Loan_Modelling-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Bank_Personal_Loan_Modelling (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>91107</td>\n",
       "      <td>4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>34</td>\n",
       "      <td>90089</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>94720</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>100</td>\n",
       "      <td>94112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>91330</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
       "0   1   25           1      49     91107       4    1.6          1         0   \n",
       "1   2   45          19      34     90089       3    1.5          1         0   \n",
       "2   3   39          15      11     94720       1    1.0          1         0   \n",
       "3   4   35           9     100     94112       1    2.7          2         0   \n",
       "4   5   35           8      45     91330       4    1.0          2         0   \n",
       "\n",
       "   Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
       "0              0                   1           0       0           0  \n",
       "1              0                   1           0       0           0  \n",
       "2              0                   0           0       0           0  \n",
       "3              0                   0           0       0           0  \n",
       "4              0                   0           0       0           1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    0\n",
       "Age                   0\n",
       "Experience            0\n",
       "Income                0\n",
       "ZIP Code              0\n",
       "Family                0\n",
       "CCAvg                 0\n",
       "Education             0\n",
       "Mortgage              0\n",
       "Personal Loan         0\n",
       "Securities Account    0\n",
       "CD Account            0\n",
       "Online                0\n",
       "CreditCard            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green;font-weight:bold; font-size:2em\">2. Study the data distribution in each attribute, share your findings(15 marks)<span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,15))\n",
    "columns=df.columns\n",
    "\n",
    "for x, i in zip(columns, range(len(columns))):\n",
    "    ax=fig.add_subplot(5,3,i+1)\n",
    "    sns.distplot(df[x], kde=False,)\n",
    "#     ax.set_title(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.00000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2500.500000</td>\n",
       "      <td>45.338400</td>\n",
       "      <td>20.104600</td>\n",
       "      <td>73.774200</td>\n",
       "      <td>93152.503000</td>\n",
       "      <td>2.396400</td>\n",
       "      <td>1.937938</td>\n",
       "      <td>1.881000</td>\n",
       "      <td>56.498800</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.104400</td>\n",
       "      <td>0.06040</td>\n",
       "      <td>0.596800</td>\n",
       "      <td>0.294000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1443.520003</td>\n",
       "      <td>11.463166</td>\n",
       "      <td>11.467954</td>\n",
       "      <td>46.033729</td>\n",
       "      <td>2121.852197</td>\n",
       "      <td>1.147663</td>\n",
       "      <td>1.747659</td>\n",
       "      <td>0.839869</td>\n",
       "      <td>101.713802</td>\n",
       "      <td>0.294621</td>\n",
       "      <td>0.305809</td>\n",
       "      <td>0.23825</td>\n",
       "      <td>0.490589</td>\n",
       "      <td>0.455637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9307.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1250.750000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>91911.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2500.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>93437.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3750.250000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>94608.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>96651.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>635.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID          Age   Experience       Income      ZIP Code  \\\n",
       "count  5000.000000  5000.000000  5000.000000  5000.000000   5000.000000   \n",
       "mean   2500.500000    45.338400    20.104600    73.774200  93152.503000   \n",
       "std    1443.520003    11.463166    11.467954    46.033729   2121.852197   \n",
