scikit-learn/4b_medical_adherence_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medical Compliance Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective: To predict patients likelihood of adherence to a prescribed regimen\n",
    "\n",
    "###  ** Data: ** \n",
    "Data has Patient details such as Age, Gender, Diabetes along with the target variable 'Adherence' Yes: If patient has adhered to regimen, No: Not adhered to regimen. Data is split in training and test datasets.\n",
    "\n",
    "### ** Evaluation: ** For each patient id in test set, we must predict if patient is going to adhere to the prescribed regimen. The model will be evaluated on precision and recall for both the outcomes. \n",
    "\n",
    "### ** Approach: ** \n",
    "**1. Data Preparation:**\n",
    "\n",
    "   a. Label encoding for string variables\n",
    "   \n",
    "   b. Check for and handle missing values\n",
    "   \n",
    "   c. Check for and handle outliers\n",
    "   \n",
    "   d. Check for and handle duplicates\n",
    "\n",
    "\n",
    "**2. Exploring the Data:**\n",
    "\n",
    "   a. Relationship between target and predictor variables\n",
    "   \n",
    "   b. Checking normality\n",
    "   \n",
    "   c. Checking correlation\n",
    "   \n",
    "   d. Checking class balance\n",
    "   \n",
    "**3. Data Preprocessing:**\n",
    "\n",
    "   a. Normalising\n",
    "   \n",
    "   b. Feature Selection - Reduction\n",
    "   \n",
    "   c. Split train-validate-test\n",
    "   \n",
    "**4. Training the Model: Learning & Evaluation:**\n",
    "\n",
    "   a. Logistic Regression\n",
    "   \n",
    "   b. Decision Trees\n",
    "   \n",
    "   c. Random Forest\n",
    "   \n",
    "   d. SVM\n",
    "   \n",
    "**5. Prediction:**\n",
    "\n",
    "   a. Best Model Fit\n",
    "   \n",
    "   b. Output desired csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import packages\n",
    "import pandas as pd\n",
    "from numpy import *\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import scipy\n",
    "import scipy.stats as stats\n",
    "import pylab as pl\n",
    "import seaborn as sns\n",
    "sns.set(style = \"whitegrid\", color_codes = True)\n",
    "#np.random.seed(sum(map(ord, \"palettes\")))\n",
    "\n",
    "# special matplotlib argument for improved plots\n",
    "from matplotlib import rcParams\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grabbing the data\n",
    "data = pd.read_csv(\"Training Data.csv\", encoding ='latin1')\n",
    "test_data = pd.read_csv(\"Test Data.csv\", encoding ='latin1')\n",
    "data_copy = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "      <th>Adherence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>F</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  Age Gender  Prescription_period  Diabetes  Alcoholism  \\\n",
       "0           1   19      M                    7         0           0   \n",
       "1           2   24      F                   59         0           0   \n",
       "2           3    4      F                   43         0           0   \n",
       "3           4   38      M                   66         0           0   \n",
       "4           5   46      F                   98         0           0   \n",
       "\n",
       "   HyperTension  Smokes  Tuberculosis  Sms_Reminder Adherence  \n",
       "0             0       0             0             0        No  \n",
       "1             0       0             0             0        No  \n",
       "2             0       0             0             0        No  \n",
       "3             0       0             0             1        No  \n",
       "4             0       0             0             1        No  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Snapshot of training data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>62</td>\n",
       "      <td>F</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>F</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>M</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  Age Gender  Prescription_period  Diabetes  Alcoholism  \\\n",
       "0           1    5      M                   28         0           0   \n",
       "1           2   62      F                    9         1           0   \n",
       "2           3    4      F                   73         0           0   \n",
       "3           4   33      M                  117         0           0   \n",
       "4           5   38      M                    8         0           0   \n",
       "\n",
       "   HyperTension  Smokes  Tuberculosis  Sms_Reminder  \n",
       "0             0       0             0             1  \n",
       "1             1       0             0             0  \n",
       "2             0       0             0             1  \n",
       "3             0       0             0             0  \n",
       "4             0       0             0             1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Snapshot of testing data\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Note how there are few categorical fields which are strings, we will need to encode them to numeric. Also, the 'Adherence' field is missing from Test Data **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adherence                                                      [No, Yes]\n",
       "Age                    [19, 24, 4, 38, 46, 20, 51, 58, 73, 48, 64, 5,...\n",
       "Alcoholism                                                        [0, 1]\n",
       "Diabetes                                                          [0, 1]\n",
       "Gender                                                            [M, F]\n",
       "HyperTension                                                      [0, 1]\n",
       "Prescription_period    [7, 59, 43, 66, 98, 73, 102, 96, 89, 97, 77, 1...\n",
       "Smokes                                                            [0, 1]\n",
       "Sms_Reminder                                                   [0, 1, 2]\n",
       "Tuberculosis                                                      [0, 1]\n",
       "patient_id             [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking out unique values in each field\n",
    "pd.Series({c: data[c].unique() for c in data})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** 1.a Label encoding for string variables **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Training\n",
    "# Only replace for the two string value variables\n",
    "data['Adherence'] = np.where(data['Adherence'] == 'No',0, 1)\n",
    "data['Gender'] = np.where(data['Gender'] == 'M',0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test\n",
    "# Only replace for the two string value variables\n",
    "test_data['Gender'] = np.where(test_data['Gender'] == 'M',0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 180212 entries, 0 to 180211\n",
      "Data columns (total 11 columns):\n",
      "patient_id             180212 non-null int64\n",
      "Age                    180212 non-null int64\n",
      "Gender                 180212 non-null int64\n",
      "Prescription_period    180212 non-null int64\n",
      "Diabetes               180212 non-null int64\n",
