ML micro Projects/Vaccine Usage Prediction_using_Logistic_Regg.ipynb

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    "<table align=\"left\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"10%\">\n",
    "            <img src=\"title.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\">\n",
    "                <font color=\"#21618C\" size=8px>\n",
    "                  <b>Vaccine Prediction\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
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    "## Problem Statement\n",
    "\n",
    "The data set is the response of people to the h1n1 flu vaccine related questionnaire. The respondents are people of age 6 months and older. This survey was designed to monitor the influenza immunization coverage in 2009-10 season. Machine learning techniques may aid a more efficient analysis in the prediction of how likely the people are to opt for the flu vaccine. In this case study, we predict, how likely it is that the people will take a H1N1 flu vaccine.        "
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    "## Data Definition\n",
    "\n",
    "**unique_id**: Unique identifier for each respondent - (Numerical)    \n",
    "\n",
    "**h1n1_worry**: Worry about the h1n1 flu(0,1,2,3) 0=Not worried at all, 1=Not very worried, 2=Somewhat worried, 3=Very worried - (Categorical)\n",
    "\n",
    "**h1n1_awareness**: Signifies the amount of knowledge or understanding the respondent has about h1n1 flu - (0,1,2) - 0=No knowledge, 1=little knowledge, 2=good knowledge- (Categorical) \n",
    " \n",
    "**antiviral_medication**: Has the respondent taken antiviral vaccination - (0,1) (Categorical)\n",
    "    \n",
    "**contact_avoidance**: Has avoided any close contact with people who have flu-like symptoms  - (0,1) - (Categorical)\n",
    "    \n",
    "**bought_face_mask**: Has the respondent bought mask or not - (0,1) - (Categorical)\n",
    "    \n",
    "**wash_hands_frequently**: Washes hands frequently or uses hand sanitizer - (0,1) - (Categorical)\n",
    "    \n",
    "**avoid_large_gatherings**: Has the respondent reduced time spent at large gatherings - (0,1) - (Categorical)\n",
    "    \n",
    "**reduced_outside_home_cont**: Has the respondent reduced contact with people outside own house - (0,1) - (Categorical)\n",
    "    \n",
    "**avoid_touch_face**: Avoids touching nose, eyes, mouth - (0,1) - (Categorical)\n",
    "\n",
    "**dr_recc_h1n1_vacc**: Doctor has recommended h1n1 vaccine - (0,1) - (Categorical)\n",
    "    \n",
    "**dr_recc_seasonal_vacc**: Doctor has recommended seasonalflu vaccine - (0,1) - (Categorical)\n",
    "    \n",
    "**chronic_medic_condition**: Has any chronic medical condition - (0,1) - (Categorical)\n",
    "    \n",
    "**cont_child_undr_6_mnth** - Has a regular contact with child the age of 6 months - (0,1) - (Categorical)\n",
    "\n",
    "**is_health_worker**: Is respondent a health worker - (0,1) - (Categorical)\n",
    "    \n",
    "**has_health_insur**: Does respondent have health insurance - (0,1) - (Categorical)\n",
    "    \n",
    "**is_h1n1_vacc_effective**:  Does respondent think that the h1n1 vaccine is effective - (1,2,3,4,5)- (1=Thinks not effective at all, 2=Thinks it is not very effective, 3=Doesn't know if it is effective or not, 4=Thinks it is somewhat effective, 5=Thinks it is highly effective) - (Categorical)\n",
    "\n",
    "**is_h1n1_risky**: What respondenst think about the risk of getting ill with h1n1 in the absence of the vaccine- (1,2,3,4,5)- (1=Thinks it is not very low risk, 2=Thinks it is somewhat low risk, 3=Doesn't know if it is risky or not, 4=Thinks it is somewhat high risk, 5=Thinks it is very highly risky) - (Categorical)\n",
    " \n",
    "**sick_from_h1n1_vacc**: Does respondent worry about getting sick by taking the h1n1 vaccine - (1,2,3,4,5)- (1=Respondent not worried at all, 2=Respondent is not very worried, 3=Doesn't know, 4=Respondent is somewhat worried, 5Respondent is very worried) - (Categorical)\n",
    "\n",
    "**is_seas_vacc_effective**: Does respondent think that the seasonal vaccine is effective- (1,2,3,4,5)- (1=Thinks not effective at all, 2=Thinks it is not very effective, 3=Doesn't know if it is effective or not, 4=Thinks it is somewhat effective, 5=Thinks it is highly effective) - (Categorical)\n",
    "\n",
    "**is_seas_flu_risky**: What respondenst think about the risk of getting ill with seasonal flu in the absence of the vaccine- (1,2,3,4,5)- (1=Thinks it is not very low risk, 2=Thinks it is somewhat low risk, 3=Doesn't know if it is risky or not, 4=Thinks it is somewhat high risk, 5=Thinks it is very highly risky) - (Categorical)\n",
    " \n",
    "**sick_from_seas_vacc**: Does respondent worry about getting sick by taking the seasonal flu vaccine - (1,2,3,4,5)- (1=Respondent not worried at all, 2=Respondent is not very worried, 3=Doesn't know, 4=Respondent is somewhat worried, 5Respondent is very worried) - (Categorical)\n",
    "\n",
    "**age_bracket** - Age bracket of the respondent - (18 - 34 Years, 35 - 44 Years, 45 - 54 Years, 55 - 64 Years, 64+ Years) - (Categorical)\n",
    "    \n",
    "**qualification** - Qualification/education level of the respondent as per their response -(<12 Years, 12 Years, College Graduate, Some College) - (Categorical)\n",
    "    \n",
    "**race**: Respondent's race - (White, Black, Other or Multiple ,Hispanic) - (Categorical) \n",
    "    \n",
    "**sex**: Respondent's sex - (Female, Male) - (Categorical)\n",
    "    \n",
    "**income_level**:Annual income of the respondent as per the 2008 poverty Census - (<=$75000-Above Poverty, >$75000, Below Poverty) - (Categorical)\n",
    "    \n",
    "**marital_status**: Respondent's marital status - (Not Married, Married) - (Categorical)\n",
    "    \n",
    "**housing_status**: Respondent's housing status - (Own, Rent) - (Categorical)\n",
    "    \n",
    "**employment**: Respondent's employment status - (Not in Labor Force, Employed, Unemployed) - (Categorical)\n",
    "    \n",
    "**census_msa**: Residence of the respondent with the MSA(metropolitan statistical area)(Non-MSA, MSA-Not Principle, CityMSA-Principle city) - (Yes, no) - (Categorical)\n",
    "    \n",
    "**no_of_adults**:  Number of adults in the respondent's house (0,1,2,3) - (Yes, no) - (Categorical)\n",
    "\n",
    "**no_of_children**: Number of children in the respondent's house(0,1,2,3) - (Yes, No) - (Categorical)\n",
    "\n",
    "**h1n1_vaccine**: (Dependent variable)Did the respondent received the h1n1 vaccine or not(1,0) - (Yes, No) - (Categorical)"
   ]
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    "## Icon Legends\n",
    "<table>\n",
    "  <tr>\n",
    "    <th width=\"25%\"> <img src=\"infer.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"alsoreadicon.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"todo.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"quicktip.png\" style=\"width:25%;\"></th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Inferences from outcome</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Additional Reads</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Lets do it</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Quick Tips</b></font></div>\n",
    "    </td>\n",
    "\n",
    "</tr>\n",
    "\n",
    "</table>\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Content\n",
    "\n",
    "1. **[Import Libraries](#import_lib)**\n",
    "2. **[Set Options](#set_options)**\n",
    "3. **[Read Data](#RD)**\n",
    "4. **[Data Analysis and Preparation](#data_preparation)**\n",
    "    - 4.1 - **[Understand the Dataset](#Data_Understanding)**\n",
    "        - 4.1.1 - **[Data Type](#Data_Types)**\n",
    "        - 4.1.2 - **[Summary Statistics](#Summary_Statistics)**\n",
    "        - 4.1.3 - **[Distribution of Variables](#distribution_variables)**\n",
    "        - 4.1.4 - **[Correlation](#correlation)**\n",
    "        - 4.1.5 - **[Missing Values](#Missing_Values)**      \n",
    "    - 4.2 - **[Prepare the Data](#Data_Preparation)**\n",
    "5. **[Logistic Regression](#LogisticReg)**\n",
    "    - 5.1 - **[Logistic Regression (using MLE)](#withStatsModels)**\n",
    "    - 5.2 - **[Logistic Regression (using SGD)](#usingSGD)**\n",
    "6. **[Conclusion and Interpretation](#conclusion)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AyalDMN-ebnc"
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   "source": [
    "<a id='import_lib'></a>\n",
    "# 1. Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Import the required libraries and functions</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
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    "# suppress display of warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 'Pandas' is used for data manipulation and analysis\n",
    "import pandas as pd \n",
    "\n",
    "# 'Numpy' is used for mathematical operations on large, multi-dimensional arrays and matrices\n",
    "import numpy as np\n",
    "\n",
    "# 'Matplotlib' is a data visualization library for 2D and 3D plots, built on numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# 'Seaborn' is based on matplotlib; used for plotting statistical graphics\n",
    "import seaborn as sns\n",
    "\n",
    "# import 'is_string_dtype' to check if the type of input is string  \n",
    "from pandas.api.types import is_string_dtype\n",
    "\n",
    "# import various functions to perform classification\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# import functions to perform logistic regression\n",
    "import statsmodels\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "source": [
    "# set the plot size using 'rcParams'\n",
    "# once the plot size is set using 'rcParams', it sets the size of all the forthcoming plots in the file\n",
    "# pass width and height in inches to 'figure.figsize' \n",
    "plt.rcParams['figure.figsize'] = [15,8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ll0ntaGOebng"
   },
   "source": [
    "<a id='set_options'></a>\n",
    "# 2. Set Options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Now we make necessary changes to :<br><br>\n",
    "1. Display complete data frames<br>\n",
    "2. To avoid the exponential number<br>\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "qJcw0J2Cebnh"
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   "source": [
    "# display all columns of the dataframe\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# display all rows of the dataframe\n",
    "pd.options.display.max_rows = None\n",
    "\n",
    "# use below code to convert the 'exponential' values to float\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yPb9_if1ebnl"
   },
   "source": [
    "<a id='RD'></a>\n",
    "# 3. Read Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Read and display data to get an insight into the data.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [
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       "      <th>unique_id</th>\n",
       "      <th>h1n1_worry</th>\n",
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       "      <td>Below Poverty</td>\n",
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       "      <td>Non-MSA</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65+ Years</td>\n",
       "      <td>12 Years</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>Below Poverty</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Not in Labor Force</td>\n",
       "      <td>MSA, Principle City</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45 - 54 Years</td>\n",
       "      <td>Some College</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>&lt;= $75,000, Above Poverty</td>\n",
       "      <td>Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Employed</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id  h1n1_worry  h1n1_awareness  antiviral_medication  \\\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",
       "   contact_avoidance  bought_face_mask  wash_hands_frequently  \\\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",
       "   avoid_large_gatherings  reduced_outside_home_cont  avoid_touch_face  \\\n",
       "0                     0.0                        1.0               1.0   \n",
       "1                     0.0                        1.0               1.0   \n",
       "2                     0.0                        0.0               0.0   \n",
       "3                     1.0                        0.0               0.0   \n",
       "4                     1.0                        0.0               1.0   \n",
       "\n",
       "   dr_recc_h1n1_vacc  dr_recc_seasonal_vacc  chronic_medic_condition  \\\n",
       "0                0.0                    0.0                      0.0   \n",
       "1                0.0                    0.0                      0.0   \n",
       "2                NaN                    NaN                      1.0   \n",
       "3                0.0                    1.0                      1.0   \n",
       "4                0.0                    0.0                      0.0   \n",
       "\n",
       "   cont_child_undr_6_mnths  is_health_worker  has_health_insur  \\\n",
       "0                      0.0               0.0               1.0   \n",
       "1                      0.0               0.0               1.0   \n",
       "2                      0.0               0.0               NaN   \n",
       "3                      0.0               0.0               NaN   \n",
       "4                      0.0               0.0               NaN   \n",
       "\n",
       "   is_h1n1_vacc_effective  is_h1n1_risky  sick_from_h1n1_vacc  \\\n",
       "0                     3.0            1.0                  2.0   \n",
       "1                     5.0            4.0                  4.0   \n",
       "2                     3.0            1.0                  1.0   \n",
       "3                     3.0            3.0                  5.0   \n",
       "4                     3.0            3.0                  2.0   \n",
       "\n",
       "   is_seas_vacc_effective  is_seas_risky  sick_from_seas_vacc    age_bracket  \\\n",
       "0                     2.0            1.0                  2.0  55 - 64 Years   \n",
       "1                     4.0            2.0                  4.0  35 - 44 Years   \n",
       "2                     4.0            1.0                  2.0  18 - 34 Years   \n",
       "3                     5.0            4.0                  1.0      65+ Years   \n",
       "4                     3.0            1.0                  4.0  45 - 54 Years   \n",
       "\n",
       "      qualification   race     sex               income_level marital_status  \\\n",
       "0        < 12 Years  White  Female              Below Poverty    Not Married   \n",
       "1          12 Years  White    Male              Below Poverty    Not Married   \n",
       "2  College Graduate  White    Male  <= $75,000, Above Poverty    Not Married   \n",
       "3          12 Years  White  Female              Below Poverty    Not Married   \n",
       "4      Some College  White  Female  <= $75,000, Above Poverty        Married   \n",
       "\n",
       "  housing_status          employment                census_msa  no_of_adults  \\\n",
       "0            Own  Not in Labor Force                   Non-MSA           0.0   \n",
       "1           Rent            Employed  MSA, Not Principle  City           0.0   \n",
       "2            Own            Employed  MSA, Not Principle  City           2.0   \n",
       "3           Rent  Not in Labor Force       MSA, Principle City           0.0   \n",
       "4            Own            Employed  MSA, Not Principle  City           1.0   \n",
       "\n",
       "   no_of_children  h1n1_vaccine  \n",
       "0             0.0             0  \n",
       "1             0.0             0  \n",
       "2             0.0             0  \n",
       "3             0.0             0  \n",
       "4             0.0             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the excel data file \n",
    "df_vaccine = pd.read_csv('h1n1_vaccine_prediction.csv')\n",
    "\n",
    "# display the top 5 rows of the dataframe\n",
    "df_vaccine.head()\n",
    "\n",
    "# Note: To display more rows, example 10, use head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dimensions of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26707, 34)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'shape' function gives the total number of rows and columns in the data\n",
    "df_vaccine.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SSHIYKY2ebns"
   },
   "source": [
    "<a id='data_preparation'></a>\n",
    "# 4. Data Analysis and Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l48tGdoxebnt"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Data preparation is the process of cleaning and transforming raw data before building predictive models. <br><br>\n",
    "                        Here, we analyze and prepare data to perform classification techniques:<br>\n",
    "                        1. Check data types. Ensure your data types are correct. Refer data definitions to validate <br>\n",
    "                        2. If data types are not as per business definition, change the data types as per requirement <br>\n",
    "                        3. Study summary statistics<br>\n",
    "                        4. Distribution of variables<br>\n",
    "                        5. Study correlation<br>\n",
    "                        6. Detect outliers<br>\n",
    "                        7. Check for missing values<br><br>\n",
    "                        Note: It is an art to explore data, and one needs more and more practice to gain expertise in this area\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y-baDwGWebnu"
   },
   "source": [
    "<a id='Data_Understanding'></a>\n",
    "## 4.1 Understand the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O71NsesEebn3"
   },
   "source": [
    "<a id='Data_Types'></a>\n",
    "### 4.1.1 Data Type\n",
