ProjectNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **H1N1 AND SEASONAL FLU VACCINES**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:** [Wallace Ouma](https://github.com/WKalawi)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business and Data Understanding\n",
    "\n",
    "Data Source: [DrivenData](https://www.drivendata.org/competitions/66/flu-shot-learning/)\n",
    "\n",
    "This project focuses on vaccination, a crucial public health strategy against infectious diseases, providing individuals with immunization and contributing to \"herd immunity\" when sufficiently prevalent within a community. While vaccines for COVID-19 are still in development at the time of this competition's launch, we will instead examine the public health response to a recent significant respiratory disease pandemic. Originating in spring 2009, the H1N1 influenza virus, commonly known as \"swine flu,\" rapidly spread worldwide, causing a pandemic. Estimates suggest this virus led to between 151,000 to 575,000 deaths globally within its first year. A vaccine for H1N1 became available to the public in October 2009. Concurrently, the United States conducted the National 2009 H1N1 Flu Survey in late 2009 and early 2010, a phone survey querying respondents about their H1N1 and seasonal flu vaccine uptake alongside various personal details. These included social, economic, and demographic backgrounds, perceptions regarding illness and vaccine efficacy risks, and behaviors related to transmission prevention. Understanding how these factors interrelate with individual vaccination behavior can offer valuable insights for future public health strategies.\n",
    "\n",
    "In this evaluation, the target variable pertains to whether an individual will voluntarily receive either an influenza or H1N1 vaccine, as indicated by past data randomly gathered from households across the United States. This data collection was conducted through random-digit-dialing of telephones within these households. Two distinct target variables exist: one for the H1N1 vaccine and another for the Seasonal Flu vaccine. These target variables are already clearly defined and binary, simplifying the preprocessing process. Given that the dataset is comprehensive, this evaluation will be approached as a supervised learning experience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "***\n",
    "\n",
    "**Import Data and Explore Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import warning\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</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",
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       "      <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",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</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",
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       "      <td>1.0</td>\n",
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       "      <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>...</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",
       "    </tr>\n",
       "    <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",
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       "      <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>...</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",
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       "      <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>...</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",
       "<p>5 rows × 38 columns</p>\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  ...  rent_or_own   employment_status  \\\n",
       "0                    1.0  ...          Own  Not in Labor Force   \n",
       "1                    1.0  ...         Rent            Employed   \n",
       "2                    0.0  ...          Own            Employed   \n",
       "3                    0.0  ...         Rent  Not in Labor Force   \n",
       "4                    1.0  ...          Own            Employed   \n",
       "\n",
       "   hhs_geo_region                census_msa  household_adults  \\\n",
       "0        oxchjgsf                   Non-MSA               0.0   \n",
       "1        bhuqouqj  MSA, Not Principle  City               0.0   \n",
       "2        qufhixun  MSA, Not Principle  City               2.0   \n",
       "3        lrircsnp       MSA, Principle City               0.0   \n",
       "4        qufhixun  MSA, Not Principle  City               1.0   \n",
       "\n",
       "   household_children  employment_industry  employment_occupation  \\\n",
       "0                 0.0                  NaN                    NaN   \n",
       "1                 0.0             pxcmvdjn               xgwztkwe   \n",
       "2                 0.0             rucpziij               xtkaffoo   \n",
       "3                 0.0                  NaN                    NaN   \n",
       "4                 0.0             wxleyezf               emcorrxb   \n",
       "\n",
       "   h1n1_vaccine  seasonal_vaccine  \n",
       "0             0                 0  \n",
       "1             0                 1  \n",
       "2             0                 0  \n",
       "3             0                 1  \n",
       "4             0                 0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV files\n",
    "training_labels = pd.read_csv('Datasets/training_set_labels.csv')\n",
    "training_features = pd.read_csv('Datasets/training_set_features.csv')\n",
    "\n",
    "# Merge the DataFrames on the common column 'respondent ID'\n",
    "df = pd.merge(training_features,training_labels, on='respondent_id', how='inner')\n",
    "\n",
    "# Write the merged DataFrame to a new CSV file\n",
    "df.to_csv('Datasets/training_set.csv', index = False)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26707, 38)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "respondent_id                    int64\n",
       "h1n1_concern                   float64\n",
       "h1n1_knowledge                 float64\n",
       "behavioral_antiviral_meds      float64\n",
       "behavioral_avoidance           float64\n",
       "behavioral_face_mask           float64\n",
       "behavioral_wash_hands          float64\n",
       "behavioral_large_gatherings    float64\n",
       "behavioral_outside_home        float64\n",
       "behavioral_touch_face          float64\n",
       "doctor_recc_h1n1               float64\n",
       "doctor_recc_seasonal           float64\n",
       "chronic_med_condition          float64\n",
       "child_under_6_months           float64\n",
       "health_worker                  float64\n",
       "health_insurance               float64\n",
       "opinion_h1n1_vacc_effective    float64\n",
       "opinion_h1n1_risk              float64\n",
       "opinion_h1n1_sick_from_vacc    float64\n",
       "opinion_seas_vacc_effective    float64\n",
       "opinion_seas_risk              float64\n",
       "opinion_seas_sick_from_vacc    float64\n",
       "age_group                       object\n",
       "education                       object\n",
       "race                            object\n",
       "sex                             object\n",
       "income_poverty                  object\n",
       "marital_status                  object\n",
       "rent_or_own                     object\n",
       "employment_status               object\n",
       "hhs_geo_region                  object\n",
       "census_msa                      object\n",
       "household_adults               float64\n",
       "household_children             float64\n",
       "employment_industry             object\n",
       "employment_occupation           object\n",
       "h1n1_vaccine                     int64\n",
       "seasonal_vaccine                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inspecting the data tyoes in the dataset\n",
    "df.dtypes  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in the DataFrame and sum them up\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Target Variables**\n",
    "\n",
    "***Seasonal Flu Vaccine***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seasonal_vaccine\n",
       "0    14272\n",
       "1    12435\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of occurrences of each unique value in the 'seasonal_vaccine' column\n",
    "df['seasonal_vaccine'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of occurrences of each unique value in the 'seasonal_vaccine' column\n",
    "temp = df[\"seasonal_vaccine\"].value_counts()\n",
    "\n",
    "# Create a DataFrame to store the counts\n",
    "dfflubal = pd.DataFrame({'seasonal_vaccine': temp.index,'values': temp.values})\n",
    "\n",
    "# Create a bar plot to visualize the counts\n",
    "plt.figure(figsize = (3,3))\n",
    "sns.barplot(x = 'seasonal_vaccine', y=\"values\", data=dfflubal)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count of individuals who received the flu shot is almost equivalent to those who didn't. This balance contrasts with the considerable difference seen in the recipients of the `H1N1 vaccine`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***H1N1 Vaccine***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h1n1_vaccine\n",
