handin.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KEN3450, Data Analysis 2020 \n",
    "\n",
    "**Kaggle Competition 2020**<br>\n",
    "\n",
    "Team: MammaMia!\n",
    "\n",
    "Members:\n",
    "- Lucas Giovanni Uberti-Bona Marín\n",
    "- Giacomo Anerdi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "#import your classifiers here\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diagnosing the Maastricht Flu \n",
    "\n",
    "You are given the early data for an outbreak of a dangerous virus originating from a group of primates being kept in a Maastricht biomedical research lab in the basement of Henri-Paul Spaaklaan building, this virus is dubbed the \"Maastricht Flu\".\n",
    "\n",
    "You have the medical records of $n$ number of patients in `flu_train.csv`. There are two general types of patients in the data, flu patients and healthy (this is recorded in the column labeled `flu`, a 0 indicates the absences of the virus and a 1 indicates presence). Notice that the dataset is unbalanced and you can expect a similar imbalance in the testing set.\n",
    "\n",
    "**Your task:** build a model to predict if a given patient has the flu. Your goal is to catch as many flu patients as possible without misdiagnosing too many healthy patients.\n",
    "\n",
    "**The deliverable:** submit your final solution via Kaggle competition using the `flu_test.csv` data.\n",
    "\n",
    "Maastricht Gemeente will use your model to diagnose sets of future patients (held by us). You can expect that there will be an increase in the number of flu patients in any groups of patients in the future.\n",
    "\n",
    "Here are some benchmarks for comparison and for expectation management. Notice that because the dataset is unbalanced, we expect that there is going to be a large difference in the accuracy for each class, thus `accuracy` is a metric that might be misleading in this case (see also below). That's why the baselines below are based on the expected accuracy **per class** and also they give you an estimate for the AUROC on all patients in the testing data. This is the score you see in the Kaggle submission as well.\n",
    "\n",
    "**Baseline Model:** \n",
    "- ~50% expected accuracy on healthy patients in training data\n",
    "- ~50% expected accuracy on flu patients in training data\n",
    "- ~50% expected accuracy on healthy patients in testing data (future data, no info on the labels)\n",
    "- ~50% expected accuracy on flu patients in testing data (future data, no info on the labels)\n",
    "- ~50% expected AUROC on all patients in testing data (future data, no info on the labels)\n",
    "\n",
    "**Reasonable Model:** \n",
    "- ~70% expected accuracy on healthy patients in training data\n",
    "- ~55% expected accuracy on flu patients, in training data\n",
    "- ~70% expected accuracy on healthy patients in testing data (future data, no info on the labels, to be checked upon your submission)\n",
    "- ~57% expected accuracy on flu patients, in testing data (future data, no info on the labels, to be checked upon your submission)\n",
    "- ~65% expected AUROC on all patients, in testing data (future data, no info on the labels, to be checked from Kaggle)\n",
    "\n",
    "**Grading:**\n",
    "Your grade will be based on:\n",
    "1. your model's ability to out-perform the benchmarks (they are kind of low, so we won't care much about this)\n",
    "2. your ability to carefully and thoroughly follow the data analysis pipeline\n",
    "3. the extend to which all choices are reasonable and defensible by methods you have learned in this class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Read the data, clean and explore the data\n",
    "\n",
    "There are a large number of missing values in the data. Nearly all predictors have some degree of missingness. Not all missingness are alike: NaN in the `'pregnancy'` column is meaningful and informative, as patients with NaN's in the pregnancy column are males, where as NaN's in other predictors may appear randomly. \n",
    "\n",
    "\n",
    "**What do you do?:** We make no attempt to interpret the predictors and we make no attempt to model the missing values in the data in any meaningful way. We replace all missing values with 0.\n",
    "\n",
    "However, it would be more complete to look at the data and allow the data to inform your decision on how to address missingness. For columns where NaN values are informative, you might want to treat NaN as a distinct value; You might want to drop predictors with too many missing values and impute the ones with few missing values using a model. There are many acceptable strategies here, as long as the appropriateness of the method in the context of the task and the data is discussed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Race1</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>HHIncome</th>\n",
       "      <th>HHIncomeMid</th>\n",
       "      <th>Poverty</th>\n",
       "      <th>HomeRooms</th>\n",
       "      <th>...</th>\n",
       "      <th>AgeRegMarij</th>\n",
       "      <th>HardDrugs</th>\n",
       "      <th>SexEver</th>\n",
       "      <th>SexAge</th>\n",
       "      <th>SexNumPartnLife</th>\n",
       "      <th>SexNumPartYear</th>\n",
       "      <th>SameSex</th>\n",
       "      <th>SexOrientation</th>\n",
       "      <th>PregnantNow</th>\n",
       "      <th>flu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51624</td>\n",
       "      <td>male</td>\n",
       "      <td>34</td>\n",
       "      <td>White</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>25000-34999</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>1.36</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51630</td>\n",
       "      <td>female</td>\n",
       "      <td>49</td>\n",
       "      <td>White</td>\n",
       "      <td>Some College</td>\n",
       "      <td>LivePartner</td>\n",
       "      <td>35000-44999</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>1.91</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51638</td>\n",
       "      <td>male</td>\n",
       "      <td>9</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75000-99999</td>\n",
       "      <td>87500.0</td>\n",
       "      <td>1.84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51646</td>\n",
       "      <td>male</td>\n",
       "      <td>8</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55000-64999</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>2.33</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51647</td>\n",
       "      <td>female</td>\n",
       "      <td>45</td>\n",
       "      <td>White</td>\n",
       "      <td>College Grad</td>\n",
       "      <td>Married</td>\n",
       "      <td>75000-99999</td>\n",
       "      <td>87500.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bisexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  Gender  Age  Race1     Education MaritalStatus     HHIncome  \\\n",
       "0  51624    male   34  White   High School       Married  25000-34999   \n",
       "1  51630  female   49  White  Some College   LivePartner  35000-44999   \n",
       "2  51638    male    9  White           NaN           NaN  75000-99999   \n",
       "3  51646    male    8  White           NaN           NaN  55000-64999   \n",
       "4  51647  female   45  White  College Grad       Married  75000-99999   \n",
       "\n",
       "   HHIncomeMid  Poverty  HomeRooms  ... AgeRegMarij HardDrugs  SexEver  \\\n",
       "0      30000.0     1.36        6.0  ...         NaN       Yes      Yes   \n",
       "1      40000.0     1.91        5.0  ...         NaN       Yes      Yes   \n",
       "2      87500.0     1.84        6.0  ...         NaN       NaN      NaN   \n",
       "3      60000.0     2.33        7.0  ...         NaN       NaN      NaN   \n",
       "4      87500.0     5.00        6.0  ...         NaN        No      Yes   \n",
       "\n",
       "   SexAge  SexNumPartnLife  SexNumPartYear  SameSex SexOrientation  \\\n",
       "0    16.0              8.0             1.0       No   Heterosexual   \n",
       "1    12.0             10.0             1.0      Yes   Heterosexual   \n",
       "2     NaN              NaN             NaN      NaN            NaN   \n",
       "3     NaN              NaN             NaN      NaN            NaN   \n",
       "4    13.0             20.0             0.0      Yes       Bisexual   \n",
       "\n",
       "  PregnantNow  flu  \n",
       "0         NaN    0  \n",
       "1         NaN    0  \n",
       "2         NaN    0  \n",
       "3         NaN    0  \n",
       "4         NaN    0  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train\n",
    "df = pd.read_csv('data/flu_train.csv')\n",
    "df = df[~np.isnan(df['flu'])]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Race1</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>HHIncome</th>\n",
       "      <th>HHIncomeMid</th>\n",
       "      <th>Poverty</th>\n",
       "      <th>HomeRooms</th>\n",
       "      <th>...</th>\n",
       "      <th>RegularMarij</th>\n",
       "      <th>AgeRegMarij</th>\n",
       "      <th>HardDrugs</th>\n",
       "      <th>SexEver</th>\n",
       "      <th>SexAge</th>\n",
       "      <th>SexNumPartnLife</th>\n",
       "      <th>SexNumPartYear</th>\n",
       "      <th>SameSex</th>\n",
       "      <th>SexOrientation</th>\n",
       "      <th>PregnantNow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51625</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000-24999</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51678</td>\n",
       "      <td>male</td>\n",
       "      <td>60</td>\n",
       "      <td>White</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>15000-19999</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51694</td>\n",
       "      <td>male</td>\n",
       "      <td>38</td>\n",
       "      <td>White</td>\n",
       "      <td>Some College</td>\n",
       "      <td>Married</td>\n",
       "      <td>20000-24999</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51695</td>\n",
       "      <td>male</td>\n",
       "      <td>8</td>\n",
       "      <td>White</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000-74999</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51711</td>\n",
       "      <td>female</td>\n",
       "      <td>59</td>\n",
       "      <td>Other</td>\n",
       "      <td>8th Grade</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>20000-24999</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  Gender  Age  Race1     Education MaritalStatus     HHIncome  \\\n",
       "0  51625    male    4  Other           NaN           NaN  20000-24999   \n",
       "1  51678    male   60  White   High School       Married  15000-19999   \n",
       "2  51694    male   38  White  Some College       Married  20000-24999   \n",
       "3  51695    male    8  White           NaN           NaN  65000-74999   \n",
       "4  51711  female   59  Other     8th Grade       Widowed  20000-24999   \n",
       "\n",
       "   HHIncomeMid  Poverty  HomeRooms  ... RegularMarij AgeRegMarij  HardDrugs  \\\n",
       "0      22500.0     1.07        9.0  ...          NaN         NaN        NaN   \n",
       "1      17500.0     1.03        5.0  ...          NaN         NaN         No   \n",
       "2      22500.0     1.15        6.0  ...           No         NaN         No   \n",
       "3      70000.0     3.55        5.0  ...          NaN         NaN        NaN   \n",
       "4      22500.0     1.37        4.0  ...          NaN         NaN        NaN   \n",
       "\n",
       "   SexEver  SexAge  SexNumPartnLife  SexNumPartYear SameSex SexOrientation  \\\n",
       "0      NaN     NaN              NaN             NaN     NaN            NaN   \n",
       "1      Yes    20.0              1.0             NaN      No            NaN   \n",
       "2      Yes    23.0              1.0             1.0      No   Heterosexual   \n",
       "3      NaN     NaN              NaN             NaN     NaN            NaN   \n",
       "4      NaN     NaN              NaN             NaN     NaN            NaN   \n",
       "\n",
       "   PregnantNow  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "\n",
       "[5 rows x 71 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test\n",
    "df_test = pd.read_csv('data/flu_test.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x train shape: (5246, 71)\n",
      "x test shape: (1533, 70)\n",
      "train class 0: 4936, train class 1: 310\n"
     ]
    }
   ],
   "source": [
    "#What's up in each set\n",
    "\n",
    "x = df.values[:, :-1]\n",
    "y = df.values[:, -1]\n",
    "\n",
    "x_test = df_test.values[:, :-1]\n",
    "\n",
    "print('x train shape:', x.shape)\n",
    "print('x test shape:', x_test.shape)\n",
    "print('train class 0: {}, train class 1: {}'.format(len(y[y==0]), len(y[y==1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Data Exploration ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initial expection for the data's missing values, quartiles, min/max and standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>HHIncomeMid</th>\n",
       "      <th>Poverty</th>\n",
       "      <th>HomeRooms</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length</th>\n",
       "      <th>HeadCirc</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>...</th>\n",
       "      <th>CompHrsDayChild</th>\n",
       "      <th>AlcoholDay</th>\n",
       "      <th>AlcoholYear</th>\n",
       "      <th>SmokeAge</th>\n",
       "      <th>AgeFirstMarij</th>\n",
       "      <th>AgeRegMarij</th>\n",
       "      <th>SexAge</th>\n",
       "      <th>SexNumPartnLife</th>\n",
       "      <th>SexNumPartYear</th>\n",
       "      <th>flu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5246.000000</td>\n",
       "      <td>5246.000000</td>\n",
       "      <td>4798.000000</td>\n",
       "      <td>4843.000000</td>\n",
       "      <td>5210.000000</td>\n",
       "      <td>5206.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>5014.000000</td>\n",
       "      <td>5010.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>417.000000</td>\n",
       "      <td>2404.000000</td>\n",
       "      <td>2882.000000</td>\n",
       "      <td>1479.000000</td>\n",
       "      <td>1331.000000</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>2673.000000</td>\n",
       "      <td>2773.000000</td>\n",
       "      <td>2405.000000</td>\n",
       "      <td>5246.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>61707.937286</td>\n",
       "      <td>35.078155</td>\n",
       "      <td>54079.824927</td>\n",
       "      <td>2.614229</td>\n",
       "      <td>6.147025</td>\n",
       "      <td>68.701921</td>\n",
       "      <td>84.363483</td>\n",
       "      <td>41.101639</td>\n",
       "      <td>160.341424</td>\n",
       "      <td>26.371601</td>\n",
       "      <td>...</td>\n",
       "      <td>2.225420</td>\n",
       "      <td>2.959651</td>\n",
       "      <td>72.353921</td>\n",
       "      <td>17.939148</td>\n",
       "      <td>16.983471</td>\n",
       "      <td>17.545310</td>\n",
       "      <td>17.456042</td>\n",
       "      <td>14.617021</td>\n",
       "      <td>1.354678</td>\n",
       "      <td>0.059093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5876.826538</td>\n",
       "      <td>23.032443</td>\n",
       "      <td>32956.594998</td>\n",
       "      <td>1.672579</td>\n",
       "      <td>2.223687</td>\n",
       "      <td>29.774856</td>\n",
       "      <td>13.827255</td>\n",
       "      <td>2.381561</td>\n",
       "      <td>21.043275</td>\n",
       "      <td>7.464134</td>\n",
       "      <td>...</td>\n",
       "      <td>2.517635</td>\n",
       "      <td>3.522538</td>\n",
       "      <td>101.779814</td>\n",
       "      <td>5.452497</td>\n",
       "      <td>3.874501</td>\n",
       "      <td>4.478533</td>\n",
       "      <td>3.740811</td>\n",
       "      <td>61.385945</td>\n",
       "      <td>2.503281</td>\n",
       "      <td>0.235820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>51624.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>47.100000</td>\n",
