Flu-Shot Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FLU SHOT LEARNING:Predict H1N1 and Seasonal Flu Vaccines\n",
    "HOSTED BY DRIVENDATA. [Link](https://www.drivendata.org/competitions/66/flu-shot-learning/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we are predicting whether people got H1N1 and seasonal flu vaccines using information they shared about their backgrounds, opinions, and health behaviors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # data analysis\n",
    "import numpy as np # numerical computing\n",
    "import matplotlib.pyplot as plt # visuaization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>h1n1_concern</th>\n",
       "      <th>h1n1_knowledge</th>\n",
       "      <th>behavioral_antiviral_meds</th>\n",
       "      <th>behavioral_avoidance</th>\n",
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       "      <th>behavioral_touch_face</th>\n",
       "      <th>doctor_recc_h1n1</th>\n",
       "      <th>...</th>\n",
       "      <th>income_poverty</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>rent_or_own</th>\n",
       "      <th>employment_status</th>\n",
       "      <th>hhs_geo_region</th>\n",
       "      <th>census_msa</th>\n",
       "      <th>household_adults</th>\n",
       "      <th>household_children</th>\n",
       "      <th>employment_industry</th>\n",
       "      <th>employment_occupation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>respondent_id</th>\n",
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       "      <td>Not Married</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Employed</td>\n",
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       "      <td>Not Married</td>\n",
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       "      <td>Not Married</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Not in Labor Force</td>\n",
       "      <td>lrircsnp</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>emcorrxb</td>\n",
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      "text/plain": [
       "               h1n1_concern  h1n1_knowledge  behavioral_antiviral_meds  \\\n",
       "respondent_id                                                            \n",
       "0                       1.0             0.0                        0.0   \n",
       "1                       3.0             2.0                        0.0   \n",
       "2                       1.0             1.0                        0.0   \n",
       "3                       1.0             1.0                        0.0   \n",
       "4                       2.0             1.0                        0.0   \n",
       "\n",
       "               behavioral_avoidance  behavioral_face_mask  \\\n",
       "respondent_id                                               \n",
       "0                               0.0                   0.0   \n",
       "1                               1.0                   0.0   \n",
       "2                               1.0                   0.0   \n",
       "3                               1.0                   0.0   \n",
       "4                               1.0                   0.0   \n",
       "\n",
       "               behavioral_wash_hands  behavioral_large_gatherings  \\\n",
       "respondent_id                                                       \n",
       "0                                0.0                          0.0   \n",
       "1                                1.0                          0.0   \n",
       "2                                0.0                          0.0   \n",
       "3                                1.0                          1.0   \n",
       "4                                1.0                          1.0   \n",
       "\n",
       "               behavioral_outside_home  behavioral_touch_face  \\\n",
       "respondent_id                                                   \n",
       "0                                  1.0                    1.0   \n",
       "1                                  1.0                    1.0   \n",
       "2                                  0.0                    0.0   \n",
       "3                                  0.0                    0.0   \n",
       "4                                  0.0                    1.0   \n",
       "\n",
       "               doctor_recc_h1n1  ...             income_poverty  \\\n",
       "respondent_id                    ...                              \n",
       "0                           0.0  ...              Below Poverty   \n",
       "1                           0.0  ...              Below Poverty   \n",
       "2                           NaN  ...  <= $75,000, Above Poverty   \n",
       "3                           0.0  ...              Below Poverty   \n",
       "4                           0.0  ...  <= $75,000, Above Poverty   \n",
       "\n",
       "               marital_status  rent_or_own   employment_status  \\\n",
       "respondent_id                                                    \n",
       "0                 Not Married          Own  Not in Labor Force   \n",
       "1                 Not Married         Rent            Employed   \n",
       "2                 Not Married          Own            Employed   \n",
       "3                 Not Married         Rent  Not in Labor Force   \n",
       "4                     Married          Own            Employed   \n",
       "\n",
       "               hhs_geo_region                census_msa  household_adults  \\\n",
       "respondent_id                                                               \n",
       "0                    oxchjgsf                   Non-MSA               0.0   \n",
       "1                    bhuqouqj  MSA, Not Principle  City               0.0   \n",
       "2                    qufhixun  MSA, Not Principle  City               2.0   \n",
       "3                    lrircsnp       MSA, Principle City               0.0   \n",
       "4                    qufhixun  MSA, Not Principle  City               1.0   \n",
       "\n",
       "               household_children  employment_industry  employment_occupation  \n",
       "respondent_id                                                                  \n",
       "0                             0.0                  NaN                    NaN  \n",
       "1                             0.0             pxcmvdjn               xgwztkwe  \n",
       "2                             0.0             rucpziij               xtkaffoo  \n",
       "3                             0.0                  NaN                    NaN  \n",
       "4                             0.0             wxleyezf               emcorrxb  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DATA FOR TRAINING THE MODEL\n",
    "X_train = pd.read_csv('F:\\\\bizz\\\\DATA SETS\\\\Flu Shot Learning\\\\training_set_features.csv', index_col = 'respondent_id')\n",
    "X_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               h1n1_concern  h1n1_knowledge  behavioral_antiviral_meds  \\\n",
       "respondent_id                                                            \n",
       "26707                   2.0             2.0                        0.0   \n",
       "26708                   1.0             1.0                        0.0   \n",
       "26709                   2.0             2.0                        0.0   \n",
       "26710                   1.0             1.0                        0.0   \n",
       "26711                   3.0             1.0                        1.0   \n",
       "\n",
       "               behavioral_avoidance  behavioral_face_mask  \\\n",
       "respondent_id                                               \n",
       "26707                           1.0                   0.0   \n",
       "26708                           0.0                   0.0   \n",
       "26709                           0.0                   1.0   \n",
       "26710                           0.0                   0.0   \n",
       "26711                           1.0                   0.0   \n",
       "\n",
       "               behavioral_wash_hands  behavioral_large_gatherings  \\\n",
       "respondent_id                                                       \n",
       "26707                            1.0                          1.0   \n",
       "26708                            0.0                          0.0   \n",
       "26709                            1.0                          1.0   \n",
       "26710                            0.0                          0.0   \n",
       "26711                            1.0                          1.0   \n",
       "\n",
       "               behavioral_outside_home  behavioral_touch_face  \\\n",
       "respondent_id                                                   \n",
       "26707                              0.0                    1.0   \n",
       "26708                              0.0                    0.0   \n",
       "26709                              1.0                    1.0   \n",
       "26710                              0.0                    0.0   \n",
       "26711                              1.0                    1.0   \n",
       "\n",
       "               doctor_recc_h1n1  ...             income_poverty  \\\n",
       "respondent_id                    ...                              \n",
       "26707                       0.0  ...                  > $75,000   \n",
       "26708                       0.0  ...              Below Poverty   \n",
       "26709                       0.0  ...                  > $75,000   \n",
       "26710                       1.0  ...  <= $75,000, Above Poverty   \n",
       "26711                       0.0  ...  <= $75,000, Above Poverty   \n",
       "\n",
       "               marital_status  rent_or_own   employment_status  \\\n",
       "respondent_id                                                    \n",
       "26707             Not Married         Rent            Employed   \n",