      "Alcoholism             180212 non-null int64\n",
      "HyperTension           180212 non-null int64\n",
      "Smokes                 180212 non-null int64\n",
      "Tuberculosis           180212 non-null int64\n",
      "Sms_Reminder           180212 non-null int64\n",
      "Adherence              180212 non-null int64\n",
      "dtypes: int64(11)\n",
      "memory usage: 15.1 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.b Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training\n",
    "data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test\n",
    "test_data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** 1.c  Outliers **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are only two continious fields we will check these for outliers only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in data: 180212\n",
      "Rows in df2: 180209\n",
      "Outliers removed 0.00166470601292\n"
     ]
    }
   ],
   "source": [
    "#In Age\n",
    "df2 = data.copy()\n",
    "df2 = df2[((df2.Age - df2.Age.mean()) / df2.Age.std()).abs() < 3]\n",
    "print(\"Rows in data:\", data.patient_id.count())\n",
    "print(\"Rows in df2:\", df2.patient_id.count())\n",
    "print(\"Outliers removed\", 100.00-((df2.patient_id.count()/data.patient_id.count())*100))\n",
    "del df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in data: 180212\n",
      "Rows in df2: 180212\n",
      "Outliers removed 0.0\n"
     ]
    }
   ],
   "source": [
    "# In Prescription Perion\n",
    "df2 = data.copy()\n",
    "df2 = df2[((df2.Prescription_period - df2.Prescription_period.mean()) / df2.Prescription_period.std()).abs() < 3]\n",
    "print(\"Rows in data:\", data.patient_id.count())\n",
    "print(\"Rows in df2:\", df2.patient_id.count())\n",
    "print(\"Outliers removed\", 100.00-((df2.patient_id.count()/data.patient_id.count())*100))\n",
    "del df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180209"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Outliers are only existing in Age variable\n",
    "#Since outliers are less than 1% we continue with the clean data\n",
    "data = data[((data.Age - data.Age.mean()) / data.Age.std()).abs() < 3]\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** 1.d  Duplicates **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicates in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "      <th>Adherence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "      <td>180209.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>90105.769135</td>\n",
       "      <td>37.794139</td>\n",
       "      <td>0.668307</td>\n",
       "      <td>54.668485</td>\n",
       "      <td>0.078525</td>\n",
       "      <td>0.025043</td>\n",
       "      <td>0.216510</td>\n",
       "      <td>0.052561</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.573972</td>\n",
       "      <td>0.301811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>52022.823660</td>\n",
       "      <td>22.850290</td>\n",
       "      <td>0.470823</td>\n",
       "      <td>35.752590</td>\n",
       "      <td>0.268997</td>\n",
       "      <td>0.156257</td>\n",
       "      <td>0.411867</td>\n",
       "      <td>0.223156</td>\n",
       "      <td>0.018395</td>\n",
       "      <td>0.499823</td>\n",
       "      <td>0.459045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45053.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>90106.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>135158.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>180212.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id            Age         Gender  Prescription_period  \\\n",
       "count  180209.000000  180209.000000  180209.000000        180209.000000   \n",
       "mean    90105.769135      37.794139       0.668307            54.668485   \n",
       "std     52022.823660      22.850290       0.470823            35.752590   \n",
       "min         1.000000       0.000000       0.000000             1.000000   \n",
       "25%     45053.000000      19.000000       0.000000            22.000000   \n",
       "50%     90106.000000      38.000000       1.000000            51.000000   \n",
       "75%    135158.000000      56.000000       1.000000            86.000000   \n",
       "max    180212.000000     104.000000       1.000000           120.000000   \n",
       "\n",
       "            Diabetes     Alcoholism   HyperTension         Smokes  \\\n",
       "count  180209.000000  180209.000000  180209.000000  180209.000000   \n",
       "mean        0.078525       0.025043       0.216510       0.052561   \n",
       "std         0.268997       0.156257       0.411867       0.223156   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "        Tuberculosis   Sms_Reminder      Adherence  \n",
       "count  180209.000000  180209.000000  180209.000000  \n",
       "mean        0.000338       0.573972       0.301811  \n",
       "std         0.018395       0.499823       0.459045  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000  \n",
       "50%         0.000000       1.000000       0.000000  \n",
       "75%         0.000000       1.000000       1.000000  \n",
       "max         1.000000       2.000000       1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.a Relationship between target and predictor variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "      <th>Adherence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  Age  Gender  Prescription_period  Diabetes  Alcoholism  \\\n",
       "0           1   19       0                    7         0           0   \n",
       "1           2   24       1                   59         0           0   \n",
       "2           3    4       1                   43         0           0   \n",
       "3           4   38       0                   66         0           0   \n",
       "4           5   46       1                   98         0           0   \n",
       "\n",
       "   HyperTension  Smokes  Tuberculosis  Sms_Reminder  Adherence  \n",
       "0             0       0             0             0          0  \n",
       "1             0       0             0             0          0  \n",
       "2             0       0             0             0          0  \n",
       "3             0       0             0             1          0  \n",
       "4             0       0             0             1          0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AUDzMzqa97GzaO25+5lQ0NBLmpTfb+MO+Frr6AxOOl+Q5WLe4gOXzczFLcZ5pw6CprF9S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\n/exnXHjhhQnlGzduJDs7Oz7mPz8/P2F/Xl5ePPZEOvPII48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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14a1e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# distribution of age# distri \n",
    "facet = sns.FacetGrid(data, hue=\"Adherence\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, data['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "0     59774\n",
      "1    120435\n",
      "dtype: int64 Females are: 33.17 %\n",
      "Diabetes\n",
      "0    166058\n",
      "1     14151\n",
      "dtype: int64 Diabetes patients are: 7.85 %\n",
