SLC - In-Class Lab Exercise _Day2_KNN_NB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IFZNFZ27CUet"
   },
   "source": [
    "<table align=\"center\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"15%\">\n",
    "            <img src=\"in_class.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"center\">\n",
    "                <font color=\"#21618C\" size=8px>\n",
    "                    <b> Inclass - Lab <br>(Day 2)\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6-O6_cf1CUez"
   },
   "source": [
    "### About the data set (Bank Client Data)\n",
    "\n",
    "#### Bank client data:\n",
    "**age:** Age of the client<br>\n",
    "**duration:** last contact duration, in seconds.<br> \n",
    "\n",
    "#### Other attributes:\n",
    "**campaign:** number of contacts performed during this campaign and for this client<br>\n",
    "**pdays:** number of days that passed by after the client was last contacted from a previous campaign (999 means client was not previously contacted)<br>\n",
    "**previous:** number of contacts performed before this campaign and for this client<br>\n",
    "\n",
    "#### Social and economic context attributes\n",
    "**emp.var.rate:** employment variation rate - quarterly indicator<br>\n",
    "**cons.price.idx:** consumer price index - monthly indicator<br>\n",
    "**cons.conf.idx:** consumer confidence index - monthly indicator<br>\n",
    "**euribor3m:** euribor 3 month rate - daily indicator<br>\n",
    "**nr.employed:** number of employees - quarterly indicator<br>\n",
    "\n",
    "**y** - (Output variable) has the client subscribed a term deposit?\n",
    "\n",
    "Data citation: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cAHXHDVGCUe0"
   },
   "source": [
    "## Table of Content\n",
    "\n",
    "1. **[Data Preprocessing](#dp)**\n",
    "1. **[K Nearest Neighbors (KNN)](#knn)**\n",
    "2. **[Naive Bayes](#nb)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_qAEL9QQCUe1"
   },
   "source": [
    "**Import the required libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 1434,
     "status": "ok",
     "timestamp": 1637051795151,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "U7F6ZbGACUe1"
   },
   "outputs": [],
   "source": [
    "# import 'Pandas' \n",
    "import pandas as pd \n",
    "\n",
    "# import 'Numpy' \n",
    "import numpy as np\n",
    "\n",
    "# import subpackage of Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# import 'Seaborn' \n",
    "import seaborn as sns\n",
    "\n",
    "# to suppress warnings \n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "# display all columns of the dataframe\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# display all rows of the dataframe\n",
    "pd.options.display.max_rows = None\n",
    " \n",
    "# to display the float values upto 6 decimal places     \n",
    "pd.options.display.float_format = '{:.6f}'.format\n",
    "\n",
    "# import train-test split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# import StandardScaler to perform scaling\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "# import various functions from sklearn \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# import the functions for visualizing the decision tree\n",
    "import pydotplus\n",
    "from IPython.display import Image  \n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1637051798118,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "kH6RCzeHCUe3"
   },
   "outputs": [],
   "source": [
    "# set the plot size using 'rcParams'\n",
    "# once the plot size is set using 'rcParams', it sets the size of all the forthcoming plots in the file\n",
    "# pass width and height in inches to 'figure.figsize' \n",
    "plt.rcParams['figure.figsize'] = [15,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 47486,
     "status": "ok",
     "timestamp": 1637051850100,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "Rx8SzwQ_C-Ki",
    "outputId": "6665a6f9-6835-4678-a61b-5363d84319f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C2p9ve_DCUe4"
   },
   "source": [
    "#### Load the csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "executionInfo": {
     "elapsed": 359,
     "status": "ok",
     "timestamp": 1637055886217,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "Ht_UJ1Y3CUe5",
    "outputId": "5c98179c-4ad6-455d-9518-f280000f8612"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.858000</td>\n",
       "      <td>5191.000000</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>691</td>\n",
       "      <td>10</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.918000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>4.960000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.444000</td>\n",
       "      <td>-36.100000</td>\n",
       "      <td>4.963000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>400</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.100000</td>\n",
       "      <td>94.601000</td>\n",
       "      <td>-49.500000</td>\n",
       "      <td>1.032000</td>\n",
       "      <td>4963.600000</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>903</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-47.100000</td>\n",
       "      <td>1.415000</td>\n",
       "      <td>5099.100000</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
       "0   32       205         2    999         0      1.100000       93.994000   \n",
       "1   32       691        10    999         0      1.400000       93.918000   \n",
       "2   45        45         8    999         0      1.400000       93.444000   \n",
       "3   33       400         1      5         2     -1.100000       94.601000   \n",
       "4   47       903         2    999         1     -1.800000       93.075000   \n",
       "\n",
       "   cons.conf.idx  euribor3m  nr.employed    y  \n",
       "0     -36.400000   4.858000  5191.000000   no  \n",
       "1     -42.700000   4.960000  5228.100000  yes  \n",
       "2     -36.100000   4.963000  5228.100000   no  \n",
       "3     -49.500000   1.032000  4963.600000  yes  \n",
       "4     -47.100000   1.415000  5099.100000  yes  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the csv file\n",
    "df_bank = pd.read_csv('/content/drive/My Drive/SLC/bank.csv')\n",
    "\n",
    "# display the first five observations\n",
    "df_bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 431,
     "status": "ok",
     "timestamp": 1634640971590,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "G17CzEwNCUe6",
    "outputId": "b1e2ce4b-0d85-48a5-f12a-d6c8f562b228"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "duration            int64\n",
       "campaign            int64\n",
       "pdays               int64\n",
       "previous            int64\n",
       "emp.var.rate      float64\n",