       "26708             Not Married         Rent            Employed   \n",
       "26709                 Married          Own            Employed   \n",
       "26710                 Married          Own  Not in Labor Force   \n",
       "26711             Not Married          Own            Employed   \n",
       "\n",
       "               hhs_geo_region                census_msa  household_adults  \\\n",
       "respondent_id                                                               \n",
       "26707                mlyzmhmf  MSA, Not Principle  City               1.0   \n",
       "26708                bhuqouqj                   Non-MSA               3.0   \n",
       "26709                lrircsnp                   Non-MSA               1.0   \n",
       "26710                lrircsnp  MSA, Not Principle  City               1.0   \n",
       "26711                lzgpxyit                   Non-MSA               0.0   \n",
       "\n",
       "               household_children  employment_industry  employment_occupation  \n",
       "respondent_id                                                                  \n",
       "26707                         0.0             atmlpfrs               hfxkjkmi  \n",
       "26708                         0.0             atmlpfrs               xqwwgdyp  \n",
       "26709                         0.0             nduyfdeo               pvmttkik  \n",
       "26710                         0.0                  NaN                    NaN  \n",
       "26711                         1.0             fcxhlnwr               mxkfnird  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DATA FOR TESTING \n",
    "X_test= pd.read_csv('F:\\\\bizz\\\\DATA SETS\\\\Flu Shot Learning\\\\test_set_features.csv', index_col = 'respondent_id')\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h1n1_vaccine</th>\n",
       "      <th>seasonal_vaccine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>respondent_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "               h1n1_vaccine  seasonal_vaccine\n",
       "respondent_id                                \n",
       "0                         0                 0\n",
       "1                         0                 1\n",
       "2                         0                 0\n",
       "3                         0                 1\n",
       "4                         0                 0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training labels\n",
    "y_train = pd.read_csv('F:\\\\bizz\\\\DATA SETS\\\\Flu Shot Learning\\\\training_set_labels.csv', index_col = 'respondent_id')\n",
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>h1n1_concern</th>\n",
       "      <th>h1n1_knowledge</th>\n",
       "      <th>behavioral_antiviral_meds</th>\n",
       "      <th>behavioral_avoidance</th>\n",
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       "      <th>behavioral_wash_hands</th>\n",
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       "      <th>behavioral_touch_face</th>\n",
       "      <th>doctor_recc_h1n1</th>\n",
       "      <th>...</th>\n",
       "      <th>health_worker</th>\n",
       "      <th>health_insurance</th>\n",
       "      <th>opinion_h1n1_vacc_effective</th>\n",
       "      <th>opinion_h1n1_risk</th>\n",
       "      <th>opinion_h1n1_sick_from_vacc</th>\n",
       "      <th>opinion_seas_vacc_effective</th>\n",
       "      <th>opinion_seas_risk</th>\n",
       "      <th>opinion_seas_sick_from_vacc</th>\n",
       "      <th>household_adults</th>\n",
       "      <th>household_children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2.396043</td>\n",
       "      <td>3.770328</td>\n",
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       "      <td>3.339895</td>\n",
       "      <td>2.257314</td>\n",
       "      <td>0.156585</td>\n",
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       "    <tr>\n",
       "      <th>h1n1_knowledge</th>\n",
       "      <td>0.625224</td>\n",
       "      <td>10.000000</td>\n",
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       "      <td>0.888393</td>\n",
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       "      <td>0.861621</td>\n",
       "      <td>0.938291</td>\n",
       "      <td>...</td>\n",
       "      <td>1.702522</td>\n",
       "      <td>1.194085</td>\n",
       "      <td>1.210085</td>\n",
       "      <td>0.730860</td>\n",
       "      <td>0.195008</td>\n",
       "      <td>0.861032</td>\n",
       "      <td>0.768246</td>\n",
       "      <td>0.624829</td>\n",
       "      <td>0.248738</td>\n",
       "      <td>0.505833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_antiviral_meds</th>\n",
       "      <td>0.903009</td>\n",
       "      <td>0.108358</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.492470</td>\n",
       "      <td>1.462605</td>\n",
       "      <td>0.641192</td>\n",
       "      <td>1.062874</td>\n",
       "      <td>1.276788</td>\n",
       "      <td>0.708683</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094651</td>\n",
       "      <td>0.639882</td>\n",
       "      <td>0.302105</td>\n",
       "      <td>1.051632</td>\n",
       "      <td>0.790906</td>\n",
       "      <td>0.150026</td>\n",
       "      <td>0.853154</td>\n",
       "      <td>0.843046</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.848217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_avoidance</th>\n",
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       "      <td>10.000000</td>\n",
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       "      <td>3.381295</td>\n",
       "      <td>2.276750</td>\n",
       "      <td>2.203478</td>\n",
       "      <td>3.353354</td>\n",
       "      <td>0.681451</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011797</td>\n",
       "      <td>0.326622</td>\n",
       "      <td>1.122997</td>\n",
       "      <td>1.178509</td>\n",
       "      <td>1.314054</td>\n",
       "      <td>1.195538</td>\n",
       "      <td>1.295041</td>\n",
       "      <td>0.829418</td>\n",
       "      <td>0.191218</td>\n",
       "      <td>0.403284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_face_mask</th>\n",
       "      <td>1.560317</td>\n",
       "      <td>0.300496</td>\n",
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       "      <td>0.649461</td>\n",
       "      <td>10.000000</td>\n",
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       "      <td>1.043353</td>\n",
       "      <td>0.842816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.699919</td>\n",
       "      <td>0.402568</td>\n",
       "      <td>0.376515</td>\n",
       "      <td>1.311648</td>\n",
       "      <td>1.071292</td>\n",
       "      <td>0.415556</td>\n",
       "      <td>1.101606</td>\n",
       "      <td>0.900095</td>\n",
       "      <td>0.139909</td>\n",
       "      <td>0.058255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_wash_hands</th>\n",
       "      <td>2.935651</td>\n",
       "      <td>0.897846</td>\n",
       "      <td>0.641192</td>\n",
       "      <td>3.381295</td>\n",
       "      <td>0.833635</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.953639</td>\n",
       "      <td>1.926190</td>\n",
       "      <td>3.650641</td>\n",
       "      <td>0.887292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537612</td>\n",
       "      <td>0.319194</td>\n",
       "      <td>1.369872</td>\n",
       "      <td>1.687112</td>\n",
       "      <td>1.500239</td>\n",
       "      <td>1.385167</td>\n",
       "      <td>1.724644</td>\n",
       "      <td>0.880293</td>\n",
       "      <td>0.096693</td>\n",
       "      <td>0.477636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_large_gatherings</th>\n",
       "      <td>2.550310</td>\n",
       "      <td>0.488976</td>\n",
       "      <td>1.062874</td>\n",
       "      <td>2.276750</td>\n",
       "      <td>1.809069</td>\n",
       "      <td>1.953639</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.840846</td>\n",
       "      <td>2.536835</td>\n",
       "      <td>0.822423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.323193</td>\n",
       "      <td>0.590001</td>\n",
       "      <td>0.522986</td>\n",
       "      <td>1.264625</td>\n",
       "      <td>1.859239</td>\n",
       "      <td>0.784907</td>\n",
       "      <td>1.328653</td>\n",
       "      <td>1.354463</td>\n",
       "      <td>0.319381</td>\n",
       "      <td>0.094487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_outside_home</th>\n",
       "      <td>2.464241</td>\n",
       "      <td>0.682187</td>\n",
       "      <td>1.276788</td>\n",
       "      <td>2.203478</td>\n",
       "      <td>1.633818</td>\n",
       "      <td>1.926190</td>\n",
       "      <td>5.840846</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.677191</td>\n",
       "      <td>0.703456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.346187</td>\n",
       "      <td>0.613812</td>\n",
       "      <td>0.507480</td>\n",
       "      <td>1.255491</td>\n",
       "      <td>1.748343</td>\n",
       "      <td>0.674695</td>\n",
       "      <td>1.202371</td>\n",
       "      <td>1.381326</td>\n",
       "      <td>0.275272</td>\n",
       "      <td>0.095578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_touch_face</th>\n",
       "      <td>2.480159</td>\n",
       "      <td>0.861621</td>\n",
       "      <td>0.708683</td>\n",
       "      <td>3.353354</td>\n",
       "      <td>1.043353</td>\n",
       "      <td>3.650641</td>\n",
       "      <td>2.536835</td>\n",
       "      <td>2.677191</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.882211</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676479</td>\n",
       "      <td>0.110245</td>\n",
       "      <td>1.041259</td>\n",
       "      <td>1.460800</td>\n",
       "      <td>1.317723</td>\n",
       "      <td>1.057976</td>\n",
       "      <td>1.437350</td>\n",