       "      <td>34.200000</td>\n",
       "      <td>83.600000</td>\n",
       "      <td>12.880000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>56623.250000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>22500.000000</td>\n",
       "      <td>1.110000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>73.950000</td>\n",
       "      <td>39.600000</td>\n",
       "      <td>155.400000</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>61669.500000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>2.320000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>70.600000</td>\n",
       "      <td>86.150000</td>\n",
       "      <td>41.200000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66841.250000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>87500.000000</td>\n",
       "      <td>4.340000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>95.325000</td>\n",
       "      <td>42.900000</td>\n",
       "      <td>173.600000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>71915.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>112.200000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>200.400000</td>\n",
       "      <td>80.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID          Age    HHIncomeMid      Poverty    HomeRooms  \\\n",
       "count   5246.000000  5246.000000    4798.000000  4843.000000  5210.000000   \n",
       "mean   61707.937286    35.078155   54079.824927     2.614229     6.147025   \n",
       "std     5876.826538    23.032443   32956.594998     1.672579     2.223687   \n",
       "min    51624.000000     0.000000    2500.000000     0.000000     1.000000   \n",
       "25%    56623.250000    15.000000   22500.000000     1.110000     5.000000   \n",
       "50%    61669.500000    33.000000   50000.000000     2.320000     6.000000   \n",
       "75%    66841.250000    53.000000   87500.000000     4.340000     7.000000   \n",
       "max    71915.000000    80.000000  100000.000000     5.000000    13.000000   \n",
       "\n",
       "            Weight      Length   HeadCirc       Height          BMI  ...  \\\n",
       "count  5206.000000  356.000000  61.000000  5014.000000  5010.000000  ...   \n",
       "mean     68.701921   84.363483  41.101639   160.341424    26.371601  ...   \n",
       "std      29.774856   13.827255   2.381561    21.043275     7.464134  ...   \n",
       "min       2.800000   47.100000  34.200000    83.600000    12.880000  ...   \n",
       "25%      53.000000   73.950000  39.600000   155.400000    21.200000  ...   \n",
       "50%      70.600000   86.150000  41.200000   165.000000    25.700000  ...   \n",
       "75%      87.300000   95.325000  42.900000   173.600000    30.500000  ...   \n",
       "max     223.000000  112.200000  45.400000   200.400000    80.600000  ...   \n",
       "\n",
       "       CompHrsDayChild   AlcoholDay  AlcoholYear     SmokeAge  AgeFirstMarij  \\\n",
       "count       417.000000  2404.000000  2882.000000  1479.000000    1331.000000   \n",
       "mean          2.225420     2.959651    72.353921    17.939148      16.983471   \n",
       "std           2.517635     3.522538   101.779814     5.452497       3.874501   \n",
       "min           0.000000     1.000000     0.000000     6.000000       1.000000   \n",
       "25%           0.000000     1.000000     3.000000    15.000000      15.000000   \n",
       "50%           1.000000     2.000000    24.000000    17.000000      16.000000   \n",
       "75%           6.000000     3.000000   104.000000    19.000000      18.000000   \n",
       "max           6.000000    82.000000   364.000000    72.000000      48.000000   \n",
       "\n",
       "       AgeRegMarij       SexAge  SexNumPartnLife  SexNumPartYear          flu  \n",
       "count   629.000000  2673.000000      2773.000000     2405.000000  5246.000000  \n",
       "mean     17.545310    17.456042        14.617021        1.354678     0.059093  \n",
       "std       4.478533     3.740811        61.385945        2.503281     0.235820  \n",
       "min       5.000000     9.000000         0.000000        0.000000     0.000000  \n",
       "25%      15.000000    15.000000         2.000000        1.000000     0.000000  \n",
       "50%      17.000000    17.000000         5.000000        1.000000     0.000000  \n",
       "75%      19.000000    19.000000        12.000000        1.000000     0.000000  \n",
       "max      52.000000    50.000000      2000.000000       69.000000     1.000000  \n",
       "\n",
       "[8 rows x 44 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it can be seen, many features contain missing values. However, in some of these columns the missing value has meaning. For example a missing value in SmokeAge means that the individual has never smoked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    int64\n",
       "Gender               object\n",
       "Age                   int64\n",
       "Race1                object\n",
       "Education            object\n",
       "MaritalStatus        object\n",
       "HHIncome             object\n",
       "HHIncomeMid         float64\n",
       "Poverty             float64\n",
       "HomeRooms           float64\n",
       "HomeOwn              object\n",
       "Work                 object\n",
       "Weight              float64\n",
       "Length              float64\n",
       "HeadCirc            float64\n",
       "Height              float64\n",
       "BMI                 float64\n",
       "BMICatUnder20yrs     object\n",
       "BMI_WHO              object\n",
       "Pulse               float64\n",
       "BPSysAve            float64\n",
       "BPDiaAve            float64\n",
       "BPSys1              float64\n",
       "BPDia1              float64\n",
       "BPSys2              float64\n",
       "BPDia2              float64\n",
       "BPSys3              float64\n",
       "BPDia3              float64\n",
       "Testosterone        float64\n",
       "DirectChol          float64\n",
       "                     ...   \n",
       "nBabies             float64\n",
       "Age1stBaby          float64\n",
       "SleepHrsNight       float64\n",
       "SleepTrouble         object\n",
       "PhysActive           object\n",
       "PhysActiveDays      float64\n",
       "TVHrsDay             object\n",
       "CompHrsDay           object\n",
       "TVHrsDayChild       float64\n",
       "CompHrsDayChild     float64\n",
       "Alcohol12PlusYr      object\n",
       "AlcoholDay          float64\n",
       "AlcoholYear         float64\n",
       "SmokeNow             object\n",
       "Smoke100             object\n",
       "Smoke100n            object\n",
       "SmokeAge            float64\n",
       "Marijuana            object\n",
       "AgeFirstMarij       float64\n",
       "RegularMarij         object\n",
       "AgeRegMarij         float64\n",
       "HardDrugs            object\n",
       "SexEver              object\n",
       "SexAge              float64\n",
       "SexNumPartnLife     float64\n",
       "SexNumPartYear      float64\n",
       "SameSex              object\n",
       "SexOrientation       object\n",
       "PregnantNow          object\n",
       "flu                   int64\n",
       "Length: 72, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysing different features ###\n",
    "\n",
    "**Gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Gender'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values in this column. As it can be seen, there the two genders present in the dataset and they are quite balanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing values. It can be observed that the age is not normally distributed in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Race**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Race1'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Race1'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing values. As the classes are quite unbalanced where `White` is in the majority of instances it is decided to aggregate the other classes together. This means that the cleaned data just contains whether any given individual is white or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Education**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1672"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Education'].value_counts().plot.bar(rot=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In which age groups is the data missing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Education'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8th Grade         0.188678\n",
       "9 - 11th Grade    0.172576\n",
       "College Grad      0.242017\n",
       "High School       0.226814\n",
       "Some College      0.222016\n",
       "Age               1.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(df['Education'])\n",
    "pd.concat([dummies, df['Age']], axis=1).corr()['Age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1672 instance don't have a value in the 'Education' feature. It looks like this feature tells at what point a given individual has stopped his/her education. This means that the dataset has missing values for young people that are still at school or at university. This is changed into the education level that has been currently achieved by estimating in which education category each individual is using the age.\n",
    "For the remaining few people with missing values, which are older than 35 years old, it can be assumed that they have finished their education have the values filled in by looking at what is the most prominent category per age group. Depending on different time periods the level of education that people got is different. For example, in the 60s going to university was much less likely than it is today. It can be assumed that the data was collected in the same time period which could be even of a few years. This means that `Age` would be closely related to when any given individual was born."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Marital Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1666"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MaritalStatus'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['MaritalStatus'].value_counts().plot.bar(rot=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['MaritalStatus'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the education example, most missing values arise from young indivuals where it can be assumed that they never married. Instead, for the remaining missing values of the older people, they filled in using the most common label depending on the age group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**HHIncome**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "448"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HHIncome'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['HHIncome'].value_counts().plot.bar(rot=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column is not needed as there is already the `HHIncomeMid` column for each category. As a result it is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**HHIncomeMid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "448"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HHIncomeMid'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['HHIncomeMid'].value_counts().sort_index().plot.bar(rot=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values are filled in by taking using the median as the data is right skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Poverty**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "403"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Poverty'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Poverty'].hist(bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A clear distribution cannot be seen. The missing values are filled in by taking the median as the data doesn't follow a clear distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Home Rooms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HomeRooms'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['HomeRooms'].hist(bins=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data appears to be normally distributed. There are very few data point missing. These are filled in by taking the mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Home Own**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HomeOwn'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['HomeOwn'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rNRcRAXwJ2J+ZX5i3qPF8EXFWRJxWXd9Eb2x/P71iv7bJbJl5W2Zuy8xhesfXtzPzhjZki4jNEfHuY9fpjQfvowX7NDNfAV6KiPOrWZcDT7ch2zzX8/ZwC7Qj24vAxRFxavWYPfZ3W/3jrckXM+a9iHAl8C/0xlx/v+Es99Eb9/oxvTOUW+iNtz4KPAf8LXBGQ9k+RO8p5D8DT1aXK9uQD/gl4Ikq2z7gD6v5vwB8D3ie3tPidzW8f0eBPW3JVmX4fnV56tjx34Z9WuW4CJiq9uvfAKe3KNtm4N+BLfPmtSXb54BnqsfCXwDvWovjzU+KSlIh2jDkIkkaAAtdkgphoUtSISx0SSqEhS5JhbDQJakQFrokFcJCl6RC/C+V/GYoYCeAUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['HomeOwn'].isna()]['Age'].hist(bins=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, there are very few missing values which are filled by using the most common label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Work**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1357"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Work'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Work'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1356"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['Work'].isna()) & (df['Education'].isna()) & (df['Age']<=20)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like most of the individuals with missing values in the 'Work' column have not finished their education yet. These are filled with `NotWorking` so that the model can more easily differentiate out of the individuals that are not studying the correlation between `Working` and `Flu`. Remaining are filled by plugging in the most common category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Weight**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Weight'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Weight'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Weight'].isna()]['Age'].hist(bins=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are few values missing. These are filled in by using the mean of individuals grouped by age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Length**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4890"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Length'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Length'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Length'].notna()]['Age'].value_counts().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column is only for kids between 0 and 3 years old. This column is merged with the `Height` column as they practically contain the same information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Head Circumference**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HeadCirc'].notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['HeadCirc'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['HeadCirc'].notna()]['Age'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[df['Age']==0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values are present only for babies. However, even many of these instances still have missing values. Therefore, it was decided to drop this feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Height**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Height'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Height'].hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Height'].isna()]['Age'].hist(bins=32)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['Height'].isna()) & (df['Length'].isna())]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values present in both Height and Length 164\n",
      "Values present in neither Height and Length 40\n"
     ]
    }
   ],
   "source": [
    "print('Values present in both Height and Length', len(df[(df['Height'].notna()) & (df['Length']).notna()]))\n",
