\n",
       "      <td>0.315202</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.767304</td>\n",
       "      <td>0.319858</td>\n",
       "      <td>-0.649827</td>\n",
       "      <td>-0.407716</td>\n",
       "      <td>-0.302099</td>\n",
       "      <td>0.077752</td>\n",
       "      <td>-0.289418</td>\n",
       "      <td>0.087186</td>\n",
       "      <td>-0.958783</td>\n",
       "      <td>0.662810</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.079130</td>\n",
       "      <td>0.917244</td>\n",
       "      <td>0.295500</td>\n",
       "      <td>0.352911</td>\n",
       "      <td>-0.746481</td>\n",
       "      <td>0.228552</td>\n",
       "      <td>-0.011835</td>\n",
       "      <td>0.004322</td>\n",
       "      <td>-0.685825</td>\n",
       "      <td>0.546941</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.403295</td>\n",
       "      <td>1.059203</td>\n",
       "      <td>0.613169</td>\n",
       "      <td>0.486861</td>\n",
       "      <td>-0.928126</td>\n",
       "      <td>0.618119</td>\n",
       "      <td>0.333727</td>\n",
       "      <td>0.345301</td>\n",
       "      <td>-0.534181</td>\n",
       "      <td>0.740057</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.191465</td>\n",
       "      <td>-0.344365</td>\n",
       "      <td>-0.006621</td>\n",
       "      <td>0.053371</td>\n",
       "      <td>-1.021729</td>\n",
       "      <td>0.389825</td>\n",
       "      <td>-0.017500</td>\n",
       "      <td>0.374827</td>\n",
       "      <td>-0.716153</td>\n",
       "      <td>0.817303</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.479920</td>\n",
       "      <td>-0.512562</td>\n",
       "      <td>-0.726797</td>\n",
       "      <td>-0.849927</td>\n",
       "      <td>2.398945</td>\n",
       "      <td>0.671527</td>\n",
       "      <td>0.226093</td>\n",
       "      <td>0.818671</td>\n",
       "      <td>-0.928455</td>\n",
       "      <td>1.087665</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.266078</td>\n",
       "      <td>0.097875</td>\n",
       "      <td>-1.074793</td>\n",
       "      <td>-0.960187</td>\n",
       "      <td>-0.654732</td>\n",
       "      <td>0.662102</td>\n",
       "      <td>0.248753</td>\n",
       "      <td>0.716758</td>\n",
       "      <td>-0.473523</td>\n",
       "      <td>0.817303</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.379554</td>\n",
       "      <td>-0.430949</td>\n",
       "      <td>-0.183017</td>\n",
       "      <td>-0.617211</td>\n",
       "      <td>-0.586383</td>\n",
       "      <td>0.613930</td>\n",
       "      <td>0.277077</td>\n",
       "      <td>0.491979</td>\n",
       "      <td>-0.564510</td>\n",
       "      <td>0.933172</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.084448</td>\n",
       "      <td>0.641612</td>\n",
       "      <td>1.284975</td>\n",
       "      <td>0.876592</td>\n",
       "      <td>0.268554</td>\n",
       "      <td>0.727030</td>\n",
       "      <td>0.401706</td>\n",
       "      <td>0.687232</td>\n",
       "      <td>-0.503852</td>\n",
       "      <td>0.662810</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.077474</td>\n",
       "      <td>0.309363</td>\n",
       "      <td>0.286671</td>\n",
       "      <td>0.103489</td>\n",
       "      <td>-0.720532</td>\n",
       "      <td>0.647441</td>\n",
       "      <td>0.311067</td>\n",
       "      <td>0.437689</td>\n",
       "      <td>-0.564510</td>\n",
       "      <td>0.585564</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.360693</td>\n",
       "      <td>0.285335</td>\n",
       "      <td>-0.509324</td>\n",
       "      <td>-0.390844</td>\n",
       "      <td>-0.351681</td>\n",
       "      <td>1.215036</td>\n",
       "      <td>0.928547</td>\n",
       "      <td>0.950110</td>\n",
       "      <td>-0.352208</td>\n",
       "      <td>0.971795</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.808131</td>\n",
       "      <td>1.627176</td>\n",
       "      <td>0.787454</td>\n",
       "      <td>0.919248</td>\n",
       "      <td>-0.153818</td>\n",
       "      <td>1.057953</td>\n",
       "      <td>0.775593</td>\n",
       "      <td>1.142506</td>\n",
       "      <td>-0.443195</td>\n",
       "      <td>1.203534</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.683182</td>\n",
       "      <td>0.544050</td>\n",
       "      <td>1.051411</td>\n",
       "      <td>0.289261</td>\n",
       "      <td>-0.433699</td>\n",
       "      <td>1.359553</td>\n",
       "      <td>1.104161</td>\n",
       "      <td>0.542459</td>\n",
       "      <td>0.042066</td>\n",
       "      <td>0.933172</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.083889</td>\n",
       "      <td>2.508341</td>\n",
       "      <td>-1.103968</td>\n",
       "      <td>-0.671534</td>\n",
       "      <td>0.393435</td>\n",
       "      <td>1.465322</td>\n",
       "      <td>1.228790</td>\n",
       "      <td>1.259658</td>\n",
       "      <td>-0.230893</td>\n",
       "      <td>1.319404</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.404366</td>\n",
       "      <td>0.398294</td>\n",
       "      <td>0.661923</td>\n",
       "      <td>1.169170</td>\n",
       "      <td>2.227726</td>\n",
       "      <td>0.866311</td>\n",
       "      <td>0.520670</td>\n",
       "      <td>0.751999</td>\n",
       "      <td>-0.594838</td>\n",
       "      <td>1.203534</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.168617</td>\n",
       "      <td>1.048432</td>\n",
       "      <td>-0.082246</td>\n",
       "      <td>0.450336</td>\n",
       "      <td>0.088299</td>\n",
       "      <td>1.284153</td>\n",
       "      <td>1.075836</td>\n",
       "      <td>0.483407</td>\n",
       "      <td>-0.382537</td>\n",
       "      <td>1.358027</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.634273</td>\n",
       "      <td>0.458916</td>\n",
       "      <td>0.092998</td>\n",
       "      <td>-0.052880</td>\n",
       "      <td>-0.379947</td>\n",
       "      <td>1.539675</td>\n",
       "      <td>1.274110</td>\n",
       "      <td>0.907249</td>\n",
       "      <td>-0.109578</td>\n",
       "      <td>1.358027</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.224438</td>\n",
       "      <td>-0.553575</td>\n",
       "      <td>-0.350649</td>\n",
       "      <td>-0.561218</td>\n",
       "      <td>0.088994</td>\n",
       "      <td>0.866311</td>\n",
       "      <td>0.554660</td>\n",
       "      <td>1.083454</td>\n",
       "      <td>-0.564510</td>\n",
       "      <td>0.971795</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.046296</td>\n",
       "      <td>-0.369636</td>\n",
       "      <td>-1.031029</td>\n",
       "      <td>-0.956344</td>\n",
       "      <td>1.840803</td>\n",
       "      <td>0.166766</td>\n",
       "      <td>-0.159124</td>\n",
       "      <td>0.635800</td>\n",
       "      <td>-0.837468</td>\n",
       "      <td>0.624187</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.803490</td>\n",
       "      <td>0.406579</td>\n",
       "      <td>1.510702</td>\n",
       "      <td>1.054734</td>\n",
       "      <td>1.245823</td>\n",
       "      <td>0.771014</td>\n",
       "      <td>0.424366</td>\n",
       "      <td>0.820576</td>\n",
       "      <td>-0.685825</td>\n",
       "      <td>1.126288</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.091992</td>\n",
       "      <td>0.392839</td>\n",
       "      <td>-0.365941</td>\n",
       "      <td>-0.223114</td>\n",
       "      <td>-0.628319</td>\n",
       "      <td>0.951136</td>\n",
       "      <td>0.628305</td>\n",
       "      <td>0.840578</td>\n",
       "      <td>-0.443195</td>\n",
       "      <td>1.126288</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.078736</td>\n",
       "      <td>0.747667</td>\n",
       "      <td>-0.338301</td>\n",
       "      <td>0.341217</td>\n",
       "      <td>-0.638977</td>\n",
       "      <td>1.227603</td>\n",
       "      <td>0.996527</td>\n",
       "      <td>0.255770</td>\n",
       "      <td>-0.412866</td>\n",
       "      <td>1.242157</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.945933</td>\n",
       "      <td>1.623861</td>\n",
       "      <td>-0.993344</td>\n",
       "      <td>-0.939333</td>\n",
       "      <td>-0.638050</td>\n",
       "      <td>0.612883</td>\n",
       "      <td>0.311067</td>\n",
       "      <td>0.048135</td>\n",
       "      <td>-0.625167</td>\n",
       "      <td>0.546941</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.021057</td>\n",
       "      <td>0.680761</td>\n",
       "      <td>-0.386671</td>\n",
       "      <td>-0.749858</td>\n",
       "      <td>-0.323646</td>\n",
       "      <td>-0.320192</td>\n",
       "      <td>-0.776605</td>\n",
       "      <td>-0.328085</td>\n",
       "      <td>-1.049770</td>\n",
       "      <td>0.353825</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.213599</td>\n",
       "      <td>-0.379165</td>\n",
       "      <td>0.822548</td>\n",
       "      <td>0.537847</td>\n",
       "      <td>-0.871594</td>\n",
       "      <td>-0.097134</td>\n",
       "      <td>-0.442372</td>\n",
       "      <td>-0.347134</td>\n",
       "      <td>-0.867797</td>\n",
       "      <td>0.431072</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.131132</td>\n",
       "      <td>1.054729</td>\n",
       "      <td>-0.190504</td>\n",
       "      <td>0.050030</td>\n",
       "      <td>-1.102357</td>\n",
       "      <td>-0.346373</td>\n",
       "      <td>-0.765275</td>\n",
       "      <td>-2.199658</td>\n",
       "      <td>-0.928455</td>\n",
       "      <td>0.199333</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1.095396</td>\n",
       "      <td>1.097869</td>\n",
       "      <td>0.742923</td>\n",
       "      <td>1.134589</td>\n",
       "      <td>0.112858</td>\n",
       "      <td>-0.277256</td>\n",
       "      <td>-0.685965</td>\n",
       "      <td>-0.496669</td>\n",
       "      <td>-0.837468</td>\n",
       "      <td>0.160710</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1.162274</td>\n",
       "      <td>1.183762</td>\n",
       "      <td>-0.460825</td>\n",
       "      <td>-0.515546</td>\n",
       "      <td>-1.102357</td>\n",
       "      <td>-0.466804</td>\n",
       "      <td>-0.918228</td>\n",
       "      <td>-2.132034</td>\n",
       "      <td>-1.171085</td>\n",
       "      <td>-0.109652</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.048438</td>\n",
       "      <td>0.050578</td>\n",
       "      <td>-0.726413</td>\n",
       "      <td>-0.448981</td>\n",
       "      <td>-1.102357</td>\n",
       "      <td>-1.184151</td>\n",
       "      <td>-1.671668</td>\n",
       "      <td>-2.264425</td>\n",
       "      <td>-1.413715</td>\n",
       "      <td>-0.650376</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>1.636164</td>\n",
       "      <td>2.034882</td>\n",
       "      <td>2.344126</td>\n",
       "      <td>3.066481</td>\n",
       "      <td>1.127197</td>\n",
       "      <td>1.206658</td>\n",
       "      <td>2.016219</td>\n",
       "      <td>1.081549</td>\n",
       "      <td>1.528175</td>\n",
       "      <td>0.160710</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>1.908758</td>\n",
       "      <td>1.663218</td>\n",
       "      <td>2.162354</td>\n",
       "      <td>2.147562</td>\n",
       "      <td>-0.413774</td>\n",
       "      <td>1.327089</td>\n",
       "      <td>1.987894</td>\n",
       "      <td>0.536744</td>\n",
       "      <td>2.225736</td>\n",