       "0    21033\n",
       "1     5674\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of occurrences of each unique value in the 'h1n1_vaccine' column\n",
    "df['h1n1_vaccine'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of occurrences of each unique value in the 'h1n1_vaccine' column\n",
    "temp = df[\"h1n1_vaccine\"].value_counts()\n",
    "\n",
    "# Create a DataFrame to store the counts\n",
    "dfh1n1bal = pd.DataFrame({'h1n1_vaccine': temp.index,'values': temp.values})\n",
    "\n",
    "# Create a bar plot to visualize the counts\n",
    "plt.figure(figsize = (3,3))\n",
    "sns.barplot(x = 'h1n1_vaccine', y=\"values\", data=dfh1n1bal)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference in the number of individuals who received the `H1N1 vaccine` compared to those who did not is significant. The number of individuals who did not receive the vaccine is nearly four times greater than those who did. To accurately assess this situation, we should consider alternative metrics instead of relying solely on conventional averages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Null Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through each column in the DataFrame\n",
    "for column in df.columns:\n",
    "    # Replace missing values in the current column with the most frequent value\n",
    "    df[column].fillna(df[column].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26707, 38)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve the new shape of the DataFrame\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "respondent_id                  0\n",
       "h1n1_concern                   0\n",
       "h1n1_knowledge                 0\n",
       "behavioral_antiviral_meds      0\n",
       "behavioral_avoidance           0\n",
       "behavioral_face_mask           0\n",
       "behavioral_wash_hands          0\n",
       "behavioral_large_gatherings    0\n",
       "behavioral_outside_home        0\n",
       "behavioral_touch_face          0\n",
       "doctor_recc_h1n1               0\n",
       "doctor_recc_seasonal           0\n",
       "chronic_med_condition          0\n",
       "child_under_6_months           0\n",
       "health_worker                  0\n",
       "health_insurance               0\n",
       "opinion_h1n1_vacc_effective    0\n",
       "opinion_h1n1_risk              0\n",
       "opinion_h1n1_sick_from_vacc    0\n",
       "opinion_seas_vacc_effective    0\n",
       "opinion_seas_risk              0\n",
       "opinion_seas_sick_from_vacc    0\n",
       "age_group                      0\n",
       "education                      0\n",
       "race                           0\n",
       "sex                            0\n",
       "income_poverty                 0\n",
       "marital_status                 0\n",
       "rent_or_own                    0\n",
       "employment_status              0\n",
       "hhs_geo_region                 0\n",
       "census_msa                     0\n",
       "household_adults               0\n",
       "household_children             0\n",
       "employment_industry            0\n",
       "employment_occupation          0\n",
       "h1n1_vaccine                   0\n",
       "seasonal_vaccine               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recheck for missing values in the DataFrame and sum them up\n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In examining the dataset, it becomes evident that several features, including `health_insurance`, `income_poverty`, `employment_industry`, and `employment_occupation`, contain significant amounts of missing values. Notably, there seems to be a potential correlation between `employment_industry` and `employment_occupation`, likely due to their similar nature. For now, the analysis will proceed without these variables. However, it's essential to acknowledge that their absence might impact the model's accuracy. Should the model's performance suffer, a reevaluation of these features will be necessary to determine suitable replacement values. Despite these omissions, the dataset still retains numerous other features without null values, which will be the basis for evaluation.\n",
    "\n",
    "By initially excluding the features with substantial null values, the analysis aims to mitigate potential biases introduced by incomplete data. This approach allows for a focused examination of the dataset's remaining 32 features, providing insights into their individual contributions to the model's predictive capabilities. However, it's crucial to remain vigilant throughout the analysis process, as the exclusion of certain variables may inadvertently overlook valuable information. Regular reassessment and refinement of the modeling strategy will be essential to ensure a comprehensive understanding of the dataset and accurate predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Engineering for Variable Exploration**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, variables previously categorized as \"object\" will be coded into integers. The aim is to convert categorical responses into numerical ones, facilitating their more intuitive utilization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert age groups from categorical to numerical values\n",
    "# Assigning numerical values to each age group for better representation\n",
    "df.age_group[df.age_group == '18 - 34 Years'] = 1\n",
    "df.age_group[df.age_group == '35 - 44 Years'] = 2\n",
    "df.age_group[df.age_group == '45 - 54 Years'] = 3\n",
    "df.age_group[df.age_group == '55 - 64 Years'] = 4\n",
    "df.age_group[df.age_group == '65+ Years'] = 5\n",
    "\n",
    "# Convert the 'age_group' column to integer type\n",
    "df.age_group = df.age_group.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert education levels from categorical to numerical values\n",
    "# Assigning numerical values to each education level for better representation\n",
    "df.education[df.education == '< 12 Years'] = 1\n",
    "df.education[df.education == '12 Years'] = 2\n",
    "df.education[df.education == 'College Graduate'] = 3\n",
    "df.education[df.education == 'Some College'] = 4\n",
    "\n",
    "# Convert the 'education' column to integer type\n",
    "df.education = df.education.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert race categories from categorical to numerical values\n",
    "# Assigning numerical values to each race category for better representation\n",
    "df.race[df.race == 'Black'] = 1\n",
    "df.race[df.race == 'Hispanic'] = 2\n",
    "df.race[df.race == 'Other or Multiple'] = 3\n",
    "df.race[df.race == 'White'] = 4\n",
    "\n",
    "# Convert the 'race' column to integer type\n",
    "df.race = df.race.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert gender categories from categorical to numerical values\n",
    "# Assigning numerical values to each gender category for better representation\n",
    "df.sex[df.sex == 'Female'] = 0\n",
    "df.sex[df.sex == 'Male'] = 1\n",
    "\n",
    "# Convert the 'sex' column to integer type\n",
    "df.sex = df.sex.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert marital status categories from categorical to numerical values\n",
    "# Assigning numerical values to each marital status category for better representation\n",
    "df.marital_status[df.marital_status == 'Married'] = 0\n",
    "df.marital_status[df.marital_status == 'Not Married'] = 1\n",
    "\n",
    "# Convert the 'marital_status' column to integer type\n",
    "df.marital_status = df.marital_status.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert housing status categories from categorical to numerical values\n",
    "# Assigning numerical values to each housing status category for better representation\n",
    "df.rent_or_own[df.rent_or_own == 'Rent'] = 0\n",
    "df.rent_or_own[df.rent_or_own == 'Own'] = 1\n",
    "\n",
    "# Convert the 'rent_or_own' column to integer type\n",
    "df.rent_or_own = df.rent_or_own.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert employment status categories from categorical to numerical values\n",
    "# Assigning numerical values to each employment status category for better representation\n",
    "df.employment_status[df.employment_status == 'Employed'] = 1\n",
    "df.employment_status[df.employment_status == 'Not in Labor Force'] = 2\n",
    "df.employment_status[df.employment_status == 'Unemployed'] = 3\n",
    "\n",
    "# Convert the 'employment_status' column to integer type\n",
    "df.employment_status = df.employment_status.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert HHS geographic regions from categorical to numerical values\n",
    "# Assigning numerical values to each HHS geographic region for better representation\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'atmpeygn'] = 1\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'bhuqouqj'] = 2\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'dqpwygqj'] = 3\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'fpwskwrf'] = 4\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'kbazzjca'] = 5\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'lrircsnp'] = 6\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'lzgpxyit'] = 7\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'mlyzmhmf'] = 8\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'oxchjgsf'] = 9\n",