       "cons.price.idx    float64\n",
       "cons.conf.idx     float64\n",
       "euribor3m         float64\n",
       "nr.employed       float64\n",
       "y                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types of variables\n",
    "df_bank.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "haZmrRfDCUe7"
   },
   "source": [
    "<a id=\"dp\"> </a>\n",
    "## 1. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "76eyjnAaCUe7"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>1. Remove the outliers (if any).</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ZpuxvnzCUe7"
   },
   "source": [
    "**Outlier analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1637056760604,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "IZBy2gkSCUe8",
    "outputId": "262adc0c-38cd-41c9-a61c-e3039bce5258"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9640, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the size of data\n",
    "df_bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 344,
     "status": "ok",
     "timestamp": 1637056907602,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "UhI8M_fZCUe8",
    "outputId": "60144cbe-107f-4d4d-fc82-d57084d84e55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6555, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the first quartile\n",
    "Q1 = df_bank.quantile(0.25)\n",
    "\n",
    "#calculate the third quartile\n",
    "Q3 = df_bank.quantile(0.75)\n",
    "\n",
    "# The Interquartile Range (IQR) is defined as the difference between the third and first quartile\n",
    "# calculate IQR\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# remove the outliers from the dataframe\n",
    "df_bank = df_bank[~((df_bank < (Q1 - 1.5 * IQR)) | (df_bank > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "\n",
    "# reset the row index\n",
    "df_bank = df_bank.reset_index(drop=True)\n",
    "\n",
    "# check the shape of the data\n",
    "df_bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 376,
     "status": "ok",
     "timestamp": 1637056916955,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "cADyM_CgRckc",
    "outputId": "a38e59a3-fa85-4d31-dbcf-40a7f2325908"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     4055\n",
       "yes    2500\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bank['y'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "wH0CvjeMCUe9"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>2. Separate the dependent and the independent variables. Also, in the target variable, replace yes with 0 and no with 1.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xCPjU0pxCUe9"
   },
   "source": [
    "#### Split the dependent and independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1637057077792,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "qhZXOSChCUe9"
   },
   "outputs": [],
   "source": [
    "# store the target variable \"y\" in a dataframe 'df_target'\n",
    "df_target = df_bank['y']\n",
    "\n",
    "# store all the independent variables in a dataframe 'df_feature' \n",
    "# drop the column \"y\" using drop()\n",
    "# 'axis = 1' drops the specified column\n",
    "df_feature = df_bank.drop('y', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 962,
     "status": "ok",
     "timestamp": 1637057164526,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "cAMtZLG5CUe-",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# if the value in the target variable is 'yes' then replace it with 1 else with 0\n",
    "for i in range(len(df_target)):\n",
    "    if df_target[i] == 'yes':\n",
    "        df_target[i] = 1\n",
    "    else:\n",
    "        df_target[i] = 0\n",
    "        \n",
    "# change the datatype of the target variable to integer\n",
    "df_target = df_target.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 328,
     "status": "ok",
     "timestamp": 1637057186535,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "mDjFanvnD5cl",
    "outputId": "47c66756-ca4e-47c8-ee6b-adb61c255426"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6555 entries, 0 to 6554\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             6555 non-null   int64  \n",
      " 1   duration        6555 non-null   int64  \n",
      " 2   campaign        6555 non-null   int64  \n",
      " 3   pdays           6555 non-null   int64  \n",
      " 4   previous        6555 non-null   int64  \n",
      " 5   emp.var.rate    6555 non-null   float64\n",
      " 6   cons.price.idx  6555 non-null   float64\n",
      " 7   cons.conf.idx   6555 non-null   float64\n",
      " 8   euribor3m       6555 non-null   float64\n",
      " 9   nr.employed     6555 non-null   float64\n",
      "dtypes: float64(5), int64(5)\n",
      "memory usage: 512.2 KB\n"
     ]
    }
   ],
   "source": [
    "df_feature.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "shMhiny2CUe-"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>3. Replace the value \"unknown\" from each column with NaN.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "executionInfo": {
     "elapsed": 372,
     "status": "ok",
     "timestamp": 1634700770994,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "f8aBosR0CUe-",
    "outputId": "d528d59d-f00c-4ee5-ba59-003a2d456ae3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.858000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>243</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>92.843000</td>\n",
       "      <td>-50.000000</td>\n",
       "      <td>1.531000</td>\n",
       "      <td>5099.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>93.200000</td>\n",
       "      <td>-42.000000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>5195.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43</td>\n",
       "      <td>550</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.855000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
       "0   32       205         2    999         0      1.100000       93.994000   \n",
       "1   25       243         3    999         0     -1.800000       92.843000   \n",
       "2   36       214         1    999         0     -0.100000       93.200000   \n",
       "3   43       550         1    999         0      1.100000       93.994000   \n",
       "4   52        64         1    999         0      1.100000       93.994000   \n",
       "\n",
       "   cons.conf.idx  euribor3m  nr.employed  \n",
       "0     -36.400000   4.858000  5191.000000  \n",
       "1     -50.000000   1.531000  5099.100000  \n",
       "2     -42.000000   4.120000  5195.800000  \n",
       "3     -36.400000   4.857000  5191.000000  \n",
       "4     -36.400000   4.855000  5191.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace: replace the existing value with the new value\n",
    "# to_replace: existing value to replace\n",
