bank_lone_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Gender</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>Home Ownership</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>M</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>91107</td>\n",
       "      <td>4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Home Owner</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>34.0</td>\n",
       "      <td>90089</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>15</td>\n",
       "      <td>11.0</td>\n",
       "      <td>94720</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>8</td>\n",
       "      <td>45.0</td>\n",
       "      <td>91330</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Age Gender  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "0   1   25      M           1    49.0     91107       4    1.6          1   \n",
       "1   2   45      M          19    34.0     90089       3    1.5          1   \n",
       "2   3   39      M          15    11.0     94720       1    1.0          1   \n",
       "3   4   35      M           9   100.0     94112       1    2.7          2   \n",
       "4   5   35      M           8    45.0     91330       4    1.0          2   \n",
       "\n",
       "   Mortgage Home Ownership Personal Loan  Securities Account  CD Account  \\\n",
       "0         0     Home Owner             0                   1           0   \n",
       "1         0           Rent             0                   1           0   \n",
       "2         0           Rent             0                   0           0   \n",
       "3         0           Rent             0                   0           0   \n",
       "4         0           Rent             0                   0           0   \n",
       "\n",
       "   Online  CreditCard  \n",
       "0     0.0           0  \n",
       "1     0.0           0  \n",
       "2     0.0           0  \n",
       "3     0.0           0  \n",
       "4     0.0           1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_excel('New bank_loan_data.xlsx') #Read the excel file\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type of each column:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   ID                  5000 non-null   int64  \n",
      " 1   Age                 5000 non-null   int64  \n",
      " 2   Gender              3404 non-null   object \n",
      " 3   Experience          5000 non-null   int64  \n",
      " 4   Income              4933 non-null   float64\n",
      " 5   ZIP Code            5000 non-null   int64  \n",
      " 6   Family              5000 non-null   int64  \n",
      " 7   CCAvg               5000 non-null   float64\n",
      " 8   Education           5000 non-null   int64  \n",
      " 9   Mortgage            5000 non-null   int64  \n",
      " 10  Home Ownership      3811 non-null   object \n",
      " 11  Personal Loan       5000 non-null   object \n",
      " 12  Securities Account  5000 non-null   int64  \n",
      " 13  CD Account          5000 non-null   int64  \n",
      " 14  Online              4960 non-null   float64\n",
      " 15  CreditCard          5000 non-null   int64  \n",
      "dtypes: float64(3), int64(10), object(3)\n",
      "memory usage: 625.1+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(\"Data type of each column:\")\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that the <mark>columns `Gender`, `Income`, `Home Ownership`, and `Online` have missing data</mark>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Personal Loan\n",
      "0    4520\n",
      "1     479\n",
      "        1\n",
      "Name: count, dtype: int64 \n",
      "\n",
      "Online\n",
      "1.0    2961\n",
      "0.0    1999\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Personal Loan'].value_counts(),\"\\n\")\n",
    "print(df['Online'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can clearly see that both <mark>`Personal Loan` and `Online` columns contain only binary value so their data type should be converted to int type</mark>. Also note that one entry in Personal Loan is null string. So, <mark>as both the attributes have null values we cannot convert them to int without cleaning them first</mark>. I also think that <mark>`Mortgage` should be converted to float</mark> as it represents money like `Income`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summery Statistics:\n",
      "                 ID          Age Gender   Experience       Income  \\\n",
      "count   5000.000000  5000.000000   3404  5000.000000  4933.000000   \n",
      "unique          NaN          NaN      5          NaN          NaN   \n",
      "top             NaN          NaN      M          NaN          NaN   \n",
      "freq            NaN          NaN   1677          NaN          NaN   \n",
      "mean    2500.500000    46.008200    NaN    20.104600    72.758159   \n",
      "std     1443.520003    25.444898    NaN    11.467954    45.425519   \n",
      "min        1.000000     0.000000    NaN    -3.000000     8.000000   \n",
      "25%     1250.750000    35.000000    NaN    10.000000    39.000000   \n",
      "50%     2500.500000    45.000000    NaN    20.000000    63.000000   \n",
      "75%     3750.250000    55.000000    NaN    30.000000    95.000000   \n",
      "max     5000.000000   978.000000    NaN    43.000000   224.000000   \n",
      "\n",
      "            ZIP Code       Family        CCAvg    Education     Mortgage  \\\n",
      "count    5000.000000  5000.000000  5000.000000  5000.000000  5000.000000   \n",
      "unique           NaN          NaN          NaN          NaN          NaN   \n",
      "top              NaN          NaN          NaN          NaN          NaN   \n",
      "freq             NaN          NaN          NaN          NaN          NaN   \n",
