vax_hesitancy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dced5572-5fac-4c4c-874c-1eebf165984b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CondaValueError: too few arguments, must supply command line package specs or --file\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge category_encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "39a93f90-9fb4-4244-b5ee-d67c6f476ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from category_encoders import OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f6d4f53-3dee-4931-b31e-daf06bdd9913",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('vh_data15.csv')\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bedc872a-f421-466b-842b-5a83dbd68604",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>County_Density</th>\n",
       "      <th>Vaccine_Trust_Index</th>\n",
       "      <th>Personal_Responsibility</th>\n",
       "      <th>Trust_Science_Apolitical</th>\n",
       "      <th>Trust_Science_Politicians</th>\n",
       "      <th>Trust_Science_Media</th>\n",
       "      <th>Trust_Science_Community</th>\n",
       "      <th>Trust_National</th>\n",
       "      <th>Trust_State</th>\n",
       "      <th>Trust_Local</th>\n",
       "      <th>Trust_Media</th>\n",
       "      <th>Perceived_Risk</th>\n",
       "      <th>Perceived_Network_Risk</th>\n",
       "      <th>Doctor_Comfort</th>\n",
       "      <th>Fear_Needles</th>\n",
       "      <th>Condition_Pregnancy</th>\n",
       "      <th>Condition_Asthma</th>\n",
       "      <th>Condition_Lung</th>\n",
       "      <th>Condition_Diabetes</th>\n",
       "      <th>Condition_Immune</th>\n",
       "      <th>Condition_Obesity</th>\n",
       "      <th>Condition_Heart</th>\n",
       "      <th>Condition_Organ</th>\n",
       "      <th>County_Cases</th>\n",
       "      <th>County_Cases2wk</th>\n",
       "      <th>Male</th>\n",
       "      <th>Race</th>\n",
       "      <th>Age</th>\n",
       "      <th>PS_Index</th>\n",
       "      <th>Natural_Science_Literacy</th>\n",
       "      <th>College_Degree</th>\n",
       "      <th>Pandemic_Impact</th>\n",
       "      <th>Pandemic_Impact_Network</th>\n",
       "      <th>Infected_Personal</th>\n",
       "      <th>Infected_Network</th>\n",
       "      <th>Biden</th>\n",
       "      <th>Trump</th>\n",
       "      <th>Party_ID</th>\n",
       "      <th>Household_Income</th>\n",
       "      <th>Vaccine_Required</th>\n",
       "      <th>Evangelical</th>\n",
       "      <th>Vaccine_Hesitant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137.851795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.078749</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.751406</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071312</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.103752</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.119295</td>\n",
       "      <td>0.002288</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.912917</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056909</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1541.026670</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082678</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   County_Density  Vaccine_Trust_Index  Personal_Responsibility  \\\n",
       "0      137.851795             0.000000                     10.0   \n",
       "1       38.751406             9.000000                      5.0   \n",
       "2       18.103752             8.666667                      7.0   \n",
       "3       26.912917             4.000000                      7.0   \n",
       "4     1541.026670             7.000000                      6.0   \n",
       "\n",
       "   Trust_Science_Apolitical  Trust_Science_Politicians  Trust_Science_Media  \\\n",
       "0                       5.0                        5.0                  5.0   \n",
       "1                       8.0                        4.0                  6.0   \n",
       "2                       6.0                        1.0                  1.0   \n",
       "3                       6.0                        6.0                  4.0   \n",
       "4                       6.0                        1.0                  2.0   \n",
       "\n",
       "   Trust_Science_Community  Trust_National  Trust_State  Trust_Local  \\\n",
       "0                      5.0             5.0          5.0          5.0   \n",
       "1                      9.0             8.0          2.0          7.0   \n",
       "2                      6.0             7.0          7.0          9.0   \n",
       "3                      6.0             6.0          6.0          6.0   \n",
       "4                      6.0             6.0          2.0          7.0   \n",
       "\n",
       "   Trust_Media  Perceived_Risk  Perceived_Network_Risk  Doctor_Comfort  \\\n",
       "0          5.0             0.0                     0.0             8.0   \n",
       "1          7.0            10.0                     9.0             8.0   \n",
       "2          1.0             9.0                     8.0            10.0   \n",
       "3          5.0             6.0                     3.0             7.0   \n",
       "4          4.0             8.0                     8.0             8.0   \n",
       "\n",
       "   Fear_Needles  Condition_Pregnancy  Condition_Asthma  Condition_Lung  \\\n",