       "      <td>-0.418637</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>1.402279</td>\n",
       "      <td>1.271036</td>\n",
       "      <td>1.610896</td>\n",
       "      <td>1.776270</td>\n",
       "      <td>-0.217996</td>\n",
       "      <td>0.572041</td>\n",
       "      <td>1.087166</td>\n",
       "      <td>1.281564</td>\n",
       "      <td>1.528175</td>\n",
       "      <td>-0.186899</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>1.490917</td>\n",
       "      <td>1.292855</td>\n",
       "      <td>2.755079</td>\n",
       "      <td>1.926624</td>\n",
       "      <td>0.599407</td>\n",
       "      <td>0.064138</td>\n",
       "      <td>0.548995</td>\n",
       "      <td>1.111075</td>\n",
       "      <td>1.588832</td>\n",
       "      <td>-0.225522</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>0.374735</td>\n",
       "      <td>0.490224</td>\n",
       "      <td>0.461148</td>\n",
       "      <td>0.937624</td>\n",
       "      <td>0.881374</td>\n",
       "      <td>0.383541</td>\n",
       "      <td>1.138151</td>\n",
       "      <td>0.861532</td>\n",
       "      <td>1.497846</td>\n",
       "      <td>-0.380014</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>0.556090</td>\n",
       "      <td>0.877750</td>\n",
       "      <td>-0.319683</td>\n",
       "      <td>0.661807</td>\n",
       "      <td>0.779199</td>\n",
       "      <td>-0.240604</td>\n",
       "      <td>0.084469</td>\n",
       "      <td>1.068214</td>\n",
       "      <td>1.164230</td>\n",
       "      <td>-0.225522</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>0.504841</td>\n",
       "      <td>1.017257</td>\n",
       "      <td>0.209189</td>\n",
       "      <td>1.105669</td>\n",
       "      <td>1.186510</td>\n",
       "      <td>-1.095137</td>\n",
       "      <td>-0.838919</td>\n",
       "      <td>0.835815</td>\n",
       "      <td>-0.230893</td>\n",
       "      <td>-0.264145</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>0.039768</td>\n",
       "      <td>0.065887</td>\n",
       "      <td>-0.944078</td>\n",
       "      <td>-0.446475</td>\n",
       "      <td>-0.338011</td>\n",
       "      <td>-0.277256</td>\n",
       "      <td>0.548995</td>\n",
       "      <td>0.562461</td>\n",
       "      <td>1.315874</td>\n",
       "      <td>-0.688999</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>0.802521</td>\n",
       "      <td>0.796433</td>\n",
       "      <td>-0.245015</td>\n",
       "      <td>1.135257</td>\n",
       "      <td>-0.661219</td>\n",
       "      <td>0.183522</td>\n",
       "      <td>0.962537</td>\n",
       "      <td>0.717711</td>\n",
       "      <td>1.255216</td>\n",
       "      <td>-0.302768</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>0.882183</td>\n",
       "      <td>0.797380</td>\n",
       "      <td>0.178798</td>\n",
       "      <td>0.649945</td>\n",
       "      <td>2.106784</td>\n",
       "      <td>-0.281445</td>\n",
       "      <td>0.294072</td>\n",
       "      <td>0.837720</td>\n",
       "      <td>1.406860</td>\n",
       "      <td>-0.225522</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>0.216941</td>\n",
       "      <td>0.064249</td>\n",
       "      <td>-1.177675</td>\n",
       "      <td>-0.776086</td>\n",
       "      <td>-0.006231</td>\n",
       "      <td>0.057855</td>\n",
       "      <td>0.871898</td>\n",
       "      <td>1.022496</td>\n",
       "      <td>0.739627</td>\n",
       "      <td>0.006217</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>0.650032</td>\n",
       "      <td>0.454832</td>\n",
       "      <td>-0.565372</td>\n",
       "      <td>0.387827</td>\n",
       "      <td>0.710619</td>\n",
       "      <td>-0.310767</td>\n",
       "      <td>0.345057</td>\n",
       "      <td>1.510153</td>\n",
       "      <td>0.769956</td>\n",
       "      <td>-0.032406</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>1.096586</td>\n",
       "      <td>0.940128</td>\n",
       "      <td>0.014302</td>\n",
       "      <td>0.712927</td>\n",
       "      <td>-0.356546</td>\n",
       "      <td>0.713416</td>\n",
       "      <td>1.353419</td>\n",
       "      <td>1.191081</td>\n",
       "      <td>1.679819</td>\n",
       "      <td>-0.418637</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>1.170536</td>\n",
       "      <td>1.041448</td>\n",
       "      <td>0.267668</td>\n",
       "      <td>1.492266</td>\n",
       "      <td>0.859364</td>\n",
       "      <td>0.177238</td>\n",
       "      <td>0.985197</td>\n",
       "      <td>1.750172</td>\n",
       "      <td>1.376531</td>\n",
       "      <td>-0.148275</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>-0.088530</td>\n",
       "      <td>0.387744</td>\n",
       "      <td>-1.143380</td>\n",
       "      <td>-0.736047</td>\n",
       "      <td>0.764602</td>\n",
       "      <td>-0.247934</td>\n",
       "      <td>0.333727</td>\n",
       "      <td>1.742552</td>\n",
       "      <td>0.800285</td>\n",
       "      <td>0.083463</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>1.228166</td>\n",
       "      <td>1.311793</td>\n",
       "      <td>0.900510</td>\n",
       "      <td>2.211296</td>\n",
       "      <td>0.236349</td>\n",
       "      <td>0.118594</td>\n",
       "      <td>0.673624</td>\n",
       "      <td>1.892088</td>\n",
       "      <td>0.982257</td>\n",
       "      <td>-1.075231</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>1.105868</td>\n",
       "      <td>0.966168</td>\n",
       "      <td>1.019899</td>\n",
       "      <td>1.589663</td>\n",
       "      <td>1.705728</td>\n",
       "      <td>0.378305</td>\n",
       "      <td>1.336424</td>\n",
       "      <td>2.097818</td>\n",
       "      <td>1.133901</td>\n",
       "      <td>-0.611753</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>0.903705</td>\n",
       "      <td>1.382614</td>\n",
       "      <td>1.215107</td>\n",
       "      <td>1.052897</td>\n",
       "      <td>0.085982</td>\n",
       "      <td>0.241119</td>\n",
       "      <td>0.837908</td>\n",
       "      <td>1.501581</td>\n",
       "      <td>1.285545</td>\n",
       "      <td>-0.380014</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>0.012262</td>\n",
       "      <td>0.583022</td>\n",
       "      <td>0.610097</td>\n",
       "      <td>-0.707089</td>\n",
       "      <td>0.051460</td>\n",
       "      <td>0.240072</td>\n",
       "      <td>1.166475</td>\n",
       "      <td>0.843435</td>\n",
       "      <td>1.649490</td>\n",
       "      <td>-0.534507</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>0.128304</td>\n",
       "      <td>0.184370</td>\n",
       "      <td>-0.289547</td>\n",
       "      <td>-0.816515</td>\n",
       "      <td>-0.700838</td>\n",
       "      <td>-0.270973</td>\n",
       "      <td>0.248753</td>\n",
       "      <td>1.132981</td>\n",
       "      <td>1.103572</td>\n",
       "      <td>-0.495884</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>0.655234</td>\n",
       "      <td>1.328364</td>\n",
       "      <td>1.694200</td>\n",
       "      <td>0.452479</td>\n",
       "      <td>0.906397</td>\n",
       "      <td>-0.173581</td>\n",
       "      <td>0.209098</td>\n",
       "      <td>1.583492</td>\n",
       "      <td>0.315025</td>\n",
       "      <td>-0.341391</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>0.714190</td>\n",
       "      <td>1.034938</td>\n",
       "      <td>1.373461</td>\n",
       "      <td>1.195567</td>\n",
       "      <td>-0.499963</td>\n",
       "      <td>0.273583</td>\n",
       "      <td>0.622640</td>\n",
       "      <td>0.776763</td>\n",
       "      <td>1.437189</td>\n",
       "      <td>-0.380014</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>0.864639</td>\n",
       "      <td>1.006057</td>\n",
       "      <td>0.701463</td>\n",
       "      <td>1.573792</td>\n",
       "      <td>0.611455</td>\n",
       "      <td>-0.775734</td>\n",
       "      <td>-0.521682</td>\n",
       "      <td>1.114885</td>\n",
       "      <td>0.315025</td>\n",
       "      <td>-0.380014</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>0.206843</td>\n",
       "      <td>0.199994</td>\n",
       "      <td>-0.974022</td>\n",
       "      <td>-0.702913</td>\n",
       "      <td>0.273883</td>\n",
       "      <td>-1.023926</td>\n",
       "      <td>-1.037193</td>\n",
       "      <td>0.956777</td>\n",
       "      <td>0.709298</td>\n",
       "      <td>-0.611753</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>0.880755</td>\n",
       "      <td>1.144544</td>\n",
       "      <td>0.708181</td>\n",
       "      <td>0.453649</td>\n",
       "      <td>-1.075713</td>\n",
       "      <td>-0.752695</td>\n",
       "      <td>-0.425377</td>\n",
       "      <td>-1.384357</td>\n",
       "      <td>1.528175</td>\n",
       "      <td>-2.118055</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1.449658</td>\n",
       "      <td>1.528992</td>\n",
       "      <td>0.682269</td>\n",
       "      <td>1.015140</td>\n",
       "      <td>0.178890</td>\n",
       "      <td>0.483027</td>\n",
       "      <td>1.211795</td>\n",
       "      <td>1.014877</td>\n",
       "      <td>1.831463</td>\n",
       "      <td>-0.418637</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>0.145949</td>\n",
       "      <td>0.215499</td>\n",
       "      <td>-0.934097</td>\n",
       "      <td>0.233295</td>\n",
       "      <td>0.901068</td>\n",
       "      <td>-0.253170</td>\n",
       "      <td>0.118459</td>\n",
       "      <td>0.766286</td>\n",
       "      <td>1.467517</td>\n",
       "      <td>-1.422839</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>0.766413</td>\n",
       "      <td>0.096898</td>\n",
       "      <td>0.601843</td>\n",
       "      <td>0.327518</td>\n",
       "      <td>0.263225</td>\n",
       "      <td>-1.522404</td>\n",
       "      <td>-1.309110</td>\n",
       "      <td>0.131951</td>\n",
       "      <td>0.557655</td>\n",
       "      <td>-1.229723</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>1.386571</td>\n",
       "      <td>0.923083</td>\n",
       "      <td>0.985157</td>\n",
       "      <td>1.400717</td>\n",
       "      <td>0.280138</td>\n",
       "      <td>-1.707763</td>\n",
       "      <td>-1.365760</td>\n",
       "      <td>0.013847</td>\n",
       "      <td>0.739627</td>\n",
       "      <td>-1.422839</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1.131470</td>\n",
       "      <td>0.817028</td>\n",
       "      <td>0.922392</td>\n",
       "      <td>1.286948</td>\n",
       "      <td>0.362388</td>\n",
       "      <td>-0.423868</td>\n",
       "      <td>0.469686</td>\n",
       "      <td>0.335776</td>\n",
       "      <td>1.255216</td>\n",
       "      <td>-1.268346</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1199 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ndvi_ne   ndvi_nw   ndvi_se   ndvi_sw  precipitation_amt_mm  \\\n",
       "0    -0.122207 -0.253846 -0.095140 -0.328502             -0.814598   \n",
       "1     0.215513  0.064732 -0.580536 -0.587307             -0.573640   \n",
       "2    -0.767304  0.319858 -0.649827 -0.407716             -0.302099   \n",
       "3    -0.079130  0.917244  0.295500  0.352911             -0.746481   \n",
       "4     0.403295  1.059203  0.613169  0.486861             -0.928126   \n",
       "6    -0.191465 -0.344365 -0.006621  0.053371             -1.021729   \n",
       "7    -0.479920 -0.512562 -0.726797 -0.849927              2.398945   \n",
       "8    -0.266078  0.097875 -1.074793 -0.960187             -0.654732   \n",
