3.Feature Selection using Confusion Matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('training_data_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test=pd.read_csv('test_data_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop('Unnamed: 0', axis=1)\n",
    "test=test.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>amount_tsh</th>\n",
       "      <th>days_since_recorded</th>\n",
       "      <th>funder</th>\n",
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       "      <th>installer</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>2</th>\n",
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       "      <td>other</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>37.460664</td>\n",
       "      <td>-3.821329</td>\n",
       "      <td>Pangani</td>\n",
       "      <td>Majengo</td>\n",
       "      <td>Manyara</td>\n",
       "      <td>...</td>\n",
       "      <td>00s</td>\n",
       "      <td>gravity</td>\n",
       "      <td>per bucket</td>\n",
       "      <td>soft</td>\n",
       "      <td>enough</td>\n",
       "      <td>dam</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0</td>\n",
       "      <td>309</td>\n",
       "      <td>Unicef</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>Ruvuma / Southern Coast</td>\n",
       "      <td>Mahakamani</td>\n",
       "      <td>Mtwara</td>\n",
       "      <td>...</td>\n",
       "      <td>80s</td>\n",
       "      <td>submersible</td>\n",
       "      <td>never pay</td>\n",
       "      <td>soft</td>\n",
       "      <td>dry</td>\n",
       "      <td>borehole</td>\n",
       "      <td>groundwater</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>non functional</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>250.0</td>\n",
       "      <td>874</td>\n",
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       "      <td>1057.545585</td>\n",
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       "      <td>31.130847</td>\n",
       "      <td>-1.825359</td>\n",
       "      <td>Lake Victoria</td>\n",
       "      <td>Kyanyamisa</td>\n",
       "      <td>Kagera</td>\n",
       "      <td>...</td>\n",
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       "      <td>seasonal</td>\n",
       "      <td>rainwater harvesting</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 25 columns</p>\n",
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      "text/plain": [
       "   amount_tsh  days_since_recorded  funder   gps_height installer  longitude  \\\n",
       "0      6000.0                  995   other  1390.000000     other  34.938093   \n",
       "1       200.0                  272   other  1399.000000     other  34.698766   \n",
       "2        25.0                  281   other   686.000000     other  37.460664   \n",
       "3        50.0                  309  Unicef   263.000000     other  38.486161   \n",
       "4       250.0                  874   other  1057.545585     other  31.130847   \n",
       "\n",
       "    latitude                    basin  subvillage   region       ...        \\\n",
       "0  -9.856322               Lake Nyasa    Mnyusi B   Iringa       ...         \n",
       "1  -2.147466            Lake Victoria     Nyamara     Mara       ...         \n",
       "2  -3.821329                  Pangani     Majengo  Manyara       ...         \n",
       "3 -11.155298  Ruvuma / Southern Coast  Mahakamani   Mtwara       ...         \n",
       "4  -1.825359            Lake Victoria  Kyanyamisa   Kagera       ...         \n",
       "\n",
       "   construction_year extraction_type payment_type water_quality  \\\n",
       "0                90s         gravity     annually          soft   \n",
       "1                10s         gravity    never pay          soft   \n",
       "2                00s         gravity   per bucket          soft   \n",
       "3                80s     submersible    never pay          soft   \n",
       "4            unknown         gravity    never pay          soft   \n",
       "\n",
       "  quantity_group           source_type source_class  \\\n",
       "0         enough                spring  groundwater   \n",
       "1   insufficient  rainwater harvesting      surface   \n",
       "2         enough                   dam      surface   \n",
       "3            dry              borehole  groundwater   \n",
       "4       seasonal  rainwater harvesting      surface   \n",
       "\n",
       "               waterpoint_type waterpoint_type_group    status_group  \n",
       "0           communal standpipe    communal standpipe      functional  \n",
       "1           communal standpipe    communal standpipe      functional  \n",
       "2  communal standpipe multiple    communal standpipe      functional  \n",
       "3  communal standpipe multiple    communal standpipe  non functional  \n",
       "4           communal standpipe    communal standpipe      functional  \n",
       "\n",
       "[5 rows x 25 columns]"
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  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.iloc[:,0] = label_encoder.fit_transform(data.iloc[:,0]).astype('float64')\n",
    "test.iloc[:,0] = label_encoder.fit_transform(test.iloc[:,0]).astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-3.821329</td>\n",
       "      <td>Pangani</td>\n",
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       "      <td>Manyara</td>\n",
       "      <td>...</td>\n",
       "      <td>00s</td>\n",
       "      <td>gravity</td>\n",
       "      <td>per bucket</td>\n",
       "      <td>soft</td>\n",
       "      <td>enough</td>\n",
       "      <td>dam</td>\n",
       "      <td>surface</td>\n",
       "      <td>communal standpipe multiple</td>\n",
       "      <td>communal standpipe</td>\n",
       "      <td>functional</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>309</td>\n",
       "      <td>Unicef</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>Ruvuma / Southern Coast</td>\n",
       "      <td>Mahakamani</td>\n",
       "      <td>Mtwara</td>\n",
       "      <td>...</td>\n",
       "      <td>80s</td>\n",
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       "      <td>soft</td>\n",
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       "      <td>non functional</td>\n",
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       "      <th>4</th>\n",
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       "      <td>seasonal</td>\n",
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       "   amount_tsh  days_since_recorded  funder   gps_height installer  longitude  \\\n",
       "0        62.0                  995   other  1390.000000     other  34.938093   \n",
       "1        25.0                  272   other  1399.000000     other  34.698766   \n",
       "2        12.0                  281   other   686.000000     other  37.460664   \n",