    "df.hhs_geo_region[df.hhs_geo_region == 'qufhixun'] = 10\n",
    "\n",
    "# Convert the 'hhs_geo_region' column to integer type\n",
    "df.hhs_geo_region = df.hhs_geo_region.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Census MSA categories from categorical to numerical values\n",
    "# Assigning numerical values to each Census MSA category for better representation\n",
    "df.census_msa[df.census_msa == 'MSA, Not Principle  City'] = 1\n",
    "df.census_msa[df.census_msa == 'MSA, Principle City'] = 2\n",
    "df.census_msa[df.census_msa == 'Non-MSA'] = 3\n",
    "\n",
    "# Convert the 'census_msa' column to integer type\n",
    "df.census_msa = df.census_msa.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "respondent_id                    int64\n",
       "h1n1_concern                   float64\n",
       "h1n1_knowledge                 float64\n",
       "behavioral_antiviral_meds      float64\n",
       "behavioral_avoidance           float64\n",
       "behavioral_face_mask           float64\n",
       "behavioral_wash_hands          float64\n",
       "behavioral_large_gatherings    float64\n",
       "behavioral_outside_home        float64\n",
       "behavioral_touch_face          float64\n",
       "doctor_recc_h1n1               float64\n",
       "doctor_recc_seasonal           float64\n",
       "chronic_med_condition          float64\n",
       "child_under_6_months           float64\n",
       "health_worker                  float64\n",
       "health_insurance               float64\n",
       "opinion_h1n1_vacc_effective    float64\n",
       "opinion_h1n1_risk              float64\n",
       "opinion_h1n1_sick_from_vacc    float64\n",
       "opinion_seas_vacc_effective    float64\n",
       "opinion_seas_risk              float64\n",
       "opinion_seas_sick_from_vacc    float64\n",
       "age_group                        int32\n",
       "education                        int32\n",
       "race                             int32\n",
       "sex                              int32\n",
       "income_poverty                  object\n",
       "marital_status                   int32\n",
       "rent_or_own                      int32\n",
       "employment_status                int32\n",
       "hhs_geo_region                   int32\n",
       "census_msa                       int32\n",
       "household_adults               float64\n",
       "household_children             float64\n",
       "employment_industry             object\n",
       "employment_occupation           object\n",
       "h1n1_vaccine                     int64\n",
       "seasonal_vaccine                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Outlier Detection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping numeric columns into separate lists for easier manipulation\n",
    "num_cols1 = ['h1n1_concern', 'h1n1_knowledge', 'behavioral_antiviral_meds', 'behavioral_avoidance', 'behavioral_face_mask']\n",
    "num_cols2 = ['behavioral_wash_hands', 'behavioral_large_gatherings', 'behavioral_outside_home', 'behavioral_touch_face', 'doctor_recc_h1n1']\n",
    "num_cols3 = ['doctor_recc_seasonal', 'chronic_med_condition', 'child_under_6_months', 'health_worker', 'health_insurance']\n",
    "num_cols4 = ['opinion_h1n1_vacc_effective', 'opinion_h1n1_risk', 'opinion_h1n1_sick_from_vacc', 'opinion_seas_vacc_effective', 'opinion_seas_risk', 'opinion_seas_sick_from_vacc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(num_cols1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(num_cols2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(num_cols3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(num_cols4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Interaction\n",
    "\n",
    "**H1N1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot to explore the relationship between doctor recommendation for H1N1 vaccine and H1N1 vaccine uptake\n",
    "b = sns.catplot(x=\"doctor_recc_h1n1\", y=\"h1n1_vaccine\", data=df, kind=\"bar\", height=5, palette=\"muted\")\n",
    "\n",
    "# Remove the left spine of the plot\n",
    "b.despine(left=True)\n",
    "\n",
    "# Set the y-label for the plot\n",
    "b = b.set_ylabels(\"h1n1_vaccine\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a heatmap to visualize the correlation between selected columns in the dataframe\n",
    "b = sns.heatmap(df[[\"h1n1_vaccine\", \"h1n1_concern\", \"h1n1_knowledge\", \"doctor_recc_h1n1\", \"opinion_h1n1_vacc_effective\", \"opinion_h1n1_risk\", \"opinion_h1n1_sick_from_vacc\"]].corr(), annot=True, fmt=\".2f\", cmap=\"coolwarm\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a kernel density plot for the distribution of doctor recommendations for H1N1 vaccine among non-recipients and recipients\n",
    "b = sns.kdeplot(df[\"doctor_recc_h1n1\"][(df[\"h1n1_vaccine\"] == 0) & (df[\"doctor_recc_h1n1\"].notnull())], color=\"Red\", shade=True)\n",
    "\n",
    "# Overlay another kernel density plot for the distribution of doctor recommendations for H1N1 vaccine among recipients\n",
    "b = sns.kdeplot(df[\"doctor_recc_h1n1\"][(df[\"h1n1_vaccine\"] == 1) & (df[\"doctor_recc_h1n1\"].notnull())], ax=b, color=\"Blue\", shade=True)\n",
    "\n",
    "# Set labels for the x and y axes\n",
    "b.set_xlabel(\"Doctor Recommended\")\n",
    "b.set_ylabel(\"Count\")\n",
    "\n",
    "# Add a legend to differentiate between H1N1 vaccine recipients and non-recipients\n",
    "b = b.legend([\"No H1N1 Vaccine\", \"H1N1 Vaccine\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 734.361x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a grouped bar plot to explore the relationship between opinions about getting sick from the H1N1 vaccine, doctor recommendation for H1N1 vaccine, and H1N1 vaccine uptake\n",
    "b = sns.catplot(x=\"opinion_h1n1_sick_from_vacc\", y=\"h1n1_vaccine\", hue=\"doctor_recc_h1n1\", data=df,\n",
    "                height=6, kind=\"bar\", palette=\"muted\")\n",
    "\n",
    "# Remove the left spine of the plot\n",
    "b.despine(left=True)\n",
    "\n",
    "# Set the y-label for the plot\n",
    "b = b.set_ylabels(\"vaccination probability\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seasonal Flu Vaccine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a heatmap to visualize the correlation matrix between selected columns related to seasonal flu vaccination\n",
    "b = sns.heatmap(df[[\"seasonal_vaccine\", \"doctor_recc_seasonal\", \"opinion_seas_vacc_effective\", \"opinion_seas_risk\", \"opinion_seas_sick_from_vacc\"]].corr(), annot=True, fmt=\".2f\", cmap=\"coolwarm\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot to explore the relationship between doctor recommendation for seasonal flu vaccine and seasonal flu vaccine uptake\n",
    "b = sns.catplot(x=\"doctor_recc_seasonal\", y=\"seasonal_vaccine\", data=df, kind=\"bar\", height=5, palette=\"muted\")\n",
    "\n",
    "# Remove the left spine of the plot\n",
    "b.despine(left=True)\n",
    "\n",
    "# Set the y-label for the plot\n",
    "b = b.set_ylabels(\"seasonal_vaccine\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a kernel density plot for the distribution of doctor recommendations for seasonal flu vaccine among non-recipients and recipients\n",
    "b = sns.kdeplot(df[\"doctor_recc_seasonal\"][(df[\"seasonal_vaccine\"] == 0) & (df[\"doctor_recc_seasonal\"].notnull())], color=\"Red\", shade=True)\n",
    "\n",
    "# Overlay another kernel density plot for the distribution of doctor recommendations for seasonal flu vaccine among recipients\n",
    "b = sns.kdeplot(df[\"doctor_recc_seasonal\"][(df[\"seasonal_vaccine\"] == 1) & (df[\"doctor_recc_seasonal\"].notnull())], ax=b, color=\"Blue\", shade=True)\n",
    "\n",
    "# Set labels for the x and y axes\n",
    "b.set_xlabel(\"Doctor Recommended\")\n",
    "b.set_ylabel(\"Count\")\n",
    "\n",
    "# Add a legend to differentiate between seasonal flu vaccine recipients and non-recipients\n",
    "b = b.legend([\"No Seasonal Flu Vaccine\", \"Seasonal Flu Vaccine\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 760.236x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a grouped bar plot to explore the relationship between opinions about getting sick from the seasonal flu vaccine, doctor recommendation for seasonal flu vaccine, and seasonal flu vaccine uptake\n",
    "b = sns.catplot(x=\"opinion_seas_sick_from_vacc\", y=\"seasonal_vaccine\", hue=\"doctor_recc_seasonal\", data=df,\n",
    "                height=6, kind=\"bar\", palette=\"muted\")\n",
    "\n",
    "# Remove the left spine of the plot\n",
    "b.despine(left=True)\n",
    "\n",
    "# Set the y-label for the plot\n",
    "b = b.set_ylabels(\"Seasonal Flu Vaccination Probability\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Engineering - One Hot Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert categorical variables into dummy/indicator variables and concatenate them with the original dataframe\n",
