Project3_Main_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting H1N1 Flu Vaccination Status: A Machine Learning Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In this project, we want to use data from the National Flu Survey (NHFS 2009) to predict whether or not respondents got the H1N1 vaccine. It is important to understand past vaccination patterns in order to understand those of more recent pandemics, such as COVID-19. The most influential factors determining vaccination status are Doctor Recommendation of H1N1 vaccine, Health Insurance, opinion of H1N1 Vaccine effectiveness, opinion of H1N1 risk.\n",
    "We used six machine learning models to make the predictions. We used Decision Tree Classifier, Logistic Regression, Random Forest, K-Nearest Neighborhood Classifier, Gradient Boosting Classifier, and XG Boosting Classifier. The Gradient Boosting Classifier yielded the best accuracy and precision score.\n",
    "\n",
    "\n",
    "## Business Problem\n",
    "\n",
    "Vaccination, as one of the most successful public health interventions to ever be implemented, led to the elimination and control of diseases that were once common throughout the world. Despite overwhelming medical evidence and the unanimous position of medical professionals in favor of vaccination, the number of people skeptical about vaccination has grown in many countries in recent years. The consequence of this alarming trend is a decline in immunization coverage. Several outbreaks of infectious diseases have been linked to undervaccinated communities. The problem of expanding vaccine rejection has begun to be so widespread that it became the subject of many studies aimed at understanding the sources and correlations of attitudes toward vaccination.\n",
    "\n",
    "The aim of this study is to predict how likely individuals are to receive their H1N1 flu vaccine. We believe the prediction outputs (model and analysis) of this study will give public health professionals and policy makers, as an end user, a clear understanding of factors associated with low vaccination rates. This in turn, enables end users to systematically act on those features hindering people to get vaccinated. \n",
    "\n",
    "The methodologies used in these models can be used as a reference for future work, and also can be compared with different models for performance evaluation. In cognizant of the data type we have, and our objectives, multiple machine learning classification models, such as Logistic regression, Decision tree, Random forest, kNN, Gradient Boosting, and XGBoosting are implemented. \n",
    "\n",
    "So as to classify exactly those who got H1N1 flu shot from those that did not, we need a higher accuracy of the model outputs, as well as a high precision score, which is related to a low false positive rate(those who considered vaccinated but did actually not get h1n1 flu shot). This will be further assessed using the ROC curve, accuracy score, precision score, and confusion matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Business Understanding\n",
    "\n",
    "**End Users**: Public health officials of the American Public Health Association (APHA)\n",
    "\n",
    "**True business problem**:\n",
    "- Predicting who is vaccinated or not accurately? (Deliverable: Model)\n",
    "- What are the factors that influence people get H1N1 vaccine or not?  (Deliverable: Analysis)\n",
    "\n",
    "\n",
    "**Context**:\n",
    "- False negative: Saying people did not get the vaccine when they actually did. \n",
    "- Outcome: Not a big problem\n",
    "\n",
    "- False positive: Saying people got the vaccine when they actually did not. \n",
    "- Outcome: Big problem\n",
    "\n",
    "**Evaluation**:\n",
    "We will focus on accuracy, f1, and precision scores for our model iterations in order to minimize False Positives, because in our business context false positives are a much more costly mistake than false negatives.\n",
    "\n",
    "- **Accuracy**\n",
    "- **Precision**\n",
    "- Recall\n",
    "- **F1-Score**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Relevant Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "import category_encoders as ce\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_validate, cross_val_score\n",
    "\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
    "from sklearn.metrics import roc_auc_score, plot_roc_curve, plot_confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "import xgboost     # extreme gradient boosting\n",
    "\n",
    "# To visualize the 100 many the columns in data\n",
    "pd.options.display.max_columns=100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following evaluate function is copied from (https://github.com/lindseyberlin/Cat-in-the-Dat-Project/blob/main/notebooks/Lindsey/EDA-Initial-Models.ipynb). \n",
    "\n",
    "We will use this function to evaluate the performance of our models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(estimator, X_tr, X_te, y_tr, y_te, roc_auc='skip'):\n",
    "    '''\n",
    "    Evaluation function to show a few scores for both the train and test set\n",
    "    Also shows a confusion matrix for the test set\n",
    "    \n",
    "    roc_auc allows you to set how to calculate the roc_auc score: \n",
    "    'dec' for decision_function or 'proba' for predict_proba \n",
    "    If roc_auc == 'skip', then it ignores calculating the roc_auc_score\n",
    "    \n",
    "    Inputs:\n",
    "        estimator: a fit sklearn-style model or pipeline\n",
    "        X_tr: array or pandas dataframe\n",
    "            training input variables\n",
    "        X_te: array or pandas dataframe\n",
    "            testing input variables\n",
    "        y_tr: array or pandas series\n",
    "            training output variable\n",
    "        y_te: array or pandas series\n",
    "            testing output variable\n",
    "        roc_auc: str\n",
    "            'skip': default, skips calculating roc_auc\n",
    "            'dec': use decision_function to calculate roc_auc\n",
    "            'proba': use predict_proba to calculate roc_auc\n",
    "    '''\n",
    "    # Grab predictions\n",
    "    tr_preds = estimator.predict(X_tr)\n",
    "    te_preds = estimator.predict(X_te)\n",
    "    \n",
    "    # output needed for roc_auc_score\n",
    "    if roc_auc == 'skip': # skips calculating the roc_auc_score\n",
    "        train_out = False\n",
    "        test_out = False\n",
    "    elif roc_auc == 'dec': \n",
    "        train_out = estimator.decision_function(X_tr)\n",
    "        test_out = estimator.decision_function(X_te)\n",
    "    elif roc_auc == 'proba': \n",
    "        train_out = estimator.predict_proba(X_tr)[:, 1] # proba for the 1 class\n",
    "        test_out = estimator.predict_proba(X_te)[:, 1]\n",
    "    else:\n",
    "        raise Exception(\"The value for roc_auc should be 'skip', 'dec' or 'proba'\")    \n",
    "    \n",
    "    print(\"Training Scores:\")\n",
    "    print(f\"Train Accuracy: {accuracy_score(y_tr, tr_preds)}\")\n",
    "    print(f\"Train Precision: {precision_score(y_tr, tr_preds)}\")\n",
    "    print(f\"Train Recall: {recall_score(y_tr, tr_preds)}\")\n",
    "    print(f\"Train F1-Score: {f1_score(y_tr, tr_preds)}\")\n",
    "    if type(train_out) == np.ndarray: # checking for roc_auc\n",
    "        print(f\"ROC-AUC: {roc_auc_score(y_train, train_out)}\")\n",
    "        \n",
    "    print(\"*\" * 10)\n",
    "    print(\"Testing Scores:\")\n",
    "    print(f\"Test Accuracy: {accuracy_score(y_te, te_preds)}\")\n",
    "    print(f\"Test Precision: {precision_score(y_te, te_preds)}\")\n",
    "    print(f\"Test Recall: {recall_score(y_te, te_preds)}\")\n",
    "    print(f\"Test F1-Score: {f1_score(y_te, te_preds)}\")\n",
    "    if type(test_out) == np.ndarray: # checking for roc_auc\n",
    "        print(f\"ROC-AUC: {roc_auc_score(y_test, test_out)}\")    \n",
    "    \n",
    "    # Plot confusion matrix for test set\n",
    "    plot_confusion_matrix(estimator, X_te, y_te, cmap=\"plasma\", normalize='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding\n",
    "\n",
    "This data comes from a NHFS National Flu Survey from 2009, which inquires about whether or not people received the seasonal flu and/or the H1N1 flu vaccination, as well as their demographic, behavioral, and health factors. There are 26,000 respondents to this survey. In this project we chose H1N1 vaccination rate as our target variable. We used all features in the survey, and filled missing values using the Iterative Imputer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LabelsandFeatures.txt     training_set_features.csv\r\n",
      "test_set_features.csv     training_set_labels.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   respondent_id  h1n1_concern  h1n1_knowledge  behavioral_antiviral_meds  \\\n",
       "0              0           1.0             0.0                        0.0   \n",
       "1              1           3.0             2.0                        0.0   \n",
       "2              2           1.0             1.0                        0.0   \n",
       "3              3           1.0             1.0                        0.0   \n",
       "4              4           2.0             1.0                        0.0   \n",
       "\n",
       "   behavioral_avoidance  behavioral_face_mask  behavioral_wash_hands  \\\n",
       "0                   0.0                   0.0                    0.0   \n",
       "1                   1.0                   0.0                    1.0   \n",
       "2                   1.0                   0.0                    0.0   \n",
       "3                   1.0                   0.0                    1.0   \n",
       "4                   1.0                   0.0                    1.0   \n",
       "\n",
       "   behavioral_large_gatherings  behavioral_outside_home  \\\n",
       "0                          0.0                      1.0   \n",
       "1                          0.0                      1.0   \n",
       "2                          0.0                      0.0   \n",
       "3                          1.0                      0.0   \n",
       "4                          1.0                      0.0   \n",
       "\n",
       "   behavioral_touch_face  doctor_recc_h1n1  doctor_recc_seasonal  \\\n",
       "0                    1.0               0.0                   0.0   \n",
       "1                    1.0               0.0                   0.0   \n",
       "2                    0.0               NaN                   NaN   \n",
       "3                    0.0               0.0                   1.0   \n",
       "4                    1.0               0.0                   0.0   \n",
       "\n",
       "   chronic_med_condition  child_under_6_months  health_worker  \\\n",
       "0                    0.0                   0.0            0.0   \n",
       "1                    0.0                   0.0            0.0   \n",
       "2                    1.0                   0.0            0.0   \n",
       "3                    1.0                   0.0            0.0   \n",
       "4                    0.0                   0.0            0.0   \n",
       "\n",
       "   health_insurance  opinion_h1n1_vacc_effective  opinion_h1n1_risk  \\\n",
       "0               1.0                          3.0                1.0   \n",
       "1               1.0                          5.0                4.0   \n",
       "2               NaN                          3.0                1.0   \n",
       "3               NaN                          3.0                3.0   \n",
       "4               NaN                          3.0                3.0   \n",
       "\n",
       "   opinion_h1n1_sick_from_vacc  opinion_seas_vacc_effective  \\\n",
       "0                          2.0                          2.0   \n",
       "1                          4.0                          4.0   \n",
       "2                          1.0                          4.0   \n",
       "3                          5.0                          5.0   \n",
       "4                          2.0                          3.0   \n",
       "\n",
       "   opinion_seas_risk  opinion_seas_sick_from_vacc      age_group  \\\n",
       "0                1.0                          2.0  55 - 64 Years   \n",
       "1                2.0                          4.0  35 - 44 Years   \n",
       "2                1.0                          2.0  18 - 34 Years   \n",
       "3                4.0                          1.0      65+ Years   \n",
       "4                1.0                          4.0  45 - 54 Years   \n",
       "\n",
       "          education   race     sex             income_poverty marital_status  \\\n",
       "0        < 12 Years  White  Female              Below Poverty    Not Married   \n",
       "1          12 Years  White    Male              Below Poverty    Not Married   \n",
       "2  College Graduate  White    Male  <= $75,000, Above Poverty    Not Married   \n",
       "3          12 Years  White  Female              Below Poverty    Not Married   \n",
       "4      Some College  White  Female  <= $75,000, Above Poverty        Married   \n",
       "\n",
       "  rent_or_own   employment_status hhs_geo_region                census_msa  \\\n",
       "0         Own  Not in Labor Force       oxchjgsf                   Non-MSA   \n",
       "1        Rent            Employed       bhuqouqj  MSA, Not Principle  City   \n",
       "2         Own            Employed       qufhixun  MSA, Not Principle  City   \n",
       "3        Rent  Not in Labor Force       lrircsnp       MSA, Principle City   \n",
       "4         Own            Employed       qufhixun  MSA, Not Principle  City   \n",
       "\n",
       "   household_adults  household_children employment_industry  \\\n",
       "0               0.0                 0.0                 NaN   \n",
       "1               0.0                 0.0            pxcmvdjn   \n",
       "2               2.0                 0.0            rucpziij   \n",
       "3               0.0                 0.0                 NaN   \n",
       "4               1.0                 0.0            wxleyezf   \n",
       "\n",
       "  employment_occupation  \n",
       "0                   NaN  \n",
       "1              xgwztkwe  \n",
       "2              xtkaffoo  \n",
       "3                   NaN  \n",
       "4              emcorrxb  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading in the feature data\n",
    "data1 = pd.read_csv('Data/training_set_features.csv')\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>respondent_id</th>\n",
       "      <th>h1n1_vaccine</th>\n",
       "      <th>seasonal_vaccine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   respondent_id  h1n1_vaccine  seasonal_vaccine\n",
       "0              0             0                 0\n",
       "1              1             0                 1\n",
       "2              2             0                 0\n",
       "3              3             0                 1\n",
       "4              4             0                 0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading in the target data\n",
    "data2 = pd.read_csv('Data/training_set_labels.csv')\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.534392\n",
       "1    0.465608\n",
       "Name: seasonal_vaccine, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the percentage breakdown of the two classes in one possible target (seasonal flu rate)\n",
    "data2['seasonal_vaccine'].value_counts(normalize=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.787546\n",
       "1    0.212454\n",
       "Name: h1n1_vaccine, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the percentage breakdown of the two classes in one possible target (H1N1 flu rate)\n",
    "data2['h1n1_vaccine'].value_counts(normalize=True)   # class imbalance problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>respondent_id</th>\n",
       "      <th>h1n1_concern</th>\n",
       "      <th>h1n1_knowledge</th>\n",
       "      <th>behavioral_antiviral_meds</th>\n",
       "      <th>behavioral_avoidance</th>\n",
       "      <th>behavioral_face_mask</th>\n",
       "      <th>behavioral_wash_hands</th>\n",
       "      <th>behavioral_large_gatherings</th>\n",
       "      <th>behavioral_outside_home</th>\n",
       "      <th>behavioral_touch_face</th>\n",
       "      <th>doctor_recc_h1n1</th>\n",
       "      <th>doctor_recc_seasonal</th>\n",
       "      <th>chronic_med_condition</th>\n",
       "      <th>child_under_6_months</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>age_group</th>\n",