       "      <td>0.900972</td>\n",
       "      <td>0.005530</td>\n",
       "      <td>0.236063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor_recc_h1n1</th>\n",
       "      <td>1.502062</td>\n",
       "      <td>0.938291</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.681451</td>\n",
       "      <td>0.842816</td>\n",
       "      <td>0.887292</td>\n",
       "      <td>0.822423</td>\n",
       "      <td>0.703456</td>\n",
       "      <td>0.882211</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.016389</td>\n",
       "      <td>0.671566</td>\n",
       "      <td>1.523046</td>\n",
       "      <td>2.628259</td>\n",
       "      <td>1.131112</td>\n",
       "      <td>1.075120</td>\n",
       "      <td>2.023199</td>\n",
       "      <td>0.571278</td>\n",
       "      <td>0.041121</td>\n",
       "      <td>0.290400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor_recc_seasonal</th>\n",
       "      <td>1.355671</td>\n",
       "      <td>0.719204</td>\n",
       "      <td>0.309094</td>\n",
       "      <td>0.740883</td>\n",
       "      <td>0.694811</td>\n",
       "      <td>1.020440</td>\n",
       "      <td>0.935571</td>\n",
       "      <td>0.856224</td>\n",
       "      <td>1.008082</td>\n",
       "      <td>5.918677</td>\n",
       "      <td>...</td>\n",
       "      <td>0.594020</td>\n",
       "      <td>1.171948</td>\n",
       "      <td>1.186142</td>\n",
       "      <td>1.684430</td>\n",
       "      <td>0.671783</td>\n",
       "      <td>1.809024</td>\n",
       "      <td>2.400866</td>\n",
       "      <td>0.253565</td>\n",
       "      <td>0.407694</td>\n",
       "      <td>0.483802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronic_med_condition</th>\n",
       "      <td>0.951621</td>\n",
       "      <td>0.226018</td>\n",
       "      <td>0.084652</td>\n",
       "      <td>0.394354</td>\n",
       "      <td>0.681131</td>\n",
       "      <td>0.302598</td>\n",
       "      <td>1.047212</td>\n",
       "      <td>0.988582</td>\n",
       "      <td>0.288765</td>\n",
       "      <td>1.582752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264811</td>\n",
       "      <td>0.660882</td>\n",
       "      <td>0.441445</td>\n",
       "      <td>1.216571</td>\n",
       "      <td>0.819429</td>\n",
       "      <td>0.917372</td>\n",
       "      <td>1.620611</td>\n",
       "      <td>0.525872</td>\n",
       "      <td>0.713465</td>\n",
       "      <td>1.082372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_under_6_months</th>\n",
       "      <td>0.495743</td>\n",
       "      <td>0.215976</td>\n",
       "      <td>0.287875</td>\n",
       "      <td>0.004143</td>\n",
       "      <td>0.397256</td>\n",
       "      <td>0.361882</td>\n",
       "      <td>0.211683</td>\n",
       "      <td>0.181947</td>\n",
       "      <td>0.266396</td>\n",
       "      <td>0.793893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.790782</td>\n",
       "      <td>0.268358</td>\n",
       "      <td>0.070937</td>\n",
       "      <td>0.865164</td>\n",
       "      <td>0.372123</td>\n",
       "      <td>0.036531</td>\n",
       "      <td>0.502666</td>\n",
       "      <td>0.375825</td>\n",
       "      <td>0.448279</td>\n",
       "      <td>0.995618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_worker</th>\n",
       "      <td>0.335619</td>\n",
       "      <td>1.702522</td>\n",
       "      <td>0.094651</td>\n",
       "      <td>0.011797</td>\n",
       "      <td>0.699919</td>\n",
       "      <td>0.537612</td>\n",
       "      <td>0.323193</td>\n",
       "      <td>0.346187</td>\n",
       "      <td>0.676479</td>\n",
       "      <td>1.016389</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.466800</td>\n",
       "      <td>0.535807</td>\n",
       "      <td>1.238693</td>\n",
       "      <td>0.090149</td>\n",
       "      <td>0.303946</td>\n",
       "      <td>0.891423</td>\n",
       "      <td>0.178935</td>\n",
       "      <td>0.133796</td>\n",
       "      <td>0.376977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_insurance</th>\n",
       "      <td>0.036546</td>\n",
       "      <td>1.194085</td>\n",
       "      <td>0.639882</td>\n",
       "      <td>0.326622</td>\n",
       "      <td>0.402568</td>\n",
       "      <td>0.319194</td>\n",
       "      <td>0.590001</td>\n",
       "      <td>0.613812</td>\n",
       "      <td>0.110245</td>\n",
       "      <td>0.671566</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466800</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.586748</td>\n",
       "      <td>0.057657</td>\n",
       "      <td>0.333141</td>\n",
       "      <td>0.912471</td>\n",
       "      <td>0.502325</td>\n",
       "      <td>0.658859</td>\n",
       "      <td>0.786973</td>\n",
       "      <td>0.694019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_h1n1_vacc_effective</th>\n",
       "      <td>2.396043</td>\n",
       "      <td>1.210085</td>\n",
       "      <td>0.302105</td>\n",
       "      <td>1.122997</td>\n",
       "      <td>0.376515</td>\n",
       "      <td>1.369872</td>\n",
       "      <td>0.522986</td>\n",
       "      <td>0.507480</td>\n",
       "      <td>1.041259</td>\n",
       "      <td>1.523046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.535807</td>\n",
       "      <td>0.586748</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.613040</td>\n",
       "      <td>0.623715</td>\n",
       "      <td>4.724671</td>\n",
       "      <td>2.581727</td>\n",
       "      <td>0.124912</td>\n",
       "      <td>0.109700</td>\n",
       "      <td>0.152422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_h1n1_risk</th>\n",
       "      <td>3.770328</td>\n",
       "      <td>0.730860</td>\n",
       "      <td>1.051632</td>\n",
       "      <td>1.178509</td>\n",
       "      <td>1.311648</td>\n",
       "      <td>1.687112</td>\n",
       "      <td>1.264625</td>\n",
       "      <td>1.255491</td>\n",
       "      <td>1.460800</td>\n",
       "      <td>2.628259</td>\n",
       "      <td>...</td>\n",
       "      <td>1.238693</td>\n",
       "      <td>0.057657</td>\n",
       "      <td>2.613040</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.350617</td>\n",
       "      <td>2.244691</td>\n",
       "      <td>5.642078</td>\n",
       "      <td>1.986014</td>\n",
       "      <td>0.301812</td>\n",
       "      <td>0.953247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_h1n1_sick_from_vacc</th>\n",
       "      <td>3.600697</td>\n",
       "      <td>0.195008</td>\n",
       "      <td>0.790906</td>\n",
       "      <td>1.314054</td>\n",
       "      <td>1.071292</td>\n",
       "      <td>1.500239</td>\n",
       "      <td>1.859239</td>\n",
       "      <td>1.748343</td>\n",
       "      <td>1.317723</td>\n",
       "      <td>1.131112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090149</td>\n",
       "      <td>0.333141</td>\n",
       "      <td>0.623715</td>\n",
       "      <td>3.350617</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.780965</td>\n",
       "      <td>2.670133</td>\n",
       "      <td>4.915461</td>\n",
       "      <td>0.147689</td>\n",
       "      <td>0.711635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_seas_vacc_effective</th>\n",
       "      <td>2.351359</td>\n",
       "      <td>0.861032</td>\n",
       "      <td>0.150026</td>\n",
       "      <td>1.195538</td>\n",
       "      <td>0.415556</td>\n",
       "      <td>1.385167</td>\n",
       "      <td>0.784907</td>\n",
       "      <td>0.674695</td>\n",
       "      <td>1.057976</td>\n",
       "      <td>1.075120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303946</td>\n",
       "      <td>0.912471</td>\n",
       "      <td>4.724671</td>\n",
       "      <td>2.244691</td>\n",
       "      <td>0.780965</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.448003</td>\n",
       "      <td>0.173402</td>\n",
       "      <td>0.225789</td>\n",
       "      <td>0.765028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_seas_risk</th>\n",
       "      <td>3.339895</td>\n",
       "      <td>0.768246</td>\n",
       "      <td>0.853154</td>\n",
       "      <td>1.295041</td>\n",
       "      <td>1.101606</td>\n",
       "      <td>1.724644</td>\n",
       "      <td>1.328653</td>\n",
       "      <td>1.202371</td>\n",
       "      <td>1.437350</td>\n",
       "      <td>2.023199</td>\n",
       "      <td>...</td>\n",
       "      <td>0.891423</td>\n",
       "      <td>0.502325</td>\n",
       "      <td>2.581727</td>\n",
       "      <td>5.642078</td>\n",
       "      <td>2.670133</td>\n",
       "      <td>3.448003</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.003787</td>\n",
       "      <td>0.061114</td>\n",
       "      <td>0.258977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_seas_sick_from_vacc</th>\n",
       "      <td>2.257314</td>\n",
       "      <td>0.624829</td>\n",
       "      <td>0.843046</td>\n",
       "      <td>0.829418</td>\n",
       "      <td>0.900095</td>\n",
       "      <td>0.880293</td>\n",
       "      <td>1.354463</td>\n",
       "      <td>1.381326</td>\n",
       "      <td>0.900972</td>\n",
       "      <td>0.571278</td>\n",
       "      <td>...</td>\n",
       "      <td>0.178935</td>\n",
       "      <td>0.658859</td>\n",
       "      <td>0.124912</td>\n",
       "      <td>1.986014</td>\n",
       "      <td>4.915461</td>\n",
       "      <td>0.173402</td>\n",
       "      <td>2.003787</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.229249</td>\n",
       "      <td>0.572859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_adults</th>\n",
       "      <td>0.156585</td>\n",
       "      <td>0.248738</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.191218</td>\n",
       "      <td>0.139909</td>\n",
       "      <td>0.096693</td>\n",
       "      <td>0.319381</td>\n",
       "      <td>0.275272</td>\n",
       "      <td>0.005530</td>\n",