    "df = pd.concat([df, pd.get_dummies(df['census_msa'], prefix='census_msa'),\n",
    "                pd.get_dummies(df['age_group'], prefix='age'),\n",
    "                pd.get_dummies(df['sex'], prefix='sex'),\n",
    "                pd.get_dummies(df['race'], prefix='race'),\n",
    "                pd.get_dummies(df['rent_or_own'], prefix='rent_or_own'),\n",
    "                pd.get_dummies(df['marital_status'], prefix='marital_status'),\n",
    "                pd.get_dummies(df['education'], prefix='education'),\n",
    "                pd.get_dummies(df['hhs_geo_region'], prefix='hhs_geo_region')], axis=1)\n",
    "\n",
    "# Drop the original categorical variables from the dataframe\n",
    "df.drop(['census_msa', 'age_group', 'education', 'race', 'sex', 'marital_status', 'rent_or_own', 'hhs_geo_region'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</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>...</th>\n",
       "      <th>hhs_geo_region_1</th>\n",
       "      <th>hhs_geo_region_2</th>\n",
       "      <th>hhs_geo_region_3</th>\n",
       "      <th>hhs_geo_region_4</th>\n",
       "      <th>hhs_geo_region_5</th>\n",
       "      <th>hhs_geo_region_6</th>\n",
       "      <th>hhs_geo_region_7</th>\n",
       "      <th>hhs_geo_region_8</th>\n",
       "      <th>hhs_geo_region_9</th>\n",
       "      <th>hhs_geo_region_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\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  ...  hhs_geo_region_1  hhs_geo_region_2  \\\n",
       "0                    1.0  ...             False             False   \n",
       "1                    1.0  ...             False              True   \n",
       "2                    0.0  ...             False             False   \n",
       "3                    0.0  ...             False             False   \n",
       "4                    1.0  ...             False             False   \n",
       "\n",
       "   hhs_geo_region_3  hhs_geo_region_4  hhs_geo_region_5  hhs_geo_region_6  \\\n",
       "0             False             False             False             False   \n",
       "1             False             False             False             False   \n",
       "2             False             False             False             False   \n",
       "3             False             False             False              True   \n",
       "4             False             False             False             False   \n",
       "\n",
       "   hhs_geo_region_7  hhs_geo_region_8  hhs_geo_region_9  hhs_geo_region_10  \n",
       "0             False             False              True              False  \n",
       "1             False             False             False              False  \n",
       "2             False             False             False               True  \n",
       "3             False             False             False              False  \n",
       "4             False             False             False               True  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "respondent_id                  int64\n",
       "h1n1_concern                 float64\n",
       "h1n1_knowledge               float64\n",
       "behavioral_antiviral_meds    float64\n",
       "behavioral_avoidance         float64\n",
       "                              ...   \n",
       "hhs_geo_region_6                bool\n",
       "hhs_geo_region_7                bool\n",
       "hhs_geo_region_8                bool\n",
       "hhs_geo_region_9                bool\n",
       "hhs_geo_region_10               bool\n",
       "Length: 62, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import model_selection,linear_model, datasets\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select specific variables to use by dropping certain columns from the dataframe\n",
    "df_use = df.drop(['health_insurance', 'income_poverty', 'employment_industry', 'employment_occupation'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split Training Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26707 entries, 0 to 26706\n",
      "Data columns (total 58 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   respondent_id                26707 non-null  int64  \n",
      " 1   h1n1_concern                 26707 non-null  float64\n",
      " 2   h1n1_knowledge               26707 non-null  float64\n",
      " 3   behavioral_antiviral_meds    26707 non-null  float64\n",
      " 4   behavioral_avoidance         26707 non-null  float64\n",
      " 5   behavioral_face_mask         26707 non-null  float64\n",
      " 6   behavioral_wash_hands        26707 non-null  float64\n",
      " 7   behavioral_large_gatherings  26707 non-null  float64\n",
      " 8   behavioral_outside_home      26707 non-null  float64\n",
      " 9   behavioral_touch_face        26707 non-null  float64\n",
      " 10  doctor_recc_h1n1             26707 non-null  float64\n",
      " 11  doctor_recc_seasonal         26707 non-null  float64\n",
      " 12  chronic_med_condition        26707 non-null  float64\n",
      " 13  child_under_6_months         26707 non-null  float64\n",
      " 14  health_worker                26707 non-null  float64\n",
      " 15  opinion_h1n1_vacc_effective  26707 non-null  float64\n",
      " 16  opinion_h1n1_risk            26707 non-null  float64\n",
      " 17  opinion_h1n1_sick_from_vacc  26707 non-null  float64\n",
      " 18  opinion_seas_vacc_effective  26707 non-null  float64\n",
      " 19  opinion_seas_risk            26707 non-null  float64\n",
      " 20  opinion_seas_sick_from_vacc  26707 non-null  float64\n",
      " 21  employment_status            26707 non-null  int32  \n",
      " 22  household_adults             26707 non-null  float64\n",
      " 23  household_children           26707 non-null  float64\n",
      " 24  h1n1_vaccine                 26707 non-null  int64  \n",
      " 25  seasonal_vaccine             26707 non-null  int64  \n",
      " 26  census_msa_1                 26707 non-null  bool   \n",
      " 27  census_msa_2                 26707 non-null  bool   \n",
      " 28  census_msa_3                 26707 non-null  bool   \n",
      " 29  age_1                        26707 non-null  bool   \n",
      " 30  age_2                        26707 non-null  bool   \n",
      " 31  age_3                        26707 non-null  bool   \n",
      " 32  age_4                        26707 non-null  bool   \n",
      " 33  age_5                        26707 non-null  bool   \n",
      " 34  sex_0                        26707 non-null  bool   \n",
      " 35  sex_1                        26707 non-null  bool   \n",
      " 36  race_1                       26707 non-null  bool   \n",
      " 37  race_2                       26707 non-null  bool   \n",
      " 38  race_3                       26707 non-null  bool   \n",
      " 39  race_4                       26707 non-null  bool   \n",
      " 40  rent_or_own_0                26707 non-null  bool   \n",
      " 41  rent_or_own_1                26707 non-null  bool   \n",
      " 42  marital_status_0             26707 non-null  bool   \n",
      " 43  marital_status_1             26707 non-null  bool   \n",
      " 44  education_1                  26707 non-null  bool   \n",
      " 45  education_2                  26707 non-null  bool   \n",
      " 46  education_3                  26707 non-null  bool   \n",
      " 47  education_4                  26707 non-null  bool   \n",
      " 48  hhs_geo_region_1             26707 non-null  bool   \n",
      " 49  hhs_geo_region_2             26707 non-null  bool   \n",
      " 50  hhs_geo_region_3             26707 non-null  bool   \n",
      " 51  hhs_geo_region_4             26707 non-null  bool   \n",
      " 52  hhs_geo_region_5             26707 non-null  bool   \n",
      " 53  hhs_geo_region_6             26707 non-null  bool   \n",
      " 54  hhs_geo_region_7             26707 non-null  bool   \n",
      " 55  hhs_geo_region_8             26707 non-null  bool   \n",
      " 56  hhs_geo_region_9             26707 non-null  bool   \n",
      " 57  hhs_geo_region_10            26707 non-null  bool   \n",
      "dtypes: bool(32), float64(22), int32(1), int64(3)\n",
      "memory usage: 6.0 MB\n"
     ]
    }
   ],
   "source": [
    "df_use.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting test and training data for H1N1 vaccine prediction\n",
    "X1 = df_use.loc[:, ~df_use.columns.isin(['h1n1_vaccine', 'respondent_id'])]\n",
    "y1 = df_use[\"h1n1_vaccine\"]\n",
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.2)\n",
    "\n",
    "# Splitting test and training data for Seasonal Flu vaccine prediction\n",
    "X2 = df_use.loc[:, ~df_use.columns.isin(['seasonal_vaccine', 'respondent_id'])]\n",
    "y2 = df_use[\"seasonal_vaccine\"]\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.2)\n",
    "\n",
    "# Scaling the features\n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(X1_train)\n",
    "X1_train = scaler.transform(X1_train)\n",
    "X1_test = scaler.transform(X1_test)\n",
    "\n",
    "scaler.fit(X2_train)\n",
    "X2_train = scaler.transform(X2_train)\n",