       "min       1.000000    23.000000    -3.000000     8.000000   9307.000000   \n",
       "25%    1250.750000    35.000000    10.000000    39.000000  91911.000000   \n",
       "50%    2500.500000    45.000000    20.000000    64.000000  93437.000000   \n",
       "75%    3750.250000    55.000000    30.000000    98.000000  94608.000000   \n",
       "max    5000.000000    67.000000    43.000000   224.000000  96651.000000   \n",
       "\n",
       "            Family        CCAvg    Education     Mortgage  Personal Loan  \\\n",
       "count  5000.000000  5000.000000  5000.000000  5000.000000    5000.000000   \n",
       "mean      2.396400     1.937938     1.881000    56.498800       0.096000   \n",
       "std       1.147663     1.747659     0.839869   101.713802       0.294621   \n",
       "min       1.000000     0.000000     1.000000     0.000000       0.000000   \n",
       "25%       1.000000     0.700000     1.000000     0.000000       0.000000   \n",
       "50%       2.000000     1.500000     2.000000     0.000000       0.000000   \n",
       "75%       3.000000     2.500000     3.000000   101.000000       0.000000   \n",
       "max       4.000000    10.000000     3.000000   635.000000       1.000000   \n",
       "\n",
       "       Securities Account  CD Account       Online   CreditCard  \n",
       "count         5000.000000  5000.00000  5000.000000  5000.000000  \n",
       "mean             0.104400     0.06040     0.596800     0.294000  \n",
       "std              0.305809     0.23825     0.490589     0.455637  \n",
       "min              0.000000     0.00000     0.000000     0.000000  \n",
       "25%              0.000000     0.00000     0.000000     0.000000  \n",
       "50%              0.000000     0.00000     1.000000     0.000000  \n",
       "75%              0.000000     0.00000     1.000000     1.000000  \n",
       "max              1.000000     1.00000     1.000000     1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,15))\n",
    "columns=df.columns\n",
    "\n",
    "for x, i in zip(columns, range(len(columns))):\n",
    "    ax=fig.add_subplot(5,3,i+1)\n",
    "    df.boxplot(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. ID - ID is the identification number for each person and it doesnot hold any informational value.\n",
    "##### 2. Age - As we can see from the graph, \"Age\" column is normally distributed. Most of the persons are between the age range 30 to 60. There seems to be no outliers.\n",
    "##### 3. Experience - This column also seems to be perfectly normally distributed with seemingly no outliers.\n",
    "##### 4. Income - As we can see from BOXPLOT graph, there a presence of outliers which make this column rightly skewed .From descriptive statistices we notice that 75% of the income is at 98K and max is at 224K which shows high skewness in income distribution.\n",
    "##### 5. ZIP Code - ZIP Code column can have informational value as there can be correlation between the area of living and possibility of good income and EMI paying capability and hence higher probability of accepting the loan offer.\n",
    "##### 6. Family - The number of family members can play a role in determining the chances of accepting the offer as the need for fund can be more. The number of family members is between 1000 - 1500 with **Number of family members = 1000** being the lowest.\n",
    "##### 7. CCAvg - 75% of the CCAvg value is at  2500 Dollar per  month and max value is  10k Dollar per month. This column shows high skewness on right side of distribution.\n",
    "##### 8. Education - 3 being the highest Education and 1 being the lowest education level, we can see that around 40 percentage of the people are at the lowest education level and only 25 percentage of the people are at the highest education level.\n",
    "##### 9. Mortgage - From the BOXPLOT it can be seen that around 80 percentage of people have zero or near to zero house mortgage value. But 20 percentage of the people have high values of house mortage and values go to as high as 635k dollars. This makes this column super highly skewed having lot of outliers.\n",
    "##### 10. Personal Loan - This columns indicates that how much percentage of people accepted the offer last time and as we can see from the graph, only minuscle proportion of people accepted the offer.\n",