    "# value: the value to replace with\n",
    "df_feature = df_feature.replace(to_replace='unknown', value=np.nan)\n",
    "\n",
    "# print the first 5 observations\n",
    "df_feature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 363,
     "status": "ok",
     "timestamp": 1634605216637,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "G6caty11EA7P",
    "outputId": "b3beb3be-b498-4296-9326-ba755e2e2065"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6555, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feature.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 536,
     "status": "ok",
     "timestamp": 1634642870946,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "LI-HEfXTTqDA",
    "outputId": "ae31a847-f09f-43ad-8252-110b7b04d2f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6555 entries, 0 to 6554\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             6555 non-null   int64  \n",
      " 1   duration        6555 non-null   int64  \n",
      " 2   campaign        6555 non-null   int64  \n",
      " 3   pdays           6555 non-null   int64  \n",
      " 4   previous        6555 non-null   int64  \n",
      " 5   emp.var.rate    6555 non-null   float64\n",
      " 6   cons.price.idx  6555 non-null   float64\n",
      " 7   cons.conf.idx   6555 non-null   float64\n",
      " 8   euribor3m       6555 non-null   float64\n",
      " 9   nr.employed     6555 non-null   float64\n",
      "dtypes: float64(5), int64(5)\n",
      "memory usage: 512.2 KB\n"
     ]
    }
   ],
   "source": [
    "df_feature.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "5F5UC-OxCUe_"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>4. Look for the null values and treat the null values.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osAV5I2ACUe_"
   },
   "source": [
    "First run a check for the presence of missing values and their percentage for each column. Then choose the right approach to treat them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "executionInfo": {
     "elapsed": 431,
     "status": "ok",
     "timestamp": 1637057296909,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "84pYPCcqCUe_",
    "outputId": "22f922b5-c628-4959-9dbe-9671904634d9",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percentage of Missing Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Total  Percentage of Missing Values\n",
       "nr.employed         0                      0.000000\n",
       "euribor3m           0                      0.000000\n",
       "cons.conf.idx       0                      0.000000\n",
       "cons.price.idx      0                      0.000000\n",
       "emp.var.rate        0                      0.000000\n",
       "previous            0                      0.000000\n",
       "pdays               0                      0.000000\n",
       "campaign            0                      0.000000\n",
       "duration            0                      0.000000\n",
       "age                 0                      0.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the variables on the basis of total null values in the variable\n",
    "# 'isnull().sum()' returns the number of missing values in each variable\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest number of missing values will appear first\n",
    "Total = df_feature.isnull().sum().sort_values(ascending=False)          \n",
    "\n",
    "# calculate percentage of missing values\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest percentage of missing values will appear first\n",
    "Percent = (df_feature.isnull().sum()*100/df_feature.isnull().count()).sort_values(ascending=False)   \n",
    "\n",
    "# concat the 'Total' and 'Percent' columns using 'concat' function\n",
    "# pass a list of column names in parameter 'keys' \n",
    "# 'axis = 1' concats along the columns\n",
    "missing_data = pd.concat([Total, Percent], axis = 1, keys = ['Total', 'Percentage of Missing Values'])    \n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1Tg_tUuCUfA"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>5. Remove the unnecessary variables that will not contribute to the model.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 366,
     "status": "ok",
     "timestamp": 1637057320288,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "DLX55mIICUfA",
    "outputId": "b3dde26c-e7d5-423f-a47a-bb3c67101a0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               10.184894\n",
       "duration         253.406706\n",
       "campaign           1.283539\n",
       "pdays              0.000000\n",
       "previous           0.000000\n",
       "emp.var.rate       1.626927\n",
       "cons.price.idx     0.585132\n",
       "cons.conf.idx      4.659679\n",
       "euribor3m          1.779666\n",
       "nr.employed       74.431765\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the standard deviation for the numeric variables\n",
    "df_feature.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aqqEgwXACUfA"
   },
   "source": [
    "**Interpretation:** The variables `pdays` and `previous` have standard deviation 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 409,
     "status": "ok",
     "timestamp": 1637057351897,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "TaMPpIwsCUfA"
   },
   "outputs": [],
   "source": [
    "# create a list of the columns to drop\n",
    "columns_to_remove = ['pdays', 'previous']\n",
    "\n",
    "# drop the columns using drop()\n",
    "# 'axis = 1' drops the specified column\n",
    "df_feature = df_feature.drop(columns=columns_to_remove, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "8lzCojBCCUfB"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>6. Plot the distribution of all the numeric variables and find the value of skewness for each variable.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EMWDhKwaCUfB"
   },
   "source": [
    "**Distribution of numeric independent variables.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 578
    },
    "executionInfo": {
     "elapsed": 2116,
     "status": "ok",
     "timestamp": 1637057450370,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "HANailDfCUfB",
    "outputId": "c9031ab5-7c58-487d-87b1-2ad30f5b8aa0",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age               0.598601\n",
       "duration          1.041608\n",
       "campaign          1.324147\n",
       "emp.var.rate     -0.575913\n",
       "cons.price.idx   -0.214375\n",
       "cons.conf.idx     0.104692\n",
       "euribor3m        -0.545969\n",
       "nr.employed      -0.880652\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for the independent numeric variables, we plot the histogram to check the distribution of the variables\n",