      "mean    93152.503000     2.396400     1.937913     1.880600    56.498800   \n",
      "std      2121.852197     1.147663     1.747666     0.839812   101.713802   \n",
      "min      9307.000000     1.000000     0.000000     1.000000     0.000000   \n",
      "25%     91911.000000     1.000000     0.700000     1.000000     0.000000   \n",
      "50%     93437.000000     2.000000     1.500000     2.000000     0.000000   \n",
      "75%     94608.000000     3.000000     2.500000     3.000000   101.000000   \n",
      "max     96651.000000     4.000000    10.000000     3.000000   635.000000   \n",
      "\n",
      "       Home Ownership  Personal Loan  Securities Account  CD Account  \\\n",
      "count            3811         5000.0         5000.000000  5000.00000   \n",
      "unique              3            3.0                 NaN         NaN   \n",
      "top      Home Mortage            0.0                 NaN         NaN   \n",
      "freq             1705         4520.0                 NaN         NaN   \n",
      "mean              NaN            NaN            0.104400     0.06040   \n",
      "std               NaN            NaN            0.305809     0.23825   \n",
      "min               NaN            NaN            0.000000     0.00000   \n",
      "25%               NaN            NaN            0.000000     0.00000   \n",
      "50%               NaN            NaN            0.000000     0.00000   \n",
      "75%               NaN            NaN            0.000000     0.00000   \n",
      "max               NaN            NaN            1.000000     1.00000   \n",
      "\n",
      "             Online   CreditCard  \n",
      "count   4960.000000  5000.000000  \n",
      "unique          NaN          NaN  \n",
      "top             NaN          NaN  \n",
      "freq            NaN          NaN  \n",
      "mean       0.596976     0.294000  \n",
      "std        0.490555     0.455637  \n",
      "min        0.000000     0.000000  \n",
      "25%        0.000000     0.000000  \n",
      "50%        1.000000     0.000000  \n",
      "75%        1.000000     1.000000  \n",
      "max        1.000000     1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(\"Summery Statistics:\")\n",
    "print(df.describe(include= 'all'))  #Since, Personal Loan which contains binary values is of object type not int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "M    1677\n",
       "F    1607\n",
       "O      96\n",
       "-      21\n",
       "#       3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the <mark>values `-` and `#` are invalid and these entries must be removed</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There <mark>may also be some issues in the `Age` and `Experience` columns as the min of 0 and -3 doesn't make sense</mark> for these attributes respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "df.set_index(\"ID\", inplace=True)\n",
    "df.replace(\"?\", np.nan, inplace=True)\n",
    "df.replace(\"\", np.nan, inplace=True)\n",
    "df.replace(\" \", np.nan, inplace=True) #For Personal Loan specific case\n",
    "\n",
    "#For later removal of invalid data in Gender\n",
    "df['Gender'].replace(\"-\", np.nan, inplace=True)\n",
    "df['Gender'].replace(\"#\", np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "meanIncome = df['Income'].mean(axis=0)\n",
    "df['Income'].replace(np.nan, meanIncome, inplace=True)\n",
    "\n",
    "df.dropna(subset=['Home Ownership', 'Personal Loan', 'Gender','Online'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I could have dropped Gender and Online column to increase the number of data point about 700, mostly attributed to Gender, but it's said that Gender also plays an important role in Personal Loan approval, even though it shouldn't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   False\n",
       "Gender                False\n",
       "Experience            False\n",
       "Income                False\n",
       "ZIP Code              False\n",
       "Family                False\n",
       "CCAvg                 False\n",
       "Education             False\n",
       "Mortgage              False\n",
       "Home Ownership        False\n",
       "Personal Loan         False\n",
       "Securities Account    False\n",
       "CD Account            False\n",
       "Online                False\n",
       "CreditCard            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any() #Here, `any` evaluates each element to determine if the overall value is True or False."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated(subset=None).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3183, 15)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correcting data formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                     int64\n",
       "Gender                 object\n",
       "Experience              int64\n",
       "Income                float64\n",
       "ZIP Code                int64\n",
       "Family                  int64\n",
       "CCAvg                 float64\n",
       "Education               int64\n",
       "Mortgage              float64\n",
       "Home Ownership         object\n",
       "Personal Loan           int32\n",
       "Securities Account      int64\n",
       "CD Account              int64\n",
       "Online                  int32\n",
       "CreditCard              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Personal Loan','Online']] = df[['Personal Loan', 'Online']].astype('int') #As both contain only binary data\n",
    "df[['Mortgage']] = df[['Mortgage']].astype('float') #like income, morgage also deals with money\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Addressing Logical Anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Gender</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>Home Ownership</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",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