       "3        18.0                  309  Unicef   263.000000     other  38.486161   \n",
       "4        27.0                  874   other  1057.545585     other  31.130847   \n",
       "\n",
       "    latitude                    basin  subvillage   region       ...        \\\n",
       "0  -9.856322               Lake Nyasa    Mnyusi B   Iringa       ...         \n",
       "1  -2.147466            Lake Victoria     Nyamara     Mara       ...         \n",
       "2  -3.821329                  Pangani     Majengo  Manyara       ...         \n",
       "3 -11.155298  Ruvuma / Southern Coast  Mahakamani   Mtwara       ...         \n",
       "4  -1.825359            Lake Victoria  Kyanyamisa   Kagera       ...         \n",
       "\n",
       "   construction_year extraction_type payment_type water_quality  \\\n",
       "0                90s         gravity     annually          soft   \n",
       "1                10s         gravity    never pay          soft   \n",
       "2                00s         gravity   per bucket          soft   \n",
       "3                80s     submersible    never pay          soft   \n",
       "4            unknown         gravity    never pay          soft   \n",
       "\n",
       "  quantity_group           source_type source_class  \\\n",
       "0         enough                spring  groundwater   \n",
       "1   insufficient  rainwater harvesting      surface   \n",
       "2         enough                   dam      surface   \n",
       "3            dry              borehole  groundwater   \n",
       "4       seasonal  rainwater harvesting      surface   \n",
       "\n",
       "               waterpoint_type waterpoint_type_group    status_group  \n",
       "0           communal standpipe    communal standpipe      functional  \n",
       "1           communal standpipe    communal standpipe      functional  \n",
       "2  communal standpipe multiple    communal standpipe      functional  \n",
       "3  communal standpipe multiple    communal standpipe  non functional  \n",
       "4           communal standpipe    communal standpipe      functional  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr = data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>gps_height</th>\n",
       "      <td>0.239961</td>\n",
       "      <td>-0.137255</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.274860</td>\n",
       "      <td>0.284231</td>\n",
       "      <td>-0.025937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>-0.148192</td>\n",
       "      <td>0.048938</td>\n",
       "      <td>-0.274860</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.425802</td>\n",
       "      <td>-0.099771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.121791</td>\n",
       "      <td>-0.256633</td>\n",
       "      <td>0.284231</td>\n",
       "      <td>-0.425802</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>-0.024886</td>\n",
       "      <td>-0.086592</td>\n",
       "      <td>-0.025937</td>\n",
       "      <td>-0.099771</td>\n",
       "      <td>0.102269</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     amount_tsh  days_since_recorded  gps_height  longitude  \\\n",
       "amount_tsh             1.000000             0.178297    0.239961  -0.148192   \n",
       "days_since_recorded    0.178297             1.000000   -0.137255   0.048938   \n",
       "gps_height             0.239961            -0.137255    1.000000  -0.274860   \n",
       "longitude             -0.148192             0.048938   -0.274860   1.000000   \n",
       "latitude              -0.121791            -0.256633    0.284231  -0.425802   \n",
       "population            -0.024886            -0.086592   -0.025937  -0.099771   \n",
       "\n",
       "                     latitude  population  \n",
       "amount_tsh          -0.121791   -0.024886  \n",
       "days_since_recorded -0.256633   -0.086592  \n",
       "gps_height           0.284231   -0.025937  \n",
       "longitude           -0.425802   -0.099771  \n",
       "latitude             1.000000    0.102269  \n",
       "population           0.102269    1.000000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "integer_columns = ['days_since_recorded', 'population','gps_height','longitude','latitude','amount_tsh', 'population']\n",
    "categorical_cols=[col for col in data.columns if col not in integer_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "data[categorical_cols]=data[categorical_cols].apply(LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>amount_tsh</th>\n",
       "      <th>days_since_recorded</th>\n",
       "      <th>funder</th>\n",
       "      <th>gps_height</th>\n",
       "      <th>installer</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>basin</th>\n",
       "      <th>subvillage</th>\n",
       "      <th>region</th>\n",
       "      <th>...</th>\n",
       "      <th>construction_year</th>\n",
       "      <th>extraction_type</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quantity_group</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_class</th>\n",
       "      <th>waterpoint_type</th>\n",
       "      <th>waterpoint_type_group</th>\n",
       "      <th>status_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.0</td>\n",
       "      <td>995</td>\n",
       "      <td>8</td>\n",
       "      <td>1390.000000</td>\n",
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       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>272</td>\n",
       "      <td>8</td>\n",
       "      <td>1399.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>34.698766</td>\n",
       "      <td>-2.147466</td>\n",
       "      <td>4</td>\n",
       "      <td>15838</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>281</td>\n",
       "      <td>8</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>37.460664</td>\n",
       "      <td>-3.821329</td>\n",
       "      <td>5</td>\n",
       "      <td>9074</td>\n",
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       "      <td>...</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>309</td>\n",
       "      <td>3</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>7</td>\n",
       "      <td>8982</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>27.0</td>\n",
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       "      <td>8</td>\n",
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       "      <td>7</td>\n",
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       "      <td>7698</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   amount_tsh  days_since_recorded  funder   gps_height  installer  longitude  \\\n",
       "0        62.0                  995       8  1390.000000          7  34.938093   \n",
       "1        25.0                  272       8  1399.000000          7  34.698766   \n",
       "2        12.0                  281       8   686.000000          7  37.460664   \n",