    "The main data types in Pandas dataframes are the object, float, int64, bool, and datetime64. To understand each attribute of our data, it is always good for us to know the data type of each column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>In our dataset, we have numerical and categorical variables. The numeric variables should have data type 'int'/'float' while categorical variables should have data type 'object'.<br><br> \n",
    "                        1. Check for the data type <br>\n",
    "                        2. For any incorrect data type, change the data type with the appropriate type<br>\n",
    "                        3. Recheck for the data type\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Check for the data type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "TnYrzq7Yebn4",
    "outputId": "4bb73ba1-917e-4175-f29d-72a57c788b84",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unique_id                      int64\n",
       "h1n1_worry                   float64\n",
       "h1n1_awareness               float64\n",
       "antiviral_medication         float64\n",
       "contact_avoidance            float64\n",
       "bought_face_mask             float64\n",
       "wash_hands_frequently        float64\n",
       "avoid_large_gatherings       float64\n",
       "reduced_outside_home_cont    float64\n",
       "avoid_touch_face             float64\n",
       "dr_recc_h1n1_vacc            float64\n",
       "dr_recc_seasonal_vacc        float64\n",
       "chronic_medic_condition      float64\n",
       "cont_child_undr_6_mnths      float64\n",
       "is_health_worker             float64\n",
       "has_health_insur             float64\n",
       "is_h1n1_vacc_effective       float64\n",
       "is_h1n1_risky                float64\n",
       "sick_from_h1n1_vacc          float64\n",
       "is_seas_vacc_effective       float64\n",
       "is_seas_risky                float64\n",
       "sick_from_seas_vacc          float64\n",
       "age_bracket                   object\n",
       "qualification                 object\n",
       "race                          object\n",
       "sex                           object\n",
       "income_level                  object\n",
       "marital_status                object\n",
       "housing_status                object\n",
       "employment                    object\n",
       "census_msa                    object\n",
       "no_of_adults                 float64\n",
       "no_of_children               float64\n",
       "h1n1_vaccine                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'dtypes' gives the data type for each column\n",
    "df_vaccine.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uvOvxensebn6"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>From the above output, it is clearly seen that the data type of variables 'unique_id' and 'h1n1_vaccine'is correctly identified as 'int64'.<br><br>\n",
    "                        However, variables like 'h1n1_worry', 'h1n1_awareness' etc are identified as float. But as per data attribute information we have, 'these variables are categorical, which are wrongly interpreted as float, so we convert these variables' data type to 'object'.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Change the incorrect data type.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "ogPgThuFebn8",
    "outputId": "df7aca8e-88e3-4091-f1cf-eae08588d4d6",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# use 'for' loop to change the data type of variables \n",
    "for col in ['h1n1_worry','h1n1_awareness', 'antiviral_medication', 'contact_avoidance', 'bought_face_mask',\n",
    "            'wash_hands_frequently', 'avoid_large_gatherings', 'reduced_outside_home_cont', 'avoid_touch_face', \n",
    "            'dr_recc_h1n1_vacc', 'dr_recc_seasonal_vacc', 'chronic_medic_condition','cont_child_undr_6_mnths',\n",
    "           'is_health_worker', 'has_health_insur', 'is_h1n1_vacc_effective', 'is_h1n1_risky', 'sick_from_h1n1_vacc', \n",
    "            'is_seas_vacc_effective', 'is_seas_risky', 'sick_from_seas_vacc', 'no_of_adults', 'no_of_children']:\n",
    "\n",
    "    # use .astype() to change the data type\n",
    "    df_vaccine[col] = df_vaccine[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7rG9ZJ7bebn-"
   },
   "source": [
    "**3. Recheck the data type after the conversion.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "Zp6_mQOBebn_",
    "outputId": "01c0bf54-e49e-4959-f839-127a94b1ad44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unique_id                     int64\n",
       "h1n1_worry                   object\n",
       "h1n1_awareness               object\n",
       "antiviral_medication         object\n",
       "contact_avoidance            object\n",
       "bought_face_mask             object\n",
       "wash_hands_frequently        object\n",
       "avoid_large_gatherings       object\n",
       "reduced_outside_home_cont    object\n",
       "avoid_touch_face             object\n",
       "dr_recc_h1n1_vacc            object\n",
       "dr_recc_seasonal_vacc        object\n",
       "chronic_medic_condition      object\n",
       "cont_child_undr_6_mnths      object\n",
       "is_health_worker             object\n",
       "has_health_insur             object\n",
       "is_h1n1_vacc_effective       object\n",
       "is_h1n1_risky                object\n",
       "sick_from_h1n1_vacc          object\n",
       "is_seas_vacc_effective       object\n",
       "is_seas_risky                object\n",
       "sick_from_seas_vacc          object\n",
       "age_bracket                  object\n",
       "qualification                object\n",
       "race                         object\n",
       "sex                          object\n",
       "income_level                 object\n",
       "marital_status               object\n",
       "housing_status               object\n",
       "employment                   object\n",
       "census_msa                   object\n",
       "no_of_adults                 object\n",
       "no_of_children               object\n",
       "h1n1_vaccine                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recheck the data types of all variables\n",
    "df_vaccine.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "udGKF9feeboB"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "           <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Now we have all the variables with corrected data type as required.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "           <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>The field 'unique_id' is a unique id of each of the survey respondents. We will drop this field as it will not be used in the analysis.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#drop the field 'unique_id'\n",
    "# axis=1: it stands for column\n",
    "# inplace=True: it perform operations on original data\n",
    "df_vaccine.drop('unique_id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26707, 33)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verify the shape\n",
    "df_vaccine.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>For convenience, we shall split the target variable from the data frame.\n",
    "Let dataframe 'df_target' be the dataframe containing the dependent variable and dataframe 'df_features' be the set of all independent variables.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# splitting features and the target variable\n",
    "# consider all the columns except 'h1n1_vaccine' using 'iloc'\n",
    "df_features = df_vaccine.iloc[:, df_vaccine.columns != 'h1n1_vaccine']\n",
    "\n",
    "# consider the target variable\n",
    "df_target = df_vaccine.iloc[:, df_vaccine.columns == 'h1n1_vaccine']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the dataframe containing features (df_features) for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BFOkFedXeboC"
   },
   "source": [
    "<a id='Summary_Statistics'></a>\n",
    "### 4.1.2 Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> In our dataset df_features, all the variables are categorical variables. We check for summary statistics :<br><br>\n",
    "                        1. For categorical variables, using the describe()<br>\n",
    "                        As all variables are categorical, it returns the summary statistics of such variables without having to use describe(include=object) \n",
    "                        </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. For numerical variables, use the describe()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BCq-Il09eboC",
    "outputId": "a7837b7a-f994-4ad1-d962-be936a04043e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>h1n1_worry</th>\n",
       "      <td>26615</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_awareness</th>\n",
       "      <td>26591</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>antiviral_medication</th>\n",
       "      <td>26636</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>25335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact_avoidance</th>\n",
       "      <td>26499</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bought_face_mask</th>\n",
       "      <td>26688</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wash_hands_frequently</th>\n",
       "      <td>26665</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avoid_large_gatherings</th>\n",
       "      <td>26620</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reduced_outside_home_cont</th>\n",
       "      <td>26625</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avoid_touch_face</th>\n",
       "      <td>26579</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>18001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dr_recc_h1n1_vacc</th>\n",
       "      <td>24547</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dr_recc_seasonal_vacc</th>\n",
       "      <td>24547</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronic_medic_condition</th>\n",
       "      <td>25736</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>18446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cont_child_undr_6_mnths</th>\n",
       "      <td>25887</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_health_worker</th>\n",
       "      <td>25903</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_health_insur</th>\n",
       "      <td>14433</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_h1n1_vacc_effective</th>\n",
       "      <td>26316</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_h1n1_risky</th>\n",
       "      <td>26319</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sick_from_h1n1_vacc</th>\n",
       "      <td>26312</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_seas_vacc_effective</th>\n",
       "      <td>26245</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_seas_risky</th>\n",
       "      <td>26193</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sick_from_seas_vacc</th>\n",
       "      <td>26170</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_bracket</th>\n",
       "      <td>26707</td>\n",
       "      <td>5</td>\n",
       "      <td>65+ Years</td>\n",
       "      <td>6843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qualification</th>\n",
       "      <td>25300</td>\n",
       "      <td>4</td>\n",
       "      <td>College Graduate</td>\n",
       "      <td>10097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>26707</td>\n",
       "      <td>4</td>\n",
       "      <td>White</td>\n",
       "      <td>21222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>26707</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>15858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_level</th>\n",
       "      <td>22284</td>\n",
       "      <td>3</td>\n",
       "      <td>&lt;= $75,000, Above Poverty</td>\n",
       "      <td>12777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_status</th>\n",
       "      <td>25299</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>13555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_status</th>\n",
       "      <td>24665</td>\n",
       "      <td>2</td>\n",
       "      <td>Own</td>\n",
       "      <td>18736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment</th>\n",
       "      <td>25244</td>\n",
       "      <td>3</td>\n",
       "      <td>Employed</td>\n",
       "      <td>13560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>census_msa</th>\n",
       "      <td>26707</td>\n",
       "      <td>3</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>11645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_adults</th>\n",
       "      <td>26458</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_children</th>\n",
       "      <td>26458</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count unique                        top   freq\n",
       "h1n1_worry                 26615      4                          2  10575\n",
       "h1n1_awareness             26591      3                          1  14598\n",
       "antiviral_medication       26636      2                          0  25335\n",
       "contact_avoidance          26499      2                          1  19228\n",
       "bought_face_mask           26688      2                          0  24847\n",
       "wash_hands_frequently      26665      2                          1  22015\n",
       "avoid_large_gatherings     26620      2                          0  17073\n",
       "reduced_outside_home_cont  26625      2                          0  17644\n",
       "avoid_touch_face           26579      2                          1  18001\n",
       "dr_recc_h1n1_vacc          24547      2                          0  19139\n",
       "dr_recc_seasonal_vacc      24547      2                          0  16453\n",
       "chronic_medic_condition    25736      2                          0  18446\n",
       "cont_child_undr_6_mnths    25887      2                          0  23749\n",
       "is_health_worker           25903      2                          0  23004\n",
       "has_health_insur           14433      2                          1  12697\n",
       "is_h1n1_vacc_effective     26316      5                          4  11683\n",
       "is_h1n1_risky              26319      5                          2   9919\n",
       "sick_from_h1n1_vacc        26312      5                          2   9129\n",
       "is_seas_vacc_effective     26245      5                          4  11629\n",
       "is_seas_risky              26193      5                          2   8954\n",
       "sick_from_seas_vacc        26170      5                          1  11870\n",
       "age_bracket                26707      5                  65+ Years   6843\n",
       "qualification              25300      4           College Graduate  10097\n",
       "race                       26707      4                      White  21222\n",
       "sex                        26707      2                     Female  15858\n",
       "income_level               22284      3  <= $75,000, Above Poverty  12777\n",
       "marital_status             25299      2                    Married  13555\n",
       "housing_status             24665      2                        Own  18736\n",
       "employment                 25244      3                   Employed  13560\n",
       "census_msa                 26707      3   MSA, Not Principle  City  11645\n",
       "no_of_adults               26458      4                          1  14474\n",
       "no_of_children             26458      4                          0  18672"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the describe() returns the statistical summary of the variables\n",
    "# by default, it returns the summary of all categorical variables as tere are no numerical variables in the dataset\n",
    "# use .transpose() for better readability, however its optional\n",
    "df_features.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BvaxGBkKeboF"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "<b>The summary contains information about the total number of observations, number of unique classes, the most occurring class and frequency of the same.\n",
    "<br><br>\n",
    "    Let us consider the 'h1n1_worry' variable to see the statistics- <br>\n",
    "count: Number of observations i.e., 26615<br>\n",
    "unique: Number of unique values or classes in the variable i.e., it has 4 classes in it <br>\n",
    "top: The most occurring class in this variable is 2<br> \n",
    "frequency: Frequency of the most repeated class; out of 20000 observations 0 has a frequency of 10575\n",
    "                    </b>  \n",
    "               </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='distribution_variables'></a>\n",
    "### 4.1.3 Distribution of Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Check the distribution of all the variables <br><br>\n",
    "                        1. Distribution of numeric independent variables<br>\n",
    "                        2. Distribution of categoric independent variables<br>\n",
    "                        3. Distribution of dependent variable\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-tWX9ptGeboe"
   },
   "source": [
    "#### 1. Distribution of numeric independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are no independent numeric variables, we skip this step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v1hJh7QHebow"
   },
   "source": [
    "#### 2. Distribution of categorical independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the independent categoric variables, we plot the bar plot to check the distribution of each variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G7HE9bl9eboz",
    "outputId": "1d6ec8fb-1114-4614-9c66-6d7d9104892b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x2160 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a list of all categorical variables\n",
    "# initiate an empty list to store the categorical variables\n",
    "categorical=[]\n",
    "\n",
    "# use for loop to check the data type of each variable\n",
    "for column in df_features:\n",
    "    \n",
    "    # use 'if' statement with condition to check the categorical type \n",