       "      <td>0.041121</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133796</td>\n",
       "      <td>0.786973</td>\n",
       "      <td>0.109700</td>\n",
       "      <td>0.301812</td>\n",
       "      <td>0.147689</td>\n",
       "      <td>0.225789</td>\n",
       "      <td>0.061114</td>\n",
       "      <td>0.229249</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.895709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_children</th>\n",
       "      <td>0.505245</td>\n",
       "      <td>0.505833</td>\n",
       "      <td>0.848217</td>\n",
       "      <td>0.403284</td>\n",
       "      <td>0.058255</td>\n",
       "      <td>0.477636</td>\n",
       "      <td>0.094487</td>\n",
       "      <td>0.095578</td>\n",
       "      <td>0.236063</td>\n",
       "      <td>0.290400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.376977</td>\n",
       "      <td>0.694019</td>\n",
       "      <td>0.152422</td>\n",
       "      <td>0.953247</td>\n",
       "      <td>0.711635</td>\n",
       "      <td>0.765028</td>\n",
       "      <td>0.258977</td>\n",
       "      <td>0.572859</td>\n",
       "      <td>1.895709</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             h1n1_concern  h1n1_knowledge  \\\n",
       "h1n1_concern                    10.000000        0.625224   \n",
       "h1n1_knowledge                   0.625224       10.000000   \n",
       "behavioral_antiviral_meds        0.903009        0.108358   \n",
       "behavioral_avoidance             2.337130        0.888393   \n",
       "behavioral_face_mask             1.560317        0.300496   \n",
       "behavioral_wash_hands            2.935651        0.897846   \n",
       "behavioral_large_gatherings      2.550310        0.488976   \n",
       "behavioral_outside_home          2.464241        0.682187   \n",
       "behavioral_touch_face            2.480159        0.861621   \n",
       "doctor_recc_h1n1                 1.502062        0.938291   \n",
       "doctor_recc_seasonal             1.355671        0.719204   \n",
       "chronic_med_condition            0.951621        0.226018   \n",
       "child_under_6_months             0.495743        0.215976   \n",
       "health_worker                    0.335619        1.702522   \n",
       "health_insurance                 0.036546        1.194085   \n",
       "opinion_h1n1_vacc_effective      2.396043        1.210085   \n",
       "opinion_h1n1_risk                3.770328        0.730860   \n",
       "opinion_h1n1_sick_from_vacc      3.600697        0.195008   \n",
       "opinion_seas_vacc_effective      2.351359        0.861032   \n",
       "opinion_seas_risk                3.339895        0.768246   \n",
       "opinion_seas_sick_from_vacc      2.257314        0.624829   \n",
       "household_adults                 0.156585        0.248738   \n",
       "household_children               0.505245        0.505833   \n",
       "\n",
       "                             behavioral_antiviral_meds  behavioral_avoidance  \\\n",
       "h1n1_concern                                  0.903009              2.337130   \n",
       "h1n1_knowledge                                0.108358              0.888393   \n",
       "behavioral_antiviral_meds                    10.000000              0.492470   \n",
       "behavioral_avoidance                          0.492470             10.000000   \n",
       "behavioral_face_mask                          1.462605              0.649461   \n",
       "behavioral_wash_hands                         0.641192              3.381295   \n",
       "behavioral_large_gatherings                   1.062874              2.276750   \n",
       "behavioral_outside_home                       1.276788              2.203478   \n",
       "behavioral_touch_face                         0.708683              3.353354   \n",
       "doctor_recc_h1n1                              0.512348              0.681451   \n",
       "doctor_recc_seasonal                          0.309094              0.740883   \n",
       "chronic_med_condition                         0.084652              0.394354   \n",
       "child_under_6_months                          0.287875              0.004143   \n",
       "health_worker                                 0.094651              0.011797   \n",
       "health_insurance                              0.639882              0.326622   \n",
       "opinion_h1n1_vacc_effective                   0.302105              1.122997   \n",
       "opinion_h1n1_risk                             1.051632              1.178509   \n",
       "opinion_h1n1_sick_from_vacc                   0.790906              1.314054   \n",
       "opinion_seas_vacc_effective                   0.150026              1.195538   \n",
       "opinion_seas_risk                             0.853154              1.295041   \n",
       "opinion_seas_sick_from_vacc                   0.843046              0.829418   \n",
       "household_adults                              0.449000              0.191218   \n",
       "household_children                            0.848217              0.403284   \n",
       "\n",
       "                             behavioral_face_mask  behavioral_wash_hands  \\\n",
       "h1n1_concern                             1.560317               2.935651   \n",
       "h1n1_knowledge                           0.300496               0.897846   \n",
       "behavioral_antiviral_meds                1.462605               0.641192   \n",
       "behavioral_avoidance                     0.649461               3.381295   \n",
       "behavioral_face_mask                    10.000000               0.833635   \n",
       "behavioral_wash_hands                    0.833635              10.000000   \n",
       "behavioral_large_gatherings              1.809069               1.953639   \n",
       "behavioral_outside_home                  1.633818               1.926190   \n",
       "behavioral_touch_face                    1.043353               3.650641   \n",
       "doctor_recc_h1n1                         0.842816               0.887292   \n",
       "doctor_recc_seasonal                     0.694811               1.020440   \n",
       "chronic_med_condition                    0.681131               0.302598   \n",
       "child_under_6_months                     0.397256               0.361882   \n",
       "health_worker                            0.699919               0.537612   \n",
       "health_insurance                         0.402568               0.319194   \n",
       "opinion_h1n1_vacc_effective              0.376515               1.369872   \n",
       "opinion_h1n1_risk                        1.311648               1.687112   \n",
       "opinion_h1n1_sick_from_vacc              1.071292               1.500239   \n",
       "opinion_seas_vacc_effective              0.415556               1.385167   \n",
       "opinion_seas_risk                        1.101606               1.724644   \n",
       "opinion_seas_sick_from_vacc              0.900095               0.880293   \n",
       "household_adults                         0.139909               0.096693   \n",
       "household_children                       0.058255               0.477636   \n",
       "\n",
       "                             behavioral_large_gatherings  \\\n",
       "h1n1_concern                                    2.550310   \n",
       "h1n1_knowledge                                  0.488976   \n",
       "behavioral_antiviral_meds                       1.062874   \n",
       "behavioral_avoidance                            2.276750   \n",
       "behavioral_face_mask                            1.809069   \n",
       "behavioral_wash_hands                           1.953639   \n",
       "behavioral_large_gatherings                    10.000000   \n",
       "behavioral_outside_home                         5.840846   \n",
       "behavioral_touch_face                           2.536835   \n",
       "doctor_recc_h1n1                                0.822423   \n",
       "doctor_recc_seasonal                            0.935571   \n",
       "chronic_med_condition                           1.047212   \n",
       "child_under_6_months                            0.211683   \n",
       "health_worker                                   0.323193   \n",
       "health_insurance                                0.590001   \n",
       "opinion_h1n1_vacc_effective                     0.522986   \n",
       "opinion_h1n1_risk                               1.264625   \n",
       "opinion_h1n1_sick_from_vacc                     1.859239   \n",
       "opinion_seas_vacc_effective                     0.784907   \n",
       "opinion_seas_risk                               1.328653   \n",
       "opinion_seas_sick_from_vacc                     1.354463   \n",
       "household_adults                                0.319381   \n",
       "household_children                              0.094487   \n",
       "\n",
       "                             behavioral_outside_home  behavioral_touch_face  \\\n",
       "h1n1_concern                                2.464241               2.480159   \n",
       "h1n1_knowledge                              0.682187               0.861621   \n",
       "behavioral_antiviral_meds                   1.276788               0.708683   \n",
       "behavioral_avoidance                        2.203478               3.353354   \n",
       "behavioral_face_mask                        1.633818               1.043353   \n",
       "behavioral_wash_hands                       1.926190               3.650641   \n",
       "behavioral_large_gatherings                 5.840846               2.536835   \n",
       "behavioral_outside_home                    10.000000               2.677191   \n",
       "behavioral_touch_face                       2.677191              10.000000   \n",
       "doctor_recc_h1n1                            0.703456               0.882211   \n",