       "0           0.0                  0.0               0.0             0.0   \n",
       "1           6.0                  0.0               0.0             0.0   \n",
       "2           3.0                  0.0               0.0             1.0   \n",
       "3           2.0                  0.0               0.0             1.0   \n",
       "4           0.0                  0.0               0.0             0.0   \n",
       "\n",
       "   Condition_Diabetes  Condition_Immune  Condition_Obesity  Condition_Heart  \\\n",
       "0                 0.0               0.0                0.0              0.0   \n",
       "1                 0.0               0.0                1.0              1.0   \n",
       "2                 0.0               0.0                0.0              1.0   \n",
       "3                 0.0               0.0                0.0              0.0   \n",
       "4                 0.0               0.0                0.0              0.0   \n",
       "\n",
       "   Condition_Organ  County_Cases  County_Cases2wk  Male  Race   Age  PS_Index  \\\n",
       "0              0.0      0.078749         0.002252     1     1  67.0  0.500000   \n",
       "1              0.0      0.071312         0.002439     1     1  70.0  0.500000   \n",
       "2              0.0      0.119295         0.002288     0     1  69.0  0.333333   \n",
       "3              0.0      0.056909         0.001455     0     1  64.0  0.500000   \n",
       "4              0.0      0.082678         0.001198     1     1  56.0  0.333333   \n",
       "\n",
       "   Natural_Science_Literacy  College_Degree  Pandemic_Impact  \\\n",
       "0                  0.666667             0.0              0.0   \n",
       "1                  0.833333             1.0              4.5   \n",
       "2                  1.000000             0.0              5.5   \n",
       "3                  0.833333             0.0              6.0   \n",
       "4                  0.833333             1.0              2.5   \n",
       "\n",
       "   Pandemic_Impact_Network  Infected_Personal  Infected_Network Biden Trump  \\\n",
       "0                      0.0                0.0               0.0    No   Yes   \n",
       "1                      5.0                0.0               0.0   Yes    No   \n",
       "2                      6.0                0.0               1.0    No   Yes   \n",
       "3                      7.0                0.0               0.0   Yes    No   \n",
       "4                      2.5                0.0               1.0   Yes   Yes   \n",
       "\n",
       "     Party_ID  Household_Income  Vaccine_Required  Evangelical  \\\n",
       "0  Republican               1.0                 0          1.0   \n",
       "1    Democrat               3.0                 0          0.0   \n",
       "2  Republican               6.0                 0          0.0   \n",
       "3    Democrat               4.0                 0          1.0   \n",
       "4  Republican               3.0                 0          1.0   \n",
       "\n",
       "   Vaccine_Hesitant  \n",
       "0                 1  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 1  \n",
       "4                 0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3fc9e634-479d-47e2-b343-45e7f68d8920",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform race column to 4 onehotencoding columns\n",
    "# party_id, biden and trump columns to onehotencoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1f2f00f-940b-4054-8883-d03f2b038b0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "662972b3-c0c2-4252-8467-9cbb339ef723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3353, 42)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a30e2a6-5693-4b6d-8312-9f965f152560",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert column names to lower case\n",
    "df.columns = df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c68712f6-7db5-4f84-a263-3aeb7f5e4398",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county_density</th>\n",
       "      <th>vaccine_trust_index</th>\n",
       "      <th>personal_responsibility</th>\n",
       "      <th>trust_science_apolitical</th>\n",
       "      <th>trust_science_politicians</th>\n",
       "      <th>trust_science_media</th>\n",
       "      <th>trust_science_community</th>\n",
       "      <th>trust_national</th>\n",
       "      <th>trust_state</th>\n",
       "      <th>trust_local</th>\n",
       "      <th>trust_media</th>\n",
       "      <th>perceived_risk</th>\n",
       "      <th>perceived_network_risk</th>\n",
       "      <th>doctor_comfort</th>\n",
       "      <th>fear_needles</th>\n",
       "      <th>condition_pregnancy</th>\n",
       "      <th>condition_asthma</th>\n",
       "      <th>condition_lung</th>\n",
       "      <th>condition_diabetes</th>\n",
       "      <th>condition_immune</th>\n",
       "      <th>condition_obesity</th>\n",
       "      <th>condition_heart</th>\n",
       "      <th>condition_organ</th>\n",
       "      <th>county_cases</th>\n",
       "      <th>county_cases2wk</th>\n",
       "      <th>male</th>\n",
       "      <th>race</th>\n",
       "      <th>age</th>\n",
       "      <th>ps_index</th>\n",
       "      <th>natural_science_literacy</th>\n",
       "      <th>college_degree</th>\n",
       "      <th>pandemic_impact</th>\n",