    "    if is_string_dtype(df_features[column]):\n",
    "        \n",
    "        # append the variables with 'categoric' data type in the list 'categorical'\n",
    "        categorical.append(column)\n",
    "\n",
    "# plot the count plot for each categorical variable \n",
    "# set the number of rows in the subplot using the parameter, 'nrows'\n",
    "# set the number of columns in the subplot using the parameter, 'ncols'\n",
    "# 'figsize' sets the figure size\n",
    "fig, ax = plt.subplots(nrows = 8, ncols = 4, figsize=(25, 30))\n",
    "\n",
    "# use for loop to plot the count plot for each variable\n",
    "for variable, subplot in zip(categorical, ax.flatten()):\n",
    "    \n",
    "    # use countplot() to plot the graph\n",
    "    # pass the axes for the plot to the parameter, 'ax'\n",
    "    sns.countplot(df_vaccine[variable], ax = subplot)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> Most of the variables are binary. A few variables have 3, 4 and even 5 categories, as seen above.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Distribution of dependent variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In section 4.1.1, we have split the dependent variable (h1n1_vaccine) and created a dataframe 'df_target'. Use this dataframe to check the distribution of the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21033\n",
       "1     5674\n",
       "Name: h1n1_vaccine, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get counts of 0's and 1's in the 'h1n1_vaccine' variable using 'value_counts()'\n",
    "# store the values in 'class_frequency'\n",
    "class_frequency = df_target.h1n1_vaccine.value_counts()\n",
    "class_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the countplot of the variable 'h1n1_vaccine'\n",
    "sns.countplot(x = df_target.h1n1_vaccine)\n",
    "\n",
    "# use below code to print the values in the graph\n",
    "# 'x' and 'y' gives position of the text\n",
    "# 's' is the text on the plot\n",
    "plt.text(x = -0.05, y = df_target.h1n1_vaccine.value_counts()[0] + 30, s = str((class_frequency[0])*100/len(df_target.h1n1_vaccine)) + '%')\n",
    "plt.text(x = 0.95, y = df_target.h1n1_vaccine.value_counts()[1] +20, s = str((class_frequency[1])*100/len(df_target.h1n1_vaccine)) + '%')\n",
    "\n",
    "# add plot and axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Count Plot for Target Variable (h1n1_vaccine)', fontsize = 15)\n",
    "plt.xlabel('Target Variable', fontsize = 15)\n",
    "plt.ylabel('Count', fontsize = 15)\n",
    "\n",
    "# to show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>There are a total of 26707 respondents who do not have taken the survey, and 5674 have taken the h1n1 flu vaccine. We see that the data set is  imbalanced between the two classes of the target variable.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vkZahcS9eboQ"
   },
   "source": [
    "<a id='correlation'></a>\n",
    "### 4.1.4 Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K_oo8YEAeboS"
   },
   "source": [
    "Correlation is a statistic that measures the degree to which two variables move with each other. A correlation coefficient near  1  indicates the strong relationship between them; a weak correlation indicates the extent to which one variable increases as the other decreases. Correlation among multiple variables can be represented in the form of a matrix. This allows us to see which variables are correlated. Since all the independent variables are categorical variables, we skip this step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G-aTB0DseboJ"
   },
   "source": [
    "<a id='Missing_Values'></a>\n",
    "### 4.1.5 Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>First, run a check for the presence of missing values and their percentage for each column. Then choose the right approach to remove them.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XsY-ydhgeboM",
    "outputId": "1287fead-9adc-4382-a939-bdba17105d0f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percentage of Missing Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>has_health_insur</th>\n",
       "      <td>12274</td>\n",
       "      <td>45.957989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_level</th>\n",
       "      <td>4423</td>\n",
       "      <td>16.561201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dr_recc_h1n1_vacc</th>\n",
       "      <td>2160</td>\n",
       "      <td>8.087767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dr_recc_seasonal_vacc</th>\n",
       "      <td>2160</td>\n",
       "      <td>8.087767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_status</th>\n",
       "      <td>2042</td>\n",
       "      <td>7.645936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment</th>\n",
       "      <td>1463</td>\n",
       "      <td>5.477965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital_status</th>\n",
       "      <td>1408</td>\n",
       "      <td>5.272026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qualification</th>\n",
       "      <td>1407</td>\n",
       "      <td>5.268282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronic_medic_condition</th>\n",
       "      <td>971</td>\n",
       "      <td>3.635751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cont_child_undr_6_mnths</th>\n",
       "      <td>820</td>\n",
       "      <td>3.070356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_health_worker</th>\n",
       "      <td>804</td>\n",
       "      <td>3.010447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sick_from_seas_vacc</th>\n",
       "      <td>537</td>\n",
       "      <td>2.010709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_seas_risky</th>\n",
       "      <td>514</td>\n",
       "      <td>1.924589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_seas_vacc_effective</th>\n",
       "      <td>462</td>\n",
       "      <td>1.729884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sick_from_h1n1_vacc</th>\n",
       "      <td>395</td>\n",
       "      <td>1.479013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_h1n1_vacc_effective</th>\n",
       "      <td>391</td>\n",
       "      <td>1.464036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_h1n1_risky</th>\n",
       "      <td>388</td>\n",
       "      <td>1.452803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_children</th>\n",
       "      <td>249</td>\n",
       "      <td>0.932340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_of_adults</th>\n",
       "      <td>249</td>\n",
       "      <td>0.932340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact_avoidance</th>\n",
       "      <td>208</td>\n",
       "      <td>0.778822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avoid_touch_face</th>\n",
       "      <td>128</td>\n",
       "      <td>0.479275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_awareness</th>\n",
       "      <td>116</td>\n",
       "      <td>0.434343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_worry</th>\n",
       "      <td>92</td>\n",
       "      <td>0.344479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avoid_large_gatherings</th>\n",
       "      <td>87</td>\n",
       "      <td>0.325757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reduced_outside_home_cont</th>\n",
       "      <td>82</td>\n",
       "      <td>0.307036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>antiviral_medication</th>\n",
       "      <td>71</td>\n",
       "      <td>0.265848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wash_hands_frequently</th>\n",
       "      <td>42</td>\n",
       "      <td>0.157262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bought_face_mask</th>\n",
       "      <td>19</td>\n",
       "      <td>0.071142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_bracket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>census_msa</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h1n1_vaccine</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Total  Percentage of Missing Values\n",
       "has_health_insur           12274                     45.957989\n",
       "income_level                4423                     16.561201\n",
       "dr_recc_h1n1_vacc           2160                      8.087767\n",
       "dr_recc_seasonal_vacc       2160                      8.087767\n",
       "housing_status              2042                      7.645936\n",
       "employment                  1463                      5.477965\n",
       "marital_status              1408                      5.272026\n",
       "qualification               1407                      5.268282\n",
       "chronic_medic_condition      971                      3.635751\n",
       "cont_child_undr_6_mnths      820                      3.070356\n",
       "is_health_worker             804                      3.010447\n",
       "sick_from_seas_vacc          537                      2.010709\n",
       "is_seas_risky                514                      1.924589\n",
       "is_seas_vacc_effective       462                      1.729884\n",
       "sick_from_h1n1_vacc          395                      1.479013\n",
       "is_h1n1_vacc_effective       391                      1.464036\n",
       "is_h1n1_risky                388                      1.452803\n",
       "no_of_children               249                      0.932340\n",
       "no_of_adults                 249                      0.932340\n",
       "contact_avoidance            208                      0.778822\n",
       "avoid_touch_face             128                      0.479275\n",
       "h1n1_awareness               116                      0.434343\n",
       "h1n1_worry                    92                      0.344479\n",
       "avoid_large_gatherings        87                      0.325757\n",
       "reduced_outside_home_cont     82                      0.307036\n",
       "antiviral_medication          71                      0.265848\n",
       "wash_hands_frequently         42                      0.157262\n",
       "bought_face_mask              19                      0.071142\n",
       "age_bracket                    0                      0.000000\n",
       "race                           0                      0.000000\n",
       "sex                            0                      0.000000\n",
       "census_msa                     0                      0.000000\n",
       "h1n1_vaccine                   0                      0.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the variables on the basis of total null values in the variable\n",
    "# 'isnull().sum()' returns the number of missing values in each variable\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest number of missing values will appear first\n",
    "Total = df_vaccine.isnull().sum().sort_values(ascending = False)          \n",
    "\n",
    "# calculate the percentage of missing values\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest percentage of missing values will appear first\n",
    "Percent = (df_vaccine.isnull().sum()*100/df_vaccine.isnull().count()).sort_values(ascending = False)   \n",
    "\n",
    "# concat the 'Total' and 'Percent' columns using 'concat' function\n",
    "# 'keys' is the list of column names\n",
    "# 'axis = 1' concats along the columns\n",
    "missing_data = pd.concat([Total, Percent], axis = 1, keys = ['Total', 'Percentage of Missing Values'])    \n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Only the variables 'age_bracket', 'race', 'sex', 'census_msa' and 'h1n1_vaccine' do not contain missing values. All other variables have missing values.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot a heatmap for visualization of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap to check null values\n",
    "# 'cbar = False' does not show the color axis \n",
    "sns.heatmap(df_vaccine.isnull(), cbar=False)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The horizontal lines in the heatmap correspond to the missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"quicktip.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>How to deal with missing data?<br><br>\n",
    "\n",
    "\n",
    "Drop data<br>\n",
    "a. Drop the whole row<br>\n",
    "b. Drop the whole column<br><br>\n",
    "\n",
    "Replace data<br>\n",
    "\n",
    "a. Replace it by mean<br>\n",
    "\n",
    "b. Replace it by frequency<br>\n",
    "c. Replace it based on other functions<br><br>\n",
    "\n",
    "Whole columns should be dropped only if most entries in the column are empty. In our dataset, column has_health_insur has almost 46% missing values. Column income_level has 16% missing values. Hence dropping these two columns. Also columns dr_recc_h1n1_vacc and dr_recc_seasonal_vacc have 8% missing values. Has these two columns mostly tell about doctor's recommendation and not about the respondents, I choose to drop these two columns as well. As a execrcise we can re-run the model with these two columns later.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_vaccine.drop(['has_health_insur','income_level','dr_recc_h1n1_vacc','dr_recc_seasonal_vacc'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26707, 29)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vaccine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_vaccine.isnull(), cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_vaccine.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23426, 29)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vaccine.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After replacing the null values for both the variables, recheck the null values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_vaccine.isnull(), cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h1n1_worry                   0\n",
       "h1n1_awareness               0\n",
       "antiviral_medication         0\n",
       "contact_avoidance            0\n",
       "bought_face_mask             0\n",
       "wash_hands_frequently        0\n",
       "avoid_large_gatherings       0\n",
       "reduced_outside_home_cont    0\n",
       "avoid_touch_face             0\n",
       "chronic_medic_condition      0\n",
       "cont_child_undr_6_mnths      0\n",
       "is_health_worker             0\n",
       "is_h1n1_vacc_effective       0\n",
       "is_h1n1_risky                0\n",
       "sick_from_h1n1_vacc          0\n",
       "is_seas_vacc_effective       0\n",
       "is_seas_risky                0\n",
       "sick_from_seas_vacc          0\n",
       "age_bracket                  0\n",
       "qualification                0\n",
       "race                         0\n",
       "sex                          0\n",
       "marital_status               0\n",
       "housing_status               0\n",
       "employment                   0\n",
       "census_msa                   0\n",
       "no_of_adults                 0\n",
       "no_of_children               0\n",
       "h1n1_vaccine                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recheck the null values\n",
    "# 'isnull().sum()' returns the number of missing values in each variable\n",
    "df_vaccine.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Dogz55FZeboP"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> The above output shows that there are no missing values in the data.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Data_Preparation'></a>\n",
    "## 4.2 Prepare the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To build the classification models, we need to encode the categorical variables using dummy encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> \n",
    "                  To dummy encode, we do the following: <br><br>\n",
    "                    1. Filter numerical and categorical variables<br>\n",
    "                    2. Dummy encode the categorical variables<br>\n",
    "                    3. Concatenate numerical and dummy encoded categorical variables</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Filter numerical and categorical variables **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no numerical variables except the dependent variable(h1n1_vaccine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h1n1_worry                   object\n",
       "h1n1_awareness               object\n",
       "antiviral_medication         object\n",
       "contact_avoidance            object\n",
       "bought_face_mask             object\n",
       "wash_hands_frequently        object\n",
       "avoid_large_gatherings       object\n",
       "reduced_outside_home_cont    object\n",
       "avoid_touch_face             object\n",
       "chronic_medic_condition      object\n",
       "cont_child_undr_6_mnths      object\n",
       "is_health_worker             object\n",
       "is_h1n1_vacc_effective       object\n",
       "is_h1n1_risky                object\n",
       "sick_from_h1n1_vacc          object\n",
       "is_seas_vacc_effective       object\n",
       "is_seas_risky                object\n",
       "sick_from_seas_vacc          object\n",
       "age_bracket                  object\n",
       "qualification                object\n",
       "race                         object\n",
       "sex                          object\n",
       "marital_status               object\n",
       "housing_status               object\n",
       "employment                   object\n",
       "census_msa                   object\n",
       "no_of_adults                 object\n",
       "no_of_children               object\n",
       "h1n1_vaccine                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vaccine.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a list of all categorical variables\n",
    "# initiate an empty list to store the categorical variables\n",
    "categorical=[]\n",
    "\n",
    "# use for loop to check the data type of each variable\n",
    "for column in df_vaccine:\n",
    "    \n",
    "    # use 'if' statement with condition to check the categorical type \n",
    "    if is_string_dtype(df_vaccine[column]):\n",
    "        \n",