       "      <th>education</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</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",
       "      <th>h1n1_vaccine</th>\n",
       "      <th>seasonal_vaccine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>0.0</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>55 - 64 Years</td>\n",
       "      <td>&lt; 12 Years</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>Below Poverty</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Not in Labor Force</td>\n",
       "      <td>oxchjgsf</td>\n",
       "      <td>Non-MSA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35 - 44 Years</td>\n",
       "      <td>12 Years</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Below Poverty</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Employed</td>\n",
       "      <td>bhuqouqj</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>pxcmvdjn</td>\n",
       "      <td>xgwztkwe</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18 - 34 Years</td>\n",
       "      <td>College Graduate</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>&lt;= $75,000, Above Poverty</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Employed</td>\n",
       "      <td>qufhixun</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>rucpziij</td>\n",
       "      <td>xtkaffoo</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65+ Years</td>\n",
       "      <td>12 Years</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>Below Poverty</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Not in Labor Force</td>\n",
       "      <td>lrircsnp</td>\n",
       "      <td>MSA, Principle City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45 - 54 Years</td>\n",
       "      <td>Some College</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>&lt;= $75,000, Above Poverty</td>\n",
       "      <td>Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Employed</td>\n",
       "      <td>qufhixun</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>wxleyezf</td>\n",
       "      <td>emcorrxb</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   respondent_id  h1n1_concern  h1n1_knowledge  behavioral_antiviral_meds  \\\n",
       "0              0           1.0             0.0                        0.0   \n",
       "1              1           3.0             2.0                        0.0   \n",
       "2              2           1.0             1.0                        0.0   \n",
       "3              3           1.0             1.0                        0.0   \n",
       "4              4           2.0             1.0                        0.0   \n",
       "\n",
       "   behavioral_avoidance  behavioral_face_mask  behavioral_wash_hands  \\\n",
       "0                   0.0                   0.0                    0.0   \n",
       "1                   1.0                   0.0                    1.0   \n",
       "2                   1.0                   0.0                    0.0   \n",
       "3                   1.0                   0.0                    1.0   \n",
       "4                   1.0                   0.0                    1.0   \n",
       "\n",
       "   behavioral_large_gatherings  behavioral_outside_home  \\\n",
       "0                          0.0                      1.0   \n",
       "1                          0.0                      1.0   \n",
       "2                          0.0                      0.0   \n",
       "3                          1.0                      0.0   \n",
       "4                          1.0                      0.0   \n",
       "\n",
       "   behavioral_touch_face  doctor_recc_h1n1  doctor_recc_seasonal  \\\n",
       "0                    1.0               0.0                   0.0   \n",
       "1                    1.0               0.0                   0.0   \n",
       "2                    0.0               NaN                   NaN   \n",
       "3                    0.0               0.0                   1.0   \n",
       "4                    1.0               0.0                   0.0   \n",
       "\n",
       "   chronic_med_condition  child_under_6_months  health_worker  \\\n",
       "0                    0.0                   0.0            0.0   \n",
       "1                    0.0                   0.0            0.0   \n",
       "2                    1.0                   0.0            0.0   \n",
       "3                    1.0                   0.0            0.0   \n",
       "4                    0.0                   0.0            0.0   \n",
       "\n",
       "   health_insurance  opinion_h1n1_vacc_effective  opinion_h1n1_risk  \\\n",
       "0               1.0                          3.0                1.0   \n",
       "1               1.0                          5.0                4.0   \n",
       "2               NaN                          3.0                1.0   \n",
       "3               NaN                          3.0                3.0   \n",
       "4               NaN                          3.0                3.0   \n",
       "\n",
       "   opinion_h1n1_sick_from_vacc  opinion_seas_vacc_effective  \\\n",
       "0                          2.0                          2.0   \n",
       "1                          4.0                          4.0   \n",
       "2                          1.0                          4.0   \n",
       "3                          5.0                          5.0   \n",
       "4                          2.0                          3.0   \n",
       "\n",
       "   opinion_seas_risk  opinion_seas_sick_from_vacc      age_group  \\\n",
       "0                1.0                          2.0  55 - 64 Years   \n",
       "1                2.0                          4.0  35 - 44 Years   \n",
       "2                1.0                          2.0  18 - 34 Years   \n",
       "3                4.0                          1.0      65+ Years   \n",
       "4                1.0                          4.0  45 - 54 Years   \n",
       "\n",
       "          education   race     sex             income_poverty marital_status  \\\n",
       "0        < 12 Years  White  Female              Below Poverty    Not Married   \n",
       "1          12 Years  White    Male              Below Poverty    Not Married   \n",
       "2  College Graduate  White    Male  <= $75,000, Above Poverty    Not Married   \n",
       "3          12 Years  White  Female              Below Poverty    Not Married   \n",
       "4      Some College  White  Female  <= $75,000, Above Poverty        Married   \n",
       "\n",
       "  rent_or_own   employment_status hhs_geo_region                census_msa  \\\n",
       "0         Own  Not in Labor Force       oxchjgsf                   Non-MSA   \n",
       "1        Rent            Employed       bhuqouqj  MSA, Not Principle  City   \n",
       "2         Own            Employed       qufhixun  MSA, Not Principle  City   \n",
       "3        Rent  Not in Labor Force       lrircsnp       MSA, Principle City   \n",
       "4         Own            Employed       qufhixun  MSA, Not Principle  City   \n",
       "\n",
       "   household_adults  household_children employment_industry  \\\n",
       "0               0.0                 0.0                 NaN   \n",
       "1               0.0                 0.0            pxcmvdjn   \n",
       "2               2.0                 0.0            rucpziij   \n",
       "3               0.0                 0.0                 NaN   \n",
       "4               1.0                 0.0            wxleyezf   \n",
       "\n",
       "  employment_occupation  h1n1_vaccine  seasonal_vaccine  \n",
       "0                   NaN             0                 0  \n",
       "1              xgwztkwe             0                 1  \n",
       "2              xtkaffoo             0                 0  \n",
       "3                   NaN             0                 1  \n",
       "4              emcorrxb             0                 0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging feature and target data\n",
    "df = data1.merge(data2, how='left', on='respondent_id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26707 entries, 0 to 26706\n",
      "Data columns (total 38 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   respondent_id                26707 non-null  int64  \n",
      " 1   h1n1_concern                 26615 non-null  float64\n",
      " 2   h1n1_knowledge               26591 non-null  float64\n",
      " 3   behavioral_antiviral_meds    26636 non-null  float64\n",
      " 4   behavioral_avoidance         26499 non-null  float64\n",
      " 5   behavioral_face_mask         26688 non-null  float64\n",
      " 6   behavioral_wash_hands        26665 non-null  float64\n",
      " 7   behavioral_large_gatherings  26620 non-null  float64\n",
      " 8   behavioral_outside_home      26625 non-null  float64\n",
      " 9   behavioral_touch_face        26579 non-null  float64\n",
      " 10  doctor_recc_h1n1             24547 non-null  float64\n",
      " 11  doctor_recc_seasonal         24547 non-null  float64\n",
      " 12  chronic_med_condition        25736 non-null  float64\n",
      " 13  child_under_6_months         25887 non-null  float64\n",
      " 14  health_worker                25903 non-null  float64\n",
      " 15  health_insurance             14433 non-null  float64\n",
      " 16  opinion_h1n1_vacc_effective  26316 non-null  float64\n",
      " 17  opinion_h1n1_risk            26319 non-null  float64\n",
      " 18  opinion_h1n1_sick_from_vacc  26312 non-null  float64\n",
      " 19  opinion_seas_vacc_effective  26245 non-null  float64\n",
      " 20  opinion_seas_risk            26193 non-null  float64\n",
      " 21  opinion_seas_sick_from_vacc  26170 non-null  float64\n",
      " 22  age_group                    26707 non-null  object \n",
      " 23  education                    25300 non-null  object \n",
      " 24  race                         26707 non-null  object \n",
      " 25  sex                          26707 non-null  object \n",
      " 26  income_poverty               22284 non-null  object \n",
      " 27  marital_status               25299 non-null  object \n",
      " 28  rent_or_own                  24665 non-null  object \n",
      " 29  employment_status            25244 non-null  object \n",
      " 30  hhs_geo_region               26707 non-null  object \n",
      " 31  census_msa                   26707 non-null  object \n",
      " 32  household_adults             26458 non-null  float64\n",
      " 33  household_children           26458 non-null  float64\n",
      " 34  employment_industry          13377 non-null  object \n",
      " 35  employment_occupation        13237 non-null  object \n",
      " 36  h1n1_vaccine                 26707 non-null  int64  \n",
      " 37  seasonal_vaccine             26707 non-null  int64  \n",
      "dtypes: float64(23), int64(3), object(12)\n",
      "memory usage: 7.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Getting info about null values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "respondent_id                      0\n",
       "h1n1_concern                      92\n",
       "h1n1_knowledge                   116\n",
       "behavioral_antiviral_meds         71\n",
       "behavioral_avoidance             208\n",
       "behavioral_face_mask              19\n",
       "behavioral_wash_hands             42\n",
       "behavioral_large_gatherings       87\n",
       "behavioral_outside_home           82\n",
       "behavioral_touch_face            128\n",
       "doctor_recc_h1n1                2160\n",
       "doctor_recc_seasonal            2160\n",
       "chronic_med_condition            971\n",
       "child_under_6_months             820\n",
       "health_worker                    804\n",
       "health_insurance               12274\n",
       "opinion_h1n1_vacc_effective      391\n",
       "opinion_h1n1_risk                388\n",
       "opinion_h1n1_sick_from_vacc      395\n",
       "opinion_seas_vacc_effective      462\n",
       "opinion_seas_risk                514\n",
       "opinion_seas_sick_from_vacc      537\n",
       "age_group                          0\n",
       "education                       1407\n",
       "race                               0\n",
       "sex                                0\n",
       "income_poverty                  4423\n",
       "marital_status                  1408\n",
       "rent_or_own                     2042\n",
       "employment_status               1463\n",
       "hhs_geo_region                     0\n",
       "census_msa                         0\n",
       "household_adults                 249\n",
       "household_children               249\n",
       "employment_industry            13330\n",
       "employment_occupation          13470\n",
       "h1n1_vaccine                       0\n",
       "seasonal_vaccine                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting number of null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>respondent_id</th>\n",
       "      <th>h1n1_concern</th>\n",
       "      <th>h1n1_knowledge</th>\n",
       "      <th>behavioral_antiviral_meds</th>\n",
       "      <th>behavioral_avoidance</th>\n",
       "      <th>behavioral_face_mask</th>\n",
       "      <th>behavioral_wash_hands</th>\n",
       "      <th>behavioral_large_gatherings</th>\n",
       "      <th>behavioral_outside_home</th>\n",
       "      <th>behavioral_touch_face</th>\n",
       "      <th>doctor_recc_h1n1</th>\n",
       "      <th>doctor_recc_seasonal</th>\n",
       "      <th>chronic_med_condition</th>\n",
       "      <th>child_under_6_months</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",
       "      <th>h1n1_vaccine</th>\n",
       "      <th>seasonal_vaccine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26707.000000</td>\n",
       "      <td>26615.000000</td>\n",
       "      <td>26591.000000</td>\n",
       "      <td>26636.000000</td>\n",
       "      <td>26499.000000</td>\n",
       "      <td>26688.000000</td>\n",
       "      <td>26665.000000</td>\n",
       "      <td>26620.00000</td>\n",
       "      <td>26625.000000</td>\n",
       "      <td>26579.000000</td>\n",
       "      <td>24547.000000</td>\n",
       "      <td>24547.000000</td>\n",
       "      <td>25736.000000</td>\n",
       "      <td>25887.000000</td>\n",
       "      <td>25903.000000</td>\n",
       "      <td>14433.00000</td>\n",
       "      <td>26316.000000</td>\n",
       "      <td>26319.000000</td>\n",
       "      <td>26312.000000</td>\n",
       "      <td>26245.000000</td>\n",
       "      <td>26193.000000</td>\n",
       "      <td>26170.000000</td>\n",
       "      <td>26458.000000</td>\n",
       "      <td>26458.000000</td>\n",
       "      <td>26707.000000</td>\n",
       "      <td>26707.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13353.000000</td>\n",
       "      <td>1.618486</td>\n",
       "      <td>1.262532</td>\n",
       "      <td>0.048844</td>\n",
       "      <td>0.725612</td>\n",
       "      <td>0.068982</td>\n",
       "      <td>0.825614</td>\n",
       "      <td>0.35864</td>\n",
       "      <td>0.337315</td>\n",
       "      <td>0.677264</td>\n",
       "      <td>0.220312</td>\n",
       "      <td>0.329735</td>\n",
       "      <td>0.283261</td>\n",
       "      <td>0.082590</td>\n",
       "      <td>0.111918</td>\n",
       "      <td>0.87972</td>\n",
       "      <td>3.850623</td>\n",
       "      <td>2.342566</td>\n",
       "      <td>2.357670</td>\n",
       "      <td>4.025986</td>\n",
       "      <td>2.719162</td>\n",
       "      <td>2.118112</td>\n",
       "      <td>0.886499</td>\n",
       "      <td>0.534583</td>\n",
       "      <td>0.212454</td>\n",
       "      <td>0.465608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7709.791156</td>\n",
       "      <td>0.910311</td>\n",
       "      <td>0.618149</td>\n",
       "      <td>0.215545</td>\n",
       "      <td>0.446214</td>\n",
       "      <td>0.253429</td>\n",
       "      <td>0.379448</td>\n",
       "      <td>0.47961</td>\n",
       "      <td>0.472802</td>\n",
       "      <td>0.467531</td>\n",
       "      <td>0.414466</td>\n",
       "      <td>0.470126</td>\n",
       "      <td>0.450591</td>\n",
       "      <td>0.275266</td>\n",
       "      <td>0.315271</td>\n",
       "      <td>0.32530</td>\n",
       "      <td>1.007436</td>\n",
       "      <td>1.285539</td>\n",
       "      <td>1.362766</td>\n",
       "      <td>1.086565</td>\n",
       "      <td>1.385055</td>\n",
       "      <td>1.332950</td>\n",
       "      <td>0.753422</td>\n",
       "      <td>0.928173</td>\n",
       "      <td>0.409052</td>\n",