       "3        18.0                  309       3   263.000000          7  38.486161   \n",
       "4        27.0                  874       8  1057.545585          7  31.130847   \n",
       "\n",
       "    latitude  basin  subvillage  region      ...       construction_year  \\\n",
       "0  -9.856322      1       11807       3      ...                       5   \n",
       "1  -2.147466      4       15838       9      ...                       1   \n",
       "2  -3.821329      5        9074       8      ...                       0   \n",
       "3 -11.155298      7        8982      12      ...                       4   \n",
       "4  -1.825359      4        7698       4      ...                       6   \n",
       "\n",
       "   extraction_type  payment_type  water_quality  quantity_group  source_type  \\\n",
       "0                1             0              6               1            6   \n",
       "1                1             2              6               2            3   \n",
       "2                1             5              6               1            1   \n",
       "3                8             2              6               0            0   \n",
       "4                1             2              6               3            3   \n",
       "\n",
       "   source_class  waterpoint_type  waterpoint_type_group  status_group  \n",
       "0             0                1                      1             0  \n",
       "1             1                1                      1             0  \n",
       "2             1                2                      1             0  \n",
       "3             0                2                      1             2  \n",
       "4             1                1                      1             0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr=data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>amount_tsh</th>\n",
       "      <th>days_since_recorded</th>\n",
       "      <th>funder</th>\n",
       "      <th>gps_height</th>\n",
       "      <th>installer</th>\n",
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       "      <th>...</th>\n",
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       "      <th>payment_type</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amount_tsh</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_since_recorded</th>\n",
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       "      <td>1.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>funder</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.154870</td>\n",
       "      <td>-0.029354</td>\n",
       "      <td>0.129598</td>\n",
       "      <td>0.022691</td>\n",
       "      <td>0.011657</td>\n",
       "      <td>0.034702</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.068286</td>\n",
       "      <td>0.049989</td>\n",
       "      <td>0.095296</td>\n",
       "      <td>-0.063741</td>\n",
       "      <td>-0.011148</td>\n",
       "      <td>-0.074819</td>\n",
       "      <td>0.003931</td>\n",
       "      <td>0.012562</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>-0.043570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gps_height</th>\n",
       "      <td>0.239961</td>\n",
       "      <td>-0.137255</td>\n",
       "      <td>-0.025098</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.038761</td>\n",
       "      <td>-0.208046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100815</td>\n",
       "      <td>-0.249200</td>\n",
       "      <td>-0.082505</td>\n",
       "      <td>0.116012</td>\n",
       "      <td>-0.014288</td>\n",
       "      <td>0.204156</td>\n",
       "      <td>-0.037071</td>\n",
       "      <td>-0.090798</td>\n",
       "      <td>-0.080919</td>\n",
       "      <td>-0.122505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installer</th>\n",
       "      <td>-0.088914</td>\n",
       "      <td>-0.090383</td>\n",
       "      <td>0.154870</td>\n",
       "      <td>-0.098448</td>\n",
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       "      <td>0.080358</td>\n",
       "      <td>-0.011520</td>\n",
       "      <td>-0.024024</td>\n",
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       "      <td>-0.012208</td>\n",
       "      <td>-0.013457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>-0.148192</td>\n",
       "      <td>0.048938</td>\n",
       "      <td>-0.029354</td>\n",
       "      <td>-0.274860</td>\n",
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       "      <td>0.009328</td>\n",
       "      <td>-0.142783</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.015785</td>\n",
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       "      <td>0.020594</td>\n",
       "      <td>-0.163355</td>\n",
       "      <td>-0.144605</td>\n",
       "      <td>0.004366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.121791</td>\n",
       "      <td>-0.256633</td>\n",
       "      <td>0.129598</td>\n",
       "      <td>0.284231</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>-0.007440</td>\n",
       "      <td>-0.025485</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155502</td>\n",
       "      <td>0.036418</td>\n",
       "      <td>0.145438</td>\n",
       "      <td>-0.010955</td>\n",
       "      <td>0.117193</td>\n",
       "      <td>0.064626</td>\n",
       "      <td>-0.040928</td>\n",
       "      <td>0.160127</td>\n",
       "      <td>0.146218</td>\n",
       "      <td>-0.014547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basin</th>\n",
       "      <td>-0.077444</td>\n",
       "      <td>0.215408</td>\n",
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       "      <td>-0.075529</td>\n",
       "      <td>-0.026707</td>\n",
       "      <td>-0.074808</td>\n",
       "      <td>0.031636</td>\n",
       "      <td>0.009864</td>\n",
       "      <td>0.013518</td>\n",
       "      <td>0.043413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subvillage</th>\n",
       "      <td>-0.043323</td>\n",
       "      <td>-0.059879</td>\n",
       "      <td>0.011657</td>\n",
       "      <td>-0.038761</td>\n",
       "      <td>-0.011520</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>-0.007440</td>\n",
       "      <td>0.057696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011206</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042279</td>\n",
       "      <td>0.004133</td>\n",
       "      <td>0.022473</td>\n",
       "      <td>0.002076</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>-0.025664</td>\n",
       "      <td>-0.012342</td>\n",
       "      <td>-0.017225</td>\n",
       "      <td>-0.019984</td>\n",
       "      <td>-0.006752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0.093536</td>\n",
       "      <td>-0.018730</td>\n",
       "      <td>0.034702</td>\n",
       "      <td>-0.208046</td>\n",
       "      <td>-0.024024</td>\n",
       "      <td>-0.142783</td>\n",