    "print('Values present in neither Height and Length', len(df[(df['Height'].isna()) & (df['Length']).isna()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data seems to be normally distributed. Most of the missing values are from young children. Where possible the `Length` value is used to fill in the missing values. The remaining missing values are filled in by taking the mean per age group the individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BMI**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "236"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['BMI'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['BMI'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values are calculate using the BMI formula which uses the columns `Height` and `Weight` that have no more missing values.\n",
    "$$BMI=\\frac{\\textrm{Weight}}{\\textrm{Height}^2}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BMI Category Under 20 years**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "941"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Age'] < 20]['BMICatUnder20yrs'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['BMICatUnder20yrs'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column is dropped as there is a numerical BMI value already which can be seen as being more informative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BMI WHO**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI_WHO'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD9CAYAAAC1DKAUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAF2ZJREFUeJzt3X20ZFV95vHvE1owqBGkrwx2N3arrZFxEiVXxHE0KhPedGxM1IFktGOY6RWD77oUZEYSHdfCMeMLKwlZHeiAGQISgkMnkiAiDEnWgDTKOyI3iHa3aLeCqMNSg/7mj7M7KS51+77UfYE+389ate45++x9zq5zq+qps8+pqlQVkqT++Zml7oAkaWkYAJLUUwaAJPWUASBJPWUASFJPGQCS1FPTBkCSTUl2JLllUvlbknw5ya1J/sdA+SlJJpLckeSogfKjW9lEkpPn925IkmYr030OIMlLgB8An6yq57SylwGnAq+oqh8leXJV7UhyCHA+cBjwFOBzwDPbqr4C/AqwDbgOOKGqbluA+yRJmoFl01WoqquTrJ5U/Cbg9Kr6Uauzo5WvAy5o5V9NMkEXBgATVXUXQJILWl0DQJKWyLQBMIVnAi9O8iHgh8C7q+o6YAVwzUC9ba0MYOuk8hdMt5Hly5fX6tWr59hFSeqn66+//ttVNTZdvbkGwDLgScDhwPOBC5M8bY7reogkG4ANAAcffDBbtmyZj9VKUm8k+dpM6s31KqBtwMXV+QLwU2A5sB1YNVBvZSubqvxhqmpjVY1X1fjY2LQBJkmao7kGwP8GXgaQ5JnA3sC3gc3A8Un2SbIGWAt8ge6k79oka5LsDRzf6kqSlsi0Q0BJzgdeCixPsg04DdgEbGqXhv4YWF/d5US3JrmQ7uTug8BJVfWTtp43A5cBewGbqurWBbg/kqQZmvYy0KU0Pj5engOQpNlJcn1VjU9Xz08CS1JPGQCS1FMGgCT1lAEgST1lAEhST831k8CPWqtP/sxSd2FG7j79FUvdBUl7OI8AJKmnDABJ6ikDQJJ6ygCQpJ4yACSppwwASeopA0CSesoAkKSeMgAkqacMAEnqKQNAknrKAJCknpo2AJJsSrKj/f7v5GXvSlJJlrf5JDkjyUSSm5IcOlB3fZI72239/N4NSdJszeQI4Bzg6MmFSVYBRwJfHyg+BljbbhuAM1vdJ9H9mPwLgMOA05LsP0rHJUmjmTYAqupq4N4hiz4GvAcY/FX5dcAnq3MNsF+Sg4CjgMur6t6qug+4nCGhIklaPHM6B5BkHbC9qm6ctGgFsHVgflsrm6pckrREZv2DMEn2Bd5HN/wz75JsoBs+4uCDD16ITUiSmNsRwNOBNcCNSe4GVgJfTPKvgO3AqoG6K1vZVOUPU1Ubq2q8qsbHxsbm0D1J0kzMOgCq6uaqenJVra6q1XTDOYdW1TeBzcAb2tVAhwP3V9U9wGXAkUn2byd/j2xlkqQlMpPLQM8H/i/wrCTbkpy4m+qXAncBE8CfAL8DUFX3Ah8Ermu3D7QySdISmfYcQFWdMM3y1QPTBZw0Rb1NwKZZ9k+StEBmfRJYGrT65M8sdRdm5O7TX7HUXZgR96cWk18FIUk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPGQCS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9ZQBIEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPzeRH4Tcl2ZHkloGyjyT5cpKbknw6yX4Dy05JMpHkjiRHDZQf3comkpw8/3dFkjQbMzkCOAc4elLZ5cBzquoXgK8ApwAkOQQ4HvjXrc0fJdkryV7AHwLHAIcAJ7S6kqQlMm0AVNXVwL2Tyj5bVQ+22WuAlW16HXBBVf2oqr4KTACHtdtEVd1VVT8GLmh1JUlLZD7OAfwW8DdtegWwdWDZtlY2VfnDJNmQZEuSLTt37pyH7kmShhkpAJKcCjwInDc/3YGq2lhV41U1PjY2Nl+rlSRNsmyuDZP8JvBK4Iiqqla8HVg1UG1lK2M35ZKkJTCnI4AkRwPvAV5VVQ8MLNoMHJ9knyRrgLXAF4DrgLVJ1iTZm+5E8ebRui5JGsW0RwBJzgdeCixPsg04je6qn32Ay5MAXFNVv11Vtya5ELiNbmjopKr6SVvPm4HLgL2ATVV16wLcH0nSDE0bAFV1wpDis3dT/0PAh4aUXwpcOqveSZIWjJ8ElqSeMgAkqacMAEnqKQNAknrKAJCknjIAJKmnDABJ6ikDQJJ6ygCQpJ4yACSppwwASeopA0CSesoAkKSeMgAkqacMAEnqKQNAknrKAJCknjIAJKmnpg2AJJuS7Ehyy0DZk5JcnuTO9nf/Vp4kZySZSHJTkkMH2qxv9e9Msn5h7o4kaaZmcgRwDnD0pLKTgSuqai1wRZsHOAZY224bgDOhCwy6H5N/AXAYcNqu0JAkLY1pA6CqrgbunVS8Dji3TZ8LHDdQ/snqXAPsl+Qg4Cjg8qq6t6ruAy7n4aEiSVpEcz0HcGBV3dOmvwkc2KZXAFsH6m1rZVOVP0ySDUm2JNmyc+fOOXZPkjSdkU8CV1UBNQ992bW+jVU1XlXjY2Nj87VaSdIkcw2Ab7WhHdrfHa18O7BqoN7KVjZVuSRpicw1ADYDu67kWQ9cMlD+hnY10OHA/W2o6DLgyCT7t5O/R7YySdISWTZdhSTnAy8FlifZRnc1z+nAhUlOBL4GvK5VvxQ4FpgAHgDeCFBV9yb5IHBdq/eBqpp8YlmStIimDYCqOmGKRUcMqVvASVOsZxOwaVa9kyQtGD8JLEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPGQCS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9ZQBIEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPjRQASd6R5NYktyQ5P8ljk6xJcm2SiSSfSrJ3q7tPm59oy1fPxx2QJM3NnAMgyQrgrcB4VT0H2As4Hvgw8LGqegZwH3Bia3IicF8r/1irJ0laIqMOAS0DfjbJMmBf4B7g5cBFbfm5wHFtel2bpy0/IklG3L4kaY7mHABVtR34feDrdC/89wPXA9+tqgdbtW3Aija9Atja2j7Y6h8web1JNiTZkmTLzp0759o9SdI0RhkC2p/uXf0a4CnA44CjR+1QVW2sqvGqGh8bGxt1dZKkKYwyBPTvga9W1c6q+ifgYuBFwH5tSAhgJbC9TW8HVgG05U8EvjPC9iVJIxglAL4OHJ5k3zaWfwRwG3Al8JpWZz1wSZve3OZpyz9fVTXC9iVJIxjlHMC1dCdzvwjc3Na1EXgv8M4kE3Rj/Ge3JmcDB7TydwInj9BvSdKIlk1fZWpVdRpw2qTiu4DDhtT9IfDaUbYnSZo/fhJYknrKAJCknhppCEiSHqlWn/yZpe7CjNx9+iuWbNseAUhSTxkAktRTBoAk9ZQBIEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPGQCS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9dRIAZBkvyQXJflyktuTvDDJk5JcnuTO9nf/VjdJzkgykeSmJIfOz12QJM3FqEcAnwD+tqp+HvhF4Ha6H3u/oqrWAlfwLz/+fgywtt02AGeOuG1J0gjmHABJngi8BDgboKp+XFXfBdYB57Zq5wLHtel1wCercw2wX5KD5txzSdJIRjkCWAPsBP40yZeSnJXkccCBVXVPq/NN4MA2vQLYOtB+Wyt7iCQbkmxJsmXnzp0jdE+StDujBMAy4FDgzKp6HvD/+JfhHgCqqoCazUqramNVjVfV+NjY2AjdkyTtzigBsA3YVlXXtvmL6ALhW7uGdtrfHW35dmDVQPuVrUyStATmHABV9U1ga5JntaIjgNuAzcD6VrYeuKRNbwbe0K4GOhy4f2CoSJK0yJaN2P4twHlJ9gbuAt5IFyoXJjkR+Brwulb3UuBYYAJ4oNWVJC2RkQKgqm4AxocsOmJI3QJOGmV7kqT54yeBJamnDABJ6ikDQJJ6ygCQpJ4yACSppwwASeopA0CSesoAkKSeMgAkqacMAEnqKQNAknrKAJCknjIAJKmnDABJ6ikDQJJ6ygCQpJ4yACSppwwASeqpkQMgyV5JvpTkr9v8miTXJplI8qn2e8Ek2afNT7Tlq0fdtiRp7ubjCOBtwO0D8x8GPlZVzwDuA05s5ScC97Xyj7V6kqQlMlIAJFkJvAI4q80HeDlwUatyLnBcm17X5mnLj2j1JUlLYNQjgI8D7wF+2uYPAL5bVQ+2+W3Aija9AtgK0Jbf3+pLkpbAnAMgySuBHVV1/Tz2hyQbkmxJsmXnzp3zuWpJ0oBRjgBeBLwqyd3ABXRDP58A9kuyrNVZCWxv09uBVQBt+ROB70xeaVVtrKrxqhofGxsboXuSpN2ZcwBU1SlVtbKqVgPHA5+vqt8ArgRe06qtBy5p05vbPG3556uq5rp9SdJoFuJzAO8F3plkgm6M/+xWfjZwQCt/J3DyAmxbkjRDy6avMr2qugq4qk3fBRw2pM4PgdfOx/YkSaPzk8CS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9ZQBIEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPGQCS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9dScAyDJqiRXJrktya1J3tbKn5Tk8iR3tr/7t/IkOSPJRJKbkhw6X3dCkjR7oxwBPAi8q6oOAQ4HTkpyCN2PvV9RVWuBK/iXH38/BljbbhuAM0fYtiRpRHMOgKq6p6q+2Ka/D9wOrADWAee2aucCx7XpdcAnq3MNsF+Sg+bcc0nSSOblHECS1cDzgGuBA6vqnrbom8CBbXoFsHWg2bZWJklaAiMHQJLHA38JvL2qvje4rKoKqFmub0OSLUm27Ny5c9TuSZKmMFIAJHkM3Yv/eVV1cSv+1q6hnfZ3RyvfDqwaaL6ylT1EVW2sqvGqGh8bGxule5Kk3RjlKqAAZwO3V9VHBxZtBta36fXAJQPlb2hXAx0O3D8wVCRJWmTLRmj7IuD1wM1Jbmhl7wNOBy5MciLwNeB1bdmlwLHABPAA8MYRti1JGtGcA6Cq/h7IFIuPGFK/gJPmuj1J0vzyk8CS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9ZQBIEk9ZQBIUk8ZAJLUUwaAJPWUASBJPWUASFJPGQCS1FMGgCT1lAEgST1lAEhSTxkAktRTBoAk9dSiB0CSo5PckWQiycmLvX1JUmdRAyDJXsAfAscAhwAnJDlkMfsgSeos9hHAYcBEVd1VVT8GLgDWLXIfJEksfgCsALYOzG9rZZKkRbZsqTswWZINwIY2+4Mkdyxlf2ZoOfDt+VxhPjyfa3vUcX/OL/fn/Hm07MunzqTSYgfAdmDVwPzKVvbPqmojsHExOzWqJFuqanyp+7GncH/OL/fn/NnT9uViDwFdB6xNsibJ3sDxwOZF7oMkiUU+AqiqB5O8GbgM2AvYVFW3LmYfJEmdRT8HUFWXApcu9nYX2KNqyOpRwP05v9yf82eP2pepqqXugyRpCfhVEJLUUwaAJPXUHhMASTYl2ZHkloGy5ya5JskNSbYkOWyKtj9pdW5IsturkpK8Pcm+c+jfvkk+k+TLSW5NcvqQOr+WpJIMvcwsyduS3NLav322fZhlf1cluTLJbW17b2vlv5tk+8D+OnaK9jP+zqeF2KdJnprkiiQ3Jbkqycop1vEfW51bk6W7uj3JY5N8IcmNrS+/18rXJLm27cdPtavnhrU/pdW5I8lRc+zDVVM99h5Npngt+Eh7nNyU5NNJ9pui7Wwet29u9SrJ8oHyJyb5q4H/5RunaH9V29au59KT53qf56yq9ogb8BLgUOCWgbLPAse06WOBq6Zo+4NZbOduYPkc+rcv8LI2vTfwd7v61sqeAFwNXAOMD2n/HOCWtp5lwOeAZyzg/jwIOHSgb1+h+/6m3wXePU3bvYB/BJ7W7uuNwCGLuU+BvwDWt+mXA382pP0BwNeBsTZ/LnDEEj1+Azy+TT8GuBY4HLgQOL6V/zHwpiFtD2n7eB9gTdv3e82hD1cNe+w92m5TvBYcCSxr0x8GPjyk3Wwft88DVk9+/ALv27V+YAy4F9j7kbi/95gjgKq6mm5HP6QY+Lk2/UTgG6NsI8lbgacAVya5spWdkOTm9s58yneQVfVAVV3Zpn8MfJHug3C7fJDugfnDKVbxbODatp4Hgf8D/Ooo92d3quqeqvpim/4+cDsz/9qOGX/n0wLu00OAz7fpK6fY/tOAO6tqZ5v/HPBrM7yP86o6P2izj2m3oguvi1r5ucBxQ5qvAy6oqh9V1VeBCbr/wcMkWd3eCZ+X5PYkFw07+kryg4Hp1yQ5p02/tv1fbkxy9dzu7cIa9lpQVZ9tzxvo3mQNOyKc1XeVVdWXquruYYuAJyQJ8PjWlweH1Ftye0wATOHtwEeSbAV+HzhlinqPbUNE1yQZ9gQDoKrOoAuRl1XVy5I8he5F++XAc4Hn7679Lu3w8z8AV7T5Q4FVVfWZ3TS7BXhxkgPaE/ZYHvqp6gWTZDXdu51rW9Gb26H0piT7D2ky4+98Wqh9SvfubVdAvpruCXnApGYTwLPai+IyuhfXRdmnwyTZK8kNwA7gcrp3o98deOGaaj/O9ju2ngX8UVU9G/ge8Duz6Ob7gaOq6heBV82i3SPJbwF/M6R8vr6r7A/o3rB9A7gZeFtV/XSKun/ahn/+WwuMRbWnB8CbgHdU1SrgHcDZU9R7anUf7/514ONJnj7D9T+fblhpZ3uSnkd3+Dml9kJzPnBGVd2V5GeAjwLv2l27qrqd7oXxs8DfAjcAP5lhP+csyeOBvwTeXlXfA84Enk734nwP8D/neZMj79NW/G7gl5N8Cfhluq8cecj+qqr76B4jn6IbPrp7cp3FVFU/qarn0r07PQz4+QXa1Naq+oc2/b+AfzeLtv8AnJPkv9ANmTyqJDmV7t34eQu4maPonp9PoXue/EGSnxtS7zeq6t8AL2631y9gn4ba0wNgPXBxm/4Lpjgsrqrt7e9ddONyz1vAPm2kG3b4eJt/At34/lVJ7qYb99087GRcVZ1dVb9UVS8B7qMbl18wSR5D9+J/XlVd3PrwrfZC9VPgTxi+T6f9zqd5NnmfUlXfqKpfrarnAae2su9OblhVf1VVL6iqFwJ3sMD7dCZaP68EXgjs1wIOpt6Ps93fkz/8M+zDQINljx3o228D/7Vt7/ohR1WPWEl+E3gl3QvvsPs8X4/bNwIXt2G9CeCrDAnzgded7wN/zhSvTwtpTw+Ab9C9+4NuSOHOyRWS7J9knza9HHgRcNtu1vl9uhdtgC/Qvctcnu7Hbk6gG5sfKsl/pzsX8c9X8FTV/VW1vKpWV9VquvHJV1XVliHtn9z+Hkw3vPHnu+nnSNrh6NnA7VX10YHygwaqvZpuaGqy2X7n07zu01a+vB1dQTf0t2mK9rv26f50QyFn7aafCybJ2K4rU5L8LPArdOddrgRe06qtBy4Z0nwzcHySfZKsAdbS7cepHJzkhW3614G/H1LnW0me3fbhqwf6+fSquraq3g/sZAmHzGYjydHAe+ieWw9MUW2+vqvs68ARbbsH0g253TVYIcmyXVcOtTdar2T4c2lhLeUZ6Pm80Q0B3AP8E93Y3Yl0h7bX040HXwv8Uqs7DpzVpv8t3Tjdje3vidNs5y107xSvbPMntHa3MOTKgoF2K+neVd1Od3h4A/Cfh9S7inZlAN0h5KUDy/6OLpxuZIGvVmn7roCbBvp7LPBn7f7eRPfkOGiKvh5L9276H4FTF3uf0r1o3tn6cBawz0C7GyY9bm5rt+OX8PH7C8CX2n69BXh/K38a3Yv5BN1R7D6t/FXABwban9r29R0MXF02ZDurgS/TDf3cTneEt++Qx95r2vquoRvTPqeVXzzwv/kE7dsEHkk3hr8WTNCN7+96nPzxPDxu39rW/yDdm82zBtb52YH99J8mP/aAx9G9Nt0E3Nr25ayv3Br15ldBSD3STuj/dVU9Z4m7okeAPX0ISJI0BY8AppDk03Qfqhn03qq6bAZtr6X7UM6g11fVzfPVv0cj9+niaSdnrxiy6Iiq+s5i9+fRbJTH7SOdASBJPeUQkCT1lAEgST1lAEhSTxkAktRTBoAk9dT/B8S5JQqCt7KoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['BMI_WHO'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column is dropped as there is a numerical BMI value already which can be seen as being more informative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pulse**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "870"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pulse'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Pulse'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Pulse'].isna()]['Age'].hist(bins=32)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most missing values are from kids. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('Age', 'Pulse')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Values are filled in with the mean for each age group. A bin size of 10 years is used since as seen from the graph, the values over ages do not drastically change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BPSysAve**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "877"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BPSysAve'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEhlJREFUeJzt3X9s3Hd9x/Hnew3rRoySdGFWcKK507KiQEfaWKUINNl0g7RMBCRUpapKgE7hj3QrU6WRMmmAUKVMojAoG1KgXcvIarpSligtP0oWg5jUlqaEJmnImlFDY6UxJW3ApUKke++P+2YcqZv7YZ/v/MnzIZ38/X7uc3ev851f/vrj8zkyE0lSuX6r2wEkSZ1l0UtS4Sx6SSqcRS9JhbPoJalwFr0kFc6il6TCWfSSVDiLXpIKt6DbAQCWLl2ag4ODbV32ueeeY+HChbMbaBb0ai7o3Wzmao25WlNirj179jydma9sODEzu35as2ZNtmv37t1tX7aTejVXZu9mM1drzNWaEnMBD2cTHevSjSQVzqKXpMJZ9JJUOItekgpn0UtS4Sx6SSqcRS9JhbPoJalwFr0kFa4n3gJhJvZNnOA9m+9tOG98y9vmII0k9R6P6CWpcBa9JBXOopekwln0klQ4i16SCmfRS1LhLHpJKpxFL0mFs+glqXAWvSQVzqKXpMJZ9JJUOItekgpn0UtS4Sx6SSqcRS9JhbPoJalwDYs+IlZExO6IeCwiDkTE9dX4RyJiIiL2Vqcr6i5zY0QcjohDEfHWTt4BSdKZNfOvBE8CN2TmIxHxCmBPRNxfnffJzPx4/eSIWAWsB14DvAr4ZkT8cWa+MJvBJUnNaXhEn5lHM/ORavvnwEFg4AwXWQeMZuYvM/MJ4DBwyWyElSS1rqU1+ogYBC4CHqyGrouIRyPitohYUo0NAE/WXewIZ/7GIEnqoMjM5iZG9AHfAm7KzHsioh94GkjgY8CyzHxfRHwGeCAzv1hd7lbgq5l592nXtxHYCNDf379mdHS0rTswefwEx55vPO/CgUVtXX+7pqam6Ovrm9PbbFavZjNXa8zVmhJzjYyM7MnMoUbzmlmjJyJeBnwZ2JaZ9wBk5rG68z8H7Kx2J4AVdRdfXo39hszcCmwFGBoayuHh4WaivMgt27Zz877Gd2P86vauv11jY2O0e586rVezmas15mrN2ZyrmVfdBHArcDAzP1E3vqxu2juB/dX2DmB9RJwbEecDK4GHZi+yJKkVzRzRvxG4BtgXEXursQ8BV0XEampLN+PA+wEy80BE3AU8Ru0VO5t8xY0kdU/Dos/M7wAxzVn3neEyNwE3zSBXVwxuvrepeeNb3tbhJJI0e/zLWEkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ4i16SCmfRS1LhLHpJKpxFL0mFs+glqXAWvSQVzqKXpMJZ9JJUOItekgpn0UtS4Sx6SSqcRS9JhbPoJalwFr0kFc6il6TCWfSSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBWuYdFHxIqI2B0Rj0XEgYi4vho/LyLuj4jHq49LqvGIiE9HxOGIeDQiLu70nZAkvbRmjuhPAjdk5irgUmBTRKwCNgO7MnMlsKvaB7gcWFmdNgKfnfXUkqSmNSz6zDyamY9U2z8HDgIDwDrgjmraHcA7qu11wBey5gFgcUQsm/XkkqSmtLRGHxGDwEXAg0B/Zh6tznoK6K+2B4An6y52pBqTJHVBZGZzEyP6gG8BN2XmPRHxbGYurjv/mcxcEhE7gS2Z+Z1qfBfwwcx8+LTr20htaYf+/v41o6Ojbd2ByeMnOPZ843kXDixqOGffxImmbrOZ65qamqKvr6+p65trvZrNXK0xV2tKzDUyMrInM4cazVvQzJVFxMuALwPbMvOeavhYRCzLzKPV0sxkNT4BrKi7+PJq7Ddk5lZgK8DQ0FAODw83E+VFbtm2nZv3Nb4b41c3vv73bL63qdts5rrGxsZo9z51Wq9mM1drzNWaszlXM6+6CeBW4GBmfqLurB3Ahmp7A7C9bvzd1atvLgVO1C3xSJLmWDNH9G8ErgH2RcTeauxDwBbgroi4FvgRcGV13n3AFcBh4BfAe2c1sSSpJQ2Lvlprj5c4+7Jp5iewaYa5JEmzxL+MlaTCWfSSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ4i16SCmfRS1LhLHpJKlxT/3ikBINN/lMRSSqNR/SSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ4i16SCmfRS1LhLHpJKpxFL0mFs+glqXANiz4ibouIyYjYXzf2kYiYiIi91emKuvNujIjDEXEoIt7aqeCSpOY0c0R/O7B2mvFPZubq6nQfQESsAtYDr6ku888Rcc5shZUkta5h0Wfmt4HjTV7fOmA0M3+ZmU8Ah4FLZpBPkjRDM1mjvy4iHq2WdpZUYwPAk3VzjlRjkqQuicxsPCliENiZma+t9vuBp4EEPgYsy8z3RcRngAcy84vVvFuBr2bm3dNc50ZgI0B/f/+a0dHRtu7A5PETHHu+rYu27cKBRQ3nTE1N0dfXNwdpWter2czVGnO1psRcIyMjezJzqNG8tv5nbGYeO7UdEZ8Ddla7E8CKuqnLq7HprmMrsBVgaGgoh4eH24nCLdu2c/O+uf3Xt+NXDzecMzY2Rrv3qdN6NZu5WmOu1pzNudpauomIZXW77wROvSJnB7A+Is6NiPOBlcBDM4soSZqJhofCEXEnMAwsjYgjwIeB4YhYTW3pZhx4P0BmHoiIu4DHgJPApsx8oTPRJUnNaFj0mXnVNMO3nmH+TcBNMwklSZo9/mWsJBXOopekwln0klQ4i16SCmfRS1LhLHpJKpxFL0mFs+glqXAWvSQVzqKXpMJZ9JJUOItekgpn0UtS4Sx6SSqcRS9JhbPoJalwFr0kFc6il6TCWfSSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ4i16SCtew6CPitoiYjIj9dWPnRcT9EfF49XFJNR4R8emIOBwRj0bExZ0ML0lqrJkj+tuBtaeNbQZ2ZeZKYFe1D3A5sLI6bQQ+OzsxJUntalj0mflt4Phpw+uAO6rtO4B31I1/IWseABZHxLLZCitJal27a/T9mXm02n4K6K+2B4An6+YdqcYkSV0Smdl4UsQgsDMzX1vtP5uZi+vOfyYzl0TETmBLZn6nGt8FfDAzH57mOjdSW96hv79/zejoaFt3YPL4CY4939ZF23bhwKKGc6ampujr65uDNK3r1Wzmao25WlNirpGRkT2ZOdRo3oK2rh2ORcSyzDxaLc1MVuMTwIq6ecursRfJzK3AVoChoaEcHh5uK8gt27Zz875270Z7xq8ebjhnbGyMdu9Tp/VqNnO1xlytOZtztbt0swPYUG1vALbXjb+7evXNpcCJuiUeSVIXNDwUjog7gWFgaUQcAT4MbAHuiohrgR8BV1bT7wOuAA4DvwDe24HMkqQWNCz6zLzqJc66bJq5CWyaaShJ0uzxL2MlqXAWvSQVzqKXpMJZ9JJUOItekgpn0UtS4Sx6SSqcRS9JhbPoJalwFr0kFc6il6TCWfSSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBXOopekwln0klQ4i16SCmfRS1LhLHpJKpxFL0mFs+glqXAWvSQVzqKXpMJZ9JJUuAUzuXBEjAM/B14ATmbmUEScB3wJGATGgSsz85mZxZQktWs2juhHMnN1Zg5V+5uBXZm5EthV7UuSuqQTSzfrgDuq7TuAd3TgNiRJTZpp0SfwjYjYExEbq7H+zDxabT8F9M/wNiRJMxCZ2f6FIwYycyIifh+4H/grYEdmLq6b80xmLpnmshuBjQD9/f1rRkdH28owefwEx55v66Jtu3BgUcM5U1NT9PX1zUGa1vVqNnO1xlytKTHXyMjInrpl85c0o1/GZuZE9XEyIr4CXAIci4hlmXk0IpYBky9x2a3AVoChoaEcHh5uK8Mt27Zz874Z3Y2WjV893HDO2NgY7d6nTuvVbOZqjblaczbnanvpJiIWRsQrTm0DbwH2AzuADdW0DcD2mYaUJLVvJofC/cBXIuLU9fxbZn4tIr4L3BUR1wI/Aq6ceUxJUrvaLvrM/CHwumnGfwpcNpNQkqTZ41/GSlLhLHpJKpxFL0mFs+glqXAWvSQVzqKXpMJZ9JJUOItekgpn0UtS4Sx6SSqcRS9JhbPoJalwFr0kFc6il6TCWfSSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBVuQbcDSJ0wuPnehnPGt7xtDpJI3WfRzwPNlBZYXJ2yb+IE72nwGPi5Vy9z6UaSCucR/VloNo9QZ3OJxCNnqTMsenVcs0tPN1zY4SCF8Rtjb2v2eX/72oUdTmLRd0wzX4TgF6KkzrPoC+KRc2v8fP2ar1IqW8eKPiLWAp8CzgE+n5lbOnVb81mzZaPe5iujzi7z7eu2I0UfEecA/wT8OXAE+G5E7MjMxzpxe9J8MddHzvOtkE7nTxqzo1NH9JcAhzPzhwARMQqsA4oo+maefPP9x/1eLYhezTWbenVJqflcJ+f0l8S9mquXdKroB4An6/aPAK/v0G1Jmmd69Rt2r+aaqcjM2b/SiHcBazPzL6v9a4DXZ+Z1dXM2Ahur3QuAQ23e3FLg6RnE7ZRezQW9m81crTFXa0rM9QeZ+cpGkzp1RD8BrKjbX16N/b/M3ApsnekNRcTDmTk00+uZbb2aC3o3m7laY67WnM25OvUWCN8FVkbE+RHx28B6YEeHbkuSdAYdOaLPzJMRcR3wdWovr7wtMw904rYkSWfWsdfRZ+Z9wH2duv46M17+6ZBezQW9m81crTFXa87aXB35ZawkqXf4NsWSVLh5XfQRsTYiDkXE4YjY3MUct0XEZETsrxs7LyLuj4jHq49LupBrRUTsjojHIuJARFzfC9ki4nci4qGI+H6V66PV+PkR8WD1eH6p+kX+nIuIcyLiexGxs1dyRcR4ROyLiL0R8XA11gvPscURcXdE/CAiDkbEG7qdKyIuqD5Pp04/i4gPdDtXle1vquf8/oi4s/pa6Pjza94Wfd3bLFwOrAKuiohVXYpzO7D2tLHNwK7MXAnsqvbn2knghsxcBVwKbKo+R93O9kvgzZn5OmA1sDYiLgX+AfhkZv4R8Axw7RznOuV64GDdfq/kGsnM1XUvxev24wi197P6Wma+Gngdtc9bV3Nl5qHq87QaWAP8AvhKt3NFxADw18BQZr6W2gtV1jMXz6/MnJcn4A3A1+v2bwRu7GKeQWB/3f4hYFm1vQw41AOfs+3U3n+oZ7IBLwceofaX008DC6Z7fOcwz3JqJfBmYCcQPZJrHFh62lhXH0dgEfAE1e/6eiXXaVneAvxXL+Ti1+8YcB61F8LsBN46F8+veXtEz/RvszDQpSzT6c/Mo9X2U0B/N8NExCBwEfAgPZCtWh7ZC0wC9wP/AzybmSerKd16PP8R+Fvgf6v93+uRXAl8IyL2VH9VDt1/HM8HfgL8S7XU9fmIWNgDueqtB+6struaKzMngI8DPwaOAieAPczB82s+F/28kbVv1V17eVNE9AFfBj6QmT+rP69b2TLzhaz9aL2c2pvgvXquM5wuIv4CmMzMPd3OMo03ZebF1JYqN0XEn9af2aXHcQFwMfDZzLwIeI7TlkO6+dyv1rrfDvz76ed1I1f1O4F11L5BvgpYyIuXfDtiPhd9w7dZ6LJjEbEMoPo42Y0QEfEyaiW/LTPv6aVsAJn5LLCb2o+siyPi1N92dOPxfCPw9ogYB0apLd98qgdynToaJDMnqa03X0L3H8cjwJHMfLDav5ta8Xc71ymXA49k5rFqv9u5/gx4IjN/kpm/Au6h9pzr+PNrPhd9r7/Nwg5gQ7W9gdr6+JyKiABuBQ5m5id6JVtEvDIiFlfbv0vt9wYHqRX+u7qVKzNvzMzlmTlI7fn0n5l5dbdzRcTCiHjFqW1q68776fLjmJlPAU9GxAXV0GXU3oq868/9ylX8etkGup/rx8ClEfHy6mvz1Oer88+vbv2SZJZ+uXEF8N/U1nf/ros57qS25vYrakc511Jb290FPA58EzivC7neRO3H00eBvdXpim5nA/4E+F6Vaz/w99X4HwIPAYep/bh9bhcf02FgZy/kqm7/+9XpwKnnercfxyrDauDh6rH8D2BJj+RaCPwUWFQ31gu5Pgr8oHre/ytw7lw8v/zLWEkq3HxeupEkNcGil6TCWfSSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcP8Hf73+PHk4MXEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['BPSysAve'].isna()]['Age'].hist(bins=32)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From