       "      <td>0.498825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6676.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13353.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20029.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26706.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       respondent_id  h1n1_concern  h1n1_knowledge  behavioral_antiviral_meds  \\\n",
       "count   26707.000000  26615.000000    26591.000000               26636.000000   \n",
       "mean    13353.000000      1.618486        1.262532                   0.048844   \n",
       "std      7709.791156      0.910311        0.618149                   0.215545   \n",
       "min         0.000000      0.000000        0.000000                   0.000000   \n",
       "25%      6676.500000      1.000000        1.000000                   0.000000   \n",
       "50%     13353.000000      2.000000        1.000000                   0.000000   \n",
       "75%     20029.500000      2.000000        2.000000                   0.000000   \n",
       "max     26706.000000      3.000000        2.000000                   1.000000   \n",
       "\n",
       "       behavioral_avoidance  behavioral_face_mask  behavioral_wash_hands  \\\n",
       "count          26499.000000          26688.000000           26665.000000   \n",
       "mean               0.725612              0.068982               0.825614   \n",
       "std                0.446214              0.253429               0.379448   \n",
       "min                0.000000              0.000000               0.000000   \n",
       "25%                0.000000              0.000000               1.000000   \n",
       "50%                1.000000              0.000000               1.000000   \n",
       "75%                1.000000              0.000000               1.000000   \n",
       "max                1.000000              1.000000               1.000000   \n",
       "\n",
       "       behavioral_large_gatherings  behavioral_outside_home  \\\n",
       "count                  26620.00000             26625.000000   \n",
       "mean                       0.35864                 0.337315   \n",
       "std                        0.47961                 0.472802   \n",
       "min                        0.00000                 0.000000   \n",
       "25%                        0.00000                 0.000000   \n",
       "50%                        0.00000                 0.000000   \n",
       "75%                        1.00000                 1.000000   \n",
       "max                        1.00000                 1.000000   \n",
       "\n",
       "       behavioral_touch_face  doctor_recc_h1n1  doctor_recc_seasonal  \\\n",
       "count           26579.000000      24547.000000          24547.000000   \n",
       "mean                0.677264          0.220312              0.329735   \n",
       "std                 0.467531          0.414466              0.470126   \n",
       "min                 0.000000          0.000000              0.000000   \n",
       "25%                 0.000000          0.000000              0.000000   \n",
       "50%                 1.000000          0.000000              0.000000   \n",
       "75%                 1.000000          0.000000              1.000000   \n",
       "max                 1.000000          1.000000              1.000000   \n",
       "\n",
       "       chronic_med_condition  child_under_6_months  health_worker  \\\n",
       "count           25736.000000          25887.000000   25903.000000   \n",
       "mean                0.283261              0.082590       0.111918   \n",
       "std                 0.450591              0.275266       0.315271   \n",
       "min                 0.000000              0.000000       0.000000   \n",
       "25%                 0.000000              0.000000       0.000000   \n",
       "50%                 0.000000              0.000000       0.000000   \n",
       "75%                 1.000000              0.000000       0.000000   \n",
       "max                 1.000000              1.000000       1.000000   \n",
       "\n",
       "       health_insurance  opinion_h1n1_vacc_effective  opinion_h1n1_risk  \\\n",
       "count       14433.00000                 26316.000000       26319.000000   \n",
       "mean            0.87972                     3.850623           2.342566   \n",
       "std             0.32530                     1.007436           1.285539   \n",
       "min             0.00000                     1.000000           1.000000   \n",
       "25%             1.00000                     3.000000           1.000000   \n",
       "50%             1.00000                     4.000000           2.000000   \n",
       "75%             1.00000                     5.000000           4.000000   \n",
       "max             1.00000                     5.000000           5.000000   \n",
       "\n",
       "       opinion_h1n1_sick_from_vacc  opinion_seas_vacc_effective  \\\n",
       "count                 26312.000000                 26245.000000   \n",
       "mean                      2.357670                     4.025986   \n",
       "std                       1.362766                     1.086565   \n",
       "min                       1.000000                     1.000000   \n",
       "25%                       1.000000                     4.000000   \n",
       "50%                       2.000000                     4.000000   \n",
       "75%                       4.000000                     5.000000   \n",
       "max                       5.000000                     5.000000   \n",
       "\n",
       "       opinion_seas_risk  opinion_seas_sick_from_vacc  household_adults  \\\n",
       "count       26193.000000                 26170.000000      26458.000000   \n",
       "mean            2.719162                     2.118112          0.886499   \n",
       "std             1.385055                     1.332950          0.753422   \n",
       "min             1.000000                     1.000000          0.000000   \n",
       "25%             2.000000                     1.000000          0.000000   \n",
       "50%             2.000000                     2.000000          1.000000   \n",
       "75%             4.000000                     4.000000          1.000000   \n",
       "max             5.000000                     5.000000          3.000000   \n",
       "\n",
       "       household_children  h1n1_vaccine  seasonal_vaccine  \n",
       "count        26458.000000  26707.000000      26707.000000  \n",
       "mean             0.534583      0.212454          0.465608  \n",
       "std              0.928173      0.409052          0.498825  \n",
       "min              0.000000      0.000000          0.000000  \n",
       "25%              0.000000      0.000000          0.000000  \n",
       "50%              0.000000      0.000000          0.000000  \n",
       "75%              1.000000      0.000000          1.000000  \n",
       "max              3.000000      1.000000          1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore numerical columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>age_group</th>\n",
       "      <th>education</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</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>employment_industry</th>\n",
       "      <th>employment_occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26707</td>\n",
       "      <td>25300</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>22284</td>\n",
       "      <td>25299</td>\n",
       "      <td>24665</td>\n",
       "      <td>25244</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>13377</td>\n",
       "      <td>13237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>65+ Years</td>\n",
       "      <td>College Graduate</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>&lt;= $75,000, Above Poverty</td>\n",
       "      <td>Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Employed</td>\n",
       "      <td>lzgpxyit</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>fcxhlnwr</td>\n",
       "      <td>xtkaffoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>6843</td>\n",
       "      <td>10097</td>\n",
       "      <td>21222</td>\n",
       "      <td>15858</td>\n",
       "      <td>12777</td>\n",
       "      <td>13555</td>\n",
       "      <td>18736</td>\n",
       "      <td>13560</td>\n",
       "      <td>4297</td>\n",
       "      <td>11645</td>\n",
       "      <td>2468</td>\n",
       "      <td>1778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age_group         education   race     sex             income_poverty  \\\n",
       "count       26707             25300  26707   26707                      22284   \n",
       "unique          5                 4      4       2                          3   \n",
       "top     65+ Years  College Graduate  White  Female  <= $75,000, Above Poverty   \n",
       "freq         6843             10097  21222   15858                      12777   \n",
       "\n",
       "       marital_status rent_or_own employment_status hhs_geo_region  \\\n",
       "count           25299       24665             25244          26707   \n",
       "unique              2           2                 3             10   \n",
       "top           Married         Own          Employed       lzgpxyit   \n",
       "freq            13555       18736             13560           4297   \n",
       "\n",
       "                      census_msa employment_industry employment_occupation  \n",
       "count                      26707               13377                 13237  \n",
       "unique                         3                  21                    23  \n",
       "top     MSA, Not Principle  City            fcxhlnwr              xtkaffoo  \n",
       "freq                       11645                2468                  1778  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore object columns\n",
    "df[[c for c in df.columns if df[c].dtype =='object']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided to choose the H1N1 vaccination rate as our target variable, because so many of the features are related to H1N1 vaccination. This target has a class imbalance problem which we want to deal with in this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following visualizations represent the top four most influential features in determining vaccination status for H1N1. There are more exploratory visualizations in the notebooks in the notebook folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a copy of main dataframe to use for visualizations\n",
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "# Graph for relationship between H1N1 vaccination and Doctor recommendation\n",
    "dr = sns.barplot(x=df2['doctor_recc_h1n1'].dropna(), y=df2['h1n1_vaccine']*100)\n",
    "dr.set_xlabel('')\n",
    "dr.set_ylabel('Vaccination Percentage', fontsize=13)\n",
    "dr.set_title('H1N1 Vaccination Percentage by Doctor Recommendation', fontsize=13);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dictionary for mapping in order to create better names for x axis in graphs below\n",
    "ins_dict = {1: 'Health Insurance',\n",
    "           0: 'No Health Insurance'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the column that will be used to create clear x axis tick marks in graph below\n",
    "df2['health_ins_words'] = df2['health_insurance'].replace(ins_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "# Health Insurance graph\n",
    "ins = sns.barplot(x=df2['health_ins_words'], y=df2['h1n1_vaccine']*100)\n",
    "ins.set_xlabel('')\n",
    "ins.set_ylabel('Vaccination Percentage', fontsize=13)\n",
    "ins.set_title('H1N1 Vaccination Percentage by Health Insurance', fontsize=13);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "# Graph for vaccine percentage differrentiation depending on opinion of effectiveness of H1N1 Vaccine\n",
    "ins = sns.barplot(x=df2['opinion_h1n1_vacc_effective'].dropna(), y=df2['h1n1_vaccine']*100)\n",
    "ins.set_xlabel('Belief in Effectivenes of Vaccine')\n",
    "ins.set_ylabel('Vaccination Percentage', fontsize=13)\n",
    "ins.set_title('H1N1 Vaccination Percentage by Effectiveness Belief', fontsize=13);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": [
    "# Risk graph exploring vaccine percentage depending on risk perception of H1N1\n",
    "ins = sns.barplot(x=df2['opinion_h1n1_risk'].dropna(), y=df2['h1n1_vaccine']*100)\n",
    "ins.set_xlabel('Risk perception of contracting H1N1')\n",
    "ins.set_ylabel('Vaccination Percentage', fontsize=13)\n",
    "ins.set_title('H1N1 Vaccination Percentage by risk perception of H1N1', fontsize=13);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "\n",
    "There were a few changes we made to the data set. First, we dropped the \"respondent_id\" and \"seasonal_vaccine\" columns because they were not relevant for our purposes. We also added columns due to categorical columns that we transformed with OneHotEncoder. We also filled null values with Iterative Imputer, which was a better alternative to simple imputer for our dataset. We replaced category names with frequency counts with CountEncoder for the columns which had more than 10 unique categories. We used pipelines to make preprocessing and modelling more efficient, and also to prevent data leakage. We also decided to split training and testing data twice so that we could have a holdout set to test our final model's generalizability at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our X and y\n",
    "X = df.drop(columns = ['respondent_id', 'h1n1_vaccine', 'seasonal_vaccine'], axis=1)\n",
    "y = df['h1n1_vaccine']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train - Holdout Set Split\n",
    "X_train, X_hold, y_train, y_hold = train_test_split(X, y, test_size=0.1, random_state=42, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regular Train Test Split\n",
    "X_tr, X_te, y_tr, y_te = train_test_split(X_train, y_train, test_size=0.2, random_state=42, stratify=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used the stratify argument for y (our target) in both splits to help deal with the class imbalance problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up lists for each columns datatypes\n",
    "num_cols = []\n",
    "ohe_cols = []\n",
    "freq_cols = []\n",
    "\n",
    "for c in X.columns:\n",
    "    if X[c].dtype in ['float64', 'int64']:\n",
    "        num_cols.append(c)\n",
    "    elif X[c].nunique() < 10:\n",
    "        ohe_cols.append(c)\n",
    "    else:\n",
    "        freq_cols.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Columns: ['h1n1_concern', 'h1n1_knowledge', 'behavioral_antiviral_meds', 'behavioral_avoidance', 'behavioral_face_mask', 'behavioral_wash_hands', 'behavioral_large_gatherings', 'behavioral_outside_home', 'behavioral_touch_face', 'doctor_recc_h1n1', 'doctor_recc_seasonal', 'chronic_med_condition', 'child_under_6_months', 'health_worker', 'health_insurance', 'opinion_h1n1_vacc_effective', 'opinion_h1n1_risk', 'opinion_h1n1_sick_from_vacc', 'opinion_seas_vacc_effective', 'opinion_seas_risk', 'opinion_seas_sick_from_vacc', 'household_adults', 'household_children']\n",
      "\n",
      "\n",
      "Object Columns (with less than 10 unique values): ['age_group', 'education', 'race', 'sex', 'income_poverty', 'marital_status', 'rent_or_own', 'employment_status', 'census_msa']\n",
      "\n",
      "\n",
      "Object Columns (with more than 10 unique values): ['hhs_geo_region', 'employment_industry', 'employment_occupation']\n"
     ]
    }
   ],
   "source": [
    "# We wanted to see each column category\n",
    "print(f'Numerical Columns:', num_cols)\n",
    "print('\\n')\n",
    "print(f'Object Columns (with less than 10 unique values):', ohe_cols)\n",
    "print('\\n')\n",
    "print(f'Object Columns (with more than 10 unique values):', freq_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing with Pipelines\n",
    "num_transformer = Pipeline(steps=[\n",
    "    ('num_imputer', IterativeImputer(max_iter=100, random_state=42)),   # replace missing values by IterativeImputer\n",
    "    ('minmaxscaler', MinMaxScaler())                                    # Scale the numbers by MinMaxScaler\n",
    "])\n",
    "\n",
    "ohe_transformer = Pipeline(steps=[\n",
    "    ('ohe_imputer', SimpleImputer(strategy='constant', fill_value='Unknown')),  # replace missing categories by SimpleImputer\n",
    "    ('ohe_encoder', OneHotEncoder(handle_unknown='ignore'))                     # Transform each category into a new column\n",
    "])\n",
    "\n",
    "freq_transformer = Pipeline(steps=[\n",
    "    ('freq_encoder', ce.count.CountEncoder(normalize=True, min_group_size=.05)), # replace category names with frequency counts by CountEncoder\n",