       "10    0.379554 -0.430949 -0.183017 -0.617211             -0.586383   \n",
       "11    1.084448  0.641612  1.284975  0.876592              0.268554   \n",
       "12    0.077474  0.309363  0.286671  0.103489             -0.720532   \n",
       "16    0.360693  0.285335 -0.509324 -0.390844             -0.351681   \n",
       "17    0.808131  1.627176  0.787454  0.919248             -0.153818   \n",
       "18    0.683182  0.544050  1.051411  0.289261             -0.433699   \n",
       "19   -0.083889  2.508341 -1.103968 -0.671534              0.393435   \n",
       "20    0.404366  0.398294  0.661923  1.169170              2.227726   \n",
       "21   -0.168617  1.048432 -0.082246  0.450336              0.088299   \n",
       "22    0.634273  0.458916  0.092998 -0.052880             -0.379947   \n",
       "23    0.224438 -0.553575 -0.350649 -0.561218              0.088994   \n",
       "25    0.046296 -0.369636 -1.031029 -0.956344              1.840803   \n",
       "26    0.803490  0.406579  1.510702  1.054734              1.245823   \n",
       "27    0.091992  0.392839 -0.365941 -0.223114             -0.628319   \n",
       "28    1.078736  0.747667 -0.338301  0.341217             -0.638977   \n",
       "29    0.945933  1.623861 -0.993344 -0.939333             -0.638050   \n",
       "30   -0.021057  0.680761 -0.386671 -0.749858             -0.323646   \n",
       "31   -0.213599 -0.379165  0.822548  0.537847             -0.871594   \n",
       "32   -0.131132  1.054729 -0.190504  0.050030             -1.102357   \n",
       "33    1.095396  1.097869  0.742923  1.134589              0.112858   \n",
       "34    1.162274  1.183762 -0.460825 -0.515546             -1.102357   \n",
       "35    0.048438  0.050578 -0.726413 -0.448981             -1.102357   \n",
       "...        ...       ...       ...       ...                   ...   \n",
       "1423  1.636164  2.034882  2.344126  3.066481              1.127197   \n",
       "1424  1.908758  1.663218  2.162354  2.147562             -0.413774   \n",
       "1425  1.402279  1.271036  1.610896  1.776270             -0.217996   \n",
       "1426  1.490917  1.292855  2.755079  1.926624              0.599407   \n",
       "1427  0.374735  0.490224  0.461148  0.937624              0.881374   \n",
       "1428  0.556090  0.877750 -0.319683  0.661807              0.779199   \n",
       "1429  0.504841  1.017257  0.209189  1.105669              1.186510   \n",
       "1432  0.039768  0.065887 -0.944078 -0.446475             -0.338011   \n",
       "1433  0.802521  0.796433 -0.245015  1.135257             -0.661219   \n",
       "1434  0.882183  0.797380  0.178798  0.649945              2.106784   \n",
       "1435  0.216941  0.064249 -1.177675 -0.776086             -0.006231   \n",
       "1436  0.650032  0.454832 -0.565372  0.387827              0.710619   \n",
       "1437  1.096586  0.940128  0.014302  0.712927             -0.356546   \n",
       "1438  1.170536  1.041448  0.267668  1.492266              0.859364   \n",
       "1439 -0.088530  0.387744 -1.143380 -0.736047              0.764602   \n",
       "1440  1.228166  1.311793  0.900510  2.211296              0.236349   \n",
       "1441  1.105868  0.966168  1.019899  1.589663              1.705728   \n",
       "1442  0.903705  1.382614  1.215107  1.052897              0.085982   \n",
       "1443  0.012262  0.583022  0.610097 -0.707089              0.051460   \n",
       "1445  0.128304  0.184370 -0.289547 -0.816515             -0.700838   \n",
       "1446  0.655234  1.328364  1.694200  0.452479              0.906397   \n",
       "1447  0.714190  1.034938  1.373461  1.195567             -0.499963   \n",
       "1448  0.864639  1.006057  0.701463  1.573792              0.611455   \n",
       "1449  0.206843  0.199994 -0.974022 -0.702913              0.273883   \n",
       "1450  0.880755  1.144544  0.708181  0.453649             -1.075713   \n",
       "1451  1.449658  1.528992  0.682269  1.015140              0.178890   \n",
       "1452  0.145949  0.215499 -0.934097  0.233295              0.901068   \n",
       "1453  0.766413  0.096898  0.601843  0.327518              0.263225   \n",
       "1454  1.386571  0.923083  0.985157  1.400717              0.280138   \n",
       "1455  1.131470  0.817028  0.922392  1.286948              0.362388   \n",
       "\n",
       "      reanalysis_air_temp_k  reanalysis_avg_temp_k  \\\n",
       "0                 -0.808198              -1.190146   \n",
       "1                 -0.340090              -0.634981   \n",
       "2                  0.077752              -0.289418   \n",
       "3                  0.228552              -0.011835   \n",
       "4                  0.618119               0.333727   \n",
       "6                  0.389825              -0.017500   \n",
       "7                  0.671527               0.226093   \n",
       "8                  0.662102               0.248753   \n",
       "10                 0.613930               0.277077   \n",
       "11                 0.727030               0.401706   \n",
       "12                 0.647441               0.311067   \n",
       "16                 1.215036               0.928547   \n",
       "17                 1.057953               0.775593   \n",
       "18                 1.359553               1.104161   \n",
       "19                 1.465322               1.228790   \n",
       "20                 0.866311               0.520670   \n",
       "21                 1.284153               1.075836   \n",
       "22                 1.539675               1.274110   \n",
       "23                 0.866311               0.554660   \n",
       "25                 0.166766              -0.159124   \n",
       "26                 0.771014               0.424366   \n",
       "27                 0.951136               0.628305   \n",
       "28                 1.227603               0.996527   \n",
       "29                 0.612883               0.311067   \n",
       "30                -0.320192              -0.776605   \n",
       "31                -0.097134              -0.442372   \n",
       "32                -0.346373              -0.765275   \n",
       "33                -0.277256              -0.685965   \n",
       "34                -0.466804              -0.918228   \n",
       "35                -1.184151              -1.671668   \n",
       "...                     ...                    ...   \n",
       "1423               1.206658               2.016219   \n",
       "1424               1.327089               1.987894   \n",
       "1425               0.572041               1.087166   \n",
       "1426               0.064138               0.548995   \n",
       "1427               0.383541               1.138151   \n",
       "1428              -0.240604               0.084469   \n",
       "1429              -1.095137              -0.838919   \n",
       "1432              -0.277256               0.548995   \n",
       "1433               0.183522               0.962537   \n",
       "1434              -0.281445               0.294072   \n",
       "1435               0.057855               0.871898   \n",
       "1436              -0.310767               0.345057   \n",
       "1437               0.713416               1.353419   \n",
       "1438               0.177238               0.985197   \n",
       "1439              -0.247934               0.333727   \n",
       "1440               0.118594               0.673624   \n",
       "1441               0.378305               1.336424   \n",
       "1442               0.241119               0.837908   \n",
       "1443               0.240072               1.166475   \n",
       "1445              -0.270973               0.248753   \n",
       "1446              -0.173581               0.209098   \n",
       "1447               0.273583               0.622640   \n",
       "1448              -0.775734              -0.521682   \n",
       "1449              -1.023926              -1.037193   \n",
       "1450              -0.752695              -0.425377   \n",
       "1451               0.483027               1.211795   \n",
       "1452              -0.253170               0.118459   \n",
       "1453              -1.522404              -1.309110   \n",
       "1454              -1.707763              -1.365760   \n",
       "1455              -0.423868               0.469686   \n",
       "\n",
       "      reanalysis_dew_point_temp_k  reanalysis_max_air_temp_k  \\\n",
       "0                       -1.926304                  -1.171085   \n",
       "1                       -0.901462                  -0.837468   \n",
       "2                        0.087186                  -0.958783   \n",
       "3                        0.004322                  -0.685825   \n",
       "4                        0.345301                  -0.534181   \n",
       "6                        0.374827                  -0.716153   \n",
       "7                        0.818671                  -0.928455   \n",
       "8                        0.716758                  -0.473523   \n",
       "10                       0.491979                  -0.564510   \n",
       "11                       0.687232                  -0.503852   \n",
       "12                       0.437689                  -0.564510   \n",
       "16                       0.950110                  -0.352208   \n",
       "17                       1.142506                  -0.443195   \n",
       "18                       0.542459                   0.042066   \n",
       "19                       1.259658                  -0.230893   \n",
       "20                       0.751999                  -0.594838   \n",
       "21                       0.483407                  -0.382537   \n",
       "22                       0.907249                  -0.109578   \n",
       "23                       1.083454                  -0.564510   \n",
       "25                       0.635800                  -0.837468   \n",
       "26                       0.820576                  -0.685825   \n",
       "27                       0.840578                  -0.443195   \n",
       "28                       0.255770                  -0.412866   \n",
       "29                       0.048135                  -0.625167   \n",
       "30                      -0.328085                  -1.049770   \n",
       "31                      -0.347134                  -0.867797   \n",
       "32                      -2.199658                  -0.928455   \n",
       "33                      -0.496669                  -0.837468   \n",
       "34                      -2.132034                  -1.171085   \n",
       "35                      -2.264425                  -1.413715   \n",
       "...                           ...                        ...   \n",
       "1423                     1.081549                   1.528175   \n",
       "1424                     0.536744                   2.225736   \n",
       "1425                     1.281564                   1.528175   \n",
       "1426                     1.111075                   1.588832   \n",
       "1427                     0.861532                   1.497846   \n",
       "1428                     1.068214                   1.164230   \n",
       "1429                     0.835815                  -0.230893   \n",
       "1432                     0.562461                   1.315874   \n",
       "1433                     0.717711                   1.255216   \n",
       "1434                     0.837720                   1.406860   \n",
       "1435                     1.022496                   0.739627   \n",