https://www.stanfordchildrens.org/en/topic/default?id=high-blood-pressure-in-children-and-adolescents-90-P01794 the mean of 105 is manually filled in for kids between 0 and 10 years old as all instances within this age group have `BPSysAve` missing. The remaining missing values for people older than 10 years old is filled in with the mean per age group. Only `BPSysAve` is kept while `BPSys1`, `BPSys2` and `BPSys3` are dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BPDiaAve**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "877"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BPDiaAve'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['BPDiaAve'].isna()]['Age'].hist(bins=32)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From https://www.stanfordchildrens.org/en/topic/default?id=high-blood-pressure-in-children-and-adolescents-90-P01794 the mean of 60 is manually filled in for kids between 0 and 10 years old as all instances within this age group have `BPDiaAve` missing. The remaining missing values for people older than 10 years old is filled in with the mean per age group. Only `BPDiaAve` is kept while `BPDia1`, `BPDia2` and `BPDia3` are dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Testosterone**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3238"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Testosterone'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Testosterone'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Testosterone'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many missing values. This column is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DirectChol**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DirectChol'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['DirectChol'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.29\n",
       "1    1.16\n",
       "2    1.34\n",
       "3    1.55\n",
       "4    2.12\n",
       "Name: DirectChol, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['DirectChol'].notna(), 'DirectChol'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many unknown values. These are filled using the median as the distribution is skewed to the left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TotChol**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TotChol'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['TotChol'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many unknown values. These are filled using the median as the distribution is skewed to the left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**UrineVol1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "609"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['UrineVol1'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['UrineVol1'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values are filled in with the median as the distribution is skewed to the left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**UrineFlow1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "946"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['UrineFlow1'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['UrineVol1'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, missing values are filled in with the median as the distribution is skewed to the left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**UrineVol2** and **UrineFlow2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4483"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['UrineVol2'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4483"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['UrineFlow2'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two columns are dropped as there are too many missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Diabetes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Diabetes'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Diabetes'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Age'] < 1]['Diabetes'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD8CAYAAACW/ATfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEFhJREFUeJzt3G9sXfV9x/H3d6SsbbwloamsKIkWJiIQgpU2FqOiqmyyTRSqwgPEQNGWVpHyYKyja6cSNmlsDyaBNEoZmipZQJtJES6lbEEpbcfSeFMnkY4AbQIpI6NpSRTidpB0pmgb63cP7nHnmYDtc+71vae/90u68j2/e/58rnX8ycnv/onMRJL08+0X+h1AktR7lr0kFcCyl6QCWPaSVADLXpIKYNlLUgEse0kqgGUvSQWw7CWpAMv6HQBg9erVuWHDhlrbvvrqqyxfvry7gZZAG3O3MTO0M7eZl04bc89kPnDgwI8y890L2igz+37btGlT1rVv377a2/ZTG3O3MXNmO3Obeem0MfdMZuCJXGDPOo0jSQWw7CWpAJa9JBXAspekAlj2klQAy16SCmDZS1IB5i37iLg/IqYi4tCssXMi4rGIeL76uaoaj4j4q4g4EhHfiYj39TK8JGlhFnJl/wXgyjljO4C9mbkR2FstA3wI2FjdtgOf605MSVIT835dQmb+U0RsmDN8DTBa3d8JTAK3VON/U32y6/GIWBkRazLzRLcCz3Xw+Gk+uuMr86539ParexVBkgZe3Tn74VkF/hIwXN1fC7w4a71j1ZgkqY+icxE+z0qdK/s9mXlRtXwqM1fOevyVzFwVEXuA2zPzm9X4XuCWzHziDPvcTmeqh+Hh4U0TExO1nsDUy6c5+dr86128dkWt/ffK9PQ0Q0ND/Y6xKG3MDO3Mbeal08bcM5nHxsYOZObIQrap+62XJ2emZyJiDTBVjR8H1s9ab1019gaZOQ6MA4yMjOTo6GitIPfs2s2dB+d/Gke31Nt/r0xOTlL3OfdLGzNDO3Obeem0MXedzHWncR4Btlb3twK7Z43/bvWunMuA072cr5ckLcy8l8QR8QCdF2NXR8Qx4DbgduDBiNgGfB+4vlr9UeAq4AjwE+BjPcgsSVqkhbwb58Y3eWjzGdZN4KamoSRJ3eUnaCWpAJa9JBXAspekAlj2klQAy16SCmDZS1IBLHtJKoBlL0kFsOwlqQCWvSQVwLKXpAJY9pJUAMtekgpg2UtSASx7SSqAZS9JBbDsJakAlr0kFcCyl6QCWPaSVADLXpIKYNlLUgEse0kqgGUvSQWw7CWpAJa9JBXAspekAlj2klQAy16SCmDZS1IBLHtJKoBlL0kFaFT2EfGHEfFMRByKiAci4u0RcW5E7I+IIxHxxYg4u1thJUn11C77iFgL/AEwkpkXAWcBNwB3AHdl5nnAK8C2bgSVJNXXdBpnGfCOiFgGvBM4AVwBPFQ9vhO4tuExJEkN1S77zDwO/CXwAzolfxo4AJzKzNer1Y4Ba5uGlCQ1E5lZb8OIVcCXgd8GTgFfonNF/2fVFA4RsR74ajXNM3f77cB2gOHh4U0TExO1cky9fJqTr82/3sVrV9Taf69MT08zNDTU7xiL0sbM0M7cZl46bcw9k3lsbOxAZo4sZJtlDY73G8D3MvOHABHxMHA5sDIillVX9+uA42faODPHgXGAkZGRHB0drRXinl27ufPg/E/j6JZ6+++VyclJ6j7nfmljZmhnbjMvnTbmrpO5yZz9D4DLIuKdERHAZuBZYB9wXbXOVmB3g2NIkrqgyZz9fjrTNk8CB6t9jQO3AJ+MiCPAu4D7upBTktRAk2kcMvM24LY5wy8AlzbZrySpu/wErSQVwLKXpAJY9pJUAMtekgpg2UtSASx7SSqAZS9JBbDsJakAlr0kFcCyl6QCWPaSVADLXpIKYNlLUgEse0kqgGUvSQWw7CWpAJa9JBXAspekAlj2klQAy16SCmDZS1IBLHtJKoBlL0kFsOwlqQCWvSQVwLKXpAJY9pJUAMtekgpg2UtSASx7SSqAZS9JBbDsJakAjco+IlZGxEMR8d2IOBwR74+IcyLisYh4vvq5qlthJUn1NL2yvxv4WmZeALwHOAzsAPZm5kZgb7UsSeqj2mUfESuADwL3AWTmf2XmKeAaYGe12k7g2qYhJUnNNLmyPxf4IfD5iHgqIu6NiOXAcGaeqNZ5CRhuGlKS1ExkZr0NI0aAx4HLM3N/RNwN/Bj4eGaunLXeK5n5hnn7iNgObAcYHh7eNDExUSvH1MunOfna/OtdvHZFrf33yvT0NENDQ/2OsShtzAztzG3mpdPG3DOZx8bGDmTmyEK2WdbgeMeAY5m5v1p+iM78/MmIWJOZJyJiDTB1po0zcxwYBxgZGcnR0dFaIe7ZtZs7D87/NI5uqbf/XpmcnKTuc+6XNmaGduY289JpY+46mWtP42TmS8CLEXF+NbQZeBZ4BNhajW0Fdtc9hiSpO5pc2QN8HNgVEWcDLwAfo/MPyIMRsQ34PnB9w2NIkhpqVPaZ+TRwpvmizU32K0nqLj9BK0kFsOwlqQCWvSQVwLKXpAJY9pJUAMtekgpg2UtSASx7SSqAZS9JBbDsJakAlr0kFcCyl6QCWPaSVADLXpIKYNlLUgEse0kqgGUvSQWw7CWpAJa9JBXAspekAlj2klQAy16SCmDZS1IBLHtJKoBlL0kFsOwlqQCWvSQVwLKXpAJY9pJUAMtekgpg2UtSASx7SSpA47KPiLMi4qmI2FMtnxsR+yPiSER8MSLObh5TktREN67sbwYOz1q+A7grM88DXgG2deEYkqQGGpV9RKwDrgburZYDuAJ4qFplJ3Btk2NIkppremX/WeDTwE+r5XcBpzLz9Wr5GLC24TEkSQ1FZtbbMOLDwFWZ+XsRMQr8EfBR4PFqCoeIWA98NTMvOsP224HtAMPDw5smJiZq5Zh6+TQnX5t/vYvXrqi1/16Znp5maGio3zEWpY2ZoZ25zbx02ph7JvPY2NiBzBxZyDbLGhzvcuAjEXEV8Hbgl4G7gZURsay6ul8HHD/Txpk5DowDjIyM5OjoaK0Q9+zazZ0H538aR7fU23+vTE5OUvc590sbM0M7c5t56bQxd53MtadxMvPWzFyXmRuAG4BvZOYWYB9wXbXaVmB33WNIkrqjF++zvwX4ZEQcoTOHf18PjiFJWoQm0zg/k5mTwGR1/wXg0m7sV5LUHX6CVpIKYNlLUgEse0kqgGUvSQWw7CWpAJa9JBXAspekAlj2klQAy16SCmDZS1IBLHtJKoBlL0kFsOwlqQCWvSQVwLKXpAJY9pJUAMtekgpg2UtSASx7SSqAZS9JBbDsJakAlr0kFcCyl6QCWPaSVADLXpIKYNlLUgEse0kqgGUvSQWw7CWpAJa9JBXAspekAlj2klSA2mUfEesjYl9EPBsRz0TEzdX4ORHxWEQ8X/1c1b24kqQ6mlzZvw58KjMvBC4DboqIC4EdwN7M3AjsrZYlSX1Uu+wz80RmPlnd/w/gMLAWuAbYWa22E7i2aUhJUjNdmbOPiA3Ae4H9wHBmnqgeegkY7sYxJEn1RWY220HEEPCPwF9k5sMRcSozV856/JXMfMO8fURsB7YDDA8Pb5qYmKh1/KmXT3PytfnXu3jtilr775Xp6WmGhob6HWNR2pgZ2pnbzEunjblnMo+NjR3IzJGFbLOsyQEj4m3Al4FdmflwNXwyItZk5omIWANMnWnbzBwHxgFGRkZydHS0VoZ7du3mzoPzP42jW+rtv1cmJyep+5z7pY2ZoZ25zbx02pi7TuYm78YJ4D7gcGZ+ZtZDjwBbq/tbgd11jyFJ6o4mV/aXA78DHIyIp6uxPwZuBx6MiG3A94Hrm0WUJDVVu+wz85tAvMnDm+vuV5LUfX6CVpIKYNlLUgEse0kqgGUvSQWw7CWpAJa9JBXAspekAlj2klQAy16SCmDZS1IBLHtJKoBlL0kFsOwlqQCWvSQVwLKXpAJY9pJUAMtekgpg2UtSASx7SSqAZS9JBbDsJakAlr0kFcCyl6QCWPaSVADLXpIKYNlLUgEse0kqgGUvSQWw7CWpAJa9JBXAspekAlj2klSAnpR9RFwZEc9FxJGI2NGLY0iSFq7rZR8RZwF/DXwIuBC4MSIu7PZxJEkLt6wH+7wUOJKZLwBExARwDfBsD47Veht2fGXedY7efvUSJJHUTQv524al+/vuxTTOWuDFWcvHqjFJUp/04sp+QSJiO7C9WpyOiOdq7mo18KN5j3dHzb33zoJyw0BlX3DmAdPG3GZeOn3NXfPveybzryx0g16U/XFg/azlddXY/5OZ48B404NFxBOZOdJ0P0utjbnbmBnamdvMS6eNuetk7sU0zr8AGyPi3Ig4G7gBeKQHx5EkLVDXr+wz8/WI+H3g68BZwP2Z+Uy3jyNJWriezNln5qPAo73Y9xk0ngrqkzbmbmNmaGduMy+dNuZedObIzF4EkSQNEL8uQZIK0Oqyb8vXMkTE/RExFRGHZo2dExGPRcTz1c9V/cw4V0Ssj4h9EfFsRDwTETdX4wObOyLeHhHfiohvV5n/vBo/NyL2V+fJF6s3DgyUiDgrIp6KiD3VchsyH42IgxHxdEQ8UY0N7PkBEBErI+KhiPhuRByOiPe3IPP51e945vbjiPjEYnO3tuxb9rUMXwCunDO2A9ibmRuBvdXyIHkd+FRmXghcBtxU/X4HOfd/Aldk5nuAS4ArI+Iy4A7grsw8D3gF2NbHjG/mZuDwrOU2ZAYYy8xLZr0NcJDPD4C7ga9l5gXAe+j8zgc6c2Y+V/2OLwE2AT8B/pbF5s7MVt6A9wNfn7V8K3Brv3O9Rd4NwKFZy88Ba6r7a4Dn+p1xnvy7gd9sS27gncCTwK/T+fDJsjOdN4Nwo/NZlL3AFcAeIAY9c5XrKLB6ztjAnh/ACuB7VK9VtiHzGZ7DbwH/XCd3a6/saf/XMgxn5onq/kvAcD/DvJWI2AC8F9jPgOeupkOeBqaAx4B/A05l5uvVKoN4nnwW+DTw02r5XQx+ZoAE/j4iDlSfiIfBPj/OBX4IfL6aMrs3IpYz2JnnugF4oLq/qNxtLvufG9n5p3kg3xYVEUPAl4FPZOaPZz82iLkz83+y89/ddXS+lO+CPkd6SxHxYWAqMw/0O0sNH8jM99GZSr0pIj44+8EBPD+WAe8DPpeZ7wVeZc7UxwBm/pnqdZuPAF+a+9hCcre57Bf0tQwD7GRErAGofk71Oc8bRMTb6BT9rsx8uBoe+NwAmXkK2EdnCmRlRMx8pmTQzpPLgY9ExFFggs5Uzt0MdmYAMvN49XOKzhzypQz2+XEMOJaZ+6vlh+iU/yBnnu1DwJOZebJaXlTuNpd927+W4RFga3V/K5058YEREQHcBxzOzM/Memhgc0fEuyNiZXX/HXReYzhMp/Svq1YbqMyZeWtmrsvMDXTO4W9k5hYGODNARCyPiF+auU9nLvkQA3x+ZOZLwIsRcX41tJnOV68PbOY5buT/pnBgsbn7/YJDwxcrrgL+lc687J/0O89b5HwAOAH8N52ri2105mX3As8D/wCc0++cczJ/gM5/C78DPF3drhrk3MCvAU9VmQ8Bf1qN/yrwLeAInf8C/2K/s75J/lFgTxsyV/m+Xd2emfn7G+Tzo8p3CfBEdY78HbBq0DNXuZcD/w6smDW2qNx+glaSCtDmaRxJ0gJZ9pJUAMtekgpg2UtSASx7SSqAZS9JBbDsJakAlr0kFeB/AXseBjqwp/lgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Diabetes'].isna()]['Age'].hist(bins=32)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most missing values are from babies where it can be assumed they don't have diabetes. The few remaining ones are also filled with the most common label `No`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Diabetes Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['DiabetesAge'].isna()) & (df['Diabetes'].isna())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['DiabetesAge'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most missing values for this features are because the individuals don't have diabetes. These entries are filled in with a 0. The instances where the data is actually missing is for the same individuals that had a missing value in the `Diabetes` column. Since these missing values were filled with No, the NaN values in the `DiabetesAge` column are also filled with 0. In short, all missing values in this column are filled in with the value 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Health General**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1463"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HealthGen'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['HealthGen'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['HealthGen'].isna()]['Age'].hist(bins=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Age'] <= 11]['HealthGen'].notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Age'] <= 20]['HealthGen'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many missing values. Individuals with `Age` $< 12$ are filled in with `Good` as it is the most common label for the entire population. The remaining ones are filled by using the most common label per age group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Days Mental Health Bad**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1468"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DaysMentHlthBad'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['DaysMentHlthBad'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['DaysMentHlthBad'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most missing values are in younger individuals. For individuals with `Age` $\\le 12$ a value of 0 is filled in. The remaining ones are filled in using the mean value per age group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Little Interest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1954"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LittleInterest'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['LittleInterest'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['LittleInterest'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lots of missing data but most of it is from the younger individuals. It is assumed that people with `Age` $\\le 15$ have a value of `None` and the rest are filled in by taking the most common label per age group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Depressed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1951"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Depressed'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Depressed'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Depressed'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to the `LittleInterest` feature, a large amount of the missing data is from the younger individuals. It is assumed that people less than with `Age` $\\le 15$ have a value of 0 and the rest are filled in by taking the mean per age group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Number Pregnancies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1370"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nPregnancies'].isna()) & (df['Gender']=='female')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['nPregnancies']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nPregnancies'].isna()) & (df['nBabies']>0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['nPregnancies'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nPregnancies'].notna()) & (df['Gender']=='male')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there is never the value 0. It is then assumed that a null value means that the individual has had 0 pregnancies. As a sanity check, there are no males that have had a pregnancy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Number Babies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nPregnancies'].notna()) & (df['nBabies']).isna()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['nBabies']==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['nBabies'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many instances have the amount of pregnancies different from the amount of babies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "455"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nPregnancies'] != df['nBabies']) & (df['nBabies'].notna())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there instances where the amount of pregnancies is less than the amount of babies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nPregnancies'] < df['nBabies']) & (df['nBabies'].notna())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nPregnancies'].notna()) & (df['nBabies'].isna())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly to `nPregnancies`, all missing values are filled instances with missing `nBabies` and 0 `nPregnancies` are filled in with 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age 1st Baby**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4301"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['nBabies']!=0) & (df['Age1stBaby']).isna()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age1stBaby'].hist(bins=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['nBabies'].notna()) & (df['Age1stBaby'].isna())]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data appears to be normally distributed. Missing values where `nBabies` is 0 are filled in with 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sleep Hours Night**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1367"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SleepHrsNight'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SleepHrsNight'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SleepHrsNight'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 0), (13, 0), (14, 0), (15, 0), (16, 83), (17, 81), (18, 67), (19, 78), (20, 76), (21, 74), (22, 71), (23, 65), (24, 66), (25, 69), (26, 64), (27, 58), (28, 85), (29, 68), (30, 81), (31, 84), (32, 71), (33, 70), (34, 60), (35, 50), (36, 68), (37, 63), (38, 69), (39, 86), (40, 74), (41, 65), (42, 54), (43, 73), (44, 66), (45, 60), (46, 59), (47, 65), (48, 68), (49, 65), (50, 69), (51, 59), (52, 52), (53, 55), (54, 73), (55, 53), (56, 77), (57, 46), (58, 44), (59, 53), (60, 57), (61, 36), (62, 40), (63, 57), (64, 51), (65, 59), (66, 31), (67, 34), (68, 40), (69, 27), (70, 53), (71, 34), (72, 33), (73, 27), (74, 40), (75, 28), (76, 30), (77, 27), (78, 25), (79, 24)]\n"