    "##### 11. Securities Amount - Only small portion of people have Securities Account in Bank and the proportion of people who have securities account in bank is in line with people who accepted the offer last year, not necessarily that same people accepted the offer.\n",
    "##### 12. CD Account - Lesser number of people surveyed last year had CD account in bank as compared to the securities account in BanK.\n",
    "##### 13. Online - From the distribution graph it can be seen that around 60 percentage of the people use banking facilities.\n",
    "##### 14. Credit Card - From the distribution graph it can be seen that around 80 percentage of the people use Credit Card issued by the bank."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green;font-weight:bold; font-size:2em\">3. Get the target column distribution. Your comments (5 marks)<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Personal Loan'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " sns.distplot(df['Personal Loan'], kde=False,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of people for which loans were given: 0.096 \n",
      "Percentage of people for which loans were not given:0.904 \n"
     ]
    }
   ],
   "source": [
    "loan_given=len(df[df['Personal Loan']==1])\n",
    "loan_not_given=len(df[df['Personal Loan']==0])\n",
    "\n",
    "print(f\"Percentage of people for which loans were given: {(loan_given)/len(df)} \") \n",
    "print(f\"Percentage of people for which loans were not given:{(loan_not_given)/len(df)} \")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-weight:bold;\">Comment -<span>\n",
    "\n",
    "<span style=\"color:red; font-weight:bold;\">From the above we can see that only 9.6% of the people last year accepted the offer.<span>\n",
    "    \n",
    "    \n",
    "<span style=\"color:red; font-weight:bold;\">Offer acceptance is highly imbalanced.<span>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green;font-weight:bold; font-size:2em\">4. Split the data into training and test set in the ratio of 70:30 respectively (5 marks)<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Personal Loan', 'ID'], axis=1)\n",
    "y=df['Personal Loan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.3, random_state = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green;font-weight:bold; font-size:2em\"> 5. Use different classification models (Logistic, K-NN and Naïve Bayes) to predict the likelihood of a customer buying personal loans (15 marks)<span>\n",
    "    \n",
    "    \n",
    "<span style=\"color:green;font-weight:bold; font-size:2em\">  6. Print the confusion matrix for all the above models (5 marks)<span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <span style=\"color:red; font-size:2em\">**Logistic Regression Model**<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0        1         2         3         4         5         6  \\\n",
      "0 -0.000528 -0.00201  0.036089 -0.000066  0.015165  0.009454  0.016447   \n",
      "\n",
      "          7         8         9        10        11  intercept  \n",
      "0  0.000833  0.000534  0.004681 -0.000177 -0.000067  -0.000106  \n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model=LogisticRegression(solver='liblinear')\n",
    "model.fit(x_train,y_train)\n",
    "y_predict=model.predict(x_test)\n",
    "\n",
    "coef_df = pd.DataFrame(model.coef_)\n",
    "coef_df['intercept']=model.intercept_\n",
    "\n",
    "print(coef_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9091428571428571\n",
      "0.9073333333333333\n"
     ]
    }
   ],
   "source": [
    "print(model.score(x_train, y_train))\n",
    "print(model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-size:1em\"> **>> We have predicted the likelihood of a customer buying personal loan.**<span>\n",
    "    \n",
    " <span style=\"color:red; font-size:1em\">**>> The training accuracy for Logistic Regression Model is=> 90.9%**<span>\n",
    "     \n",
    " <span style=\"color:red; font-size:1em\">**>> The test accuracy for Logistic Regression Model is => 90.7%**<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=metrics.confusion_matrix(y_test, y_predict, labels=[1, 0])\n",
    "\n",
    "df_cm = pd.DataFrame(cm, index = [i for i in [\"1\",\"0\"]],\n",
    "                  columns = [i for i in [\"Predict 1\",\"Predict 0\"]])\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.heatmap(df_cm, annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Predict 1  Predict 0\n",