    "        # append the variables with 'categoric' data type in the list 'categorical'\n",
    "        categorical.append(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "DLtmzY72eboU"
   },
   "outputs": [],
   "source": [
    "# dataframe with categorical features\n",
    "# 'categorical' contains a list of categorical variables\n",
    "df_cat = df_vaccine[categorical]\n",
    "\n",
    "# dataframe with numerical features\n",
    "# use 'drop()' to drop the categorical variables\n",
    "# 'axis = 1' drops the corresponding column(s)\n",
    "df_num = df_vaccine.drop(categorical, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Dummy encode the categorical variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h1n1_worry</th>\n",
       "      <th>h1n1_awareness</th>\n",
       "      <th>antiviral_medication</th>\n",
       "      <th>contact_avoidance</th>\n",
       "      <th>bought_face_mask</th>\n",
       "      <th>wash_hands_frequently</th>\n",
       "      <th>avoid_large_gatherings</th>\n",
       "      <th>reduced_outside_home_cont</th>\n",
       "      <th>avoid_touch_face</th>\n",
       "      <th>chronic_medic_condition</th>\n",
       "      <th>cont_child_undr_6_mnths</th>\n",
       "      <th>is_health_worker</th>\n",
       "      <th>is_h1n1_vacc_effective</th>\n",
       "      <th>is_h1n1_risky</th>\n",
       "      <th>sick_from_h1n1_vacc</th>\n",
       "      <th>is_seas_vacc_effective</th>\n",
       "      <th>is_seas_risky</th>\n",
       "      <th>sick_from_seas_vacc</th>\n",
       "      <th>age_bracket</th>\n",
       "      <th>qualification</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>housing_status</th>\n",
       "      <th>employment</th>\n",
       "      <th>census_msa</th>\n",
       "      <th>no_of_adults</th>\n",
       "      <th>no_of_children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>55 - 64 Years</td>\n",
       "      <td>&lt; 12 Years</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Not in Labor Force</td>\n",
       "      <td>Non-MSA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>35 - 44 Years</td>\n",
       "      <td>12 Years</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Employed</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>18 - 34 Years</td>\n",
       "      <td>College Graduate</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Employed</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>65+ Years</td>\n",
       "      <td>12 Years</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>Not Married</td>\n",
       "      <td>Rent</td>\n",
       "      <td>Not in Labor Force</td>\n",
       "      <td>MSA, Principle City</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>45 - 54 Years</td>\n",
       "      <td>Some College</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>Married</td>\n",
       "      <td>Own</td>\n",
       "      <td>Employed</td>\n",
       "      <td>MSA, Not Principle  City</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  h1n1_worry h1n1_awareness antiviral_medication contact_avoidance  \\\n",
       "0          1              0                    0                 0   \n",
       "1          3              2                    0                 1   \n",
       "2          1              1                    0                 1   \n",
       "3          1              1                    0                 1   \n",
       "4          2              1                    0                 1   \n",
       "\n",
       "  bought_face_mask wash_hands_frequently avoid_large_gatherings  \\\n",
       "0                0                     0                      0   \n",
       "1                0                     1                      0   \n",
       "2                0                     0                      0   \n",
       "3                0                     1                      1   \n",
       "4                0                     1                      1   \n",
       "\n",
       "  reduced_outside_home_cont avoid_touch_face chronic_medic_condition  \\\n",
       "0                         1                1                       0   \n",
       "1                         1                1                       0   \n",
       "2                         0                0                       1   \n",
       "3                         0                0                       1   \n",
       "4                         0                1                       0   \n",
       "\n",
       "  cont_child_undr_6_mnths is_health_worker is_h1n1_vacc_effective  \\\n",
       "0                       0                0                      3   \n",
       "1                       0                0                      5   \n",
       "2                       0                0                      3   \n",
       "3                       0                0                      3   \n",
       "4                       0                0                      3   \n",
       "\n",
       "  is_h1n1_risky sick_from_h1n1_vacc is_seas_vacc_effective is_seas_risky  \\\n",
       "0             1                   2                      2             1   \n",
       "1             4                   4                      4             2   \n",
       "2             1                   1                      4             1   \n",
       "3             3                   5                      5             4   \n",
       "4             3                   2                      3             1   \n",
       "\n",
       "  sick_from_seas_vacc    age_bracket     qualification   race     sex  \\\n",
       "0                   2  55 - 64 Years        < 12 Years  White  Female   \n",
       "1                   4  35 - 44 Years          12 Years  White    Male   \n",
       "2                   2  18 - 34 Years  College Graduate  White    Male   \n",
       "3                   1      65+ Years          12 Years  White  Female   \n",
       "4                   4  45 - 54 Years      Some College  White  Female   \n",
       "\n",
       "  marital_status housing_status          employment                census_msa  \\\n",
       "0    Not Married            Own  Not in Labor Force                   Non-MSA   \n",
       "1    Not Married           Rent            Employed  MSA, Not Principle  City   \n",
       "2    Not Married            Own            Employed  MSA, Not Principle  City   \n",
       "3    Not Married           Rent  Not in Labor Force       MSA, Principle City   \n",
       "4        Married            Own            Employed  MSA, Not Principle  City   \n",
       "\n",
       "  no_of_adults no_of_children  \n",
       "0            0              0  \n",
       "1            0              0  \n",
       "2            2              0  \n",
       "3            0              0  \n",
       "4            1              0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first five observations of the 'df_cat'\n",
    "df_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "rLNKCK7lebo3"
   },
   "outputs": [],
   "source": [
    "# use 'get_dummies()' from pandas to create dummy variables\n",
    "# use 'drop_first = True' to create (n-1) dummy variables\n",
    "df_cat_dummies = pd.get_dummies(df_cat, drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age_bracket_65+ Years</th>\n",
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       "      <th>race_Other or Multiple</th>\n",
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       "      <th>marital_status_Not Married</th>\n",
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       "      <th>employment_Not in Labor Force</th>\n",
       "      <th>employment_Unemployed</th>\n",
       "      <th>census_msa_MSA, Principle City</th>\n",
       "      <th>census_msa_Non-MSA</th>\n",
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       "      <th>no_of_adults_2.0</th>\n",
       "      <th>no_of_adults_3.0</th>\n",
       "      <th>no_of_children_1.0</th>\n",
       "      <th>no_of_children_2.0</th>\n",
       "      <th>no_of_children_3.0</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   h1n1_worry_1.0  h1n1_worry_2.0  h1n1_worry_3.0  h1n1_awareness_1.0  \\\n",
       "0               1               0               0                   0   \n",
       "1               0               0               1                   0   \n",
       "2               1               0               0                   1   \n",
       "3               1               0               0                   1   \n",
       "4               0               1               0                   1   \n",
       "\n",
       "   h1n1_awareness_2.0  antiviral_medication_1.0  contact_avoidance_1.0  \\\n",
       "0                   0                         0                      0   \n",
       "1                   1                         0                      1   \n",
       "2                   0                         0                      1   \n",
       "3                   0                         0                      1   \n",
       "4                   0                         0                      1   \n",
       "\n",
       "   bought_face_mask_1.0  wash_hands_frequently_1.0  \\\n",
       "0                     0                          0   \n",
       "1                     0                          1   \n",
       "2                     0                          0   \n",
       "3                     0                          1   \n",
       "4                     0                          1   \n",
       "\n",
       "   avoid_large_gatherings_1.0  reduced_outside_home_cont_1.0  \\\n",
       "0                           0                              1   \n",
       "1                           0                              1   \n",
       "2                           0                              0   \n",
       "3                           1                              0   \n",
       "4                           1                              0   \n",
       "\n",
       "   avoid_touch_face_1.0  chronic_medic_condition_1.0  \\\n",
       "0                     1                            0   \n",
       "1                     1                            0   \n",
       "2                     0                            1   \n",
       "3                     0                            1   \n",
       "4                     1                            0   \n",
       "\n",
       "   cont_child_undr_6_mnths_1.0  is_health_worker_1.0  \\\n",
       "0                            0                     0   \n",
       "1                            0                     0   \n",
       "2                            0                     0   \n",
       "3                            0                     0   \n",
       "4                            0                     0   \n",
       "\n",
       "   is_h1n1_vacc_effective_2.0  is_h1n1_vacc_effective_3.0  \\\n",
       "0                           0                           1   \n",
       "1                           0                           0   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   is_h1n1_vacc_effective_4.0  is_h1n1_vacc_effective_5.0  is_h1n1_risky_2.0  \\\n",
       "0                           0                           0                  0   \n",
       "1                           0                           1                  0   \n",
       "2                           0                           0                  0   \n",
       "3                           0                           0                  0   \n",
       "4                           0                           0                  0   \n",
       "\n",
       "   is_h1n1_risky_3.0  is_h1n1_risky_4.0  is_h1n1_risky_5.0  \\\n",
       "0                  0                  0                  0   \n",
       "1                  0                  1                  0   \n",
       "2                  0                  0                  0   \n",
       "3                  1                  0                  0   \n",
       "4                  1                  0                  0   \n",
       "\n",
       "   sick_from_h1n1_vacc_2.0  sick_from_h1n1_vacc_3.0  sick_from_h1n1_vacc_4.0  \\\n",
       "0                        1                        0                        0   \n",
       "1                        0                        0                        1   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        1                        0                        0   \n",
       "\n",
       "   sick_from_h1n1_vacc_5.0  is_seas_vacc_effective_2.0  \\\n",
       "0                        0                           1   \n",
       "1                        0                           0   \n",
       "2                        0                           0   \n",
       "3                        1                           0   \n",
       "4                        0                           0   \n",
       "\n",
       "   is_seas_vacc_effective_3.0  is_seas_vacc_effective_4.0  \\\n",
       "0                           0                           0   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           0   \n",
       "4                           1                           0   \n",
       "\n",
       "   is_seas_vacc_effective_5.0  is_seas_risky_2.0  is_seas_risky_3.0  \\\n",
       "0                           0                  0                  0   \n",
       "1                           0                  1                  0   \n",
       "2                           0                  0                  0   \n",
       "3                           1                  0                  0   \n",
       "4                           0                  0                  0   \n",
       "\n",
       "   is_seas_risky_4.0  is_seas_risky_5.0  sick_from_seas_vacc_2.0  \\\n",
       "0                  0                  0                        1   \n",
       "1                  0                  0                        0   \n",
       "2                  0                  0                        1   \n",
       "3                  1                  0                        0   \n",
       "4                  0                  0                        0   \n",
       "\n",
       "   sick_from_seas_vacc_3.0  sick_from_seas_vacc_4.0  sick_from_seas_vacc_5.0  \\\n",
       "0                        0                        0                        0   \n",
       "1                        0                        1                        0   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        0                        1                        0   \n",
       "\n",
       "   age_bracket_35 - 44 Years  age_bracket_45 - 54 Years  \\\n",
       "0                          0                          0   \n",
       "1                          1                          0   \n",
       "2                          0                          0   \n",
       "3                          0                          0   \n",
       "4                          0                          1   \n",
       "\n",
       "   age_bracket_55 - 64 Years  age_bracket_65+ Years  qualification_< 12 Years  \\\n",
       "0                          1                      0                         1   \n",
       "1                          0                      0                         0   \n",
       "2                          0                      0                         0   \n",
       "3                          0                      1                         0   \n",
       "4                          0                      0                         0   \n",
       "\n",
       "   qualification_College Graduate  qualification_Some College  race_Hispanic  \\\n",
       "0                               0                           0              0   \n",
       "1                               0                           0              0   \n",
       "2                               1                           0              0   \n",
       "3                               0                           0              0   \n",
       "4                               0                           1              0   \n",
       "\n",
       "   race_Other or Multiple  race_White  sex_Male  marital_status_Not Married  \\\n",
       "0                       0           1         0                           1   \n",
       "1                       0           1         1                           1   \n",
       "2                       0           1         1                           1   \n",
       "3                       0           1         0                           1   \n",
       "4                       0           1         0                           0   \n",
       "\n",
       "   housing_status_Rent  employment_Not in Labor Force  employment_Unemployed  \\\n",
       "0                    0                              1                      0   \n",
       "1                    1                              0                      0   \n",
       "2                    0                              0                      0   \n",
       "3                    1                              1                      0   \n",
       "4                    0                              0                      0   \n",
       "\n",
       "   census_msa_MSA, Principle City  census_msa_Non-MSA  no_of_adults_1.0  \\\n",
       "0                               0                   1                 0   \n",
       "1                               0                   0                 0   \n",
       "2                               0                   0                 0   \n",
       "3                               1                   0                 0   \n",
       "4                               0                   0                 1   \n",
       "\n",
       "   no_of_adults_2.0  no_of_adults_3.0  no_of_children_1.0  no_of_children_2.0  \\\n",
       "0                 0                 0                   0                   0   \n",
       "1                 0                 0                   0                   0   \n",
       "2                 1                 0                   0                   0   \n",