       "1436                     1.510153                   0.769956   \n",
       "1437                     1.191081                   1.679819   \n",
       "1438                     1.750172                   1.376531   \n",
       "1439                     1.742552                   0.800285   \n",
       "1440                     1.892088                   0.982257   \n",
       "1441                     2.097818                   1.133901   \n",
       "1442                     1.501581                   1.285545   \n",
       "1443                     0.843435                   1.649490   \n",
       "1445                     1.132981                   1.103572   \n",
       "1446                     1.583492                   0.315025   \n",
       "1447                     0.776763                   1.437189   \n",
       "1448                     1.114885                   0.315025   \n",
       "1449                     0.956777                   0.709298   \n",
       "1450                    -1.384357                   1.528175   \n",
       "1451                     1.014877                   1.831463   \n",
       "1452                     0.766286                   1.467517   \n",
       "1453                     0.131951                   0.557655   \n",
       "1454                     0.013847                   0.739627   \n",
       "1455                     0.335776                   1.255216   \n",
       "\n",
       "      reanalysis_min_air_temp_k ...  3  4  5  6  7  8  9  10  11  12  \n",
       "0                      0.122086 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "1                      0.315202 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "2                      0.662810 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "3                      0.546941 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "4                      0.740057 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "6                      0.817303 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "7                      1.087665 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "8                      0.817303 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "10                     0.933172 ...  0  0  0  0  1  0  0   0   0   0  \n",
       "11                     0.662810 ...  0  0  0  0  1  0  0   0   0   0  \n",
       "12                     0.585564 ...  0  0  0  0  1  0  0   0   0   0  \n",
       "16                     0.971795 ...  0  0  0  0  0  1  0   0   0   0  \n",
       "17                     1.203534 ...  0  0  0  0  0  1  0   0   0   0  \n",
       "18                     0.933172 ...  0  0  0  0  0  0  1   0   0   0  \n",
       "19                     1.319404 ...  0  0  0  0  0  0  1   0   0   0  \n",
       "20                     1.203534 ...  0  0  0  0  0  0  1   0   0   0  \n",
       "21                     1.358027 ...  0  0  0  0  0  0  1   0   0   0  \n",
       "22                     1.358027 ...  0  0  0  0  0  0  0   1   0   0  \n",
       "23                     0.971795 ...  0  0  0  0  0  0  0   1   0   0  \n",
       "25                     0.624187 ...  0  0  0  0  0  0  0   1   0   0  \n",
       "26                     1.126288 ...  0  0  0  0  0  0  0   1   0   0  \n",
       "27                     1.126288 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "28                     1.242157 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "29                     0.546941 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "30                     0.353825 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "31                     0.431072 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "32                     0.199333 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "33                     0.160710 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "34                    -0.109652 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "35                    -0.650376 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "...                         ... ... .. .. .. .. .. .. ..  ..  ..  ..  \n",
       "1423                   0.160710 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "1424                  -0.418637 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "1425                  -0.186899 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "1426                  -0.225522 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "1427                  -0.380014 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "1428                  -0.225522 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "1429                  -0.264145 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "1432                  -0.688999 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "1433                  -0.302768 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "1434                  -0.225522 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "1435                   0.006217 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "1436                  -0.032406 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "1437                  -0.418637 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "1438                  -0.148275 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "1439                   0.083463 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "1440                  -1.075231 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "1441                  -0.611753 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "1442                  -0.380014 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "1443                  -0.534507 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "1445                  -0.495884 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "1446                  -0.341391 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "1447                  -0.380014 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "1448                  -0.380014 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "1449                  -0.611753 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "1450                  -2.118055 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "1451                  -0.418637 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "1452                  -1.422839 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "1453                  -1.229723 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "1454                  -1.422839 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "1455                  -1.268346 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "\n",
       "[1199 rows x 33 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_process(dengue_features_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>reanalysis_avg_temp_k</th>\n",
       "      <th>reanalysis_dew_point_temp_k</th>\n",
       "      <th>reanalysis_max_air_temp_k</th>\n",
       "      <th>reanalysis_min_air_temp_k</th>\n",
       "      <th>...</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>12</th>\n",
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       "      <td>0.025780</td>\n",
       "      <td>-0.050108</td>\n",
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       "      <td>-0.818442</td>\n",
       "      <td>-0.964555</td>\n",
       "      <td>-1.573061</td>\n",
       "      <td>-1.396223</td>\n",
       "      <td>-0.728707</td>\n",
       "      <td>0.052798</td>\n",
       "      <td>0.031005</td>\n",
       "      <td>0.019366</td>\n",
       "      <td>-0.064241</td>\n",
       "      <td>0.115226</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.715372</td>\n",
       "      <td>-0.876513</td>\n",
       "      <td>-0.705330</td>\n",
       "      <td>-1.188132</td>\n",
       "      <td>-0.981948</td>\n",
       "      <td>0.100093</td>\n",
       "      <td>0.069564</td>\n",
       "      <td>0.063916</td>\n",
       "      <td>0.001710</td>\n",
       "      <td>0.100691</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.706002</td>\n",
       "      <td>-1.017569</td>\n",
       "      <td>-1.041595</td>\n",
       "      <td>-0.818210</td>\n",
       "      <td>-1.086090</td>\n",
       "      <td>0.111399</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>0.019715</td>\n",
       "      <td>0.039396</td>\n",
       "      <td>0.124915</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.351191</td>\n",
       "      <td>-0.593691</td>\n",
       "      <td>-1.821617</td>\n",
       "      <td>-1.357235</td>\n",
       "      <td>-1.064465</td>\n",
       "      <td>0.115743</td>\n",
       "      <td>0.084712</td>\n",
       "      <td>0.037144</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>0.144294</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.980855</td>\n",
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       "      <td>-0.945177</td>\n",
       "      <td>-1.274773</td>\n",
       "      <td>0.938986</td>\n",
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       "      <td>0.087467</td>\n",
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       "      <td>-0.012422</td>\n",
       "      <td>0.158828</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>6</th>\n",
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       "      <td>-0.455715</td>\n",
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       "      <td>0.100894</td>\n",
       "      <td>0.080579</td>\n",
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       "      <td>0.153984</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.706002</td>\n",
       "      <td>-0.293236</td>\n",
       "      <td>-0.789688</td>\n",
       "      <td>-0.915216</td>\n",
       "      <td>-0.209420</td>\n",
       "      <td>0.149731</td>\n",
       "      <td>0.128436</td>\n",
       "      <td>0.097520</td>\n",
       "      <td>0.062950</td>\n",
       "      <td>0.153984</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.638538</td>\n",
       "      <td>-0.522216</td>\n",
       "      <td>-1.340186</td>\n",
       "      <td>-0.908718</td>\n",
       "      <td>-0.857888</td>\n",
       "      <td>0.155384</td>\n",
       "      <td>0.130502</td>\n",
       "      <td>0.110767</td>\n",
       "      <td>0.015842</td>\n",
       "      <td>0.202431</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.252077</td>\n",
       "      <td>-0.119212</td>\n",
       "      <td>-0.650716</td>\n",
       "      <td>-0.393519</td>\n",
       "      <td>-0.587575</td>\n",
       "      <td>0.150352</td>\n",
       "      <td>0.126026</td>\n",
       "      <td>0.117739</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.996160</td>\n",
       "      <td>-0.828055</td>\n",
       "      <td>0.667174</td>\n",
       "      <td>0.146125</td>\n",
       "      <td>-0.620297</td>\n",
       "      <td>0.136701</td>\n",
       "      <td>0.117075</td>\n",
       "      <td>0.089084</td>\n",
       "      <td>-0.021844</td>\n",
       "      <td>0.226655</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.706002</td>\n",
       "      <td>-0.876513</td>\n",
       "      <td>-0.142772</td>\n",
       "      <td>-0.271605</td>\n",
       "      <td>-0.962599</td>\n",
       "      <td>0.148008</td>\n",
       "      <td>0.129813</td>\n",
       "      <td>0.106305</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.595179</td>\n",
       "      <td>2.172999</td>\n",
       "      <td>0.885834</td>\n",
       "      <td>0.481004</td>\n",
       "      <td>-0.989631</td>\n",
       "      <td>0.155247</td>\n",