      "Alcoholism\n",
      "0    175696\n",
      "1      4513\n",
      "dtype: int64 Alcoholics are: 2.5 %\n",
      "HyperTension\n",
      "0    141192\n",
      "1     39017\n",
      "dtype: int64 HyperTension patients are: 21.65 %\n",
      "Smokes\n",
      "0    170737\n",
      "1      9472\n",
      "dtype: int64 Smokers are: 5.26 %\n",
      "Tuberculosis\n",
      "0    180148\n",
      "1        61\n",
      "dtype: int64 Tuberculosis patients are: 0.03 %\n"
     ]
    }
   ],
   "source": [
    "# Checking out the size in groups for binary predictors\n",
    "dlen = len(data)\n",
    "\n",
    "print(data.groupby('Gender').size(), \"Females are:\", round((len(data[data['Gender'] == 0])/dlen)*100,2),\"%\" )\n",
    "print(data.groupby('Diabetes').size(), \"Diabetes patients are:\", round((len(data[data['Diabetes'] == 1])/dlen)*100,2) ,\"%\")\n",
    "print(data.groupby('Alcoholism').size(), \"Alcoholics are:\", round((len(data[data['Alcoholism'] == 1])/dlen)*100,2),\"%\" )\n",
    "print(data.groupby('HyperTension').size(), \"HyperTension patients are:\", round((len(data[data['HyperTension'] == 1])/dlen)*100,2),\"%\" )\n",
    "print(data.groupby('Smokes').size(), \"Smokers are:\", round((len(data[data['Smokes'] == 1])/dlen)*100,2),\"%\" )\n",
    "print(data.groupby('Tuberculosis').size(), \"Tuberculosis patients are:\", round((len(data[data['Tuberculosis'] == 1])/dlen)*100,2),\"%\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are very few Tuberculosis patients and alcoholics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.a Checking normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ruhama.ahale/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2881: FutureWarning: \n",
      "mpl_style had been deprecated and will be removed in a future version.\n",
      "Use `matplotlib.pyplot.style.use` instead.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106dfe780>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DPwiITFIvFBHRY+CxKYq1H8zErshOVS6vyWUFX1t1AiNUT0ZE9GjjqSciIpJiURARkRSL\ngoiIpFgUREQkxaIgIiIpFgUREUmxKIiISIpFQUREUiwKIiKSYlEQEZEUi4KIiKRYFEREJMWiICIi\nKRYFERFJsSiIiEhKtetRZGdnY/Hixbh27RpsNhtatWqFESNGoE2bNsoYvV6PmJiYct/XtGlTrFy5\nUq0YRESkMtWKwtXVFX379kX79u1RUlKCpKQkzJkzB4sWLYKvr2+5sRs3blT+r9Fo1IpAREQNQLVT\nT87Oznj++efh6ekJLy8vjB49GiUlJThx4kSFsVqtVvnn4MCzX0RE9qzBnqUdHBzg6OgIs9ncUHdB\nRESNoMGumZ2ZmYmCggJ06NChwrJJkyZBo9EgLCwM77zzDlxcXGo8Z12XWyyWes9RXzXNoAZui0cn\nhz1ksJcc9pDBXnLYQ4ZSDVYU33zzDcLCwhAaGqrc5ufnh4SEBHh5ecFsNmP9+vWYP38+Zs+e3VAx\niIionhqkKM6cOYMjR45g3rx55W7XaDTlXmFMmDABUVFRSE9PR+vWraudt3nz5pKlWdLlpa1cnznq\nq2YZ1MBt8SjlsIcM9pLDHjLYSw61Muj1+npnUf09Cr1ejyVLlmDChAnw9/eXjvX09ISrq6sqK0JE\nRA1D1aLIz8/H3LlzMWTIEDz77LPVjjcajSgoKEBgYKCaMYiISEWqnXoqLi7G/Pnz0a1bN0RERMBm\nswG4f7qp9Fdgr1+/DpvNhqCgIGRnZ2PlypXo3LlzufcxiIjIvqhWFP/5z39w/vx5nD9/Hps3b1Zu\n79ChAxISEgAAV65cwZYtW5CdnQ03Nzd0794dQ4cOVSsClfHaqoqfX6koq8olHs5a9cIQ0SNNtaKI\niIhARESEdEyvXr3Qq1cvte6SqrArspN0eWZmJv6cklXtOCIigH8UkIiIqsGiICIiKRYFERFJNdgn\nsx8GvoFLRKS+x6Yo+AYuEVHD4KknIiKSYlEQEZEUi4KIiKRYFEREJMWiICIiKRYFERFJsSiIiEiK\nRUFERFIsCiIikmJREBGRFIuCiIikWBRERCTFoiAiIikWBRERSbEoiIhIikVBRERSLAoiIpJiURAR\nkRSLgoiIpFgUREQkxaIgIiIpFgUREUmxKIiISIpFQUREUo4P404zMzOxevVqXLp0CR4eHujTpw8G\nDhz4MKIQEVE1Gr0oTCYT5syZg65duyI2NhZ37tzB/Pnz4ezsjH79+jV2HCIiqkajF8X+/fuh0Wgw\ncuRIODg44KmnnsKgQYOwefNm9OnTBw4OPBtGRI1r4MCBSE9Pl45p3bo1tm/f3kiJ7EujF8WpU6fQ\nrl27coXQsWNH5OTk4MaNG3jyyScbO5KqNjwzCM3u3pKOOVPNHMaAYAw7uUW9UKj8gfD0//vv/3/L\nDwL67XnwcToWAOAp/6Z7eqQEvqh8qcbj9O11p5Bntilf69d9CKvhZpXjHX1C4D/y03K3eThrkTQy\nvF45qtPoRXH37l106tQJAGA0GtG0aVP4+voqy2RFkZmZWef7tVgs9Z6jJhLfn4H/7esnzaDT6aRz\n/DklCxEq51y6dGm1ORp625TVWPvjUchhDxnsJYc9PE7L5pA9VtV4nOaZbeVz9F1dqwylORp6ezV6\nURQXF8PJyQmHDh3C4sWL0alTJ0ybNg0AYDabVb2v6OhoXL9+vdxtfcv8FN2yZUusWLFC1fsE7u+4\nUnX5CcFNp1E908OQ+voE+Opv12uOe/5B6PndPx7pDPaSwx4y2FOO2jxOgYqPVTUep6MWz8GZmfXb\nFu8GBAF967ctqtPoReHs7Izi4mJ4e3vD1dUVAQEBSnM2adJE+r3Nmzev1X19++23yv9LG7e2c9TW\nrsgH5o/8vtEzVKexcow4lWTXGewlhz1ksJcc9vA4bcwcjbEt9Hp9vb4feAhF4e/vD6PRiI4dOyIx\nMREAcOvW/XOFAQEBjR2HiIiq0ei/YhQeHo5ffvkFJSUlym1nzpyBl5cXWrRo0dhxiIioGo1eFC+9\n9BIAYP369SgsLMTly5eRlJSEN998k78aS0Rkhxr91JOLiwtmzpyJ1atXIyoqCu7u7ujTpw8/bEdE\nZKceyp/wCAkJQXx8/MO4ayIiqiWe6yEiIikWBRERSbEoiIhISiOEEA87RE3s2bPnYUcgInokvfzy\ny/X6/kemKIiI6OHgqSciIpJiURARkRSLgoiIpFgUREQkpU1ISEh42CHUtHTpUvz444/o2rVrveZJ\nSEjA3r17sWvXLhQWFqJt27YA7v8Bw2XLluHAgQM4dOgQXnjhBWi12jrfz4cffohXXnml2nEmkwnJ\nycno0KFDjebdvXs3QkND6z3PqFGjcPLkSRw9ehRt27aFq6trneaq6XoC9/fhgQMHcPDgQXTv3h3L\nly9HYmIiAgMDERwcXOf7eXBZ2a8r+z7Z/dZ2O5bau3cv1qxZg+3bt+O7777DkSNHUFxcjKeeeqpW\n81Tm5MmTWLp0KQ4ePIi8vDycPn26xvlKc61cuRL79++vNldt9mdtlF2HoqIitG7dutzy2hwLDyo9\nlr///v6fFA8KCqrTPgTu7/+4uDicPXu20ueaqo6thtpuDe2h/AmPhmK1WnHjxg1oNBpYLJZqrwxV\nnbi4ODg5OSE+Ph4RERFwcXHBV199hZkzZ8LNzQ1FRUVwcnJSKb2ci4sLBg0aVOPxu3fvrvSArO08\nwcHBSEhIwJUrV7BkyRIkJCRAo9HUaa6aKLsPS40fPx6bN29W9X5qQna/dV333r17o3fv3khNTUVR\nURH69u1b35iKzZs3Iy4uDm5ubnXO9d577+H1119XNVdtVLcO9TkWSo/lkpISTJ06Fb17967z8avT\n6aDRaHDnzh1Vnmvs3WNVFGfPnkWbNm2g1Wpx8uRJdO7cGRs2bMCpU6eg0+mg0+kwduxY+Pv7Iy0t\nDV9//TU0Gg369++PF154odI5S0pKoNFooNPp8PXXX8Nms2HBggUQQmDWrFkAgKSkJCQnJwMAXnnl\nFYwaNQrA/Svs2Ww2FBcXo23btpgxYwYAYNWqVThw4IByEadSa9euxYEDB2CxWNCsWTMsWLAAjo6O\n+OKLL3D8+HEUFRWhVatWmDVrFhwdHTFx4kT87ne/Q0ZGBp577jnloF+6dClu3bqFhIQEPP3008rt\nqampyhPUp5/+90pdqamp+OGHHwAAvXr1UgomNjYWOTk5iIuLw9NPPw1vb2/cunULISEhlc41ceJE\neHh4ICMjAzqdDm+++SZef/11pKWlISMjA5GRkXBxcUGPHj0wePBgpKam4vDhw7hw4QIA4C9/+Qvc\n3d3h5uaGS5cuobi4WFkHADh9+jTWrVunXEK3d+/eOHXqFPR6PXJycuDk5ASz2Yxly5Yp1zgpuz5W\nqxVr1qzBhQsXlDlL1//mzZuYMWMGOnXqhBs3bmDSpEkAgH379ikXjind3ufOnYMQAh4eHvj000+V\nYyknJwcA4O3tjYiICOh0OqSmpiIzMxNFRUUYNmwYdu3ahbi4uEqPtaqOydjYWISFhSm5hw0bBgD4\n/vvvsXfvXjg5OSEiIgItWrTAv//9b2i1Wuzfvx9ZWVlo27Ytrl69ij59+mDr1q3w8vKCTqdDSEgI\nbt68f0U3g8GArl274sKFC8jPzwdw/weNzMxMnD9/XlnHRYsWoWXLlujfvz+KioqwfPlynDlzBlar\nFQEBAYiIiICfn1+l65CamoqLFy/i1q1bymPH0dER6enp+Oqrr1BSUgI/Pz9lHX744QfMmzcPqamp\nWLt2LZo2bYo+ffogJSUFzzzzTIX1DwwMrHSflx2Tm5sLAMjNzYXJZMJf//pXFBUVYciQIUrm5s2b\n4+rVqzCZTHB3d4fJZFLuNy4uDv7+/khNTcU333yD7OxsdOvWTXmuiYqKUp4zyn7qwGAwICoqqsLt\nVT1vfPHFF7hw4QKys7MhhMCKFSvQpEmTWh8fD27bmJgY6XEmJR4jK1asED/99JM4fvy4WLZsmRBC\niClTpgibzSa2bdsmDh48KIQQwmazidjYWFFYWCisVquYNWuWMJvN5eaaPXu2mDVrlkhISBA//vij\nMv///M//CIvFooyzWCxi2LBhwmAwCLPZLEaNGiXy8vKEEEIMHTpU3LhxQ1itVjFp0iQhhBAGg0GM\nGDFCZGVliYKCAjFu3Dgl09ixY8Xf/vY3UVRUVC7Tvn37xLx588TUqVPLZRw3bpwoKCgQNptNxMXF\nCaPRqCybNm1aldup7LKcnBwRFxcnLBaLsFqtIj4+Xty7d08IIUR0dLSYPHmykn/9+vXi1KlTVc41\nbtw4kZOTI2w2m/jwww/FxIkTlW09duxYkZubK2bOnCni4uKEwWAQe/fuFaNGjRK5ubnKfvj888+V\nffjOO+8oc69du1Z88MEHYvLkySIvL0/MnDlTDB8+XFy8eFHExcWJ+Ph4cffuXREdHS1dH6PRqKzP\nyJEjxYwZM8SoUaNEdHS0Mn7mzJkiPz9fXL9+XcTGxopjx45Vur0nT55c7liKj48XcXFx5fbblClT\nxFdffSWSk5PFtm3bxKZNm8SRI0fEvn37xM6dO5X1kx2TD+Yue4zm5uaWm2Pfvn1i7ty54sKFC2L8\n+PFi48aNIjk5WWzcuFFER0eLw4cPC6PRKEaOHCnu3LkjrFarGDp0qLh8+bKwWq1iyJAh4pNPPhHb\ntm1T9m9ptilTpijZxo4dKwoKCkR8fLyYPn26MBgM0nUoPYbLPnZKSkrE1KlTyx23pevwpz/9SVy4\ncEHs27dPREVFldt+n332mTh27Jiy/rJjuOw2GjlypJg+fbqYOnWquH79uhBCiKlTp5bLPGrUKGEw\nGMS+ffuUbVd2v5VasWKFiImJUZ5rjEajGDJkiMjKyhK5ubliyJAhQgghjEajGDp0aIXbZc8bn3/+\nuYiKihImk6nOx0dl27a6eWQem1cUJSUlSEtLU35quHTpEmw2G3r27IkJEyagRYsWyqcT8/LyYDAY\nMG/ePABAQUEBjEYjAgMDy80ZFxdX7vKsrq6u8Pb2hqOjI5YvX46rV6/ij3/8IywWi/ITqMViwdWr\nVxEWFobg4GAcO3YMeXl5sNlsAIAbN27Aw8MDvr6+AABPT08lU35+Pm7fvo25c+dWyBQeHo7U1NRy\n+Tw9PZX3DEJDQ3Hv3j14e3vXarvp9XqEhobC0fH+odCmTRvcvn0bPj4+8PLyAgBotVrodDoYDAY0\na9asyrk8PT1x/fp1/PzzzxBCwGQyKdvaarVi0aJFKCwsxJNPPgmDwQCTyYSSkhIsWrQIAJCfn48T\nJ04oP9WWlJTAZrNBq9WioKAAPj4+uHbtGhYuXAi9Xg8hBAwGA0JDQ/HGG2/g4MGDMJvNyj6qbH1K\nt49Op0NwcDDeffdd7N+/H5cuXYJWq0WbNm0AAIcPH8bdu3fRqlWrKrf36dOnyx1LVqsVer0eq1ev\nRq9evQAALVq0ULZZkyZN8Ouvv5a7aFcp2TH5YO5SMTEx2L17N/Ly8tClSxe0a9cOPXv2xAsvvICP\nPvoIAJR9aDKZ4OPjA41GA4PBgMDAQGRlZSEgIABubm4oLCyEVquFzWbDrVu38Ic//KFCNqvVio8/\n/hgFBQVwdXWFq6sr3n//ffz973/HunXr0K1bN+njKjw8XNkvpfM2bdq03DHr4OCAnj174rvvvsM/\n//lP9OnTB1qtFk5OTsr2E//3U3np+mdkZFS5z8tuo2bNmiE+Ph7x8fFwd3dXjrGymb28vPDNN98g\nIyMDzZs3h5eXV4X9VvpcU1hYiD179uDSpUt49dVX4ebmpjyuS9+3vHfvHlxdXSvcfvv27SqfNwCg\nXbt25Z57ant8VLZtq5tH5rEpigsXLqB9+/Z4//33AQDLli3D2bNnlZfMZd9L8PT0REhICKZOnVru\nzdnqBAQE4Mcff0ReXh7Gjx+PtWvXwsXFBS4uLli4cKFyMJRydHTEm2++ieLiYuzatQsA0KpVK+Tl\n5UGv16NJkybIzs5WMnl7e+OVV17BgAEDapQnOzsbeXl5cHNzw9WrVzF48GBlmdVqRUlJSbUXg/L3\n98eVK1dgtVqh0Whw8eJFvP766xXGmc1mGAwG6RuI2dnZWL16NRYuXIgZM2bA0dFR2dZ3797F5MmT\n4erqivi1Uz3VAAAEdUlEQVT4eAQGBiIzMxNNmzZV9sMvv/yC3bt3K/twyJAhOHPmDJ555hm4ubkp\n9z958mTMnz8fbm5uCAwMxLfffotmzZrh5ZdfxrfffouTJ09Wuz4Prr8QAjabDRcvXsS4ceOwatUq\nCCHQrl27Kre3Tqer9FgqLi7GrFmzanWNlbock76+vsrxNWvWLMydO1fZ3w+WkaenJ4xGI2w2G3x8\nfHDnzh34+/vDZrPBbDYr+1Wr1SqnqTw9PSGEgKenJ4KCglBQUIDS332Jjo5GXl6e8kQ0YsQIzJs3\nr1br4OHhgezsbNy7d0957JQes0IIlJSUwGKxwGq1StffYDBg4sSJle7zstto165daNKkCUaMGIEv\nv/wSH374IRwcHCrNvHv3biQlJeGNN96ocL+lzzU3b95EbGwsli1bhnv37sFsNqOkpAT5+fnKD4YB\nAQGV3h4SElLl80Zlant8VLZt6zJPqcemKI4cOYLu3bsrX/fo0QNHjx6FEAIff/wxtFotmjVrhsjI\nSLi6umL48OGYN28eNBoNvL29MWHChGrvQ6fT4fe//z0WLFgAnU4HFxcXhISEYNiwYYiNjQVw/1XH\n8uXLAdz/aSIhIQEmk0l55dC0aVO89NJLiI2NhbOzM8xmMwBAo9Gga9eu+OGHH3D8+PEaZXJ1dcWa\nNWuQmZmJHj16wMPDQ1kWHh6OuXPnws/PD1FRUeXeHC7L09MTr776KmbPng3g/vndsq8aSt/ryM7O\nxvTp06ucpzSPzWbD6NGj8eSTTyoP+OHDh2POnDmYNGkSrFYrBg0aBA8PD2g0GnTu3FnZD9nZ2Xj3\n3XeV+Zo0aYIvvvgCnTp1gpeXF8xmMwoLCxETEwMvLy/4+fkhMTERRqMRI0aMgJubG0wmE7p161bl\n+lS1/qtXr0Z8fDx69eqFoKAg+Pn5ISgoCGazGRs2bMDNmzcrbO+DBw9Co9Eox9Ldu3dhtVrh4+OD\n1157TbrvHlR2npoek4mJibh27RpMJhNee+01rF27FteuXUNJSQmGDx+OFStWKGNdXFzQpk0bbNq0\nCR4eHnj++efxj3/8AwDg7u5ebhvpdDp88MEHWLRoEUJCQrBu3TqEhITg8OHDSEhIgLe3t7It0tLS\n4OLigvnz56NPnz4ICQmp8TpoNBpER0dj6dKlSiGVvmrMyclB27ZtceLEiUq/d8OGDUhOToaDgwNM\nJhO6du1a6T4vu41KH4Ph4eE4dOgQDhw4UGG7Z2dnw9vbG3q9vtwPCWWVPtds2rQJwH+fa9zc3BAX\nF4cWLVoohe3p6Vnp7Q4ODlU+b1S1rWpzfFS2bSdPnlyn4wzA4/UexYPy8/NFYmKisFqtoqSkRCxY\nsEBcuHDhYcdSjex9iIfB3vLUx4IFC0ROTk652x6n9SOqjcfmFUVldDod9Hq9cr42PDxcOQdNdVPV\nx24el4/jnD17Fv/617/w4osvKj+B0qPjcT8+Hxb+9VgiIpLin/AgIiIpFgUREUmxKIiISIpFQURE\nUiwKIiKSYlEQEZHU/wcp5tu9FH7VuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14b125c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.options.display.mpl_style = 'default'\n",
    "#cols =['Age','Prescription_period']\n",
    "features = ['Age','Gender','Prescription_period','Diabetes','Alcoholism','HyperTension','Smokes','Tuberculosis','Sms_Reminder','Adherence'] \n",
    "data_var = data[features]\n",
    "data_var.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=masked_array(data = [32375.641677179745 293716.19887388445 --],\n",
       "             mask = [False False  True],\n",
       "       fill_value = 1e+20)\n",
       ", pvalue=array([ 0.,  0.,  0.]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a subset of data with only features to show coorrelation between features\n",
    "features = data[['Age','Prescription_period','Sms_Reminder']]\n",
    "series = features.values\n",
    "scipy.stats.mstats.normaltest(series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, we will need to normalize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9PaqqqrB161bk5OTg7bffhs1mk9wuIqJAcA5/NVQU4Sc5vqlV+MI/LnbBPjDo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\nY5gQEZHXGCZEROQ1hgkREXnt/wP6oWdwCHSAjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106e258d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "h = sorted(data['Age'])  #sorted\n",
    "\n",
    "fit = stats.norm.pdf(h, np.mean(h), np.std(h))  #this is a fitting indeed\n",
    "\n",
    "pl.plot(h,fit,'-o')\n",
    "\n",
    "pl.hist(h,normed=True)      #use this to draw histogram of your data\n",
    "\n",
    "pl.show()     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.a Checking correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L8uXLOXDgALdu3SI3N5fjx4+XaME4fPgwN2/epLCwkFOnTnHs2LEST1qMHDmS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\n4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15130390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there is some multicollinearity between Age-Hypertension, Diabetes-Age/Hypertension, Alcoholism-Smokes and such"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.a Checking class balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(180209, 11)\n",
      "(119788, 10)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adherence\n",
      "0    125820\n",
      "1     54389\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('Adherence').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i8Xj81J+vry/mzJkDjUaDqVOnIjs7G06nE2fOnBlT6+XlJf4pldKpm0wm1NTU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\n3eanV9QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ab3dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "sns.countplot(data['Adherence'],label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of 0: 69.82\n",
      "Proportion of 1: 30.18\n"
     ]
    }
   ],
   "source": [
    "#Do we need to balance the data?\n",
    "print(\"Proportion of 0:\", round((125820/dlen)*100,2))\n",
    "print(\"Proportion of 1:\", round((54389/dlen)*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that 30.18% of the data has Adhered to the prescription, this is a good enough amount of data for us to model on, so we will not consider the data inbalanced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a Normalising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Training **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180209\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "      <th>Adherence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.182692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.487395</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.365385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.546218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.442308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id       Age  Gender  Prescription_period  Diabetes  Alcoholism  \\\n",
       "0           1  0.182692     0.0             0.050420       0.0         0.0   \n",
       "1           2  0.230769     1.0             0.487395       0.0         0.0   \n",
       "2           3  0.038462     1.0             0.352941       0.0         0.0   \n",
       "3           4  0.365385     0.0             0.546218       0.0         0.0   \n",
       "4           5  0.442308     1.0             0.815126       0.0         0.0   \n",
       "\n",
       "   HyperTension  Smokes  Tuberculosis  Sms_Reminder  Adherence  \n",
       "0           0.0     0.0           0.0           0.0          0  \n",
       "1           0.0     0.0           0.0           0.0          0  \n",