       "3                 0                 0                   0                   0   \n",
       "4                 0                 0                   0                   0   \n",
       "\n",
       "   no_of_children_3.0  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the first five observations of the data with dummy encoded variables\n",
    "df_cat_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removal of missing values and dummy encoding the data, the dataframe `df_cat_dummies` contains all the independent variables and the dataframe df_num contains the target variable. We will rename these dataframes as X and y respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df_num contains only the target variable 'h1n1_vaccine'.\n",
    "# We store it in dataframe 'y'\n",
    "y = pd.DataFrame(df_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, use this 'y' as a target variable to build the classification models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df_cat_dummies contain all the dummy encoded independent variables\n",
    "# We store it in dataframe 'X'\n",
    "X = pd.DataFrame(df_cat_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h1n1_worry_1.0</th>\n",
       "      <th>h1n1_worry_2.0</th>\n",
       "      <th>h1n1_worry_3.0</th>\n",
       "      <th>h1n1_awareness_1.0</th>\n",
       "      <th>h1n1_awareness_2.0</th>\n",
       "      <th>antiviral_medication_1.0</th>\n",
       "      <th>contact_avoidance_1.0</th>\n",
       "      <th>bought_face_mask_1.0</th>\n",
       "      <th>wash_hands_frequently_1.0</th>\n",
       "      <th>avoid_large_gatherings_1.0</th>\n",
       "      <th>reduced_outside_home_cont_1.0</th>\n",
       "      <th>avoid_touch_face_1.0</th>\n",
       "      <th>chronic_medic_condition_1.0</th>\n",
       "      <th>cont_child_undr_6_mnths_1.0</th>\n",
       "      <th>is_health_worker_1.0</th>\n",
       "      <th>is_h1n1_vacc_effective_2.0</th>\n",
       "      <th>is_h1n1_vacc_effective_3.0</th>\n",
       "      <th>is_h1n1_vacc_effective_4.0</th>\n",
       "      <th>is_h1n1_vacc_effective_5.0</th>\n",
       "      <th>is_h1n1_risky_2.0</th>\n",
       "      <th>is_h1n1_risky_3.0</th>\n",
       "      <th>is_h1n1_risky_4.0</th>\n",
       "      <th>is_h1n1_risky_5.0</th>\n",
       "      <th>sick_from_h1n1_vacc_2.0</th>\n",
       "      <th>sick_from_h1n1_vacc_3.0</th>\n",
       "      <th>sick_from_h1n1_vacc_4.0</th>\n",
       "      <th>sick_from_h1n1_vacc_5.0</th>\n",
       "      <th>is_seas_vacc_effective_2.0</th>\n",
       "      <th>is_seas_vacc_effective_3.0</th>\n",
       "      <th>is_seas_vacc_effective_4.0</th>\n",
       "      <th>is_seas_vacc_effective_5.0</th>\n",
       "      <th>is_seas_risky_2.0</th>\n",
       "      <th>is_seas_risky_3.0</th>\n",
       "      <th>is_seas_risky_4.0</th>\n",
       "      <th>is_seas_risky_5.0</th>\n",
       "      <th>sick_from_seas_vacc_2.0</th>\n",
       "      <th>sick_from_seas_vacc_3.0</th>\n",
       "      <th>sick_from_seas_vacc_4.0</th>\n",
       "      <th>sick_from_seas_vacc_5.0</th>\n",
       "      <th>age_bracket_35 - 44 Years</th>\n",
       "      <th>age_bracket_45 - 54 Years</th>\n",
       "      <th>age_bracket_55 - 64 Years</th>\n",
       "      <th>age_bracket_65+ Years</th>\n",
       "      <th>qualification_&lt; 12 Years</th>\n",
       "      <th>qualification_College Graduate</th>\n",
       "      <th>qualification_Some College</th>\n",
       "      <th>race_Hispanic</th>\n",
       "      <th>race_Other or Multiple</th>\n",
       "      <th>race_White</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>marital_status_Not Married</th>\n",
       "      <th>housing_status_Rent</th>\n",
       "      <th>employment_Not in Labor Force</th>\n",
       "      <th>employment_Unemployed</th>\n",
       "      <th>census_msa_MSA, Principle City</th>\n",
       "      <th>census_msa_Non-MSA</th>\n",
       "      <th>no_of_adults_1.0</th>\n",
       "      <th>no_of_adults_2.0</th>\n",
       "      <th>no_of_adults_3.0</th>\n",
       "      <th>no_of_children_1.0</th>\n",
       "      <th>no_of_children_2.0</th>\n",
       "      <th>no_of_children_3.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   h1n1_worry_1.0  h1n1_worry_2.0  h1n1_worry_3.0  h1n1_awareness_1.0  \\\n",
       "0               1               0               0                   0   \n",
       "1               0               0               1                   0   \n",
       "2               1               0               0                   1   \n",
       "3               1               0               0                   1   \n",
       "4               0               1               0                   1   \n",
       "\n",
       "   h1n1_awareness_2.0  antiviral_medication_1.0  contact_avoidance_1.0  \\\n",
       "0                   0                         0                      0   \n",
       "1                   1                         0                      1   \n",
       "2                   0                         0                      1   \n",
       "3                   0                         0                      1   \n",
       "4                   0                         0                      1   \n",
       "\n",
       "   bought_face_mask_1.0  wash_hands_frequently_1.0  \\\n",
       "0                     0                          0   \n",
       "1                     0                          1   \n",
       "2                     0                          0   \n",
       "3                     0                          1   \n",
       "4                     0                          1   \n",
       "\n",
       "   avoid_large_gatherings_1.0  reduced_outside_home_cont_1.0  \\\n",
       "0                           0                              1   \n",
       "1                           0                              1   \n",
       "2                           0                              0   \n",
       "3                           1                              0   \n",
       "4                           1                              0   \n",
       "\n",
       "   avoid_touch_face_1.0  chronic_medic_condition_1.0  \\\n",
       "0                     1                            0   \n",
       "1                     1                            0   \n",
       "2                     0                            1   \n",
       "3                     0                            1   \n",
       "4                     1                            0   \n",
       "\n",
       "   cont_child_undr_6_mnths_1.0  is_health_worker_1.0  \\\n",
       "0                            0                     0   \n",
       "1                            0                     0   \n",
       "2                            0                     0   \n",
       "3                            0                     0   \n",
       "4                            0                     0   \n",
       "\n",
       "   is_h1n1_vacc_effective_2.0  is_h1n1_vacc_effective_3.0  \\\n",
       "0                           0                           1   \n",
       "1                           0                           0   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   is_h1n1_vacc_effective_4.0  is_h1n1_vacc_effective_5.0  is_h1n1_risky_2.0  \\\n",
       "0                           0                           0                  0   \n",
       "1                           0                           1                  0   \n",
       "2                           0                           0                  0   \n",
       "3                           0                           0                  0   \n",
       "4                           0                           0                  0   \n",
       "\n",
       "   is_h1n1_risky_3.0  is_h1n1_risky_4.0  is_h1n1_risky_5.0  \\\n",
       "0                  0                  0                  0   \n",
       "1                  0                  1                  0   \n",
       "2                  0                  0                  0   \n",
       "3                  1                  0                  0   \n",
       "4                  1                  0                  0   \n",
       "\n",
       "   sick_from_h1n1_vacc_2.0  sick_from_h1n1_vacc_3.0  sick_from_h1n1_vacc_4.0  \\\n",
       "0                        1                        0                        0   \n",
       "1                        0                        0                        1   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        1                        0                        0   \n",
       "\n",
       "   sick_from_h1n1_vacc_5.0  is_seas_vacc_effective_2.0  \\\n",
       "0                        0                           1   \n",
       "1                        0                           0   \n",
       "2                        0                           0   \n",
       "3                        1                           0   \n",
       "4                        0                           0   \n",
       "\n",
       "   is_seas_vacc_effective_3.0  is_seas_vacc_effective_4.0  \\\n",
       "0                           0                           0   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           0   \n",
       "4                           1                           0   \n",
       "\n",
       "   is_seas_vacc_effective_5.0  is_seas_risky_2.0  is_seas_risky_3.0  \\\n",
       "0                           0                  0                  0   \n",
       "1                           0                  1                  0   \n",
       "2                           0                  0                  0   \n",
       "3                           1                  0                  0   \n",
       "4                           0                  0                  0   \n",
       "\n",
       "   is_seas_risky_4.0  is_seas_risky_5.0  sick_from_seas_vacc_2.0  \\\n",
       "0                  0                  0                        1   \n",
       "1                  0                  0                        0   \n",
       "2                  0                  0                        1   \n",
       "3                  1                  0                        0   \n",
       "4                  0                  0                        0   \n",
       "\n",
       "   sick_from_seas_vacc_3.0  sick_from_seas_vacc_4.0  sick_from_seas_vacc_5.0  \\\n",
       "0                        0                        0                        0   \n",
       "1                        0                        1                        0   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        0                        1                        0   \n",
       "\n",
       "   age_bracket_35 - 44 Years  age_bracket_45 - 54 Years  \\\n",
       "0                          0                          0   \n",
       "1                          1                          0   \n",
       "2                          0                          0   \n",
       "3                          0                          0   \n",
       "4                          0                          1   \n",
       "\n",
       "   age_bracket_55 - 64 Years  age_bracket_65+ Years  qualification_< 12 Years  \\\n",
       "0                          1                      0                         1   \n",
       "1                          0                      0                         0   \n",
       "2                          0                      0                         0   \n",
       "3                          0                      1                         0   \n",
       "4                          0                      0                         0   \n",
       "\n",
       "   qualification_College Graduate  qualification_Some College  race_Hispanic  \\\n",
       "0                               0                           0              0   \n",
       "1                               0                           0              0   \n",
       "2                               1                           0              0   \n",
       "3                               0                           0              0   \n",
       "4                               0                           1              0   \n",
       "\n",
       "   race_Other or Multiple  race_White  sex_Male  marital_status_Not Married  \\\n",
       "0                       0           1         0                           1   \n",
       "1                       0           1         1                           1   \n",
       "2                       0           1         1                           1   \n",
       "3                       0           1         0                           1   \n",
       "4                       0           1         0                           0   \n",
       "\n",
       "   housing_status_Rent  employment_Not in Labor Force  employment_Unemployed  \\\n",
       "0                    0                              1                      0   \n",
       "1                    1                              0                      0   \n",
       "2                    0                              0                      0   \n",
       "3                    1                              1                      0   \n",
       "4                    0                              0                      0   \n",
       "\n",
       "   census_msa_MSA, Principle City  census_msa_Non-MSA  no_of_adults_1.0  \\\n",
       "0                               0                   1                 0   \n",
       "1                               0                   0                 0   \n",
       "2                               0                   0                 0   \n",
       "3                               1                   0                 0   \n",
       "4                               0                   0                 1   \n",
       "\n",
       "   no_of_adults_2.0  no_of_adults_3.0  no_of_children_1.0  no_of_children_2.0  \\\n",
       "0                 0                 0                   0                   0   \n",
       "1                 0                 0                   0                   0   \n",
       "2                 1                 0                   0                   0   \n",
       "3                 0                 0                   0                   0   \n",
       "4                 0                 0                   0                   0   \n",
       "\n",
       "   no_of_children_3.0  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the first five observations of X\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this 'X' as a set of predictors to build the classification models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a generalized function to calculate the metrics for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a generalized function to calculate the metrics values for test set\n",
    "def get_test_report(model):\n",
    "    \n",
    "    # return the performace measures on test set\n",
    "    return(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a generalized function to calculate the kappa score for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a generalized function to calculate the metrics values for test set\n",
    "def kappa_score(model):\n",
    "    \n",
    "    # return the kappa score on test set\n",
    "    return(cohen_kappa_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define a function to plot the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "Av39nL7SebpS",
    "outputId": "eb72d174-cce1-49f2-abcc-b8a6111b2008"
   },
   "outputs": [],
   "source": [
    "# define a to plot a confusion matrix for the model\n",
    "def plot_confusion_matrix(model):\n",
    "    \n",
    "    # create a confusion matrix\n",
    "    # pass the actual and predicted target values to the confusion_matrix()\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    # label the confusion matrix  \n",
    "    # pass the matrix as 'data'\n",
    "    # pass the required column names to the parameter, 'columns'\n",
    "    # pass the required row names to the parameter, 'index'\n",
    "    conf_matrix = pd.DataFrame(data = cm,columns = ['Predicted:0','Predicted:1'], index = ['Actual:0','Actual:1'])\n",
    "\n",
    "    # plot a heatmap to visualize the confusion matrix\n",
    "    # 'annot' prints the value of each grid \n",
    "    # 'fmt = d' returns the integer value in each grid\n",
    "    # 'cmap' assigns color to each grid\n",
    "    # as we do not require different colors for each grid in the heatmap,\n",
    "    # use 'ListedColormap' to assign the specified color to the grid\n",
    "    # 'cbar = False' will not return the color bar to the right side of the heatmap\n",
    "    # 'linewidths' assigns the width to the line that divides each grid\n",
    "    # 'annot_kws = {'size':25})' assigns the font size of the annotated text \n",
    "    sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = ListedColormap(['lightskyblue']), cbar = False, \n",
    "                linewidths = 0.1, annot_kws = {'size':25})\n",
    "\n",
    "    # set the font size of x-axis ticks using 'fontsize'\n",
    "    plt.xticks(fontsize = 20)\n",
    "\n",
    "    # set the font size of y-axis ticks using 'fontsize'\n",
    "    plt.yticks(fontsize = 20)\n",
    "\n",
    "    # display the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define a function to plot the ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define a function to plot the ROC curve and print the ROC-AUC score\n",
    "def plot_roc(model):\n",
    "    \n",
    "    # the roc_curve() returns the values for false positive rate, true positive rate and threshold\n",
    "    # pass the actual target values and predicted probabilities to the function\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "\n",
    "    # plot the ROC curve\n",
    "    plt.plot(fpr, tpr)\n",
    "\n",
    "    # set limits for x and y axes\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "\n",