       "      <th>pandemic_impact_network</th>\n",
       "      <th>infected_personal</th>\n",
       "      <th>infected_network</th>\n",
       "      <th>biden</th>\n",
       "      <th>trump</th>\n",
       "      <th>party_id</th>\n",
       "      <th>household_income</th>\n",
       "      <th>vaccine_required</th>\n",
       "      <th>evangelical</th>\n",
       "      <th>vaccine_hesitant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137.851795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.078749</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.751406</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071312</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.103752</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.119295</td>\n",
       "      <td>0.002288</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.912917</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056909</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1541.026670</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082678</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Republican</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   county_density  vaccine_trust_index  personal_responsibility  \\\n",
       "0      137.851795             0.000000                     10.0   \n",
       "1       38.751406             9.000000                      5.0   \n",
       "2       18.103752             8.666667                      7.0   \n",
       "3       26.912917             4.000000                      7.0   \n",
       "4     1541.026670             7.000000                      6.0   \n",
       "\n",
       "   trust_science_apolitical  trust_science_politicians  trust_science_media  \\\n",
       "0                       5.0                        5.0                  5.0   \n",
       "1                       8.0                        4.0                  6.0   \n",
       "2                       6.0                        1.0                  1.0   \n",
       "3                       6.0                        6.0                  4.0   \n",
       "4                       6.0                        1.0                  2.0   \n",
       "\n",
       "   trust_science_community  trust_national  trust_state  trust_local  \\\n",
       "0                      5.0             5.0          5.0          5.0   \n",
       "1                      9.0             8.0          2.0          7.0   \n",
       "2                      6.0             7.0          7.0          9.0   \n",
       "3                      6.0             6.0          6.0          6.0   \n",
       "4                      6.0             6.0          2.0          7.0   \n",
       "\n",
       "   trust_media  perceived_risk  perceived_network_risk  doctor_comfort  \\\n",
       "0          5.0             0.0                     0.0             8.0   \n",
       "1          7.0            10.0                     9.0             8.0   \n",
       "2          1.0             9.0                     8.0            10.0   \n",
       "3          5.0             6.0                     3.0             7.0   \n",
       "4          4.0             8.0                     8.0             8.0   \n",
       "\n",
       "   fear_needles  condition_pregnancy  condition_asthma  condition_lung  \\\n",
       "0           0.0                  0.0               0.0             0.0   \n",
       "1           6.0                  0.0               0.0             0.0   \n",
       "2           3.0                  0.0               0.0             1.0   \n",
       "3           2.0                  0.0               0.0             1.0   \n",
       "4           0.0                  0.0               0.0             0.0   \n",
       "\n",
       "   condition_diabetes  condition_immune  condition_obesity  condition_heart  \\\n",
       "0                 0.0               0.0                0.0              0.0   \n",
       "1                 0.0               0.0                1.0              1.0   \n",
       "2                 0.0               0.0                0.0              1.0   \n",
       "3                 0.0               0.0                0.0              0.0   \n",
       "4                 0.0               0.0                0.0              0.0   \n",
       "\n",
       "   condition_organ  county_cases  county_cases2wk  male  race   age  ps_index  \\\n",
       "0              0.0      0.078749         0.002252     1     1  67.0  0.500000   \n",
       "1              0.0      0.071312         0.002439     1     1  70.0  0.500000   \n",
       "2              0.0      0.119295         0.002288     0     1  69.0  0.333333   \n",
       "3              0.0      0.056909         0.001455     0     1  64.0  0.500000   \n",
       "4              0.0      0.082678         0.001198     1     1  56.0  0.333333   \n",
       "\n",
       "   natural_science_literacy  college_degree  pandemic_impact  \\\n",
       "0                  0.666667             0.0              0.0   \n",
       "1                  0.833333             1.0              4.5   \n",
       "2                  1.000000             0.0              5.5   \n",
       "3                  0.833333             0.0              6.0   \n",
       "4                  0.833333             1.0              2.5   \n",
       "\n",