       "2           0.0     0.0           0.0           0.0          0  \n",
       "3           0.0     0.0           0.0           0.5          0  \n",
       "4           0.0     0.0           0.0           0.5          0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Gender = data.Gender.astype(float)\n",
    "data.Diabetes = data.Diabetes.astype(float)\n",
    "data.Alcoholism = data.Alcoholism.astype(float)\n",
    "data.HyperTension = data.HyperTension.astype(float)\n",
    "data.Smokes = data.Smokes.astype(float)\n",
    "data.Tuberculosis = data.Tuberculosis.astype(float)\n",
    "\n",
    "\n",
    "for item in data: #converts everything to floats\n",
    "    data[item] = pd.to_numeric(data[item])\n",
    "\n",
    "def normalize(data, toNormalize): #normalizes \n",
    "    result = data.copy()\n",
    "    for item in data.columns:\n",
    "        if (item in toNormalize):\n",
    "            max_value = data[item].max()\n",
    "            min_value = data[item].min()\n",
    "            result[item] = (data[item] - min_value) / (max_value - min_value)\n",
    "    return result\n",
    "toNormalize = ['Age', 'Prescription_period', 'Sms_Reminder'] #columns to normalize\n",
    "data = normalize(data, toNormalize)\n",
    "#data = data.dropna()\n",
    "print(len(data))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ruhama.ahale/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2881: FutureWarning: \n",
      "mpl_style had been deprecated and will be removed in a future version.\n",
      "Use `matplotlib.pyplot.style.use` instead.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cb8ea20>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cb8e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.options.display.mpl_style = 'default'\n",
    "#cols =['Age','Prescription_period']\n",
    "features = ['Age','Gender','Prescription_period','Diabetes','Alcoholism','HyperTension','Smokes','Tuberculosis','Sms_Reminder','Adherence'] \n",
    "data_var = data[features]\n",
    "data_var.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Testing **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119788\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.044248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.226891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.548673</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.035398</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.605042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.292035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.336283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id       Age  Gender  Prescription_period  Diabetes  Alcoholism  \\\n",
       "0           1  0.044248     0.0             0.226891       0.0         0.0   \n",
       "1           2  0.548673     1.0             0.067227       1.0         0.0   \n",
       "2           3  0.035398     1.0             0.605042       0.0         0.0   \n",
       "3           4  0.292035     0.0             0.974790       0.0         0.0   \n",
       "4           5  0.336283     0.0             0.058824       0.0         0.0   \n",
       "\n",
       "   HyperTension  Smokes  Tuberculosis  Sms_Reminder  \n",
       "0           0.0     0.0           0.0           0.5  \n",
       "1           1.0     0.0           0.0           0.0  \n",
       "2           0.0     0.0           0.0           0.5  \n",
       "3           0.0     0.0           0.0           0.0  \n",
       "4           0.0     0.0           0.0           0.5  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.Gender = test_data.Gender.astype(float)\n",
    "test_data.Diabetes = test_data.Diabetes.astype(float)\n",
    "test_data.Alcoholism = test_data.Alcoholism.astype(float)\n",
    "test_data.HyperTension = test_data.HyperTension.astype(float)\n",
    "test_data.Smokes = test_data.Smokes.astype(float)\n",
    "test_data.Tuberculosis = test_data.Tuberculosis.astype(float)\n",
    "\n",
    "\n",
    "for item in test_data: #converts everything to floats\n",
    "    test_data[item] = pd.to_numeric(test_data[item])\n",
    "\n",
    "def normalize(data, toNormalize): #normalizes \n",
    "    result = data.copy()\n",
    "    for item in data.columns:\n",
    "        if (item in toNormalize):\n",
    "            max_value = data[item].max()\n",
    "            min_value = data[item].min()\n",
    "            result[item] = (data[item] - min_value) / (max_value - min_value)\n",
    "    return result\n",
    "toNormalize = ['Age', 'Prescription_period', 'Sms_Reminder'] #columns to normalize\n",
    "test_data = normalize(test_data, toNormalize)\n",
    "#data = data.dropna()\n",
    "print(len(test_data))\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ruhama.ahale/anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2881: FutureWarning: \n",
      "mpl_style had been deprecated and will be removed in a future version.\n",
      "Use `matplotlib.pyplot.style.use` instead.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cb8e550>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cbc0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.options.display.mpl_style = 'default'\n",
    "#cols =['Age','Prescription_period']\n",
    "features = ['Age','Gender','Prescription_period','Diabetes','Alcoholism','HyperTension','Smokes','Tuberculosis','Sms_Reminder'] \n",
    "data_var = test_data[features]\n",
    "data_var.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fixed_train = data.copy()\n",
    "fixed_test = test_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** 3.b Feature Selection**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are only 10 predictors in the variable, we do not want to exclude any features as that could lead to loss of information. Instead, we will try to handle this in the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** 3.c Split Train-Validation-Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ruhama.ahale/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/ruhama.ahale/anaconda/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#logistic\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Decision tree\n",
    "from sklearn.neighbors import KNeighborsClassifier # KNN classifier\n",
    "from sklearn import svm # for suport vector machine algorithm\n",
    "from sklearn import metrics # for checking the model accuracy\n",
    "from sklearn.tree import DecisionTreeClassifier # for using DTA\n",
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "      <th>Adherence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>0.230769</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.487395</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.352941</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.365385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.546218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.442308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Gender  Prescription_period  Diabetes  Alcoholism  HyperTension  \\\n",
       "0  0.182692     0.0             0.050420       0.0         0.0           0.0   \n",
       "1  0.230769     1.0             0.487395       0.0         0.0           0.0   \n",