    "    # plot the straight line showing worst prediction for the model\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "\n",
    "    # add plot and axes labels\n",
    "    # set text size using 'fontsize'\n",
    "    plt.title('ROC Curve for h1n1_vaccine Classifier', fontsize = 15)\n",
    "    plt.xlabel('False positive rate (1-Specificity)', fontsize = 15)\n",
    "    plt.ylabel('True positive rate (Sensitivity)', fontsize = 15)\n",
    "\n",
    "    # add the AUC score to the plot\n",
    "    # 'x' and 'y' gives position of the text\n",
    "    # 's' is the text \n",
    "    # use round() to round-off the AUC score upto 4 digits\n",
    "    plt.text(x = 0.02, y = 0.9, s = ('AUC Score:',round(roc_auc_score(y_test, y_pred_prob),4)))\n",
    "\n",
    "    # plot the grid\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a generalized function to create a dataframe containing the scores for the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create an empty dataframe to store the scores for various classification algorithms\n",
    "score_card = pd.DataFrame(columns=['Model', 'AUC Score', 'Precision Score', 'Recall Score', 'Accuracy Score',\n",
    "                                   'Kappa Score', 'f1-score'])\n",
    "\n",
    "# append the result table for all performance scores\n",
    "# performance measures considered for comparision are 'AUC', 'Precision', 'Recall','Accuracy','Kappa Score', and 'f1-score'\n",
    "# compile the required information in a user defined function \n",
    "def update_score_card(model_name):\n",
    "    \n",
    "    # assign 'score_card' as global variable\n",
    "    global score_card\n",
    "\n",
    "    # append the results to the dataframe 'score_card'\n",
    "    # 'ignore_index = True' do not consider the index labels\n",
    "    score_card = score_card.append({'Model': model_name,\n",
    "                                    'AUC Score' : roc_auc_score(y_test, y_pred_prob),\n",
    "                                    'Precision Score': metrics.precision_score(y_test, y_pred),\n",
    "                                    'Recall Score': metrics.recall_score(y_test, y_pred),\n",
    "                                    'Accuracy Score': metrics.accuracy_score(y_test, y_pred),\n",
    "                                    'Kappa Score': cohen_kappa_score(y_test, y_pred),\n",
    "                                    'f1-score': metrics.f1_score(y_test, y_pred)}, \n",
    "                                    ignore_index = True)\n",
    "    return(score_card)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='LogisticReg'></a>\n",
    "# 5. Logistic Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic regression is one of the techniques used for classification. The estimates of the parameters are obtained by maximizing the likelihood function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"alsoreadicon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>I love to know more:  <br></b>\n",
    "                     1. <a href=\"https://bit.ly/2qE1xTB\">Logistic Regression<br></a>\n",
    "                     2. <a href=\"https://bit.ly/34XFnub\">Accuracy Measures<br></a>\n",
    "                     3. <a href=\"https://bit.ly/2CCGFPg\">Understanding ROC and AUC</a>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='withStatsModels'></a>\n",
    "## 5.1 Logistic Regression (using MLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>To build the logistic model using Statsmodel, we do the following: <br><br>\n",
    "                       1. Introduce the intercept term<br>\n",
    "                       2. Split the data into training and test sets<br> \n",
    "                       3. Build the model<br>\n",
    "                       4. Do predictions on the test set <br>\n",
    "                       5. Compute accuracy measures <br>\n",
    "                       6. Tabulate the results</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Introduce the intercept term**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statmodels logistic regression function (Logit) does not include the intercept term by default. Thus, we add the intercept column in the dataframe including features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>h1n1_worry_1.0</th>\n",
       "      <th>h1n1_worry_2.0</th>\n",
       "      <th>h1n1_worry_3.0</th>\n",
       "      <th>h1n1_awareness_1.0</th>\n",
       "      <th>h1n1_awareness_2.0</th>\n",
       "      <th>antiviral_medication_1.0</th>\n",
       "      <th>contact_avoidance_1.0</th>\n",
       "      <th>bought_face_mask_1.0</th>\n",
       "      <th>wash_hands_frequently_1.0</th>\n",
       "      <th>avoid_large_gatherings_1.0</th>\n",
       "      <th>reduced_outside_home_cont_1.0</th>\n",
       "      <th>avoid_touch_face_1.0</th>\n",
       "      <th>chronic_medic_condition_1.0</th>\n",
       "      <th>cont_child_undr_6_mnths_1.0</th>\n",
       "      <th>is_health_worker_1.0</th>\n",
       "      <th>is_h1n1_vacc_effective_2.0</th>\n",
       "      <th>is_h1n1_vacc_effective_3.0</th>\n",
       "      <th>is_h1n1_vacc_effective_4.0</th>\n",
       "      <th>is_h1n1_vacc_effective_5.0</th>\n",
       "      <th>is_h1n1_risky_2.0</th>\n",
       "      <th>is_h1n1_risky_3.0</th>\n",
       "      <th>is_h1n1_risky_4.0</th>\n",
       "      <th>is_h1n1_risky_5.0</th>\n",
       "      <th>sick_from_h1n1_vacc_2.0</th>\n",
       "      <th>sick_from_h1n1_vacc_3.0</th>\n",
       "      <th>sick_from_h1n1_vacc_4.0</th>\n",
       "      <th>sick_from_h1n1_vacc_5.0</th>\n",
       "      <th>is_seas_vacc_effective_2.0</th>\n",
       "      <th>is_seas_vacc_effective_3.0</th>\n",
       "      <th>is_seas_vacc_effective_4.0</th>\n",
       "      <th>is_seas_vacc_effective_5.0</th>\n",
       "      <th>is_seas_risky_2.0</th>\n",
       "      <th>is_seas_risky_3.0</th>\n",
       "      <th>is_seas_risky_4.0</th>\n",
       "      <th>is_seas_risky_5.0</th>\n",
       "      <th>sick_from_seas_vacc_2.0</th>\n",
       "      <th>sick_from_seas_vacc_3.0</th>\n",
       "      <th>sick_from_seas_vacc_4.0</th>\n",
       "      <th>sick_from_seas_vacc_5.0</th>\n",
       "      <th>age_bracket_35 - 44 Years</th>\n",
       "      <th>age_bracket_45 - 54 Years</th>\n",
       "      <th>age_bracket_55 - 64 Years</th>\n",
       "      <th>age_bracket_65+ Years</th>\n",
       "      <th>qualification_&lt; 12 Years</th>\n",
       "      <th>qualification_College Graduate</th>\n",
       "      <th>qualification_Some College</th>\n",
       "      <th>race_Hispanic</th>\n",
       "      <th>race_Other or Multiple</th>\n",
       "      <th>race_White</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>marital_status_Not Married</th>\n",
       "      <th>housing_status_Rent</th>\n",
       "      <th>employment_Not in Labor Force</th>\n",
       "      <th>employment_Unemployed</th>\n",
       "      <th>census_msa_MSA, Principle City</th>\n",
       "      <th>census_msa_Non-MSA</th>\n",
       "      <th>no_of_adults_1.0</th>\n",
       "      <th>no_of_adults_2.0</th>\n",
       "      <th>no_of_adults_3.0</th>\n",
       "      <th>no_of_children_1.0</th>\n",
       "      <th>no_of_children_2.0</th>\n",
       "      <th>no_of_children_3.0</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  h1n1_worry_1.0  h1n1_worry_2.0  h1n1_worry_3.0  h1n1_awareness_1.0  \\\n",
       "0    1.0               1               0               0                   0   \n",
       "1    1.0               0               0               1                   0   \n",
       "2    1.0               1               0               0                   1   \n",
       "3    1.0               1               0               0                   1   \n",
       "4    1.0               0               1               0                   1   \n",
       "\n",
       "   h1n1_awareness_2.0  antiviral_medication_1.0  contact_avoidance_1.0  \\\n",
       "0                   0                         0                      0   \n",
       "1                   1                         0                      1   \n",
       "2                   0                         0                      1   \n",
       "3                   0                         0                      1   \n",
       "4                   0                         0                      1   \n",
       "\n",
       "   bought_face_mask_1.0  wash_hands_frequently_1.0  \\\n",
       "0                     0                          0   \n",
       "1                     0                          1   \n",
       "2                     0                          0   \n",
       "3                     0                          1   \n",
       "4                     0                          1   \n",
       "\n",
       "   avoid_large_gatherings_1.0  reduced_outside_home_cont_1.0  \\\n",
       "0                           0                              1   \n",
       "1                           0                              1   \n",
       "2                           0                              0   \n",
       "3                           1                              0   \n",
       "4                           1                              0   \n",
       "\n",
       "   avoid_touch_face_1.0  chronic_medic_condition_1.0  \\\n",
       "0                     1                            0   \n",
       "1                     1                            0   \n",
       "2                     0                            1   \n",
       "3                     0                            1   \n",
       "4                     1                            0   \n",
       "\n",
       "   cont_child_undr_6_mnths_1.0  is_health_worker_1.0  \\\n",
       "0                            0                     0   \n",
       "1                            0                     0   \n",
       "2                            0                     0   \n",
       "3                            0                     0   \n",
       "4                            0                     0   \n",
       "\n",
       "   is_h1n1_vacc_effective_2.0  is_h1n1_vacc_effective_3.0  \\\n",
       "0                           0                           1   \n",
       "1                           0                           0   \n",
       "2                           0                           1   \n",
       "3                           0                           1   \n",
       "4                           0                           1   \n",
       "\n",
       "   is_h1n1_vacc_effective_4.0  is_h1n1_vacc_effective_5.0  is_h1n1_risky_2.0  \\\n",
       "0                           0                           0                  0   \n",
       "1                           0                           1                  0   \n",
       "2                           0                           0                  0   \n",
       "3                           0                           0                  0   \n",
       "4                           0                           0                  0   \n",
       "\n",
       "   is_h1n1_risky_3.0  is_h1n1_risky_4.0  is_h1n1_risky_5.0  \\\n",
       "0                  0                  0                  0   \n",
       "1                  0                  1                  0   \n",
       "2                  0                  0                  0   \n",
       "3                  1                  0                  0   \n",
       "4                  1                  0                  0   \n",
       "\n",
       "   sick_from_h1n1_vacc_2.0  sick_from_h1n1_vacc_3.0  sick_from_h1n1_vacc_4.0  \\\n",
       "0                        1                        0                        0   \n",
       "1                        0                        0                        1   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        1                        0                        0   \n",
       "\n",
       "   sick_from_h1n1_vacc_5.0  is_seas_vacc_effective_2.0  \\\n",
       "0                        0                           1   \n",
       "1                        0                           0   \n",
       "2                        0                           0   \n",
       "3                        1                           0   \n",
       "4                        0                           0   \n",
       "\n",
       "   is_seas_vacc_effective_3.0  is_seas_vacc_effective_4.0  \\\n",
       "0                           0                           0   \n",
       "1                           0                           1   \n",
       "2                           0                           1   \n",
       "3                           0                           0   \n",
       "4                           1                           0   \n",
       "\n",
       "   is_seas_vacc_effective_5.0  is_seas_risky_2.0  is_seas_risky_3.0  \\\n",
       "0                           0                  0                  0   \n",
       "1                           0                  1                  0   \n",
       "2                           0                  0                  0   \n",
       "3                           1                  0                  0   \n",
       "4                           0                  0                  0   \n",
       "\n",
       "   is_seas_risky_4.0  is_seas_risky_5.0  sick_from_seas_vacc_2.0  \\\n",
       "0                  0                  0                        1   \n",
       "1                  0                  0                        0   \n",
       "2                  0                  0                        1   \n",
       "3                  1                  0                        0   \n",
       "4                  0                  0                        0   \n",
       "\n",
       "   sick_from_seas_vacc_3.0  sick_from_seas_vacc_4.0  sick_from_seas_vacc_5.0  \\\n",
       "0                        0                        0                        0   \n",
       "1                        0                        1                        0   \n",
       "2                        0                        0                        0   \n",
       "3                        0                        0                        0   \n",
       "4                        0                        1                        0   \n",
       "\n",
       "   age_bracket_35 - 44 Years  age_bracket_45 - 54 Years  \\\n",
       "0                          0                          0   \n",
       "1                          1                          0   \n",
       "2                          0                          0   \n",
       "3                          0                          0   \n",
       "4                          0                          1   \n",
       "\n",
       "   age_bracket_55 - 64 Years  age_bracket_65+ Years  qualification_< 12 Years  \\\n",
       "0                          1                      0                         1   \n",
       "1                          0                      0                         0   \n",
       "2                          0                      0                         0   \n",
       "3                          0                      1                         0   \n",
       "4                          0                      0                         0   \n",
       "\n",
       "   qualification_College Graduate  qualification_Some College  race_Hispanic  \\\n",
       "0                               0                           0              0   \n",
       "1                               0                           0              0   \n",
       "2                               1                           0              0   \n",
       "3                               0                           0              0   \n",
       "4                               0                           1              0   \n",
       "\n",
       "   race_Other or Multiple  race_White  sex_Male  marital_status_Not Married  \\\n",
       "0                       0           1         0                           1   \n",
       "1                       0           1         1                           1   \n",
       "2                       0           1         1                           1   \n",
       "3                       0           1         0                           1   \n",
       "4                       0           1         0                           0   \n",
       "\n",
       "   housing_status_Rent  employment_Not in Labor Force  employment_Unemployed  \\\n",
       "0                    0                              1                      0   \n",
       "1                    1                              0                      0   \n",
       "2                    0                              0                      0   \n",
       "3                    1                              1                      0   \n",
       "4                    0                              0                      0   \n",
       "\n",
       "   census_msa_MSA, Principle City  census_msa_Non-MSA  no_of_adults_1.0  \\\n",
       "0                               0                   1                 0   \n",
       "1                               0                   0                 0   \n",
       "2                               0                   0                 0   \n",
       "3                               1                   0                 0   \n",
       "4                               0                   0                 1   \n",
       "\n",
       "   no_of_adults_2.0  no_of_adults_3.0  no_of_children_1.0  no_of_children_2.0  \\\n",
       "0                 0                 0                   0                   0   \n",
       "1                 0                 0                   0                   0   \n",
       "2                 1                 0                   0                   0   \n",
       "3                 0                 0                   0                   0   \n",
       "4                 0                 0                   0                   0   \n",
       "\n",