     ]
    }
   ],
   "source": [
    "age_count = list()\n",
    "for i in range(df['Age'].max()):\n",
    "    age_count += [(i, len(df.loc[(df['SleepHrsNight'].notna()) & (df['Age']==i)]))]\n",
    "print(age_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All individuals with `Age` $\\le 15$ have missing values. For these instances the values are manually inputted by using the average sleep time got from https://www.sleepfoundation.org/press-release/national-sleep-foundation-recommends-new-sleep-times. For the remaining people, the sleep hours per night is found by taking the mean of the age group each particular person is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sleep Trouble**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1356"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SleepTrouble'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SleepTrouble'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SleepTrouble'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All missing values come from individuals with an age smaller or equal to 16. All these values are assumed to be `No` as younger people are less likely to have sleep trouble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Physically Active**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1042"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PhysActive'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PhysActive'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['PhysActive'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 64), (13, 95), (14, 79), (15, 76), (16, 83), (17, 81), (18, 67), (19, 78), (20, 76), (21, 74), (22, 71), (23, 65), (24, 67), (25, 69), (26, 65), (27, 58), (28, 85), (29, 68), (30, 81), (31, 84), (32, 71), (33, 70), (34, 61), (35, 50), (36, 68), (37, 63), (38, 70), (39, 86), (40, 74), (41, 65), (42, 55), (43, 73), (44, 66), (45, 60), (46, 59), (47, 65), (48, 68), (49, 65), (50, 69), (51, 60), (52, 52), (53, 55), (54, 74), (55, 53), (56, 77), (57, 46), (58, 44), (59, 54), (60, 58), (61, 36), (62, 40), (63, 57), (64, 51), (65, 60), (66, 31), (67, 34), (68, 40), (69, 27), (70, 53), (71, 34), (72, 34), (73, 27), (74, 40), (75, 28), (76, 30), (77, 27), (78, 25), (79, 24)]\n"
     ]
    }
   ],
   "source": [
    "age_count = list()\n",
    "for i in range(df['Age'].max()):\n",
    "    age_count += [(i, len(df.loc[(df['PhysActive'].notna()) & (df['Age']==i)]))]\n",
    "print(age_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For kids with `Age` $\\le 4$ years old, `No` is inserted. All the individuals where $5 \\ge$`Age` $15 \\le$ are assumed to have physical activity. The remaining missing values for the population older than $15$ years old the most common value per age group is inserted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Physically Active Days**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2872"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PhysActiveDays'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PhysActiveDays'].hist(bins=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many missing values and as a result this column is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TV Hours per Day**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2829"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TVHrsDay'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TVHrsDay'].value_counts().plot.bar(rot=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many missing values and as a result this column is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computer Hours per Day**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2827"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CompHrsDay'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['CompHrsDay'].value_counts().plot.bar(rot=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many missing values and as a result this column is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TV Hours per Day Child**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4829"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TVHrsDayChild'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TVHrsDayChild'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many missing values and as a result this column is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computer Hours per Day Child**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4829"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TVHrsDayChild'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TVHrsDayChild'].hist(bins=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are too many missing values and as a result this column is dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alcohol 12+ Years**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Alcohol12PlusYr'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADglJREFUeJzt3X+o3Xd9x/Hny1bdmELrkoUuTZcyskHcj1ruajf3h8Otv/wjrWOlZWiohThIpw43FoWt/qCjG6uOjlqINJqKs+tQMcNgjZngZKi5lZI27UqvtaUJsY2L+GOOutb3/jjfzGNMen/mnDbv5wMO55z393vO/Ry45Mn5fs+5SVUhSernRdNegCRpOgyAJDVlACSpKQMgSU0ZAElqygBIUlMGQJKaMgCS1JQBkKSmzpz2Ap7LqlWrav369dNehiS9oNx7773fqqrV8+33vA7A+vXrmZ2dnfYyJOkFJcnjC9nPQ0CS1JQBkKSmDIAkNWUAJKkpAyBJTc0bgCTrknwhyYNJDiR52zB/d5JDSe4bLleMPeadSeaSPJzk0rH5ZcNsLsm2U/OSJEkLsZCPgT4DvKOqvpbk5cC9SfYM2z5QVX8/vnOSjcA1wCuBXwQ+n+RXhs23AX8AHAT2JdlVVQ+uxAuRJC3OvAGoqsPA4eH295I8BKx9jodsAu6qqqeBbySZAy4ats1V1aMASe4a9jUAkjQFizoHkGQ98CrgK8PohiT7k+xIcvYwWws8Mfawg8PsZHNJ0hQs+JvASV4GfAJ4e1V9N8ntwPuAGq5vAd683AUl2QJsATjvvPOW+3QTsX7bZ6a9hNPKYze/ftpLkFpY0DuAJC9m9I//x6rqkwBV9WRVPVtVPwI+xI8P8xwC1o09/NxhdrL5T6iq7VU1U1Uzq1fP+6csJElLtJBPAQW4A3ioqt4/Nj9nbLergAeG27uAa5K8NMn5wAbgq8A+YEOS85O8hNGJ4l0r8zIkSYu1kENArwHeCNyf5L5h9i7g2iQXMDoE9BjwFoCqOpDkbkYnd58BtlbVswBJbgDuAc4AdlTVgRV8LZKkRVjIp4C+BOQEm3Y/x2NuAm46wXz3cz1OkjQ5fhNYkpoyAJLUlAGQpKYMgCQ1ZQAkqSkDIElNGQBJasoASFJTBkCSmjIAktSUAZCkpgyAJDVlACSpKQMgSU0ZAElqygBIUlMGQJKaMgCS1JQBkKSmDIAkNWUAJKkpAyBJTRkASWrKAEhSUwZAkpoyAJLUlAGQpKYMgCQ1ZQAkqSkDIElNGQBJasoASFJTBkCSmpo3AEnWJflCkgeTHEjytmH+iiR7kjwyXJ89zJPk1iRzSfYnuXDsuTYP+z+SZPOpe1mSpPks5B3AM8A7qmojcDGwNclGYBuwt6o2AHuH+wCXAxuGyxbgdhgFA7gReDVwEXDjsWhIkiZv3gBU1eGq+tpw+3vAQ8BaYBOwc9htJ3DlcHsTcGeNfBk4K8k5wKXAnqo6WlXfBvYAl63oq5EkLdiizgEkWQ+8CvgKsKaqDg+bvgmsGW6vBZ4Ye9jBYXay+fE/Y0uS2SSzR44cWczyJEmLsOAAJHkZ8Ang7VX13fFtVVVArcSCqmp7Vc1U1czq1atX4iklSSewoAAkeTGjf/w/VlWfHMZPDod2GK6fGuaHgHVjDz93mJ1sLkmagoV8CijAHcBDVfX+sU27gGOf5NkMfHps/qbh00AXA98ZDhXdA1yS5Ozh5O8lw0ySNAVnLmCf1wBvBO5Pct8wexdwM3B3kuuBx4Grh227gSuAOeAHwHUAVXU0yfuAfcN+762qoyvyKiRJizZvAKrqS0BOsvl1J9i/gK0nea4dwI7FLFCSdGr4TWBJasoASFJTBkCSmjIAktSUAZCkpgyAJDVlACSpKQMgSU0ZAElqygBIUlMGQJKaMgCS1JQBkKSmDIAkNWUAJKkpAyBJTRkASWrKAEhSUwZAkpoyAJLUlAGQpKYMgCQ1ZQAkqSkDIElNGQBJasoASFJTBkCSmjIAktSUAZCkpgyAJDVlACSpKQMgSU3NG4AkO5I8leSBsdm7kxxKct9wuWJs2zuTzCV5OMmlY/PLhtlckm0r/1IkSYuxkHcAHwEuO8H8A1V1wXDZDZBkI3AN8MrhMR9MckaSM4DbgMuBjcC1w76SpCk5c74dquqLSdYv8Pk2AXdV1dPAN5LMARcN2+aq6lGAJHcN+z646BVLklbEcs4B3JBk/3CI6OxhthZ4Ymyfg8PsZHNJ0pQsNQC3A78MXAAcBm5ZqQUl2ZJkNsnskSNHVuppJUnHWVIAqurJqnq2qn4EfIgfH+Y5BKwb2/XcYXay+Ymee3tVzVTVzOrVq5eyPEnSAiwpAEnOGbt7FXDsE0K7gGuSvDTJ+cAG4KvAPmBDkvOTvITRieJdS1+2JGm55j0JnOTjwGuBVUkOAjcCr01yAVDAY8BbAKrqQJK7GZ3cfQbYWlXPDs9zA3APcAawo6oOrPirkSQt2EI+BXTtCcZ3PMf+NwE3nWC+G9i9qNVJkk4ZvwksSU0ZAElqygBIUlMGQJKaMgCS1JQBkKSmDIAkNWUAJKkpAyBJTRkASWrKAEhSUwZAkpoyAJLUlAGQpKYMgCQ1ZQAkqSkDIElNGQBJasoASFJTBkCSmjIAktSUAZCkpgyAJDVlACSpKQMgSU0ZAElqygBIUlMGQJKaMgCS1JQBkKSmDIAkNWUAJKmpeQOQZEeSp5I8MDZ7RZI9SR4Zrs8e5klya5K5JPuTXDj2mM3D/o8k2XxqXo4kaaEW8g7gI8Blx822AXuragOwd7gPcDmwYbhsAW6HUTCAG4FXAxcBNx6LhiRpOuYNQFV9ETh63HgTsHO4vRO4cmx+Z418GTgryTnApcCeqjpaVd8G9vDTUZEkTdBSzwGsqarDw+1vAmuG22uBJ8b2OzjMTjaXJE3Jsk8CV1UBtQJrASDJliSzSWaPHDmyUk8rSTrOUgPw5HBoh+H6qWF+CFg3tt+5w+xk859SVduraqaqZlavXr3E5UmS5rPUAOwCjn2SZzPw6bH5m4ZPA10MfGc4VHQPcEmSs4eTv5cMM0nSlJw53w5JPg68FliV5CCjT/PcDNyd5HrgceDqYffdwBXAHPAD4DqAqjqa5H3AvmG/91bV8SeWJUkTNG8Aqurak2x63Qn2LWDrSZ5nB7BjUauTJJ0yfhNYkpoyAJLUlAGQpKYMgCQ1ZQAkqSkDIElNGQBJasoASFJTBkCSmjIAktSUAZCkpgyAJDVlACSpKQMgSU0ZAElqygBIUlMGQJKaMgCS1NS8/yWkpBe29ds+M+0lnDYeu/n1017CivIdgCQ1ZQAkqSkDIElNGQBJasoASFJTBkCSmjIAktSUAZCkpgyAJDVlACSpKQMgSU0ZAElqygBIUlMGQJKaWlYAkjyW5P4k9yWZHWavSLInySPD9dnDPEluTTKXZH+SC1fiBUiSlmYl3gH8XlVdUFUzw/1twN6q2gDsHe4DXA5sGC5bgNtX4GdLkpboVBwC2gTsHG7vBK4cm99ZI18Gzkpyzin4+ZKkBVhuAAr4XJJ7k2wZZmuq6vBw+5vAmuH2WuCJscceHGY/IcmWJLNJZo8cObLM5UmSTma5/yXk71bVoSS/AOxJ8p/jG6uqktRinrCqtgPbAWZmZhb1WEnSwi3rHUBVHRqunwI+BVwEPHns0M5w/dSw+yFg3djDzx1mkqQpWHIAkvxckpcfuw1cAjwA7AI2D7ttBj493N4FvGn4NNDFwHfGDhVJkiZsOYeA1gCfSnLsef6pqj6bZB9wd5LrgceBq4f9dwNXAHPAD4DrlvGzJUnLtOQAVNWjwG+eYP5fwOtOMC9g61J/niRpZflNYElqygBIUlMGQJKaMgCS1JQBkKSmDIAkNWUAJKkpAyBJTRkASWrKAEhSUwZAkpoyAJLUlAGQpKYMgCQ1ZQAkqSkDIElNGQBJasoASFJTBkCSmjIAktSUAZCkpgyAJDVlACSpKQMgSU0ZAElqygBIUlMGQJKaMgCS1JQBkKSmDIAkNWUAJKkpAyBJTU08AEkuS/Jwkrkk2yb98yVJIxMNQJIzgNuAy4GNwLVJNk5yDZKkkUm/A7gImKuqR6vqh8BdwKYJr0GSxOQDsBZ4Yuz+wWEmSZqwM6e9gOMl2QJsGe5+P8nD01zPaWYV8K1pL2I++dtpr0BT8rz//XwB/W7+0kJ2mnQADgHrxu6fO8z+X1VtB7ZPclFdJJmtqplpr0M6EX8/J2/Sh4D2ARuSnJ/kJcA1wK4Jr0GSxITfAVTVM0luAO4BzgB2VNWBSa5BkjQy8XMAVbUb2D3pnyvAQ2t6fvP3c8JSVdNegyRpCvxTEJLUlAE4TWTkS0kuH5v9UZLPTnNd0vGSVJJbxu7/eZJ3T3FJbRmA00SNjuX9CfD+JD+T5GXA3wBbp7sy6ac8DbwhyappL6Q7A3AaqaoHgH8F/hL4a+DOqvp6ks1JvprkviQfTPKiJGcm+WiS+5M8kOSt0129GnmG0QnfPzt+Q5L1Sf4tyf4ke5OcN/nl9fG8+yawlu09wNeAHwIzSX4NuAr4neFjuNsZff/i68Cqqvp1gCRnTWvBauk2YH+Svztu/o/AzqrameTNwK3AlRNfXRMG4DRTVf+d5J+B71fV00l+H/gtYDYJwM8y+ntM9wC/muRW4DPA56a1ZvVTVd9NcifwVuB/xjb9NvCG4fZHgeMDoRVkAE5PPxouAGH0hbu/On6nJL/B6E9zbwX+kB//DSZpEv6B0bvVD097IV15DuD093ng6mMn3JL8fJLzkqxm9D2Qf2F0vuDCaS5S/VTVUeBu4Pqx8X8wOkQJ8MfAv096XZ34DuA0V1X3J3kP8PkkLwL+l9GnhZ4F7sjouFAxOnEsTdotwA1j9/8U+HCSvwCOANdNZVVN+E1gSWrKQ0CS1JQBkKSmDIAkNWUAJKkpAyBJTRkASWrKAEhSUwZAkpr6P1XxTPDTLGuYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Alcohol12PlusYr'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Alcohol12PlusYr'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 0), (13, 0), (14, 0), (15, 0), (16, 0), (17, 0), (18, 30), (19, 35), (20, 68), (21, 69), (22, 61), (23, 60), (24, 58), (25, 53), (26, 59), (27, 50), (28, 72), (29, 56), (30, 71), (31, 73), (32, 62), (33, 61), (34, 49), (35, 41), (36, 56), (37, 56), (38, 56), (39, 71), (40, 64), (41, 53), (42, 40), (43, 61), (44, 59), (45, 55), (46, 52), (47, 63), (48, 61), (49, 59), (50, 67), (51, 48), (52, 50), (53, 52), (54, 70), (55, 50), (56, 66), (57, 45), (58, 39), (59, 49), (60, 53), (61, 34), (62, 39), (63, 53), (64, 49), (65, 54), (66, 28), (67, 31), (68, 36), (69, 23), (70, 49), (71, 31), (72, 32), (73, 25), (74, 38), (75, 22), (76, 25), (77, 23), (78, 22), (79, 21)]\n"
     ]
    }
   ],
   "source": [
    "age_count = list()\n",
    "for i in range(df['Age'].max()):\n",
    "    age_count += [(i, len(df.loc[(df['Alcohol12PlusYr'].notna()) & (df['Age']==i)]))]\n",
    "print(age_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All values are missing for people younger than 18 years old. These are filled with a `No`. the remaining ones are filled by taking the most common value per age group each given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alcohol Day**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2842"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AlcoholDay'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['AlcoholDay'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEh9JREFUeJzt3X9s3Hd9x/HnmwZYiFHSELBCks1By0ChHqG1ShFosukGaUEEJNSl6iCFovBH2MrmaQT+AYYqZRKBDbFVCxRaBNRkpVWjUGBd1owxqYWkdLhtVpFRl8ZKExhpSkrVzeW9P+4bcrj+cb7z2Xcfng/J8n0/3899v5+3fffy15/7fu8iM5Ekles5iz0ASVJ7GfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwi1Z7AEArFq1Kvv6+tq6jyeffJJly5a1dR/tZg2do4Q6rKEztFLD4cOHf5qZL56tX0cEfV9fH4cOHWrrPg4ePMjg4GBb99Fu1tA5SqjDGjpDKzVExCON9HPqRpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCtcRV8b+Junb+fWm7zvcP8HV09x/bNebm96upLJ5RC9JhTPoJalwBr0kFc6gl6TCGfSSVLhZgz4i1kXEXRHxYEQ8EBHXVu0fjYjxiLiv+rq87j4fioijEfFQRLypnQVIkmbWyOmVE8BwZt4bES8EDkfEndW6T2XmJ+o7R8RGYCvwSuClwL9ExO9l5jPzOXBJUmNmPaLPzOOZeW91++fAEWDNDHfZAoxk5tOZ+TBwFLh4PgYrSZq7yMzGO0f0Ad8GLgD+ArgaeAI4RO2o/1REfAa4OzO/VN3nBuAbmXnLpG1tB7YD9Pb2XjQyMtJqLTM6c+YMPT09bd1HI0bHTzd9396lcOKpqdf1r1ne9HYXUqf8HlpVQh3W0BlaqWFoaOhwZg7M1q/hK2Mjogf4GvCBzHwiIq4HPg5k9X038J5Gt5eZe4A9AAMDA9nuz33slM+WnO7K1kYM90+we3TqX9nYVYNNb3chdcrvoVUl1GENnWEhamjorJuIeC61kP9yZt4KkJknMvOZzPwl8FnOTc+MA+vq7r62apMkLYJGzroJ4AbgSGZ+sq59dV23twP3V7f3AVsj4vkRsR7YAHx3/oYsSZqLRqZuXge8ExiNiPuqtg8DV0bEJmpTN2PA+wAy84GI2As8SO2MnR2ecSNJi2fWoM/M7wAxxao7ZrjPdcB1LYxLkjRPvDJWkgpn0EtS4Qx6SSqcQS9JhfOjBDWtVj72cDrD/RMMzvtWJc3EI3pJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCrdksQfQqr6dX2+o33D/BFc32BdgbNebmx2SJHUUj+glqXCzBn1ErIuIuyLiwYh4ICKurdpXRsSdEfHD6vv5VXtExKcj4mhE/CAiLmx3EZKk6TVyRD8BDGfmRuASYEdEbAR2AgcycwNwoFoGuAzYUH1tB66f91FLkho2a9Bn5vHMvLe6/XPgCLAG2ALcVHW7CXhbdXsL8MWsuRtYERGr533kkqSGzOnF2IjoA14N3AP0ZubxatVjQG91ew3waN3djlVtx5G60FQv+M/1xf3JfLFfCykys7GOET3AvwHXZeatEfF4Zq6oW38qM8+PiP3Arsz8TtV+APhgZh6atL3t1KZ26O3tvWhkZKSpAkbHTzfUr3cpnHiq8e32r1ne1Hhm0+h4pzJTDe0YbytjnU7vUnjJyvb8bNtlqp/DXB9Pk7Xr8TUXZ86coaenZ7GH0ZLf9BqGhoYOZ+bAbP0aOqKPiOcCXwO+nJm3Vs0nImJ1Zh6vpmZOVu3jwLq6u6+t2n5NZu4B9gAMDAzk4OBgI0N5lkaPqob7J9g92vg/MGNXNTee2bRyFDhTDe0Ybytjnc5w/wRXNPm7XixT/Rzm+niarF2Pr7k4ePAgzT7vOoU1NKaRs24CuAE4kpmfrFu1D9hW3d4G3F7X/q7q7JtLgNN1UzySpAXWyCHJ64B3AqMRcV/V9mFgF7A3Iq4BHgGuqNbdAVwOHAV+Abx7XkcsSZqTWYO+mmuPaVZfOkX/BHa0OC5J0jzxylhJKlzXv9dNuzT6HjqS1Ok8opekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMJ5ZawWXLuuOvbDPKSpGfSF8C0bJE3HqRtJKpxH9NIicPpKC8kjekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxXxqoYvt+PNDWP6CWpcAa9JBXOqRupIHOZvhrun+DqBvv7ZmndzSN6SSqcQS9JhTPoJalwswZ9RHw+Ik5GxP11bR+NiPGIuK/6urxu3Yci4mhEPBQRb2rXwCVJjWnkiP5GYPMU7Z/KzE3V1x0AEbER2Aq8srrPP0TEefM1WEnS3M0a9Jn5beBnDW5vCzCSmU9n5sPAUeDiFsYnSWpRZObsnSL6gP2ZeUG1/FHgauAJ4BAwnJmnIuIzwN2Z+aWq3w3ANzLzlim2uR3YDtDb23vRyMhIUwWMjp9uqF/vUjjxVFO76BjW0DlKqGMuNfSvWd7ewTTpzJkz9PT0LPYwWtJKDUNDQ4czc2C2fs2eR3898HEgq++7gffMZQOZuQfYAzAwMJCDg4NNDaTR84CH+yfYPdrdlw1YQ+cooY651DB21WB7B9OkgwcP0mx2dIqFqKGps24y80RmPpOZvwQ+y7npmXFgXV3XtVWbJGmRNBX0EbG6bvHtwNkzcvYBWyPi+RGxHtgAfLe1IUqSWjHr/20RcTMwCKyKiGPAR4DBiNhEbepmDHgfQGY+EBF7gQeBCWBHZj7TnqFLkhoxa9Bn5pVTNN8wQ//rgOtaGZQkaf54ZawkFc6gl6TCGfSSVDiDXpIKZ9BLUuG6+9I+SQuiXR+83k2fXNWun8GNm5e1Zbv1PKKXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVbtagj4jPR8TJiLi/rm1lRNwZET+svp9ftUdEfDoijkbEDyLiwnYOXpI0u0aO6G8ENk9q2wkcyMwNwIFqGeAyYEP1tR24fn6GKUlq1pLZOmTmtyOib1LzFmCwun0TcBD4YNX+xcxM4O6IWBERqzPz+HwNWFI5+nZ+vaX7D/dPcPWkbYztenNL2yxRs3P0vXXh/RjQW91eAzxa1+9Y1SZJWiRRO/iepVPtiH5/Zl5QLT+emSvq1p/KzPMjYj+wKzO/U7UfAD6YmYem2OZ2atM79Pb2XjQyMtJUAaPjpxvq17sUTjzV1C46hjV0jhLqKLWG/jXL27KvRrNmrtYvP4+enp6m7js0NHQ4Mwdm6zfr1M00TpydkomI1cDJqn0cWFfXb23V9iyZuQfYAzAwMJCDg4NNDWTyv23TGe6fYPdos+V2BmvoHCXUUWoNY1cNtmVfjWbNXN24eRnN5l+jmp262Qdsq25vA26va39XdfbNJcBp5+claXHN+uc8Im6m9sLrqog4BnwE2AXsjYhrgEeAK6rudwCXA0eBXwDvbsOYJUlz0MhZN1dOs+rSKfomsKPVQUmS5o9XxkpS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwnX3x8tI0iStfuB4iTyil6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVrqUPHomIMeDnwDPARGYORMRK4KtAHzAGXJGZp1obpiSpWfNxRD+UmZsyc6Ba3gkcyMwNwIFqWZK0SNoxdbMFuKm6fRPwtjbsQ5LUoMjM5u8c8TBwCkjgHzNzT0Q8npkrqvUBnDq7POm+24HtAL29vReNjIw0NYbR8dMN9etdCieeamoXHcMaOkcJdVhDZ1i//Dx6enqauu/Q0NDhutmUabUa9GsyczwiXgLcCfwpsK8+2CPiVGaeP9N2BgYG8tChQ02NodEPAh7un2D3aHd/Fro1dI4S6rCGznDj5mUMDg42dd+IaCjoW5q6yczx6vtJ4DbgYuBERKyuBrEaONnKPiRJrWk66CNiWUS88Oxt4I3A/cA+YFvVbRtwe6uDlCQ1r5X/eXqB22rT8CwBvpKZ34yI7wF7I+Ia4BHgitaHKUlqVtNBn5k/Al41Rfv/AJe2MihJ0vzxylhJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUuLYFfURsjoiHIuJoROxs134kSTNrS9BHxHnA3wOXARuBKyNiYzv2JUmaWbuO6C8GjmbmjzLzf4ERYEub9iVJmkG7gn4N8Gjd8rGqTZK0wCIz53+jEe8ANmfme6vldwKvycz31/XZDmyvFl8OPDTvA/l1q4Cftnkf7WYNnaOEOqyhM7RSw+9k5otn67SkyY3PZhxYV7e8tmr7lczcA+xp0/6fJSIOZebAQu2vHayhc5RQhzV0hoWooV1TN98DNkTE+oh4HrAV2NemfUmSZtCWI/rMnIiI9wPfAs4DPp+ZD7RjX5KkmbVr6obMvAO4o13bb8KCTRO1kTV0jhLqsIbO0PYa2vJirCSpc/gWCJJUuCKDPiLWRcRdEfFgRDwQEddW7Ssj4s6I+GH1/fzFHut0IuK3IuK7EfGfVQ0fq9rXR8Q91VtLfLV6sbujRcR5EfH9iNhfLXdVDRExFhGjEXFfRByq2rrmsQQQESsi4paI+K+IOBIRr+2mGiLi5dXP/+zXExHxgW6qASAi/rx6Pt8fETdXz/O2Px+KDHpgAhjOzI3AJcCO6i0YdgIHMnMDcKBa7lRPA2/IzFcBm4DNEXEJ8DfApzLzd4FTwDWLOMZGXQscqVvuxhqGMnNT3Wlw3fRYAvg74JuZ+QrgVdR+H11TQ2Y+VP38NwEXAb8AbqOLaoiINcCfAQOZeQG1E1W2shDPh8ws/gu4Hfgjahdlra7aVgMPLfbYGhz/C4B7gddQu7BiSdX+WuBbiz2+Wca+ltoT8A3AfiC6sIYxYNWktq55LAHLgYepXpPrxhomjfuNwH90Ww2ce8eAldROhNkPvGkhng+lHtH/SkT0Aa8G7gF6M/N4teoxoHeRhtWQasrjPuAkcCfw38DjmTlRdemGt5b4W+CvgF9Wyy+i+2pI4J8j4nB1RTd012NpPfAT4AvVFNrnImIZ3VVDva3AzdXtrqkhM8eBTwA/Bo4Dp4HDLMDzoeigj4ge4GvABzLzifp1Wfvz2dGnHGXmM1n7V3UttTeKe8UiD2lOIuItwMnMPLzYY2nR6zPzQmrvxrojIv6gfmUXPJaWABcC12fmq4EnmTTF0QU1AFDNX78V+KfJ6zq9hur1gy3U/vC+FFgGbF6IfRcb9BHxXGoh/+XMvLVqPhERq6v1q6kdKXe8zHwcuIvav3UrIuLs9Q/PemuJDvM64K0RMUbtHUzfQG2uuJtqOHskRmaepDYvfDHd9Vg6BhzLzHuq5VuoBX831XDWZcC9mXmiWu6mGv4QeDgzf5KZ/wfcSu050vbnQ5FBHxEB3AAcycxP1q3aB2yrbm+jNnffkSLixRGxorq9lNprDEeoBf47qm4dXUNmfigz12ZmH7V/t/81M6+ii2qIiGUR8cKzt6nND99PFz2WMvMx4NGIeHnVdCnwIF1UQ50rOTdtA91Vw4+BSyLiBVVGnf09tP35UOQFUxHxeuDfgVHOzQ1/mNo8/V7gt4FHgCsy82eLMshZRMTvAzdRe2X+OcDezPzriHgZtaPjlcD3gT/JzKcXb6SNiYhB4C8z8y3dVEM11tuqxSXAVzLzuoh4EV3yWAKIiE3A54DnAT8C3k31uKJ7alhGLSxflpmnq7Zu+z18DPhjamcGfh94L7U5+bY+H4oMeknSOUVO3UiSzjHoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkq3P8DFo+VnopKzSoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['AlcoholDay'].notna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, people