       "      <td>-0.025485</td>\n",
       "      <td>-0.114880</td>\n",
       "      <td>-0.011206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164590</td>\n",
       "      <td>0.277008</td>\n",
       "      <td>0.056709</td>\n",
       "      <td>-0.073382</td>\n",
       "      <td>0.034766</td>\n",
       "      <td>-0.140439</td>\n",
       "      <td>-0.010899</td>\n",
       "      <td>0.279421</td>\n",
       "      <td>0.257183</td>\n",
       "      <td>0.091252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>-0.024886</td>\n",
       "      <td>-0.086592</td>\n",
       "      <td>0.057651</td>\n",
       "      <td>-0.025937</td>\n",
       "      <td>0.027228</td>\n",
       "      <td>-0.099771</td>\n",
       "      <td>0.102269</td>\n",
       "      <td>-0.029898</td>\n",
       "      <td>-0.000864</td>\n",
       "      <td>0.109926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030210</td>\n",
       "      <td>0.126756</td>\n",
       "      <td>0.062281</td>\n",
       "      <td>-0.048807</td>\n",
       "      <td>0.034192</td>\n",
       "      <td>-0.079292</td>\n",
       "      <td>0.012253</td>\n",
       "      <td>0.125506</td>\n",
       "      <td>0.104900</td>\n",
       "      <td>-0.012008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>public_meeting</th>\n",
       "      <td>0.032509</td>\n",
       "      <td>-0.089298</td>\n",
       "      <td>0.059163</td>\n",
       "      <td>0.015711</td>\n",
       "      <td>-0.003503</td>\n",
       "      <td>-0.048005</td>\n",
       "      <td>0.045487</td>\n",
       "      <td>0.016602</td>\n",
       "      <td>0.012726</td>\n",
       "      <td>-0.028418</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023946</td>\n",
       "      <td>0.009791</td>\n",
       "      <td>0.124099</td>\n",
       "      <td>0.032079</td>\n",
       "      <td>-0.028307</td>\n",
       "      <td>-0.008829</td>\n",
       "      <td>-0.071904</td>\n",
       "      <td>-0.023053</td>\n",
       "      <td>-0.027216</td>\n",
       "      <td>-0.033983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scheme_management</th>\n",
       "      <td>-0.042761</td>\n",
       "      <td>-0.067353</td>\n",
       "      <td>-0.033738</td>\n",
       "      <td>0.131377</td>\n",
       "      <td>-0.057240</td>\n",
       "      <td>-0.144352</td>\n",
       "      <td>0.034709</td>\n",
       "      <td>-0.041219</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.010546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050211</td>\n",
       "      <td>-0.041816</td>\n",
       "      <td>-0.037621</td>\n",
       "      <td>0.044573</td>\n",
       "      <td>-0.131733</td>\n",
       "      <td>0.053290</td>\n",
       "      <td>-0.050297</td>\n",
       "      <td>-0.039097</td>\n",
       "      <td>-0.053811</td>\n",
       "      <td>-0.064742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scheme_name</th>\n",
       "      <td>0.083287</td>\n",
       "      <td>0.035528</td>\n",
       "      <td>-0.021645</td>\n",
       "      <td>0.009727</td>\n",
       "      <td>-0.046902</td>\n",
       "      <td>-0.102220</td>\n",
       "      <td>0.034302</td>\n",
       "      <td>0.022467</td>\n",
       "      <td>0.013663</td>\n",
       "      <td>0.115154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128959</td>\n",
       "      <td>0.225567</td>\n",
       "      <td>-0.044772</td>\n",
       "      <td>-0.081137</td>\n",
       "      <td>0.054636</td>\n",
       "      <td>-0.042615</td>\n",
       "      <td>-0.082162</td>\n",
       "      <td>0.386842</td>\n",
       "      <td>0.373257</td>\n",
       "      <td>0.036578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>permit</th>\n",
       "      <td>-0.088108</td>\n",
       "      <td>-0.066694</td>\n",
       "      <td>0.138277</td>\n",
       "      <td>0.082871</td>\n",
       "      <td>0.108553</td>\n",
       "      <td>0.098299</td>\n",
       "      <td>0.055979</td>\n",
       "      <td>-0.090888</td>\n",
       "      <td>-0.033814</td>\n",
       "      <td>-0.016626</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023794</td>\n",
       "      <td>-0.136905</td>\n",
       "      <td>-0.023220</td>\n",
       "      <td>0.050908</td>\n",
       "      <td>-0.008774</td>\n",
       "      <td>0.061436</td>\n",
       "      <td>0.088929</td>\n",
       "      <td>-0.151327</td>\n",
       "      <td>-0.130991</td>\n",
       "      <td>-0.031936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>construction_year</th>\n",
       "      <td>0.105198</td>\n",
       "      <td>0.169050</td>\n",
       "      <td>-0.068286</td>\n",
       "      <td>0.100815</td>\n",
       "      <td>-0.141000</td>\n",
       "      <td>-0.320469</td>\n",
       "      <td>0.155502</td>\n",
       "      <td>-0.214819</td>\n",
       "      <td>-0.042279</td>\n",
       "      <td>0.164590</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119346</td>\n",
       "      <td>-0.035393</td>\n",
       "      <td>-0.018510</td>\n",
       "      <td>-0.005146</td>\n",
       "      <td>0.072254</td>\n",
       "      <td>-0.080963</td>\n",
       "      <td>0.216712</td>\n",
       "      <td>0.205038</td>\n",
       "      <td>0.128198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extraction_type</th>\n",
       "      <td>-0.072663</td>\n",
       "      <td>0.019294</td>\n",
       "      <td>0.049989</td>\n",
       "      <td>-0.249200</td>\n",
       "      <td>0.035103</td>\n",
       "      <td>-0.067787</td>\n",
       "      <td>0.036418</td>\n",
       "      <td>0.131235</td>\n",
       "      <td>0.004133</td>\n",
       "      <td>0.277008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119346</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.147280</td>\n",
       "      <td>-0.133783</td>\n",
       "      <td>-0.006442</td>\n",
       "      <td>-0.333867</td>\n",
       "      <td>-0.262935</td>\n",
       "      <td>0.477329</td>\n",
       "      <td>0.439278</td>\n",
       "      <td>0.147886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_type</th>\n",
       "      <td>-0.389181</td>\n",
       "      <td>-0.273204</td>\n",
       "      <td>0.095296</td>\n",
       "      <td>-0.082505</td>\n",
       "      <td>0.078990</td>\n",
       "      <td>-0.007441</td>\n",
       "      <td>0.145438</td>\n",
       "      <td>-0.029961</td>\n",
       "      <td>0.022473</td>\n",
       "      <td>0.056709</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035393</td>\n",
       "      <td>0.147280</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047366</td>\n",
       "      <td>0.006905</td>\n",
       "      <td>-0.173454</td>\n",
       "      <td>-0.037780</td>\n",
       "      <td>0.038424</td>\n",
       "      <td>0.025069</td>\n",
       "      <td>0.055772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water_quality</th>\n",