       "   no_of_children_3.0  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the intercept column using 'add_constant()'\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# print the first five bservations after adding intercept\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Split the dataset into train and test sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9XWUmyXrebo_",
    "outputId": "7dcfd65a-f799-464c-d257-c7df8d4b7a06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (16398, 63)\n",
      "y_train (16398, 1)\n",
      "X_test (7028, 63)\n",
      "y_test (7028, 1)\n"
     ]
    }
   ],
   "source": [
    "# split data into train subset and test subset\n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "# 'test_size' returns the proportion of data to be included in the test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 10)\n",
    "\n",
    "# check the dimensions of the train & test subset using 'shape'\n",
    "# print dimension of train set\n",
    "print(\"X_train\",X_train.shape)\n",
    "print(\"y_train\",y_train.shape)\n",
    "\n",
    "# print dimension of test set\n",
    "print(\"X_test\",X_test.shape)\n",
    "print(\"y_test\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Build a logistic regression model using statsmodels `Logit()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.414325\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:           h1n1_vaccine   No. Observations:                16398\n",
      "Model:                          Logit   Df Residuals:                    16335\n",
      "Method:                           MLE   Df Model:                           62\n",
      "Date:                Tue, 25 Aug 2020   Pseudo R-squ.:                  0.1990\n",
      "Time:                        13:34:34   Log-Likelihood:                -6794.1\n",
      "converged:                       True   LL-Null:                       -8482.4\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "==================================================================================================\n",
      "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------\n",
      "const                             -4.4648      0.297    -15.041      0.000      -5.047      -3.883\n",
      "h1n1_worry_1.0                    -0.0658      0.089     -0.739      0.460      -0.240       0.109\n",
      "h1n1_worry_2.0                    -0.1000      0.090     -1.109      0.267      -0.277       0.077\n",
      "h1n1_worry_3.0                    -0.2456      0.101     -2.430      0.015      -0.444      -0.047\n",
      "h1n1_awareness_1.0                -0.0167      0.094     -0.177      0.860      -0.202       0.168\n",
      "h1n1_awareness_2.0                 0.1793      0.099      1.805      0.071      -0.015       0.374\n",
      "antiviral_medication_1.0           0.2231      0.099      2.262      0.024       0.030       0.416\n",
      "contact_avoidance_1.0             -0.0414      0.055     -0.746      0.455      -0.150       0.067\n",
      "bought_face_mask_1.0               0.1483      0.082      1.804      0.071      -0.013       0.309\n",
      "wash_hands_frequently_1.0          0.1179      0.071      1.649      0.099      -0.022       0.258\n",
      "avoid_large_gatherings_1.0        -0.1337      0.057     -2.347      0.019      -0.245      -0.022\n",
      "reduced_outside_home_cont_1.0     -0.0560      0.058     -0.967      0.334      -0.170       0.058\n",
      "avoid_touch_face_1.0               0.0750      0.054      1.391      0.164      -0.031       0.181\n",
      "chronic_medic_condition_1.0        0.2745      0.048      5.685      0.000       0.180       0.369\n",
      "cont_child_undr_6_mnths_1.0        0.3852      0.075      5.142      0.000       0.238       0.532\n",
      "is_health_worker_1.0               1.0208      0.063     16.149      0.000       0.897       1.145\n",
      "is_h1n1_vacc_effective_2.0        -0.2255      0.268     -0.842      0.400      -0.751       0.300\n",
      "is_h1n1_vacc_effective_3.0         0.3436      0.238      1.441      0.150      -0.124       0.811\n",
      "is_h1n1_vacc_effective_4.0         0.9134      0.231      3.957      0.000       0.461       1.366\n",
      "is_h1n1_vacc_effective_5.0         1.7691      0.232      7.634      0.000       1.315       2.223\n",
      "is_h1n1_risky_2.0                  0.4774      0.069      6.899      0.000       0.342       0.613\n",
      "is_h1n1_risky_3.0                  0.7898      0.145      5.435      0.000       0.505       1.075\n",
      "is_h1n1_risky_4.0                  1.3443      0.077     17.525      0.000       1.194       1.495\n",
      "is_h1n1_risky_5.0                  1.7373      0.102     17.094      0.000       1.538       1.937\n",
      "sick_from_h1n1_vacc_2.0           -0.3338      0.060     -5.551      0.000      -0.452      -0.216\n",
      "sick_from_h1n1_vacc_3.0           -1.1660      0.610     -1.911      0.056      -2.362       0.030\n",
      "sick_from_h1n1_vacc_4.0           -0.1008      0.067     -1.506      0.132      -0.232       0.030\n",
      "sick_from_h1n1_vacc_5.0           -0.0953      0.100     -0.950      0.342      -0.292       0.101\n",
      "is_seas_vacc_effective_2.0        -0.0714      0.190     -0.377      0.706      -0.443       0.300\n",
      "is_seas_vacc_effective_3.0         0.4532      0.210      2.157      0.031       0.041       0.865\n",
      "is_seas_vacc_effective_4.0         0.1020      0.167      0.609      0.542      -0.226       0.430\n",
      "is_seas_vacc_effective_5.0         0.2093      0.168      1.244      0.213      -0.120       0.539\n",
      "is_seas_risky_2.0                  0.4478      0.081      5.548      0.000       0.290       0.606\n",
      "is_seas_risky_3.0                  0.7621      0.183      4.155      0.000       0.403       1.122\n",
      "is_seas_risky_4.0                  0.6335      0.083      7.595      0.000       0.470       0.797\n",
      "is_seas_risky_5.0                  0.7018      0.099      7.125      0.000       0.509       0.895\n",
      "sick_from_seas_vacc_2.0           -0.1054      0.057     -1.838      0.066      -0.218       0.007\n",
      "sick_from_seas_vacc_3.0           -0.5849      0.571     -1.024      0.306      -1.704       0.534\n",
      "sick_from_seas_vacc_4.0           -0.1661      0.066     -2.522      0.012      -0.295      -0.037\n",
      "sick_from_seas_vacc_5.0           -0.3793      0.108     -3.520      0.000      -0.591      -0.168\n",
      "age_bracket_35 - 44 Years         -0.0085      0.082     -0.104      0.917      -0.168       0.152\n",
      "age_bracket_45 - 54 Years         -0.0719      0.077     -0.932      0.351      -0.223       0.079\n",
      "age_bracket_55 - 64 Years          0.2601      0.079      3.271      0.001       0.104       0.416\n",
      "age_bracket_65+ Years              0.3193      0.086      3.701      0.000       0.150       0.488\n",
      "qualification_< 12 Years          -0.1897      0.093     -2.034      0.042      -0.372      -0.007\n",
      "qualification_College Graduate     0.1607      0.063      2.557      0.011       0.038       0.284\n",
      "qualification_Some College         0.1208      0.064      1.892      0.058      -0.004       0.246\n",
      "race_Hispanic                      0.2276      0.125      1.815      0.070      -0.018       0.473\n",
      "race_Other or Multiple             0.3526      0.127      2.787      0.005       0.105       0.601\n",
      "race_White                         0.2702      0.095      2.830      0.005       0.083       0.457\n",
      "sex_Male                           0.1347      0.047      2.875      0.004       0.043       0.227\n",
      "marital_status_Not Married        -0.0870      0.063     -1.391      0.164      -0.210       0.036\n",
      "housing_status_Rent               -0.0331      0.059     -0.563      0.573      -0.148       0.082\n",
      "employment_Not in Labor Force      0.1674      0.055      3.016      0.003       0.059       0.276\n",
      "employment_Unemployed              0.0572      0.106      0.539      0.590      -0.151       0.265\n",
      "census_msa_MSA, Principle City     0.0719      0.052      1.372      0.170      -0.031       0.175\n",
      "census_msa_Non-MSA                 0.1104      0.053      2.066      0.039       0.006       0.215\n",
      "no_of_adults_1.0                   0.0718      0.069      1.048      0.295      -0.062       0.206\n",
      "no_of_adults_2.0                   0.0480      0.089      0.542      0.588      -0.126       0.222\n",
      "no_of_adults_3.0                  -0.0617      0.123     -0.503      0.615      -0.302       0.178\n",
      "no_of_children_1.0                 0.0821      0.074      1.106      0.269      -0.063       0.228\n",
      "no_of_children_2.0                 0.0251      0.081      0.310      0.757      -0.134       0.184\n",
      "no_of_children_3.0                -0.0515      0.100     -0.513      0.608      -0.248       0.145\n",
      "==================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# build the model on train data (X_train and y_train)\n",
    "# use fit() to fit the logistic regression model\n",
    "log_reg_model = sm.Logit(y_train, X_train).fit()\n",
    "\n",
    "# print the summary of the model\n",
    "print(log_reg_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** The `Pseudo R-squ.` obtained from the above model summary is the value of `McFadden's R-squared`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Do predictions on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UP5DA9_qebpC",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9480     0.193582\n",
       "25478    0.663039\n",
       "16789    0.091390\n",
       "8036     0.121057\n",
       "7121     0.156698\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let 'y_pred_prob' be the predicted values of y\n",
    "y_pred_prob = log_reg_model.predict(X_test)\n",
    "\n",
    "# print the y_pred_prob\n",
    "y_pred_prob.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the target variable can take only two values either 0 or 1. We decide the threshold of 0.5. If `y_pred_prob` is less than 0.5, then consider it to be 0 else consider it to be 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen that the dataset is not balanced. We may check model performance parameters for different threshold values to classify as 0 and 1. As an exercise check the impact on performance parameters for different threshold values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "s-aj8refebpI"
   },
   "outputs": [],
   "source": [
    "# convert probabilities to 0 and 1 using 'if_else'\n",
    "y_pred = ['0' if x < 0.5 else '1' for x in y_pred_prob]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NQyMfFD5ebpP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the predicted values to type 'float32'\n",
    "y_pred = np.array(y_pred, dtype=np.float32)\n",
    "\n",
    "# print the first five predictions\n",
    "y_pred[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Calculate the performance measures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function to plot the confusion matrix\n",
    "# pass the logistic regression model to the function\n",
    "plot_confusion_matrix(log_reg_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate performance measures on the test set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UP5DA9_qebpC",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.95      0.89      5476\n",
      "           1       0.65      0.33      0.44      1552\n",
      "\n",
      "    accuracy                           0.81      7028\n",
      "   macro avg       0.74      0.64      0.66      7028\n",
      "weighted avg       0.79      0.81      0.79      7028\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# compute the performance measures on test data\n",
    "# call the function 'get_test_report'\n",
    "# pass the logstic regression model to the function\n",
    "test_report = get_test_report(log_reg_model)\n",
    "\n",
    "# print the performace measures\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** The accuracy is 81% for this model. Also, there is significant difference between specificity and sensitivity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3426774787926872\n"
     ]
    }
   ],
   "source": [
    "# compute kappa score on test set\n",
    "# call the function 'kappa_score'\n",
    "# pass the logstic regression model to the function\n",
    "kappa_value = kappa_score(log_reg_model)\n",
    "\n",
    "# print the kappa value\n",
    "print(kappa_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** As the kappa score for the logistic regression is 0.3426, we can say that there is low to moderate agreement between the actual and predicted values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the ROC curve.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function 'plot_roc' to plot the ROC curve\n",
    "# pass the logstic regression model to the function\n",
    "plot_roc(log_reg_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>The dotted line represents the ROC curve of a purely random classifier; a good classifier stays as far away from that line as possible (toward the top-left corner).<br><br>\n",
    "We see our classifier (logistic regression) to be away from the dotted line with the AUC score 0.7944.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. Tabulate the results.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we tabulate the results, so that is easy for us to compare the models built."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>AUC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Kappa Score</th>\n",
       "      <th>f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.794406</td>\n",
       "      <td>0.654384</td>\n",
       "      <td>0.33183</td>\n",
       "      <td>0.813745</td>\n",
       "      <td>0.342677</td>\n",
       "      <td>0.440359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  AUC Score  Precision Score  Recall Score  \\\n",
       "0  Logistic Regression   0.794406         0.654384       0.33183   \n",
       "\n",
       "   Accuracy Score  Kappa Score  f1-score  \n",
       "0        0.813745     0.342677  0.440359  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the function 'update_score_card' to store the performance measures\n",
    "# pass the 'Logistic Regression' as model name to the function\n",
    "update_score_card(model_name = 'Logistic Regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>It can be seen from the result tabulation, that most of the accuracy measures for logistic regression (without scaled features) and logistic regression with scaled features are nearly equal. This implies that there is no significant difference between the model with scaled features and unscaled features.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "chj2_KBtebpz"
   },
   "source": [
    "<a id='usingSGD'></a>\n",
    "## 5.2 Logistic Regression (using SGD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>To build the logistic model on scaled features using SGD from sklearn, we do the following: <br><br>\n",
    "                       1. Scale the data<br>\n",
    "                       2. Split the data into training and test sets<br> \n",
    "                       3. Build the model<br>\n",
    "                       4. Do predictions on the test set <br>\n",
    "                       5. Compute accuracy measures <br>\n",
    "                       6. Tabulate the results\n",
    "</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Scale the data features**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not need to scale the data, as all the varaibles are categorical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Split the data into training and test sets**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has been split in section 5.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Build the model**\n",
    "\n",
    "The `SGDClassifier()` from sklearn contains an intercept term. Thus, there is no need to add the column of intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "OeNBFnOQebp1",
    "outputId": "4e896ca6-e6ea-4966-f157-5dfc920f1f98"
   },
   "outputs": [],
   "source": [
    "# instantiate the 'SGDClassifier' to build model using SGD\n",
    "# to perform logistic regression, consider the log-loss function \n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "SGD = SGDClassifier(loss = 'log', random_state = 10)\n",
    "\n",
    "# fit the model on scaled training data\n",
    "logreg_with_SGD = SGD.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Do predictions on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# predict probabilities on the test set\n",
    "# consider the probability of positive class by subsetting with '[:,1]'\n",
    "y_pred_prob = logreg_with_SGD.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use predict() to predict the class labels of target variable\n",