    "    ('freq_imputer', IterativeImputer(max_iter=100, random_state=42))            # replace missing values by IterativeImputer\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessor defined using ColumnTransformer by packaging the all components together\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', num_transformer, num_cols),\n",
    "        ('ohe', ohe_transformer, ohe_cols),\n",
    "        ('freq', freq_transformer, freq_cols)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('num_imputer',\n",
       "                                                  IterativeImputer(max_iter=100,\n",
       "                                                                   random_state=42)),\n",
       "                                                 ('minmaxscaler',\n",
       "                                                  MinMaxScaler())]),\n",
       "                                 ['h1n1_concern', '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...\n",
       "                                 ['age_group', 'education', 'race', 'sex',\n",
       "                                  'income_poverty', 'marital_status',\n",
       "                                  'rent_or_own', 'employment_status',\n",
       "                                  'census_msa']),\n",
       "                                ('freq',\n",
       "                                 Pipeline(steps=[('freq_encoder',\n",
       "                                                  CountEncoder(combine_min_nan_groups=True,\n",
       "                                                               min_group_size=0.05,\n",
       "                                                               normalize=True)),\n",
       "                                                 ('freq_imputer',\n",
       "                                                  IterativeImputer(max_iter=100,\n",
       "                                                                   random_state=42))]),\n",
       "                                 ['hhs_geo_region', 'employment_industry',\n",
       "                                  'employment_occupation'])])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting preprocessor to see the components as a whole\n",
    "preprocessor.fit(X_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19228, 59)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let'see what this looks like after the preprocessor transformation\n",
    "X_tr_transformed = preprocessor.transform(X_tr)\n",
    "X_tr_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>0.076659</td>\n",
       "      <td>0.499168</td>\n",
       "      <td>0.504213</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.016076</td>\n",
       "      <td>0.963804</td>\n",
       "      <td>0.038064</td>\n",
       "      <td>0.894508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116478</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110629</td>\n",
       "      <td>0.920662</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.296807</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.279800</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <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>1.0</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>0.160235</td>\n",
       "      <td>0.499168</td>\n",
       "      <td>0.504213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.016076</td>\n",
       "      <td>0.963804</td>\n",
       "      <td>0.038064</td>\n",
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       "      <td>0.062409</td>\n",
       "      <td>0.00</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0    1         2         3         4         5    6         7    8   \\\n",
       "0  1.000000  0.5  0.016076  0.963804  0.038064  0.894508  0.0  1.000000  0.0   \n",
       "1  0.666667  1.0  0.016076  0.000000  0.038064  0.894508  0.0  0.013143  1.0   \n",
       "2  0.333333  0.5  0.016076  0.963804  0.038064  0.894508  0.0  0.013143  0.0   \n",
       "3  0.333333  0.5  0.016076  0.963804  0.038064  0.894508  0.0  0.013143  1.0   \n",
       "4  0.333333  1.0  0.016076  0.963804  0.038064  0.894508  0.0  0.013143  1.0   \n",
       "\n",
       "         9         10   11   12        13        14    15        16    17  \\\n",
       "0  0.116478  0.141558  1.0  0.0  1.000000  0.779010  0.75  0.296807  0.75   \n",
       "1  0.116478  0.141558  0.0  0.0  0.110629  0.920662  0.75  0.765602  0.25   \n",
       "2  1.000000  1.000000  0.0  0.0  0.110629  0.920662  0.75  0.296807  0.00   \n",
       "3  0.116478  1.000000  0.0  0.0  0.110629  0.920662  0.75  0.296807  0.25   \n",
       "4  0.116478  0.141558  0.0  1.0  1.000000  0.920662  0.25  0.062409  0.00   \n",
       "\n",
       "     18        19    20        21        22   23   24   25   26   27   28  \\\n",
       "0  0.75  0.279800  0.75  0.333333  0.333333  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "1  0.75  0.759933  0.25  0.333333  0.000000  0.0  0.0  1.0  0.0  0.0  1.0   \n",
       "2  1.00  0.279800  0.00  0.333333  0.000000  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       "3  0.75  0.279800  0.25  0.000000  0.000000  0.0  0.0  0.0  0.0  1.0  1.0   \n",
       "4  0.75  0.279800  0.25  0.666667  0.333333  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "\n",
       "    29   30   31   32   33   34   35   36   37   38   39   40   41   42   43  \\\n",
       "0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  1.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0   \n",
       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       "4  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    44   45   46   47   48   49   50   51   52   53   54   55        56  \\\n",
       "0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.106407   \n",
       "1  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.160235   \n",
       "2  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.076659   \n",
       "3  1.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.160235   \n",
       "4  1.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.160235   \n",
       "\n",
       "         57        58  \n",
       "0  0.499168  0.504213  \n",
       "1  0.340961  0.371854  \n",
       "2  0.499168  0.504213  \n",
       "3  0.499168  0.504213  \n",
       "4  0.340961  0.371854  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize it with Pandas dataframe \n",
    "pd.DataFrame(X_tr_transformed).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In each model, we will put this preprocessor into the modelling pipeline to make sure null values are filled and categorical variables are transformed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "We wanted to use a variety of different models so as to find the most accurate model. Because there are many different hyperparameters for each model and we did not know the optimal combinations, we used GridSearrchCV to find the best combinations for each model. We specified class weight to be balanced in order to address the class imbalance issue for our models, whenever possible. We also analyzed the accuracy score, precision score, fl score, and roc-auc curve for each model. We also compared the different roc-auc curves of each model, to choose the final model. Additionally, we looked closely at the confusion matrix to see whether or not we were minimizing false positives. Gradient Boosting Classifier gave us the best accuracy and precision scores, so we chose it to be our final model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Understanding\n",
    "\n",
    "- Let's use DummyClassifier to make prediction based on the most frequent class in the target variable, which is 0 in our case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the up the dummy model to go through the pipeline\n",
    "dummy_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', DummyClassifier(strategy=\"most_frequent\"))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42)),\n",
       "                                                                  ('minmaxscaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['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",
       "                                                   'beha...\n",
       "                                                   'rent_or_own',\n",
       "                                                   'employment_status',\n",
       "                                                   'census_msa']),\n",
       "                                                 ('freq',\n",
       "                                                  Pipeline(steps=[('freq_encoder',\n",
       "                                                                   CountEncoder(combine_min_nan_groups=True,\n",
       "                                                                                min_group_size=0.05,\n",
       "                                                                                normalize=True)),\n",
       "                                                                  ('freq_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42))]),\n",
       "                                                  ['hhs_geo_region',\n",
       "                                                   'employment_industry',\n",
       "                                                   'employment_occupation'])])),\n",
       "                ('classifier', DummyClassifier(strategy='most_frequent'))])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the dummy model\n",
    "dummy_model.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 0.7875494071146245\n",
      "Train Precision: 0.0\n",
      "Train Recall: 0.0\n",
      "Train F1-Score: 0.0\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.7874376039933444\n",
      "Test Precision: 0.0\n",
      "Test Recall: 0.0\n",
      "Test F1-Score: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brookesmyth/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "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": [
    "# Evaluate dummy model\n",
    "evaluate(dummy_model, X_tr, X_te, y_tr, y_te, roc_auc='skip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": [
    "# Plotting the roc-auc curve for dummy model\n",
    "plot_roc_curve(dummy_model, X_tr, y_tr, name='Dummy model'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the mean of the accuracy score is a little over 78% if we always guess the majority class, which is 0 in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling Iterations\n",
    "Now we will start to iterate over multiple models!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL 1: Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the DecisionTreeClassifier to go though the pipeline\n",
    "dtc = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', DecisionTreeClassifier(class_weight='balanced', max_depth = 5))\n",
    "])\n",
    "\n",
    "# The parameters are chosen based on GridSearchCV done in Project3_model_iterations.ipynb notebook results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42)),\n",
       "                                                                  ('minmaxscaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['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",
       "                                                   'beha...\n",
       "                                                   'employment_status',\n",
       "                                                   'census_msa']),\n",
       "                                                 ('freq',\n",
       "                                                  Pipeline(steps=[('freq_encoder',\n",
       "                                                                   CountEncoder(combine_min_nan_groups=True,\n",
       "                                                                                min_group_size=0.05,\n",
       "                                                                                normalize=True)),\n",
       "                                                                  ('freq_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42))]),\n",
       "                                                  ['hhs_geo_region',\n",
       "                                                   'employment_industry',\n",
       "                                                   'employment_occupation'])])),\n",
       "                ('classifier',\n",
       "                 DecisionTreeClassifier(class_weight='balanced', max_depth=5))])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the model to the training data\n",
    "dtc.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 0.7920220511753693\n",
      "Train Precision: 0.5071858288770054\n",
      "Train Recall: 0.7429620563035496\n",
      "Train F1-Score: 0.6028404012315026\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.7886855241264559\n",
      "Test Precision: 0.5019710906701709\n",
      "Test Recall: 0.7475538160469667\n",
      "Test F1-Score: 0.6006289308176102\n"
     ]
    },
    {
     "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": [
    "# Evaluating the decision tree model for various metrics\n",
    "evaluate(dtc, X_tr, X_te, y_tr, y_te, roc_auc='skip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": [
    "# roc-auc curve for decision tree model\n",
    "plot_roc_curve(dtc, X_te, y_te, name='Decision Tree Classifier'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This decision tree model is not overfitting, but we have a low precision score, as well as a low f1 score. However, the AUC for this model is 0.84, which is fairly high, meaning that it does an adequate job of maximizing true positives and minimizing the false positives. This model is not overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL 2: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the logistic regression model to go through the pipeline\n",
    "logreg = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', LogisticRegression(max_iter=500, C=1, class_weight= 'balanced', solver= 'lbfgs',           \n",
    "                                      random_state=42))\n",
    "])\n",
    "\n",
    "# The parameters are chosen based on GridSearchCV done in Project3_model_iterations.ipynb notebook results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42)),\n",
       "                                                                  ('minmaxscaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['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",
       "                                                   'beha...\n",
       "                                                   'census_msa']),\n",
       "                                                 ('freq',\n",
       "                                                  Pipeline(steps=[('freq_encoder',\n",
       "                                                                   CountEncoder(combine_min_nan_groups=True,\n",
       "                                                                                min_group_size=0.05,\n",
       "                                                                                normalize=True)),\n",
       "                                                                  ('freq_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42))]),\n",
       "                                                  ['hhs_geo_region',\n",
       "                                                   'employment_industry',\n",
       "                                                   'employment_occupation'])])),\n",
       "                ('classifier',\n",
       "                 LogisticRegression(C=1, class_weight='balanced', max_iter=500,\n",
       "                                    random_state=42))])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the logistic regression model on the training data\n",
    "logreg.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 0.7756396921156646\n",
      "Train Precision: 0.4816946442845723\n",
      "Train Recall: 0.7375764993880048\n",
      "Train F1-Score: 0.5827852998065763\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.7791181364392679\n",
      "Test Precision: 0.4872448979591837\n",
      "Test Recall: 0.7475538160469667\n",