younger than 18 years old have all the values missing which are then filled with a 0. The remaining ones are filled by taking the most common value per age group each given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Alcohol Year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2364"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AlcoholYear'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tBj4G/I80L+CjFA+IB9gOXJ+m16d50vK1qb+ZmXVA2SOAPwQ+C/wozV8KvBERZ9P8CLAoTS8CXgNIy8+k/mZm1gEqHtXbwhulXwWui4hPSaoBvwV8AtifhnmQtAT4SkRcIekwsC4iRtKybwFXRcTr49Y7AAwA9Pb2Xjk0NNRSPoCTp85w4s2W396ylYvmTbnv6OgoPT09M5imGs5ZLees1mzI2c6M/f39ByNioue1v0uZh8J/GPg1SdcBFwH/GPgCMF/SBemv/MXAsdT/GLAEGJF0ATAP+O74lUbEIDAI0NfXF7VareWA9+3YydZDpZ5735KjG2tT7luv1ymzje3inNVyzmrNhpzdmLHlIaCIuCMiFkfEUmAD8EREbAT2ATekbpuAnWl6V5onLX8iWj38MDOz0mbiPoDbgc9IGqYY438gtT8AXJraPwNsmYHPNjOzKapkfCQi6kA9Tb8CrJ6gz98Dv17F55mZWXm+E9jMLFPtP0OagaVb/nLKfW9beZZPTKN/M0fv/lhl6zKz85uPAMzMMuUCYGaWKRcAM7NMuQCYmWXKBcDMLFMuAGZmmXIBMDPLlAuAmVmmXADMzDLlO4HPM9O5C3k6mt2x7DuQzWYfHwGYmWXKBcDMLFMuAGZmmXIBMDPLVMsFQNISSfskvSDpiKRbU/slkvZIejn9XJDaJeleScOSnpe0qqqNMDOz6StzBHAWuC0iVgBrgJslraB41OPeiFgO7OWdRz9eCyxPrwHg/hKfbWZmJZV5KPzxiPh6mv474EVgEbAe2J66bQeuT9PrgYeisB+YL+mylpObmVkplZwDkLQU+EXgANAbEcfTou8AvWl6EfBaw9tGUpuZmXWAIqLcCqQe4K+BuyLiMUlvRMT8huWnI2KBpC8Dd0fE11L7XuD2iHhm3PoGKIaI6O3tvXJoaKjlbCdPneHEmy2/vS16L6brM0LznCsXzWtfmHMYHR2lp6en0zGacs5qzYac7czY399/MCL6mvUrdSewpPcAfw7siIjHUvMJSZdFxPE0xHMytR8DljS8fXFqe5eIGAQGAfr6+qJWq7Wc774dO9l6qLtvdr5t5dmuzwjNcx7dWGtfmHOo1+uU+TfTLs5ZrdmQsxszlrkKSMADwIsR8QcNi3YBm9L0JmBnQ/tN6WqgNcCZhqEiMzNrszJ/en4Y+A3gkKRnU9tvA3cDj0jaDHwb+Hhathu4DhgGvg98ssRnm5lZSS0XgDSWr0kWr52gfwA3t/p5ZhMZ/+V3zb60rkr+Ajyb7XwnsJlZprr/7KOZWQdV9RXr0z06bccRpo8AzMwy5QJgZpYpFwAzs0y5AJiZZcongc1aVObkYJnLVX35qVXFRwBmZpnyEYCZTVlVl0ROl496ZoaPAMzMMuUCYGaWKRcAM7NMuQCYmWXKBcDMLFMuAGZmmfJloGbW9ZpdftrO50CcT9p+BCBpnaSXJA1L2tLuzzczs0JbC4CkOcAXgWuBFcCNkla0M4OZmRXaPQS0GhiOiFcAJA0B64EX2pzDKtapO0TNrHXtLgCLgNca5keAq9qcwWxWa2ex9dj6+U3Fs9rb9GHSDcC6iPjXaf43gKsi4paGPgPAQJr9APBSiY9cCLxe4v3tMBsygnNWzTmrNRtytjPjP42I9zfr1O4jgGPAkob5xantbRExCAxW8WGSnomIvirWNVNmQ0Zwzqo5Z7VmQ85uzNjuq4CeBpZLWibpQmADsKvNGczMjDYfAUTEWUm3AI8Dc4BtEXGknRnMzKzQ9hvBImI3sLtNH1fJUNIMmw0ZwTmr5pzVmg05uy5jW08Cm5lZ9/B3AZmZZeq8LADd/HUTko5KOiTpWUnPpLZLJO2R9HL6uaADubZJOinpcEPbhLlUuDft3+clrepwzt+RdCzt02clXdew7I6U8yVJ17Qp4xJJ+yS9IOmIpFtTe1ftz3Pk7Lb9eZGkpyQ9l3L+p9S+TNKBlOdL6cISJM1N88Np+dIO53xQ0qsN+/ODqb1jv0dvi4jz6kVxcvlbwOXAhcBzwIpO52rIdxRYOK7t94EtaXoL8PkO5PoIsAo43CwXcB3wFUDAGuBAh3P+DvBbE/Rdkf77zwWWpX8Xc9qQ8TJgVZr+CeCbKUtX7c9z5Oy2/SmgJ02/BziQ9tMjwIbU/kfAb6bpTwF/lKY3AF9q0/6cLOeDwA0T9O/Y79HY63w8Anj76yYi4gfA2NdNdLP1wPY0vR24vt0BIuJJ4NS45slyrQceisJ+YL6kyzqYczLrgaGIeCsiXgWGKf59zKiIOB4RX0/Tfwe8SHEXfFftz3PknEyn9mdExGiafU96BfBR4NHUPn5/ju3nR4G1ktTBnJPp2O/RmPOxAEz0dRPn+kfdbgF8VdLBdNczQG9EHE/T3wF6OxPtx0yWqxv38S3pMHpbwxBax3Om4YdfpPhrsGv357ic0GX7U9IcSc8CJ4E9FEcfb0TE2QmyvJ0zLT8DXNqJnBExtj/vSvvzHklzx+dM2v7f/XwsAN3ulyNiFcU3ot4s6SONC6M4Nuy6S7O6NVdyP/BTwAeB48DWzsYpSOoB/hz4dxHx/xqXddP+nCBn1+3PiPhhRHyQ4tsDVgM/2+FIExqfU9IVwB0UeT8EXALc3sGI73I+FoCmXzfRSRFxLP08CfwFxT/mE2OHfunnyc4lfJfJcnXVPo6IE+kX70fAH/POsETHckp6D8X/VHdExGOpuev250Q5u3F/jomIN4B9wC9RDJmM3cvUmOXtnGn5POC7Hcq5Lg21RUS8BfwJXbQ/z8cC0LVfNyHpfZJ+YmwauBo4TJFvU+q2CdjZmYQ/ZrJcu4Cb0lUMa4AzDUMbbTdu3PRfUOxTKHJuSFeFLAOWA0+1IY+AB4AXI+IPGhZ11f6cLGcX7s/3S5qfpi8GfoXifMU+4IbUbfz+HNvPNwBPpCOuTuT8RkPRF8V5isb92dnfo3afdW7Hi+Ls+jcpxgk/1+k8Dbkup7iK4jngyFg2ivHJvcDLwF8Bl3Qg28MUh/v/QDEWuXmyXBRXLXwx7d9DQF+Hc/5pyvE8xS/VZQ39P5dyvgRc26aMv0wxvPM88Gx6Xddt+/McObttf/488Dcpz2HgP6b2yykK0DDwZ8Dc1H5Rmh9Oyy/vcM4n0v48DPxP3rlSqGO/R2Mv3wlsZpap83EIyMzMpsAFwMwsUy4AZmaZcgEwM8uUC4CZWaZcAMzMMuUCYGaWKRcAM7NM/X8hCgYFvZpfYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['AlcoholYear'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same as the `AlcoholDay` column, people younger than 18 years old have all the values missing which are then filled with a 0. The remaining ones are filled by taking the most common value per age group each given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Smoke Now**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3707"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SmokeNow'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SmokeNow'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SmokeNow'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SmokeNow']=='Yes']['Age'].hist(bins=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All people of 20 years old or younger have missing values. These are filled with `No`. The remaining missing values for the older people, are filled in using the most common label depending on the age group they are part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Smoke Past 100 Months**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1665"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Smoke100'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Smoke100'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['Smoke100'].isna()) & (df['Age']>=20)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All missing values are from individuals younger than 20 years old. As these people are assumed to not smoke, it follows that they should not identify as smokers. This means that all the missing values are filled in with `No`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Smoker Identify**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1665"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Smoke100n'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Smoke100n'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like the `Smoke100` feature, all missing values are from individuals younger than 20 years old. Therefore, all these people it assumed that they do not smoke."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Smoke Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3767"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SmokeAge'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SmokeAge'].hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['SmokeNow']=='Yes') & (df['SmokeAge'].isna())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tha data appears to be normally distributed. The individuals which have the value `No` in the `SmokeNow` column have their entry filled with a 0. The few entries of `SmokeAge` of people that smoke but have a missing value are filled in by taking the mean of the age group a given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Marijuana**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2835"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Marijuana'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Marijuana'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['Marijuana'].notna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 0), (13, 0), (14, 0), (15, 0), (16, 0), (17, 0), (18, 65), (19, 73), (20, 68), (21, 69), (22, 61), (23, 59), (24, 58), (25, 53), (26, 59), (27, 49), (28, 70), (29, 54), (30, 71), (31, 72), (32, 62), (33, 60), (34, 48), (35, 40), (36, 55), (37, 55), (38, 56), (39, 71), (40, 64), (41, 52), (42, 40), (43, 61), (44, 58), (45, 53), (46, 52), (47, 62), (48, 61), (49, 58), (50, 66), (51, 47), (52, 49), (53, 52), (54, 70), (55, 48), (56, 63), (57, 41), (58, 39), (59, 47), (60, 0), (61, 0), (62, 0), (63, 0), (64, 0), (65, 0), (66, 0), (67, 0), (68, 0), (69, 0), (70, 0), (71, 0), (72, 0), (73, 0), (74, 0), (75, 0), (76, 0), (77, 0), (78, 0), (79, 0)]\n"
     ]
    }
   ],
   "source": [
    "age_count = list()\n",
    "for i in range(df['Age'].max()):\n",
    "    age_count += [(i, len(df.loc[(df['Marijuana'].notna()) & (df['Age']==i)]))]\n",
    "print(age_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['Marijuana'].isna()) & (df['SmokeNow']=='Yes')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The individuals which have the value `No` in the `SmokeNow` column have their entry filled with a `No` in the `Marijuana` column as well.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age First Marijuana**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3915"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AgeFirstMarij'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['AgeFirstMarij'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['Marijuana']=='Yes') & (df['AgeFirstMarij'].isna())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most missing values are from people that do not smoke marijuana. The entries for these instance are therefore filled with a 0. The remaining missing values are filled with the mean of the age group the given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Regular Marijuana**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2835"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RegularMarij'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['RegularMarij'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "213"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df_test['Marijuana']=='Yes') & (df['RegularMarij'].isna())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instances that have `No` in the `Marijuana` feature have their missing value in `RegularMarij` set to `No` as well. The remaining missing values, if any, are filled in by using the most common value of the age group the given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Age Regular Marijuana**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4617"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AgeRegMarij'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['AgeRegMarij'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "703"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['AgeRegMarij'].isna()) & (df['Marijuana'] == 'Yes')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, instances that have `No` in the `Marijuana` feature have their missing value in `AgeRegMarij` set to 0. The remaining missing values, are filled in by using the mean value of the age group the given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hard Drugs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2448"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HardDrugs'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['HardDrugs'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['HardDrugs'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 0), (13, 0), (14, 0), (15, 0), (16, 0), (17, 0), (18, 65), (19, 73), (20, 68), (21, 69), (22, 61), (23, 59), (24, 58), (25, 53), (26, 59), (27, 49), (28, 70), (29, 54), (30, 71), (31, 72), (32, 62), (33, 60), (34, 48), (35, 40), (36, 55), (37, 55), (38, 56), (39, 71), (40, 64), (41, 52), (42, 40), (43, 61), (44, 58), (45, 54), (46, 52), (47, 63), (48, 61), (49, 58), (50, 67), (51, 47), (52, 49), (53, 52), (54, 70), (55, 47), (56, 63), (57, 42), (58, 39), (59, 47), (60, 49), (61, 33), (62, 38), (63, 51), (64, 47), (65, 53), (66, 26), (67, 30), (68, 34), (69, 23), (70, 0), (71, 0), (72, 0), (73, 0), (74, 0), (75, 0), (76, 0), (77, 0), (78, 0), (79, 0)]\n"
     ]
    }
   ],
   "source": [
    "age_count = list()\n",
    "for i in range(df['Age'].max()):\n",
    "    age_count += [(i, len(df.loc[(df['HardDrugs'].notna()) & (df['Age']==i)]))]\n",
    "print(age_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instances where the value is missing and `Age` $\\le 18$ have the `HardDrugs` attribute set to `No`. The remaining missing values, are filled in by using the most common value of the age group the given individual is part of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sex Ever**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2450"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SexEver'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SexEver'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SexEver'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values are filled in the following way. If the individual's age is less than the mode of `SexAge`, then the entry is filled with `No`. Otherwise, it is filled with `Yes`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sex Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2573"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SexAge'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SexAge'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['SexAge'].isna()) & (df['SexEver'].notna())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feature appears to be normally distributed. Instances where there is a missing value and `SexEver` is `No` are filled with 0. The remaining missing values are filled in with `Yes`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sex Number of Partners Life**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2473"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SexNumPartnLife'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SexNumPartnLife'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['SexNumPartnLife'].isna()) & (df['SexEver'] == 'Yes')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SexNumPartnLife'].isna()]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 0), (1, 0), (2, 0), (3, 0), (4, 0), (5, 0), (6, 0), (7, 0), (8, 0), (9, 0), (10, 0), (11, 0), (12, 0), (13, 0), (14, 0), (15, 0), (16, 0), (17, 0), (18, 65), (19, 73), (20, 68), (21, 69), (22, 61), (23, 58), (24, 58), (25, 52), (26, 58), (27, 48), (28, 70), (29, 54), (30, 70), (31, 72), (32, 61), (33, 59), (34, 48), (35, 40), (36, 55), (37, 55), (38, 56), (39, 71), (40, 64), (41, 51), (42, 40), (43, 59), (44, 58), (45, 53), (46, 51), (47, 61), (48, 61), (49, 58), (50, 66), (51, 47), (52, 49), (53, 52), (54, 66), (55, 48), (56, 64), (57, 41), (58, 38), (59, 47), (60, 49), (61, 33), (62, 37), (63, 51), (64, 46), (65, 53), (66, 27), (67, 27), (68, 32), (69, 23), (70, 0), (71, 0), (72, 0), (73, 0), (74, 0), (75, 0), (76, 0), (77, 0), (78, 0), (79, 0)]\n"
     ]
    }
   ],
   "source": [
    "age_count = list()\n",
    "for i in range(df['Age'].max()):\n",
    "    age_count += [(i, len(df.loc[(df['SexNumPartnLife'].notna()) & (df['Age']==i)]))]\n",