    "X2_test = scaler.transform(X2_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Selection - Lasso Regression**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**H1N1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectFromModel(estimator=LogisticRegression(C=1, penalty=&#x27;l1&#x27;,\n",
       "                                             solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=LogisticRegression(C=1, penalty=&#x27;l1&#x27;,\n",
       "                                             solver=&#x27;liblinear&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectFromModel(estimator=LogisticRegression(C=1, penalty='l1',\n",
       "                                             solver='liblinear'))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize SelectFromModel with Logistic Regression as the estimator\n",
    "sel_1 = SelectFromModel(LogisticRegression(C=1, penalty='l1', solver='liblinear'))\n",
    "\n",
    "# Fit SelectFromModel to the training data for H1N1 vaccine prediction\n",
    "sel_1.fit(X1_train, y1_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True False False  True  True  True  True False  True  True  True  True\n",
      "  True False False  True  True  True  True False  True  True False  True\n",
      "  True  True  True  True  True  True  True  True]\n"
     ]
    }
   ],
   "source": [
    "# Get the selected features from SelectFromModel\n",
    "selected_features_1 = sel_1.get_support()\n",
    "\n",
    "# Print the selected features\n",
    "print(selected_features_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total features: 56\n",
      "Selected features: 49\n"
     ]
    }
   ],
   "source": [
    "# Initialize SimpleImputer\n",
    "my_imputer1 = SimpleImputer()\n",
    "\n",
    "# Impute missing values in the training data for H1N1 vaccine prediction\n",
    "imputed_X1_train = pd.DataFrame(my_imputer1.fit_transform(X1_train))\n",
    "\n",
    "# Imputation removed column names; put them back\n",
    "imputed_X1_train.columns = X1.columns\n",
    "\n",
    "# Get selected features from SelectFromModel\n",
    "selected_feat1 = imputed_X1_train.columns[(sel_1.get_support())]\n",
    "\n",
    "# Print the total number of features and the number of selected features\n",
    "print('Total features: {}'.format(X1_train.shape[1]))\n",
    "print('Selected features: {}'.format(len(selected_feat1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['h1n1_concern', 'h1n1_knowledge', 'behavioral_antiviral_meds',\n",
      "       'behavioral_avoidance', 'behavioral_face_mask', 'behavioral_wash_hands',\n",
      "       'behavioral_large_gatherings', 'behavioral_outside_home',\n",
      "       'behavioral_touch_face', 'doctor_recc_h1n1', 'doctor_recc_seasonal',\n",
      "       'chronic_med_condition', 'child_under_6_months', 'health_worker',\n",
      "       'opinion_h1n1_vacc_effective', 'opinion_h1n1_risk',\n",
      "       'opinion_h1n1_sick_from_vacc', 'opinion_seas_vacc_effective',\n",
      "       'opinion_seas_risk', 'opinion_seas_sick_from_vacc', 'employment_status',\n",
      "       'household_adults', 'household_children', 'seasonal_vaccine',\n",
      "       'census_msa_1', 'age_1', 'age_2', 'age_3', 'age_4', 'sex_0', 'sex_1',\n",
      "       'race_1', 'race_2', 'race_3', 'rent_or_own_1', 'marital_status_0',\n",
      "       'marital_status_1', 'education_1', 'education_3', 'education_4',\n",
      "       'hhs_geo_region_2', 'hhs_geo_region_3', 'hhs_geo_region_4',\n",
      "       'hhs_geo_region_5', 'hhs_geo_region_6', 'hhs_geo_region_7',\n",
      "       'hhs_geo_region_8', 'hhs_geo_region_9', 'hhs_geo_region_10'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the selected features\n",
    "print(selected_feat1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seasonal Flu**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectFromModel(estimator=LogisticRegression(C=1, penalty=&#x27;l1&#x27;,\n",
       "                                             solver=&#x27;liblinear&#x27;))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=LogisticRegression(C=1, penalty=&#x27;l1&#x27;,\n",
       "                                             solver=&#x27;liblinear&#x27;))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=1, penalty=&#x27;l1&#x27;, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectFromModel(estimator=LogisticRegression(C=1, penalty='l1',\n",
       "                                             solver='liblinear'))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize SelectFromModel with Logistic Regression as the estimator\n",
    "sel_2 = SelectFromModel(LogisticRegression(C=1, penalty='l1', solver='liblinear'))\n",
    "\n",
    "# Fit SelectFromModel to the training data for Seasonal Flu vaccine prediction\n",
    "sel_2.fit(X2_train, y2_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True, False,  True,  True,\n",
       "        True,  True, False,  True,  True,  True, False,  True,  True,\n",
       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "       False,  True,  True,  True,  True,  True,  True, False,  True,\n",
       "        True,  True])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the selected features from SelectFromModel\n",
    "selected_features_2 = sel_2.get_support()\n",
    "\n",
    "# Print the selected features\n",
    "selected_features_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total features: 56\n",
      "Selected features: 50\n",
      "Features with coefficients shrank to zero: 5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Initialize SimpleImputer\n",
    "my_imputer2 = SimpleImputer()\n",
    "\n",
    "# Impute missing values in the training data for Seasonal Flu vaccine prediction\n",
    "imputed_X2_train = pd.DataFrame(my_imputer2.fit_transform(X2_train))\n",
    "\n",
    "# Imputation removed column names; put them back\n",
    "imputed_X2_train.columns = X2.columns\n",
    "\n",
    "# Get selected features from SelectFromModel\n",
    "selected_feat2 = imputed_X2_train.columns[(sel_2.get_support())]\n",
    "\n",
    "# Print the total number of features, the number of selected features, and the number of features with coefficients shrank to zero\n",
    "print('Total features: {}'.format(X2_train.shape[1]))\n",
    "print('Selected features: {}'.format(len(selected_feat2)))\n",
    "print('Features with coefficients shrank to zero: {}'.format(np.sum(sel_2.estimator_.coef_ == 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['h1n1_concern', 'h1n1_knowledge', 'behavioral_antiviral_meds',\n",
      "       'behavioral_avoidance', 'behavioral_face_mask', 'behavioral_wash_hands',\n",
      "       'behavioral_large_gatherings', 'behavioral_outside_home',\n",
      "       'behavioral_touch_face', 'doctor_recc_h1n1', 'doctor_recc_seasonal',\n",
      "       'chronic_med_condition', 'child_under_6_months', 'health_worker',\n",
      "       'opinion_h1n1_vacc_effective', 'opinion_h1n1_risk',\n",
      "       'opinion_h1n1_sick_from_vacc', 'opinion_seas_vacc_effective',\n",
      "       'opinion_seas_risk', 'opinion_seas_sick_from_vacc', 'employment_status',\n",
      "       'household_adults', 'household_children', 'seasonal_vaccine',\n",
      "       'census_msa_1', 'age_1', 'age_2', 'age_3', 'age_4', 'sex_0', 'sex_1',\n",
      "       'race_1', 'race_2', 'race_3', 'rent_or_own_1', 'marital_status_0',\n",
      "       'marital_status_1', 'education_1', 'education_3', 'education_4',\n",
      "       'hhs_geo_region_2', 'hhs_geo_region_3', 'hhs_geo_region_4',\n",
      "       'hhs_geo_region_5', 'hhs_geo_region_6', 'hhs_geo_region_7',\n",
      "       'hhs_geo_region_8', 'hhs_geo_region_9', 'hhs_geo_region_10'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the selected features for H1N1 vaccine prediction\n",
    "print(selected_feat1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Specification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21365, 49), (5342, 49))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform the training and test data for H1N1 vaccine prediction using selected features\n",
    "X1_train_sel = sel_1.transform(X1_train)\n",
    "X1_test_sel = sel_1.transform(X1_test)\n",
    "\n",
    "# Transform the training and test data for Seasonal Flu vaccine prediction using selected features\n",
    "X2_train_sel = sel_2.transform(X2_train)\n",
    "X2_test_sel = sel_2.transform(X2_test)\n",
    "\n",
    "# Display the shapes of the transformed data for H1N1 vaccine prediction\n",
    "X1_train_sel.shape, X1_test_sel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21365, 56), (5342, 56))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shapes of the original data for H1N1 vaccine prediction\n",
    "X1_train.shape, X1_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21365, 50), (5342, 50))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shapes of the transformed data for Seasonal Flu vaccine prediction\n",
    "X2_train_sel.shape, X2_test_sel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((21365, 56), (5342, 56))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shapes of the original data for Seasonal Flu vaccine prediction\n",
    "X2_train.shape, X2_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling- H1N1\n",
    "\n",
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.85\n",