      "1         43        106\n",
      "0         33       1318\n"
     ]
    }
   ],
   "source": [
    "print(df_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-size:1.5em\">**Above is the Confusion Matrix for Logistic Regression Model**<span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <span style=\"color:red; font-size:2em\">**Naive Bayes' Model**<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb_model=GaussianNB()\n",
    "nb_model.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8942857142857142"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=nb_model.predict(x_train)\n",
    "train_accuracy=metrics.accuracy_score(y_train,y_predict)\n",
    "train_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.884"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_predict=nb_model.predict(x_test)\n",
    "test_accuracy=metrics.accuracy_score(y_test,y_test_predict)\n",
    "test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8942857142857142\n",
      "0.884\n"
     ]
    }
   ],
   "source": [
    "print(nb_model.score(x_train, y_train))\n",
    "print(nb_model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-size:1em\"> **>> We have predicted the likelihood of a customer buying personal loan.**<span>\n",
    "    \n",
    " <span style=\"color:red; font-size:1em\">**>> The training accuracy for Naive Bayes' Model is=> 89.4%**<span>\n",
    "     \n",
    " <span style=\"color:red; font-size:1em\">**>> The test accuracy for Naive Bayes' Model is => 88.4%**<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_cm=metrics.confusion_matrix(y_test, y_test_predict, labels=[1, 0])\n",
    "\n",
    "nb_df_cm = pd.DataFrame(nb_cm, index = [i for i in [\"1\",\"0\"]],\n",
    "                  columns = [i for i in [\"Predict 1\",\"Predict 0\"]])\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.heatmap(nb_df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Predict 1  Predict 0\n",
      "1         85         64\n",
      "0        110       1241\n"
     ]
    }
   ],
   "source": [
    "print(nb_df_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-size:1.5em\">**Above is the Confusion Matrix for Naive Bayes' Model**<span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-size:2em\">**KNN**<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the best value of K for good accuracy\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "scores =[]\n",
    "for k in range(1,50):\n",
    "    NNH_train = KNeighborsClassifier(n_neighbors = k, weights = 'distance' )\n",
    "    NNH_train.fit(x_train, y_train)\n",
    "    scores.append(NNH_train.score(x_test, y_test))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d4edf8488>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,50),scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=29, weights='distance')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NNH = KNeighborsClassifier(n_neighbors = 29, weights = 'distance' )\n",
    "NNH.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_predicted_labels = NNH.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.9026666666666666\n"
     ]
    }
   ],
   "source": [
    "print(NNH.score(x_train, y_train))\n",
    "print(NNH.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-size:1em\"> **>> We have predicted the likelihood of a customer buying personal loan.**<span>\n",
    "    \n",
    " <span style=\"color:red; font-size:1em\">**>> The training accuracy for Naive Bayes' Model is=> 100%**<span>\n",
    "     \n",
    " <span style=\"color:red; font-size:1em\">**>> The test accuracy for Naive Bayes' Model is => 90.26%**<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "KNN_cm=metrics.confusion_matrix(y_test, KNN_predicted_labels, labels=[1,0])\n",
    "\n",
    "KNN_df_cm = pd.DataFrame(KNN_cm, index = [i for i in [\"1\",\"0\"]],\n",
    "                  columns = [i for i in [\"Predict 1\",\"Predict 0\"]])\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.heatmap(KNN_df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Predict 1  Predict 0\n",
      "1          5        144\n",
      "0          2       1349\n"
     ]
    }
   ],
   "source": [
    "print(KNN_df_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-size:1.5em\">**Above is the Confusion Matrix for KNN Model**<span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:green;font-weight:bold; font-size:2em\"> 7. Give your reasoning on which is the best model in this case and why it performs better? (5 marks)<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us put Confusion Matrix of all the three models together for comparison of models.\n",