       "      <td>0.028363</td>\n",
       "      <td>-0.000065</td>\n",
       "      <td>-0.063741</td>\n",
       "      <td>0.116012</td>\n",
       "      <td>-0.003624</td>\n",
       "      <td>-0.051622</td>\n",
       "      <td>-0.010955</td>\n",
       "      <td>-0.075529</td>\n",
       "      <td>0.002076</td>\n",
       "      <td>-0.073382</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018510</td>\n",
       "      <td>-0.133783</td>\n",
       "      <td>-0.047366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031869</td>\n",
       "      <td>0.097019</td>\n",
       "      <td>0.083834</td>\n",
       "      <td>-0.162096</td>\n",
       "      <td>-0.149837</td>\n",
       "      <td>-0.003094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quantity_group</th>\n",
       "      <td>-0.010587</td>\n",
       "      <td>-0.040233</td>\n",
       "      <td>-0.011148</td>\n",
       "      <td>-0.014288</td>\n",
       "      <td>0.082401</td>\n",
       "      <td>0.015785</td>\n",
       "      <td>0.117193</td>\n",
       "      <td>-0.026707</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>0.034766</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005146</td>\n",
       "      <td>-0.006442</td>\n",
       "      <td>0.006905</td>\n",
       "      <td>-0.031869</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000585</td>\n",
       "      <td>0.117300</td>\n",
       "      <td>0.006436</td>\n",
       "      <td>0.010060</td>\n",
       "      <td>-0.131425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_type</th>\n",
       "      <td>0.083274</td>\n",
       "      <td>0.062734</td>\n",
       "      <td>-0.074819</td>\n",
       "      <td>0.204156</td>\n",
       "      <td>-0.138065</td>\n",
       "      <td>-0.047479</td>\n",
       "      <td>0.064626</td>\n",
       "      <td>-0.074808</td>\n",
       "      <td>-0.025664</td>\n",
       "      <td>-0.140439</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072254</td>\n",
       "      <td>-0.333867</td>\n",
       "      <td>-0.173454</td>\n",
       "      <td>0.097019</td>\n",
       "      <td>-0.000585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.087867</td>\n",
       "      <td>-0.023123</td>\n",
       "      <td>0.025987</td>\n",
       "      <td>-0.079656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_class</th>\n",
       "      <td>0.010639</td>\n",
       "      <td>0.040683</td>\n",
       "      <td>0.003931</td>\n",
       "      <td>-0.037071</td>\n",
       "      <td>0.021601</td>\n",
       "      <td>0.020594</td>\n",
       "      <td>-0.040928</td>\n",
       "      <td>0.031636</td>\n",
       "      <td>-0.012342</td>\n",
       "      <td>-0.010899</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080963</td>\n",
       "      <td>-0.262935</td>\n",
       "      <td>-0.037780</td>\n",
       "      <td>0.083834</td>\n",
       "      <td>0.117300</td>\n",
       "      <td>-0.087867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.339715</td>\n",
       "      <td>-0.321849</td>\n",
       "      <td>-0.026771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterpoint_type</th>\n",
       "      <td>0.009254</td>\n",
       "      <td>-0.060015</td>\n",
       "      <td>0.012562</td>\n",
       "      <td>-0.090798</td>\n",
       "      <td>-0.007337</td>\n",
       "      <td>-0.163355</td>\n",
       "      <td>0.160127</td>\n",
       "      <td>0.009864</td>\n",
       "      <td>-0.017225</td>\n",
       "      <td>0.279421</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216712</td>\n",
       "      <td>0.477329</td>\n",
       "      <td>0.038424</td>\n",
       "      <td>-0.162096</td>\n",
       "      <td>0.006436</td>\n",
       "      <td>-0.023123</td>\n",
       "      <td>-0.339715</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982154</td>\n",
       "      <td>0.206507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterpoint_type_group</th>\n",
       "      <td>0.006656</td>\n",
       "      <td>-0.044362</td>\n",
       "      <td>0.009434</td>\n",
       "      <td>-0.080919</td>\n",
       "      <td>-0.012208</td>\n",
       "      <td>-0.144605</td>\n",
       "      <td>0.146218</td>\n",
       "      <td>0.013518</td>\n",
       "      <td>-0.019984</td>\n",
       "      <td>0.257183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205038</td>\n",
       "      <td>0.439278</td>\n",
       "      <td>0.025069</td>\n",
       "      <td>-0.149837</td>\n",
       "      <td>0.010060</td>\n",
       "      <td>0.025987</td>\n",
       "      <td>-0.321849</td>\n",
       "      <td>0.982154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status_group</th>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.028992</td>\n",
       "      <td>-0.043570</td>\n",
       "      <td>-0.122505</td>\n",
       "      <td>-0.013457</td>\n",
       "      <td>0.004366</td>\n",
       "      <td>-0.014547</td>\n",
       "      <td>0.043413</td>\n",
       "      <td>-0.006752</td>\n",
       "      <td>0.091252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128198</td>\n",
       "      <td>0.147886</td>\n",
       "      <td>0.055772</td>\n",
       "      <td>-0.003094</td>\n",
       "      <td>-0.131425</td>\n",
       "      <td>-0.079656</td>\n",
       "      <td>-0.026771</td>\n",
       "      <td>0.206507</td>\n",
       "      <td>0.206431</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       amount_tsh  days_since_recorded    funder  gps_height  \\\n",
       "amount_tsh               1.000000             0.178297 -0.073544    0.239961   \n",
       "days_since_recorded      0.178297             1.000000 -0.054307   -0.137255   \n",
       "funder                  -0.073544            -0.054307  1.000000   -0.025098   \n",
       "gps_height               0.239961            -0.137255 -0.025098    1.000000   \n",
       "installer               -0.088914            -0.090383  0.154870   -0.098448   \n",
       "longitude               -0.148192             0.048938 -0.029354   -0.274860   \n",
       "latitude                -0.121791            -0.256633  0.129598    0.284231   \n",
       "basin                   -0.077444             0.215408  0.022691   -0.528646   \n",
       "subvillage              -0.043323            -0.059879  0.011657   -0.038761   \n",
       "region                   0.093536            -0.018730  0.034702   -0.208046   \n",
       "population              -0.024886            -0.086592  0.057651   -0.025937   \n",
       "public_meeting           0.032509            -0.089298  0.059163    0.015711   \n",
       "scheme_management       -0.042761            -0.067353 -0.033738    0.131377   \n",
       "scheme_name              0.083287             0.035528 -0.021645    0.009727   \n",
       "permit                  -0.088108            -0.066694  0.138277    0.082871   \n",