       "   pandemic_impact_network  infected_personal  infected_network biden trump  \\\n",
       "0                      0.0                0.0               0.0    No   Yes   \n",
       "1                      5.0                0.0               0.0   Yes    No   \n",
       "2                      6.0                0.0               1.0    No   Yes   \n",
       "3                      7.0                0.0               0.0   Yes    No   \n",
       "4                      2.5                0.0               1.0   Yes   Yes   \n",
       "\n",
       "     party_id  household_income  vaccine_required  evangelical  \\\n",
       "0  Republican               1.0                 0          1.0   \n",
       "1    Democrat               3.0                 0          0.0   \n",
       "2  Republican               6.0                 0          0.0   \n",
       "3    Democrat               4.0                 0          1.0   \n",
       "4  Republican               3.0                 0          1.0   \n",
       "\n",
       "   vaccine_hesitant  \n",
       "0                 1  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 1  \n",
       "4                 0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a3c7d589-009a-45b6-91f4-ac26f8b05bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat       1194\n",
       "Republican     1033\n",
       "Independent    1008\n",
       "Other party      72\n",
       "Libertarian      45\n",
       "Name: party_id, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['party_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b023eada-a019-49ed-91a3-212a5c67fe76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3353, 42)\n",
      "(3353, 41)\n",
      "(3353,)\n"
     ]
    }
   ],
   "source": [
    "# split data into feature matrix and target vector\n",
    "target = 'vaccine_hesitant'\n",
    "y = df[target]\n",
    "X = df.drop(columns = target)\n",
    "\n",
    "print(df.shape)\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b2afbff-d59e-4980-a557-cfb45ad15916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2682, 41) (2682,)\n",
      "(671, 41) (671,)\n"
     ]
    }
   ],
   "source": [
    "# split data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.80, test_size=0.20, random_state=42)\n",
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "13488690-c2f0-404a-8024-fa48dd13ef17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7548464062033999"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# establish a baseline\n",
    "y.value_counts(normalize=True).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e48603b9-012e-4d94-ac69-a2887dd637ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a DecisionTree model\n",
    "model_dt = make_pipeline(\n",
    "    OneHotEncoder(),\n",
    "    SimpleImputer(),\n",
    "    DecisionTreeClassifier(random_state=42)\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf47a8c6-8c5f-4edf-b450-a566405d5113",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a RandomForest model\n",
    "model_rf = make_pipeline(\n",
    "    OneHotEncoder(),\n",
    "    SimpleImputer(),\n",
    "    RandomForestClassifier(n_estimators=25, random_state=42)\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7c0ee22a-7520-4161-b12f-dea5b987ff42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get cross validation scores\n",
    "\n",
    "# DecisionTree scores\n",
    "cv_scores_dt = cross_val_score(model_dt, X_train, y_train, cv=10, n_jobs=-1)\n",
    "\n",
    "# RandomForest scores\n",
    "cv_scores_rf = cross_val_score(model_rf, X_train, y_train, cv=10, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a661da98-8af4-473d-8ba6-24044ade91ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82156134, 0.80669145, 0.81343284, 0.81343284, 0.85447761,\n",
       "       0.82835821, 0.82462687, 0.80970149, 0.82089552, 0.83208955])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_scores_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c76eb5b9-30ad-4aef-b74b-c7c8fa6d7532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.85873606, 0.86988848, 0.87313433, 0.88059701, 0.89925373,\n",
       "       0.89552239, 0.91044776, 0.90298507, 0.88432836, 0.87686567])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_scores_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c787b1f9-9634-47b4-8c97-9e26fd4c6427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score DecisionTreeClassifier\n",
      "Mean CV accuracy score: 0.8225267713477225\n",
      "STD CV accuracy score: 0.013171775124140628\n",
      "------------------------------\n",
      "CV score RandomForestClassifier\n",
      "Mean CV accuracy score: 0.8851758863674194\n",
      "STD CV accuracy score: 0.015587120267983427\n"
     ]
    }
   ],
   "source": [
    "print('CV score DecisionTreeClassifier')\n",
    "print('Mean CV accuracy score:', cv_scores_dt.mean())\n",
    "print('STD CV accuracy score:', cv_scores_dt.std())\n",
    "print('---' * 10)\n",
    "print('CV score RandomForestClassifier')\n",
    "print('Mean CV accuracy score:', cv_scores_rf.mean())\n",
    "print('STD CV accuracy score:', cv_scores_rf.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "decef1a6-79da-45b3-a0e2-118d9db5ea98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('ordinalencoder', OrdinalEncoder()),\n",
       "                                       ('simpleimputer', SimpleImputer()),\n",