    "# Initialising lists with models and model names.\n",
    "\n",
    "models=[LogisticRegression(solver='liblinear'),GaussianNB(),KNeighborsClassifier(n_neighbors = 29, weights = 'distance' )]\n",
    "model_names=['LR','NB','KNN']\n",
    "\n",
    "CM=[]             # Set the confusion matrix to empty list for appending all 3 confusion matrix\n",
    "\n",
    "Recall=[]        # Setting the parameters to empty list for appending the values for all 3 models.\n",
    "Specificity=[]\n",
    "Accuracy=[]\n",
    "Precision=[]\n",
    "Neg_Pred_value=[]\n",
    "F1_Score=[]\n",
    "\n",
    "TPs=[]\n",
    "FPs=[]\n",
    "TNs=[]\n",
    "FNs=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying FOR-LOOP for calculating confusion matrix & Calculating matrix parameters.\n",
    "\n",
    "for x,model in zip(model_names, models):\n",
    "    \n",
    "    model.fit(x_train,y_train)\n",
    "    \n",
    "    y_predict  = model.predict(x_test)\n",
    "    \n",
    "    \n",
    "    confusion_matrix=metrics.confusion_matrix(y_test, y_predict, labels=[1, 0])\n",
    "    cm = pd.DataFrame(confusion_matrix, index = [i for i in [\"1\",\"0\"]],\n",
    "                  columns = [i for i in [x+\"_Predict 1\",x+\"_Predict 0\"]])\n",
    "    CM.append(cm)\n",
    "           \n",
    "    TP = cm.iloc[0,0]\n",
    "    FP= cm.iloc[1,0]\n",
    "    TN = cm.iloc[1,1]\n",
    "    FN= cm.iloc[0,1]\n",
    "    \n",
    "    TPs.append(TP)\n",
    "    FPs.append(FP)\n",
    "    TNs.append(TN)\n",
    "    FNs.append(FN)\n",
    "    \n",
    "    Recall.append(TP/(TP+FN))\n",
    "    Specificity.append(TN/(TN+FP))\n",
    "    Accuracy.append((TP+TN)/(TP+TN+FP+FN))\n",
    "    Precision.append(TP/(TP+FP))\n",
    "    Neg_Pred_value.append(TN/(TN+FN))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LR_Predict 1</th>\n",
       "      <th>LR_Predict 0</th>\n",
       "      <th>NB_Predict 1</th>\n",
       "      <th>NB_Predict 0</th>\n",
       "      <th>KNN_Predict 1</th>\n",
       "      <th>KNN_Predict 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43</td>\n",
       "      <td>106</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>1318</td>\n",
       "      <td>110</td>\n",
       "      <td>1241</td>\n",
       "      <td>2</td>\n",
       "      <td>1349</td>\n",
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      ],
      "text/plain": [
       "   LR_Predict 1  LR_Predict 0  NB_Predict 1  NB_Predict 0  KNN_Predict 1  \\\n",
       "1            43           106            85            64              5   \n",
       "0            33          1318           110          1241              2   \n",
       "\n",
       "   KNN_Predict 0  \n",
       "1            144  \n",
       "0           1349  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculation F1 Score using list of Precison & Recall.\n",
    "for i,j in zip(Precision, Recall):\n",
    "    f1_score=2*((i*j)/(i+j))\n",
    "    F1_Score.append(f1_score)\n",
    "    \n",
    "CM_Main=pd.concat(CM,axis=1)  # Printing the CM of all three models.\n",
    "CM_Main\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TPs</th>\n",
       "      <th>FNs</th>\n",
       "      <th>TNs</th>\n",
       "      <th>FPs</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Neg_Pred_value</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>43</td>\n",
       "      <td>106</td>\n",
       "      <td>1318</td>\n",
       "      <td>33</td>\n",
       "      <td>0.288591</td>\n",
       "      <td>0.975574</td>\n",
       "      <td>0.907333</td>\n",
       "      <td>0.565789</td>\n",
       "      <td>0.925562</td>\n",
       "      <td>0.382222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>1241</td>\n",
       "      <td>110</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.918579</td>\n",
       "      <td>0.884000</td>\n",
       "      <td>0.435897</td>\n",
       "      <td>0.950958</td>\n",
       "      <td>0.494186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>5</td>\n",
       "      <td>144</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "      <td>0.033557</td>\n",
       "      <td>0.998520</td>\n",
       "      <td>0.902667</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.903550</td>\n",