       "construction_year        0.105198             0.169050 -0.068286    0.100815   \n",
       "extraction_type         -0.072663             0.019294  0.049989   -0.249200   \n",
       "payment_type            -0.389181            -0.273204  0.095296   -0.082505   \n",
       "water_quality            0.028363            -0.000065 -0.063741    0.116012   \n",
       "quantity_group          -0.010587            -0.040233 -0.011148   -0.014288   \n",
       "source_type              0.083274             0.062734 -0.074819    0.204156   \n",
       "source_class             0.010639             0.040683  0.003931   -0.037071   \n",
       "waterpoint_type          0.009254            -0.060015  0.012562   -0.090798   \n",
       "waterpoint_type_group    0.006656            -0.044362  0.009434   -0.080919   \n",
       "status_group            -0.044921            -0.028992 -0.043570   -0.122505   \n",
       "\n",
       "                       installer  longitude  latitude     basin  subvillage  \\\n",
       "amount_tsh             -0.088914  -0.148192 -0.121791 -0.077444   -0.043323   \n",
       "days_since_recorded    -0.090383   0.048938 -0.256633  0.215408   -0.059879   \n",
       "funder                  0.154870  -0.029354  0.129598  0.022691    0.011657   \n",
       "gps_height             -0.098448  -0.274860  0.284231 -0.528646   -0.038761   \n",
       "installer               1.000000   0.153143 -0.007988  0.080358   -0.011520   \n",
       "longitude               0.153143   1.000000 -0.425802  0.216088    0.009328   \n",
       "latitude               -0.007988  -0.425802  1.000000 -0.218502   -0.007440   \n",
       "basin                   0.080358   0.216088 -0.218502  1.000000    0.057696   \n",
       "subvillage             -0.011520   0.009328 -0.007440  0.057696    1.000000   \n",
       "region                 -0.024024  -0.142783 -0.025485 -0.114880   -0.011206   \n",
       "population              0.027228  -0.099771  0.102269 -0.029898   -0.000864   \n",
       "public_meeting         -0.003503  -0.048005  0.045487  0.016602    0.012726   \n",
       "scheme_management      -0.057240  -0.144352  0.034709 -0.041219    0.000700   \n",
       "scheme_name            -0.046902  -0.102220  0.034302  0.022467    0.013663   \n",
       "permit                  0.108553   0.098299  0.055979 -0.090888   -0.033814   \n",
       "construction_year      -0.141000  -0.320469  0.155502 -0.214819   -0.042279   \n",
       "extraction_type         0.035103  -0.067787  0.036418  0.131235    0.004133   \n",
       "payment_type            0.078990  -0.007441  0.145438 -0.029961    0.022473   \n",
       "water_quality          -0.003624  -0.051622 -0.010955 -0.075529    0.002076   \n",
       "quantity_group          0.082401   0.015785  0.117193 -0.026707    0.006003   \n",
       "source_type            -0.138065  -0.047479  0.064626 -0.074808   -0.025664   \n",
       "source_class            0.021601   0.020594 -0.040928  0.031636   -0.012342   \n",
       "waterpoint_type        -0.007337  -0.163355  0.160127  0.009864   -0.017225   \n",
       "waterpoint_type_group  -0.012208  -0.144605  0.146218  0.013518   -0.019984   \n",
       "status_group           -0.013457   0.004366 -0.014547  0.043413   -0.006752   \n",
       "\n",
       "                         region      ...       construction_year  \\\n",
       "amount_tsh             0.093536      ...                0.105198   \n",
       "days_since_recorded   -0.018730      ...                0.169050   \n",
       "funder                 0.034702      ...               -0.068286   \n",
       "gps_height            -0.208046      ...                0.100815   \n",
       "installer             -0.024024      ...               -0.141000   \n",
       "longitude             -0.142783      ...               -0.320469   \n",
       "latitude              -0.025485      ...                0.155502   \n",
       "basin                 -0.114880      ...               -0.214819   \n",
       "subvillage            -0.011206      ...               -0.042279   \n",
       "region                 1.000000      ...                0.164590   \n",
       "population             0.109926      ...                0.030210   \n",
       "public_meeting        -0.028418      ...               -0.023946   \n",
       "scheme_management      0.010546      ...                0.050211   \n",
       "scheme_name            0.115154      ...                0.128959   \n",
       "permit                -0.016626      ...               -0.023794   \n",
       "construction_year      0.164590      ...                1.000000   \n",
       "extraction_type        0.277008      ...                0.119346   \n",
       "payment_type           0.056709      ...               -0.035393   \n",
       "water_quality         -0.073382      ...               -0.018510   \n",
       "quantity_group         0.034766      ...               -0.005146   \n",
       "source_type           -0.140439      ...                0.072254   \n",
       "source_class          -0.010899      ...               -0.080963   \n",
       "waterpoint_type        0.279421      ...                0.216712   \n",
       "waterpoint_type_group  0.257183      ...                0.205038   \n",
       "status_group           0.091252      ...                0.128198   \n",
       "\n",
       "                       extraction_type  payment_type  water_quality  \\\n",
       "amount_tsh                   -0.072663     -0.389181       0.028363   \n",
       "days_since_recorded           0.019294     -0.273204      -0.000065   \n",
       "funder                        0.049989      0.095296      -0.063741   \n",
       "gps_height                   -0.249200     -0.082505       0.116012   \n",
       "installer                     0.035103      0.078990      -0.003624   \n",
       "longitude                    -0.067787     -0.007441      -0.051622   \n",
       "latitude                      0.036418      0.145438      -0.010955   \n",
       "basin                         0.131235     -0.029961      -0.075529   \n",
       "subvillage                    0.004133      0.022473       0.002076   \n",
       "region                        0.277008      0.056709      -0.073382   \n",
       "population                    0.126756      0.062281      -0.048807   \n",
       "public_meeting                0.009791      0.124099       0.032079   \n",
       "scheme_management            -0.041816     -0.037621       0.044573   \n",
       "scheme_name                   0.225567     -0.044772      -0.081137   \n",
       "permit                       -0.136905     -0.023220       0.050908   \n",
       "construction_year             0.119346     -0.035393      -0.018510   \n",
       "extraction_type               1.000000      0.147280      -0.133783   \n",
       "payment_type                  0.147280      1.000000      -0.047366   \n",