       "      <td>0.135321</td>\n",
       "      <td>0.122689</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>0.221810</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.704753</td>\n",
       "      <td>-0.848823</td>\n",
       "      <td>-1.233413</td>\n",
       "      <td>-1.242772</td>\n",
       "      <td>-0.695415</td>\n",
       "      <td>0.161589</td>\n",
       "      <td>0.143240</td>\n",
       "      <td>0.107560</td>\n",
       "      <td>0.025264</td>\n",
       "      <td>0.236344</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.005126</td>\n",
       "      <td>-0.647889</td>\n",
       "      <td>-0.163600</td>\n",
       "      <td>0.049118</td>\n",
       "      <td>0.363361</td>\n",
       "      <td>0.162141</td>\n",
       "      <td>0.141174</td>\n",
       "      <td>0.148763</td>\n",
       "      <td>0.015842</td>\n",
       "      <td>0.231499</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.706002</td>\n",
       "      <td>-0.876513</td>\n",
       "      <td>-1.863930</td>\n",
       "      <td>-1.468475</td>\n",
       "      <td>-0.964306</td>\n",
       "      <td>0.181858</td>\n",
       "      <td>0.162519</td>\n",
       "      <td>0.142210</td>\n",
       "      <td>0.053528</td>\n",
       "      <td>0.226655</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.100222</td>\n",
       "      <td>0.834858</td>\n",
       "      <td>0.419755</td>\n",
       "      <td>0.079288</td>\n",
       "      <td>-0.176413</td>\n",
       "      <td>0.161865</td>\n",
       "      <td>0.139797</td>\n",
       "      <td>0.138096</td>\n",
       "      <td>0.020553</td>\n",
       "      <td>0.207276</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.001121</td>\n",
       "      <td>-0.876513</td>\n",
       "      <td>0.237303</td>\n",
       "      <td>-0.032415</td>\n",
       "      <td>-0.526398</td>\n",
       "      <td>0.175722</td>\n",
       "      <td>0.155290</td>\n",
       "      <td>0.157129</td>\n",
       "      <td>0.048817</td>\n",
       "      <td>0.221810</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.706002</td>\n",
       "      <td>-0.753681</td>\n",
       "      <td>-0.210971</td>\n",
       "      <td>-0.879967</td>\n",
       "      <td>0.637089</td>\n",
       "      <td>0.185512</td>\n",
       "      <td>0.163897</td>\n",
       "      <td>0.165984</td>\n",
       "      <td>0.062950</td>\n",
       "      <td>0.221810</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.554667</td>\n",
       "      <td>3.731480</td>\n",
       "      <td>-0.764192</td>\n",
       "      <td>-0.756479</td>\n",
       "      <td>2.518472</td>\n",
       "      <td>0.165174</td>\n",
       "      <td>0.145650</td>\n",
       "      <td>0.159918</td>\n",
       "      <td>0.067660</td>\n",
       "      <td>0.216965</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.135213</td>\n",
       "      <td>0.288265</td>\n",
       "      <td>0.558846</td>\n",
       "      <td>-0.051832</td>\n",
       "      <td>0.292225</td>\n",
       "      <td>0.162899</td>\n",
       "      <td>0.142551</td>\n",
       "      <td>0.150436</td>\n",
       "      <td>0.053528</td>\n",
       "      <td>0.221810</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.209392</td>\n",
       "      <td>-0.140406</td>\n",
       "      <td>0.948707</td>\n",
       "      <td>0.650494</td>\n",
       "      <td>3.289862</td>\n",
       "      <td>0.144629</td>\n",
       "      <td>0.122239</td>\n",
       "      <td>0.123804</td>\n",
       "      <td>0.053528</td>\n",
       "      <td>0.183052</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-1.563982</td>\n",
       "      <td>0.053251</td>\n",
       "      <td>0.812968</td>\n",
       "      <td>0.745025</td>\n",
       "      <td>0.477746</td>\n",
       "      <td>0.153592</td>\n",
       "      <td>0.137731</td>\n",
       "      <td>0.140746</td>\n",
       "      <td>0.058239</td>\n",
       "      <td>0.149139</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.109813</td>\n",
       "      <td>-0.239630</td>\n",
       "      <td>-0.265404</td>\n",
       "      <td>-0.051909</td>\n",
       "      <td>-0.910813</td>\n",
       "      <td>0.160900</td>\n",
       "      <td>0.136354</td>\n",
       "      <td>0.105677</td>\n",
       "      <td>0.039396</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.439637</td>\n",
       "      <td>-0.041891</td>\n",
       "      <td>-0.688064</td>\n",
       "      <td>-0.636111</td>\n",
       "      <td>0.633105</td>\n",
       "      <td>0.137666</td>\n",
       "      <td>0.113976</td>\n",
       "      <td>0.107420</td>\n",
       "      <td>0.058239</td>\n",
       "      <td>0.178207</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.498300</td>\n",
       "      <td>-0.344949</td>\n",
       "      <td>1.622735</td>\n",
       "      <td>0.556891</td>\n",
       "      <td>1.523148</td>\n",
       "      <td>0.113536</td>\n",
       "      <td>0.094697</td>\n",
       "      <td>0.092222</td>\n",
       "      <td>0.020553</td>\n",
       "      <td>0.168518</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.185030</td>\n",
       "      <td>-0.339032</td>\n",
       "      <td>-1.678815</td>\n",
       "      <td>-0.839406</td>\n",
       "      <td>-1.086090</td>\n",
       "      <td>0.143388</td>\n",
       "      <td>0.127403</td>\n",
       "      <td>0.068727</td>\n",
       "      <td>0.001710</td>\n",
       "      <td>0.197586</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1.295687</td>\n",
       "      <td>-0.911659</td>\n",
       "      <td>-0.796870</td>\n",
       "      <td>-0.768083</td>\n",
       "      <td>-0.738097</td>\n",
       "      <td>0.130083</td>\n",
       "      <td>0.107435</td>\n",
       "      <td>0.118784</td>\n",
       "      <td>0.020553</td>\n",
       "      <td>0.187897</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.074464</td>\n",
       "      <td>-0.351103</td>\n",
       "      <td>0.382050</td>\n",
       "      <td>-0.278411</td>\n",
       "      <td>-0.726430</td>\n",
       "      <td>0.132978</td>\n",
       "      <td>0.114665</td>\n",
       "      <td>0.090548</td>\n",
       "      <td>0.053528</td>\n",
       "      <td>0.173363</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.706002</td>\n",
       "      <td>-0.564107</td>\n",
       "      <td>0.173953</td>\n",
       "      <td>-0.191771</td>\n",
       "      <td>-0.446158</td>\n",
       "      <td>0.112502</td>\n",
       "      <td>0.088844</td>\n",
       "      <td>0.081276</td>\n",
       "      <td>-0.012422</td>\n",
       "      <td>0.178207</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>1.644529</td>\n",
       "      <td>1.879357</td>\n",
       "      <td>2.840796</td>\n",
       "      <td>1.750977</td>\n",
       "      <td>0.127761</td>\n",
       "      <td>0.047765</td>\n",
       "      <td>0.061646</td>\n",
       "      <td>0.181879</td>\n",
       "      <td>0.199562</td>\n",
       "      <td>0.032865</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>1.590377</td>\n",
       "      <td>1.643896</td>\n",
       "      <td>1.490796</td>\n",
       "      <td>0.865289</td>\n",
       "      <td>-0.085929</td>\n",
       "      <td>0.085822</td>\n",
       "      <td>0.126026</td>\n",
       "      <td>0.121782</td>\n",
       "      <td>0.232537</td>\n",
       "      <td>0.018331</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>0.110125</td>\n",
       "      <td>1.008270</td>\n",
       "      <td>0.199090</td>\n",
       "      <td>-0.700911</td>\n",
       "      <td>1.823054</td>\n",
       "      <td>0.061968</td>\n",
       "      <td>0.096418</td>\n",
       "      <td>0.150506</td>\n",
       "      <td>0.204272</td>\n",
       "      <td>0.003797</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>0.899287</td>\n",
       "      <td>0.710166</td>\n",
       "      <td>0.335337</td>\n",
       "      <td>0.561688</td>\n",
       "      <td>0.348564</td>\n",
       "      <td>0.059417</td>\n",
       "      <td>0.089532</td>\n",
       "      <td>0.157617</td>\n",
       "      <td>0.171297</td>\n",
       "      <td>-0.010738</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.892074</td>\n",
       "      <td>1.030991</td>\n",
       "      <td>0.769338</td>\n",
       "      <td>0.468240</td>\n",
       "      <td>-0.080808</td>\n",
       "      <td>0.030185</td>\n",
       "      <td>0.041333</td>\n",
       "      <td>0.126872</td>\n",
       "      <td>0.081793</td>\n",
       "      <td>-0.010738</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>0.469799</td>\n",
       "      <td>0.111421</td>\n",
       "      <td>0.572193</td>\n",
       "      <td>-0.331943</td>\n",
       "      <td>-0.143122</td>\n",
       "      <td>-0.055028</td>\n",
       "      <td>-0.024768</td>\n",
       "      <td>0.062312</td>\n",
       "      <td>0.213694</td>\n",
       "      <td>-0.068875</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>0.869407</td>\n",
       "      <td>0.435188</td>\n",
       "      <td>0.433581</td>\n",
       "      <td>0.344159</td>\n",
       "      <td>0.166458</td>\n",
       "      <td>-0.019798</td>\n",
       "      <td>0.005184</td>\n",
       "      <td>0.064125</td>\n",
       "      <td>0.218405</td>\n",
       "      <td>-0.073719</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>0.441689</td>\n",
       "      <td>0.025610</td>\n",
       "      <td>-0.518030</td>\n",
       "      <td>-0.406205</td>\n",
       "      <td>1.681353</td>\n",
       "      <td>0.015294</td>\n",
       "      <td>0.050629</td>\n",
       "      <td>0.132240</td>\n",
       "      <td>0.223115</td>\n",
       "      <td>-0.034961</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1.598125</td>\n",
       "      <td>1.788374</td>\n",
       "      <td>1.629737</td>\n",
       "      <td>2.347711</td>\n",
       "      <td>0.152516</td>\n",
       "      <td>0.011778</td>\n",
       "      <td>0.031694</td>\n",
       "      <td>0.099891</td>\n",
       "      <td>0.246669</td>\n",
       "      <td>-0.078564</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1.011563</td>\n",
       "      <td>1.047017</td>\n",
       "      <td>1.078881</td>\n",
       "      <td>1.008967</td>\n",
       "      <td>1.696718</td>\n",
       "      <td>0.030116</td>\n",
       "      <td>0.085057</td>\n",
       "      <td>0.145765</td>\n",
       "      <td>0.232537</td>\n",
       "      <td>-0.020427</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0.962037</td>\n",
       "      <td>0.477687</td>\n",
       "      <td>0.904717</td>\n",
       "      <td>0.876686</td>\n",
       "      <td>2.591030</td>\n",
       "      <td>0.021016</td>\n",
       "      <td>0.052006</td>\n",
       "      <td>0.116065</td>\n",
       "      <td>0.147743</td>\n",
       "      <td>-0.039806</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1.155297</td>\n",
       "      <td>1.450559</td>\n",
       "      <td>0.466976</td>\n",
       "      <td>1.163526</td>\n",
       "      <td>0.805252</td>\n",
       "      <td>0.017638</td>\n",
       "      <td>0.049596</td>\n",
       "      <td>0.135865</td>\n",
       "      <td>0.157165</td>\n",
       "      <td>-0.010738</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1.452326</td>\n",
       "      <td>1.586290</td>\n",
       "      <td>1.632820</td>\n",
       "      <td>1.963632</td>\n",
       "      <td>0.029025</td>\n",
       "      <td>-0.018419</td>\n",
       "      <td>0.038235</td>\n",
       "      <td>0.109651</td>\n",
       "      <td>0.176008</td>\n",