       "doctor_recc_seasonal                        0.856224               1.008082   \n",
       "chronic_med_condition                       0.988582               0.288765   \n",
       "child_under_6_months                        0.181947               0.266396   \n",
       "health_worker                               0.346187               0.676479   \n",
       "health_insurance                            0.613812               0.110245   \n",
       "opinion_h1n1_vacc_effective                 0.507480               1.041259   \n",
       "opinion_h1n1_risk                           1.255491               1.460800   \n",
       "opinion_h1n1_sick_from_vacc                 1.748343               1.317723   \n",
       "opinion_seas_vacc_effective                 0.674695               1.057976   \n",
       "opinion_seas_risk                           1.202371               1.437350   \n",
       "opinion_seas_sick_from_vacc                 1.381326               0.900972   \n",
       "household_adults                            0.275272               0.005530   \n",
       "household_children                          0.095578               0.236063   \n",
       "\n",
       "                             doctor_recc_h1n1  ...  health_worker  \\\n",
       "h1n1_concern                         1.502062  ...       0.335619   \n",
       "h1n1_knowledge                       0.938291  ...       1.702522   \n",
       "behavioral_antiviral_meds            0.512348  ...       0.094651   \n",
       "behavioral_avoidance                 0.681451  ...       0.011797   \n",
       "behavioral_face_mask                 0.842816  ...       0.699919   \n",
       "behavioral_wash_hands                0.887292  ...       0.537612   \n",
       "behavioral_large_gatherings          0.822423  ...       0.323193   \n",
       "behavioral_outside_home              0.703456  ...       0.346187   \n",
       "behavioral_touch_face                0.882211  ...       0.676479   \n",
       "doctor_recc_h1n1                    10.000000  ...       1.016389   \n",
       "doctor_recc_seasonal                 5.918677  ...       0.594020   \n",
       "chronic_med_condition                1.582752  ...       0.264811   \n",
       "child_under_6_months                 0.793893  ...       0.790782   \n",
       "health_worker                        1.016389  ...      10.000000   \n",
       "health_insurance                     0.671566  ...       0.466800   \n",
       "opinion_h1n1_vacc_effective          1.523046  ...       0.535807   \n",
       "opinion_h1n1_risk                    2.628259  ...       1.238693   \n",
       "opinion_h1n1_sick_from_vacc          1.131112  ...       0.090149   \n",
       "opinion_seas_vacc_effective          1.075120  ...       0.303946   \n",
       "opinion_seas_risk                    2.023199  ...       0.891423   \n",
       "opinion_seas_sick_from_vacc          0.571278  ...       0.178935   \n",
       "household_adults                     0.041121  ...       0.133796   \n",
       "household_children                   0.290400  ...       0.376977   \n",
       "\n",
       "                             health_insurance  opinion_h1n1_vacc_effective  \\\n",
       "h1n1_concern                         0.036546                     2.396043   \n",
       "h1n1_knowledge                       1.194085                     1.210085   \n",
       "behavioral_antiviral_meds            0.639882                     0.302105   \n",
       "behavioral_avoidance                 0.326622                     1.122997   \n",
       "behavioral_face_mask                 0.402568                     0.376515   \n",
       "behavioral_wash_hands                0.319194                     1.369872   \n",
       "behavioral_large_gatherings          0.590001                     0.522986   \n",
       "behavioral_outside_home              0.613812                     0.507480   \n",
       "behavioral_touch_face                0.110245                     1.041259   \n",
       "doctor_recc_h1n1                     0.671566                     1.523046   \n",
       "doctor_recc_seasonal                 1.171948                     1.186142   \n",
       "chronic_med_condition                0.660882                     0.441445   \n",
       "child_under_6_months                 0.268358                     0.070937   \n",
       "health_worker                        0.466800                     0.535807   \n",
       "health_insurance                    10.000000                     0.586748   \n",
       "opinion_h1n1_vacc_effective          0.586748                    10.000000   \n",
       "opinion_h1n1_risk                    0.057657                     2.613040   \n",
       "opinion_h1n1_sick_from_vacc          0.333141                     0.623715   \n",
       "opinion_seas_vacc_effective          0.912471                     4.724671   \n",
       "opinion_seas_risk                    0.502325                     2.581727   \n",
       "opinion_seas_sick_from_vacc          0.658859                     0.124912   \n",
       "household_adults                     0.786973                     0.109700   \n",
       "household_children                   0.694019                     0.152422   \n",
       "\n",
       "                             opinion_h1n1_risk  opinion_h1n1_sick_from_vacc  \\\n",
       "h1n1_concern                          3.770328                     3.600697   \n",
       "h1n1_knowledge                        0.730860                     0.195008   \n",
       "behavioral_antiviral_meds             1.051632                     0.790906   \n",
       "behavioral_avoidance                  1.178509                     1.314054   \n",
       "behavioral_face_mask                  1.311648                     1.071292   \n",
       "behavioral_wash_hands                 1.687112                     1.500239   \n",
       "behavioral_large_gatherings           1.264625                     1.859239   \n",
       "behavioral_outside_home               1.255491                     1.748343   \n",
       "behavioral_touch_face                 1.460800                     1.317723   \n",
       "doctor_recc_h1n1                      2.628259                     1.131112   \n",
       "doctor_recc_seasonal                  1.684430                     0.671783   \n",
       "chronic_med_condition                 1.216571                     0.819429   \n",
       "child_under_6_months                  0.865164                     0.372123   \n",
       "health_worker                         1.238693                     0.090149   \n",
       "health_insurance                      0.057657                     0.333141   \n",
       "opinion_h1n1_vacc_effective           2.613040                     0.623715   \n",
       "opinion_h1n1_risk                    10.000000                     3.350617   \n",
       "opinion_h1n1_sick_from_vacc           3.350617                    10.000000   \n",
       "opinion_seas_vacc_effective           2.244691                     0.780965   \n",
       "opinion_seas_risk                     5.642078                     2.670133   \n",
       "opinion_seas_sick_from_vacc           1.986014                     4.915461   \n",
       "household_adults                      0.301812                     0.147689   \n",
       "household_children                    0.953247                     0.711635   \n",
       "\n",
       "                             opinion_seas_vacc_effective  opinion_seas_risk  \\\n",
       "h1n1_concern                                    2.351359           3.339895   \n",
       "h1n1_knowledge                                  0.861032           0.768246   \n",
       "behavioral_antiviral_meds                       0.150026           0.853154   \n",
       "behavioral_avoidance                            1.195538           1.295041   \n",
       "behavioral_face_mask                            0.415556           1.101606   \n",
       "behavioral_wash_hands                           1.385167           1.724644   \n",
       "behavioral_large_gatherings                     0.784907           1.328653   \n",
       "behavioral_outside_home                         0.674695           1.202371   \n",
       "behavioral_touch_face                           1.057976           1.437350   \n",
       "doctor_recc_h1n1                                1.075120           2.023199   \n",
       "doctor_recc_seasonal                            1.809024           2.400866   \n",
       "chronic_med_condition                           0.917372           1.620611   \n",
       "child_under_6_months                            0.036531           0.502666   \n",
       "health_worker                                   0.303946           0.891423   \n",
       "health_insurance                                0.912471           0.502325   \n",
       "opinion_h1n1_vacc_effective                     4.724671           2.581727   \n",
       "opinion_h1n1_risk                               2.244691           5.642078   \n",
       "opinion_h1n1_sick_from_vacc                     0.780965           2.670133   \n",
       "opinion_seas_vacc_effective                    10.000000           3.448003   \n",
       "opinion_seas_risk                               3.448003          10.000000   \n",
       "opinion_seas_sick_from_vacc                     0.173402           2.003787   \n",
       "household_adults                                0.225789           0.061114   \n",
       "household_children                              0.765028           0.258977   \n",
       "\n",
       "                             opinion_seas_sick_from_vacc  household_adults  \\\n",
       "h1n1_concern                                    2.257314          0.156585   \n",
       "h1n1_knowledge                                  0.624829          0.248738   \n",
       "behavioral_antiviral_meds                       0.843046          0.449000   \n",