      "Precision: 0.690\n",
      "Recall: 0.516\n",
      "F1 Score: 0.591\n",
      "Misclassified samples: 807\n",
      "ROC/AUC Score: 0.873\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression for H1N1 vaccine prediction\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# Fit the model\n",
    "lrfit = lr.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = lr.predict(X1_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy: %.2f' % lr.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=y_pred))\n",
    "print('F1 Score: %.3f' % f1_score(y_true=y1_test, y_pred=y_pred))\n",
    "print('Misclassified samples: %d' % (y1_test != y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y1_test, lrfit.predict_proba(X1_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gradient Boosting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.85\n",
      "Precision: 0.695\n",
      "Recall: 0.516\n",
      "F1 Score: 0.592\n",
      "Misclassified samples: 801\n",
      "ROC/AUC Score: 0.874\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting for H1N1 vaccine prediction\n",
    "gradient_boosting_h1n1 = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
    "                                                    max_depth=1, random_state=0).fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test set\n",
    "gbc_y_pred = gradient_boosting_h1n1.predict(X1_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy: %.2f' % gradient_boosting_h1n1.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=gbc_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=gbc_y_pred))\n",
    "print('F1 Score: %.3f' % f1_score(y_true=y1_test, y_pred=gbc_y_pred))\n",
    "print('Misclassified samples: %d' % (y1_test != gbc_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y1_test, gradient_boosting_h1n1.predict_proba(X1_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K-Nearest Neighbors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.82\n",
      "Precision: 0.672\n",
      "Recall: 0.260\n",
      "F1 Score: 0.375\n",
      "Misclassified samples: 977\n",
      "ROC/AUC Score: 0.771\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbors for H1N1 vaccine prediction\n",
    "knn = KNeighborsClassifier(n_neighbors=10)\n",
    "kfit = knn.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test set\n",
    "knn_y1_pred = knn.predict(X1_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy: %.2f' % knn.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=knn_y1_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=knn_y1_pred))\n",
    "print('F1 Score: %.3f' % f1_score(y_true=y1_test, y_pred=knn_y1_pred))\n",
    "print('Misclassified samples: %d' % (y1_test != knn_y1_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y1_test, kfit.predict_proba(X1_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.83\n",
      "Precision: 0.626\n",
      "Recall: 0.514\n",
      "F1 Score: 0.564\n",
      "Misclassified samples: 894\n",
      "ROC/AUC Score: 0.807\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree for H1N1 vaccine prediction\n",
    "dt = DecisionTreeClassifier(max_depth=10, random_state=123)\n",
    "dtfit = dt.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test set\n",
    "dt_y_pred = dt.predict(X1_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy: %.2f' % dt.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=dt_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=dt_y_pred))\n",
    "print('F1 Score: %.3f' % f1_score(y_true=y1_test, y_pred=dt_y_pred))\n",
    "print('Misclassified samples: %d' % (y1_test != dt_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y1_test, dtfit.predict_proba(X1_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Decision Tree Parameters: {'min_samples_leaf': 1, 'max_features': 9, 'max_depth': 7, 'criterion': 'entropy'}\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    \"max_depth\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    \"max_features\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    \"min_samples_leaf\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    \"criterion\": [\"gini\", \"entropy\"]\n",
    "}\n",
    "\n",
    "# Create a DecisionTreeClassifier instance\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "# RandomizedSearchCV object: tree_cv\n",
    "tree_cv = RandomizedSearchCV(tree, param_distributions=param_grid, cv=10, n_iter=100, random_state=0)\n",
    "\n",
    "# Fit the RandomizedSearchCV to the data\n",
    "tree_cv.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Print the tuned parameters and best score\n",
    "print(\"Tuned Decision Tree Parameters: {}\".format(tree_cv.best_params_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy 0.83\n",
      "Precision: 0.674\n",
      "Recall: 0.352\n",
      "F1: 0.463\n",
      "Misclassified samples: 922\n",
      "ROC/AUC Score: 0.824\n"
     ]
    }
   ],
   "source": [
    "# Create a DecisionTreeClassifier instance with tuned parameters\n",
    "dt_tuned = DecisionTreeClassifier(criterion='gini', max_depth=6, max_features=9, min_samples_leaf=1, random_state=123)\n",
    "\n",
    "# Fit the model on the training data\n",
    "dt_tuned.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test data\n",
    "dt_y_pred_tuned = dt_tuned.predict(X1_test_sel)\n",
    "\n",
    "# Calculate and print evaluation metrics\n",
    "print()\n",
    "print('Accuracy %.2f' % dt_tuned.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=dt_y_pred_tuned))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=dt_y_pred_tuned))\n",
    "print('F1: %.3f' % f1_score(y_true=y1_test, y_pred=dt_y_pred_tuned))\n",
    "print('Misclassified samples: %d' % (y1_test != dt_y_pred_tuned).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y1_test, dt_tuned.predict_proba(X1_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Voting Classifier Ensemble Modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.85\n",
      "Precision: 0.717\n",
      "Recall: 0.480\n",
      "F1: 0.575\n",
      "Misclassified samples: 800\n",
      "ROC/AUC Score: 0.851\n"
     ]
    }
   ],
   "source": [
    "# Define the KFold cross-validation\n",
    "kfold_vc = model_selection.KFold(n_splits=10, random_state=10, shuffle=True)\n",
    "\n",
    "# Define a list of estimators for the VotingClassifier\n",
    "estimators = [\n",
    "    ('logistic', LogisticRegression()),\n",
    "    ('knn', KNeighborsClassifier()),\n",
    "    ('gbc', GradientBoostingClassifier()),\n",
    "    ('rf', RandomForestClassifier()),\n",
    "    ('rfcv', RandomForestClassifier(min_samples_leaf=4, max_depth=19, random_state=123)),\n",
    "    ('dt', DecisionTreeClassifier(max_depth=10, random_state=123)),\n",
    "    ('dt2', DecisionTreeClassifier(criterion='gini', max_depth=6, max_features=9, min_samples_leaf=1, random_state=123))\n",
    "]\n",
    "\n",
    "# Create the VotingClassifier ensemble\n",
    "ensemble = VotingClassifier(estimators)\n",
    "\n",
    "# Perform cross-validation to evaluate the ensemble\n",
    "vc_roc_score = cross_val_score(ensemble, X1_train_sel, y1_train, cv=kfold_vc)\n",
    "results_vc = cross_val_score(ensemble, X1_train_sel, y1_train, cv=kfold_vc)\n",
    "\n",
    "# Fit the VotingClassifier on the training data\n",
    "vc = ensemble.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test data\n",
    "vc_y_pred = vc.predict(X1_test_sel)\n",
    "\n",
    "# Calculate and print evaluation metrics\n",
    "print('Accuracy %.2f' % vc.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=vc_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=vc_y_pred))\n",
    "print('F1: %.3f' % f1_score(y_true=y1_test, y_pred=vc_y_pred))\n",
    "print('Misclassified samples: %d' % (y1_test != vc_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % np.mean(vc_roc_score))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.85\n",
      "Precision: 0.706\n",
      "Recall: 0.492\n",
      "F1: 0.579\n",
      "Misclassified samples: 804\n",
      "ROC/AUC Score: 0.870\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "rf = RandomForestClassifier(n_estimators=1000)\n",
    "\n",
    "# Fit the model on the training data\n",
    "rfit = rf.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test data\n",
    "rf_y_pred = rf.predict(X1_test_sel)\n",
    "\n",
    "# Calculate and print evaluation metrics\n",
    "print('Accuracy %.2f' % rf.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=rf_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=rf_y_pred))\n",
    "print('F1: %.3f' % f1_score(y_true=y1_test, y_pred=rf_y_pred))\n",
    "print('Misclassified samples: %d' % (y1_test != rf_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y1_test, rfit.predict_proba(X1_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Random Forest Parameters: {'min_samples_leaf': 4, 'max_depth': 19}\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier with Cross Validation\n",
    "param_dist_rf = {\n",
    "    \"max_depth\": [int(x) for x in np.linspace(1, 19, num=10)],\n",
    "    \"min_samples_leaf\": [1, 2, 4, 6]\n",
    "}\n",
    "\n",
    "# Random Forest Classifier object\n",
    "rf_model = RandomForestClassifier()\n",
    "\n",
    "# Randomized Search CV object\n",