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       "      <th>79</th>\n",
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       "      <td>72.758159</td>\n",
       "      <td>93305</td>\n",
       "      <td>2</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>90277</td>\n",
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       "      <td>5.6</td>\n",
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      ],
      "text/plain": [
       "      Age Gender  Experience      Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "ID                                                                             \n",
       "79      0      M          30   72.758159     93305       2    2.6          3   \n",
       "91     13      M          30  118.000000     90277       4    5.6          2   \n",
       "1667    2      F          25  190.000000     95138       2    4.2          2   \n",
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       "2216    7      F           3  193.000000     94501       3    4.0          2   \n",
       "2318    8      M           5  129.000000     95814       3    5.9          3   \n",
       "\n",
       "      Mortgage Home Ownership  Personal Loan  Securities Account  CD Account  \\\n",
       "ID                                                                             \n",
       "79         0.0           Rent              1                   0           0   \n",
       "91         0.0           Rent              1                   0           0   \n",
       "1667       0.0           Rent              1                   0           0   \n",
       "1799       0.0   Home Mortage              1                   0           0   \n",
       "2216       0.0           Rent              1                   0           0   \n",
       "2318     319.0           Rent              1                   1           1   \n",
       "\n",
       "      Online  CreditCard  \n",
       "ID                        \n",
       "79         0           0  \n",
       "91         1           0  \n",
       "1667       1           0  \n",
       "1799       1           0  \n",
       "2216       1           0  \n",
       "2318       0           1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age']<18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is common knowledge that an individual must be at least 18 yrs old to get a loan. Looking at the data we can clearly see that the `Age` must be mistaken. How can a 0yr old boy have 30 years of experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
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      "text/plain": [
       "      Age Gender  Experience      Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "ID                                                                             \n",
       "79      0      M          30   72.758159     93305       2    2.6          3   \n",
       "91     13      M          30  118.000000     90277       4    5.6          2   \n",
       "1667    2      F          25  190.000000     95138       2    4.2          2   \n",
       "1799    4      M          20  185.000000     94086       3    2.7          1   \n",
       "\n",
       "      Mortgage Home Ownership  Personal Loan  Securities Account  CD Account  \\\n",
       "ID                                                                             \n",
       "79         0.0           Rent              1                   0           0   \n",
       "91         0.0           Rent              1                   0           0   \n",
       "1667       0.0           Rent              1                   0           0   \n",
       "1799       0.0   Home Mortage              1                   0           0   \n",
       "\n",
       "      Online  CreditCard  \n",
       "ID                        \n",
       "79         0           0  \n",
       "91         1           0  \n",
       "1667       1           0  \n",
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age']<=df['Experience']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see that such <mark>mistakes only exist on those entries where the `Age` was below 18. So we must remove these entries</mark>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "df = df[df['Age']>=18]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, having <mark>-ve years of experience is impossible. So these entries must also be removed</mark>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
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      "text/plain": [
       "     Age Gender  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "ID                                                                        \n",
       "90    25      M          -1   113.0     94303       4   2.30          3   \n",
       "316   24      M          -2    51.0     90630       3   0.30          3   \n",
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       "541   25      F          -1   109.0     94010       4   2.30          3   \n",
       "\n",
       "     Mortgage Home Ownership  Personal Loan  Securities Account  CD Account  \\\n",
       "ID                                                                            \n",
       "90        0.0           Rent              0                   0           0   \n",
       "316       0.0           Rent              0                   0           0   \n",
       "452      89.0   Home Mortage              0                   0           0   \n",
       "525       0.0     Home Owner              0                   0           0   \n",
       "541     314.0     Home Owner              0                   0           0   \n",