       "      <td>0.064103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TPs  FNs   TNs  FPs    Recall  Specificity  Accuracy  \\\n",
       "Logistic Regression   43  106  1318   33  0.288591     0.975574  0.907333   \n",
       "Naive Bayes           85   64  1241  110  0.570470     0.918579  0.884000   \n",
       "KNN                    5  144  1349    2  0.033557     0.998520  0.902667   \n",
       "\n",
       "                     Precision  Neg_Pred_value  F1_Score  \n",
       "Logistic Regression   0.565789        0.925562  0.382222  \n",
       "Naive Bayes           0.435897        0.950958  0.494186  \n",
       "KNN                   0.714286        0.903550  0.064103  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zipping lists of all the parameters to form the DataFrame for better comparions of all 3 models.\n",
    "data_tuples = list(zip(TPs,FNs,TNs,FPs,Recall,Specificity,Accuracy,Precision,Neg_Pred_value,F1_Score))\n",
    "df1=pd.DataFrame(data_tuples,index=['Logistic Regression','Naive Bayes','KNN'],\n",
    "               columns=['TPs','FNs','TNs','FPs','Recall','Specificity','Accuracy','Precision','Neg_Pred_value','F1_Score'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### To interpret the models we need to first understand what is our requirement and accordingly what do we need from model.\n",
    "##### We need to find the likelihood of a customer accepting the loan offer. \n",
    "##### But likelihood of a customer accepting the loan offer depends on the customer's assessment of his ability to pay the EMI of the loan.\n",
    "##### Hence, Customer's EMI paying capacity will determine his likelihood of accepting the loan offer.\n",
    "##### It is fine as far as model predicts true values. We need to check to how much extent the model is predicting false values.\n",
    "  **>>There two types of false values: False Positives & False Negatives**\n",
    "  \n",
    "<span style=\"color:purple; font-weight:bold;\">>>TPs -Acceptance of offer is predicted & customer accepts the offer -- means the customer has assessed that he has good emi paying capacity<span>.\n",
    "    \n",
    "<span style=\"color:purple; font-weight:bold;\">>>FPs - Acceptance of offer is predicted & customer does not accept the offer - means the customer has assessed that he does not have good EMI paying capacity.<span>\n",
    "    \n",
    "<span style=\"color:purple; font-weight:bold;\">>>FNs -No Acceptance of offer is predicted & customer has the capacity to pay the emi and wants emi. - Opportunity Loss<span>\n",
    "    \n",
    "<span style=\"color:purple; font-weight:bold;\">>>TNs - No Acceptance of offer is predicted & Customer also does not have the ability to pay the EMI and hence does not want the offer.<span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:purple; font-weight:bold;\">>> This means we need to minimise the False Positive values as Falsely giving the offer to a customer who does not have the EMI paying capacity may result in NPAs for bank. <span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. **False Negatives** are lost opportunities. The cost of lost opportunities is a small amount of       interest charged on loan.\n",
    " 2. **False Postitives** are predicting the customer will accept the offer and in reality cutomer has not accepted the offer. This happens when customer has assessed that he cannot pay the EMI and hence **FALSE POSITIVES** are heavy cost of extending offer to wrong people and Banks need to avoid\n",
    "the heavy cost to reduce the NPAs. **This is because if a customer defaults entire loan amount is to be written off from the bank's profits.**\n",
    " 3. Hence there is a **trade-off** between **forgone opportunies of small amount of interest charged on loan and heavy cost of NPAs.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:blue; font-weight:bold;\"> Conclusion:- It is important to select a model which minimizes both on False Positives & False Negatives. But practically it may not be possible to minimize both together. We have to pick either FPs or FNs to minimize more.<span>\n",
    "    \n",
    "<span style=\"color:blue; font-weight:bold;\">Here, we will pick a model which minimizes False Positives. FP means:- Acceptance of offer is predicted by model and the customer does not accept the offer.<span>\n",
    "    \n",