      "Test F1-Score: 0.58996138996139\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pz9TyO4RhZrkUAaUmpTo6I2m8pIWSFkk6v4Ny75FUkjSpszqd1MwsswilOjqSbBdwKTABOAA4TdIB7ZS7EJiZJjYnNTPLqDz6meboxKHAoohYHBGbgOuAk9oo90XKmz29nCY6JzUzy6Q8oT3dAQyQ9EjFcVZFVUOBFyq+L03ONZM0FJgITEkbnwcKzCyzDAMFqyJiTDvX0ixA+2PgGxFRktLd00nNzLIJKJW6ZPRzKTC84vswYHmrMmOA65KENgA4TtLmiLilvUqd1Mwsky5cT202MFrSSGAZMBk4vcW9IkZu+SzpCuC2jhIaOKmZWWZdM00q2Vf4HMqjmo3A1IiYL+ns5Hrq52iVnNTMLJsunCYVEdOB6a3OtZnMIuJf0tTppGZmmQReT83MCsYT2s2sUNJMgaoVJzUzy8RLD5lZ4fiZmpkViltqBfWecYv5wkV309AYTL/yXVx38WEtrh996nwmf+VhADau682Pv3wsix8fBMA1C6awYd12NJUaKG0Wn//Ax7s9/p7o7llD+bcLD6Op1MDHTl7Ilz79WIvrP/2fd3Lj7aMA2Fxq4OnFu7Lwvmvov+smDv7QR9ipz5s0NgaNjU3c/ZtptfgVcqFHJjVJU4ETgJcj4sBq3adWGhqaOPc/f8+/nvgRVi7bmcvuv4qHbt+H558a0FxmxXP9OO9Dp7Fu7Q4ceuxivvLTmZwz9ozm61+dMJnXVvepRfg9UqkkvvH9I/jt5TMYssd6xk3+MOOPWsK+o9Y2l/niJx7ni594HIAZ9w5nytUH0n/XTc3Xb5k6nd37v9HdoedK3l/pqOYqHVcA46tYf03tN2YFyxb3Y8Vz/dj8ZiP3/HZ/jjhhUYsyC/48lHVrdyh/fngIA4e+XotQLTH38YGMHPEaew1/ne16NzFxwmLuuGdEu+Vvmj6Kkycs7sYI60RAKeVRC1VLahFxH/BKteqvtQFD1rFy6c7N31cu25kBg9tPWhM+/hgP39k8jY0IcdG06/nZrCs5/hPzqhmqJVa83Iche6xv/j7kbRtY8VLfNstu2NjIHx4Yxonjnm0+J8Gkz47ngx85iStv2Lfq8eZVoNRHLdT8mVqyvtJZANuxW42jyUBv/b+h9ib5HnTk80w48zG+PO6jzee+dPTprH5xZ/oNXM9Ft17Pkqd35/EHhrf589Y12noOpDb+HgFm/nEEhx78Uouu5+1X3cbgQRtYuXoHJp01ntEjX+WIMS9WK9xca8rxM7WaLxIZEZdHxJiIGNOLnWodTmqrlu3MwGFbW2YDh77O6hffGv/eB77MVy+dybdOPZnXXtmx+fzqF8utvLUr+zJr2mj2G7Oi+kH3cEPetoHlL25tmS1/qQ97DNrQZtmb79ibkyc80+Lc4KTswN3/xnFHP8/cJwa09aM9QqQ8aqHmSa1ePTVnMENHrWGPPdfSq3eJoyY9yYO379OizKBhr/GdX9/C//308SxdtLUVukOfTey40xvNn8cc/RzPLei5/0C6y8EHrmTx87vw/NKd2PRmAzffsTfjxy55S7nXXu/Ng48MZsJRW6+t39CL19f3bv5874ND2X+fNd0We56UBwrSHbVQ8+5nvWoqNfDTrx7Dhb+7gYbG4I6r3snzTw7ghE/9BYDbfnkwZ3zzAXbZbSNf+vFdAM2vbvQftIHvXnczQPnVgOsPYPZde9fsd+kpevUK/t+/PcQ/nz2eppI4feLT7LfPWv7n+v0A+MRHngLg9rv3YuwRy+jbZ3Pzz65cvSMf//LRQPlVj1OOe4aj37+s+3+JnKjVIEAaiiq9cCLpWmAs5dUqXwK+HRG/7Ohn+mrP2L/XN6sSj1XHg09eXOsQLIPDJ77AnMf/tk1P8AdrVHxcP0hV9sKYPKeD5byromottYg4rVp1m1ntBNBU6yA64O6nmWWW496nk5qZZeeWmpkVxpZ9P/PKSc3MMivVOoAOOKmZWSYeKDCzwnFSM7NCyfEjNSc1M8vG3U8zK5ggctxWc1Izs8w8+mlmheHup5kVTrSzuOZbC1Y3jrY4qZlZZnluqXmRSDPLZEv3M83RGUnjJS2UtEjS+W1cP0nSY5LmSXpE0vs7q9MtNTPLrNQF/UpJjcClwDhgKTBb0rSIWFBR7G5gWkSEpHcB1wP7dVSvW2pmlkl5/4F0/+vEocCiiFgcEZuA64CTWtwrYl1sXcm2Lyme0jmpmVlmGbqfA5Ju45bjrIpqhgIvVHxfmpxrQdJESU8BtwOf7Cw2dz/NLLPUG7QHqzpYzrutWt7SEouIm4GbJR0JXAAc09EtndTMLJPyQEGXvKuxFKjc7HYYsLzd+0bcJ2mUpAERsaq9cu5+mllmXTT6ORsYLWmkpO2AycC0ygKS9pGk5PMhwHbA6o4qdUvNzDIJoktGPyNis6RzgJlAIzA1IuZLOju5PgU4BThT0pvARuDU6GQLPCc1M8usi7qfRMR0YHqrc1MqPl8IXJilTic1M8ss9UBBDTipmVkmXThQUBVOamaWmddTM7NCyfOEdic1M8ukq0Y/q8VJzcwya0q7nloNOKmZWSYeKDCzwslvSnNSM7O/g1tqZlYYAWx2UjOz4vC+n2ZWIB4oMLNikV/pMLMC8WbGZlY47n6aWWGUp0nlt63mpGZmmbmlZmaF4qRmZoXhVzrMrHCavJy3mRWFW2pmVihB8KZHP82sSNxSM7NCcVIzs8IIgpLc/TSzggjwxitmVhwBbMpxS00R+cm4klYCz9c6jioYAKyqdRCWSVH/zvaMiIHbUoGkGZT/fNJYFRHjt+V+WeUqqRWVpEciYkyt47D0/HdWvxpqHYCZWVdyUjOzQnFS6x6X1zoAy8x/Z3XKz9TMrFDcUjOzQnFSM7NCcVKrIknjJS2UtEjS+bWOxzonaaqklyU9UetY7O/jpFYlkhqBS4EJwAHAaZIOqG1UlsIVQLe+LGpdy0mteg4FFkXE4ojYBFwHnFTjmKwTEXEf8Eqt47C/n5Na9QwFXqj4vjQ5Z2ZV5KRWPW2t4u73Z8yqzEmtepYCwyu+DwOW1ygWsx7DSa16ZgOjJY2UtB0wGZhW45jMCs9JrUoiYjNwDjATeBK4PiLm1zYq64yka4GHgH0lLZX0qVrHZNl4mpSZFYpbamZWKE5qZlYoTmpmVihOamZWKE5qZlYoTmp1RFJJ0jxJT0i6QVKfbajrCkmTks+/6GiyvaSxko74O+7xnKS37DrU3vlWZdZlvNd3JH0ta4xWPE5q9WVjRBwUEQcCm4CzKy8mK4NkFhGfjogFHRQZC2ROama14KRWv+4H9klaUfdI+jXwuKRGST+UNFvSY5I+C6CySyQtkHQ7MGhLRZLulTQm+Txe0lxJj0q6W9JelJPneUkr8QOSBkq6MbnHbEnvS352d0l3SvqLpP+m7fmvLUi6RdIcSfMlndXq2sVJLHdLGpicGyVpRvIz90var0v+NK0wvEN7HZLUi/I6bTOSU4cCB0bEs0lieDUi3iNpe+ABSXcCBwP7Au8E3gYsAKa2qncg8HPgyKSu3SLiFUlTgHUR8aOk3K+B/4qIWZJGUJ41sT/wbWBWRHxP0vFAiyTVjk8m99gRmC3pxohYDfQF5kbEVyV9K6n7HMobopwdEX+V9F7gMuCDf8cfoxWUk1p92VHSvOTz/cAvKXcLH46IZ5PzxwLv2vK8DNgVGA0cCVwbESVguaQ/tFH/YcB9W+qKiPbWFTsGOEBqbojtImnn5B4nJz97u6Q1KX6ncyVNTD4PT2JdDTQBv0nO/wq4SdJOye97Q8W9t09xD+tBnNTqy8aIOKjyRPKPe33lKeCLETGzVbnj6HzpI6UoA+XHFodHxMY2Ykk9707SWMoJ8vCI2CDpXmCHdopHct+1rf8MzCr5mVrxzAQ+J6k3gKS3S+oL3AdMTp65DQaOauNnHwL+UdLI5Gd3S86/DuxcUe5Oyl1BknIHJR/vAz6anJsA9O8k1l2BNUlC249yS3GLBmBLa/N0yt3a14BnJf1zcg9Jencn97AexkmteH5B+XnZ3GTzkP+m3CK/Gfgr8DjwM+CPrX8wIlZSfg52k6RH2dr9uxWYuGWgADgXGJMMRCxg6yjsd4EjJc2l3A1e0kmsM4Bekh4DLgD+VHFtPfAOSXMoPzP7XnL+o8Cnkvjm4yXSrRWv0mFmheKWmpkVipOamRWKk5qZFYqTmpkVipOamRWKk5qZFYqTmpkVyv8CVxG5Geq/S88AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluating the logistic regression for various metrics\n",
    "evaluate(logreg, X_tr, X_te, y_tr, y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+e5nz+aELOLN5owBHZIwJF9W9UBYJXIfTx9CnqrpKRK4AHgfigV51E2Lo+/U7y3lnaTYAw89ubknAGFOnqrsj+D+gNbAYGC8i24DzgcdU9f06iC0s/PzNTD5fsxuAN37Wh0Fd0gIckTEm3FSXCDKAHqrqFpE4IA/oqKq76ia00DdiwgKyth8AnCai1jrIGBMI1TUfLVJVN4CqFgLrTzYJiMgwEVknIhtF5LEqylwkIlkislpEvjmZ7Qezmd9nlyaBpb+/xJKAMSZgqrsjOFNEVng+C9DBMy2AqmqP6jbsqWOYAFyKM47BEhGZraprvMokAa8Cw1T1RxEJi+ciq3fm8/DbzjCT79xzPikJsQGOyBgTzqpLBF1Pc9t9gY2quhlARGYAVwNrvMrcBMxU1R8BVHXPae4zKHyQtROA285vQ5+2TQIcjTEm3FXX6dzpdjTXEvAe6zgb6FeuTGcgWkS+xunh9CVVfbP8hkTkLuAugPT09NMMK7D+uWgbk+ZtBuCPV50V4GiMMcbHwetPkVQyT8tNRwHnApcDQ4EnRKRzhZVUJ6lqhqpmpKam1n6kdWTWsmyeeH8VAHcPbI9IZT8iY4ypW/7sySwbp/npca1wuqcoXyZPVY8AR0RkHnAOsN6PcQXML99y6gX+MaYvF3YO3oRmjAktPt0RiEi8iJzsKOlLgE4i0k5EYoAbgNnlynwAXCAiUSLSAOfR0dqT3E9QWLxlX+lnSwLGmPqkxkQgIlcCWcCnnumeIlL+hF6BqpYAY4G5OCf3t1V1tYjcIyL3eMqs9Wx3Bc6La5NVddUpHku99namU13yrzvKV5MYY0xg+fJo6EmcFkBfA6hqloi09WXjqjoHmFNu3sRy0+OAcb5sL1i53cq7ni4kzu+QEuBojDGmLF8eDZWoar7fIwlhA577CoBe6UlERlgFsTGmfvHljmCViNwERIpIJ+BB4L/+DSt0rN6Zz878QsCpJDbGmPrGlzuCB3DGKz4G/BunO+qH/BhTyCgqcXP5+PkAzLjrPBrFRQc4ImOMqciXO4Iuqvo74Hf+DibU3P3PTABEoF87e4PYGFM/+ZII/iYiZwDvADNUdbWfYwp6qkqXJz6lqMQNwLqnL7OXx4wx9VaNj4ZUdRBwEZALTBKRlSLye38HFsxGv7GkNAl8cP9PiIny5wvcxhhzenw6Q6nqLlUdD9yD807B//gzqGC2btchvlmfC8CKJ4dwTuukwAZkjDE18OWFsq4i8qSIrAJewWkx1MrvkQWpP8x23od75aZeVjlsjAkKvtQRvAFMB4aoavm+goyXNTsPsmiz05XEsLOaBzgaY4zxTY2JQFXPq4tAQsHw8d8C8Oy1ZxMVafUCxpjgUGUiEJG3VfU6EVlJ2e6jfRqhLNwc71SuTUoDbugb3GMmGGPCS3V3BL/wfL+iLgIJdg9OXwbAX645O8CRGGPMyany+YWq5ng+3qeq27y/gPvqJrzgcKzExa6DTjcS1qmcMSbY+PIg+9JK5l1W24EEs4lfO0NP/vayM+3FMWNM0KmujuBenCv/9iKywmtRIrDA34EFk4nfbALgmt4tAxyJMcacvOrqCP4NfAL8BXjMa/4hVd1X+Srh578b8zha7KJTWgJpiXGBDscYY05adYlAVXWriNxffoGINLFk4Phi7R4AHh/eNcCRGGPMqanpjuAKYClO81Hvh98KtPdjXEFjyoItxEZFMOjMtECHYowxp6TKRKCqV3i+t6u7cIJLYbELgFjrVM4YE8R86WvoJyLS0PP5FhH5m4jYG1PAi5+vB+DuCzsEOBJjjDl1vlzKvgYUiMg5wG+AbcA//RpVkFiy1akmudcSgTEmiPk6eL0CVwMvqepLOE1Iw973Px4gLTGWCBuQ3hgTxHzpffSQiPwWuBW4QEQigbDvX/mhGU6XEh1SEwIciTHGnB5f7giuxxm4foyq7gJaAuP8GlU9l72/gPeznB65X76pV4CjMcaY0+PLUJW7gGlAYxG5AihU1Tf9Hlk9NuC5/wDw/KgeNE2IDXA0xhhzenxpNXQdsBj4KXAd8J2IjPJ3YPXVl2t3l36+LqN1ACMxxpja4Usdwe+APqq6B0BEUoEvgHf9GVh9pKrc8Y9MACbecm6AozHGmNrhSx1BxPEk4LHXx/VCztJt+wHolJbAsO42FKUxJjT4ckfwqYjMxRm3GJzK4zn+C6n+GjVxIQDPjbLB2YwxocOXMYt/LSLXAgNw+huapKqz/B5ZPbPvSFHp597pyQGMxBhjald14xF0Al4AOgArgV+p6o66Cqy++cuctQA8P9LuBowxoaW6Z/1TgI+AkTg9kL58shsXkWEisk5ENorIY9WU6yMirvrcGunT1bsAGNHLBp8xxoSW6h4NJarq657P60Tk+5PZsOcN5Ak4Q11mA0tEZLaqrqmk3HPA3JPZfl2LjowgLTGWGOtp1BgTYqpLBHEi0osT4xDEe0+rak2JoS+wUVU3A4jIDJz+itaUK/cA8B7Q5yRjr1P7jhRxxwDrkdsYE3qqSwQ5wN+8pnd5TStwcQ3bbgls95rOBvp5FxCRlsA1nm1VmQhE5C7gLoD09LrvATsn/ygABUUldb5vY4zxt+oGphl0mtuurEtOLTf9d+BRVXWJVN2Dp6pOAiYBZGRklN+G3+0+eAyAjDZN6nrXxhjjd768R3CqsgHvPhhaATvLlckAZniSQFNguIiUqOr7fozrpP3NMwBNSkJMgCMxxpja589EsAToJCLtgB3ADcBN3gW8h8EUkanAR/UtCQDkHy0GYGCn1ABHYowxtc9viUBVS0RkLE5roEhgiqquFpF7PMsn+mvftW3D7kN0b9nIBqAxxoSkGhOBOM9tbgbaq+pTnvGKm6vq4prWVdU5lOuOoqoEoKqjfYq4juUXFFNQ5KJhjD9vnowxJnB8aRT/KnA+cKNn+hDO+wFh4bstewEY3DUtwJEYY4x/+HKZ209Ve4vIMgBV3S8iYVNr+vHKHAAu635GgCMxxhj/8OWOoNjz9q9C6XgEbr9GVY8c72yudZMGAY7EGGP8w5dEMB6YBaSJyJ+A+cCf/RpVPaGqfLshj2aNbDhKY0zo8qUb6mkishQYjPOS2AhVXev3yOqB8V9uBKBXa+t22hgTunxpNZQOFAAfes9T1R/9GVigqSovfuG8SPacdT1tjAlhvlQWf4xTPyBAHNAOWAec5ce4Am5T7hEAerRqTOMG0QGOxhhj/MeXR0Nne0+LSG/gbr9FVE9MmrcJgEeGdAlwJMYY418n3bm+p/vpet1ldG043tHcgI5NAxyJMcb4ly91BA97TUYAvYFcv0VUD6gq36zPpX1qQyKtWwljTIjzpY4g0etzCU6dwXv+Cad++OtnTiVxq2R7d8AYE/qqTQSeF8kSVPXXdRRPvVBQ5ALgtZt7BzgSY4zxvyrrCEQkSlVdOI+CwobbrUxZsIWoCKFhrHU0Z4wJfdWd6RbjJIEsEZkNvAMcOb5QVWf6ObaAWLb9AAAl7jofCM0YYwLCl0veJsBenHGFj79PoEBIJoIteU6ue2N0yDeMMsYYoPpEkOZpMbSKEwnguJC9XD7qGaD+rJaNAhyJMcbUjeoSQSSQgG+D0IeMJz5YDWAD0RhjwkZ1Z7scVX2qziKpB7I89QOAVRQbY8JGdW8Wh92bVPPWO+/J/f36noENxBhj6lB1iWBwnUVRT/ztc+dFsv4dUgIciTHG1J0qE4Gq7qvLQALN7Wku2rpJPGmN4gIcjTHG1J2T7nQuVK3amQ/A0G7NAxyJMcbULUsEHut2HQLg0m7NAhyJMcbULUsEHsuzDwDQtmnDwAZijDF1zBKBx+qdBwFoZvUDxpgwY4nAY9mPB2jWKDbQYRhjTJ2zRADc+6+lAKQ3sfEHjDHhJ+wTwYbdh/hk1S4A/nlHvwBHY4wxdS/sE8GlL84D4M/XnE1cdGSAozHGmLoX9h3qREcKsVGR3NQvPdChGGNMQPj1jkBEhonIOhHZKCKPVbL8ZhFZ4fn6r4ic4894ylNVil3KmJ+0rcvdGmNMveK3ROAZ73gCcBnQDbhRRLqVK7YFuFBVewBPA5P8FU9lNuU6g9DYYGTGmHDmzzuCvsBGVd2sqkXADOBq7wKq+l9V3e+ZXAS08mM8FSz3dDvdu01SXe7WGGPqFX8mgpbAdq/pbM+8qtwBfFLZAhG5S0QyRSQzNze31gLcmHsYgE5pibW2TWOMCTb+TAQ+j2wmIoNwEsGjlS1X1UmqmqGqGampqbUWYN6hYwCk2Ytkxpgw5s9WQ9lAa6/pVsDO8oVEpAcwGbhMVff6MZ4yXG7lnaXZtElpQGyUNRs1xoQvf94RLAE6iUg7EYkBbgBmexcQkXRgJnCrqq73YywVfJC1A4DWyfY2sTEmvPntjkBVS0RkLDAXiASmqOpqEbnHs3wi8D9ACvCqiACUqGqGv2Lytmizc/Px8o296mJ3xhhTb/n1hTJVnQPMKTdvotfnO4E7/RlDVd7OzAagcXx0IHZvjDH1Rth2MZGaGEuEQEREZXXaxhgTPsI2EeQeOsat57UJdBjGGBNwYZkI1uYcDHQIxhhTb4RlIvj7F04DpaFn2UD1xhgTlokge/9RAPp3bBrgSIwxJvDCMhFERQhdz2gU6DCMMaZeCMtEUOxSWibFBzoMY4ypF8IyEazJOUiUNRs1xhggTBNBTFQEbrVBCIwxBsIwERQWuygqcdMhLSHQoRhjTL0Qdokg77DT9XRCbNgP12yMMUAYJoKteQUApCXaGATGGANhmAg2HR+VrJmNSmaMMRCGicDlGam+VbI1HzXGGAjDRDBvgzPmcWKc1REYYwyEYSKIj3aGpbThKY0xxhF2iWBT7mHOamHdSxhjzHFhlwhUYf+RokCHYYwx9UbYJQKXKr3aJAc6DGOMqTfCKhEUlbjZnHuE2KiwOmxjjKlWWJ0RN+c57xDERIbVYRtjTLXCqg3l/iPFAJzXPiXAkZjaVlxcTHZ2NoWFhYEOxZiAiouLo1WrVkRHR/u8Tlglgg17DgHQJqVBgCMxtS07O5vExETatm2LiHUxbsKTqrJ3716ys7Np166dz+uF1TOSldn5ANbzaAgqLCwkJSXFkoAJayJCSkrKSd8Zh1UiWLptPwCN4ny/ZTLBw5KAMaf2fxBWiWD3wUI6N7O7AWOM8RY2icDtVo4UuUhqEBPoUEyISkg49YuMO++8kzVr1lS5fOrUqezcudPn8vXd7NmzefbZZ2tlW0ePHuXCCy/E5XKVznvxxReJi4sjPz+/dN7UqVMZO3ZsmXUvuugiMjMzATh8+DB33303HTp04KyzzmLgwIF89913pxWbqvLggw/SsWNHevTowffff19puS+//JLevXvTs2dPBgwYwMaNG8ssX7JkCZGRkbz77rsAFBUVMXDgQEpKSk4rvuPCJxF4hqY831oMmXpo8uTJdOvWrcrl5RNBTeWrUhsnDu8T7qm66qqreOyxx057OwBTpkzh2muvJTLyRP9h06dPp0+fPsyaNcvn7dx55500adKEDRs2sHr1aqZOnUpeXt5pxfbJJ5+wYcMGNmzYwKRJk7j33nsrLXfvvfcybdo0srKyuOmmm3jmmWdKl7lcLh599FGGDh1aOi8mJobBgwfz1ltvnVZ8x4VNq6G8w063EoUlp/9HbOq3P364mjU7D9bqNru1aMQfrjzLp7Kqym9+8xs++eQTRITf//73XH/99bjdbsaOHcs333xDu3btcLvdjBkzhlGjRnHRRRfxwgsv0KtXL+644w4yMzMREcaMGUPr1q3JzMzk5ptvJj4+noULF3LZZZfxwgsvkJGRwaeffsrjjz+Oy+WiadOmfPnll2XimTp1Kh9//DGFhYUcOXKEDz/8kAceeICVK1dSUlLCk08+ydVXX01BQQGjR4/mhx9+oGvXrmzdupUJEyaQkZFBQkICDz/8MHPnzuWvf/0rW7duZfz48RQVFdGvXz9effVVgAqx//KXv2T8+PFMnDiRqKgounXrxowZM5g6dSqZmZm88sorbNu2jTFjxpCbm0tqaipvvPEG6enpjB49mkaNGpGZmcmuXbt4/vnnGTVqVIWf97Rp0/j3v/9dOr1p0yYOHz7MuHHj+POf/8zo0aNr/J1t2rSJ7777jmnTphER4Vwft2/fnvbt2/v0O6/KBx98wG233YaIcN5553HgwAFycnI444wzypQTEQ4edP5m8/PzadGiRemyl19+mZEjR7JkyZIy64wYMYLf/va33HzzzacVI4RRIih2uQHomGp1BMa/Zs6cSVZWFsuXLycvL48+ffowcOBAFixYwNatW1m5ciV79uyha9eujBkzpsy6WVlZ7Nixg1WrVgFw4MABkpKSeOWVV0pP/N5yc3P5+c9/zrx582jXrh379u2rNKaFCxeyYsUKmjRpwuOPP87FF1/MlClTOHDgAH379uWSSy7htddeIzk5mRUrVrBq1Sp69uxZuv6RI0fo3r07Tz31FGvXruW5555jwYIFREdHc9999zFt2jTOOuusCrEDPPvss2zZsoXY2NjSed7Gjh3Lbbfdxu23386UKVN48MEHef/99wHIyclh/vz5/PDDD1x11VUVEkFRURGbN2+mbdu2pfOmT5/OjTfeyAUXXMC6devYs2cPaWlp1f7OVq9eTc+ePcvcVVTl+uuvZ926dRXmP/zww9x2221l5u3YsYPWrVuXTrdq1YodO3ZUSASTJ09m+PDhxMfH06hRIxYtWlS6/qxZs/jqq68qJILu3btXmHeqwiYR5HrGKo6x7iVCnq9X7v4yf/58brzxRiIjI2nWrBkXXnghS5YsYf78+fz0pz8lIiKC5s2bM2jQoArrtm/fns2bN/PAAw9w+eWXM2TIkGr3tWjRIgYOHFjaZrxJkyaVlrv00ktLl3322WfMnj2bF154AXCa3v7444/Mnz+fX/ziF4BzkunRo0fp+pGRkYwcORJwnmcvXbqUPn36AM4z+rS0NK688spKY+/Rowc333wzI0aMYMSIERViW7hwITNnzgTg1ltv5Te/+U3pshEjRhAREUG3bt3YvXt3hXXz8vJISkoqM2/GjBnMmjWLiIgIrr32Wt555x3uv//+KlvTnGwrm5N5HKOeR9I17e/FF19kzpw59OvXj3HjxvHwww8zefJkHnroIZ577rlKE1RkZCQxMTEcOnSIxMTTG3HRr4lARIYBLwGRwGRVfbbccvEsHw4UAKNVtfLalNOUvf8ocGI8AmP8pbJ//urme0tOTmb58uXMnTuXCRMm8PbbbzNlypRq9+XLiaxhw4Zl1nnvvffo0qWLz/HFxcWVnoxUldtvv52//OUvFcpVFvvHH3/MvHnzmD17Nk8//TSrV6+uNlbv44mNPTG2eGXxxcfHl2kzv2LFCjZs2MCll14KOHcM7du35/777yclJYX9+/eXWX/fvn00bdqUpKQkli9fjtvtLn00VJWTuSNo1aoV27dvL53Ozs4u89gHnLu65cuX069fv9LtDxs2DIDMzExuuOEGwEl6c+bMISoqqjShHjt2jLi4uGrj9YXfLo9FJBKYAFwGdANuFJHytVuXAZ08X3cBr/krnkjPH1fbpg1rKGnM6Rk4cCBvvfUWLpeL3Nxc5s2bR9++fRkwYADvvfcebreb3bt38/XXX1dYNy8vD7fbzciRI3n66adLW5kkJiZy6NChCuXPP/98vvnmG7Zs2QJQ5aMhb0OHDuXll18uPbEuW7YMgAEDBvD2228DsGbNGlauXFnp+oMHD+bdd99lz549pfvctm1bpbG73W62b9/OoEGDeP755zlw4ACHDx8us73+/fszY8YMwHneP2DAgBqP4bjk5GRcLldpMpg+fTpPPvkkW7duZevWrezcuZMdO3awbds2+vTpw4IFC9i1axfgnGSPHTtG69at6dChAxkZGfzhD38o/bls2LCBDz74oMI+33rrLbKysip8lU8C4FSKv/nmm6gqixYtonHjxhUeCyUnJ5Ofn8/69esB+Pzzz+natSsAW7ZsKT2WUaNG8eqrr5Ymgb1795KamnpSXUlUxZ93BH2Bjaq6GUBEZgBXA95t3q4G3lTnJ79IRJJE5AxVzfFjXMb41TXXXMPChQs555xzEBGef/55mjdvzsiRI/nyyy/p3r07nTt3pl+/fjRu3LjMujt27OBnP/sZbrdTp3X8qnv06NHcc889pZXFx6WmpjJp0iSuvfZa3G43aWlpfP7559XG98QTT/DQQw/Ro0cPVJW2bdvy0Ucfcd9993H77bfTo0cPevXqRY8ePSrEB9CtWzeeeeYZhgwZgtvtJjo6mgkTJhAfH18hdpfLxS233EJ+fj6qyi9/+csKj3LGjx/PmDFjGDduXGll8ckYMmQI8+fP55JLLmHGjBl88sknZZZfc801zJgxg0cffZSXXnqJ4cOH43a7SUhIYPr06aV3AJMnT+aRRx6hY8eONGjQgJSUFMaNG3dSsZQ3fPhw5syZU7pN72MbPnw4kydPpkWLFrz++uuMHDmSiIgIkpOTq70LPO4///kPw4cPP634SqmqX76AUTiPg45P3wq8Uq7MR8AAr+kvgYxKtnUXkAlkpqen66nI3LpP7/1Xpu7YX3BK65v6bc2aNYEOwSeHDh1SVdW8vDxt37695uTkBDiiE0pKSvTo0aOqqrpx40Zt06aNHjt2LMBR1ez777/XW265JdBh1LlrrrlGf/jhh0qXVfb/AGRqFedrf94RVPbgsvxDPl/KoKqTgEkAGRkZNT9orcS5bZI5t825p7KqMbXmiiuu4MCBAxQVFfHEE0/QvHnzQIdUqqCggEGDBlFcXIyq8tprrxETU/9fwOzVqxeDBg3C5XL51OonFBQVFTFixIgK9Tynyp+JIBto7TXdCth5CmWMCRmV1QvUF4mJiaVv2Qab8s1wQ11MTEyldRKnyp9tKZcAnUSknYjEADcAs8uVmQ3cJo7zgHy1+gFzitSHVjnGhLpT+T/w2x2BqpaIyFhgLk7z0SmqulpE7vEsnwjMwWk6uhGn+ejP/BWPCW1xcXHs3bvXuqI2YU094xGcbJNSCbarqIyMDA3W21fjPzZCmTGOqkYoE5GlqppR2Tph82axCW3R0dEnNSKTMeYE62/BGGPCnCUCY4wJc5YIjDEmzAVdZbGI5ALbTnH1psDpjTQRfOyYw4Mdc3g4nWNuo6qplS0IukRwOkQks6pa81Blxxwe7JjDg7+O2R4NGWNMmLNEYIwxYS7cEsGkQAcQAHbM4cGOOTz45ZjDqo7AGGNMReF2R2CMMaYcSwTGGBPmQjIRiMgwEVknIhtF5LFKlouIjPcsXyEivQMRZ23y4Zhv9hzrChH5r4icE4g4a1NNx+xVro+IuERkVF3G5w++HLOIXCQiWSKyWkS+qesYa5sPf9uNReRDEVnuOeag7sVYRKaIyB4RWVXF8to/f1U1dFmwfuF0eb0JaA/EAMuBbuXKDAc+wRkh7Tzgu0DHXQfH3B9I9ny+LByO2avcVzhdno8KdNx18HtOwhkXPN0znRbouOvgmB8HnvN8TgX2ATGBjv00jnkg0BtYVcXyWj9/heIdQV9go6puVtUiYAZwdbkyVwNvqmMRkCQiZ9R1oLWoxmNW1f+q6n7P5CKc0eCCmS+/Z4AHgPeAPXUZnJ/4csw3ATNV9UcAVQ324/blmBVIFGcgigScRFBSt2HWHlWdh3MMVan181coJoKWwHav6WzPvJMtE0xO9njuwLmiCGY1HrOItASuASbWYVz+5MvvuTOQLCJfi8hSEam98QwDw5djfgXoijPM7UrgF6rqrpvwAqLWz1+hOB5BZcNTlW8j60uZYOLz8YjIIJxEMMCvEfmfL8f8d+BRVXWFyKhlvhxzFHAuMBiIBxaKyCJVXe/v4PzEl2MeCmQBFwMdgM9F5FtVPejn2AKl1s9foZgIsoHWXtOtcK4UTrZMMPHpeESkBzAZuExV99ZRbP7iyzFnADM8SaApMFxESlT1/TqJsPb5+redp6pHgCMiMg84BwjWRODLMf8MeFadB+gbRWQLcCawuG5CrHO1fv4KxUdDS4BOItJORGKAG4DZ5crMBm7z1L6fB+Srak5dB1qLajxmEUkHZgK3BvHVobcaj1lV26lqW1VtC7wL3BfESQB8+9v+ALhARKJEpAHQD1hbx3HWJl+O+UecOyBEpBnQBdhcp1HWrVo/f4XcHYGqlojIWGAuTouDKaq6WkTu8SyfiNOCZDiwESjAuaIIWj4e8/8AKcCrnivkEg3inht9POaQ4ssxq+paEfkUWAG4gcmqWmkzxGDg4+/5aWCqiKzEeWzyqKoGbffUIjIduAhoKiLZwB+AaPDf+cu6mDDGmDAXio+GjDHGnARLBMYYE+YsERhjTJizRGCMMWHOEoExxoQ5SwSmXvL0Fprl9dW2mrKHa2F/U0Vki2df34vI+aewjcki0s3z+fFyy/57ujF6tnP857LK0+NmUg3le4rI8NrYtwld1nzU1EsiclhVE2q7bDXbmAp8pKrvisgQ4AVV7XEa2zvtmGraroj8A1ivqn+qpvxoIENVx9Z2LCZ02B2BCQoikiAiX3qu1leKSIWeRkXkDBGZ53XFfIFn/hARWehZ9x0RqekEPQ/o6Fn3Yc+2VonIQ555DUXkY0//96tE5HrP/K9FJENEngXiPXFM8yw77Pn+lvcVuudOZKSIRIrIOBFZIk4f83f78GNZiKezMRHpK844E8s837t43sR9CrjeE8v1ntinePazrLKfowlDge57277sq7IvwIXTkVgWMAvnLfhGnmVNcd6qPH5He9jz/RHgd57PkUCip+w8oKFn/qPA/1Syv6l4xisAfgp8h9N520qgIU73xquBXsBI4HWvdRt7vn+Nc/VdGpNXmeMxXgP8w/M5BqcXyXjgLuD3nvmxQCbQrpI4D3sd3zvAMM90IyDK8/kS4D3P59HAK17r/xm4xfM5CacPooaB/n3bV2C/Qq6LCRMyjqpqz+MTIhIN/FlEBuJ0ndASaAbs8lpnCTDFU/Z9Vc0SkQuBbsACT9caMThX0pUZJyK/B3JxemgdDMxSpwM3RGQmcAHwKfCCiDyH8zjp25M4rk+A8SISCwwD5qnqUc/jqB5yYhS1xkAnYEu59eNFJAtoCywFPvcq/w8R6YTTE2V0FfsfAlwlIr/yTMcB6QR3f0TmNFkiMMHiZpzRp85V1WIR2YpzEiulqvM8ieJy4J8iMg7YD3yuqjf6sI9fq+q7xydE5JLKCqnqehE5F6e/l7+IyGeq+pQvB6GqhSLyNU7XydcD04/vDnhAVefWsImjqtpTRBoDHwH3A+Nx+tv5j6pe46lY/7qK9QUYqarrfInXhAerIzDBojGwx5MEBgFtyhcQkTaeMq8D/4cz3N8i4CcicvyZfwMR6ezjPucBIzzrNMR5rPOtiLQAClT1X8ALnv2UV+y5M6nMDJyOwi7A6UwNz/d7j68jIp09+6yUquYDDwK/8qzTGNjhWTzaq+ghnEdkx80FHhDP7ZGI9KpqHyZ8WCIwwWIakCEimTh3Bz9UUuYiIEtEluE8x39JVXNxTozTRWQFTmI405cdqur3OHUHi3HqDCar6jLgbGCx5xHN74BnKll9ErDieGVxOZ/hjEv7hTrDL4IzTsQa4HtxBi3/X2q4Y/fEshyna+bnce5OFuDUHxz3H6Db8cpinDuHaE9sqzzTJsxZ81FjjAlzdkdgjDFhzhKBMcaEOUsExhgT5iwRGGNMmLNEYIwxYc4SgTHGhDlLBMYYE+b+Hx3YJ8fKLcngAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the roc-auc curve for the logistic regression model\n",
    "plot_roc_curve(logreg, X_te, y_te, name='logistic regression');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This logistic regression model has low precision and fl scores, and has an AUC equal to the decision tree AUC above. This model is not overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL 3: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the RandomForestClassifier to go through the pipeline\n",
    "rfc = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', RandomForestClassifier(class_weight= 'balanced', max_depth= 5, min_samples_leaf= 1,\n",
    "                                          n_estimators= 200, random_state=42))\n",
    "])\n",
    "\n",
    "# The parameters are chosen based on GridSearchCV done in Project3_model_iterations.ipynb notebook results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42)),\n",