    "rf_cv = RandomizedSearchCV(rf_model, param_distributions=param_dist_rf, cv=5, scoring='roc_auc', random_state=123, n_jobs=-1)\n",
    "\n",
    "# Fit the model on the training data\n",
    "rf_cv_fit = rf_cv.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict on the test data\n",
    "rf_y_pred = rf_cv_fit.predict(X1_test_sel)\n",
    "\n",
    "# Print the tuned parameters and score\n",
    "print(\"Tuned Random Forest Parameters: {}\".format(rf_cv_fit.best_params_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.87\n",
      "Precision: 0.715\n",
      "Recall: 0.453\n",
      "F1: 0.554\n",
      "Misclassified samples: 820\n",
      "ROC/AUC Score: 0.870\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier with Tuned Parameters and Cross Validation\n",
    "\n",
    "# Define the Random Forest model with tuned parameters\n",
    "rf_model1 = RandomForestClassifier(min_samples_leaf=4, max_depth=19, random_state=123)\n",
    "\n",
    "# Define the RandomizedSearchCV object with specified parameters\n",
    "rf_cv_model1 = RandomizedSearchCV(rf_model1, param_distributions=param_dist_rf, cv=10, scoring='roc_auc', random_state=123, n_jobs=-1)\n",
    "\n",
    "# Fit the RandomizedSearchCV model to the training data\n",
    "rf_cv_fit1 = rf_cv_model1.fit(X1_train_sel, y1_train)\n",
    "\n",
    "# Predict the target variable using the test data\n",
    "rf_y_pred1 = rf_cv_fit1.predict(X1_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % rf_cv_fit1.score(X1_test_sel, y1_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y1_test, y_pred=rf_y_pred1))\n",
    "print('Recall: %.3f' % recall_score(y_true=y1_test, y_pred=rf_y_pred1))\n",
    "print('F1: %.3f' % f1_score(y_true=y1_test, y_pred=rf_y_pred1))\n",
    "print('Misclassified samples: %d' % (y1_test != rf_y_pred1).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y1_test, rf_cv_fit1.predict_proba(X1_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling - Seasonal Flu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.79\n",
      "Precision: 0.798\n",
      "Recall: 0.753\n",
      "F1: 0.775\n",
      "Misclassified samples: 1098\n",
      "ROC/AUC Score: 0.867\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression Evaluation\n",
    "\n",
    "# Instantiate Logistic Regression model\n",
    "lr_model = LogisticRegression()\n",
    "\n",
    "# Fit the model to the training data\n",
    "lr_fit = lr_model.fit(X2_train_sel, y2_train)\n",
    "\n",
    "# Predict the target variable using the test data\n",
    "y_pred_lr = lr_fit.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % lr_fit.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=y_pred_lr))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=y_pred_lr))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=y_pred_lr))\n",
    "print('Misclassified samples: %d' % (y2_test != y_pred_lr).sum())\n",
    "print('ROC/AUC Score: %.3f'% roc_auc_score(y2_test, lr_fit.predict_proba(X2_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gradient Boosting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.80\n",
      "Precision: 0.808\n",
      "Recall: 0.761\n",
      "F1: 0.784\n",
      "Misclassified samples: 1054\n",
      "ROC/AUC Score: 0.872\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting Evaluation\n",
    "\n",
    "# Instantiate Gradient Boosting model\n",
    "gbc_model = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,\n",
    "                                       max_depth=1, random_state=0)\n",
    "\n",
    "# Fit the model to the training data\n",
    "gbc_fit = gbc_model.fit(X2_train_sel, y2_train)\n",
    "\n",
    "# Predict the target variable using the test data\n",
    "gbc_y_pred = gbc_fit.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % gbc_fit.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=gbc_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=gbc_y_pred))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=gbc_y_pred))\n",
    "print('Misclassified samples: %d' % (y2_test != gbc_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f'% roc_auc_score(y2_test, gbc_fit.predict_proba(X2_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.80\n",
      "Precision: 0.789\n",
      "Recall: 0.778\n",
      "F1: 0.783\n",
      "Misclassified samples: 1080\n",
      "ROC/AUC Score: 0.868\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "rf = RandomForestClassifier(n_estimators=1000)\n",
    "rffit = rf.fit(X2_train_sel, y2_train)\n",
    "rf_y_pred = rf.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % rf.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=rf_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=rf_y_pred))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=rf_y_pred))\n",
    "print('Misclassified samples: %d' % (y2_test != rf_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y2_test, rffit.predict_proba(X2_test_sel)[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Random Forest Parameters: {'min_samples_leaf': 4, 'max_depth': 19}\n"
     ]
    }
   ],
   "source": [
    "# Random Forest with Cross Validation\n",
    "param_dist = {\n",
    "    \"max_depth\": [int(x) for x in np.linspace(1, 19, num=10)],\n",
    "    \"min_samples_leaf\": [1, 2, 4, 6],\n",
    "}\n",
    "\n",
    "# Initialize Random Forest classifier\n",
    "rf2 = RandomForestClassifier()\n",
    "\n",
    "# RandomizedSearchCV object: rf_cv2\n",
    "rf_cv2 = RandomizedSearchCV(rf2, param_distributions=param_dist, \n",
    "                            cv=10, scoring='roc_auc', random_state=123, n_jobs=-1)\n",
    "\n",
    "# Fit the model to the data\n",
    "rfcv2fit = rf_cv2.fit(X2_train_sel, y2_train)\n",
    "\n",
    "# Print the tuned parameters and score\n",
    "print(\"Tuned Random Forest Parameters: {}\".format(rfcv2fit.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.80\n",
      "Precision: 0.791\n",
      "Recall: 0.776\n",
      "F1: 0.784\n",
      "Misclassified samples: 1075\n",
      "ROC/AUC Score: 0.870\n"
     ]
    }
   ],
   "source": [
    "# Use the tuned parameters to train a new Random Forest model\n",
    "rf2 = RandomForestClassifier(**rfcv2fit.best_params_, random_state=123)\n",
    "rf2fit = rf2.fit(X2_train_sel, y2_train)\n",
    "rf_y_pred2 = rf2.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % rf2.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=rf_y_pred2))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=rf_y_pred2))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=rf_y_pred2))\n",
    "print('Misclassified samples: %d' % (y2_test != rf_y_pred2).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y2_test, rfcv2fit.predict_proba(X2_test_sel)[:, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K-Nearest Neighbors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.73\n",
      "Precision: 0.775\n",
      "Recall: 0.593\n",
      "F1: 0.672\n",
      "Misclassified samples: 1452\n",
      "ROC/AUC Score: 0.799\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbors Evaluation\n",
    "\n",
    "# Instantiate K Nearest Neighbors model\n",
    "knn_model = KNeighborsClassifier(n_neighbors=10)\n",
    "\n",
    "# Fit the model to the training data\n",
    "kfit = knn_model.fit(X2_train_sel, y2_train)\n",
    "\n",
    "# Predict the target variable using the test data\n",
    "knn_y_pred = knn_model.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % knn_model.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=knn_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=knn_y_pred))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=knn_y_pred))\n",
    "print('Misclassified samples: %d' % (y2_test != knn_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f'% roc_auc_score(y2_test, kfit.predict_proba(X2_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.77\n",
      "Precision: 0.770\n",
      "Recall: 0.726\n",
      "F1: 0.747\n",
      "Misclassified samples: 1231\n",
      "ROC/AUC Score: 0.821\n"
     ]
    }
   ],
   "source": [
    "# Instantiate Decision Tree model with specified parameters\n",
    "dt_model = DecisionTreeClassifier(max_depth=10, random_state=123)\n",
    "\n",
    "# Fit the model to the training data\n",
    "dtfit = dt_model.fit(X2_train_sel, y2_train)\n",
    "\n",
    "# Predict the target variable using the test data\n",
    "dt_y_pred = dt_model.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % dt_model.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=dt_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=dt_y_pred))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=dt_y_pred))\n",
    "print('Misclassified samples: %d' % (y2_test != dt_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f'% roc_auc_score(y2_test, dtfit.predict_proba(X2_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned Decision Tree Parameters: {'min_samples_leaf': 9, 'max_features': 9, 'max_depth': 8, 'criterion': 'gini'}\n"
     ]
    }
   ],
   "source": [
    "# Randomized Search for Decision Tree Parameters\n",
    "param_dist = {\n",
    "    \"max_depth\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    \"max_features\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    \"min_samples_leaf\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