    "# Note: the hist() function considers the numeric variables only, by default\n",
    "# we drop the target variable using drop()\n",
    "# 'axis=1' drops the specified column\n",
    "df_feature.hist()\n",
    "\n",
    "# adjust the subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# display the plot\n",
    "plt.show()  \n",
    "\n",
    "# print the skewness for each numeric independent variable\n",
    "print('Skewness:')\n",
    "# we drop the target variable using drop()\n",
    "# 'axis=1' drops the specified column\n",
    "# skew() returns the coefficient of skewness for each variable\n",
    "df_feature.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "ih-bnZ0NCUfB"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>7. Plot the distribution of the target variable.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 384
    },
    "executionInfo": {
     "elapsed": 469,
     "status": "ok",
     "timestamp": 1634700794906,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "l-UuZERXCUfC",
    "outputId": "c95ab9e1-e3d8-4798-d24b-28b841d2adc3"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get counts of 0's and 1's in the target variable\n",
    "df_target.value_counts()\n",
    "\n",
    "# plot the countplot of the target variable\n",
    "sns.countplot(x = df_target)\n",
    "\n",
    "# use below code to print the values in the graph\n",
    "# 'x' and 'y' gives position of the text\n",
    "# 's' is the text \n",
    "plt.text(x = -0.05, y = df_target.value_counts()[0] + 1, s = str(round((df_target.value_counts()[0])*100/len(df_target),2)) + '%')\n",
    "plt.text(x = 0.95, y = df_target.value_counts()[1] +1, s = str(round((df_target.value_counts()[1])*100/len(df_target),2)) + '%')\n",
    "\n",
    "# add plot and axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Count Plot for Target Variable', fontsize = 15)\n",
    "plt.xlabel('Target Variable', fontsize = 15)\n",
    "plt.ylabel('Count', fontsize = 15)\n",
    "\n",
    "# to show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "wO0n1csyCUfC"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>8. Scale all the numeric variables using standard scalar.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xxSTfsSKCUfD"
   },
   "source": [
    "We scale the variables to get all the variables in the same range. With this, we can avoid a problem in which some features come to dominate solely because they tend to have larger values than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1637057946411,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "LtJH3YB4CUfD",
    "outputId": "fee2b7f2-0244-4403-8fc7-a38b77f5c749"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.722787</td>\n",
       "      <td>-0.480765</td>\n",
       "      <td>-0.024486</td>\n",
       "      <td>0.701361</td>\n",
       "      <td>0.749298</td>\n",
       "      <td>0.910658</td>\n",
       "      <td>0.765585</td>\n",
       "      <td>0.394074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.410132</td>\n",
       "      <td>-0.330797</td>\n",
       "      <td>0.754670</td>\n",
       "      <td>-1.081276</td>\n",
       "      <td>-1.217928</td>\n",
       "      <td>-2.008221</td>\n",
       "      <td>-1.104009</td>\n",
       "      <td>-0.840708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.330018</td>\n",
       "      <td>-0.445246</td>\n",
       "      <td>-0.803642</td>\n",
       "      <td>-0.036282</td>\n",
       "      <td>-0.607764</td>\n",
       "      <td>-0.291233</td>\n",
       "      <td>0.350869</td>\n",
       "      <td>0.458567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.357326</td>\n",
       "      <td>0.880787</td>\n",
       "      <td>-0.803642</td>\n",
       "      <td>0.701361</td>\n",
       "      <td>0.749298</td>\n",
       "      <td>0.910658</td>\n",
       "      <td>0.765023</td>\n",
       "      <td>0.394074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.241055</td>\n",
       "      <td>-1.037225</td>\n",
       "      <td>-0.803642</td>\n",
       "      <td>0.701361</td>\n",
       "      <td>0.749298</td>\n",
       "      <td>0.910658</td>\n",
       "      <td>0.763899</td>\n",
       "      <td>0.394074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  duration  campaign  emp.var.rate  cons.price.idx  cons.conf.idx  \\\n",
       "0 -0.722787 -0.480765 -0.024486      0.701361        0.749298       0.910658   \n",
       "1 -1.410132 -0.330797  0.754670     -1.081276       -1.217928      -2.008221   \n",
       "2 -0.330018 -0.445246 -0.803642     -0.036282       -0.607764      -0.291233   \n",
       "3  0.357326  0.880787 -0.803642      0.701361        0.749298       0.910658   \n",
       "4  1.241055 -1.037225 -0.803642      0.701361        0.749298       0.910658   \n",
       "\n",
       "   euribor3m  nr.employed  \n",
       "0   0.765585     0.394074  \n",
       "1  -1.104009    -0.840708  \n",
       "2   0.350869     0.458567  \n",
       "3   0.765023     0.394074  \n",
       "4   0.763899     0.394074  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize the standard scalar\n",
    "X_scaler = StandardScaler()\n",
    "\n",
    "# scale all the numerical columns\n",
    "# standardize all the columns of the dataframe 'df_feature'\n",
    "num_scaled = X_scaler.fit_transform(df_feature)\n",
    "\n",
    "# create a dataframe of scaled numerical variables\n",
    "# pass the required column names to the parameter 'columns'\n",
    "X = pd.DataFrame(num_scaled, columns = df_feature.columns)\n",
    "\n",
    "# display first five observations\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C8Ts17MNCUfE"
   },
   "source": [
    "<a id=\"knn\"> </a>\n",
    "## 2. K Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WSDy58lmCUfE"
   },
   "source": [
    "## We shall use the bank marketing dataset that we cleaned above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8EterByuCUfE"
   },
   "source": [
    "Before applying classification techniques to predict whether the client subscribed the term deposit or not, let us split the dataset in train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 385,
     "status": "ok",
     "timestamp": 1637057950575,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "hydLH3ZoCUfE",
    "outputId": "ab40bca6-c573-46ff-d2a2-65ac7fa0816e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (5244, 8)\n",
      "y_train (5244,)\n",
      "X_test (1311, 8)\n",
      "y_test (1311,)\n"
     ]
    }
   ],
   "source": [
    "# split data into train subset and test subset\n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "# 'test_size' returns the proportion of data to be included in the testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, df_target, random_state = 10,test_size = 0.2)\n",
    "\n",