       "2  0.038462     1.0             0.352941       0.0         0.0           0.0   \n",
       "3  0.365385     0.0             0.546218       0.0         0.0           0.0   \n",
       "4  0.442308     1.0             0.815126       0.0         0.0           0.0   \n",
       "\n",
       "   Smokes  Tuberculosis  Sms_Reminder  Adherence  \n",
       "0     0.0           0.0           0.0          0  \n",
       "1     0.0           0.0           0.0          0  \n",
       "2     0.0           0.0           0.0          0  \n",
       "3     0.0           0.0           0.5          0  \n",
       "4     0.0           0.0           0.5          0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.copy()\n",
    "df.drop(df.columns[0], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Prescription_period</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>HyperTension</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Tuberculosis</th>\n",
       "      <th>Sms_Reminder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.044248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.226891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.548673</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.067227</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.035398</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.605042</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.292035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.974790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.336283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Gender  Prescription_period  Diabetes  Alcoholism  HyperTension  \\\n",
       "0  0.044248     0.0             0.226891       0.0         0.0           0.0   \n",
       "1  0.548673     1.0             0.067227       1.0         0.0           1.0   \n",
       "2  0.035398     1.0             0.605042       0.0         0.0           0.0   \n",
       "3  0.292035     0.0             0.974790       0.0         0.0           0.0   \n",
       "4  0.336283     0.0             0.058824       0.0         0.0           0.0   \n",
       "\n",
       "   Smokes  Tuberculosis  Sms_Reminder  \n",
       "0     0.0           0.0           0.5  \n",
       "1     0.0           0.0           0.0  \n",
       "2     0.0           0.0           0.5  \n",
       "3     0.0           0.0           0.0  \n",
       "4     0.0           0.0           0.5  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = test_data.copy()\n",
    "test_df.drop(test_df.columns[0], axis=1, inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, val = train_test_split(df, test_size=0.2)\n",
    "X_train = train.ix[:,0:9]\n",
    "y_train = train.ix[:,-1]\n",
    "y_train_df = pd.DataFrame(y_train)\n",
    "X_val = val.ix[:,0:9]\n",
    "y_val = val.ix[:,-1]\n",
    "y_val_df = pd.DataFrame(y_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36042\n",
      "(144167, 9)\n",
      "(36042, 9)\n",
      "(144167,)\n",
      "(36042,)\n"
     ]
    }
   ],
   "source": [
    "val_len = len(X_val)\n",
    "print(val_len)\n",
    "print(X_train.shape)\n",
    "print(X_val.shape)\n",
    "print(y_train.shape)\n",
    "print(y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119788, 9)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144167, 10)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Training and Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#defining the function to calculate precision recall\n",
    "def precision_recall(TP, FP, FN, TN, c):\n",
    "    if c == 'PY':\n",
    "        score = TP/(TP+FP)\n",
    "    elif c == 'RY':\n",
    "        score = TP/(TP+FN)\n",
    "    elif c == 'PN':\n",
    "        score = TN/(TN+FN)\n",
    "    elif c == 'RN':\n",
    "        score = TN/(TN+FP)\n",
    "    return(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ** 4.a Logistic Regression **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic Regression is:  87.77 %\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.90      0.91     25128\n",
      "          1       0.79      0.82      0.80     10914\n",
      "\n",
      "avg / total       0.88      0.88      0.88     36042\n",
      "\n",
      "[[22717  2411]\n",
      " [ 1996  8918]]\n",
      "Precision for Yes is : 90.41 %\n",
      "Recall for Yes is : 91.92 %\n",
      "Precision for No is : 81.71 %\n",
      "Recall for No is : 78.72 %\n",
      "The result is telling us that we have 31635 ( 87.77 %) correct predictions and 4407 ( 12.23 %) incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train.values)\n",
    "\n",
    "\n",
    "prediction = logreg.predict(X_val)\n",
    "print('The accuracy of Logistic Regression is: ', round(accuracy_score(y_val, prediction)*100,2),\"%\")\n",
    "\n",
    "print(classification_report(y_val.values, prediction))\n",
    "cm = confusion_matrix(y_val.values, prediction)\n",
    "print(cm)\n",
    "\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "\n",
    "print(\"Precision for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'PY')*100,2), \"%\")\n",
    "print(\"Recall for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'RY')*100,2), \"%\")\n",
    "print(\"Precision for No is :\", round(precision_recall(TP, FP, FN, TN, 'PN')*100,2), \"%\")\n",
    "print(\"Recall for No is :\", round(precision_recall(TP, FP, FN, TN, 'RN')*100,2), \"%\")\n",
    "\n",
    "print(\"The result is telling us that we have\", TP+TN, \"(\", round(((TP+TN)/val_len)*100,2),\"%)\", \"correct predictions and\", FP+FN ,  \"(\", round(((FP+FN)/val_len)*100,2),\"%)\", \"incorrect predictions.\")  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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TEIUQQtSTBj/xMzs7278Zl8ViQa/XExQURHR09El71lfk+ISu5uz4plRSF1IXJ5K6+EtT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\neAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1743cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#roc curve\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_val, logreg.predict(X_val))\n",
    "fpr, tpr, thresholds = roc_curve(y_val, logreg.predict_proba(X_val)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ** For Logistic regression, the f-score is 88% **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.b. Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Decision Tree is:  85.32 %\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.90      0.89     25128\n",
      "          1       0.76      0.75      0.76     10914\n",
      "\n",
      "avg / total       0.85      0.85      0.85     36042\n",
      "\n",
      "[[22540  2588]\n",
      " [ 2704  8210]]\n",
      "Precision for Yes is : 89.7 %\n",
      "Recall for Yes is : 89.29 %\n",
      "Precision for No is : 75.22 %\n",
      "Recall for No is : 76.03 %\n",
      "The result is telling us that we have 30750 ( 85.32 %) correct predictions and 5292 ( 14.68 %) incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train.values)\n",
    "prediction = model.predict(X_val)\n",
    "print('The accuracy of Decision Tree is: ', round(accuracy_score(y_val, prediction)*100,2), \"%\") \n",
    "\n",
    "print(classification_report(y_val.values, prediction))\n",