       "water_quality                -0.133783     -0.047366       1.000000   \n",
       "quantity_group               -0.006442      0.006905      -0.031869   \n",
       "source_type                  -0.333867     -0.173454       0.097019   \n",
       "source_class                 -0.262935     -0.037780       0.083834   \n",
       "waterpoint_type               0.477329      0.038424      -0.162096   \n",
       "waterpoint_type_group         0.439278      0.025069      -0.149837   \n",
       "status_group                  0.147886      0.055772      -0.003094   \n",
       "\n",
       "                       quantity_group  source_type  source_class  \\\n",
       "amount_tsh                  -0.010587     0.083274      0.010639   \n",
       "days_since_recorded         -0.040233     0.062734      0.040683   \n",
       "funder                      -0.011148    -0.074819      0.003931   \n",
       "gps_height                  -0.014288     0.204156     -0.037071   \n",
       "installer                    0.082401    -0.138065      0.021601   \n",
       "longitude                    0.015785    -0.047479      0.020594   \n",
       "latitude                     0.117193     0.064626     -0.040928   \n",
       "basin                       -0.026707    -0.074808      0.031636   \n",
       "subvillage                   0.006003    -0.025664     -0.012342   \n",
       "region                       0.034766    -0.140439     -0.010899   \n",
       "population                   0.034192    -0.079292      0.012253   \n",
       "public_meeting              -0.028307    -0.008829     -0.071904   \n",
       "scheme_management           -0.131733     0.053290     -0.050297   \n",
       "scheme_name                  0.054636    -0.042615     -0.082162   \n",
       "permit                      -0.008774     0.061436      0.088929   \n",
       "construction_year           -0.005146     0.072254     -0.080963   \n",
       "extraction_type             -0.006442    -0.333867     -0.262935   \n",
       "payment_type                 0.006905    -0.173454     -0.037780   \n",
       "water_quality               -0.031869     0.097019      0.083834   \n",
       "quantity_group               1.000000    -0.000585      0.117300   \n",
       "source_type                 -0.000585     1.000000     -0.087867   \n",
       "source_class                 0.117300    -0.087867      1.000000   \n",
       "waterpoint_type              0.006436    -0.023123     -0.339715   \n",
       "waterpoint_type_group        0.010060     0.025987     -0.321849   \n",
       "status_group                -0.131425    -0.079656     -0.026771   \n",
       "\n",
       "                       waterpoint_type  waterpoint_type_group  status_group  \n",
       "amount_tsh                    0.009254               0.006656     -0.044921  \n",
       "days_since_recorded          -0.060015              -0.044362     -0.028992  \n",
       "funder                        0.012562               0.009434     -0.043570  \n",
       "gps_height                   -0.090798              -0.080919     -0.122505  \n",
       "installer                    -0.007337              -0.012208     -0.013457  \n",
       "longitude                    -0.163355              -0.144605      0.004366  \n",
       "latitude                      0.160127               0.146218     -0.014547  \n",
       "basin                         0.009864               0.013518      0.043413  \n",
       "subvillage                   -0.017225              -0.019984     -0.006752  \n",
       "region                        0.279421               0.257183      0.091252  \n",
       "population                    0.125506               0.104900     -0.012008  \n",
       "public_meeting               -0.023053              -0.027216     -0.033983  \n",
       "scheme_management            -0.039097              -0.053811     -0.064742  \n",
       "scheme_name                   0.386842               0.373257      0.036578  \n",
       "permit                       -0.151327              -0.130991     -0.031936  \n",
       "construction_year             0.216712               0.205038      0.128198  \n",
       "extraction_type               0.477329               0.439278      0.147886  \n",
       "payment_type                  0.038424               0.025069      0.055772  \n",
       "water_quality                -0.162096              -0.149837     -0.003094  \n",
       "quantity_group                0.006436               0.010060     -0.131425  \n",
       "source_type                  -0.023123               0.025987     -0.079656  \n",
       "source_class                 -0.339715              -0.321849     -0.026771  \n",
       "waterpoint_type               1.000000               0.982154      0.206507  \n",
       "waterpoint_type_group         0.982154               1.000000      0.206431  \n",
       "status_group                  0.206507               0.206431      1.000000  \n",
       "\n",
       "[25 rows x 25 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x193a014c668>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KIYRosiQTIoQQQogGIWtChBBCCNEgtDr19g7WQiYhTZBKa/W/lh9X3p/Bt4tS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\nCIVCoVAoFIXwP4nvSx0k9Hq6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x193a0144898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59400, 25)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns = np.full((corr.shape[0],), True, dtype=bool)\n",
    "for i in range(corr.shape[0]):\n",
    "    for j in range(i+1, corr.shape[0]):\n",
    "        if corr.iloc[i,j] >= 0.9:\n",
    "            if columns[j]:\n",
    "                columns[j] = False\n",
    "selected_columns = data.columns[columns]\n",
    "data = data[selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59400, 24)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['amount_tsh', 'days_since_recorded', 'funder', 'gps_height',\n",
       "       'installer', 'longitude', 'latitude', 'basin', 'subvillage', 'region',\n",
       "       'population', 'public_meeting', 'scheme_management', 'scheme_name',\n",
       "       'permit', 'construction_year', 'extraction_type', 'payment_type',\n",
       "       'water_quality', 'quantity_group', 'source_type', 'source_class',\n",
       "       'waterpoint_type', 'status_group'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "selected_columns = selected_columns[1:].values\n",
    "import statsmodels.formula.api as sm\n",
    "def backwardElimination(x, Y, sl, columns):\n",
    "    numVars = len(x[0])\n",
    "    for i in range(0, numVars):\n",
    "        regressor_OLS = sm.OLS(Y, x).fit()\n",
    "        maxVar = max(regressor_OLS.pvalues).astype(float)\n",
    "        if maxVar > sl:\n",
    "            for j in range(0, numVars - i):\n",
    "                if (regressor_OLS.pvalues[j].astype(float) == maxVar):\n",
    "                    x = np.delete(x, j, 1)\n",