    "# check the dimensions of the train & test subset using 'shape'\n",
    "# print dimension of train set\n",
    "print('X_train', X_train.shape)\n",
    "print('y_train', y_train.shape)\n",
    "\n",
    "# print dimension of test set\n",
    "print('X_test', X_test.shape)\n",
    "print('y_test', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "Su-sbk3ACUfF"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>9. Create a function to draw a confusion matrix (heatmap) and a function to plot a roc-auc curve.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dRdz8cJICUfF"
   },
   "source": [
    "#### Define a function to plot the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wiN2ycAHZO7E"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 372,
     "status": "ok",
     "timestamp": 1637058175140,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "c9wBCA_jCUfF"
   },
   "outputs": [],
   "source": [
    "# define a to plot a confusion matrix for the model\n",
    "def plot_confusion_matrix(model):\n",
    "    \n",
    "    # predict the target values using X_test\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # create a confusion matrix\n",
    "    # pass the actual and predicted target values to the confusion_matrix()\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    # label the confusion matrix  \n",
    "    # pass the matrix as 'data'\n",
    "    # pass the required column names to the parameter, 'columns'\n",
    "    # pass the required row names to the parameter, 'index'\n",
    "    conf_matrix = pd.DataFrame(data = cm,columns = ['Predicted:0','Predicted:1'], \n",
    "                               index = ['Actual:0','Actual:1'])\n",
    "\n",
    "    # plot a heatmap to visualize the confusion matrix\n",
    "    # 'annot' prints the value of each grid \n",
    "    # 'fmt = d' returns the integer value in each grid\n",
    "    # 'cmap' assigns color to each grid\n",
    "    # as we do not require different colors for each grid in the heatmap,\n",
    "    # use 'ListedColormap' to assign the specified color to the grid\n",
    "    # 'cbar = False' will not return the color bar to the right side of the heatmap\n",
    "    # 'linewidths' assigns the width to the line that divides each grid\n",
    "    # 'annot_kws = {'size':25})' assigns the font size of the annotated text \n",
    "    sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = ListedColormap(['lightskyblue']), cbar = False, \n",
    "                linewidths = 0.1, annot_kws = {'size':25})\n",
    "\n",
    "    # set the font size of x-axis ticks using 'fontsize'\n",
    "    plt.xticks(fontsize = 20)\n",
    "\n",
    "    # set the font size of y-axis ticks using 'fontsize'\n",
    "    plt.yticks(fontsize = 20)\n",
    "\n",
    "    # display the plot\n",
    "    plt.show()#### Define a function to plot the confusion matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qnixPYYbCUfF"
   },
   "source": [
    "#### Define a function to plot the ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 360,
     "status": "ok",
     "timestamp": 1637058284170,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "mPr-Zo8aCUfF"
   },
   "outputs": [],
   "source": [
    "# define a function to plot the ROC curve and print the ROC-AUC score\n",
    "def plot_roc(model):\n",
    "    \n",
    "    # predict the probability of target variable using X_test\n",
    "    # consider the probability of positive class by subsetting with '[:,1]'\n",
    "    y_pred_prob = model.predict_proba(X_test)[:,1]\n",
    "    \n",
    "    # the roc_curve() returns the values for false positive rate, true positive rate and threshold\n",
    "    # pass the actual target values and predicted probabilities to the function\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "\n",
    "    # plot the ROC curve\n",
    "    plt.plot(fpr, tpr)\n",
    "\n",
    "    # set limits for x and y axes\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "\n",
    "    # plot the straight line showing worst prediction for the model\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "\n",
    "    # add plot and axes labels\n",
    "    # set text size using 'fontsize'\n",
    "    plt.title('ROC curve for Admission Prediction Classifier', fontsize = 15)\n",
    "    plt.xlabel('False positive rate (1-Specificity)', fontsize = 15)\n",
    "    plt.ylabel('True positive rate (Sensitivity)', fontsize = 15)\n",
    "\n",
    "    # add the AUC score to the plot\n",
    "    # 'x' and 'y' gives position of the text\n",
    "    # 's' is the text \n",
    "    # use round() to round-off the AUC score upto 4 digits\n",
    "    plt.text(x = 0.02, y = 0.9, s = ('AUC Score:',round(roc_auc_score(y_test, y_pred_prob),4)))\n",
    "\n",
    "    # plot the grid\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "MB9Wrp5UCUfG"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>10. Build a knn model on a training dataset with euclidean distance and 3 neighbours to predict whether or not the client subscribed the term deposit. Calculate the accuracy of the model.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 355,
     "status": "ok",
     "timestamp": 1637058681426,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "HUWGT538CUfG",
    "outputId": "066131e7-582d-4052-a494-564bdbffb58b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8497330282227308\n"
     ]
    }
   ],
   "source": [
    "#By default KNN works as uniform mode of operation ( if weights='distance', it work as weighted mode)\n",
    "# instantiate the 'KNeighborsClassifier'\n",
    "# n_neighnors: number of neighbors to consider\n",
    "# default metric is minkowski, and with p=2 it is equivalent to the euclidean metric\n",
    "knn_classification = KNeighborsClassifier(n_neighbors = 3, metric='euclidean')\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "knn_model = knn_classification.fit(X_train, y_train)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred = knn_model.predict(X_test)\n",
    "\n",
    "# pass the actual and predicted target values to the accuracy_score()\n",
    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "6H74DodmCUfG"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>11. Plot a confusion matrix using the function created above and print a classification report. </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "executionInfo": {
     "elapsed": 407,
     "status": "ok",
     "timestamp": 1637058689628,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "C11oOOqZCUfG",
    "outputId": "4895c4df-0453-4efc-915a-0b289a702514"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function to plot the confusion matrix\n",
    "# pass the knn model to the function\n",
    "plot_confusion_matrix(knn_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 338,