    "<span style=\"color:blue; font-weight:bold;\">Note that ideally model should not predict likelihood of acceptance of offer by the customer when customer does not accept the offer. This non acceptance of offer by customer may be due to his assessment of his ability to not be able to pay the EMI<span>\n",
    "    \n",
    "    \n",
    "### Hence False Positive(FP) is the error we need to reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>TPs</th>\n",
       "      <th>FNs</th>\n",
       "      <th>TNs</th>\n",
       "      <th>FPs</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Neg_Pred_value</th>\n",
       "      <th>F1_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>43</td>\n",
       "      <td>106</td>\n",
       "      <td>1318</td>\n",
       "      <td>33</td>\n",
       "      <td>0.288591</td>\n",
       "      <td>0.975574</td>\n",
       "      <td>0.907333</td>\n",
       "      <td>0.565789</td>\n",
       "      <td>0.925562</td>\n",
       "      <td>0.382222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>1241</td>\n",
       "      <td>110</td>\n",
       "      <td>0.570470</td>\n",
       "      <td>0.918579</td>\n",
       "      <td>0.884000</td>\n",
       "      <td>0.435897</td>\n",
       "      <td>0.950958</td>\n",
       "      <td>0.494186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>5</td>\n",
       "      <td>144</td>\n",
       "      <td>1349</td>\n",
       "      <td>2</td>\n",
       "      <td>0.033557</td>\n",
       "      <td>0.998520</td>\n",
       "      <td>0.902667</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.903550</td>\n",
       "      <td>0.064103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TPs  FNs   TNs  FPs    Recall  Specificity  Accuracy  \\\n",
       "Logistic Regression   43  106  1318   33  0.288591     0.975574  0.907333   \n",
       "Naive Bayes           85   64  1241  110  0.570470     0.918579  0.884000   \n",
       "KNN                    5  144  1349    2  0.033557     0.998520  0.902667   \n",
       "\n",
       "                     Precision  Neg_Pred_value  F1_Score  \n",
       "Logistic Regression   0.565789        0.925562  0.382222  \n",
       "Naive Bayes           0.435897        0.950958  0.494186  \n",
       "KNN                   0.714286        0.903550  0.064103  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:blue; font-weight:bold;\">1. Note that total number of Positives(149) and Negatives(1351) are same for all three models.<span>\n",
    "\n",
    "<span style=\"color:purple; font-weight:bold;\">2. LR  Model is able to classify 97.5% of the Negative values correctly.<span>\n",
    "    \n",
    "<span style=\"color:purple; font-weight:bold;\">   NB  Model is able to classify 91.8% of the Negative values correctly.<span>\n",
    "    \n",
    "<span style=\"color:purple; font-weight:bold;\">   KNN Model is able to classify 99.8% of the Negative values correctly.<span>\n",
    "\n",
    "\n",
    "<span style=\"color:green; font-weight:bold;\">3. LR  Model is able to classify 28.8% of the Positive values correctly.<span>\n",
    "    \n",
    "<span style=\"color:green; font-weight:bold;\">   NB  Model is able to classify 57.0% of the Positive values correctly.<span>\n",
    "    \n",
    "<span style=\"color:green; font-weight:bold;\">   KNN Model is able to classify 3.35% of the Positive values correctly.<span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red; font-weight:bold; font-size:2em\">Final Conclusion :-<span>\n",
    "\n",
    "<span style=\"color:red; font-weight:bold;\">KNN has the highest accuracy of classifying Negative Values correctly.<span>\n",
    "<span style=\"color:red; font-weight:bold;\">KNN has the lowest FPs values as compared to other two models.<span>\n",
    "<span style=\"color:red; font-weight:bold;\">On the other side, KNN also has the highest value for False Negative Values.<span>\n",
    "<span style=\"color:red; font-weight:bold;\">But as discussed above, we can afford to be wrong on False Negative side.<span>\n",
    "<span style=\"color:red; font-weight:bold;\">We cannot afford to be wrong on False Postive side.\n",
    "<span style=\"color:red; font-weight:bold;\">Also, the test accuracy of KNN is comparable with other two models.<span>\n",
    "\n",
    "<span style=\"color:red; font-weight:bold;\">So, out of all the three models we select KNN as the best model.<span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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