    "print(age_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values for people that have the `SexEver` attribute to `No` are filled in with 0. The remaining missing values are filled in by using the mean of the age group that the given individual is part of. However the dataset doesn't contain any value for people of 70 years or older. These values are filled in by using the mean of the instances with $60 \\ge$ `SexNumPartnLife` $< 70$ as it is presumed that this value does not greatly change between the two age groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sex Number of Parners Year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2841"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SexNumPartYear'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SexNumPartYear'].hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "382"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['SexNumPartYear'].isna()) & (df['SexEver'] == 'Yes')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['SexNumPartYear'].isna()) & (df['SexEver'] == 'Yes')]['Age'].hist(bins=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similartly to `SexNumPartnLife`, missing values for people that have the `SexEver` attribute to `No` are filled in with 0. The remaining missing values are filled in by using the mean of the age group that the given individual is part of. However the dataset doesn't contain any value for people of 70 years or older. These values are filled in by using the mean of the instances with $60 \\ge$ `SexNumPartnYear` $< 70$ as it is presumed that this value does not greatly change between the two age groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Same Sex**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2448"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SameSex'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['SameSex'].isna()) & (df['SexEver']=='Yes')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SameSex'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SameSex'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lots of missing values. Mostly for the younger and older age groups. As such this column is removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sex Orientation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1694"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['SexOrientation'].isna()) & (df['Age'] >= 14)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q+lqSBQw+HQ7X3R97j5+1+18OtceX5wO/2N5KVfVAkqOBkxgE9xnAW4D/Hg6iCZ4L/IDBUcW44f08vI/fCmwBjm6PP+rfhrnEcwA7di3wpvGFluYweIM/cQr9JroeeE3r80wG00XfSfJ04K6quhi4GvhNBoeir0rylNb/4CTjf9zpvcCVwDuAD7faFuApSZ6c5PHA7w497xOBzUn2HX9+QZvfncfgTT1cd3/MLV9l8CdjYPDzun4X1v0G8LI2Pz8POBP4txb8+1TVp4G/BI6pqh8C321Hc2Tg6NY+lsH/YfI84LwkR7bt383gyBAefRHBrwObq+qXwGsZ/J7NSQbAjv0psLSdfLqdwScFGMxJvjLtJPBO+k10CbBPO1z9Z+B1VfUzBp8+bk2yAXgOcEVV3c7gF/PaJDcDa4BDkrwM+C3gvVV1JfDzJGdX1S+AdzL4hV/DYI573F8BXwe+MqHeo/3bftvAYB8sq6qHJ/Rxf8wtbwLObj/31wJvnuqKVbUZOJ/B1M1NwPqquprBn6C/ru3jfwLGj+xeA5yT5CbgNuC0FuAfBl5fg6vF3gasyOAQ/2+BP0nyLQbnAMZdAixr23kWU5gJGBW/CSxJnfIIQJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktSp/wMW9Cxua+0psgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['SexOrientation'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[df['SexOrientation'].isna()]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column is dropped as there are too many missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pregnant Now**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1770"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['PregnantNow'].isna()) & (df['Gender'] == 'female')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PregnantNow'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qeuRBYAa4D/hv4MnMPF2t0tTr+RfAHwL/Wy3/bEtyJfCJiDhSfascmn8dNwPfBP6+mur6u4hY3YJc3a4G7qquN5orM6eBPwe+AZwEZoEjrMD+dT4X/XkjO7+qG/t4U0QMAR8G3pqZ3+m+ralsmfnD7PzTehOdk+A9f6UznCkifgOYycwjTWdZwCsy88V0pipviIhf7b6xodfxAuDFwPsy80XAU5wxHdLkvl/Ndb8W+Kczb2siV/WewE46vyB/HljN2VO+A3E+F/2ip1lo2KmI2ABQ/TnTRIiIeBadkr8zM+9pUzaAzHwSuJ/OP1nXRsT8dzuaeD1fDrw2IqbonHH1lXTmoJvONX80SGbO0JlvfgnNv44ngBOZ+UC1/CE6xd90rnlXAJ/LzFPVctO5fg34WmZ+MzN/ANxDZ58b+P51Phd920+zcADYVV3fRWd+fEVFRAC3Ag9n5rvbki0inhcRa6vrF9J53+BhOoX/+qZyZeZNmbkpM0fo7E//lpnXNp0rIlZHxHPnr9OZdz5Gw69jZj4GPBoRl1RDlwNfajpXl2v40bQNNJ/rG8BlEfGc6mdz/vka/P7V1JskNb25cSXwX3Tmd/+4wRx30Zlz+wGdo5zr6cztHgK+AvwrcFEDuV5B55+nXwQerC5XNp0N+BXg81WuY8CfVOO/CHwGOE7nn9vPbvA1HQMOtiFX9fhfqC4Pze/rTb+OVYZtwOHqtfxnYF1Lcq0Gvg2s6RprQ653Al+u9vsPAM9eif3Lb8ZKUuHO56kbSdISWPSSVDiLXpIKZ9FLUuEsekkqnEUvSYWz6CWpcBa9JBXu/wAAe/WSuJDQmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[(df['PregnantNow'].isna()) & (df['Gender'] == 'female')]['Age'].hist(bins=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All missing values are either from males or people that are less than 20 years old or more than 45 years old. As such it can be assumed that all individual with missing values are not pregnant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Flu**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flu'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['flu'].value_counts().plot.bar(rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no rows with flu value missing that would need to be dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Model Choice\n",
    "\n",
    "The first task is to decide which classifier to use (from the ones that we learned this block), i.e. which one would best suit our task and our data. Note that our data are heavily unbalanced, thus you need to do some exploration on how different classifiers handle inbalances in the data (we will discuss some of these techniques during week 3 lecture).\n",
    "\n",
    "It would be possible to do brute force model comparison here - i.e. tune all models and compare which does best with respect to various benchmarks. However, it is also reasonable to do a first round of model comparison by running models (with out of the box parameter settings) on the training data and eliminating some models which performed very poorly.\n",
    "\n",
    "Let the best model win!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning, normalization and data splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_bin_num(dataframe, feature, bin_feature, bin_size, stat_measure, min_bin=None, max_bin=None, default_val='No'):\n",
    "    if min_bin is None:\n",
    "        min_bin = dataframe[bin_feature].min()\n",
    "    if max_bin is None:\n",
    "        max_bin = dataframe[bin_feature].max()\n",
    "    new_dataframe = dataframe.copy()\n",
    "    df_meancat = pd.DataFrame(columns=['interval', 'stat_measure'])\n",
    "    for num_bin, subset in dataframe.groupby(pd.cut(dataframe[bin_feature], np.arange(min_bin, max_bin+bin_size, bin_size), include_lowest=True)):\n",
    "        if stat_measure is 'mean':\n",
    "            row = [num_bin, subset[feature].mean()]\n",
    "        elif stat_measure is 'mode': \n",
    "            mode_ar = subset[feature].mode().values\n",
    "            if len(mode_ar) > 0:\n",
    "                row = [num_bin, mode_ar[0]]\n",
    "            else:\n",
    "                row = [num_bin, default_val]\n",
    "        else:\n",
    "            raise Exception('Unknown statistical measure: ' + stat_measure)\n",
    "        df_meancat.loc[len(df_meancat)] = row\n",
    "    for index, row_df in dataframe[dataframe[feature].isna()].iterrows():\n",
    "        for _, row_meancat in df_meancat.iterrows():\n",
    "            if row_df[bin_feature] in row_meancat['interval']:\n",
    "                new_dataframe.at[index, feature] = row_meancat['stat_measure']\n",
    "    return new_dataframe\n",
    "\n",
    "\n",
    "def make_dummy_cols(dataframe, column, prefix, drop_dummy):\n",
    "    dummy = pd.get_dummies(dataframe[column], prefix=prefix)\n",
    "    dummy = dummy.drop(columns=prefix+'_'+drop_dummy)\n",
    "    dataframe = pd.concat([dataframe, dummy], axis=1)\n",
    "    dataframe = dataframe.drop(columns=column)\n",
    "    return dataframe\n",
    "\n",
    "\n",
    "def cleaning(dataframe_raw):\n",
    "    dataframe = dataframe_raw.copy()\n",
    "\n",
    "    dataframe = dataframe.set_index('ID')\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<=13) & (dataframe['Education'].isna()), 'Education'] = 'Lower School/Kindergarten'\n",
    "    dataframe.loc[(dataframe['Age']==14) & (dataframe['Education'].isna()), 'Education'] = '8th Grade'\n",
    "    dataframe.loc[(dataframe['Age']<=17) & (dataframe['Education'].isna()), 'Education'] = '9 - 11th Grade'\n",
    "    dataframe.loc[(dataframe['Age']<=21) & (dataframe['Education'].isna()), 'Education'] = 'High School'\n",
    "    dataframe['Education'] = dataframe['Education'].fillna('Some College')\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<=20) & (dataframe['MaritalStatus'].isna()), 'MaritalStatus'] = 'NeverMarried'\n",
    "    dataframe.at[dataframe['MaritalStatus'].isna(), 'MaritalStatus'] = fill_bin_num(dataframe, 'MaritalStatus', 'Age', 5, 'mode',20)\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['HHIncome'])\n",
    "\n",
    "    dataframe.loc[dataframe['HHIncomeMid'].isna(), 'HHIncomeMid'] = dataframe['HHIncomeMid'].median()\n",
    "\n",
    "    dataframe.loc[dataframe['Poverty'].isna(), 'Poverty'] = dataframe['Poverty'].median()\n",
    "\n",
    "    dataframe.loc[dataframe['HomeRooms'].isna(), 'HomeRooms'] = dataframe['HomeRooms'].mean()\n",
    "\n",
    "    dataframe.loc[dataframe['HomeOwn'].isna(), 'HomeOwn'] = dataframe['HomeOwn'].mode().values[0]\n",
    "\n",
    "    dataframe.loc[(dataframe['Work'].isna()) & (dataframe['Education'].isna()) & (dataframe['Age']<=20), 'Work'] = 'NotWorking'\n",
    "\n",
    "    dataframe.loc[dataframe['Work'].isna(), 'Work'] = dataframe['Work'].mode().values[0]\n",
    "\n",
    "    dataframe = fill_bin_num(dataframe, 'Weight', 'Age', 2, 'mean')\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['HeadCirc'])\n",
    "\n",
    "    for index, row in dataframe.iterrows():\n",
    "        if np.isnan(row['Height']) and not np.isnan(row['Length']):\n",
    "            dataframe.at[index, 'Height'] = row['Length']\n",
    "    dataframe = fill_bin_num(dataframe, 'Height', 'Age', 2, 'mean')\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['Length'])\n",
    "\n",
    "    for index, row in dataframe[dataframe['BMI'].isna()].iterrows():\n",
    "        dataframe.at[index, 'BMI'] = row['Weight'] / ((row['Height']/100)**2)\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BMICatUnder20yrs')\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BMI_WHO')\n",
    "\n",
    "    dataframe = fill_bin_num(dataframe, 'Pulse', 'Age', 10, 'mean')\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<10) & (dataframe['BPSysAve'].isna()), 'BPSysAve'] = 105\n",
    "    dataframe = fill_bin_num(dataframe, 'BPSysAve', 'Age', 5, 'mean', 10)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<10) & (dataframe['BPDiaAve'].isna()), 'BPDiaAve'] = 60\n",
    "    dataframe = fill_bin_num(dataframe, 'BPDiaAve', 'Age', 5, 'mean', 10)\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BPSys1')\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BPDia1')\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BPSys2')\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BPDia2')\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BPSys3')\n",
    "\n",
    "    dataframe = dataframe.drop(columns='BPDia3')\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['Testosterone'])\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<10) & (dataframe['DirectChol'].isna()), 'DirectChol'] = 0 \n",
    "    dataframe = fill_bin_num(dataframe, 'DirectChol', 'Age', 5, 'mean', 10)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<10) & (dataframe['TotChol'].isna()), 'TotChol'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'TotChol', 'Age', 5, 'mean', 10)\n",
    "    \n",
    "    dataframe.loc[dataframe['UrineVol1'].isna(), 'UrineVol1'] = dataframe['UrineVol1'].median()\n",
    "\n",
    "    dataframe.loc[dataframe['UrineFlow1'].isna(), 'UrineFlow1'] = dataframe['UrineFlow1'].median()\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['UrineVol2'])\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['UrineFlow2'])\n",
    "\n",
    "    dataframe['Diabetes'] = dataframe['Diabetes'].fillna('No')\n",
    "\n",
    "    dataframe['DiabetesAge'] = dataframe['DiabetesAge'].fillna(0)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<=12) & (dataframe['HealthGen'].isna()), 'HealthGen'] = 'Good'\n",
    "    dataframe = fill_bin_num(dataframe, 'HealthGen', 'Age', 5, 'mode', 10)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<=12) & (dataframe['DaysMentHlthBad'].isna()), 'DaysMentHlthBad'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'DaysMentHlthBad', 'Age', 5, 'mean', 10)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<=15) & (dataframe['LittleInterest'].isna()), 'LittleInterest'] = 'None'\n",
    "    dataframe = fill_bin_num(dataframe, 'LittleInterest', 'Age', 5, 'mode', 15)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<=12) & (dataframe['DaysMentHlthBad'].isna()), 'DaysMentHlthBad'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'DaysMentHlthBad', 'Age', 5, 'mean', 10)\n",
    "\n",
    "    for index, row in dataframe.iterrows():\n",
    "        if np.isnan(row['nBabies']) and not np.isnan(row['nPregnancies']):\n",
    "            dataframe.at[index, 'nBabies'] = row['nPregnancies']\n",
    "    dataframe['nBabies'] = dataframe['nBabies'].fillna(0)\n",
    "\n",
    "    dataframe['nPregnancies'] = dataframe['nPregnancies'].fillna(0)\n",
    "\n",
    "    dataframe['Age1stBaby'] = dataframe['Age1stBaby'].fillna(0)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']==0) & (dataframe['SleepHrsNight'].isna()), 'SleepHrsNight'] = 14\n",
    "    dataframe.loc[(dataframe['Age']<=2) & (dataframe['SleepHrsNight'].isna()), 'SleepHrsNight'] = 12\n",
    "    dataframe.loc[(dataframe['Age']<=5) & (dataframe['SleepHrsNight'].isna()), 'SleepHrsNight'] = 10\n",
    "    dataframe.loc[(dataframe['Age']<=10) & (dataframe['SleepHrsNight'].isna()), 'SleepHrsNight'] = 9\n",
    "    dataframe.loc[(dataframe['Age']<=15) & (dataframe['SleepHrsNight'].isna()), 'SleepHrsNight'] = 8\n",
    "    dataframe['SleepHrsNight'] = dataframe['SleepHrsNight'].fillna(dataframe_raw['SleepHrsNight'].mean())\n",
    "\n",
    "    dataframe['SleepTrouble'] = dataframe['SleepTrouble'].fillna('No')\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<=4) & (dataframe['PhysActive'].isna()), 'PhysActive'] = 'No'\n",
    "    dataframe = fill_bin_num(dataframe, 'PhysActive', 'Age', 2, 'mode', 16)\n",
    "    dataframe['PhysActive'] = dataframe['PhysActive'].fillna('Yes') # Big assumption here. All kids between 4 and 16 are physically active\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['PhysActiveDays'])\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['TVHrsDay'])\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['TVHrsDayChild'])\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['CompHrsDay'])\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['CompHrsDayChild'])\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<18) & (dataframe['Alcohol12PlusYr'].isna()), 'Alcohol12PlusYr'] = 'No'\n",
    "    dataframe = fill_bin_num(dataframe, 'Alcohol12PlusYr', 'Age', 5, 'mode', 18)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<18) & (dataframe['AlcoholDay'].isna()), 'AlcoholDay'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'AlcoholDay', 'Age', 5, 'mean', 18)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<18) & (dataframe['AlcoholYear'].isna()), 'AlcoholYear'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'AlcoholYear', 'Age', 5, 'mean', 18)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<20) & (dataframe['SmokeNow'].isna()), 'SmokeNow'] = 'No'\n",
    "    dataframe = fill_bin_num(dataframe, 'SmokeNow', 'Age', 5, 'mode', 20)\n",
    "\n",
    "    dataframe['Smoke100'] = dataframe['Smoke100'].fillna('No')\n",
    "\n",
    "    dataframe['Smoke100n'] = dataframe['Smoke100n'].fillna('No')\n",
    "\n",
    "    dataframe.loc[(dataframe['SmokeNow']=='No') & (dataframe['SmokeAge'].isna()), 'SmokeAge'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'SmokeAge', 'Age', 5, 'mean', 20)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<18) & (dataframe['Marijuana'].isna()), 'Marijuana'] = 'No'\n",