     "status": "ok",
     "timestamp": 1637058772481,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "z6j_-8XwCUfG",
    "outputId": "f6e80435-14ec-4339-84ba-667e61f12b58"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.87      0.88       819\n",
      "           1       0.79      0.81      0.80       492\n",
      "\n",
      "    accuracy                           0.85      1311\n",
      "   macro avg       0.84      0.84      0.84      1311\n",
      "weighted avg       0.85      0.85      0.85      1311\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print the classification report \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "WHYja9WqCUfH"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>12. Find the optimal value of 'k' in knn with 3 fold cross validation.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qv9gkpzUCUfH"
   },
   "source": [
    "Given list of values:\n",
    "\n",
    "    n_neighbors = np.arange(1, 25, 2)\n",
    "    metric = ['hamming','euclidean','manhattan','Chebyshev']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23648,
     "status": "ok",
     "timestamp": 1637060619175,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "ILb165a2CUfH",
    "outputId": "fbb7750a-0ff7-4079-bbb9-b3e97bb6e725"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for KNN Classifier:  {'metric': 'manhattan', 'n_neighbors': 15, 'weights': 'uniform'} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary with hyperparameters and its values\n",
    "# n_neighnors: number of neighbors to consider\n",
    "# usually, we consider the odd value of 'n_neighnors' to avoid the equal number of nearest points with more than one class\n",
    "# pass the different distance metrics to the parameter, 'metric'\n",
    "hyp_paramaters = {'n_neighbors': np.arange(1, 25, 2),\n",
    "                   'metric': ['hamming','euclidean','manhattan','Chebyshev'],\n",
    "                  'weights':['uniform','distance']}\n",
    " \n",
    "# instantiate the 'KNeighborsClassifier' \n",
    "knn_classification = KNeighborsClassifier(n_jobs=-1)\n",
    "\n",
    "# use GridSearchCV() to find the optimal value of the hyperparameters\n",
    "# estimator: pass the knn model\n",
    "# param_grid: pass the list 'tuned_parameters'\n",
    "# cv: number of folds in k-fold i.e. here cv = 3\n",
    "knn_grid = GridSearchCV(estimator = knn_classification, \n",
    "                        param_grid = hyp_paramaters, \n",
    "                        cv = 3, \n",
    "                        scoring = 'accuracy', \n",
    "                        n_jobs=-1)\n",
    "\n",
    "# fit the model on X_train and y_train using fit()\n",
    "knn_grid.fit(X_train, y_train)\n",
    "\n",
    "# get the best parameters\n",
    "print('Best parameters for KNN Classifier: ', knn_grid.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "6Mxvv5usCUfH"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>13.  Build a KNN model with the best parameters and find the accuracy. Also generate a classification report.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 335,
     "status": "ok",
     "timestamp": 1637061034170,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "yu3RtameADGp",
    "outputId": "7fc78158-8a2b-4553-927f-0482cee93a35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_params_['n_neighbors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 340,
     "status": "ok",
     "timestamp": 1637061026060,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "YrJC3p6bALP1",
    "outputId": "c42c3900-5cc0-45a5-b6ac-7be71b792e4b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'manhattan'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_params_['metric']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 345,
     "status": "ok",
     "timestamp": 1637060790176,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "OG_hazzkCUfH",
    "outputId": "6c9012eb-7644-4930-a319-9aceea2a31d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.858886346300534\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'KNeighborsClassifier'\n",
    "# n_neighnors: number of neighbors to consider\n",
    "# default metric is minkowski, and with p=2 it is equivalent to the euclidean metric\n",
    "knn_classification = KNeighborsClassifier(n_neighbors = knn_grid.best_params_['n_neighbors'],metric=knn_grid.best_params_['metric'])\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "knn_model = knn_classification.fit(X_train, y_train)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred = knn_model.predict(X_test)\n",
    "\n",
    "# pass the actual and predicted target values to the accuracy_score()\n",
    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 453,
     "status": "ok",
     "timestamp": 1637060837548,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "kEmyDNoiCUfI",
    "outputId": "8e94b073-d128-47a6-e64a-bb264cef6ef9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.86      0.88       819\n",
      "           1       0.79      0.85      0.82       492\n",
      "\n",
      "    accuracy                           0.86      1311\n",
      "   macro avg       0.85      0.86      0.85      1311\n",
      "weighted avg       0.86      0.86      0.86      1311\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print the classification report \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "43UqHUcxCUfI"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>14. Find the area under the receiver operating characteristic curve and the confusion matrix for the tuned KNN model built in question 13.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "executionInfo": {
     "elapsed": 1409,
     "status": "ok",
     "timestamp": 1637061046890,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "JXBov1zLCUfI",
    "outputId": "9b6eea58-296c-4d39-c6e4-690447724604"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the ROC curve\n",
    "plot_roc(knn_classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "executionInfo": {
     "elapsed": 418,
     "status": "ok",
     "timestamp": 1637061056494,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "LJgD7Qd5CUfI",
    "outputId": "04be1896-d9a6-4243-b014-606d0e9c106b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function to plot the confusion matrix\n",
    "# pass the knn model to the function\n",
    "plot_confusion_matrix(knn_classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "Eugxfi4dCUfI"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>15. Calculate the percentage of misclassified and correctly classified observations.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 399,
     "status": "ok",
     "timestamp": 1634705658288,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "aOWnvu7TCUfJ",