       "      <td>-0.078564</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1.138907</td>\n",
       "      <td>1.323517</td>\n",
       "      <td>0.596791</td>\n",
       "      <td>0.676795</td>\n",
       "      <td>2.074873</td>\n",
       "      <td>-0.020419</td>\n",
       "      <td>0.007250</td>\n",
       "      <td>0.120597</td>\n",
       "      <td>0.072371</td>\n",
       "      <td>-0.005893</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.691199</td>\n",
       "      <td>-0.028401</td>\n",
       "      <td>0.221174</td>\n",
       "      <td>0.293877</td>\n",
       "      <td>-0.311855</td>\n",
       "      <td>0.026738</td>\n",
       "      <td>0.082302</td>\n",
       "      <td>0.148345</td>\n",
       "      <td>0.152454</td>\n",
       "      <td>0.003797</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>0.741975</td>\n",
       "      <td>0.512156</td>\n",
       "      <td>-0.030375</td>\n",
       "      <td>0.530126</td>\n",
       "      <td>1.100890</td>\n",
       "      <td>0.038941</td>\n",
       "      <td>0.071974</td>\n",
       "      <td>0.173932</td>\n",
       "      <td>0.147743</td>\n",
       "      <td>0.013486</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>0.939549</td>\n",
       "      <td>0.618879</td>\n",
       "      <td>0.781906</td>\n",
       "      <td>0.483402</td>\n",
       "      <td>1.886507</td>\n",
       "      <td>0.045628</td>\n",
       "      <td>0.095041</td>\n",
       "      <td>0.153365</td>\n",
       "      <td>0.251380</td>\n",
       "      <td>-0.010738</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>0.690396</td>\n",
       "      <td>0.604171</td>\n",
       "      <td>0.411855</td>\n",
       "      <td>0.187588</td>\n",
       "      <td>2.402379</td>\n",
       "      <td>0.011571</td>\n",
       "      <td>0.059924</td>\n",
       "      <td>0.122967</td>\n",
       "      <td>0.147743</td>\n",
       "      <td>-0.001048</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0.938121</td>\n",
       "      <td>0.809061</td>\n",
       "      <td>0.539695</td>\n",
       "      <td>-0.022978</td>\n",
       "      <td>1.419860</td>\n",
       "      <td>0.017982</td>\n",
       "      <td>0.075761</td>\n",
       "      <td>0.112300</td>\n",
       "      <td>0.270223</td>\n",
       "      <td>-0.030117</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1.224397</td>\n",
       "      <td>1.042419</td>\n",
       "      <td>0.355656</td>\n",
       "      <td>0.633011</td>\n",
       "      <td>0.174426</td>\n",
       "      <td>0.061071</td>\n",
       "      <td>0.089877</td>\n",
       "      <td>0.140885</td>\n",
       "      <td>0.345595</td>\n",
       "      <td>-0.015582</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.501568</td>\n",
       "      <td>0.877628</td>\n",
       "      <td>0.860190</td>\n",
       "      <td>1.857266</td>\n",
       "      <td>0.936141</td>\n",
       "      <td>-0.008629</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.125059</td>\n",
       "      <td>0.086504</td>\n",
       "      <td>-0.064030</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1.699857</td>\n",
       "      <td>1.748410</td>\n",
       "      <td>2.120684</td>\n",
       "      <td>1.656756</td>\n",
       "      <td>-1.021215</td>\n",
       "      <td>0.028944</td>\n",
       "      <td>0.061990</td>\n",
       "      <td>-0.037594</td>\n",
       "      <td>0.303198</td>\n",
       "      <td>-0.238441</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1.769820</td>\n",
       "      <td>1.200076</td>\n",
       "      <td>1.586106</td>\n",
       "      <td>1.635715</td>\n",
       "      <td>0.692859</td>\n",
       "      <td>0.044801</td>\n",
       "      <td>0.090565</td>\n",
       "      <td>0.136353</td>\n",
       "      <td>0.265512</td>\n",
       "      <td>-0.049496</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1.496305</td>\n",
       "      <td>1.118982</td>\n",
       "      <td>1.255197</td>\n",
       "      <td>0.824857</td>\n",
       "      <td>0.230480</td>\n",
       "      <td>-0.000701</td>\n",
       "      <td>0.011725</td>\n",
       "      <td>0.118924</td>\n",
       "      <td>0.086504</td>\n",
       "      <td>-0.049496</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>0.773387</td>\n",
       "      <td>1.209914</td>\n",
       "      <td>0.204117</td>\n",
       "      <td>1.063427</td>\n",
       "      <td>1.397666</td>\n",
       "      <td>-0.024210</td>\n",
       "      <td>-0.005833</td>\n",
       "      <td>0.090339</td>\n",
       "      <td>0.171297</td>\n",
       "      <td>-0.088254</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1.177189</td>\n",
       "      <td>1.821744</td>\n",
       "      <td>0.922852</td>\n",
       "      <td>1.970517</td>\n",
       "      <td>0.083942</td>\n",
       "      <td>0.018948</td>\n",
       "      <td>0.050629</td>\n",
       "      <td>0.080021</td>\n",
       "      <td>0.157165</td>\n",
       "      <td>-0.078564</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>0.840673</td>\n",
       "      <td>1.227564</td>\n",
       "      <td>0.982462</td>\n",
       "      <td>1.614983</td>\n",
       "      <td>0.948945</td>\n",
       "      <td>-0.010353</td>\n",
       "      <td>0.019988</td>\n",
       "      <td>0.090130</td>\n",
       "      <td>0.194851</td>\n",
       "      <td>-0.131856</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0.785256</td>\n",
       "      <td>0.905826</td>\n",
       "      <td>0.570397</td>\n",
       "      <td>0.525639</td>\n",
       "      <td>1.160644</td>\n",
       "      <td>-0.074814</td>\n",
       "      <td>-0.062983</td>\n",
       "      <td>0.043698</td>\n",
       "      <td>0.114768</td>\n",
       "      <td>-0.175459</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1.233142</td>\n",
       "      <td>1.272700</td>\n",
       "      <td>2.506296</td>\n",
       "      <td>2.195318</td>\n",
       "      <td>0.038984</td>\n",
       "      <td>-0.077365</td>\n",
       "      <td>-0.024768</td>\n",
       "      <td>-0.017097</td>\n",
       "      <td>0.176008</td>\n",
       "      <td>-0.088254</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1.414533</td>\n",
       "      <td>0.828028</td>\n",
       "      <td>1.882004</td>\n",
       "      <td>0.783703</td>\n",
       "      <td>0.387831</td>\n",
       "      <td>-0.000425</td>\n",
       "      <td>0.052350</td>\n",
       "      <td>0.030103</td>\n",
       "      <td>0.241958</td>\n",
       "      <td>-0.228751</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ndvi_ne   ndvi_nw   ndvi_se   ndvi_sw  precipitation_amt_mm  \\\n",
       "0   -0.824064 -1.010706 -1.313073 -1.191691              1.150400   \n",
       "1   -0.818442 -0.964555 -1.573061 -1.396223             -0.728707   \n",
       "2   -0.715372 -0.876513 -0.705330 -1.188132             -0.981948   \n",
       "3   -0.706002 -1.017569 -1.041595 -0.818210             -1.086090   \n",
       "4   -0.351191 -0.593691 -1.821617 -1.357235             -1.064465   \n",
       "5   -0.980855 -1.092831 -0.945177 -1.274773              0.938986   \n",
       "6   -0.982729 -1.053484 -0.941766 -0.455715              0.307875   \n",
       "7   -0.706002 -0.293236 -0.789688 -0.915216             -0.209420   \n",
       "8   -0.638538 -0.522216 -1.340186 -0.908718             -0.857888   \n",
       "9   -0.252077 -0.119212 -0.650716 -0.393519             -0.587575   \n",
       "10  -0.996160 -0.828055  0.667174  0.146125             -0.620297   \n",
       "11  -0.706002 -0.876513 -0.142772 -0.271605             -0.962599   \n",
       "12   0.595179  2.172999  0.885834  0.481004             -0.989631   \n",
       "13  -0.704753 -0.848823 -1.233413 -1.242772             -0.695415   \n",
       "14  -0.005126 -0.647889 -0.163600  0.049118              0.363361   \n",
       "15  -0.706002 -0.876513 -1.863930 -1.468475             -0.964306   \n",
       "16   1.100222  0.834858  0.419755  0.079288             -0.176413   \n",
       "17   0.001121 -0.876513  0.237303 -0.032415             -0.526398   \n",
       "18  -0.706002 -0.753681 -0.210971 -0.879967              0.637089   \n",
       "19   1.554667  3.731480 -0.764192 -0.756479              2.518472   \n",
       "20  -0.135213  0.288265  0.558846 -0.051832              0.292225   \n",
       "21  -0.209392 -0.140406  0.948707  0.650494              3.289862   \n",
       "22  -1.563982  0.053251  0.812968  0.745025              0.477746   \n",
       "23   0.109813 -0.239630 -0.265404 -0.051909             -0.910813   \n",
       "24   0.439637 -0.041891 -0.688064 -0.636111              0.633105   \n",
       "25  -0.498300 -0.344949  1.622735  0.556891              1.523148   \n",
       "26  -0.185030 -0.339032 -1.678815 -0.839406             -1.086090   \n",
       "27  -1.295687 -0.911659 -0.796870 -0.768083             -0.738097   \n",
       "28  -0.074464 -0.351103  0.382050 -0.278411             -0.726430   \n",
       "29  -0.706002 -0.564107  0.173953 -0.191771             -0.446158   \n",
       "..        ...       ...       ...       ...                   ...   \n",
       "386  1.644529  1.879357  2.840796  1.750977              0.127761   \n",
       "387  1.590377  1.643896  1.490796  0.865289             -0.085929   \n",
       "388  0.110125  1.008270  0.199090 -0.700911              1.823054   \n",
       "389  0.899287  0.710166  0.335337  0.561688              0.348564   \n",
       "390  0.892074  1.030991  0.769338  0.468240             -0.080808   \n",
       "391  0.469799  0.111421  0.572193 -0.331943             -0.143122   \n",
       "392  0.869407  0.435188  0.433581  0.344159              0.166458   \n",
       "393  0.441689  0.025610 -0.518030 -0.406205              1.681353   \n",
       "394  1.598125  1.788374  1.629737  2.347711              0.152516   \n",
       "395  1.011563  1.047017  1.078881  1.008967              1.696718   \n",
       "396  0.962037  0.477687  0.904717  0.876686              2.591030   \n",
       "397  1.155297  1.450559  0.466976  1.163526              0.805252   \n",
       "398  1.452326  1.586290  1.632820  1.963632              0.029025   \n",
       "399  1.138907  1.323517  0.596791  0.676795              2.074873   \n",
       "400  0.691199 -0.028401  0.221174  0.293877             -0.311855   \n",
       "401  0.741975  0.512156 -0.030375  0.530126              1.100890   \n",
       "402  0.939549  0.618879  0.781906  0.483402              1.886507   \n",
       "403  0.690396  0.604171  0.411855  0.187588              2.402379   \n",
       "404  0.938121  0.809061  0.539695 -0.022978              1.419860   \n",
       "405  1.224397  1.042419  0.355656  0.633011              0.174426   \n",
       "406  0.501568  0.877628  0.860190  1.857266              0.936141   \n",
       "407  1.699857  1.748410  2.120684  1.656756             -1.021215   \n",
       "408  1.769820  1.200076  1.586106  1.635715              0.692859   \n",
       "409  1.496305  1.118982  1.255197  0.824857              0.230480   \n",
       "410  0.773387  1.209914  0.204117  1.063427              1.397666   \n",
       "411  1.177189  1.821744  0.922852  1.970517              0.083942   \n",
       "412  0.840673  1.227564  0.982462  1.614983              0.948945   \n",
       "413  0.785256  0.905826  0.570397  0.525639              1.160644   \n",
       "414  1.233142  1.272700  2.506296  2.195318              0.038984   \n",
       "415  1.414533  0.828028  1.882004  0.783703              0.387831   \n",
       "\n",
       "     reanalysis_air_temp_k  reanalysis_avg_temp_k  \\\n",