       "                                                                  ('minmaxscaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['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",
       "                                                   'beha...\n",
       "                                                  Pipeline(steps=[('freq_encoder',\n",
       "                                                                   CountEncoder(combine_min_nan_groups=True,\n",
       "                                                                                min_group_size=0.05,\n",
       "                                                                                normalize=True)),\n",
       "                                                                  ('freq_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42))]),\n",
       "                                                  ['hhs_geo_region',\n",
       "                                                   'employment_industry',\n",
       "                                                   'employment_occupation'])])),\n",
       "                ('classifier',\n",
       "                 RandomForestClassifier(class_weight='balanced', max_depth=5,\n",
       "                                        n_estimators=200, random_state=42))])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the random forest model to the training data\n",
    "rfc.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 0.7942583732057417\n",
      "Train Precision: 0.5107303277324904\n",
      "Train Recall: 0.7515299877600979\n",
      "Train F1-Score: 0.608161648177496\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.788269550748752\n",
      "Test Precision: 0.5013245033112583\n",
      "Test Recall: 0.7407045009784736\n",
      "Test F1-Score: 0.5979462875197473\n"
     ]
    },
    {
     "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": [
    "# Evaluating various metrics of the random forest classifier\n",
    "evaluate(rfc, X_tr, X_te, y_tr, y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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_roc_curve(rfc, X_te, y_te, name='random forest classifier');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This random forest classifier model also has low precision and fl scores. It has an AUC score of 0.85, which is slightly better than the decision tree model above. This model is not overfitting to a great extent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL 4: kNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the KNN model to go through the pipeline\n",
    "kNN = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', KNeighborsClassifier(n_neighbors=5, p=1, weights= 'distance'))\n",
    "])\n",
    "\n",
    "# The parameters are chosen based on GridSearchCV done in Project3_model_iterations.ipynb notebook results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42)),\n",
       "                                                                  ('minmaxscaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['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",
       "                                                   'beha...\n",
       "                                                   'rent_or_own',\n",
       "                                                   'employment_status',\n",
       "                                                   'census_msa']),\n",
       "                                                 ('freq',\n",
       "                                                  Pipeline(steps=[('freq_encoder',\n",
       "                                                                   CountEncoder(combine_min_nan_groups=True,\n",
       "                                                                                min_group_size=0.05,\n",
       "                                                                                normalize=True)),\n",
       "                                                                  ('freq_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42))]),\n",
       "                                                  ['hhs_geo_region',\n",
       "                                                   'employment_industry',\n",
       "                                                   'employment_occupation'])])),\n",
       "                ('classifier', KNeighborsClassifier(p=1, weights='distance'))])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the KNN model to the training data\n",
    "kNN.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 1.0\n",
      "Train Precision: 1.0\n",
      "Train Recall: 1.0\n",
      "Train F1-Score: 1.0\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.7999168053244592\n",
      "Test Precision: 0.5622406639004149\n",
      "Test Recall: 0.2651663405088063\n",
      "Test F1-Score: 0.36037234042553196\n"
     ]
    },
    {
     "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": [
    "# evaluating the KNN model for various metrics\n",
    "evaluate(kNN, X_tr, X_te, y_tr, y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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": [
    "# Plotting the roc-auc curve for the KNN model\n",
    "plot_roc_curve(kNN, X_te, y_te, name='k Nearest Neighborhood classifier');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This KNN model is definitely overfitting; the training data has perfect scores for all metrics, whereas the testing data scores are much lower. The AUC score is also much lower than on previous models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL 5: Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the gradient boosting classifier to go through the pipeline\n",
    "gbc = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', GradientBoostingClassifier(learning_rate= 0.1, max_depth= 5, n_estimators= 200,\n",
    "        random_state=42))\n",
    "])\n",
    "\n",
    "# The parameters are chosen based on GridSearchCV done in Project3_model_iterations.ipynb notebook results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42)),\n",
       "                                                                  ('minmaxscaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['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",
       "                                                   'beha...\n",
       "                                                   'census_msa']),\n",
       "                                                 ('freq',\n",
       "                                                  Pipeline(steps=[('freq_encoder',\n",
       "                                                                   CountEncoder(combine_min_nan_groups=True,\n",
       "                                                                                min_group_size=0.05,\n",
       "                                                                                normalize=True)),\n",
       "                                                                  ('freq_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42))]),\n",
       "                                                  ['hhs_geo_region',\n",
       "                                                   'employment_industry',\n",
       "                                                   'employment_occupation'])])),\n",
       "                ('classifier',\n",
       "                 GradientBoostingClassifier(max_depth=5, n_estimators=200,\n",
       "                                            random_state=42))])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the gradient boosting classifier to the training data\n",
    "gbc.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 0.895933014354067\n",
      "Train Precision: 0.8400783289817232\n",
      "Train Recall: 0.630110159118727\n",
      "Train F1-Score: 0.7201007133864876\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.8548252911813644\n",
      "Test Precision: 0.720108695652174\n",
      "Test Recall: 0.5185909980430529\n",
      "Test F1-Score: 0.602957906712173\n"
     ]
    },
    {
     "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": [
    "# Evaluate the gradient boosting model for various metrics\n",
    "evaluate(gbc, X_tr, X_te, y_tr, y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": [
    "# Plotting the roc-auc curve for the gradient boosting model\n",
    "plot_roc_curve(gbc, X_te, y_te, name='gradient boosting classifier');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model has the highest overall scores of all the models done so far, and also does the best job of minimizing the false positives. This is our candidate for the final model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL 6: XG Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the XG Boosting model to go through the pipeline\n",
    "xgb = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', xgboost.XGBClassifier(learning_rate= 1, max_depth= 2, n_estimators= 100, random_state=42))\n",
    "])\n",
    "\n",
    "# The parameters are chosen based on GridSearchCV done in Project3_model_iterations.ipynb notebook results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('num_imputer',\n",
       "                                                                   IterativeImputer(max_iter=100,\n",
       "                                                                                    random_state=42)),\n",
       "                                                                  ('minmaxscaler',\n",
       "                                                                   MinMaxScaler())]),\n",
       "                                                  ['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",
       "                                                   'beha...\n",
       "                               colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "                               importance_type='gain',\n",
       "                               interaction_constraints='', learning_rate=1,\n",
       "                               max_delta_step=0, max_depth=2,\n",
       "                               min_child_weight=1, missing=nan,\n",
       "                               monotone_constraints='()', n_estimators=100,\n",
       "                               n_jobs=0, num_parallel_tree=1, random_state=42,\n",
       "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "                               subsample=1, tree_method='exact',\n",
       "                               validate_parameters=1, verbosity=None))])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the xg boosting model to the training data\n",
    "xgb.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 0.8668088204701477\n",
      "Train Precision: 0.7565656565656566\n",
      "Train Recall: 0.5500611995104039\n",
      "Train F1-Score: 0.6369950389794472\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.8508735440931781\n",
      "Test Precision: 0.7126917712691772\n",
      "Test Recall: 0.5\n",
      "Test F1-Score: 0.5876940770557791\n"
     ]
    },
    {
     "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": [
    "# Evaluating the XG Boost model for various metrics\n",
    "evaluate(xgb, X_tr, X_te, y_tr, y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "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_roc_curve(xgb, X_te, y_te, name='XGBoost Classifier');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model gave us similar scores to the gradient boosting model, but the gradient boosting model has the best AUC score and precision score. So, we will choose the gradient boosting classifier as the final model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of Model ROC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "plot_roc_curve(dummy_model, X_te, y_te,\n",
    "               name='Dummy model', ax=ax)\n",
    "plot_roc_curve(kNN, X_te, y_te, \n",
    "               name='k Nearest Neighborhood classifier', ax=ax)\n",
    "plot_roc_curve(logreg, X_te, y_te, \n",
    "               name='Logistic regression', ax=ax)\n",
    "plot_roc_curve(dtc, X_te, y_te,\n",
    "               name='Decision tree classification', ax=ax)\n",
    "plot_roc_curve(rfc, X_te, y_te, \n",
    "               name='Random forest classifier', ax=ax)\n",
    "plot_roc_curve(gbc, X_te, y_te, \n",
    "               name='Gradient boosting classifier', ax=ax)\n",
    "plot_roc_curve(xgb, X_te, y_te, \n",
    "               name='XGBoost Classifier', ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'Final' Model: Gradient Boosting Classifier\n",
    "\n",
    "From the ROC curves comparison above, and confusion matrix of the method, we decided to choose Gradient Boosting Classifier as our final model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', GradientBoostingClassifier(learning_rate =0.1, n_estimators=200, \n",
    "                                              max_depth=5,\n",
    "                                              random_state=42))\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_final_model = final_model.fit(X_tr, y_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Scores:\n",
      "Train Accuracy: 0.895933014354067\n",
      "Train Precision: 0.8400783289817232\n",
      "Train Recall: 0.630110159118727\n",
      "Train F1-Score: 0.7201007133864876\n",
      "**********\n",
      "Testing Scores:\n",
      "Test Accuracy: 0.8476226132534631\n",
      "Test Precision: 0.7051282051282052\n",
      "Test Recall: 0.48500881834215165\n",
      "Test F1-Score: 0.5747126436781609\n"
     ]
    },
    {
     "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": [
    "evaluate(final_model, X_tr, X_hold, y_tr, y_hold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wbOI6d18XZmCZomohEYm6dJ4jeI6gfcCAlkA3YBWQH2JcGaVqIRGJsnSqhnonLpvZEcBloUUkIiIZVeNJ6GPDT2vIaBGRHJFOG8G1CYt7AEcAG0OLSEREMiqdNoLWCa/LCdoMngwnHBERybQqE0HsQbK93f2/MxRPRqnrqIhIFW0EZtbU3b8hqArKSeo6KiJSdYngDYIkUGJmM4HHge0VG939qZBjywh1HRWRqEunjWAfYBPBvMIVzxM4kNWJQNVCIiKBqhLBfrEeQ0v5NgFUCHXe4ExQtZCISKCqRNAE2Jv0JqHPSqoWEhGpOhGsd/fbMhaJiIg0iKqeLE5VEsgJmohGRORbVSWCH2YsigxT+4CIyLcqTQTuntO3zGofEBEJ1HjQORERyS1KBCIiEadEICIScUoEIiIRp0QgIhJxSgQiIhGnRCAiEnFKBCIiEadEICIScaEmAjM72cxWmdlqMxudYvsFZrYk9vOqmRWEGY+IiOwutEQQm+/4fuAUoBdwnpn1StptDfADd+8D3A5MCCseERFJLcwSwVHAand/392/BqYBQxN3cPdX3f3z2OLrQOcQ4xERkRTCTASdgA8Tlktj6yrzc+DvqTaY2UgzKzaz4o0bN9ZjiCIiEmYiSHtmMzMbTJAIbki13d0nuHuRuxfl5eXVY4giIhJmIigFDkhY7gx8nLyTmfUBJgJD3X1TiPEAmpRGRCRZmIlgEdDdzLqZWXPgx8DMxB3MrAvwFHChu78bYixxmpRGROS7qpqzuE7cvdzMrgKeB5oAk9x9mZldHts+HhgD7As8YGYA5e5eFFZMFTQpjYjIt0JLBADuPhuYnbRufMLrS4BLwoxBRESqpieLRUQiTolARCTilAhERCJOiUBEJOIilQj0DIGIyO4ilQj0DIGIyO4ilQhAzxCIiCSLXCIQEZHvUiIQEYk4JQIRkYhTIhARiTglAhGRiFMiEBGJOCUCEZGIUyIQEYk4JQIRkYhTIhARiTglAhGRiFMiEBGJOCUCEZGIUyIQEYm4pg0dgDQOO3fupLS0lLKysoYORUTqoGXLlnTu3JlmzZql/R4lAgGgtLSU1q1b07VrV8ysocMRkVpwdzZt2kRpaSndunVL+32qGhIAysrK2HfffZUERLKYmbHvvvvWuGSvRCBxSgIi2a82/4+VCEREIk6JQBqNTz75hPPPP5+DDjqIfv36MXDgQGbMmFGnY956662MHTsWgDFjxvDiiy/W6jglJSXMnj075baXX36Ztm3bUlhYSJ8+fTjhhBP49NNPax1zsrVr1zJ16tT4cnFxMddcc029Hf+RRx7h8MMPJz8/n169esX/XhdddBFPPPFEvXzGxx9/zLBhw+LL5513Hn369OHee++t0/dS4emnn+a22277zrqCggLOO++876wbNGgQxcXF8eW1a9dy+OGHx5ffeOMNjjvuOHr06MFhhx3GJZdcwldffVWn2NasWUP//v3p3r07w4cP5+uvv0653/XXX09+fj49e/bkmmuuwd2BoN7/5ptv5tBDD6Vnz56MGzcOgFmzZnHLLbfUKbY4d8+qn379+nltnTv+VT93/Ku1fn8uW758eYN+/q5du3zAgAH+4IMPxtetXbvWx40bt9u+O3fuTPu4t9xyi99zzz11ju+vf/2rjxo1KuW2OXPm+GmnnRZfHj16tI8ZM6bOn1nZ8evT7NmzvW/fvv7RRx+5u/uOHTt8woQJ7u4+YsQIf/zxx+v9M9evX+9dunSp9ftTff8DBw70jRs3xpeXL1/uhx9+uO+///6+bdu2+Pof/OAHvmjRovjymjVrPD8/393dN2zY4F26dPFXXw2uEbt27fLHH3/cN2zYUOtY3d1/9KMf+WOPPebu7pdddpk/8MADu+3zyiuv+NFHH+3l5eVeXl7uAwYM8Dlz5ri7+6RJk/zCCy/0b775xt3dP/nkk3h8hYWFvn379t2Ol+r/M1DslVxX1WtIdvPrZ5ex/OMv6/WYvfZvwy1n5Fe6/aWXXqJ58+Zcfvnl8XUHHnggV199NQAPP/wwzz33HGVlZWzfvp2ZM2cydOhQPv/8c3bu3MlvfvMbhg4dCsBvf/tbHnnkEQ444ADy8vLo168fENzhnn766QwbNozFixdz7bXXsm3bNtq3b8/DDz9Mx44dGTRoEP3792fOnDls2bKFhx56iP79+zNmzBh27NjBggULuPHGGxk+fHjK83B3tm7dyiGHHALA5s2bufjii3n//ffZc889mTBhAn369Kl0/dy5c/nFL34BBHW98+bNY/To0axYsYLCwkJGjBhB3759GTt2LLNmzeLWW29l3bp1vP/++6xbt45f/vKX8dLC7bffzpQpUzjggANo3749/fr147rrrvtOvL/73e8YO3Ys+++/PxB0Pbz00kt3O6/bbruNZ599lh07dnD00Ufzl7/8BTNj3LhxjB8/nqZNm9KrVy+mTZuW8hw2bdrE6aefztKlSznppJP49NNPKSws5M9//jMPPfRQWt/L0UcfzSuvvMKQIUP41a9+FY/t3XffpUWLFrRv3z6+burUqVx44YWsWLGCmTNn7lYySOX+++9nxIgRDBw4MB57YimmNtydl156KV6iGzFiBLfeeitXXHHFd/YzM8rKyvj6669xd3bu3EmHDh0AePDBB5k6dSp77BFU4Oy3337x9wwaNIhZs2Zx7rnn1ilOJQJpFJYtW8YRRxxR5T6vvfYaS5YsYZ999qG8vJwZM2bQpk0bPvvsMwYMGMCQIUN48803mTZtGm+99Rbl5eUcccQR8URQYefOnVx99dU888wz5OXlMX36dG6++WYmTZoEQHl5OW+88QazZ8/m17/+NS+++CK33XYbxcXF3HfffSljmz9/PoWFhWzatIm99tqLO+64A4BbbrmFvn378vTTT/PSSy/x05/+lJKSkkrXjx07lvvvv59jjjmGbdu20bJlS+688874hR+CqqhEK1euZM6cOWzdupUePXpwxRVX8Pbbb/Pkk09W+XcAWLp0acr1ya666