    "outputId": "f357aed9-052e-4268-8bff-51c15df02de1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of correctly classified observations:  85.89\n",
      "Percentage of misclassified observations:  14.11\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_pred, y_test)\n",
    "\n",
    "total = cm[0][0]+cm[1][1]+cm[0][1]+cm[1][0]\n",
    "\n",
    "# find the percentage of correctly classified observations\n",
    "# round the result upto 2 decimal points\n",
    "correct_classified_percentage = ((cm[0][0]+cm[1][1])/total)*100\n",
    "print(\"Percentage of correctly classified observations: \", round(correct_classified_percentage, 2))\n",
    "\n",
    "# find the percentage of  misclassified observations\n",
    "# round the result upto 2 decimal points\n",
    "misclassified_percentage = ((cm[0][1]+cm[1][0])/total)*100\n",
    "print(\"Percentage of misclassified observations: \", round(misclassified_percentage, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "bzzwY9w5CUfJ"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>16. Compute the accuracy for each value of k and append the value in a list 'accuracy'. Build knn models for euclidean distance metric. Consider only the odd numbers between the range 1 and 25 for the 'k'.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oTzBqLGGCUfJ"
   },
   "outputs": [],
   "source": [
    "# consider an empty list to store the accuracy\n",
    "accuracy = []\n",
    "\n",
    "# use for loop to build a knn model for each K\n",
    "for i in np.arange(1,25,2):\n",
    "    \n",
    "    # setup a knn classifier with k neighbors\n",
    "    # use the 'euclidean' metric \n",
    "    knn = KNeighborsClassifier(i, metric = 'euclidean', n_jobs=-1)\n",
    "   \n",
    "    # fit the model using 'cross_val_score'\n",
    "    # pass the knn model as 'estimator'\n",
    "    # use 3-fold cross validation\n",
    "    score = cross_val_score(knn, X_train, y_train, cv = 3, n_jobs=-1)\n",
    "    \n",
    "    # calculate the mean score\n",
    "    score = score.mean()\n",
    "    \n",
    "    # append the accuracy \n",
    "    accuracy.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1634706560753,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "i7KP0ehWCUfJ",
    "outputId": "65e04e63-a913-4d37-cb6d-0c1b7f993adf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8249427917620137,\n",
       " 0.8499237223493518,\n",
       " 0.8579328756674295,\n",
       " 0.8636536994660564,\n",
       " 0.8680396643783371,\n",
       " 0.8665141113653699,\n",
       " 0.8640350877192983,\n",
       " 0.866323417238749,\n",
       " 0.8668954996186118,\n",
       " 0.8668954996186118,\n",
       " 0.8646071700991609,\n",
       " 0.8649885583524028]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the accuracy score\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "pCIYHBuaCUfJ"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>17. Draw a line plot to see the accuracy (list created in the above question) for each value of K using euclidean distance as a metric of KNN model and find the optimal value of 'k'.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ULtL9SrECUfK"
   },
   "source": [
    "Given list of values:\n",
    "\n",
    "    n_neighbors = np.arange(1, 25, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 385
    },
    "executionInfo": {
     "elapsed": 572,
     "status": "ok",
     "timestamp": 1634706600704,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "2iWm0IvRCUfK",
    "outputId": "bfccb2da-7d62-4fe8-9c19-269dc9067a3f"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the accuracy for different values of K \n",
    "plt.plot(range(1,25,2), accuracy)\n",
    "\n",
    "# add plot and axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Find the Optimal Value of K', fontsize = 15)\n",
    "plt.xlabel('K', fontsize = 15)\n",
    "plt.ylabel('Accuracy', fontsize = 15)\n",
    "\n",
    "# set the x-axis labels\n",
    "plt.xticks(np.arange(1, 25, step = 2))\n",
    "\n",
    "# plot a vertical line across the minimum error rate\n",
    "plt.axvline(x = 15, color = 'red')\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wAVohQFdCUfK"
   },
   "source": [
    "**Intrepretation:** The accuracy is stabilized after k = 15. Thus, we can consider 15 as the optimal value of k."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hKbfd73KfO10"
   },
   "outputs": [],
   "source": [
    "#Compare Logistic Regression model vs KNN model using KFold cross validation using roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "executionInfo": {
     "elapsed": 947,
     "status": "ok",
     "timestamp": 1637061921957,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "lgTJE-yKfO4p"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "executionInfo": {
     "elapsed": 347,
     "status": "ok",
     "timestamp": 1637062442229,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "kr8YTpXMfolO"
   },
   "outputs": [],
   "source": [
    "LR=LogisticRegression()\n",
    "KNN_final=KNeighborsClassifier(n_neighbors=23,metric='manhattan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "executionInfo": {
     "elapsed": 349,
     "status": "ok",
     "timestamp": 1637061982928,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "ivm5ht1Zfoo1"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold,cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23138,
     "status": "ok",
     "timestamp": 1637062396741,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "uDsPNDEThDIu",
    "outputId": "9663c6fc-b771-4ff6-aeaa-d25dec21a9d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'metric': 'manhattan', 'n_neighbors': 23, 'weights': 'uniform'}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier()\n",
    "kf=KFold(n_splits=5,shuffle=True,random_state=0)\n",
    "GS=GridSearchCV(knn,hyp_paramaters,cv=kf,scoring='roc_auc')\n",
    "GS.fit(X,df_target)\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 428,
     "status": "ok",
     "timestamp": 1637062185394,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "Qu1D9sC1fosb",
    "outputId": "2ecf2a3a-381f-4b36-fca8-e12fefd5e481"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bias_error 0.0782739521885395\n",
      "Variance_error 0.0030854702317669137\n"
     ]
    }
   ],
   "source": [
    "#Logistic\n",
    "kf=KFold(n_splits=5,shuffle=True,random_state=0)\n",
    "scores=cross_val_score(LR,X,df_target,cv=kf,scoring='roc_auc')\n",
    "print(\"Bias_error\",1-np.mean(scores))\n",
    "print(\"Variance_error\",np.std(scores,ddof=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 336,
     "status": "ok",
     "timestamp": 1637062483559,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "c4tiP8xTfo22",
    "outputId": "d9fe8c5e-db8b-4634-dfcb-bcc66342d047"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bias_error 0.06707176826310768\n",