    "                    columns = np.delete(columns, j)\n",
    "                    \n",
    "    regressor_OLS.summary()\n",
    "    return x, columns\n",
    "SL = 0.05\n",
    "data_modeled, selected_columns = backwardElimination(data.iloc[:,1:].values, data.iloc[:,0].values, SL, selected_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame()\n",
    "result['status_group'] = data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   status_group\n",
       "0             0\n",
       "1             0\n",
       "2             0\n",
       "3             2\n",
       "4             0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame(data = data_modeled, columns = selected_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days_since_recorded</th>\n",
       "      <th>funder</th>\n",
       "      <th>gps_height</th>\n",
       "      <th>installer</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>basin</th>\n",
       "      <th>region</th>\n",
       "      <th>public_meeting</th>\n",
       "      <th>scheme_management</th>\n",
       "      <th>scheme_name</th>\n",
       "      <th>permit</th>\n",
       "      <th>construction_year</th>\n",
       "      <th>extraction_type</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>water_quality</th>\n",
       "      <th>quantity_group</th>\n",
       "      <th>source_type</th>\n",
       "      <th>source_class</th>\n",
       "      <th>waterpoint_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>995.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1390.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.938093</td>\n",
       "      <td>-9.856322</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2246.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>272.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1399.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.698766</td>\n",
       "      <td>-2.147466</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>281.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37.460664</td>\n",
       "      <td>-3.821329</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2121.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>309.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.486161</td>\n",
       "      <td>-11.155298</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>874.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1057.545585</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.130847</td>\n",
       "      <td>-1.825359</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   days_since_recorded  funder   gps_height  installer  longitude   latitude  \\\n",
       "0                995.0     8.0  1390.000000        7.0  34.938093  -9.856322   \n",
       "1                272.0     8.0  1399.000000        7.0  34.698766  -2.147466   \n",
       "2                281.0     8.0   686.000000        7.0  37.460664  -3.821329   \n",
       "3                309.0     3.0   263.000000        7.0  38.486161 -11.155298   \n",
       "4                874.0     8.0  1057.545585        7.0  31.130847  -1.825359   \n",
       "\n",
       "   basin  region  public_meeting  scheme_management  scheme_name  permit  \\\n",
       "0    1.0     3.0             1.0                6.0       2246.0     0.0   \n",
       "1    4.0     9.0             2.0                2.0       2171.0     1.0   \n",
       "2    5.0     8.0             1.0                6.0       2121.0     1.0   \n",
       "3    7.0    12.0             1.0                6.0       2171.0     1.0   \n",
       "4    4.0     4.0             1.0                2.0       2171.0     1.0   \n",
       "\n",
       "   construction_year  extraction_type  payment_type  water_quality  \\\n",
       "0                5.0              1.0           0.0            6.0   \n",
       "1                1.0              1.0           2.0            6.0   \n",
       "2                0.0              1.0           5.0            6.0   \n",
       "3                4.0              8.0           2.0            6.0   \n",
       "4                6.0              1.0           2.0            6.0   \n",
       "\n",
       "   quantity_group  source_type  source_class  waterpoint_type  \n",
       "0             1.0          6.0           0.0              1.0  \n",
       "1             2.0          3.0           1.0              1.0  \n",
       "2             1.0          1.0           1.0              2.0  \n",
       "3             0.0          0.0           0.0              2.0  \n",
       "4             3.0          3.0           1.0              1.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59400, 20)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['days_since_recorded', 'funder', 'gps_height', 'installer', 'longitude',\n",
       "       'latitude', 'basin', 'region', 'public_meeting', 'scheme_management',\n",
       "       'scheme_name', 'permit', 'construction_year', 'extraction_type',\n",
       "       'payment_type', 'water_quality', 'quantity_group', 'source_type',\n",
       "       'source_class', 'waterpoint_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data\n",
    "y=result['status_group']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    2\n",
       "4    0\n",
       "Name: status_group, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting the Training dataframe into a matrix and predictor as y \n",
    "data_X = X.as_matrix()\n",
    "data_y = y.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.model_selection \n",
    "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(data_X, \n",
    "                                                                             data_y, \n",
    "                                                                             test_size = 0.3, \n",
    "                                                                             random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.ensemble\n",
    "\n",
    "rfc = sklearn.ensemble.RandomForestClassifier(n_estimators=1000, \n",
    "                                              min_samples_split=6,\n",
    "                                              criterion='gini', \n",
    "                                              max_features='auto',\n",
    "                                              oob_score=True,\n",
    "                                              random_state=1,\n",
    "                                              n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Train Accuracy Score : 0.941486291486\n",
      "Random Forest Classifier Test Score : 0.809090909091\n"
     ]
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)\n",
    "\n",
    "print('Random Forest Classifier Train Accuracy Score :', rfc.score(X_train, y_train))\n",
    "print('Random Forest Classifier Test Score :', rfc.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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