       "\n",
       "     Online  CreditCard  \n",
       "ID                       \n",
       "90        0           1  \n",
       "316       1           0  \n",
       "452       1           0  \n",
       "525       1           0  \n",
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df[df['Experience']<0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "df = df[df['Experience']>=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</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>3</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>15</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>94720</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</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>35</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>94112</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</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>5</th>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>8</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>91330</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4884</th>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>13</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>92646</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4928</th>\n",
       "      <td>43</td>\n",
       "      <td>O</td>\n",
       "      <td>19</td>\n",
       "      <td>72.758159</td>\n",
       "      <td>94720</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4942</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>72.758159</td>\n",
       "      <td>90049</td>\n",
       "      <td>2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Home Mortage</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>46</td>\n",
       "      <td>O</td>\n",
       "      <td>20</td>\n",
       "      <td>72.758159</td>\n",
       "      <td>90065</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Home Mortage</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>29</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>95762</td>\n",
       "      <td>3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Home Mortage</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3147 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Gender  Experience      Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "ID                                                                             \n",
       "1      25      M           1   49.000000     91107       4    1.6          1   \n",
       "2      45      M          19   34.000000     90089       3    1.5          1   \n",
       "3      39      M          15   11.000000     94720       1    1.0          1   \n",
       "4      35      M           9  100.000000     94112       1    2.7          2   \n",
       "5      35      M           8   45.000000     91330       4    1.0          2   \n",
       "...   ...    ...         ...         ...       ...     ...    ...        ...   \n",
       "4884   38      F          13  129.000000     92646       3    4.1          3   \n",
       "4928   43      O          19   72.758159     94720       1    0.7          2   \n",
       "4942   28      F           4   72.758159     90049       2    1.6          2   \n",
       "4963   46      O          20   72.758159     90065       3    3.0          3   \n",
       "4981   29      F           5  135.000000     95762       3    5.3          1   \n",
       "\n",
       "      Mortgage Home Ownership  Personal Loan  Securities Account  CD Account  \\\n",
       "ID                                                                             \n",
       "1          0.0     Home Owner              0                   1           0   \n",
       "2          0.0           Rent              0                   1           0   \n",
       "3          0.0           Rent              0                   0           0   \n",
       "4          0.0           Rent              0                   0           0   \n",
       "5          0.0           Rent              0                   0           0   \n",
       "...        ...            ...            ...                 ...         ...   \n",
       "4884       0.0           Rent              1                   0           1   \n",
       "4928       0.0           Rent              1                   0           1   \n",
       "4942       0.0   Home Mortage              1                   0           0   \n",
       "4963       0.0   Home Mortage              1                   0           1   \n",
       "4981       0.0   Home Mortage              1                   0           1   \n",
       "\n",
       "      Online  CreditCard  \n",
       "ID                        \n",
       "1          0           0  \n",
       "2          0           0  \n",
       "3          0           0  \n",
       "4          0           0  \n",
       "5          0           1  \n",
       "...      ...         ...  \n",
       "4884       1           1  \n",
       "4928       1           1  \n",
       "4942       1           0  \n",
       "4963       1           1  \n",
       "4981       1           1  \n",
       "\n",
       "[3147 rows x 15 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardization & Normalization\n",
    "No need to standardize as all related columns i.e. Income, CCAvg and Mortgage are already standardized in terms of thousands. I also didn't feel the need to normalize these data as so that we can cross compare between these columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "def handleOutlier(column):\n",
    "  print(\"\\nFor \"+column+\":\")\n",
    "  q1 = df[column].quantile(0.25)\n",
    "  q3 = df[column].quantile(0.75)\n",
    "  IQR = q3 - q1\n",
    "  lowerBound = q1 - 1.5 * IQR\n",
    "  upperBound = q3 + 1.5 * IQR \n",