       "behavioral_avoidance                            0.829418          0.191218   \n",
       "behavioral_face_mask                            0.900095          0.139909   \n",
       "behavioral_wash_hands                           0.880293          0.096693   \n",
       "behavioral_large_gatherings                     1.354463          0.319381   \n",
       "behavioral_outside_home                         1.381326          0.275272   \n",
       "behavioral_touch_face                           0.900972          0.005530   \n",
       "doctor_recc_h1n1                                0.571278          0.041121   \n",
       "doctor_recc_seasonal                            0.253565          0.407694   \n",
       "chronic_med_condition                           0.525872          0.713465   \n",
       "child_under_6_months                            0.375825          0.448279   \n",
       "health_worker                                   0.178935          0.133796   \n",
       "health_insurance                                0.658859          0.786973   \n",
       "opinion_h1n1_vacc_effective                     0.124912          0.109700   \n",
       "opinion_h1n1_risk                               1.986014          0.301812   \n",
       "opinion_h1n1_sick_from_vacc                     4.915461          0.147689   \n",
       "opinion_seas_vacc_effective                     0.173402          0.225789   \n",
       "opinion_seas_risk                               2.003787          0.061114   \n",
       "opinion_seas_sick_from_vacc                    10.000000          0.229249   \n",
       "household_adults                                0.229249         10.000000   \n",
       "household_children                              0.572859          1.895709   \n",
       "\n",
       "                             household_children  \n",
       "h1n1_concern                           0.505245  \n",
       "h1n1_knowledge                         0.505833  \n",
       "behavioral_antiviral_meds              0.848217  \n",
       "behavioral_avoidance                   0.403284  \n",
       "behavioral_face_mask                   0.058255  \n",
       "behavioral_wash_hands                  0.477636  \n",
       "behavioral_large_gatherings            0.094487  \n",
       "behavioral_outside_home                0.095578  \n",
       "behavioral_touch_face                  0.236063  \n",
       "doctor_recc_h1n1                       0.290400  \n",
       "doctor_recc_seasonal                   0.483802  \n",
       "chronic_med_condition                  1.082372  \n",
       "child_under_6_months                   0.995618  \n",
       "health_worker                          0.376977  \n",
       "health_insurance                       0.694019  \n",
       "opinion_h1n1_vacc_effective            0.152422  \n",
       "opinion_h1n1_risk                      0.953247  \n",
       "opinion_h1n1_sick_from_vacc            0.711635  \n",
       "opinion_seas_vacc_effective            0.765028  \n",
       "opinion_seas_risk                      0.258977  \n",
       "opinion_seas_sick_from_vacc            0.572859  \n",
       "household_adults                       1.895709  \n",
       "household_children                    10.000000  \n",
       "\n",
       "[23 rows x 23 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#corelation between features \n",
    "abs(X_train.corr())*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#asserting that training columns are equal\n",
    "np.testing.assert_array_equal(X_train.index.values,  y_train.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations:26707\n"
     ]
    }
   ],
   "source": [
    "n_obs = y_train.shape[0]\n",
    "print('Number of observations:{}'.format(n_obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21033\n",
       "1     5674\n",
       "Name: h1n1_vaccine, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train['h1n1_vaccine'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.787546\n",
      "1    0.212454\n",
      "Name: h1n1_vaccine, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# proportion of h1n1 vaccine \n",
    "# 0 proportion of people who did not take the vaccine\n",
    "# 1 proportion of people who took the vaccinedid\n",
    "print(y_train['h1n1_vaccine'].value_counts().div(n_obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.534392\n",
      "1    0.465608\n",
      "Name: seasonal_vaccine, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# proportion of seasonal vaccine \n",
    "# 0 proportion of people who did not take the vaccine\n",
    "# 1 proportion of people who took the vaccinedid\n",
    "print(y_train['seasonal_vaccine'].value_counts().div(n_obs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# proportion of h1n1 vaccine\n",
    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
    "n_obs = y_train.shape[0]\n",
    "(y_train['h1n1_vaccine']\n",
    "    .value_counts()\n",
    "    .div(n_obs)\n",
    "    .plot.barh(title = 'Proportion of h1n1 vaccine', ax=ax[0])\n",
    ")\n",
    "ax[0].set_ylabel('h1_vaccine')\n",
    "\n",
    "# proportion of seasonal vaccine \n",
    "(y_train['seasonal_vaccine']\n",
    "    .value_counts()\n",
    "    .div(n_obs)\n",
    "    .plot.barh(title = 'Proportion of seasonal vaccine', ax=ax[1])\n",
    ")\n",
    "ax[1].set_ylabel('seasonal_vaccine') \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>seasonal_vaccine</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_vaccine</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.497810</td>\n",
       "      <td>0.289737</td>\n",
       "      <td>0.787546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036582</td>\n",
       "      <td>0.175871</td>\n",
       "      <td>0.212454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.534392</td>\n",
       "      <td>0.465608</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "seasonal_vaccine         0         1       All\n",
       "h1n1_vaccine                                  \n",
       "0                 0.497810  0.289737  0.787546\n",
       "1                 0.036582  0.175871  0.212454\n",
       "All               0.534392  0.465608  1.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Are the two target variables dependent ?\n",
    "pd.crosstab(\n",
    "    y_train['h1n1_vaccine'],\n",
    "    y_train['seasonal_vaccine'],\n",
    "    margins = True,\n",
    "    normalize = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26707, 37)\n"
     ]
    }
   ],
   "source": [
    "joined_df = X_train.join(y_train)\n",
    "print(joined_df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12116b0>"
      ]
     },
     "execution_count": 48,
     "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": [
    "# vaccination rate for each level of h1n1_concern\n",
    "counts = (joined_df[['h1n1_concern', 'h1n1_vaccine']]\n",
    "              .groupby(['h1n1_concern', 'h1n1_vaccine'])\n",
    "              .size()\n",
    "              .unstack('h1n1_vaccine'))\n",
    "\n",
    "\n",
    "ax = counts.plot.barh()\n",
    "ax.invert_yaxis()\n",
    "ax.legend(\n",
    "    loc = 'center right',\n",
    "    bbox_to_anchor = (1.3, 0.5),\n",
    "    title = 'h1n1_vaccine'\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h1n1_concern\n",
       "0.0     3296\n",
       "1.0     8153\n",
       "2.0    10575\n",
       "3.0     4591\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h1n1_concern_counts = counts.sum(axis='columns')\n",
    "h1n1_concern_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>h1n1_vaccine</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_concern</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.864381</td>\n",
       "      <td>0.135619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.828652</td>\n",
       "      <td>0.171348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.766147</td>\n",
       "      <td>0.233853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.707907</td>\n",
       "      <td>0.292093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "h1n1_vaccine         0         1\n",
       "h1n1_concern                    \n",
       "0.0           0.864381  0.135619\n",
       "1.0           0.828652  0.171348\n",
       "2.0           0.766147  0.233853\n",
       "3.0           0.707907  0.292093"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props = counts.div(h1n1_concern_counts, axis='index')\n",
    "props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1782ef0>"
      ]
     },
     "execution_count": 51,
     "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": [
    "# Plotting proportion of h1n1_concern\n",
    "ax = props.plot.barh()\n",
    "ax.invert_yaxis()\n",
    "ax.legend(\n",
    "    loc = 'center right',\n",
    "    bbox_to_anchor = (1.05, 0.5),\n",
    "    title = 'h1n1_vaccine'\n",