irGjBkDwIUXXsisWbM444wzuPPOO1mzZg0tWrRgy5YtACnPIdHMmTM5/fTTKSkpAeChhx5K63vZsmULc+fO3S22V155Zbd/O9OnT+eFF15g1apV3HfffWklgqVLlzJixIhq91u1alWlNwIvv/wy7dq1iy9v2rSJdu3a0bRpcKnt3LkzH3300W7vGzhwIIMHD6Zjx464O1dddRU9e/YE4L333mP69OnMmDGDvLw8xo0bR/fu3QEoKipi/vz5SgTpmrpwHQvXbKZ/t30aOpRGr6o790wZNWoUCxYsoHnz5ixatAiAE088kX32Cb4/d+emm25i3rx57LHHHnz00Ud88sknzJ8/n7POOos999wTgCFDhux27FWrVrF06VJOPPFEAL755hs6duwY33722WcD0K9fP9auXZtWvMcee2z8Qn3XXXdx/fXXM378eBYsWMCTTz4JwPHHH8+mTZv44osvKl1/zDHHcO2113LBBRdw9tln07lz52o/+7TTTqNFixa0aNGC/fbbj08++YQFCxYwdOhQWrVqBcAZZ5yR1nlUZs6cOdx999189dVXbN68mfz8fM444wz69OnDBRdcwJlnnsmZZ54JUKtzgOq/l8ouvuvXrycvLy++vGjRIvLy8jjwwAPp3LkzF198MZ9//jnf+973UvaoqWkvmx49esSTWHU8Vs9f3eetXr2aFStWUFpaCgT/1ufNm8dxxx3Hv//9b1q2bElxcTFPPfUUF198MfPnzweC0sHHH39co/hTCbWx2MxONrNVZrbazEan2G5mNi62fYmZVX1LWAfPlARZeGhhp7A+QuogPz+fN998M758//33869//YuNGzfG1+21117x11OmTGHjxo0sXryYkpISOnToEO87Xd1/bHcnPz+fkpISSkpKeOedd/jnP/8Z396iRQsAmjRpQnl5eY3PZciQIcybNy/+WcnMrNL1o0ePZuLEiezYsYMBAwawcuXKaj+vIt7EmFMdP5X8/HwWL15c5T5lZWVceeWVPPHEE7zzzjtceuml8b/1c889x6hRo1i8eDH9+vWjvLy8VucA1X8vid9/olatWn2n3/xjjz3GypUr6dq1KwcffDBffvllPOnuu+++fP755/F9N2/eHK9SSudvAUHCKiwsTPlTUSqq0L59e7Zs2RL/d1RaWhqvhks0Y8YMBgwYwN57783ee+/NKaecwuuvvw4EpYhzzjkHgLPOOoslS5bE31dWVhZP9nURWiIwsybA/cApQC/gPDPrlbTbKUD32M9I4MGw4gHo320fzu/fJcyPkFo6/vjjKSsr48EHv/0nUFVvjS+++IL99tuPZs2aMWfOHD744AMAjjvuOGbMmMGOHTvYunUrzz777G7v7dGjBxs3buS1114DgiqJZcuWVRlf69at2bp1a1rnsmDBAg4++OB4PFOmTAGCaoP27dvTpk2bSte/99579O7dmxtuuIGioiJWrlxZo8+u8P3vf59nn32WsrIytm3bxnPPPZdyvxtvvJHrr7+eDRs2APDvf/873iulQsVFtn379mzbti3ek2jXrl18+OGHDB48mLvvvpstW7awbdu2lOeQjtp8LwA9e/Zk9erV8Zgef/xxlixZwtq1a1m7di3PPPMMjz32GBD0Gnr00UfjiXLy5MkMHjwYCKq/Jk+ezMKFC+PHfvTRR+N/m8Q4K5JV8k9itRAEyX3w4MHxv9nkyZPjbVmJunTpwty5cykvL2fnzp3MnTs3XjV05pln8tJLLwEwd+5cDj300Pj73n333e/0eqqtMEsERwGr3f19d/8amAYk/wWGAo/EGrVfB9qZWcfkA0nuMzOefvpp5s6dS7du3TjqqKMYMWIEd911V8r9L7jgAoqLiykqKmLKlCkcdthhABxxxBEMHz6cwsJCzjnnHI499tjd3tu8eXOeeOIJbrjhBgoKCigsLOTVV1+tMr7BgwezfPlyCgsLmT59+m7bK9oICgoK+Nvf/sbvf/97IOi+WlxcTJ8+fRg9ejSTJ0+ucv0f//hHDj/8cAoKCmjVqhWnnHIKffr0oWnTphQUFHDvvfem9fc88sgjGTJkCAUFBZx99tkUFRXRtm3b3fY79dRTGTVqFCeccAL5+fnxu/pE7dq149JLL6V3796ceeaZHHnkkUBQdfOTn/yE3r1707dvX/7rv/6Ldu3apTyHdNTme4Eg2b711lu4O/PmzaNTp0506tTpO9uXL1/O+vXrGTlyJK1bt6agoICCggK2bdsWb0Dv0KED06ZN47rrrqNHjx707NmT+fPn06ZNm7Tir8xdd93FH/7wBw455BA2bdrEz3/+cyDoBnzJJZcAMGzYMA4++GB69+4dj62iOm/06NE8+eST9O7dmxtvvJGJEyfGjz1nzhxOO+20OsUHhNd9FBgGTExYvhC4L2mfWcD3E5b/BRSlONZIoBgorm23s1tnLvVbZy6t1XujoKG7j0r927p1q7u7b9++3fv16+eLFy9u4IjCc8011/gLL7zQ0GFk1IYNG/z4449Pua0xdR9NVVGbXHGZzj64+wRgAkBRUVF6lZ9JGkMDqEgmjRw5kuXLl1NWVsaIESOq7ZWVzW666abvVOlEwbp16+Ilz7oKMxGUAgckLHcGkpu309lHRGoh8WnkXNehQ4eUPcRyWUUVXX0Is41gEdDdzLqZWXPgx8DMpH1mAj+N9R4aAHzh7utDjEmq4Gn2NBGRxqs2/49DKxG4e7mZXQU8DzQBJrn7MjO7PLZ9PDAbOBVYDXwF/CyseKRqLVu2ZNOmTRqKWiSLeWw+guSH+Kpj2XYXWFRU5ImDRkn90AxlIrmhshnKzGyxuxelek9kniyWqjVr1qxGMxqJSO7QMNQiIhGnRCAiEnFKBCIiEZd1jcVmthH4oJZvbw98Vo/hZAOdczTonKOhLud8oLvnpdqQdYmgLsysuLJW81ylc44GnXM0hHXOqhoSEYk4JQIRkYiLWiKY0NABNACdczTonKMhlHOOVBuBiIjsLmolAhERSaJEICIScTmZCMzsZDNbZWarzWx0iu1mZuNi25eYWdbP2JHGOV8QO9clZvaqmRU0RJz1qbpzTtjvSDP7xsyGZTK+MKRzzmY2yMxKzGyZmc3NdIz1LY1/223N7Fkzezt2zlk9irGZTTKzT81saSXb6//6VdnUZdn6QzDk9XvAQUBz4G2gV9I+pwJ/J5ghbQCwsKHjzsA5Hw18L/b6lCicc8J+LxEMeT6soePOwPfcDlgOdIkt79fQcWfgnG8C7oq9zgM2A80bOvY6nPNxwBHA0kq21/v1KxdLBEcBq939fXf/GpgGDE3aZyjwiAdeB9qZWcdMB1qPqj1nd3/V3T+PLb5OMBtcNkvnewa4GngS+DSTwYUknXM+H3jK3dcBuHu2n3c65+xAawsm0tibIBGUZzbM+uPu8wjOoTL1fv3KxUTQCfgwYbk0tq6m+2STmp7PzwnuKLJZtedsZp2As4DxGYwrTOl8z4cC3zOzl81ssZn9NGPRhSOdc74P6Ekwze07wC/cfVdmwmsQ9X79ysX5CFJNr5XcRzadfbJJ2udjZoMJEsH3Q40ofOmc8x+BG9z9mxyZdS2dc24K9AN+CLQCXjOz19393bCDC0k65/yfQAlwPHAw8IKZzXf3L0OOraHU+/UrFxNBKXBAwnJngjuFmu6TTdI6HzPrA0wETnH3TRmKLSzpnHMRMC2WBNoDp5pZubs/nZEI61+6/7Y/c/ftwHYzmwcUANmaCNI5558Bd3pQgb7azNYAhwFvZCbEjKv361cuVg0tArqbWTczaw78GJiZtM9M4Kex1vcBwBfuvj7Tgdajas/ZzLoATwEXZvHdYaJqz9ndu7l7V3fvCjwBXJnFSQDS+7f9DHCsmTU1sz2B/sCKDMdZn9I553UEJSDMrAPQA3g/o1FmVr1fv3KuRODu5WZ2FfA8QY+DSe6+zMwuj20fT9CD5FRgNfAVwR1F1krznMcA+wIPxO6Qyz2LR25M85xzSjrn7O4rzOwfwBJgFzDR3VN2Q8wGaX7PtwMPm9k7BNUmN7h71g5PbWaPAYOA9mZWCtwCNIPwrl8aYkJEJOJysWpIRERqQIlARCTilAhERCJOiUBEJOKUCEREIk6JQBql2GihJQk/XavYd1s9fN7DZrYm9llvmtnAWhxjopn1ir2+KWnbq3WNMXacir/L0tiIm+2q2b/QzE6tj8+W3KXuo9Iomdk2d9+7vvet4hgPA7Pc/QkzOwkY6+596nC8OsdU3XHNbDLwrrv/tor9LwKK3P2q+o5FcodKBJIVzGxvM/tX7G79HTPbbaRRM+toZvMS7piPja0/ycxei733cTOr7gI9Dzgk9t5rY8daama/jK3by8yei41/v9TMhsfWv2xmRWZ2J9AqFseU2LZtsd/TE+/QYyWRc8ysiZndY2aLLBhj/rI0/iyvERtszMyOsmCeibdiv3vEnsS9DRgei2V4LPZJsc95K9XfUSKoocfe1o9+Uv0A3xAMJFYCzCB4Cr5NbFt7gqcqK0q022K/fwXcHHvdBGgd23cesFds/Q3AmBSf9zCx+QqAHwELCQZvewfYi2B442VAX+Ac4H8T3ts29vtlgrvveEwJ+1TEeBYwOfa6OcEokq2AkcD/xNa3AIqBbini3JZwfo8DJ8eW2wBNY69PAJ6Mvb4IuC/h/XcAP4m9bkcwBtFeDf1966dhf3JuiAnJGTvcvbBiwcyaAXeY2XEEQyd0AjoAGxLeswiYFNv3aXcvMbMfAL2AV2JDazQnuJNO5R4z+x9gI8EIrT8EZngwgBtm9hRwLPAPYKyZ3UVQnTS/Buf1d2CcmbUATgbmufuOWHVUH/t2FrW2QHdgTdL7W5lZCdAVWAy8kLD/ZDPrTjASZbNKPv8kYIiZXRdbbgl0IbvHI5I6UiKQbHEBwexT/dx9p5mtJbiIxbn7vFiiOA34m5ndA3wOvODu56XxGf/t7k9ULJjZCal2cvd3zawfwXgvvzOzf7r7bemchLuXmdnLBEMnDwceq/g44Gp3f76aQ+xw90IzawvMAkYB4wjG25nj7mfFGtZfruT9Bpzj7qvSiVeiQW0Eki3aAp/GksBg4MDkHczswNg+/ws8RDDd3+vAMWZWUee/p5kdmuZnzgPOjL1nL4Jqnflmtj/wlbs/CoyNfU6ynbGSSSrTCAYKO5ZgMDViv6+oeI+ZHRr7zJTc/QvgGuC62HvaAh/FNl+UsOtWgiqyCs8DV1useGRmfSv7DIkOJQLJFlOAIjMrJigdrEyxzyCgxMzeIqjH/5O7byS4MD5mZksIEsNh6Xygu79J0HbwBkGbwUR3fwvoDbwRq6K5GfhNirdPAJZUNBYn+SfBvLQvejD9IgTzRCwH3rRg0vK/UE2JPRbL2wRDM99NUDp5haD9oMIcoFdFYzFByaFZLLalsWWJOHUfFRGJOJUIREQiTolARCTilAhERCJOiUBEJOKUCEREIk6JQEQk4pQIREQi7v8DQx2ZjHAKss0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(final_model, X_hold, y_hold, name='Gradient Boosting Classifier');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# we can calculate predictions for confusion matrix\n",
    "hold_preds_final_model = final_model.predict(X_hold)\n",
    "cm=confusion_matrix(y_hold, hold_preds_final_model, normalize='true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "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 heatmap for final model's confusion matrix for better visualization \n",
    "ax= plt.subplot()\n",
    "sns.heatmap(cm, annot=True, ax=ax, cbar=False, linewidths=1, cmap=\"YlGnBu\");  \n",
    "\n",
    "ax.set_title('Confusion Matrix')\n",
    "ax.set_xlabel('Predictions', fontsize = 15)\n",
    "ax.set_ylabel('Actuals', fontsize = 15)\n",
    "ax.xaxis.set_ticklabels(['Not Vaccinated', 'Vaccinated'])\n",
    "ax.yaxis.set_ticklabels(['Not Vaccinated', 'Vaccinated'], rotation = 0, fontsize = 10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Evaluation\n",
    "Our baseline model had an accuracy score of 78%, but a score of zero for precision, recall, and fl scores. When we compare all of our following models to this baseline, all have much better precision, recall, and f1 scores, and many have higher accuracy scores. The decision tree model is not overfitting, but it has a low precision score, as well as a low f1 score. However, it has an AUC score of 0.84, which is fairly high, meaning that it does an adequate job of maximizing true positives and minimizing the false positives. The decision tree model is not overfitting. This logistic regression model has low precision and fl scores, and has an AUC equal to the decision tree AUC above. This model is not overfitting. The random forest classifier model also has low precision and fl scores. It has an AUC score of 0.85, which is slightly better than the decision tree model above. This model is not overfitting to a great extent. The KNN model is definitely overfitting; the training data has perfect scores for all metrics, whereas the testing data scores are much lower. The AUC score is also much lower than on previous models. The Gradient Boosting model has the highest overall scores of all the models done so far, and also does the best job of minimizing the false positives. This is our candidate for the final model. The XG Boost model gave us similar scores to the gradient boosting model, but the gradient boosting model has the best AUC score and precision score. So, we will choose the gradient boosting classifier as the final model.\n",
    "\n",
    "Our final model did not overfit to the training set, we got similar AUC, precision and accuracy scores for the holdout set. Because the model does a good job of minimizing the false positive rate on the hold out data, we are fairly confident that it will generalize well to unseen data and will accurately help public health offials determine the people who didn't get the vaccine. We are going to look into feature importances to understand the relationship between the features and vaccination behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('num_imputer',\n",
       "                                                  IterativeImputer(max_iter=100,\n",
       "                                                                   random_state=42)),\n",
       "                                                 ('minmaxscaler',\n",
       "                                                  MinMaxScaler())]),\n",
       "                                 ['h1n1_concern', '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...\n",
       "                                 ['age_group', 'education', 'race', 'sex',\n",
       "                                  'income_poverty', 'marital_status',\n",
       "                                  'rent_or_own', 'employment_status',\n",
       "                                  'census_msa']),\n",
       "                                ('freq',\n",
       "                                 Pipeline(steps=[('freq_encoder',\n",
       "                                                  CountEncoder(combine_min_nan_groups=True,\n",
       "                                                               min_group_size=0.05,\n",
       "                                                               normalize=True)),\n",
       "                                                 ('freq_imputer',\n",
       "                                                  IterativeImputer(max_iter=100,\n",
       "                                                                   random_state=42))]),\n",
       "                                 ['hhs_geo_region', 'employment_industry',\n",
       "                                  'employment_occupation'])])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the features of the preproccesor\n",
    "final_model['preprocessor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19228, 59)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are looking at the shape of the preprocessor transformation.\n",
    "final_model['preprocessor'].fit_transform(X_tr).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.016076</td>\n",
       "      <td>0.963804</td>\n",
       "      <td>0.038064</td>\n",
       "      <td>0.894508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116478</td>\n",
       "      <td>0.141558</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.779010</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.296807</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.279800</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</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>0.0</td>\n",
       "      <td>1.0</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>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>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.106407</td>\n",
       "      <td>0.499168</td>\n",
       "      <td>0.504213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016076</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038064</td>\n",
       "      <td>0.894508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116478</td>\n",
       "      <td>0.141558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110629</td>\n",
       "      <td>0.920662</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.765602</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.759933</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>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>1.0</td>\n",
       "      <td>0.160235</td>\n",
       "      <td>0.340961</td>\n",
       "      <td>0.371854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.016076</td>\n",
       "      <td>0.963804</td>\n",
       "      <td>0.038064</td>\n",
       "      <td>0.894508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110629</td>\n",
       "      <td>0.920662</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.296807</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.279800</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</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>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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.076659</td>\n",
       "      <td>0.499168</td>\n",
       "      <td>0.504213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.016076</td>\n",
       "      <td>0.963804</td>\n",
       "      <td>0.038064</td>\n",
       "      <td>0.894508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116478</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110629</td>\n",
       "      <td>0.920662</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.296807</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.279800</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160235</td>\n",
       "      <td>0.499168</td>\n",
       "      <td>0.504213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016076</td>\n",
       "      <td>0.963804</td>\n",
       "      <td>0.038064</td>\n",
       "      <td>0.894508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116478</td>\n",
       "      <td>0.141558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.920662</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.062409</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.279800</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>0.160235</td>\n",
       "      <td>0.340961</td>\n",
       "      <td>0.371854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0    1         2         3         4         5    6         7    8   \\\n",
       "0  1.000000  0.5  0.016076  0.963804  0.038064  0.894508  0.0  1.000000  0.0   \n",
       "1  0.666667  1.0  0.016076  0.000000  0.038064  0.894508  0.0  0.013143  1.0   \n",
       "2  0.333333  0.5  0.016076  0.963804  0.038064  0.894508  0.0  0.013143  0.0   \n",
       "3  0.333333  0.5  0.016076  0.963804  0.038064  0.894508  0.0  0.013143  1.0   \n",
       "4  0.333333  1.0  0.016076  0.963804  0.038064  0.894508  0.0  0.013143  1.0   \n",
       "\n",
       "         9         10   11   12        13        14    15        16    17  \\\n",
       "0  0.116478  0.141558  1.0  0.0  1.000000  0.779010  0.75  0.296807  0.75   \n",
       "1  0.116478  0.141558  0.0  0.0  0.110629  0.920662  0.75  0.765602  0.25   \n",
       "2  1.000000  1.000000  0.0  0.0  0.110629  0.920662  0.75  0.296807  0.00   \n",
       "3  0.116478  1.000000  0.0  0.0  0.110629  0.920662  0.75  0.296807  0.25   \n",
       "4  0.116478  0.141558  0.0  1.0  1.000000  0.920662  0.25  0.062409  0.00   \n",
       "\n",
       "     18        19    20        21        22   23   24   25   26   27   28  \\\n",
       "0  0.75  0.279800  0.75  0.333333  0.333333  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "1  0.75  0.759933  0.25  0.333333  0.000000  0.0  0.0  1.0  0.0  0.0  1.0   \n",
       "2  1.00  0.279800  0.00  0.333333  0.000000  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       "3  0.75  0.279800  0.25  0.000000  0.000000  0.0  0.0  0.0  0.0  1.0  1.0   \n",
       "4  0.75  0.279800  0.25  0.666667  0.333333  0.0  0.0  1.0  0.0  0.0  0.0   \n",
       "\n",
       "    29   30   31   32   33   34   35   36   37   38   39   40   41   42   43  \\\n",
       "0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  1.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0   \n",
       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       "4  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "    44   45   46   47   48   49   50   51   52   53   54   55        56  \\\n",
       "0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.106407   \n",
       "1  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.160235   \n",
       "2  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.076659   \n",
       "3  1.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.160235   \n",
       "4  1.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.160235   \n",
       "\n",
       "         57        58  \n",
       "0  0.499168  0.504213  \n",
       "1  0.340961  0.371854  \n",
       "2  0.499168  0.504213  \n",
       "3  0.499168  0.504213  \n",
       "4  0.340961  0.371854  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are looking at the transformed data\n",