       "                                       ('randomforestclassifier',\n",
       "                                        RandomForestClassifier(n_estimators=25,\n",
       "                                                               random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestclassifier__max_depth': range(5, 40, 5),\n",
       "                         'randomforestclassifier__n_estimators': range(25, 125, 25),\n",
       "                         'simpleimputer__strategy': ['mean', 'median']},\n",
       "             verbose=-1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = make_pipeline(\n",
    "    OrdinalEncoder(),\n",
    "    SimpleImputer(),\n",
    "    RandomForestClassifier(n_estimators=25, random_state=42)\n",
    ");\n",
    "\n",
    "# create a hyper parameter grid for GridSearchCV model\n",
    "param_grid = {\n",
    "    'simpleimputer__strategy':['mean', 'median'],\n",
    "    'randomforestclassifier__n_estimators': range(25, 125, 25),\n",
    "    'randomforestclassifier__max_depth': range(5, 40, 5)\n",
    "}\n",
    "\n",
    "# instantiate GridSearch model\n",
    "model_rfgs = GridSearchCV(clf,\n",
    "                          param_grid=param_grid,\n",
    "                          n_jobs=-1,\n",
    "                          cv=5,\n",
    "                          verbose=-1)\n",
    "\n",
    "# fit our model\n",
    "model_rfgs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "234b781e-5ef8-4025-b6a8-ac3e31fd919b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'randomforestclassifier__max_depth': 10,\n",
       " 'randomforestclassifier__n_estimators': 50,\n",
       " 'simpleimputer__strategy': 'median'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at best parameters for GridSearch model\n",
    "model_rfgs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "29742b46-270b-421a-a2df-6484049683d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888983851691264"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the best score for GridSearch model\n",
    "model_rfgs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c6b1af25-0a01-466a-ab7b-a9662f6e7dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at predictions for GridSearch model\n",
    "model_rfgs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "30402595-532b-4af1-b58d-527a888d374c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('ordinalencoder',\n",
       "                                              OrdinalEncoder()),\n",
       "                                             ('simpleimputer', SimpleImputer()),\n",
       "                                             ('randomforestclassifier',\n",
       "                                              RandomForestClassifier(n_estimators=25,\n",
       "                                                                     random_state=42))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'randomforestclassifier__max_depth': range(5, 40, 5),\n",
       "                                        'randomforestclassifier__n_estimators': range(25, 125, 25),\n",
       "                                        'simpleimputer__strategy': ['mean',\n",
       "                                                                    'median']},\n",
       "                   verbose=-1)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate RandomizedSearch model\n",
    "model_rfrs = RandomizedSearchCV(clf,\n",
    "                                param_distributions=param_grid,\n",
    "                                n_jobs=-1,\n",
    "                                cv=5,\n",
    "                                verbose=-1)\n",
    "\n",
    "# fit our model\n",
    "model_rfrs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "373c21b3-6066-4e26-a93f-45c6b8ab9dc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'simpleimputer__strategy': 'mean',\n",
       " 'randomforestclassifier__n_estimators': 100,\n",
       " 'randomforestclassifier__max_depth': 25}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at best parameters for RandomizedSearch model\n",
    "model_rfrs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8984cc50-00a9-41d7-b763-1c6e5ef7bfff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8870313238277884"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at best score for RandomizedSearch model\n",
    "model_rfrs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5582612e-a7e6-48ab-acf2-b62b96a92fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at predictions for RandomizedSearch model\n",
    "model_rfrs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d9c1534b-aa1d-4cf0-a548-692909f14595",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plot feature importances of the better performing model (GridSearch)\n",
    "bestimator = model_rfgs.best_estimator_\n",
    "importances = bestimator.named_steps['randomforestclassifier'].feature_importances_\n",
    "features = X_train.columns\n",
    "feat_imp = pd.Series(importances, index=features).sort_values()\n",
    "feat_imp.tail(10).plot(kind='barh')\n",
    "plt.xlabel('Reduction in Gini Impurity');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86c923bc-6186-49ce-8982-d4df93705891",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tflow (python3)",
   "language": "python",
   "name": "tflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}