    "\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xa5ce3b0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAD4CAYAAAD7CAEUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAW5UlEQVR4nO3deZxdZZ3n8c8vlZA0AsWSOAOptEWaANkoIWFxdACNCAQbWnYcRRRBbWgZV7AzoxkdZnBrYdp+hcbBBgSBgAJpxQUFWrtlsUADCZExQjSLNBGwQMOSVH7zx73EUKmk7pO6S1Xyeb9e9eKec55zz++pKuqb5yzPjcxEkqRajWh1AZKk4cXgkCQVMTgkSUUMDklSEYNDklRkZKsL2Bpjx47Nzs7OVpchScPKAw888LvMHDfY9xmWwdHZ2Ul3d3ery5CkYSUifl2P9/FUlSSpiMEhSSpicEiSihgckqQiBockqYjBIUkqYnBIkooYHJKkIgaHJKmIwSFJKmJwSJKKGBySpCLDcpLDh1f20HnRt1tdhqRtwLIxb291CbWb29PqCgBHHJKkQgaHJKmIwSFJKmJwSJKKGBySpCIGhySpiMEhSSpicEiSihgckqQiDQ+OiDgmIh6NiKURcVE/20dHxI3V7fdFRGeja5Ikbb2GBkdEtAH/ABwLTAHOiIgpfZqdDTyTmfsAXwI+28iaJEmD0+gRxyHA0sx8LDNfAm4ATujT5gTg6urrm4FZERENrkuStJUaHRzjgeUbLa+oruu3TWauA3qAPRpclyRpKzU6OPobOeRWtCEizo2I7ojo7l0zNGaIlKTtUaODYwUwYaPlDmDV5tpExEigHXi67xtl5hWZOTMzZ7bt2N6gciVJA2l0cPwUmBQRe0fEDsDpwII+bRYA76q+Phm4MzM3GXFIkoaGhn6QU2aui4jzge8BbcBXM3NxRHwa6M7MBcCVwNciYimVkcbpjaxJkjQ4Df8EwMy8Hbi9z7pPbvT6BeCURtchSaoPnxyXJBUxOCRJRQwOSVIRg0OSVMTgkCQVMTgkSUUMDklSEYNDklSk4Q8ANsL08e10X3Jcq8uQtE1w0tRSjjgkSUUMDklSEYNDklTE4JAkFTE4JElFDA5JUhGDQ5JUxOCQJBUxOCRJRQwOSVIRg0OSVMTgkCQVMTgkSUUMDklSEYNDklTE4JAkFTE4JElFDA5JUhGDQ5JUxOCQJBUxOCRJRQwOSVIRg0OSVMTgkCQVMTgkSUUMDklSkZGtLmBrPLyyh86Lvt3qMiQNQcvGvL3VJTTO3J5WVwA44pAkFTI4JElFDA5JUhGDQ5JUxOCQJBUxOCRJRQwOSVIRg0OSVKTmBwAjYhxwDtC58X6Z+Z76lyVJGqpKnhy/Dfgx8AOgtzHlSJKGupLg2DEzLyx584j4KvBW4MnMnNbP9gAuA2YDa4CzMvPBkmNIkpqr5BrHtyJiduH7XwUcs4XtxwKTql/nAvMK31+S1GQlwXEBlfB4PiKejYjnIuLZLe2QmT8Cnt5CkxOAa7LiXmDXiNizoCZJUpPVdKqqekppamb+ps7HHw8s32h5RXXdb/up4VwqoxLadhlX5zIkSbWqacSRmQnc0oDjR3+H20wNV2TmzMyc2bZjewNKkSTVouRU1b0RcXCdj78CmLDRcgewqs7HkCTVUcldVW8E3h8Ry4A/UhktZGYeMIjjLwDOj4gbgEOBnszc5DSVJA3W2h12ZcVBF/JC+0T6P9kxDCxZMqjd77jjjukLFy5cVkPT9cCidevWvXfGjBlP9t1YEhzHFrQFICKuB44ExkbECuBTwCiAzLwcuJ3KrbhLqdyO++7SY0hSLVYcdCE7T5xJ56tGUrlsOwztNXlQu/f29q6bNm3a7wZqt379+li9evWUJ5544v8Cx/fdXnNwZOavI+INwKTM/Kfqk+Q7DbDPGQNsT+C8WmuQpK31QvvE4R0aTTRixIgcN25czxNPPLHJ83dQcI0jIj4FXAh8orpqFHDt4EuUpGYIQ6PAiBEjks1kRMnF8bdRGbL8ESAzVwE7D7o6SdKwUhIcL1VPLSVARLyqMSVJUnMsW76KaW86ZZP1N/3zHUx948mM6JhB98JHanqvOZd8mQkzj2WnSa+vd5lFLr/8cq655pqGHqMkOOZHxD9Sebr7HCqTHX6lMWVJUutM2/8v+OZXvsDhhx1U8z5/edTh3P/txv7BrsX73/9+zjzzzIYeo+bgyMwvADcD3wD2Az6ZmX/fqMIkqRl6e3s552OfYeobT+YtZ/w1zz//ApMnTWS/fTo3aXvVjQs48b0f4Zj/ch6TXn8CH/+fl27YdtiMA9jzPww8q0XPs8/ReehxrF+/HoA1zz/PhJnHsnbtWr5y3Tc5ePY76HrzaZx0zkdZ8/zzAPz76qd429kfoauri66uLn7yk58AcM0113DAAQfQ1dXFO9/5TgDmzp3LF77wBQCOPPJILrzwQg455BD23Xdfuru7RwCsW7eO973vfR3Tpk2bvO+++075/Oc/P7bke1ZycXxv4MeZ+bHM/CjwrxHRWXIwSRpqfvn4cs5716ksvutmdt1lZ75x+w+32P7ni/8fN867hId/OJ8bF3yf5SufKDpe+y470zVlX/7lngcA+Ofv/4ijj3wdo0aN4sRj38RPb7+WhT+4kcn77M2V198GwAf/++c44rCDWLhwIQ8++CBTp05l8eLFXHzxxdx5550sXLiQyy67rN/jrVu3jvvvv59LL72UefPm7QBw6aWXjm1vb+9dtGjRkoULFy65+uqrx/3iF7/YodY+lJyquonKQyEv662uk6Rha+8Je/HaafsBMOOAySxbvuVnkGe94RDad9mZMWNGM2Xfifx6Zfkzy6cd/xZuXPB9AG5Y8H1OO/4tACx69Ff857e9h+mzTuW6W77D4kd/BcCd//ZTPnBm5VpMW1sb7e3t3HnnnZx88smMHVsZLOy+++79HuvEE0+s9G3GDFatWhUAP/jBD3aZP3/+Hvvvv/+UAw88cPIzzzwz8pFHHhlTa/0lDwCOzMyXXl7IzJciouaEqqfp49vpvuS4Vhxa0pDX0//qJUs2fYDupd0Y/apdYK8DAWjb7Yc8/4c/bFhmh51g3H5/Wt5tIaN3W/2n9jvuxrpdJ/5pO0CMeOVyP45/1yQ+8bmpPD3mNTyweClvOuVcaGvjrI+eyK233kpXVxdXXXUVd999d+W9RoyEvV77ivfIzJpuLx49enSl1rY2ent7X943vvjFL/7mpJNO2uIM55tTMuJYHREbniCMiBOAAZ9AlCS90k477cQhhxzCBRdcwFvf+lba2toAeO6559hzzz1Zu3Yt11133Yb2s2bNYt68yscV9fb28uyzzzJr1izmz5/PU089BcDTT2/pEyxe6aijjuqZN2/euBdffDEAHnroodHPPvtszXlQEhzvB/42In4TEcupPAz4voL9JWlYuOWWW+jo6OCee+7huOOO4+ijjx5wn49//ON0dHSwZs0aOjo6mDt37hbbn3baaVx77bWcdtppG9Z95jOf4dBDD+Woo45i//3337D+sssu46677mL69OnMmDGDxYsXM3XqVObMmcMRRxxBV1cXH/7wh2vu34c+9KHf7b///i9Mnz598qRJk6aec845r1m7dm3NT0dG5dGM2kXETtX9nivasY5mzpyZ3d3drTq8pGFoyZIlTJ48uLmehrtFixatmTZtWs0zJS5cuHBsV1dXZ9/1NV/jiIjRwElAJzDy5XNrmfnpWt9DkjT8lVwcv43KVacHgBcbU44kbTsuvvhibrrplTefnnLKKcyZM6dFFdVHSXB0ZOYxDatEkrYxc+bMGfYh0Z+Si+M/iYjpDatEkjQslIw43gCcFRGPUzlVVY9PAJQkDTMN/QRASdK2p2SSw18DuwJ/Wf3atbpOktRE3/3ud9lvv/3YZ599uOSSS5p+/JLbcS8AzgG+WV11bURc4Qy5krZXnRd9u67vt6yGqZR6e3s577zzuOOOO+jo6ODggw/m+OOPZ8qUKXWtZUtKTlWdDRyamX8EiIjPAvcABockNcn999/PPvvsw8SJEwE4/fTTue2225oaHCV3VQWVGXFf1ltdJ0lqkpUrVzJhwoQNyx0dHaxcubKpNZSMOP4JuC8ibqku/xVwZf1LkiRtTn/TRNUyS2491Rwcmfl3EXE3ldtyA3h3Zv6sUYVJkjbV0dHB8uXLNyyvWLGCvfbaq6k1lFwcPwxYnJkPVpd3johDM/O+hlUnSXqFgw8+mF/+8pc8/vjjjB8/nhtuuIGvf/3rTa2h5BrHPOAPGy3/sbpOktQkI0eO5Mtf/jJHH300kydP5tRTT2Xq1KnNraGgbeRGJ9cyc31ElOwvSduUWm6fbYTZs2cze/bslhwbykYcj0XEByNiVPXrAuCxRhUmSRqaSj8B8D8BK4EVwKHAuY0oSpI0dJXcVfUkcPrmtkfEJzLzf9elKknSkFUy4hjIKXV8L0nSEFXP4PApcknaDtQzODZ9nFGStM1xxCFJw8h73vMeXv3qVzNt2rSW1VDP5zBuGriJJG1D5rbX+f16Bmxy1llncf7553PmmWfW99gFBjXiiIhPvvw6M//X4MuRJG3J4Ycfzu67797SGgY74ngv8Ol6FFLi4ZU9df8AFUmtsWzM25tzoKPnw6oXmnOsrbVqgHlj9zqwOXUMYMDgiIhnN7cJ+LP6liNJGupqGXH8Hjg4M/+974aIWN5Pe0nSNqyWaxzXAK/ZzLbmzuUrSWq5AYMjM/9bZt6/mW0X1r8kSdLmnHHGGbzuda/j0UcfpaOjgyuvbP4HsRZdHI+I8VRGHxv2y8wf1bsoSRoWzr276Ye8/vrrm37Mvko+AfCzwGnAI0BvdXUCBockbUdKRhx/BeyXmS82qhhJ0tBX9EFOwKhGFSJJGh5KRhxrgJ9HxA+BDaOOzPxg3auSpLpLMpMIp9Wrxfr16wNY39+2kuBYUP2qWURMoHI773+sFnBFZl7Wp00AlwGzqYTTWZn5YMlxJGkgY3oe46k/7s4erxppeAxg/fr1sXr16nZgUX/bSz4B8OqtOP464COZ+WBE7Aw8EBF3ZOYjG7U5FphU/ToUmFf9ryTVTceDn2UFF7K6fSLDdjLvniWD2v2JJ54Y2dvbO7aGpuuBRevWrXtvfxsHNVdVRHwnM4/d3PbM/C3w2+rr5yJiCTCeyp1ZLzsBuCYzE7g3InaNiD2r+0pSXYx66ffsfe8nWl3G4NQwe+6WTJky5eHMnDnYMmqZq+qgzW0CXlvrgSKiEzgQuK/PpvHAxlOXrKiue0VwRMS5wLkAbbuMq/WwkqQ6q2XE8VPgX+h/bLdrLQeJiJ2AbwD/NTP7TprY3/tu8mmCmXkFcAXA6D0n+WmDktQitQTHEuB9mfnLvhtqmeQwIkZRCY3rMvOb/TRZAUzYaLkDWFVDXZKkFqjlOY65W2j3N1vasXrH1JXAksz8u800WwCcGRWHAT1e35CkoWvAEUdm3gwQEaOBk4DOPvvduoXdXw+8E3g4In5eXfe3wJ9X3/ty4HYqt+IupXI77ruLeiBJaqqSu6puA3qAB9joAcAtycx/ZYD73qp3U51XUIckqYVKgqMjM49pWCWSpGGhZK6qn0TE9IZVIkkaFmp5juNhKrfHjgTeHRGPUTlVFVTONB3Q2BIlSUNJLaeq3trwKiRJw0Ytd1X9uhmFSJKGh5JrHJIkGRySpDIGhySpyKCmVW+V6ePb6b7kuFaXIakuBjdVuJrPEYckqYjBIUkqYnBIkooYHJKkIgaHJKmIwSFJKmJwSJKKGBySpCIGhySpiMEhSSpicEiSihgckqQiBockqYjBIUkqYnBIkooYHJKkIgaHJKmIwSFJKmJwSJKKGBySpCIGhySpiMEhSSpicEiSihgckqQiBockqYjBIUkqMrLVBWyNh1f20HnRt1tdhrRdWzbm7a0uYfszt6fVFQCOOCRJhQwOSVIRg0OSVMTgkCQVMTgkSUUMDklSEYNDklTE4JAkFTE4JElFGhocETEmIu6PiIURsTgi/kc/bUZHxI0RsTQi7ouIzkbWJEkanEaPOF4E3pSZXcBrgWMi4rA+bc4GnsnMfYAvAZ9tcE2SpEFoaHBkxR+qi6OqX9mn2QnA1dXXNwOzIiIaWZckaes1/BpHRLRFxM+BJ4E7MvO+Pk3GA8sBMnMd0APs0ei6JElbp+HBkZm9mflaoAM4JCKm9WnS3+ii76iEiDg3Irojort3zdCYIVKStkdNu6sqM38P3A0c02fTCmACQESMBNqBp/vZ/4rMnJmZM9t2bG9wtZKkzWn0XVXjImLX6us/A94M/KJPswXAu6qvTwbuzMxNRhySpKGh0R/ktCdwdUS0UQmp+Zn5rYj4NNCdmQuAK4GvRcRSKiON0xtckyRpEBoaHJn5EHBgP+s/udHrF4BTGlmHJKl+fHJcklTE4JAkFTE4JElFDA5JUhGDQ5JUxOCQJBUxOCRJRQwOSVKRRj853hDTx7fTfclxrS5D2s452ej2yhGHJKmIwSFJKmJwSJKKGBySpCIGhySpiMEhSSpicEiSihgckqQiBockqYjBIUkqYnBIkooYHJKkIgaHJKlIZGaraygWEc8Bj7a6jhYaC/yu1UW00Pbc/+2572D/B9v/12TmuMEWMSynVQcezcyZrS6iVSKi2/5vn/3fnvsO9n+o9N9TVZKkIgaHJKnIcA2OK1pdQIvZ/+3X9tx3sP9Dov/D8uK4JKl1huuIQ5LUIgaHJKnIkA6OiDgmIh6NiKURcVE/20dHxI3V7fdFRGfzq2yMGvr+4Yh4JCIeiogfRsRrWlFnowzU/43anRwRGREtv0Wxnmrpf0ScWv0dWBwRX292jY1Uw+//n0fEXRHxs+r/A7NbUWcjRMRXI+LJiFi0me0REf+n+r15KCIOanaNZOaQ/ALagF8BE4EdgIXAlD5t/hq4vPr6dODGVtfdxL6/Edix+voD20rfa+1/td3OwI+Ae4GZra67yT//ScDPgN2qy69udd1N7v8VwAeqr6cAy1pddx37fzhwELBoM9tnA98BAjgMuK/ZNQ7lEcchwNLMfCwzXwJuAE7o0+YE4Orq65uBWRERTayxUQbse2belZlrqov3Ah1NrrGRavnZA3wG+BzwQjOLa4Ja+n8O8A+Z+QxAZj7Z5BobqZb+J7BL9XU7sKqJ9TVUZv4IeHoLTU4ArsmKe4FdI2LP5lRXMZSDYzywfKPlFdV1/bbJzHVAD7BHU6prrFr6vrGzqfwLZFsxYP8j4kBgQmZ+q5mFNUktP/99gX0j4t8i4t6IOKZp1TVeLf2fC7wjIlYAtwN/05zShoTSvw91N5SnHOlv5ND33uFa2gxHNfcrIt4BzASOaGhFzbXF/kfECOBLwFnNKqjJavn5j6RyuupIKqPNH0fEtMz8fYNra4Za+n8GcFVmfjEiXgd8rdr/9Y0vr+Va/ndvKI84VgATNlruYNPh6IY2ETGSypB1S0O84aKWvhMRbwbmAMdn5otNqq0ZBur/zsA04O6IWEblPO+CbegCea2/+7dl5trMfJzKpJ+TmlRfo9XS/7OB+QCZeQ8whsoEgNuDmv4+NNJQDo6fApMiYu+I2IHKxe8FfdosAN5VfX0ycGdWrx4NcwP2vXqq5h+phMa2dH4bBuh/ZvZk5tjM7MzMTirXeI7PzO7WlFt3tfzu30rlBgkiYiyVU1ePNbXKxqml/78BZgFExGQqwbG6qVW2zgLgzOrdVYcBPZn522YWMGRPVWXmuog4H/gelbssvpqZiyPi00B3Zi4ArqQyRF1KZaRxeusqrp8a+/55YCfgpur9AL/JzONbVnQd1dj/bVaN/f8e8JaIeAToBT6WmU+1rur6qbH/HwG+EhEfonKa5qxt5B+NRMT1VE5Bjq1ew/kUMAogMy+nck1nNrAUWAO8u+k1biPfa0lSkwzlU1WSpCHI4JAkFTE4JElFDA5JUhGDQ5JUxOCQJBUxOCRJRf4/Ojdfa0GVXBYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = props.plot.barh(stacked=True)\n",
    "ax.invert_yaxis()\n",
    "ax.legend(\n",
    "    loc = 'center right',\n",
    "    bbox_to_anchor = (1.05, 0.5),\n",
    "    title = 'h1n1_vaccine'\n",
    "\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets factor this plot into a function that we can plot more variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vaccination_rate_plot(col, target, data, ax=None):\n",
    "    '''stacked bar chart of vaccination rate for target against col\n",
    "    \n",
    "    args:\n",
    "         col-column name of feature variable\n",
    "         target-column name of target variable\n",
    "         data- pandas dataframe that contain col(columns) and target\n",
    "    \n",
    "         ax- object to attach plot to\n",
    "    \n",
    "    '''\n",
    "    counts = (joined_df[[col, target]]\n",
    "              .groupby([col, target])\n",
    "              .size()\n",
    "              .unstack(target))\n",
    "    \n",
    "    grouped_counts = counts.sum(axis='columns')\n",
    "    props = counts.div(grouped_counts, axis='index')\n",
    "    \n",
    "    props.plot(kind='barh', stacked=True, ax=ax)\n",
    "    ax.invert_yaxis()\n",
    "    ax.legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then loop through several columns and plot against seasonal_vaccine and h1n1_vaccine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x1980 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols_to_plot = [\n",
    "    'h1n1_concern',\n",
    "    'h1n1_knowledge',\n",
    "    'opinion_h1n1_vacc_effective',\n",
    "    'opinion_h1n1_risk',\n",
    "    'opinion_h1n1_sick_from_vacc',\n",
    "    'opinion_seas_vacc_effective',\n",
    "    'opinion_seas_sick_from_vacc',\n",
    "    'opinion_seas_risk',\n",
    "    'age_group',\n",
    "    'race',\n",
    "    'sex',\n",
    "    \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(len(cols_to_plot), 2, figsize = (9, len(cols_to_plot)*2.5))\n",
    "for idx, col in enumerate(cols_to_plot):\n",
    "    vaccination_rate_plot(col, 'h1n1_vaccine', joined_df, ax=ax[idx, 0])\n",
    "    vaccination_rate_plot(col, 'seasonal_vaccine', joined_df, ax=ax[idx, 1])\n",
    "    \n",
    "ax[0, 0].legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), title='h1n1_vaccine')\n",
    "ax[0, 1].legend(loc='lower center', bbox_to_anchor=(0.5, 1.05), title='seasonal_vaccine')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can explore the various features and get some insights based on the plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL CREATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "RANDOM_SEED = 67"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h1n1_concern',\n",
       " 'h1n1_knowledge',\n",
       " 'behavioral_antiviral_meds',\n",
       " 'behavioral_avoidance',\n",
       " 'behavioral_face_mask',\n",
       " 'behavioral_wash_hands',\n",
       " 'behavioral_large_gatherings',\n",
       " 'behavioral_outside_home',\n",
       " 'behavioral_touch_face',\n",
       " 'doctor_recc_h1n1',\n",
       " 'doctor_recc_seasonal',\n",