      "Variance_error 0.0034245251915131654\n"
     ]
    }
   ],
   "source": [
    "#KNN\n",
    "kf=KFold(n_splits=5,shuffle=True,random_state=0)\n",
    "scores=cross_val_score(KNN_final,X,df_target,cv=kf,scoring='roc_auc')\n",
    "print(\"Bias_error\",1-np.mean(scores))\n",
    "print(\"Variance_error\",np.std(scores,ddof=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zQOoqQKTCUfK"
   },
   "source": [
    "<a id=\"nb\"> </a>\n",
    "## 3. Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "XQMaHofLCUfK"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>18. Build a Gaussian naive bayes model and generate a classification report. Also tell how well is the model performing.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jpBH37RFinc9"
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 376,
     "status": "ok",
     "timestamp": 1634714026506,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "_D2xx_zRCUfK",
    "outputId": "d2cd7f53-d7e2-48ac-8221-aea2efc6a9b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.76      0.80       819\n",
      "           1       0.66      0.75      0.70       492\n",
      "\n",
      "    accuracy                           0.76      1311\n",
      "   macro avg       0.75      0.76      0.75      1311\n",
      "weighted avg       0.77      0.76      0.76      1311\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'GaussianNB'\n",
    "gnb = GaussianNB()\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "gnb_model = gnb.fit(X_train, y_train)\n",
    "\n",
    "# predict the attrition for test set\n",
    "y_pred = gnb_model.predict(X_test)\n",
    "\n",
    "# print the classification report \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-S5G8bpzCUfL"
   },
   "source": [
    "**Interpretation:** The recall for the positive class (i.e. customers who will subscribe) and the recall of the negative class (i.e. customers who will not subscribe) are stable.\n",
    "Thus, we can say that our model is performing good.\n",
    "\n",
    "Note: Recall for the positive class is also known as Sensitivity and Recall for the negative class is also known as Specificity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "wEjKrvETCUfL"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>19. Find the area under the receiver operating characteristic curve and the confusion matrix for the Naive Bayes model built in question 18.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gPRnOAj3CUfL",
    "outputId": "e36c30a5-745a-487c-9c7a-81dcbfabe39c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the ROC curve\n",
    "plot_roc(gnb_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GR34SY87CUfL",
    "outputId": "783decd9-d19a-4182-db9b-074722359267"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function to plot the confusion matrix\n",
    "# pass the knn model to the function\n",
    "plot_confusion_matrix(gnb_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "4AE1KmMGCUfL"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>20. Build a Gaussian Naive Bayes model and perform 10 fold cross validation and find the average accuracy.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 398,
     "status": "ok",
     "timestamp": 1634714546352,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "xjwdmi2NCUfL",
    "outputId": "08f9a5a0-4d3f-46cb-c9d7-d6f6e86b180a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy after 10 fold cross validation:  0.78\n"
     ]
    }
   ],
   "source": [
    "# instantiate the 'GaussianNB'\n",
    "gnb = GaussianNB()\n",
    "   \n",
    "# fit the model using 'cross_val_score'\n",
    "# pass the gnb model as 'estimator'\n",
    "# use 10-fold cross validation\n",
    "score = cross_val_score(gnb, X_train, y_train, cv = 10)\n",
    "\n",
    "# calculate the mean score\n",
    "score = score.mean()\n",
    "\n",
    "print(\"Accuracy after 10 fold cross validation: \", round(score, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VS0dGp6olhU-"
   },
   "outputs": [],
   "source": [
    "knn_final=KNeighborsClassifier(n_neighbors=15,metric='manhattan')\n",
    "gnb=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dxhcf9SIlroo"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 396,
     "status": "ok",
     "timestamp": 1634714824990,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "xkYI4HqWmKKG",
    "outputId": "87463509-f148-4fc4-f38b-7080b8204b79"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6555, 8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1146,
     "status": "ok",
     "timestamp": 1634715038921,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "2y3G1RWqlzeM",
    "outputId": "7912f4f2-77b0-49b1-ad56-47e8641f0302"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9313775699384366\n",
      "0.007968783926577567\n"
     ]
    }
   ],
   "source": [
    "kf=KFold(n_splits=10,shuffle=True,random_state=0)\n",
    "scores=cross_val_score(knn_final,X,df_target,cv=kf,scoring='roc_auc')\n",
    "print(np.mean(scores))\n",
    "print(np.std(scores,ddof=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 473,
     "status": "ok",
     "timestamp": 1634715045699,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "FmFWHCTVm-6c",
    "outputId": "029c56a9-cad2-49f8-fa2c-91956754d578"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.93218558 0.93168073 0.94528786 0.91958003 0.93227448 0.91851284\n",
      " 0.93558885 0.93734336 0.93361333 0.92770864]\n"
     ]
    }
   ],
   "source": [
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 399,
     "status": "ok",
     "timestamp": 1634715089769,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "hqyzdrWhmjTw",
    "outputId": "912536d3-133a-4495-aa31-7a18362e79fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8884274389295991\n",
      "0.011847049742143416\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(gnb,X,df_target,cv=kf,scoring='roc_auc')\n",
    "print(np.mean(scores))\n",
    "print(np.std(scores,ddof=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 407,
     "status": "ok",
     "timestamp": 1634715094576,
     "user": {
      "displayName": "Mahesh Anand",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Ghy47CXBCGfpGm71ElmYPDEY5c3AQB5tdq4ROpemw=s64",
      "userId": "06788086636039591358"
     },
     "user_tz": -330
    },
    "id": "7yRif4NAnLKX",
    "outputId": "e4671c1a-50c8-4f1e-be26-d2ddfce9d1a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.88194099 0.89119449 0.89649459 0.86474207 0.90198216 0.88337084\n",
      " 0.88021356 0.89577427 0.9039787  0.88458272]\n"
     ]
    }
   ],
   "source": [
    "print(scores)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "SLC - In-Class Lab Exercise _Day2_KNN_NB.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}