    "  \n",
    "  outlier_index=[]\n",
    "  for i in df.index:\n",
    "    if df[column][i] is not np.nan:\n",
    "      if (df[column][i] < lowerBound) or (df[column][i] > upperBound):\n",
    "        outlier_index.append(i)\n",
    "  \n",
    "  print(\"Number of Outliers:\",len(outlier_index))\n",
    "  print(\"No. of rows before removing outliers\",df.shape[0])\n",
    "  df.drop(outlier_index, inplace=True)\n",
    "  print(\"No. of rows after removing outliers\",df.shape[0])\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Box whisker plot with Outliers'}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].plot(kind='box', title=\"Box whisker plot with Outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For Age:\n",
      "Number of Outliers: 7\n",
      "No. of rows before removing outliers 3147\n",
      "No. of rows after removing outliers 3140\n"
     ]
    }
   ],
   "source": [
    "handleOutlier('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Experience:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Box whisker plot with Outliers'}>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Experience'].plot(kind='box', title=\"Box whisker plot with Outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For Experience:\n",
      "Number of Outliers: 0\n",
      "No. of rows before removing outliers 3140\n",
      "No. of rows after removing outliers 3140\n"
     ]
    }
   ],
   "source": [
    "handleOutlier('Experience')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For CCAvg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Box whisker plot with Outliers'}>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['CCAvg'].plot(kind='box', title=\"Box whisker plot with Outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For CCAvg:\n",
      "Number of Outliers: 197\n",
      "No. of rows before removing outliers 3140\n",
      "No. of rows after removing outliers 2943\n"
     ]
    }
   ],
   "source": [
    "handleOutlier('CCAvg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Mortgage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Box whisker plot with Outliers'}>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Mortgage'].plot(kind='box', title=\"Box whisker plot with Outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For Mortgage:\n",
      "Number of Outliers: 162\n",
      "No. of rows before removing outliers 2943\n",
      "No. of rows after removing outliers 2781\n"
     ]
    }
   ],
   "source": [
    "handleOutlier('Mortgage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For Income:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Box whisker plot with Outliers'}>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Income'].plot(kind='box', title=\"Box whisker plot with Outliers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For Income:\n",
      "Number of Outliers: 101\n",
      "No. of rows before removing outliers 2781\n",
      "No. of rows after removing outliers 2680\n"
     ]
    }
   ],
   "source": [
    "handleOutlier('Income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2680, 15)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "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>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "      <th>Personal Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993960</td>\n",
       "      <td>-0.055826</td>\n",
       "      <td>-0.045881</td>\n",
       "      <td>-0.042042</td>\n",
       "      <td>0.054105</td>\n",
       "      <td>-0.015994</td>\n",
       "      <td>0.016307</td>\n",
       "      <td>0.012023</td>\n",
       "      <td>0.010056</td>\n",
       "      <td>0.023634</td>\n",
       "      <td>-0.024899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experience</th>\n",
       "      <td>0.993960</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.048750</td>\n",
       "      <td>-0.052111</td>\n",
       "      <td>-0.045039</td>\n",
       "      <td>0.026836</td>\n",
       "      <td>-0.015061</td>\n",
       "      <td>0.015211</td>\n",
       "      <td>0.014453</td>\n",
       "      <td>0.011446</td>\n",
       "      <td>0.022923</td>\n",
       "      <td>-0.021490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>-0.055826</td>\n",
       "      <td>-0.048750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.120314</td>\n",
       "      <td>0.542228</td>\n",
       "      <td>-0.158649</td>\n",
       "      <td>-0.041076</td>\n",
       "      <td>0.017770</td>\n",
       "      <td>0.097009</td>\n",
       "      <td>-0.020610</td>\n",
       "      <td>-0.002031</td>\n",
       "      <td>0.415306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>-0.045881</td>\n",
       "      <td>-0.052111</td>\n",
       "      <td>-0.120314</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042910</td>\n",
       "      <td>0.007549</td>\n",
       "      <td>0.022904</td>\n",
       "      <td>0.003527</td>\n",
       "      <td>-0.005234</td>\n",
       "      <td>0.017803</td>\n",
       "      <td>0.013712</td>\n",
       "      <td>0.024979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCAvg</th>\n",
       "      <td>-0.042042</td>\n",
       "      <td>-0.045039</td>\n",
       "      <td>0.542228</td>\n",
       "      <td>-0.042910</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.052375</td>\n",
       "      <td>-0.012395</td>\n",
       "      <td>0.045797</td>\n",
       "      <td>0.138538</td>\n",
       "      <td>-0.018921</td>\n",
       "      <td>-0.012377</td>\n",
       "      <td>0.409730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0.054105</td>\n",
       "      <td>0.026836</td>\n",
       "      <td>-0.158649</td>\n",
       "      <td>0.007549</td>\n",