    "    dataframe.loc[(dataframe['Marijuana'].isna()) & (dataframe['SmokeNow']=='No'), 'Marijuana'] = 'No'\n",
    "    dataframe = fill_bin_num(dataframe, 'Marijuana', 'Age', 5, 'mode', 20)\n",
    "\n",
    "    dataframe.loc[(dataframe['Marijuana']=='No') & (dataframe['AgeFirstMarij'].isna()), 'AgeFirstMarij'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'AgeFirstMarij', 'Age', 5, 'mean', 20)\n",
    "\n",
    "    dataframe.loc[(dataframe['Marijuana']=='No') & (dataframe['RegularMarij'].isna()), 'RegularMarij'] = 'No'\n",
    "    dataframe = fill_bin_num(dataframe, 'RegularMarij', 'Age', 5, 'mode', 20)\n",
    "\n",
    "    dataframe.loc[(dataframe['RegularMarij']=='No') & (dataframe['AgeRegMarij'].isna()), 'AgeRegMarij'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'AgeRegMarij', 'Age', 5, 'mean', 20)\n",
    "\n",
    "    dataframe.loc[(dataframe['Age']<18) & (dataframe['HardDrugs'].isna()), 'HardDrugs'] = 'No'\n",
    "    dataframe = fill_bin_num(dataframe, 'HardDrugs', 'Age', 5, 'mode', 18)\n",
    "\n",
    "    mode_sex_age = dataframe['SexAge'].mode()[0]\n",
    "    dataframe.loc[(dataframe['Age']<=mode_sex_age) & (dataframe['SexEver'].isna()), 'SexEver'] = 'No'\n",
    "    dataframe['SexEver'] = dataframe['SexEver'].fillna('Yes')\n",
    "\n",
    "    dataframe.loc[(dataframe['SexEver']=='No') & (dataframe['SexAge'].isna()), 'SexAge'] = 0\n",
    "    dataframe['SexAge'] = dataframe['SexAge'].fillna(mode_sex_age)\n",
    "\n",
    "    dataframe.loc[(dataframe['SexEver']=='No') & (dataframe['SexNumPartnLife'].isna()), 'SexNumPartnLife'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'SexNumPartnLife', 'Age', 5, 'mean')\n",
    "    dataframe['SexNumPartnLife'] = dataframe_raw.loc[(dataframe_raw['Age'] >= 60) & (dataframe_raw['Age'] <= 70), 'SexNumPartnLife'].mode()[0] # Missing values for the elderly. Assumed that lifetime sex partners do not increase after 60.\n",
    "\n",
    "    dataframe.loc[(dataframe['SexEver']=='No') & (dataframe['SexNumPartYear'].isna()), 'SexNumPartYear'] = 0\n",
    "    dataframe = fill_bin_num(dataframe, 'SexNumPartYear', 'Age', 10, 'mean')\n",
    "    dataframe['SexNumPartYear'] = dataframe['SexNumPartYear'].fillna(0)\n",
    "\n",
    "    dataframe['SameSex'] = dataframe['SameSex'].fillna('No')\n",
    "\n",
    "    dataframe = dataframe.drop(columns=['SexOrientation'])\n",
    "\n",
    "    dataframe['PregnantNow'] = dataframe['PregnantNow'].fillna('No')\n",
    "\n",
    "\n",
    "    # Making dummy variables\n",
    "    dataframe['male'] = 1*(dataframe['Gender'] ==  'male')\n",
    "    dataframe = dataframe.drop(columns=['Gender'])\n",
    "\n",
    "    dataframe['white'] = np.where(dataframe['Race1'] == 'white',1,0)\n",
    "    dataframe = dataframe.drop(columns=['Race1'])\n",
    "\n",
    "    dataframe = make_dummy_cols(dataframe, 'Education', 'education', '8th Grade')\n",
    "\n",
    "    dataframe = make_dummy_cols(dataframe, 'MaritalStatus', 'maritalstatus', 'Separated')\n",
    "\n",
    "    dataframe = make_dummy_cols(dataframe, 'HomeOwn', 'homeown', 'Other')\n",
    "\n",
    "    dataframe = make_dummy_cols(dataframe, 'Work', 'work', 'Looking')\n",
    "\n",
    "    dataframe['Diabetes'] = np.where(dataframe['Diabetes'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe = make_dummy_cols(dataframe, 'HealthGen', 'healthgen', 'Poor')\n",
    "\n",
    "    dataframe = make_dummy_cols(dataframe, 'LittleInterest', 'littleinterest', 'None')\n",
    "\n",
    "    dataframe = make_dummy_cols(dataframe, 'Depressed', 'depressed', 'None')\n",
    "\n",
    "    dataframe['SleepTrouble'] = np.where(dataframe['SleepTrouble'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['PhysActive'] = np.where(dataframe['PhysActive'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['Alcohol12PlusYr'] = np.where(dataframe['Alcohol12PlusYr'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['SmokeNow'] = np.where(dataframe['SmokeNow'] == 'Yes',1,0)\n",
    "    \n",
    "    dataframe['Smoke100'] = np.where(dataframe['Smoke100'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['Smoke100n'] = np.where(dataframe['Smoke100n'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['Marijuana'] = np.where(dataframe['Marijuana'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['RegularMarij'] = np.where(dataframe['RegularMarij'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['HardDrugs'] = np.where(dataframe['HardDrugs'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['SexEver'] = np.where(dataframe['SexEver'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['SameSex'] = np.where(dataframe['SameSex'] == 'Yes',1,0)\n",
    "\n",
    "    dataframe['PregnantNow'] = np.where(dataframe['PregnantNow'] == 'Yes',1,0)\n",
    "\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "data = cleaning(df).select_dtypes(include = 'number')\n",
    "norm = preprocessing.MinMaxScaler()\n",
    "data_n = norm.fit_transform(data.drop('flu', axis=1))\n",
    "ndata = pd.DataFrame(norm.fit_transform(data.drop('flu', axis=1)), index=data.index)\n",
    "ndata['flu'] = data['flu']\n",
    "num_test = cleaning(df_test).select_dtypes(include='number')\n",
    "ntest = pd.DataFrame(norm.fit_transform(num_test), index=num_test.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(ndata, stratify=ndata['flu'], test_size=0.1)\n",
    "\n",
    "X_train = train.drop('flu', axis=1)\n",
    "X_test = test.drop('flu', axis=1)\n",
    "y_train = train['flu']\n",
    "y_test = test['flu']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "\n",
    "cw = []\n",
    "for i in np.linspace(start = 0.006, stop = 0.08, num = 5):\n",
    "    cw.append({0:i, 1:1-i})\n",
    "cw.append('balanced')\n",
    "C = [x for x in np.linspace(start = 0.2, stop = 1.5, num = 5)]\n",
    "C.append(1)\n",
    "\n",
    "param_grid = {\n",
    "    'C':C,\n",
    "    'kernel':['linear', 'rbf', 'poly', 'sigmoid'],\n",
    "    'degree':[2,3,4,5,6,7,8],\n",
    "    'gamma':['auto'],\n",
    "    'shrinking':[True, False],\n",
    "    'class_weight': cw\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'SVC' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-203-7166061f37d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0msv_r\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomizedSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscorel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msv_r\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'SVC' is not defined"
     ]
    }
   ],
   "source": [
    "sv = SVC()\n",
    "sv_r = RandomizedSearchCV(sv, param_grid, scoring=scorel, cv=3, return_train_score=True, verbose=2, random_state=42, n_jobs=-2, n_iter=300)\n",
    "sv_r.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = sv_r.best_params_\n",
    "print('The best parameters are {} giving an average Balanced Accuract of {:.4f}'.format(params, sv_r.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(sv_r.best_estimator_.predict(ntest))\n",
    "result = pd.DataFrame(np.array([num_test.index, a], dtype=np.int32).T, columns=['ID', 'Prediction'])\n",
    "result.to_csv('result_svm.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import telegram \n",
    "import json\n",
    "import os\n",
    "def notify_me(message='Done'):\n",
    "    filename = os.environ['HOME']+'/.telegram'\n",
    "    with open(filename) as f:\n",
    "        json_blob = f.read()\n",
    "        credentials = json.loads(json_blob)\n",
    "    bot = telegram.Bot(token=credentials['api_key'])\n",
    "    bot.send_message(chat_id=credentials['chat_id'], text=message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "cw = []\n",
    "for i in np.linspace(start = 0.001, stop = 0.4, num = 20):\n",
    "    cw.append({0:i, 1:1-i})\n",
    "cw.append('balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w0 = 0.0599\n",
    "param_grid = {\n",
    "    'C':[x for x in np.linspace(start = 0.001, stop = 20, num = 40)],\n",
    "    'penalty':['l1', 'l2', 'elasticnet'],\n",
    "    'max_iter':[10, 100, 1000, 10000],\n",
    "    'class_weight': cw\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()\n",
    "lr_r = GridSearchCV(lr, param_grid, scoring='balanced_accuracy', cv=3, return_train_score=True, verbose=0, n_jobs=-1)\n",
    "lr_r.fit(ndata.drop('flu', axis=1), ndata['flu'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = lr_r.best_params_\n",
    "\n",
    "notify_me('The best parameters are {} giving an average ROC AUC score of {:.4f}'.format(params, lr_r.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(lr_r.cv_results_).sort_values(by='rank_test_score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = 0.064\n",
    "fin = LogisticRegression(class_weight={0:w,1:1-w}, C=2.02, penalty='l2')\n",
    "fin.fit(X_train, y_train)\n",
    "np.mean(cross_val_score(fin, X_train, y_train, scoring='balanced_accuracy', cv=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lo = lr_r.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(lr_r.best_estimator_.predict(ntest))\n",
    "result = pd.DataFrame(np.array([num_test.index, a], dtype=np.int32).T, columns=['ID', 'Prediction'])\n",
    "result.to_csv('result_lr_n.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scorel(model, X_test, y_test):\n",
    "    return 0.6*np.mean(cross_val_score(model,X_train,y_train,scoring='balanced_accuracy', cv=5))+0.4*score(model,X_test, y_test)[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "xg_c = xgb.XGBClassifier(max_depth=3)\n",
    "param_grid = {\n",
    "    'objective':['reg:squarederror', 'reg:logistic', 'binary:logistic'],\n",
    "    'scale_pos_weight':[20,21,22],\n",
    "    'colsample_bytree':[0.3],\n",
    "    'eval_metric':['aucpr', 'auc', 'mae', 'map'],\n",
    "    'alpha':[5, 10, 20],\n",
    "    'n_estimators': [5, 10, 25, 40, 50, 100, 125],\n",
    "    'learning_rate': [0.05, 0.1, 0.15]\n",
    "}\n",
    "xg_s = GridSearchCV(xg_c, param_grid, scoring='balanced_accuracy', cv=3, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_s.fit(ndata.drop('flu', axis=1), ndata['flu'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = xg_s.best_params_\n",
    "print('The best parameters are {} giving an average ROC AUC score of {:.4f}'.format(params, xg_s.best_score_))\n",
    "xg = xg_s.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(xg.predict(ntest.values))\n",
    "result = pd.DataFrame(np.array([num_test.index, a], dtype=np.int32).T, columns=['ID', 'Prediction'])\n",
    "result.to_csv('result_xg.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "w0 = 0.0599\n",
    "\n",
    "cw = []\n",
    "for i in np.linspace(start = 0.001, stop = 0.15, num = 10):\n",
    "    cw.append({0:i, 1:1-i})\n",
    "cw.append('balanced')\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators' : [20,50,70,110, 130, 150, 200],\n",
    "    'max_features' : ['auto', 'sqrt'], \n",
    "    'max_depth':[3, 5, 7, 10, 15, None],\n",
    "    'criterion' : ['gini', 'entropy'],\n",
    "    'min_samples_split' : [2, 3, 5, 7],\n",
    "    'min_samples_leaf' : [2, 3, 5, 7],\n",
    "    'class_weight': cw\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfs = RandomForestClassifier()\n",
    "rfs_random = RandomizedSearchCV(rfs, param_grid, scoring='balanced_accuracy', cv=3, return_train_score=True, random_state=42, n_jobs=-1, n_iter=1000)\n",
    "rfs_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = rfs_random.best_params_\n",
    "notify_me('The best parameters are {} giving an average ROC AUC score of {:.4f}'.format(params, rfs_random.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(**rfs_random.best_params_)\n",
    "rf.fit(ndata.drop('flu', axis=1), ndata['flu'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(rf.predict(ntest))\n",
    "result = pd.DataFrame(np.array([num_test.index, a], dtype=np.int32).T, columns=['ID', 'Prediction'])\n",
    "result.to_csv('result_rf.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_features' : ['auto', 'sqrt'], \n",
    "    'max_depth':[3, 4, 5,6, 7, 10, None],\n",
    "    'criterion' : ['gini', 'entropy'],\n",
    "    'min_samples_split' : [2, 3,4, 5, 7],\n",
    "    'min_samples_leaf' : [2, 3,4, 5, 7],\n",
    "    'class_weight': cw\n",
    "}\n",
    "clf = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_r = RandomizedSearchCV(clf, param_grid, scoring='balanced_accuracy', cv=3, return_train_score=True, verbose=0, n_iter=2000)\n",
    "clf_r.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = clf_r.best_params_\n",
    "print('The best parameters are {} giving an average ROC AUC score of {:.4f}'.format(params, clf_r.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(clf_r.best_estimator_.predict(ntest))\n",
    "result = pd.DataFrame(np.array([num_test.index, a], dtype=np.int32).T, columns=['ID', 'Prediction'])\n",
    "result.to_csv('result_dt.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame({'LR':0.69452, 'SVM':0.69214, 'XGBoost':0.67610}, index=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## On evaluation\n",
    "\n",
    "### AUROC\n",
    "\n",
    "As mentioned abbove, we will use the accuracy scores for each class and for the whole dataset, as well as the AUROC score from Kaggle platform. You can coimpute AUROC locally (e.g. on your train/validation set) by calling the relevant scikit learn function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###AUROC locally\n",
    "\n",
    "#score = roc_auc_score(real_labels, predicted_labels)\n",
    "\n",
    "#real_labels: the ground truth (0 or 1)\n",
    "#predicted_labels: labels predicted by your algorithm (0 or 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy (per class)\n",
    "\n",
    "Below there is a function that will be handy for your models. It computes the accuracy per-class, based on a model you pass as parameter and a dataset (split to x/y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extended_score(model, x_test, y_test):\n",
    "    overall = 0\n",
    "    class_0 = 0\n",
    "    class_1 = 0\n",
    "    for i in range(100):\n",
    "        sample = np.random.choice(len(x_test), len(x_test))\n",
    "        x_sub_test = x_test[sample]\n",
    "        y_sub_test = y_test[sample]\n",
    "        \n",
    "        overall += model.score(x_sub_test, y_sub_test)\n",
    "        class_0 += model.score(x_sub_test[y_sub_test==0], y_sub_test[y_sub_test==0])\n",
    "        class_1 += model.score(x_sub_test[y_sub_test==1], y_sub_test[y_sub_test==1])\n",
    "\n",
    "    return pd.Series([overall / 100., \n",
    "                      class_0 / 100.,\n",
    "                      class_1 / 100.],\n",
    "                      index=['overall accuracy', 'accuracy on class 0', 'accuracy on class 1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#same job as before, but faster?\n",
    "\n",
    "score = lambda model, x_val, y_val: pd.Series([model.score(x_val, y_val), \n",
    "                                                 model.score(x_val[y_val==0], y_val[y_val==0]),\n",
    "                                                 model.score(x_val[y_val==1], y_val[y_val==1])], \n",
    "                                                index=['overall accuracy', 'accuracy on class 0', 'accuracy on class 1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scorel(model, X_test, y_test):\n",
    "    return 0.8*np.mean(cross_val_score(model,X_train,y_train,scoring='balanced_accuracy', cv=2))+0.2*score(model,X_test, y_test)[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution extraction for Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that you extract your solutions (predictions) in the correct format required by Kaggle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Conclusions\n",
    "\n",
    "Highlight at the end of your notebook, which were the top-3 approaches that produced the best scores for you. That is, provide a table with the scores you got (on the AUROC score you get from Kaggle) and make sure that you judge these in relation to your work on the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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