       " 'chronic_med_condition',\n",
       " 'child_under_6_months',\n",
       " 'health_worker',\n",
       " 'health_insurance',\n",
       " 'opinion_h1n1_vacc_effective',\n",
       " 'opinion_h1n1_risk',\n",
       " 'opinion_h1n1_sick_from_vacc',\n",
       " 'opinion_seas_vacc_effective',\n",
       " 'opinion_seas_risk',\n",
       " 'opinion_seas_sick_from_vacc',\n",
       " 'household_adults',\n",
       " 'household_children']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = X_train.select_dtypes(include = 'number').columns.to_list()\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing\n",
    "num_pipe = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "ct = ColumnTransformer(remainder = 'drop',\n",
    "                      transformers = [\n",
    "                          ('num_pipe', num_pipe, num_cols)\n",
    "                      ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chaining\n",
    "estimators = MultiOutputClassifier(\n",
    "    estimator = LogisticRegression(penalty='l2', C=1)\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model\n",
    "model_1 = Pipeline([\n",
    "    ('ct', ct),\n",
    "    ('classifier', estimators)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data to train set and test set\n",
    "X_train_, X_test_, y_train_, y_test_ = train_test_split(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    test_size=0.33,\n",
    "    shuffle=True,\n",
    "    stratify=y_train,#stratify enforces same proportionality in an inbalance class \n",
    "    random_state=RANDOM_SEED\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 717 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[array([[0.93475789, 0.06524211],\n",
       "        [0.24167582, 0.75832418],\n",
       "        [0.87068062, 0.12931938],\n",
       "        ...,\n",
       "        [0.45857524, 0.54142476],\n",
       "        [0.75976917, 0.24023083],\n",
       "        [0.81105099, 0.18894901]]),\n",
       " array([[0.66403729, 0.33596271],\n",
       "        [0.05090096, 0.94909904],\n",
       "        [0.72742121, 0.27257879],\n",
       "        ...,\n",
       "        [0.8557033 , 0.1442967 ],\n",
       "        [0.54927872, 0.45072128],\n",
       "        [0.23566856, 0.76433144]])]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Train the model\n",
    "model_1.fit(X_train_,  y_train_)\n",
    "\n",
    "# Predict on evaluation set\n",
    "preds = model_1.predict_proba(X_test_)#not X_test\n",
    "preds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_probas[0] (8814, 2)\n",
      "test_probas[1] (8814, 2)\n"
     ]
    }
   ],
   "source": [
    "print('test_probas[0]', preds[0].shape)\n",
    "print('test_probas[1]', preds[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_preds.shape: (8814, 2)\n"
     ]
    },
    {
     "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>h1n1_vaccine</th>\n",
       "      <th>seasonal_vaccine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>respondent_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19263</th>\n",
       "      <td>0.065242</td>\n",
       "      <td>0.335963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13455</th>\n",
       "      <td>0.758324</td>\n",
       "      <td>0.949099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11103</th>\n",
       "      <td>0.129319</td>\n",
       "      <td>0.272579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26227</th>\n",
       "      <td>0.043213</td>\n",
       "      <td>0.320680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19411</th>\n",
       "      <td>0.038546</td>\n",
       "      <td>0.103736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               h1n1_vaccine  seasonal_vaccine\n",
       "respondent_id                                \n",
       "19263              0.065242          0.335963\n",
       "13455              0.758324          0.949099\n",
       "11103              0.129319          0.272579\n",
       "26227              0.043213          0.320680\n",
       "19411              0.038546          0.103736"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the predictions to a data frame\n",
    "\n",
    "y_preds =pd.DataFrame(\n",
    "    \n",
    "    {\n",
    "       'h1n1_vaccine':preds[0][:, 1],\n",
    "        'seasonal_vaccine':preds[1][:, 1],\n",
    "    },\n",
    "    \n",
    "    index = y_test_.index\n",
    "\n",
    ")\n",
    "\n",
    "print('y_preds.shape:', y_preds.shape)\n",
    "y_preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluation of the model\n",
    "def plot_roc(y_true, y_score, label_name, ax):\n",
    "    '''\n",
    "    Function to plot ROC CURVE used to evaluate model performance\n",
    "    \n",
    "    args:\n",
    "            y_true- true label\n",
    "            y_score- predictions\n",
    "            label_name- \n",
    "            ax- object to attach plot to\n",
    "       \n",
    "    '''\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_score)\n",
    "    ax.plot(fpr, tpr)\n",
    "    ax.plot([0, 1], [0, 1], color='grey', linestyle='--')\n",
    "    ax.set_xlabel('TPR')\n",
    "    ax.set_ylabel('FPR')\n",
    "    ax.set_title(\n",
    "        f'{label_name}: AUC = {roc_auc_score(y_true, y_score):.4f}'\n",
    "    \n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize = (7, 6))\n",
    "\n",
    "plot_roc(\n",
    "    y_test_['h1n1_vaccine'],\n",
    "    y_preds['h1n1_vaccine'],\n",
    "    'h1n1_vaccine',\n",
    "    ax=ax[0]\n",
    ")\n",
    "\n",
    "plot_roc(\n",
    "    y_test_['seasonal_vaccine'],\n",
    "    y_preds['seasonal_vaccine'],\n",
    "    'seasonal_vaccine',\n",
    "    ax=ax[1]\n",
    "\n",
    ")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8276405790454424"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test_, y_preds) #Our model does fairly well with a score of 82%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we train on full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 378 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#on full data_set\n",
    "model_1.fit(X_train, y_train);\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predict probability on the full data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0.87280478, 0.12719522],\n",
       "        [0.94475228, 0.05524772],\n",
       "        [0.62906841, 0.37093159],\n",
       "        ...,\n",
       "        [0.80410787, 0.19589213],\n",
       "        [0.94924508, 0.05075492],\n",
       "        [0.38418105, 0.61581895]]),\n",
       " array([[0.5736    , 0.4264    ],\n",
       "        [0.92877344, 0.07122656],\n",
       "        [0.3626208 , 0.6373792 ],\n",
       "        ...,\n",
       "        [0.598725  , 0.401275  ],\n",
       "        [0.68002776, 0.31997224],\n",
       "        [0.3578779 , 0.6421221 ]])]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_prob = model_1.predict_proba(X_test)\n",
    "test_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>h1n1_vaccine</th>\n",
       "      <th>seasonal_vaccine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>respondent_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26707</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26708</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26709</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26710</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26711</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               h1n1_vaccine  seasonal_vaccine\n",
       "respondent_id                                \n",
       "26707                   0.5               0.7\n",
       "26708                   0.5               0.7\n",
       "26709                   0.5               0.7\n",
       "26710                   0.5               0.7\n",
       "26711                   0.5               0.7"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_df = pd.read_csv('F:\\\\bizz\\\\DATA SETS\\\\Flu Shot Learning\\\\submission_format.csv', index_col = 'respondent_id')\n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.testing.assert_array_equal(X_test.index.values,\n",
    "                             submission_df.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h1n1_vaccine</th>\n",
       "      <th>seasonal_vaccine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>respondent_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26707</th>\n",
       "      <td>0.127195</td>\n",
       "      <td>0.426400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26708</th>\n",
       "      <td>0.055248</td>\n",
       "      <td>0.071227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26709</th>\n",
       "      <td>0.370932</td>\n",
       "      <td>0.637379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26710</th>\n",
       "      <td>0.464355</td>\n",
       "      <td>0.803452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26711</th>\n",
       "      <td>0.286664</td>\n",
       "      <td>0.634914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               h1n1_vaccine  seasonal_vaccine\n",
       "respondent_id                                \n",
       "26707              0.127195          0.426400\n",
       "26708              0.055248          0.071227\n",
       "26709              0.370932          0.637379\n",
       "26710              0.464355          0.803452\n",
       "26711              0.286664          0.634914"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save predictions to submission data frame\n",
    "submission_df[\"h1n1_vaccine\"] = test_prob[0][:, 1]\n",
    "submission_df[\"seasonal_vaccine\"] = test_prob[1][:, 1]\n",
    "\n",
    "submission_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df.to_csv('flu-shot submission.csv', \n",
    "                       index=True, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After submission I obtained a score of 81%"
   ]
  }
 ],
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