       "      <td>-0.052375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001306</td>\n",
       "      <td>-0.012240</td>\n",
       "      <td>-0.013177</td>\n",
       "      <td>-0.026374</td>\n",
       "      <td>-0.012730</td>\n",
       "      <td>0.105745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mortgage</th>\n",
       "      <td>-0.015994</td>\n",
       "      <td>-0.015061</td>\n",
       "      <td>-0.041076</td>\n",
       "      <td>0.022904</td>\n",
       "      <td>-0.012395</td>\n",
       "      <td>0.001306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011639</td>\n",
       "      <td>0.013451</td>\n",
       "      <td>-0.005014</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>-0.020210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Securities Account</th>\n",
       "      <td>0.016307</td>\n",
       "      <td>0.015211</td>\n",
       "      <td>0.017770</td>\n",
       "      <td>0.003527</td>\n",
       "      <td>0.045797</td>\n",
       "      <td>-0.012240</td>\n",
       "      <td>0.011639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.322644</td>\n",
       "      <td>-0.010022</td>\n",
       "      <td>-0.030994</td>\n",
       "      <td>0.038148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD Account</th>\n",
       "      <td>0.012023</td>\n",
       "      <td>0.014453</td>\n",
       "      <td>0.097009</td>\n",
       "      <td>-0.005234</td>\n",
       "      <td>0.138538</td>\n",
       "      <td>-0.013177</td>\n",
       "      <td>0.013451</td>\n",
       "      <td>0.322644</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161827</td>\n",
       "      <td>0.245873</td>\n",
       "      <td>0.274764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Online</th>\n",
       "      <td>0.010056</td>\n",
       "      <td>0.011446</td>\n",
       "      <td>-0.020610</td>\n",
       "      <td>0.017803</td>\n",
       "      <td>-0.018921</td>\n",
       "      <td>-0.026374</td>\n",
       "      <td>-0.005014</td>\n",
       "      <td>-0.010022</td>\n",
       "      <td>0.161827</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012327</td>\n",
       "      <td>-0.009976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditCard</th>\n",
       "      <td>0.023634</td>\n",
       "      <td>0.022923</td>\n",
       "      <td>-0.002031</td>\n",
       "      <td>0.013712</td>\n",
       "      <td>-0.012377</td>\n",
       "      <td>-0.012730</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>-0.030994</td>\n",
       "      <td>0.245873</td>\n",
       "      <td>-0.012327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Personal Loan</th>\n",
       "      <td>-0.024899</td>\n",
       "      <td>-0.021490</td>\n",
       "      <td>0.415306</td>\n",
       "      <td>0.024979</td>\n",
       "      <td>0.409730</td>\n",
       "      <td>0.105745</td>\n",
       "      <td>-0.020210</td>\n",
       "      <td>0.038148</td>\n",
       "      <td>0.274764</td>\n",
       "      <td>-0.009976</td>\n",
       "      <td>-0.002292</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Age  Experience    Income    Family     CCAvg  \\\n",
       "Age                 1.000000    0.993960 -0.055826 -0.045881 -0.042042   \n",
       "Experience          0.993960    1.000000 -0.048750 -0.052111 -0.045039   \n",
       "Income             -0.055826   -0.048750  1.000000 -0.120314  0.542228   \n",
       "Family             -0.045881   -0.052111 -0.120314  1.000000 -0.042910   \n",
       "CCAvg              -0.042042   -0.045039  0.542228 -0.042910  1.000000   \n",
       "Education           0.054105    0.026836 -0.158649  0.007549 -0.052375   \n",
       "Mortgage           -0.015994   -0.015061 -0.041076  0.022904 -0.012395   \n",
       "Securities Account  0.016307    0.015211  0.017770  0.003527  0.045797   \n",
       "CD Account          0.012023    0.014453  0.097009 -0.005234  0.138538   \n",
       "Online              0.010056    0.011446 -0.020610  0.017803 -0.018921   \n",
       "CreditCard          0.023634    0.022923 -0.002031  0.013712 -0.012377   \n",
       "Personal Loan      -0.024899   -0.021490  0.415306  0.024979  0.409730   \n",
       "\n",
       "                    Education  Mortgage  Securities Account  CD Account  \\\n",
       "Age                  0.054105 -0.015994            0.016307    0.012023   \n",
       "Experience           0.026836 -0.015061            0.015211    0.014453   \n",
       "Income              -0.158649 -0.041076            0.017770    0.097009   \n",
       "Family               0.007549  0.022904            0.003527   -0.005234   \n",
       "CCAvg               -0.052375 -0.012395            0.045797    0.138538   \n",
       "Education            1.000000  0.001306           -0.012240   -0.013177   \n",
       "Mortgage             0.001306  1.000000            0.011639    0.013451   \n",
       "Securities Account  -0.012240  0.011639            1.000000    0.322644   \n",
       "CD Account          -0.013177  0.013451            0.322644    1.000000   \n",
       "Online              -0.026374 -0.005014           -0.010022    0.161827   \n",
       "CreditCard          -0.012730  0.001717           -0.030994    0.245873   \n",
       "Personal Loan        0.105745 -0.020210            0.038148    0.274764   \n",
       "\n",
       "                      Online  CreditCard  Personal Loan  \n",
       "Age                 0.010056    0.023634      -0.024899  \n",
       "Experience          0.011446    0.022923      -0.021490  \n",
       "Income             -0.020610   -0.002031       0.415306  \n",
       "Family              0.017803    0.013712       0.024979  \n",
       "CCAvg              -0.018921   -0.012377       0.409730  \n",
       "Education          -0.026374   -0.012730       0.105745  \n",