    "cm = confusion_matrix(y_val.values, prediction)\n",
    "print(cm)\n",
    "\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "\n",
    "print(\"Precision for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'PY')*100,2), \"%\")\n",
    "print(\"Recall for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'RY')*100,2), \"%\")\n",
    "print(\"Precision for No is :\", round(precision_recall(TP, FP, FN, TN, 'PN')*100,2), \"%\")\n",
    "print(\"Recall for No is :\", round(precision_recall(TP, FP, FN, TN, 'RN')*100,2), \"%\")\n",
    "\n",
    "print(\"The result is telling us that we have\", TP+TN, \"(\", round(((TP+TN)/val_len)*100,2),\"%)\", \"correct predictions and\", FP+FN ,  \"(\", round(((FP+FN)/val_len)*100,2),\"%)\", \"incorrect predictions.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.89      0.92     25128\n",
      "          1       0.78      0.89      0.83     10914\n",
      "\n",
      "avg / total       0.90      0.89      0.89     36042\n",
      "\n",
      "[[22435  2693]\n",
      " [ 1230  9684]]\n",
      "Precision for Yes is : 89.28 %\n",
      "Recall for Yes is : 94.8 %\n",
      "Precision for No is : 88.73 %\n",
      "Recall for No is : 78.24 %\n",
      "The result is telling us that we have 32119 ( 89.12 %) correct predictions and 3923 ( 10.88 %) incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "parameters={'min_samples_split' : list(range(10,500,20)),'max_depth': list(range(1,20,2))}\n",
    "clf_t = DecisionTreeClassifier()\n",
    "clf = GridSearchCV(clf_t,parameters)\n",
    "clf.fit(X_train, y_train)\n",
    "prediction = clf.predict(X_val)\n",
    "\n",
    "print(classification_report(y_val.values, prediction))\n",
    "cm = confusion_matrix(y_val, prediction)\n",
    "print(cm)\n",
    "\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "\n",
    "print(\"Precision for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'PY')*100,2), \"%\")\n",
    "print(\"Recall for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'RY')*100,2), \"%\")\n",
    "print(\"Precision for No is :\", round(precision_recall(TP, FP, FN, TN, 'PN')*100,2), \"%\")\n",
    "print(\"Recall for No is :\", round(precision_recall(TP, FP, FN, TN, 'RN')*100,2), \"%\")\n",
    "\n",
    "print(\"The result is telling us that we have\", TP+TN, \"(\", round(((TP+TN)/val_len)*100,2),\"%)\", \"correct predictions and\", FP+FN ,  \"(\", round(((FP+FN)/val_len)*100,2),\"%)\", \"incorrect predictions.\")  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ** For Decision Tree, the f-score is 89% **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.c Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load scikit's random forest classifier library\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#took too long to run, didnt identify best params\n",
    "rfc = RandomForestClassifier(n_jobs=-1, oob_score = True) \n",
    "\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 700],\n",
    "    'max_features': ['auto', 'sqrt', 'log2']\n",
    "}\n",
    "\n",
    "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
    "CV_rfc.fit(X_train, y_train)\n",
    "print(CV_rfc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of RF classifier is:  0.86521280728\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.90      0.90     25128\n",
      "          1       0.77      0.80      0.78     10914\n",
      "\n",
      "avg / total       0.87      0.87      0.87     36042\n",
      "\n",
      "[[22492  2636]\n",
      " [ 2222  8692]]\n",
      "Precision for Yes is : 89.51 %\n",
      "Recall for Yes is : 91.01 %\n",
      "Precision for No is : 79.64 %\n",
      "Recall for No is : 76.73 %\n",
      "The result is telling us that we have 31184 ( 86.52 %) correct predictions and 4858 ( 13.48 %) incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "# Create a random forest Classifier. By convention, model\n",
    "\n",
    "\n",
    "model = RandomForestClassifier(n_jobs=-1, random_state=0)\n",
    "model.fit(X_train, y_train.values)\n",
    "prediction = model.predict(X_val)\n",
    "print('The accuracy of RF classifier is: ', accuracy_score(y_val, prediction))\n",
    "\n",
    "print(classification_report(y_val.values, prediction))\n",
    "cm = confusion_matrix(y_val.values, prediction)\n",
    "print(cm)\n",
    "\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "\n",
    "print(\"Precision for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'PY')*100,2), \"%\")\n",
    "print(\"Recall for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'RY')*100,2), \"%\")\n",
    "print(\"Precision for No is :\", round(precision_recall(TP, FP, FN, TN, 'PN')*100,2), \"%\")\n",
    "print(\"Recall for No is :\", round(precision_recall(TP, FP, FN, TN, 'RN')*100,2), \"%\")\n",
    "\n",
    "print(\"The result is telling us that we have\", TP+TN, \"(\", round(((TP+TN)/val_len)*100,2),\"%)\", \"correct predictions and\", FP+FN ,  \"(\", round(((FP+FN)/val_len)*100,2),\"%)\", \"incorrect predictions.\")  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ** The f-score for random forest is 87% **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.d. SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM is: 0.878475112369\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.91      0.91     25128\n",
      "          1       0.80      0.81      0.80     10914\n",
      "\n",
      "avg / total       0.88      0.88      0.88     36042\n",
      "\n",
      "[[22873  2255]\n",
      " [ 2125  8789]]\n"
     ]
    }
   ],
   "source": [
    "model=svm.SVC() #probability=True,cache_size=7000\n",
    "model.fit(X_train,y_train) \n",
    "prediction=model.predict(X_val) \n",
    "print('The accuracy of the SVM is:',metrics.accuracy_score(y_val,prediction))\n",
    "\n",
    "\n",
    "print(classification_report(y_val, prediction))\n",
    "cm = confusion_matrix(y_val, prediction)\n",
    "print(cm)\n",
    "\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision for Yes is : 91.21 %\n",
      "Recall for Yes is : 91.6 %\n",
      "Precision for No is : 80.78 %\n",
      "Recall for No is : 79.99 %\n",
      "The result is telling us that we have 31734 ( 88.05 %) correct predictions and 4308 ( 11.95 %) incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "print(\"Precision for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'PY')*100,2), \"%\")\n",
    "print(\"Recall for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'RY')*100,2), \"%\")\n",
    "print(\"Precision for No is :\", round(precision_recall(TP, FP, FN, TN, 'PN')*100,2), \"%\")\n",
    "print(\"Recall for No is :\", round(precision_recall(TP, FP, FN, TN, 'RN')*100,2), \"%\")\n",
    "\n",
    "print(\"The result is telling us that we have\", TP+TN, \"(\", round(((TP+TN)/val_len)*100,2),\"%)\", \"correct predictions and\", FP+FN ,  \"(\", round(((FP+FN)/val_len)*100,2),\"%)\", \"incorrect predictions.\") \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The f-score for SVM is 88%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### could not identify best params as code took too long to run, hence commenting this\n",
    "import time\n",
    "#import numpy as np\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier\n",