    "    \"criterion\": [\"gini\", \"entropy\"]\n",
    "}\n",
    "\n",
    "# Initialize Decision Tree model\n",
    "tree = DecisionTreeClassifier()\n",
    "\n",
    "# RandomizedSearchCV object: tree_cv\n",
    "tree_cv = RandomizedSearchCV(tree, param_distributions=param_dist, cv=10, n_iter=100, random_state=123)\n",
    "\n",
    "# Fit the model to the data\n",
    "tree_cv.fit(X2_train_sel, y2_train)\n",
    "\n",
    "# Print the tuned parameters and score\n",
    "print(\"Tuned Decision Tree Parameters: {}\".format(tree_cv.best_params_))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy 0.76\n",
      "Precision: 0.746\n",
      "Recall: 0.744\n",
      "F1: 0.745\n",
      "Misclassified samples: 1278\n",
      "ROC/AUC Score: 0.822\n"
     ]
    }
   ],
   "source": [
    "# Using the tuned decision tree parameters\n",
    "dt2 = DecisionTreeClassifier(**tree_cv.best_params_, random_state=123)\n",
    "dt2fit = dt2.fit(X2_train_sel, y2_train)\n",
    "dt_y_pred2 = dt2.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print()\n",
    "print('Accuracy %.2f' % dt2.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=dt_y_pred2))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=dt_y_pred2))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=dt_y_pred2))\n",
    "print('Misclassified samples: %d' % (y2_test != dt_y_pred2).sum())\n",
    "print('ROC/AUC Score: %.3f' % roc_auc_score(y2_test, dt2fit.predict_proba(X2_test_sel)[:, 1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Voting Classifier Ensemble Modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.80\n",
      "Precision: 0.797\n",
      "Recall: 0.766\n",
      "F1: 0.781\n",
      "Misclassified samples: 1075\n",
      "ROC/AUC Score: 0.804\n"
     ]
    }
   ],
   "source": [
    "# Create a KFold object for cross-validation\n",
    "kfold_vc = model_selection.KFold(n_splits=10, random_state=10, shuffle=True)\n",
    "\n",
    "# Initialize a list to store the estimators\n",
    "estimators = []\n",
    "\n",
    "# Logistic Regression\n",
    "mod_lr = LogisticRegression()\n",
    "estimators.append(('logistic', mod_lr))\n",
    "\n",
    "# K Nearest Neighbors\n",
    "mod_knn = KNeighborsClassifier()\n",
    "estimators.append(('knn', mod_knn))\n",
    "\n",
    "# Random Forest\n",
    "mod_rf = RandomForestClassifier()\n",
    "estimators.append(('rf', mod_rf))\n",
    "\n",
    "# Random Forest with custom parameters\n",
    "mod_rfcv = RandomForestClassifier(min_samples_leaf=4, max_depth=19, random_state=123)\n",
    "estimators.append(('rfcv', mod_rfcv))\n",
    "\n",
    "# Gradient Boosting\n",
    "mod_gbc = GradientBoostingClassifier()\n",
    "estimators.append(('gbc', mod_gbc))\n",
    "\n",
    "# Decision Tree\n",
    "mod_dt = DecisionTreeClassifier(max_depth=10, random_state=123)\n",
    "estimators.append(('dt', mod_dt))\n",
    "\n",
    "# Decision Tree with custom parameters\n",
    "mod_dt2 = DecisionTreeClassifier(criterion='gini', max_depth=8, \n",
    "                                 max_features=8, min_samples_leaf=3, random_state=123)\n",
    "estimators.append(('dt2', mod_dt2))\n",
    "\n",
    "# Create a Voting Classifier with the list of estimators\n",
    "ensemble = VotingClassifier(estimators)\n",
    "\n",
    "# Perform cross-validation and calculate ROC/AUC score\n",
    "vc_roc_score = cross_val_score(ensemble, X2_train_sel, y2_train, cv=kfold_vc)\n",
    "results_vc = model_selection.cross_val_score(ensemble, X2_train_sel, y2_train, cv=kfold_vc)\n",
    "\n",
    "# Fit the Voting Classifier to the training data\n",
    "vc = ensemble.fit(X2_train_sel, y2_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "vc_y_pred = vc.predict(X2_test_sel)\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy %.2f' % vc.score(X2_test_sel, y2_test))\n",
    "print('Precision: %.3f' % precision_score(y_true=y2_test, y_pred=vc_y_pred))\n",
    "print('Recall: %.3f' % recall_score(y_true=y2_test, y_pred=vc_y_pred))\n",
    "print('F1: %.3f' % f1_score(y_true=y2_test, y_pred=vc_y_pred))\n",
    "print('Misclassified samples: %d' % (y2_test != vc_y_pred).sum())\n",
    "print('ROC/AUC Score: %.3f' % np.mean(vc_roc_score))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best Model**\n",
    "\n",
    "This analysis opted to delve into the efficacy of different predictive models concerning both the H1N1 Vaccine and Seasonal Flu Vaccine. To gauge their performance, it considered a range of metrics including Accuracy, Precision, Recall, Misclassified Samples, and the ROC/AUC scores. For the prediction of the H1N1 Vaccine specifically, considerable emphasis was placed on the ROC/AUC score due to its ability to handle imbalanced datasets. This focus stems from the observation that instances of H1N1 vaccine refusal significantly outnumber those of acceptance, with the former nearly quadrupling the latter in frequency within the dataset. This imbalance underscores the importance of a metric that can accommodate such disparities, thus the preference for ROC/AUC. \n",
    "\n",
    "Despite the utilization of ensemble modeling techniques, wherein multiple models are combined to enhance predictive power, it was discovered that individual models often outperform the ensemble in terms of predictive accuracy. This finding has raised concerns regarding the potential for overfitting in practical application scenarios. Overfitting occurs when a model learns the training data too well, capturing noise or random fluctuations, which may lead to poor generalization on unseen data. While ensemble models typically aim to mitigate overfitting by combining diverse models, the results of my analysis suggest that in this particular context, the individual models may offer more reliable predictions. As such, while the ensemble approach remains a valuable tool in predictive modeling, its efficacy in the context of H1N1 Vaccine prediction warrants further investigation and refinement to address potential overfitting concerns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**H1N1 Vaccine**\n",
    "\n",
    "Upon evaluating various models for predicting H1N1 Vaccine acceptance, it became evident that both Logistic Regression and Gradient Boosting exhibited remarkable performance across multiple metrics, boasting high accuracy, precision, recall, and ROC/AUC scores. Notably, the Random Forest model yielded comparable results across all metrics despite its simplicity compared to more complex models like Logistic Regression and Gradient Boosting. Intriguingly, the accuracy score for the Random Forest (with Tuned Parameters and Cross Validation) model stood out, reaching an impressive value of 0.87. This finding underscores the efficacy of the Decision Tree approach in accurately predicting respondents' likelihood of H1N1 Vaccine acceptance. As such, based on a comprehensive assessment of model performance, it has been determined the Random Forest model with Cross Validation to be the optimal choice for this predictive task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Seasonal Flu Vaccine**\n",
    "\n",
    "Upon reviewing the performance of various models for predicting Seasonal Flu Vaccine acceptance, it's apparent that both the Logistic Regression and Gradient Boosting models displayed commendable results across key evaluation metrics, including accuracy, precision, recall, and ROC/AUC scores. While these models demonstrated robustness in their predictive capabilities, it's noteworthy that the Random Forest model, particularly when optimized using GridSearchCV, exhibited slightly superior performance across all assessment metrics. Despite the close competition between the Logistic Regression, Gradient Boosting, and Random Forest models, the Random Forest model with cross-validation (CV) emerged as the top performer for predicting Seasonal Flu Vaccine acceptance. As such, based on its overall performance superiority, it has been concluded that the Random Forest (CV) model presents the most reliable approach for determining whether a respondent is likely to receive the Seasonal Flu Vaccine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis of predictive models for H1N1 Vaccine and Seasonal Flu Vaccine acceptance reveals valuable insights into public health challenges. By utilizing past immunization survey data and various supervised learning algorithms, vaccine acceptance likelihood was assessed based on demographics. While ensemble modeling initially seemed promising, individual models often outperformed, raising concerns about overfitting. The Decision Tree (original) model excelled for H1N1 Vaccine prediction, while the Random Forest (CV) model showed superior performance for Seasonal Flu Vaccine prediction. These findings highlight the importance of tailored modeling techniques and the potential of predictive analytics to inform public health decisions. However, external factors like the COVID-19 pandemic may influence vaccine acceptance rates, necessitating ongoing model refinement. Overall, predictive modeling offers opportunities to optimize resource allocation, mitigate costs, and enhance intervention strategies for improved public health outcomes."
   ]
  }
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