       "Mortgage           -0.005014    0.001717      -0.020210  \n",
       "Securities Account -0.010022   -0.030994       0.038148  \n",
       "CD Account          0.161827    0.245873       0.274764  \n",
       "Online              1.000000   -0.012327      -0.009976  \n",
       "CreditCard         -0.012327    1.000000      -0.002292  \n",
       "Personal Loan      -0.009976   -0.002292       1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age','Experience','Income','Family','CCAvg','Education','Mortgage','Securities Account', 'CD Account', 'Online', 'CreditCard', 'Personal Loan']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>Besides **Income, CCAvg, Education and CD Account** the others have a negligible effect on **Personal Loan**</mark>. The attributes explicitly mentioned here also have the same type of negligible relation with the other unmentioned attributes. So, only the four mentioned attributes are relevant here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "metadata": {}
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income vs Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Income and Personal Loan:\n",
      "The Pearson Correlation Coefficient is 0.41530643124255734  with a P-value of 2.983051734467303e-112\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['Income'], df['Personal Loan'])\n",
    "print(\"For Income and Personal Loan:\\nThe Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As, P-value is <0.001 correlation of 0.41 between `Income` and `Personal Loan` is highly significant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2acf4079010>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Personal Loan\", y=\"Income\", kind=\"bar\").set(title=\"Bar Plot of Personal Loan vs Income\", ylabel=\"Income(in thousands)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see in the bar graph that <mark>in average the people who have been approved for Personal loans have almost twice the monthly Income of those who aren't approved</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CCAvg vs Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For CCAvg and Personal Loan:\n",
      "The Pearson Correlation Coefficient is 0.4097297364175141  with a P-value of 5.067299204418516e-109\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['CCAvg'], df['Personal Loan'])\n",
    "print(\"For CCAvg and Personal Loan:\\nThe Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As, P-value is <0.001 correlation of 0.41 between `CCAvg` and `Personal Loan` is highly significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2acee917910>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Personal Loan\", y=\"CCAvg\", kind=\"bar\").set(title=\"Bar Plot of Personal Loan vs CCAvg\", ylabel=\"CCAvg(in thousands)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see that that <mark>in average the people who have been approved for Personal loans have more that twice the montly Credit Card Expense compared to those who aren't approved</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Income vs CCAvg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Income and CCAvg:\n",
      "The Pearson Correlation Coefficient is 0.5422279130610437  with a P-value of 9.857524612783263e-205\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['Income'], df['CCAvg'])\n",
    "print(\"For Income and CCAvg:\\nThe Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation between Income and CCAvg is positive and as the P-value is <0.001 it is highly significant. This makes sense as peoples expense naturally grows along side their income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Income vs CCAvg (measured in thousands)'}, xlabel='Income', ylabel='CCAvg'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x=\"Income\", y=\"CCAvg\", title=\"Income vs CCAvg (measured in thousands)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that <mark>only after crossing the threshold of about 75k in income does peoples CCAvg i.e. credit card expense go above 4.2k. So, the approval of Personal Loan increasing after CCAvg of 4.2k may be due to the crossing this income threshold.</mark>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Residual Plot of Income vs CCAvg (measured in thousands)')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(x=df[['Income']], y=df[['CCAvg']]).set(title=\"Residual Plot of Income vs CCAvg (measured in thousands)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the distribution is not homogeneous, we can say that <mark>Income and CCAvg has curve-linear relationship</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Education vs Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Education and Personal Loan:\n",
      "The Pearson Correlation Coefficient is 0.10574481657512408  with a P-value of 4.085637726344147e-08\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['Education'], df['Personal Loan'])\n",
    "print(\"For Education and Personal Loan:\\nThe Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As, P-value is <0.001 correlation of 0.41 between `Education` and `Personal Loan` is highly significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac91b2ce90>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Education\", y=\"Personal Loan\", kind=\"violin\").set(title=\"Violin Plot of Education vs Personal Loan\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### Violin Plot Fundamentals (General Knowledge)\n",
    "The reason the plots extend beyond the binary values of 0 and 1 on the y-axis in the upcoming graph is due to the kernel density estimation (KDE) used in violin plots. KDE is a smoothing process that estimates the probability density function of a continuous random variable.\n",
    "\n",