    "#from sklearn import datasets\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "\n",
    "start = time.time()\n",
    "clf = OneVsRestClassifier(SVC(kernel='linear', probability=True, cache_size = 7000))\n",
    "clf.fit(X_train, y_train)\n",
    "end = time.time()\n",
    "print(\"Single SVC\", end - start, clf.score(y_val, prediction))\n",
    "print(classification_report(y_val.values, prediction))\n",
    "sproba = clf.predict_proba(test_df)\n",
    "\n",
    "\n",
    "n_estimators = 10\n",
    "start = time.time()\n",
    "clf = OneVsRestClassifier(BaggingClassifier(SVC(kernel='linear', probability=True, cache_size = 7000), max_samples=1.0 / n_estimators, n_estimators=n_estimators))\n",
    "clf.fit(X_train, y_train)\n",
    "end = time.time()\n",
    "print(\"Bagging SVC\", end - start, clf.score(y_val, prediction))\n",
    "print(classification_report(y_val.values, prediction))\n",
    "bproba = clf.predict_proba(test_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 5. Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a. Best fit model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the f-scores for each of these models, we find that **'Decision Tree' ** is the best fitting model with highest f-score of 90%, hence, we use the predicted values calculated for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.89      0.92     25128\n",
      "          1       0.78      0.89      0.83     10914\n",
      "\n",
      "avg / total       0.90      0.89      0.89     36042\n",
      "\n",
      "The confusion matrix is:\n",
      "[[22435  2693]\n",
      " [ 1230  9684]]\n",
      "Precision for Yes is : 89.28 %\n",
      "Recall for Yes is : 94.8 %\n",
      "Precision for No is : 88.73 %\n",
      "Recall for No is : 78.24 %\n",
      "The result is telling us that we have 32119 ( 89.12 %) correct predictions and 3923 ( 10.88 %) incorrect predictions.\n"
     ]
    }
   ],
   "source": [
    "parameters={'min_samples_split' : list(range(10,500,20)),'max_depth': list(range(1,20,2))}\n",
    "clf_t = DecisionTreeClassifier()\n",
    "clf = GridSearchCV(clf_t,parameters)\n",
    "clf.fit(X_train, y_train)\n",
    "prediction = clf.predict(X_val)\n",
    "\n",
    "print(classification_report(y_val.values, prediction))\n",
    "\n",
    "cm = confusion_matrix(y_val, prediction)\n",
    "print(\"The confusion matrix is:\")\n",
    "print(cm)\n",
    "\n",
    "\n",
    "TP = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "TN = cm[1][1]\n",
    "\n",
    "\n",
    "print(\"Precision for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'PY')*100,2), \"%\")\n",
    "print(\"Recall for Yes is :\", round(precision_recall(TP, FP, FN, TN, 'RY')*100,2), \"%\")\n",
    "print(\"Precision for No is :\", round(precision_recall(TP, FP, FN, TN, 'PN')*100,2), \"%\")\n",
    "print(\"Recall for No is :\", round(precision_recall(TP, FP, FN, TN, 'RN')*100,2), \"%\")\n",
    "\n",
    "print(\"The result is telling us that we have\", TP+TN, \"(\", round(((TP+TN)/val_len)*100,2),\"%)\", \"correct predictions and\", FP+FN ,  \"(\", round(((FP+FN)/val_len)*100,2),\"%)\", \"incorrect predictions.\")  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before    patient_id       Age  Gender  Prescription_period  Diabetes  Alcoholism  \\\n",
      "0           1  0.044248     0.0             0.226891       0.0         0.0   \n",
      "1           2  0.548673     1.0             0.067227       1.0         0.0   \n",
      "2           3  0.035398     1.0             0.605042       0.0         0.0   \n",
      "3           4  0.292035     0.0             0.974790       0.0         0.0   \n",
      "4           5  0.336283     0.0             0.058824       0.0         0.0   \n",
      "\n",
      "   HyperTension  Smokes  Tuberculosis  Sms_Reminder  \n",
      "0           0.0     0.0           0.0           0.5  \n",
      "1           1.0     0.0           0.0           0.0  \n",
      "2           0.0     0.0           0.0           0.5  \n",
      "3           0.0     0.0           0.0           0.0  \n",
      "4           0.0     0.0           0.0           0.5  \n",
      "After:    patient_id       Age  Gender  Prescription_period  Diabetes  Alcoholism  \\\n",
      "0           1  0.044248     0.0             0.226891       0.0         0.0   \n",
      "1           2  0.548673     1.0             0.067227       1.0         0.0   \n",
      "2           3  0.035398     1.0             0.605042       0.0         0.0   \n",
      "3           4  0.292035     0.0             0.974790       0.0         0.0   \n",
      "4           5  0.336283     0.0             0.058824       0.0         0.0   \n",
      "\n",
      "   HyperTension  Smokes  Tuberculosis  Sms_Reminder  t_predicted  probability  \n",
      "0           0.0     0.0           0.0           0.5            1     0.811887  \n",
      "1           1.0     0.0           0.0           0.0            1     0.763921  \n",
      "2           0.0     0.0           0.0           0.5            0     0.051298  \n",
      "3           0.0     0.0           0.0           0.0            0     0.060242  \n",
      "4           0.0     0.0           0.0           0.5            1     0.836200  \n",
      "t_predicted\n",
      "0    78539\n",
      "1    41249\n",
      "dtype: int64 Adhered are: 34.44 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Before\",test_data.head())\n",
    "#test_data.drop(test_data[['t_predicted','probability']], axis=1, inplace=True)\n",
    "prediction = clf.predict(test_df)\n",
    "test_data['t_predicted'] = prediction\n",
    "#test_data.head()\n",
    "probability = clf.predict_proba(test_df)[:,1]\n",
    "test_data['probability'] = probability\n",
    "print(\"After:\", test_data.head())\n",
    "\n",
    "test_len = len(test_data)\n",
    "print(test_data.groupby('t_predicted').size(), \"Adhered are:\", round((len(test_data[test_data['t_predicted'] == 1])/test_len)*100,2),\"%\" )   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>adherence</th>\n",
       "      <th>probability_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.811887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.763921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.060242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.836200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  adherence  probability_score\n",
       "0           1          1           0.811887\n",
       "1           2          1           0.763921\n",
       "2           3          0           0.051298\n",
       "3           4          0           0.060242\n",
       "4           5          1           0.836200"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_output = test_data[['patient_id','t_predicted','probability']]\n",
    "prediction_output = prediction_output.rename(columns={'t_predicted':'adherence', 'probability':'probability_score'})\n",
    "prediction_output.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## b. Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_output.to_csv('prediction_output.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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