    "y_pred = logreg_with_SGD.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Compute accuracy measures**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x0xVJkrpyZS79zLv6PM9x7/pMpu5zyOrPtbMz911xce674uIkyYhRYzJ85KgsWzg/tXPl6noOf+17s+czX7DO8VYuW5o7fnN+bv3V/ySlpGP0uAwbMWKN/VNK9n/+q3Poy98+yL8KAE2wYNbDueGC/0rS6vFx/U++mut/8tVe2x9w0mmrn1XbkH1XmXPvrfnDV1qzQw0b0ZGOMVtl5bIlq+dpS5Kttts5x//DpzN268nr9yVhC9NvaEuSrjD263heDfo1ZutJefppH8ijt1+bOffekiXzHsvShfMyvGNkJk7ZKdvt/dRMP+6UTN1n3VEga+cTQ+ivXL40i+f2PU9954o1B/DZ4YAjcugr3pFHb782jz94V5YumJtlixakY8y4TNh+1+xwwNOzz3NelvHb9Ty+0EEn/3/ZepdpmXHTlZn38L1ZMm/O6v3HT905U/c5NNOPffF6TzkAwKZXO5/4jb3WziyeO7vP9iuWLGrLvkkydtvtcuw7z86Mm67Io3dcl8WPzcqS+Y+lDBuecZN3yLa77ZNdDzsuex59YkaMHD2YrwVbtD4n134SGT0SgE3G5NoAbGobMrk2AAAAm9CAukeuUkr5cJKa5PO11jlrbZuU5G1pzbP9z+0rEQAAYOgaVGhLcmZaoe17SeastW1yt+1CGwAAQBsMNrR9LK1QNquHbbO6bQcAAKANBhXaaq1n9rFtdlp32gAAAGgTA5EAAAA0mNAGAADQYL12jyylHLO+B621/mZ99wUAAOAJfT3TdmnWf1CR4eu5HwAAAN30FdqMBAkAALCJ9Rra+hopEgAAgCeHgUgAAAAaTGgDAABosEFNrl1KKUlemuR5SXZKMqqHZrXW+ldtqA0AAGDIG3BoK6WMSnJRkmOTlLQGKSndmtRu6wEAAGiDwXSPfF+S45L8S5IpaQW0M5PsmORVSe5P8t0kI9tbIgAAwNA1mND2siRX1Vo/UmudvWplrXVGrfW7SY5PclKSd7a5RgAAgCFrMKFtWpLfdftck3Ss/lDrXUkuTHJaWyoDAABgUKFteZIl3T7PT6ubZHf3JtlzQ4sCAACgZTCh7YG0Roxc5bYkz1irzSFJ5mxoUQAAALQMJrT9Lskzu33+cZKDSilfLaWcWEr5ZJLnJLm0jfUBAAAMaYOZp+3bSXYppexea70nyWeSnJzk9LSeYytJ7khyRptrBAAAGLIGHNpqrZem2120WuuiUspRaQW3vZLck+SCWuui9pYIAAAwdA3mTts6aq0rkvygTbUAAACwlsE80wYAAMCTbMB32koprxto21rrN9avHAAAALobTPfIr6U1oXZfSlcboQ0AAKANBhPaTu9l/dZJDk/yyrSeb7twQ4sCAACgZTCjR369r+2llHPTCmyf3dCiAAAAaGnbQCS11ouT/CzJx9p1TAAAgKGu3aNH3pbkaW0+JgAAwJDV7tC2f/ofrAQAAIAB2qDJtZOklDIsyS5J3pjkBUn+d0OPCQAAQMtg5mnrTN930UqS2Unes6FFAQAA0DKYO22/Sc+hrTPJY0n+nOTcWuuj7SgMAACAwQ35f+xGrAMAAIAetHsgEgAAANpowKGtlLKylPKhftr8UyllxYaXBQAAQDK4O22laxlIOwAAANqg3d0jt0mypM3HBAAAGLL6HIiklHLMWqt272FdkgxPsmuSVye5tU21AQAADHn9jR55aZ4Y5r8meX3X0pOS1vD/725LZQAAAPQb2j6WVlgrST6cVoi7rId2K9OaWPvXtdZb2lkgAADAUNZnaKu1nrnqfSnl9Ul+XGv97MYuCgAAgJbBTK69x8YsBAAAgHUNZp62aaWU15VSJvWyfXLX9j3bVx4AAMDQNpgh/89IcnaSeb1sn5vkP5K8Z0OLAgAAoGUwoe3YJL+qtS7vaWPX+l8mOb4NdQEAAJDBhbadktzTT5v7kuy43tUAAACwhsGEtmVJJvTTZnyemNcNAACADTSY0HZDkhNLKR09bSyljExyUpKb2lEYAAAAgwtt30yya5L/KaVs331D1+f/SbJLkm+0rzwAAIChbcDztCU5J8mpSU5OckIp5bokD6b1rNtTkoxN8qskX2x3kQAAAEPVgO+01Vo7k7wwyVlJlic5Mq0Qd2Raz7v9a5ITu9oBAADQBoPpHpla6/Ja6weSTEpyYJKju14n11o/mGRlKeXk9pcJAAAwNA2me+RqXXfTVg84UkrZrZTyhiSnJ9khyfD2lAcAADC0rVdoS5JSyvC0nm97U5LnpHXXrqb1XBsAAABtMOjQVkrZM8kbkpyWZGrX6llJvpTkq7XWe9tWHQAAwBA3oNBWShmR5JS07qodl9ZdtWVJfpjWYCTn11o/vLGKBAAAGKr6DG2llOlJ3pjk9UkmJylJrkrytSTfrrXOKaUYLRIAAGAj6e9O261pPac2M8mnk5xba71xo1cFAABAkoEN+V+TXJTkPIENAADgydVfaPtQknvTGsr/d6WUm0op7y2l7LDxSwMAAKDP0FZr/XitdVqSFyT5UZJpSc5Kcl8p5cJSysufhBoBAACGrIF0j0yt9ee11pcm2SXJB9K6+/aCJN9Jq/vkU0sph220KgEAAIaoAYW2VWqtM2utZ9Va90pyQpLzkixP8rQkfy6lXF1KedtGqBMAAGBIGlRo667WenGt9RVJdk7y3iS3JTk4yWfbVBsAAMCQt96hbZVa66xa63/UWvdLcnxaXSYBAABog/7maRuUWuulSS5t5zEBAACGsg2+0wYAAMDGI7QBAAA0mNAGAADQYEIbAABAgwltAAAADSa0AQAANJjQBgAA0GBCGwAAQIMJbQAAAA0mtAEAADSY0AYAANBgQhsAAECDCW0AAAANJrQBAAA0mNAGAADQYEIbAABAgwltAAAADSa0AQAANJjQBgAA0GBCGwAAQIMJbQAAAA0mtAEAADSY0AYAANBgQhsAAECDCW0AAAANJrQBAAA0mNAGAADQYEIbAABAgwltAAAADSa0AQAANJjQBgAA0GCl1rqpa0iSRhQBAACwiZTeNox4Mqvoy1lXL9/UJQAwRJ1xSIfrEACb1BmHdPS6TfdIAACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwUZs6gJgS7Ni6eLMuOnKzLr7pszuWhbOejhJ8tRT35JDXva2XvddunBeZtx8ZWbf9cS+ix+flSQ5+m//JdOPfXGf5170+Kw8cvOVmX33zZl9942ZfffNWbpgbpLk+R/6r+xwwBF97n/nb3+aR++4LrPvvjmLZj+SJfMfS+1cmdETts2kPffPtKNflN2ffsJg/hwAbCauO/8r+ct3PrP68+nfvWGdNrPuvin3/+XSzL77psx7+N4smTcnyxYvzMgx4zJxxz2y8yHPyr4nvDKjtprY57nuv+qy3HrxeZl1x3VZumBuRm01MZP23D97/9XLstvTjm/7d4PNndAGbfboHdfnl//+lvXa974rLslvv/jB9T73rb/8Xq75wRfWe//ff/mjWbF08erPI8eOz4qVK7Jw9owsnD0j911xSXZ8yjNz/Ls+k47RY9f7PAA0y9yH7s415/V//bj91z/KLb/4zurPwztGZcTIUVm6YG5m3nZNZt52TW686Jt5znv+M9vt/dR19u/sXJnfffHDueM357dWlJKRY8dnyYK5eeDqy/PA1Zdn+rGn5Kg3fyyllLZ9P9jcCW2wEYwcNyGT9tg/k/bYL5P22C9//sYnVt8x68+YrSdn2933W73vrz/1DwM/cSkZN2n71eceu82U/O6cMwe8+97Hn5pJe+yX7fY+JOMmbZ/hHSNTa82CmQ/kxv/979z8s2/noet+nyu++ck88w0fGXhdADRW7ezMb7/04axcvjRTph+cR2+/tte2U/Y6MFtNeXem7ntoJu64R0aNm5AkWb5kUe750y9z5bfOzpJ5c3Lx2e/IqZ++MCPHjl9j/6u///nVgW3/F7wmB5/y5oyesE2WL1mU2y75Qa781tm5/dIfZfzUXXLwKW/aeF8aNjNCG7TZ1P0Oy6u/+vs11v3l25/ppfWapj3rpH67QPbl4Je8OYe89K2rP8+f+eCg9n/6689YZ10pJeOn7pIjT/tAls57PHf9/qLc8ZsLcuRpH8iwER3rXSsAzXDTz76VmbdenT2PPjETpu7aZ2jb65iTe1zfMXpspj/75IzdenJ+8W9vzpK5c3L/VZdl2tEnrW6zZP7jufGnX0+S7Hr48WtcczpGj80BL3xtli2cl2t+8IVc9+MvZ+/jT82YiZPa9C1h82YgEmizYcOGr/++wzfsd5QNOfdATJn+lCTJymVLVj8rB8Dma/7MB3LV9z6bUeO3ztNf974NPt6U6Qevfr9w9iNrbHvo+j9k5fKlSZIDTzq9x/0PfNHpKWVYVixdnLv/8PMNrge2FEIbMGCP3HJVkmTE6LEZ7ddPgM3e7845MyuWLs4Rr31PRk/YdoOP98gtf1n9fsLUXdbYtmpQriTZeudpPe7fMXpsxk6amiR58NrfbnA9sKXQPRLo07JF8zPv4Xtzyy+/m3v+9IskyYEnvs4D4gCbuVsvPi8P3/DH7HjQkb12exyIlcuXZdFjj+b+qy7L1d//XJJk/Pa7ZpfDju11n9rZ2e+2x+6/fb1rgi1NW0NbKeV9SZ5XazVWK2zG7vr9Rbnss+9dZ/3wjpHZ/4WvzcGnrt/omAA0w8I5j+TKb52d4SNHr/fAUt947aFZuXzZOuu32+eQPPvtn8jwjpFrrN9qyo6r3z9+/+3Zfv/D19l36YK5WfTYzCTJosceXa+6YEvU7u6R+yZ5dpuPCTzJho8cnTETJ2XMxEkpXc/JlWHDc+CL/iYHnnjaRn92DoCN6/df/miWLZqfQ1761oxfqxvjQI2ZODljJk7KiFFjVq/b/oAj8vTXvS9bTd5hnfY7HHhkhneMSpJc+6NzejzmdT/+clJrkqSuXJEVy5asV22wpdE9EljHbk87fvXkpp0rV+TxB+/K9T/+Sq794Rdz28Xfz/Hv/r89zr8DQPPdefkFeeDq32Tb3ffNASe+br2P87LP/WL1+8VzZ+fOyy/ItT86Jxd88P/k4FPenENf/ndrtB89fuvs/8LX5Przv5qHrv9DLvvc+3LwKW/KhKm7ZtHjs3Lrxd/PDRd+PcOGj0jnyhVJklIMvwBJP6GtlPKxQR7vkA2oBWigYcNHZNtd986z3/GJjJqwdW7+2bdz6f/9x7zk0z/NiJGjN3V5AAzC4rmz86dv/HvKsOE56o1nbvCoxauMmTgpB550Wqbue1gu/NCrc+0Pv5gp0w5c57m2Q1/+9iya/Uju/O1Pc9dvL8xdv71wje0Tdtg9uxxyTG686BsZPnL0Ol0sYajq7/+pH0xSk8OOtpoAABNzSURBVAxmxIE6kEallDcleVOSfOlLX0oO73noV6A5Dnjh63Lzz76dhbNn5IFrLs/uR5ywqUsCYBCu/Pans3T+49n3hFdk4k57ZvmSRWts71yxfPX7VduGjejI8AHOyzllr4Oy3b6H5pGbr8ytl5y3TmgbNnxEjvm7szLtWS/KHb/5Sebce0uWL1mUsdtsl10POzb7v+A1+dM3PpEkmbjDbhvwTWHL0l9oW5zkwSQfH+Dx3pDkmQNpWGs9J8mqDs31rKuX99UcaICx205d/X7+jPs3YSUArI8FMx9Iktzyy+/lll9+r8+23zztiCTJ/i94zRoTYfdn3DbbJUnmzbiv1zY7HXxUdjr4qB63PXLzlUlaA5oALf2FtuuT7FVr/fpADlZKOTYDDG3A5mf+I08EtY7RYzdhJQA01fyuYNgxetyg933k1qsy96G7k2SDpiGALU1/oe2aJIeXUnaptfpZHbZgnStX9Ptsw3Xnf2X1+56Gagag2V7wka/1uf3q738+1/zgC0mS0797wxrbOjtXppRhfc7T+dD1f8yjd16fZPDXieWLF+aP/9Xq3LXjU56ZKXsdNKj9YUvWX2i7IskrkuyXZCChzdT1kNY8M90nDq219X7FsiVZMu+x1euHjxy1zh2r7tu7W75k0RrbRowavcYwy0lrQtKlC+au/rxs4bwn3i9esMb+HWPGrfGA93XnfzVzH7or0446KVP2Pjijxk1I0gpzs++6Kdf95Ku574qLkyR7HnVitt55Wj9/BQC2JAtnzcglZ78j+5zwiux00DOy1XY7rw5wC2Y9nLt+d2Gu/eGXklozaquJPY5M+ejt1+WhG/6Y3Q7/q0zYftcMG9GRlcuX5aHr/5Arv/OZPH7/7RkzcVKOetNHn+yvB41Wah3QuCEbm2fa2KJ8/++emwWzHuq33V7HnJxnvXXNR0bPfeWBAzrHU099Sw552dvWWDd/5oM57x3PG9D+R//tv2T6sS9e/bn7r6tJMmL02AzvGJXli+avHno5SfZ4xvNz9Fs+nhEjRw3oPLA5OOOQjrgOQd932ta+xgwb0ZGOMVtl5bIlWbF08er1W223c47/h09n0h77rXP8e6+4OJec/fdJWsP5d4zdKssXL0ztXJkkmbjjHvmrf/zPTNxx93Z/NWi8Mw7p6PU2tnnagCTJ9ONeklHjt86Mm67I4w/cmSXz5mTZwnnpGD024yZvnynTn5q9nvWiTN330E1dKgCbwNhtt8ux7zw7M266Io/ecV0WPzYrS+Y/ljJseMZN3iHb7rZPdj3suOx59Im9TgkzaY/9c+CL/iaP3PKXLJj5QJYumJtR4ydmm52nZ/enn5Dpx5864JEqYShxpw2AIc+dNgA2tb7utJlmHgAAoMEG1T2ylPLhtCbP/nytdc5a2yYleVuSWmv95/aVCAAAMHQN9pm2M9MKbd9LMmetbZO7bRfaAAAA2mCwoe1jaYWyWT1sm9VtOwAAAG0wqNBWaz2zj22z07rTBgAAQJsYiAQAAKDBhDYAAIAG67V7ZCnlmPU9aK31N+u7LwAAAE/o65m2S7P+g4oMX8/9AAAA6Kav0GYkSAAAgE2s19DW10iRAAAAPDkMRAIAANBgQhsAAECDDWpy7VJKSfLSJM9LslOSUT00q7XWv2pDbQAAAEPegENbKWVUkouSHJukpDVISenWpHZbDwAAQBsMpnvk+5Icl+RfkkxJK6CdmWTHJK9Kcn+S7yYZ2d4SAQAAhq7BhLaXJbmq1vqRWuvsVStrrTNqrd9NcnySk5K8s801AgAADFmDCW3Tkvyu2+eapGP1h1rvSnJhktPaUhkAAACDCm3Lkyzp9nl+Wt0ku7s3yZ4bWhQAAAAtgwltD6Q1YuQqtyV5xlptDkkyZ0OLAgAAoGUwoe13SZ7Z7fOPkxxUSvlqKeXEUsonkzwnyaVtrA8AAGBIG8w8bd9OskspZfda6z1JPpPk5CSnp/UcW0lyR5Iz2lwjAADAkDXg0FZrvTTd7qLVWheVUo5KK7jtleSeJBfUWhe1t0QAAIChazB32tZRa12R5AdtqgUAAIC1DOaZNgAAAJ5kA77TVkp53UDb1lq/sX7lAAAA0N1gukd+La0JtftSutoIbQAAAG0wmNB2ei/rt05yeJJXpvV824UbWhQAAAAtgxk98ut9bS+lnJtWYPvshhYFAABAS9sGIqm1XpzkZ0k+1q5jAgAADHXtHj3ytiRPa/MxAQAAhqx2h7b90/9gJQAAAAzQBk2unSSllGFJdknyxiQvSPK/G3pMAAAAWgYzT1tn+r6LVpLMTvKeDS0KAACAlsHcaftNeg5tnUkeS/LnJOfWWh9tR2EAAAAMbsj/YzdiHQAAAPSg3QORAAAA0EYDDm2llJWllA/10+afSikrNrwsAAAAksHdaStdy0DaAQAA0Abt7h65TZIlbT4mAADAkNXnQCSllGPWWrV7D+uSZHiSXZO8OsmtbaoNAABgyOtv9MhL88Qw/zXJ67uWnpS0hv9/d1sqAwAAoN/Q9rG0wlpJ8uG0QtxlPbRbmdbE2r+utd7SzgIBAACGsj5DW631zFXvSymvT/LjWutnN3ZRAAAAtAxmcu09NmYhAAAArGsw87RNK6W8rpQyqZftk7u279m+8gAAAIa2wQz5f0aSs5PM62X73CT/keQ9G1oUAAAALYMJbccm+VWtdXlPG7vW/zLJ8W2oCwAAgAwutO2U5J5+2tyXZMf1rgYAAIA1DCa0LUsyoZ824/PEvG4AAABsoMGEthuSnFhK6ehpYyllZJKTktzUjsIAAAAYXGj7ZpJdk/xPKWX77hu6Pv9Pkl2SfKN95QEAAAxtA56nLck5SU5NcnKSE0op1yV5MK1n3Z6SZGySXyX5YruLBAAAGKoGfKet1tqZ5IVJzkqyPMmRaYW4I9N63u1fk5zY1Q4AAIA2GEz3yNRal9daP5BkUpIDkxzd9Tq51vrBJCtLKSe3v0wAAIChaTDdI1frupu2esCRUspupZQ3JDk9yQ5JhrenPAAAgKFtvUJbkpRShqf1fNubkjwnrbt2Na3n2gAAAGiDQYe2UsqeSd6Q5LQkU7tWz0rypSRfrbXe27bqAAAAhrgBhbZSyogkp6R1V+24tO6qLUvyw7QGIzm/1vrhjVUkAADAUNVnaCulTE/yxiSvTzI5SUlyVZKvJfl2rXVOKcVokQAAABtJf3fabk3rObWZST6d5Nxa640bvSoAAACSDGzI/5rkoiTnCWwAAABPrv5C24eS3JvWUP6/K6XcVEp5byllh41fGgAAAH2Gtlrrx2ut05K8IMmPkkxLclaS+0opF5ZSXv4k1AgAADBkDaR7ZGqtP6+1vjTJLkk+kNbdtxck+U5a3SefWko5bKNVCQAAMEQNKLStUmudWWs9q9a6V5ITkpyXZHmSpyX5cynl6lLK2zZCnQAAAEPSoEJbd7XWi2utr0iyc5L3JrktycFJPtum2gAAAIa89Q5tq9RaZ9Va/6PWul+S49PqMgkAAEAb9DdP26DUWi9Ncmk7jwkAADCUbfCdNgAAADYeoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABhPaAAAAGkxoAwAAaDChDQAAoMGENgAAgAYT2gAAABpMaAMAAGgwoQ0AAKDBhDYAAIAGE9oAAAAaTGgDAABoMKENAACgwYQ2AACABiu11k1dQ5I0oggAAIBNpPS2YcSTWUUfei0Q6F8p5U211nM2dR0ADF2uRbDx6B4JW4Y3beoCABjyXItgIxHaAAAAGkxoAwAAaDChDbYMniEAYFNzLYKNpCmjRwIAANADd9oAAAAaTGiDBiil7F5KqaWUr621/mtd63ffJIUN0uZWLwBPcC2C5hLaGDK6/gXefVlZSplVSrmklPLqTV3fxtDbBbhpSinPLKVcVEqZU0pZVEq5rpTyzlLK8E1dG0A7uRY1Tymlo5Ty96WUc0sp15RSlnXV+4ZNXRus0pTJteHJ9NGu144k+yR5cZLjSimH1VrftenK6tH7k5yV5MFNXcjGUko5OckPkixJ8r0kc5K8KMmnkxyV5GWbrjqAjca1qDnGJflM1/tHksxIssumKwfWJbQx5NRaz+z+uZTyV0l+meSdpZTP1lrv2RR19aTW+nCShzd1HRtLKWVCki8nWZnk2FrrlV3rP5TkkiQvLaW8stb63U1YJkDbuRY1yqIkL0xyTa314VLKmUk+smlLgjXpHsmQV2u9OMktSUqSw5M1u3KUUvYupXyvlDKzlNJZSjl21b6llG1LKf9WSrm5lLK4lDK3lHJxKeW5PZ2rlDK+lPKpUsoDpZQlpZRbSinvSi//X+yrX34p5Yiuuh4spSwtpTxcSvlFKeXlXdvPTHJ3V/PXr9Ud57S1jvW8ru6Js7qOdWcp5ZOllK17qes5pZTLSykLu7o0/riUsm8ff+bevDTJlCTfXRXYkqTWuiTJB7s+vmU9jguwWXEt2nTXolrrslrr/3aFU2gkd9qgpXS9rj0HxrQkf0pyW5JvJRmTZF6SlFJ2S3Jpkt2TXJ7kZ2l1sTgpyc9KKW+utX559QlKGZXk4rQuxtd2HW/rJB9K8uxBFVvKG5N8Ia07VD9JcnuS7ZI8Lclbk/xPV21bJ/n7rvP9uNshrul2rA+n1U1nTpKfJpmZ5ClJ/jHJC0spz6i1zuvW/qVpdWNc1vX6cJKjk/whyXW91Pu1JK9Pcnqt9WvdNh3f9fqzHnb7TVq/fj6zlDKq1rq0978IwBbBtWjTXIug+WqtFsuQWNK6CNYe1j8nSWfXslvXut1XtU/yr70c79KufV651vqt07oQLU4ytdv6D3Qd7wdJhnVbv0daF6ma5GtrHetrXet377Zu/yTLu/Y5oIe6du72fveejttt+3Fd23+fZOu1tp3Wte3T3dZtlWR21/mftlb7T3f7m+3ey/c4ba31V3StP6yX+m7o2r7fpv7nx2KxWNqxuBb1+B026bWoh3rO7Gr3hk39z4vFsmrRPZIhp5RyZtfy8VLKeWn9KlmSfKbWeu9azR/JEw+Ldz/GwWn9IvmDutbzVrXWx9PqCz86yandNp2e1oX1vbXWzm7t707y2UF8hbekdZf8n2utN669sdb6wCCO9Y6u1zd21d39OF9L64LffTSzk5Nsm+TbtVt3xi5nJpnby3nen2S/JD9aa/3Ertfe9lu1vseuMQCbK9eiNWzqaxE0nu6RDEWrHi6uSR5PqzvJV2ut3+yh7bW15255z+h6ndjVX39tU7pe90tazw8k2SvJ/bXWO3tof2kG/tDzkV2v/zvA9n15Rlq/VL6slNLTKI0jk0wppUyqtc5OcmjX+svWblhrnVtKuSY9dK+p6/8Qe29dhQA2d65FT2j6tQg2OaGNIafWWvpvtdqMXtZP6no9oWvpzVZdr6vuKD0yyPP0ZNVdp3YMvTwprX8P9HeRXtUVpZ3fI3ni19CJvWyfsFY7gC2Ca9EaNvW1CBpP90joW293eFaFiL+vtZY+ltPXaj+1l+NtP4iaVnUd2WkQ+/RmbpLH+vkOpVtXnXZ+jyS5tet177U3lFJGpPWMxYokdw3yuABbEteijXstgsYT2mD9/LHr9VkDaVxrnZ/kjiQ7lVKm9dDk2PU49wsG0HZl1+vwPo61TSnlgAGe+6qu13W6nZRSJiZ56gCPs8olXa/P72HbMUnGJvl9L92CAIY616K1rOe1CBpPaIP10PXg8+VJXlJK+Zue2pRSDiqlbNdt1blp/X/u30spw7q12yNPPIQ9EF9I6+7Th0op+/dw3p27fXwsrV9od+3lWJ/uev1yKWXHHo41rpRyZLdV53cd81WllKet1fzM9NLNsZSyQyll366LaXfnJZmV5JXdj1dKGZ3kX7o+fqGX2gGGNNeitl2LoPE80wbr71Vp3Sn6ainlHWnNofN4kp3TmlvmwLQerp7Z1f7sJC9OaxSvq0opP0/rwvKKtOYk++uBnLTWelMp5a1Jvpjk6lLK+WnNjTMprblx5qc1fHJqrQtKKX9K8qxSyrfSmuNnZZKf1Fqvq7VeXEo5I8m/Jbm9lHJRWpOgbpVkt7R+xfxtuu6EdR3vTWnNiXN5KaX73DgHdn2PY3oo+9/SNTdOWkMur/ou87rm+TkvyaWllO+mNXz0XyfZp2v99wbydwEYolyLNvBalCRd5181MfeqO3Wnl1KO7nr/21rrVwbyt4GNYlPPOWCxPFlLepkbp5e2u6ePOWW6tRuf1pw3f0myIK35cO5OcmGSNyUZt1b7CUk+ldaD20uS3JLk3Un27Ol86WFunG7bnpHWPDsz05pc9KG0hox+6Vrt9kpyQVoPb3em5/nSjk5rEtSHuo71aFpDLH8qa82B09X+hLQuoIvS+rXz/LQudj3Wm37mxklyVJKLuo61OMn1Sf4hyfBN/c+NxWKxtHNxLWrmtSitkTNrH0uf/xtYLBt7KbUaSRsAAKCpPNMGAADQYEIbAABAgwltAAAADSa0AQAANJjQBgAA0GBCGwAAQIMJbQAAAA0mtAEAADSY0AYAANBgQhsAAECD/f/vNU77QZ7hwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function to plot the confusion matrix\n",
    "# pass the logistic regression (SGD) model to the function\n",
    "plot_confusion_matrix(logreg_with_SGD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate performance measures on the test set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UP5DA9_qebpC",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.96      0.89      5476\n",
      "           1       0.66      0.28      0.40      1552\n",
      "\n",
      "    accuracy                           0.81      7028\n",
      "   macro avg       0.74      0.62      0.64      7028\n",
      "weighted avg       0.79      0.81      0.78      7028\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# compute the performance measures on test data\n",
    "# call the function 'get_test_report'\n",
    "# pass the logstic regression (SGD) model to the function\n",
    "test_report = get_test_report(logreg_with_SGD)\n",
    "\n",
    "# print the performace measures\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** The accuracy is 81% for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.30475321107275866\n"
     ]
    }
   ],
   "source": [
    "# compute kappa score on test set\n",
    "# call the function 'kappa_score'\n",
    "# pass the logstic regression (SGD) model to the function\n",
    "kappa_value = kappa_score(logreg_with_SGD)\n",
    "\n",
    "# print the kappa value\n",
    "print(kappa_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** As the kappa score for the logistic regression (SGD) is 0.3047, we can say that there is low to moderate agreement between the actual and predicted values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the ROC curve.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function 'plot_roc' to plot the ROC curve\n",
    "# pass the logstic regression (SGD) model to the function\n",
    "plot_roc(logreg_with_SGD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>The dotted line represents the ROC curve of a purely random classifier; a good classifier stays as far away from that line as possible (toward the top-left corner).<br><br>\n",
    "We see our classifier (logistic regression with SGD) to be away from the dotted line with the AUC score 0.7886.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. Tabulate the results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>AUC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Kappa Score</th>\n",
       "      <th>f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.794406</td>\n",
       "      <td>0.654384</td>\n",
       "      <td>0.331830</td>\n",
       "      <td>0.813745</td>\n",
       "      <td>0.342677</td>\n",
       "      <td>0.440359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression (SGD)</td>\n",
       "      <td>0.788639</td>\n",
       "      <td>0.663142</td>\n",
       "      <td>0.282861</td>\n",
       "      <td>0.809903</td>\n",
       "      <td>0.304753</td>\n",
       "      <td>0.396567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  AUC Score  Precision Score  Recall Score  \\\n",
       "0        Logistic Regression   0.794406         0.654384      0.331830   \n",
       "1  Logistic Regression (SGD)   0.788639         0.663142      0.282861   \n",
       "\n",
       "   Accuracy Score  Kappa Score  f1-score  \n",
       "0        0.813745     0.342677  0.440359  \n",
       "1        0.809903     0.304753  0.396567  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the function 'update_score_card' to store the performance measures\n",
    "# pass the 'Logistic Regression (SGD)' as model name to the function\n",
    "update_score_card(model_name = 'Logistic Regression (SGD)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"conclusion\"> </a>\n",
    "# 6. Conclusion and Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take the final conclusion, let us print the result table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>AUC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Kappa Score</th>\n",
       "      <th>f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.794406</td>\n",
       "      <td>0.654384</td>\n",
       "      <td>0.331830</td>\n",
       "      <td>0.813745</td>\n",
       "      <td>0.342677</td>\n",
       "      <td>0.440359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logistic Regression (SGD)</td>\n",
       "      <td>0.788639</td>\n",
       "      <td>0.663142</td>\n",
       "      <td>0.282861</td>\n",
       "      <td>0.809903</td>\n",
       "      <td>0.304753</td>\n",
       "      <td>0.396567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  AUC Score  Precision Score  Recall Score  \\\n",
       "0        Logistic Regression   0.794406         0.654384      0.331830   \n",
       "1  Logistic Regression (SGD)   0.788639         0.663142      0.282861   \n",
       "\n",
       "   Accuracy Score  Kappa Score  f1-score  \n",
       "0        0.813745     0.342677  0.440359  \n",
       "1        0.809903     0.304753  0.396567  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the 'score_card' to compare all the models\n",
    "score_card"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> The performance of the models were evaluated using AUC score, accuracy, precision, f1-score, recall, and kappa score. </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot the performance measures of the two models in the single graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the graph\n",
    "# by default, plot() returns the line plot\n",
    "score_card.plot()\n",
    "\n",
    "# set the text size of the title\n",
    "plt.title(label = 'Comparison of the Models', fontsize = 15)\n",
    "\n",
    "# set the model names as x-ticks\n",
    "# 'score_card.Model' retuns the model names\n",
    "# rotate the x-axis labels vertically\n",
    "plt.xticks([0,1], list(score_card.Model), rotation = 'vertical')\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> The graph was plotted for all the performance metrics of the models implemented. The X-axis has the model name as given in the table. <br><br>\n",
    "The above plot shows that the Logistic Regression(SGD) modela has lower values for Recall Score, Kappa score and F1-score when compared to the Logistic Regression model built initially. All the other performance measures are similar for both the models. Finally, it can be concluded that the Logistic Regression model can be used to predict if a person has taken a h1n1 vaccine or not. Additionally, based on the business decision, as to what is more important to predict correctly for business,i.e., predicting the people who have taken the vaccine is more important or whether it is more important to predict the people who have not taken the vaccine. Based on business decision the performance parameters like sensitivity or specificity can be fine tuned.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "wORl-2PPebnv",
    "O71NsesEebn3",
    "7rG9ZJ7bebn-",
    "BFOkFedXeboC",
    "G-aTB0DseboJ",
    "vkZahcS9eboQ",
    "-tWX9ptGeboe",
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    "cfzuv0sbebq7",
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    "YxDtqzYhebre",
    "IRmSXEhkebrl"
   ],
   "name": "Chronic Renal Gland Disorder - Logit.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}