    "pd.DataFrame(final_model['preprocessor'].fit_transform(X_tr)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.11492608e-02, 1.16198447e-02, 5.50352362e-03, 3.14729500e-03,\n",
       "       5.05099935e-03, 3.02963189e-03, 2.89312301e-03, 3.00118538e-03,\n",
       "       1.48503032e-03, 3.04514685e-01, 2.08734341e-02, 7.16371985e-03,\n",
       "       8.33912735e-03, 3.79441735e-02, 1.63029370e-01, 1.19087364e-01,\n",
       "       9.45289860e-02, 1.36968164e-02, 1.29758084e-02, 3.14383678e-02,\n",
       "       1.11118335e-02, 8.91720376e-03, 7.43287360e-03, 2.67321629e-03,\n",
       "       2.48171435e-03, 1.56316928e-03, 4.27999277e-03, 6.97286357e-03,\n",
       "       2.28525154e-03, 5.63344331e-03, 5.39853837e-03, 1.34372476e-03,\n",
       "       3.79157693e-03, 5.20576056e-03, 1.97704964e-03, 1.94393889e-03,\n",
       "       2.94627450e-03, 2.53498163e-03, 1.98454794e-03, 1.53787250e-03,\n",
       "       2.92085468e-03, 1.71900606e-03, 1.85018762e-03, 2.24159242e-03,\n",
       "       2.11303485e-03, 2.87282421e-04, 8.72081871e-04, 2.57243362e-03,\n",
       "       9.00035843e-04, 1.70701140e-03, 1.24244582e-03, 1.81431173e-03,\n",
       "       1.98048835e-03, 1.89024640e-03, 1.68921617e-03, 2.29535857e-03,\n",
       "       1.57604344e-02, 9.24109291e-03, 4.41531129e-03])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are looking at the feature importances coming out of. the gradient boosting classifier\n",
    "final_model['classifier'].feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age_group_18 - 34 Years', 'age_group_35 - 44 Years',\n",
       "       'age_group_45 - 54 Years', 'age_group_55 - 64 Years',\n",
       "       'age_group_65+ Years', 'education_12 Years',\n",
       "       'education_< 12 Years', 'education_College Graduate',\n",
       "       'education_Some College', 'education_Unknown', 'race_Black',\n",
       "       'race_Hispanic', 'race_Other or Multiple', 'race_White',\n",
       "       'sex_Female', 'sex_Male',\n",
       "       'income_poverty_<= $75,000, Above Poverty',\n",
       "       'income_poverty_> $75,000', 'income_poverty_Below Poverty',\n",
       "       'income_poverty_Unknown', 'marital_status_Married',\n",
       "       'marital_status_Not Married', 'marital_status_Unknown',\n",
       "       'rent_or_own_Own', 'rent_or_own_Rent', 'rent_or_own_Unknown',\n",
       "       'employment_status_Employed',\n",
       "       'employment_status_Not in Labor Force',\n",
       "       'employment_status_Unemployed', 'employment_status_Unknown',\n",
       "       'census_msa_MSA, Not Principle  City',\n",
       "       'census_msa_MSA, Principle City', 'census_msa_Non-MSA'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are ohe_transformed columns!\n",
    "ohe_cols_transformed = final_model['preprocessor'].transformers_[1][1]['ohe_encoder'].get_feature_names(ohe_cols)\n",
    "ohe_cols_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name of the numerical columns\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hhs_geo_region', 'employment_industry', 'employment_occupation']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name of the frequency columns\n",
    "freq_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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',\n",
       " 'age_group_18 - 34 Years',\n",
       " 'age_group_35 - 44 Years',\n",
       " 'age_group_45 - 54 Years',\n",
       " 'age_group_55 - 64 Years',\n",
       " 'age_group_65+ Years',\n",
       " 'education_12 Years',\n",
       " 'education_< 12 Years',\n",
       " 'education_College Graduate',\n",
       " 'education_Some College',\n",
       " 'education_Unknown',\n",
       " 'race_Black',\n",
       " 'race_Hispanic',\n",
       " 'race_Other or Multiple',\n",
       " 'race_White',\n",
       " 'sex_Female',\n",
       " 'sex_Male',\n",
       " 'income_poverty_<= $75,000, Above Poverty',\n",
       " 'income_poverty_> $75,000',\n",
       " 'income_poverty_Below Poverty',\n",
       " 'income_poverty_Unknown',\n",
       " 'marital_status_Married',\n",
       " 'marital_status_Not Married',\n",
       " 'marital_status_Unknown',\n",
       " 'rent_or_own_Own',\n",
       " 'rent_or_own_Rent',\n",
       " 'rent_or_own_Unknown',\n",
       " 'employment_status_Employed',\n",
       " 'employment_status_Not in Labor Force',\n",
       " 'employment_status_Unemployed',\n",
       " 'employment_status_Unknown',\n",
       " 'census_msa_MSA, Not Principle  City',\n",
       " 'census_msa_MSA, Principle City',\n",
       " 'census_msa_Non-MSA',\n",
       " 'hhs_geo_region',\n",
       " 'employment_industry',\n",
       " 'employment_occupation']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Putting all columns together\n",
    "final_cols = [*num_cols, *ohe_cols_transformed, *freq_cols]\n",
    "final_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>doctor_recc_h1n1</th>\n",
       "      <td>0.304515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_insurance</th>\n",
       "      <td>0.163029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_h1n1_vacc_effective</th>\n",
       "      <td>0.119087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_h1n1_risk</th>\n",
       "      <td>0.094529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health_worker</th>\n",
       "      <td>0.037944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_seas_risk</th>\n",
       "      <td>0.031438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doctor_recc_seasonal</th>\n",
       "      <td>0.020873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hhs_geo_region</th>\n",
       "      <td>0.015760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_h1n1_sick_from_vacc</th>\n",
       "      <td>0.013697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_seas_vacc_effective</th>\n",
       "      <td>0.012976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_knowledge</th>\n",
       "      <td>0.011620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_concern</th>\n",
       "      <td>0.011149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opinion_seas_sick_from_vacc</th>\n",
       "      <td>0.011112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_industry</th>\n",
       "      <td>0.009241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_adults</th>\n",
       "      <td>0.008917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child_under_6_months</th>\n",
       "      <td>0.008339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_children</th>\n",
       "      <td>0.007433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronic_med_condition</th>\n",
       "      <td>0.007164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group_65+ Years</th>\n",
       "      <td>0.006973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_&lt; 12 Years</th>\n",
       "      <td>0.005633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_antiviral_meds</th>\n",
       "      <td>0.005504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_College Graduate</th>\n",
       "      <td>0.005399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Black</th>\n",
       "      <td>0.005206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_face_mask</th>\n",
       "      <td>0.005051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_occupation</th>\n",
       "      <td>0.004415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group_55 - 64 Years</th>\n",
       "      <td>0.004280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_Unknown</th>\n",
       "      <td>0.003792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_avoidance</th>\n",
       "      <td>0.003147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_wash_hands</th>\n",
       "      <td>0.003030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_outside_home</th>\n",
       "      <td>0.003001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_White</th>\n",
       "      <td>0.002946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_poverty_&gt; $75,000</th>\n",
       "      <td>0.002921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_large_gatherings</th>\n",
       "      <td>0.002893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group_18 - 34 Years</th>\n",
       "      <td>0.002673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rent_or_own_Rent</th>\n",
       "      <td>0.002572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_Female</th>\n",
       "      <td>0.002535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group_35 - 44 Years</th>\n",
       "      <td>0.002482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>census_msa_Non-MSA</th>\n",
       "      <td>0.002295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_12 Years</th>\n",
       "      <td>0.002285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_status_Married</th>\n",
       "      <td>0.002242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_status_Not Married</th>\n",
       "      <td>0.002113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_Male</th>\n",
       "      <td>0.001985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_status_Unknown</th>\n",
       "      <td>0.001980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Hispanic</th>\n",
       "      <td>0.001977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Other or Multiple</th>\n",
       "      <td>0.001944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>census_msa_MSA, Not Principle  City</th>\n",
       "      <td>0.001890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_poverty_Unknown</th>\n",
       "      <td>0.001850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_status_Unemployed</th>\n",
       "      <td>0.001814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_poverty_Below Poverty</th>\n",
       "      <td>0.001719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_status_Employed</th>\n",
       "      <td>0.001707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>census_msa_MSA, Principle City</th>\n",
       "      <td>0.001689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group_45 - 54 Years</th>\n",
       "      <td>0.001563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_poverty_&lt;= $75,000, Above Poverty</th>\n",
       "      <td>0.001538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>behavioral_touch_face</th>\n",
       "      <td>0.001485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_Some College</th>\n",
       "      <td>0.001344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_status_Not in Labor Force</th>\n",
       "      <td>0.001242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rent_or_own_Unknown</th>\n",
       "      <td>0.000900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rent_or_own_Own</th>\n",
       "      <td>0.000872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_status_Unknown</th>\n",
       "      <td>0.000287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0\n",
       "doctor_recc_h1n1                          0.304515\n",
       "health_insurance                          0.163029\n",
       "opinion_h1n1_vacc_effective               0.119087\n",
       "opinion_h1n1_risk                         0.094529\n",
       "health_worker                             0.037944\n",
       "opinion_seas_risk                         0.031438\n",
       "doctor_recc_seasonal                      0.020873\n",
       "hhs_geo_region                            0.015760\n",
       "opinion_h1n1_sick_from_vacc               0.013697\n",
       "opinion_seas_vacc_effective               0.012976\n",
       "h1n1_knowledge                            0.011620\n",
       "h1n1_concern                              0.011149\n",
       "opinion_seas_sick_from_vacc               0.011112\n",
       "employment_industry                       0.009241\n",
       "household_adults                          0.008917\n",
       "child_under_6_months                      0.008339\n",
       "household_children                        0.007433\n",
       "chronic_med_condition                     0.007164\n",
       "age_group_65+ Years                       0.006973\n",
       "education_< 12 Years                      0.005633\n",
       "behavioral_antiviral_meds                 0.005504\n",
       "education_College Graduate                0.005399\n",
       "race_Black                                0.005206\n",
       "behavioral_face_mask                      0.005051\n",
       "employment_occupation                     0.004415\n",
       "age_group_55 - 64 Years                   0.004280\n",
       "education_Unknown                         0.003792\n",
       "behavioral_avoidance                      0.003147\n",
       "behavioral_wash_hands                     0.003030\n",
       "behavioral_outside_home                   0.003001\n",
       "race_White                                0.002946\n",
       "income_poverty_> $75,000                  0.002921\n",
       "behavioral_large_gatherings               0.002893\n",
       "age_group_18 - 34 Years                   0.002673\n",
       "rent_or_own_Rent                          0.002572\n",
       "sex_Female                                0.002535\n",
       "age_group_35 - 44 Years                   0.002482\n",
       "census_msa_Non-MSA                        0.002295\n",
       "education_12 Years                        0.002285\n",
       "marital_status_Married                    0.002242\n",
       "marital_status_Not Married                0.002113\n",
       "sex_Male                                  0.001985\n",
       "employment_status_Unknown                 0.001980\n",
       "race_Hispanic                             0.001977\n",
       "race_Other or Multiple                    0.001944\n",
       "census_msa_MSA, Not Principle  City       0.001890\n",
       "income_poverty_Unknown                    0.001850\n",
       "employment_status_Unemployed              0.001814\n",
       "income_poverty_Below Poverty              0.001719\n",
       "employment_status_Employed                0.001707\n",
       "census_msa_MSA, Principle City            0.001689\n",
       "age_group_45 - 54 Years                   0.001563\n",
       "income_poverty_<= $75,000, Above Poverty  0.001538\n",
       "behavioral_touch_face                     0.001485\n",
       "education_Some College                    0.001344\n",
       "employment_status_Not in Labor Force      0.001242\n",
       "rent_or_own_Unknown                       0.000900\n",
       "rent_or_own_Own                           0.000872\n",
       "marital_status_Unknown                    0.000287"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put together the final cols and the feature importances\n",
    "# Then sort them by feature importances\n",
    "pd.DataFrame.from_dict(dict(zip(final_cols, final_model['classifier'].feature_importances_)), \n",
    "                       orient='index').sort_values(by=0, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We saw that the demographic and behavioral features are not that important compared to health related factors and opinions. The doctor recommendation, health insurance, opinion of vaccine efficiency, and opinion of H1N1 risk are the top important factors in determining people's vaccination status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions & Recommendations\n",
    "\n",
    "We recommend that public health officials at the American Public Health Association (APHA) communicate to doctors the importance of recommending to patients that they get the H1N1 vaccine. We also recommend that they find a way to make the vaccine accesible to people regardless of health insurance status. Additionally, because opinion on H1N1 vaccine effectiveness and H1N1 risk to health are highly influential in determining vaccination status, we recommend that the APHA make educational outreach a priority.\n",
    "Our analysis might not fully resolve the goal of predicting H1N1 vaccination status because we were not able to fully rule out false negatives, or people who we predicted as not getting vaccinated but actually did get the vaccine. Additionally, there are other factors at play which were not tapped into by this survey's questions which could also paly a role in vaccination prediction. \n",
    "For our next steps, we would like to look at more recent flu survey data, so as to get the most recent results. We would also like to do more feature engineering to improve accuracy. Lastly, since we chose to focus only on predicting H1N1 vaccination status, we would like to focus in the future on predicting seasonal flu vaccine status."
   ]
  }
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