       "0                 0.053626               0.030661   \n",
       "1                 0.052798               0.031005   \n",
       "2                 0.100093               0.069564   \n",
       "3                 0.111399               0.087467   \n",
       "4                 0.115743               0.084712   \n",
       "5                 0.115191               0.087467   \n",
       "6                 0.129393               0.100894   \n",
       "7                 0.149731               0.128436   \n",
       "8                 0.155384               0.130502   \n",
       "9                 0.150352               0.126026   \n",
       "10                0.136701               0.117075   \n",
       "11                0.148008               0.129813   \n",
       "12                0.155247               0.135321   \n",
       "13                0.161589               0.143240   \n",
       "14                0.162141               0.141174   \n",
       "15                0.181858               0.162519   \n",
       "16                0.161865               0.139797   \n",
       "17                0.175722               0.155290   \n",
       "18                0.185512               0.163897   \n",
       "19                0.165174               0.145650   \n",
       "20                0.162899               0.142551   \n",
       "21                0.144629               0.122239   \n",
       "22                0.153592               0.137731   \n",
       "23                0.160900               0.136354   \n",
       "24                0.137666               0.113976   \n",
       "25                0.113536               0.094697   \n",
       "26                0.143388               0.127403   \n",
       "27                0.130083               0.107435   \n",
       "28                0.132978               0.114665   \n",
       "29                0.112502               0.088844   \n",
       "..                     ...                    ...   \n",
       "386               0.047765               0.061646   \n",
       "387               0.085822               0.126026   \n",
       "388               0.061968               0.096418   \n",
       "389               0.059417               0.089532   \n",
       "390               0.030185               0.041333   \n",
       "391              -0.055028              -0.024768   \n",
       "392              -0.019798               0.005184   \n",
       "393               0.015294               0.050629   \n",
       "394               0.011778               0.031694   \n",
       "395               0.030116               0.085057   \n",
       "396               0.021016               0.052006   \n",
       "397               0.017638               0.049596   \n",
       "398              -0.018419               0.038235   \n",
       "399              -0.020419               0.007250   \n",
       "400               0.026738               0.082302   \n",
       "401               0.038941               0.071974   \n",
       "402               0.045628               0.095041   \n",
       "403               0.011571               0.059924   \n",
       "404               0.017982               0.075761   \n",
       "405               0.061071               0.089877   \n",
       "406              -0.008629               0.000708   \n",
       "407               0.028944               0.061990   \n",
       "408               0.044801               0.090565   \n",
       "409              -0.000701               0.011725   \n",
       "410              -0.024210              -0.005833   \n",
       "411               0.018948               0.050629   \n",
       "412              -0.010353               0.019988   \n",
       "413              -0.074814              -0.062983   \n",
       "414              -0.077365              -0.024768   \n",
       "415              -0.000425               0.052350   \n",
       "\n",
       "     reanalysis_dew_point_temp_k  reanalysis_max_air_temp_k  \\\n",
       "0                       0.025780                  -0.050108   \n",
       "1                       0.019366                  -0.064241   \n",
       "2                       0.063916                   0.001710   \n",
       "3                       0.019715                   0.039396   \n",
       "4                       0.037144                   0.006421   \n",
       "5                       0.064195                  -0.012422   \n",
       "6                       0.080579                   0.011131   \n",
       "7                       0.097520                   0.062950   \n",
       "8                       0.110767                   0.015842   \n",
       "9                       0.117739                   0.006421   \n",
       "10                      0.089084                  -0.021844   \n",
       "11                      0.106305                   0.006421   \n",
       "12                      0.122689                   0.006421   \n",
       "13                      0.107560                   0.025264   \n",
       "14                      0.148763                   0.015842   \n",
       "15                      0.142210                   0.053528   \n",
       "16                      0.138096                   0.020553   \n",
       "17                      0.157129                   0.048817   \n",
       "18                      0.165984                   0.062950   \n",
       "19                      0.159918                   0.067660   \n",
       "20                      0.150436                   0.053528   \n",
       "21                      0.123804                   0.053528   \n",
       "22                      0.140746                   0.058239   \n",
       "23                      0.105677                   0.039396   \n",
       "24                      0.107420                   0.058239   \n",
       "25                      0.092222                   0.020553   \n",
       "26                      0.068727                   0.001710   \n",
       "27                      0.118784                   0.020553   \n",
       "28                      0.090548                   0.053528   \n",
       "29                      0.081276                  -0.012422   \n",
       "..                           ...                        ...   \n",
       "386                     0.181879                   0.199562   \n",
       "387                     0.121782                   0.232537   \n",
       "388                     0.150506                   0.204272   \n",
       "389                     0.157617                   0.171297   \n",
       "390                     0.126872                   0.081793   \n",
       "391                     0.062312                   0.213694   \n",
       "392                     0.064125                   0.218405   \n",
       "393                     0.132240                   0.223115   \n",
       "394                     0.099891                   0.246669   \n",
       "395                     0.145765                   0.232537   \n",
       "396                     0.116065                   0.147743   \n",
       "397                     0.135865                   0.157165   \n",
       "398                     0.109651                   0.176008   \n",
       "399                     0.120597                   0.072371   \n",
       "400                     0.148345                   0.152454   \n",
       "401                     0.173932                   0.147743   \n",
       "402                     0.153365                   0.251380   \n",
       "403                     0.122967                   0.147743   \n",
       "404                     0.112300                   0.270223   \n",
       "405                     0.140885                   0.345595   \n",
       "406                     0.125059                   0.086504   \n",
       "407                    -0.037594                   0.303198   \n",
       "408                     0.136353                   0.265512   \n",
       "409                     0.118924                   0.086504   \n",
       "410                     0.090339                   0.171297   \n",
       "411                     0.080021                   0.157165   \n",
       "412                     0.090130                   0.194851   \n",
       "413                     0.043698                   0.114768   \n",
       "414                    -0.017097                   0.176008   \n",
       "415                     0.030103                   0.241958   \n",
       "\n",
       "     reanalysis_min_air_temp_k ...  3  4  5  6  7  8  9  10  11  12  \n",
       "0                     0.100691 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "1                     0.115226 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "2                     0.100691 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "3                     0.124915 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "4                     0.144294 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "5                     0.158828 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "6                     0.153984 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "7                     0.153984 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "8                     0.202431 ...  0  0  0  1  0  0  0   0   0   0  \n",
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       "16                    0.207276 ...  0  0  0  0  0  1  0   0   0   0  \n",
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       "29                    0.178207 ...  0  0  0  0  0  0  0   0   1   0  \n",
       "..                         ... ... .. .. .. .. .. .. ..  ..  ..  ..  \n",
       "386                   0.032865 ...  0  0  0  0  0  0  0   0   0   1  \n",
       "387                   0.018331 ...  0  0  0  0  0  0  0   0   0   1  \n",
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       "389                  -0.010738 ...  0  0  0  0  0  0  0   0   0   1  \n",
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       "395                  -0.020427 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "396                  -0.039806 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "397                  -0.010738 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "398                  -0.078564 ...  0  0  0  0  0  0  0   0   0   0  \n",
       "399                  -0.005893 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "400                   0.003797 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "401                   0.013486 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "402                  -0.010738 ...  1  0  0  0  0  0  0   0   0   0  \n",
       "403                  -0.001048 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "404                  -0.030117 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "405                  -0.015582 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "406                  -0.064030 ...  0  1  0  0  0  0  0   0   0   0  \n",
       "407                  -0.238441 ...  0  1  0  0  0  0  0   0   0   0  \n",
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       "409                  -0.049496 ...  0  0  1  0  0  0  0   0   0   0  \n",
       "410                  -0.088254 ...  0  0  1  0  0  0  0   0   0   0  \n",
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       "415                  -0.228751 ...  0  0  0  1  0  0  0   0   0   0  \n",
       "\n",
       "[416 rows x 33 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_process(dengue_features_test, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ndvi_ne</th>\n",
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       "      <th>ndvi_se</th>\n",
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       "      <td>-0.411882</td>\n",
       "      <td>-0.981948</td>\n",
       "      <td>-0.088166</td>\n",
       "      <td>-0.189231</td>\n",
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       "      <td>0.149197</td>\n",
       "      <td>0.248984</td>\n",
       "      <td>-0.891909</td>\n",
       "      <td>True</td>\n",
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      ],
      "text/plain": [
       "    ndvi_ne   ndvi_nw   ndvi_se   ndvi_sw  precipitation_amt_mm  \\\n",
       "0 -0.824064 -1.010706 -1.313073 -1.191691              1.150400   \n",
       "1 -0.818442 -0.964555 -1.573061 -1.396223             -0.728707   \n",
       "2 -0.715372 -0.876513 -0.705330 -1.188132             -0.981948   \n",
       "3 -0.706002 -1.017569 -1.041595 -0.818210             -1.086090   \n",
       "4 -0.351191 -0.593691 -1.821617 -1.357235             -1.064465   \n",
       "\n",
       "   reanalysis_air_temp_k  reanalysis_avg_temp_k  reanalysis_dew_point_temp_k  \\\n",
       "0               0.053626               0.030661                     0.025780   \n",
       "1               0.052798               0.031005                     0.019366   \n",
       "2               0.100093               0.069564                     0.063916   \n",
       "3               0.111399               0.087467                     0.019715   \n",
       "4               0.115743               0.084712                     0.037144   \n",
       "\n",
       "   reanalysis_max_air_temp_k  reanalysis_min_air_temp_k  ...    \\\n",
       "0                  -0.050108                   0.100691  ...     \n",
       "1                  -0.064241                   0.115226  ...     \n",
       "2                   0.001710                   0.100691  ...     \n",
       "3                   0.039396                   0.124915  ...     \n",
       "4                   0.006421                   0.144294  ...     \n",
       "\n",
       "   reanalysis_relative_humidity_percent  reanalysis_sat_precip_amt_mm  \\\n",
       "0                             -0.356928                      1.150400   \n",
       "1                             -0.416180                     -0.728707   \n",
       "2                             -0.411882                     -0.981948   \n",
       "3                             -0.976465                     -1.086090   \n",
       "4                             -0.861645                     -1.064465   \n",
       "\n",
       "   reanalysis_specific_humidity_g_per_kg  reanalysis_tdtr_k  \\\n",
       "0                              -0.476845          -0.555653   \n",
       "1                              -0.542237          -0.712691   \n",
       "2                              -0.088166          -0.189231   \n",
       "3                              -0.550319          -0.213391   \n",
       "4                              -0.364429          -0.438881   \n",
       "\n",
       "   station_avg_temp_c  station_diur_temp_rng_c  station_max_temp_c  \\\n",
       "0           -0.010869                -0.192700            0.298395   \n",
       "1           -0.107336                -0.739593           -0.686308   \n",
       "2            0.284559                 0.072934            0.149197   \n",
       "3            0.311690                -0.479168            0.298395   \n",
       "4            0.218238                -0.182283            0.298395   \n",
       "\n",
       "   station_min_temp_c  station_precip_mm  is_sj  \n",
       "0           -0.050215           1.194355   True  \n",
       "1            0.085784           0.012524   True  \n",
       "2            0.248984          -0.891909   True  \n",
       "3            0.684182          -0.969927   True  \n",
       "4            0.384983           1.451526   True  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ndvi_ne                                  0\n",
       "ndvi_nw                                  0\n",
       "ndvi_se                                  0\n",
       "ndvi_sw                                  0\n",
       "precipitation_amt_mm                     0\n",
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       "reanalysis_avg_temp_k                    0\n",
       "reanalysis_dew_point_temp_k              0\n",
       "reanalysis_max_air_temp_k                0\n",
       "reanalysis_min_air_temp_k                0\n",
       "reanalysis_precip_amt_kg_per_m2          0\n",
       "reanalysis_relative_humidity_percent     0\n",
       "reanalysis_sat_precip_amt_mm             0\n",
       "reanalysis_specific_humidity_g_per_kg    0\n",
       "reanalysis_tdtr_k                        0\n",
       "station_avg_temp_c                       0\n",
       "station_diur_temp_rng_c                  0\n",
       "station_max_temp_c                       0\n",
       "station_min_temp_c                       0\n",
       "station_precip_mm                        0\n",
       "is_sj                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dengue_features_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19688ef0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check correlations between variables \n",
    "plt.figure(figsize=(14,13))\n",
    "sns.heatmap(dengue_features_train.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dengue_features_train\n",
    "\n",
    "y = dengue_labels_train.total_cases.loc[X.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(416, 21) (1199, 21)\n"
     ]
    }
   ],
   "source": [
    "print(dengue_features_test.shape, dengue_features_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 101, shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a23d7c860>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((y_test-predictions),bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 10.873948479311917\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict with Real Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(416,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_real = lm.predict(dengue_features_test)\n",
    "predictions_real.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing Dataset Features Only\n",
    "dengue_features_test = pd.read_csv('dengue_features_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate city, year, weekofyear, total_cases on the column axis \n",
    "submission1 = pd.concat([dengue_features_test['city'],dengue_features_test['year'], dengue_features_test['weekofyear'], pd.Series(predictions_real.round())],axis=1)\n",
    "\n",
    "# Set the city column as the index\n",
    "submission1.set_index('city',inplace=True)\n",
    "\n",
    "# Rename columns\n",
    "submission1.columns = ['year', 'weekofyear', 'total_cases']\n",
    "\n",
    "# convert type of 'total_cases' column to integer\n",
    "submission1['total_cases'] = submission1['total_cases'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>total_cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sj</th>\n",
       "      <td>2008</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sj</th>\n",
       "      <td>2008</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sj</th>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sj</th>\n",
       "      <td>2008</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sj</th>\n",
       "      <td>2008</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  weekofyear  total_cases\n",
       "city                               \n",
       "sj    2008          18            6\n",
       "sj    2008          19            5\n",
       "sj    2008          20           26\n",
       "sj    2008          21            0\n",
       "sj    2008          22           12"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to CSV for submission\n",
    "submission1.to_csv('submission4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = GradientBoostingRegressor(n_estimators=1000, learning_rate=0.1,\n",
    "max_depth=1, random_state=0, loss='ls').fit(X_train, y_train)\n",
    "metrics.mean_absolute_error(y_test, predictions)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References \n",
    "* [https://www.datacamp.com/community/tutorials/categorical-data] - Handling Categorical data for ML\n",
    "\n",
    "* https://machinelearningmastery.com/feature-selection-machine-learning-python - feature Selection from ML Mastery\n",
    "\n",
    "* [https://scikit-learn.org/stable/modules/feature_selection.html] - Feature Selection SKLearn documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "# load data\n",
    "names = ['city', 'year', 'weekofyear', 'ndvi_ne', 'ndvi_nw',\n",
    "       'ndvi_se', 'ndvi_sw', 'precipitation_amt_mm', 'reanalysis_air_temp_k',\n",
    "       'reanalysis_avg_temp_k', 'reanalysis_dew_point_temp_k',\n",
    "       'reanalysis_max_air_temp_k', 'reanalysis_min_air_temp_k',\n",
    "       'reanalysis_precip_amt_kg_per_m2',\n",
    "       'reanalysis_relative_humidity_percent', 'reanalysis_sat_precip_amt_mm',\n",
    "       'reanalysis_specific_humidity_g_per_kg', 'reanalysis_tdtr_k',\n",
    "       'station_avg_temp_c', 'station_diur_temp_rng_c', 'station_max_temp_c',\n",
    "       'station_min_temp_c', 'station_precip_mm', 'total_cases']\n",
    "dataframe = dengue_train[names]\n",
    "array = dataframe.values\n",
    "X = array[:,0:23]\n",
    "Y = array[:,23]\n",
    "# feature extraction\n",
    "model = LinearRegression()\n",
    "rfe = RFE(model, 3)\n",
    "fit = rfe.fit(X, Y)\n",
    "print(\"Num Features: %d\" % fit.n_features_)\n",
    "print(\"Selected Features: %s\" % fit.support_)\n",
    "print(\"Feature Ranking: %s\" % fit.ranking_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_features = ['ndvi_se', 'ndvi_sw','reanalysis_air_temp_k','reanalysis_dew_point_temp_k',\n",
    " 'reanalysis_avg_temp_k','station_min_temp_c','reanalysis_tdtr_k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = dengue_train[imp_features]\n",
    "\n",
    "y_new = dengue_train['total_cases']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_new, y_new, test_size=0.4, random_state=101)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "\n",
    "predictions = lm.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test,predictions)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot((y_test-predictions),bins=50);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Model Evaluation \n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test,predictions))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Predict with Real Dataset\n",
    "\n",
    "predictions_real = lm.predict(dengue_features_testNumeric[imp_features])\n",
    "\n",
    "# Concatenate city, year, weekofyear, total_cases on the column axis \n",
    "submission2 = pd.concat([dengue_features_test['city'],dengue_features_test['year'], dengue_features_test['weekofyear'], pd.Series(predictions_real.round())],axis=1)\n",
    "\n",
    "# Set the city column as the index\n",
    "submission2.set_index('city',inplace=True)\n",
    "\n",
    "# Rename columns\n",
    "submission2.columns = ['year', 'weekofyear', 'total_cases']\n",
    "\n",
    "# convert type of 'total_cases' column to integer\n",
    "submission2['total_cases'] = submission1['total_cases'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to CSV for submission\n",
    "submission2.to_csv('submission2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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