    "In a violin plot, the KDE can produce tails that extend beyond the actual data range, which is why you see parts of the violins going below 0 and above 1. This is a common visualization effect and does not indicate the presence of actual data points beyond the binary values. It’s simply an artifact of the KDE method used to create the plot. <mark>The width of the violins represents the density of data points at different values, and the extended tails are part of the smoothing process</mark> to give a fuller picture of the data distribution. Remember, the actual data still only contains binary values of 0 or 1 for “Personal Loan.”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education\n",
       "1    1038\n",
       "3     834\n",
       "2     808\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that <mark>people who have Masters or Professional Degrees got more personal loans compared to those who had Bachelors degree</mark>, even though people with bachelors degree were the ones who applied for more loans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac853f2910>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vvyReeeUV3Xbbbfrd736n3NxcpaWlqUmTJvrqq6/cUnefPn3UvXt3Pf300zpw4IDatGmj1atX6/3339fo0aOd3mYVFxentWvX6pVXXlFkZKSio6MdN9Wca9iwYXr11Vc1ePBgZWVlqVGjRnr33Xf16aefatq0aeWugV+KI0eO6O233y7XHhAQ4Pg0tNTUVN1xxx3q3LmzhgwZoh9//FEzZ85UixYtHGc2pF+uNw4YMEAzZ86UzWZT48aN9eGHHyo3N7fc+DNmzFDnzp11/fXXa9iwYYqOjtaBAwf00Ucfafv27ZKk3r1766233pLdbldsbKwyMzO1du1ahYSEOI3Vtm1b1apVSy+++KLy8/Pl4+OjHj16VHgPQVUey4oMHDhQzz77rHx9fZWcnOw0ez9x4oTq16+vu+++W23atFFAQIDWrl2rrVu3Op09qGzdu3fXAw88oBkzZmjPnj267bbbVFpaqn/961/q3r27RowYoTZt2igpKUmvvfaa8vLy1LVrV33++eeaP3+++vXrp+7du1daPWlpaercubNatWql3//+97rmmmuUk5OjzMxM/fe//9WXX35ZafvCBbjtvnLUGBW9BcvX19dq27atNXv2bKe351iWZc2dO9dq2rSp5ePjYzVv3txKT0+3xo0bZ537cpN0SW9z6dq1q9WiRYtf7XfuW7Asy7JOnDhhjRkzxoqMjLS8vLyspk2bWlOmTClX+65du6wuXbpYfn5+lqRffTtWTk6O9dBDD1l169a1vL29rVatWjm9palMZb0F69zntWTJEismJsby8fGxYmNjrffee6/C53/s2DErMTHRql27tnXVVVdZDz/8sLVjx45yb8GyLMvasWOHddddd1lBQUGWr6+v1axZM+uZZ55xbP/pp58czzkgIMBKSEiwdu3aZTVs2LDc8Xr99deta665xqpVq5bTW3zOfQvWxR7LsrdgTZkypdxxk2SNGzfuV47uL/bs2eM4pps2bXLaVlRUZD355JNWmzZtrDp16lj+/v5WmzZtrFmzZv3quOd7C5a/v3+5vhX9P3H69GlrypQpVvPmzS1vb2+rXr16Vq9evaysrCxHn5KSEmv8+PFWdHS05eXlZUVFRVkpKSlOb5WyrPO/5ir6/+58x3Xfvn3Wgw8+aIWHh1teXl7W1VdfbfXu3dt69913f/VYoHLYLOsi7rQAAADVjmvSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAM9ZsPacuyVFBQcFGf6QsAgEl+8yF94sQJ2e12nThxwt2lAABwSX7zIQ0AQE1FSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADOXp7gJQ/SzLUmFhoWPd399fNpvNjRUBACpCSF+BCgsL1bdvX8f6+++/r4CAADdWBACoCKe7AQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGMqtX1XZqFEjHTx4sFz7Y489prS0NJ06dUqPP/64Fi5cqKKiIiUkJGjWrFkKCwurlvrinnyzWvZT3Wyni2U/a73bMwtleXq7rZ6qlDXlQXeXAAAuc+tMeuvWrTp69KhjWbNmjSRpwIABkqQxY8Zo+fLlWrx4sTZs2KDvv/9e/fv3d2fJAABUG7fOpOvVq+e0PmnSJDVu3Fhdu3ZVfn6+5s6dqwULFqhHjx6SpPT0dMXExGjz5s3q2LGjO0oGAKDaGHNNuri4WG+//baGDBkim82mrKwslZSUqGfPno4+zZs3V4MGDZSZmenGSgEAqB5unUmfbdmyZcrLy9PgwYMlSdnZ2fL29lZQUJBTv7CwMGVnZ593nKKiIhUVFTnWCwoKqqJcAACqnDEz6blz56pXr16KjIy8rHFSU1Nlt9sdS1RUVCVVCABA9TIipA8ePKi1a9dq6NChjrbw8HAVFxcrLy/PqW9OTo7Cw8PPO1ZKSory8/Mdy+HDh6uqbAAAqpQRIZ2enq7Q0FDdcccdjra4uDh5eXkpIyPD0bZ7924dOnRI8fHx5x3Lx8dHgYGBTgsAADWR269Jl5aWKj09XUlJSfL0/P/l2O12JScna+zYsQoODlZgYKBGjhyp+Ph47uwGAFwR3B7Sa9eu1aFDhzRkyJBy26ZOnSoPDw8lJiY6fZgJAABXAreH9K233irLsirc5uvrq7S0NKWlpVVzVQAAuJ8R16QBAEB5hDQAAIYipAEAMBQhDQCAodx+4xiqn1XLS/mtBzmtAwDMQ0hfiWy23+z3RwPAbwmnuwEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFBuD+kjR47o/vvvV0hIiPz8/NSqVStt27bNsd2yLD377LOKiIiQn5+fevbsqT179rixYgAAqodbQ/qnn35Sp06d5OXlpRUrVmjnzp16+eWXddVVVzn6TJ48WTNmzNCcOXO0ZcsW+fv7KyEhQadOnXJj5QAAVD1Pd+78xRdfVFRUlNLT0x1t0dHRjn9blqVp06bpL3/5i/r27StJevPNNxUWFqZly5bp3nvvrfaagSuRZVkqLCx0rPv7+8tms7mxIpiO10zlcOtM+oMPPlC7du00YMAAhYaG6rrrrtPrr7/u2L5//35lZ2erZ8+ejja73a4OHTooMzOzwjGLiopUUFDgtAC4PIWFherbt69jOfuXL1ARXjOVw60h/d1332n27Nlq2rSpVq1apUcffVSjRo3S/PnzJUnZ2dmSpLCwMKfHhYWFObadKzU1VXa73bFERUVV7ZMAAKCKuDWkS0tLdf311+uFF17Qddddp2HDhun3v/+95syZ4/KYKSkpys/PdyyHDx+uxIoBAKg+bg3piIgIxcbGOrXFxMTo0KFDkqTw8HBJUk5OjlOfnJwcx7Zz+fj4KDAw0GkBAKAmcmtId+rUSbt373Zq+89//qOGDRtK+uUmsvDwcGVkZDi2FxQUaMuWLYqPj6/WWgEAqG5uvbt7zJgxuvHGG/XCCy/onnvu0eeff67XXntNr732miTJZrNp9OjRev7559W0aVNFR0frmWeeUWRkpPr16+fO0gEAqHJuDekbbrhBS5cuVUpKiiZMmKDo6GhNmzZN9913n6PPH//4RxUWFmrYsGHKy8tT586dtXLlSvn6+rqxcgAAqp5bQ1qSevfurd69e593u81m04QJEzRhwoRqrAoAAPdz+8eCAgCAihHSAAAYipAGAMBQhDQAAIYipAEAMJTb7+4GfkvinnzT3SVUCdvpYtnPWu/2zEJZnt5uq6cqZU15sFr3x2um5qvK1wwzaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYii/YAABUOquWl/JbD3Jax6UjpAH8Kn7h4pLZbL/Zb72qToQ0gF/HL1zALbgmDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIZya0g/99xzstlsTkvz5s0d20+dOqXhw4crJCREAQEBSkxMVE5OjhsrBgCg+rh9Jt2iRQsdPXrUsWzatMmxbcyYMVq+fLkWL16sDRs26Pvvv1f//v3dWC0AANXH0+0FeHoqPDy8XHt+fr7mzp2rBQsWqEePHpKk9PR0xcTEaPPmzerYsWN1lwoAQLVy+0x6z549ioyM1DXXXKP77rtPhw4dkiRlZWWppKREPXv2dPRt3ry5GjRooMzMTHeVCwBAtXHrTLpDhw6aN2+emjVrpqNHj2r8+PG66aabtGPHDmVnZ8vb21tBQUFOjwkLC1N2dvZ5xywqKlJRUZFjvaCgoKrKBwCgSrk1pHv16uX4d+vWrdWhQwc1bNhQ77zzjvz8/FwaMzU1VePHj6+sEgEAcBu3n+4+W1BQkK699lrt3btX4eHhKi4uVl5enlOfnJycCq9hl0lJSVF+fr5jOXz4cBVXDQBA1TAqpE+ePKl9+/YpIiJCcXFx8vLyUkZGhmP77t27dejQIcXHx593DB8fHwUGBjotAADURG493f3EE0+oT58+atiwob7//nuNGzdOtWrV0qBBg2S325WcnKyxY8cqODhYgYGBGjlypOLj47mzGwBwRXBrSP/3v//VoEGDdPz4cdWrV0+dO3fW5s2bVa9ePUnS1KlT5eHhocTERBUVFSkhIUGzZs1yZ8kAAFQbt4b0woULL7jd19dXaWlpSktLq6aKAAAwh1HXpAEAwP9HSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAENdVkgXFxdr9+7dOn36dGXVAwAA/o9LIf2///1PycnJql27tlq0aKFDhw5JkkaOHKlJkyZVaoEAAFypXArplJQUffnll1q/fr18fX0d7T179tSiRYsqrTgAAK5knq48aNmyZVq0aJE6duwom83maG/RooX27dtXacUBAHAlc2kmfezYMYWGhpZrLywsdAptAADgOpdCul27dvroo48c62XB/Pe//13x8fGVUxkAAFc4l053v/DCC+rVq5d27typ06dPa/r06dq5c6c+++wzbdiwobJrBADgiuTSTLpz587avn27Tp8+rVatWmn16tUKDQ1VZmam4uLiKrtGAACuSC7NpCWpcePGev311yuzFgAAcBaXQ1qScnNzlZubq9LSUqf21q1bX1ZRAADAxdPdWVlZatmypSIiItS6dWu1bdvWsVx33XUuFTJp0iTZbDaNHj3a0Xbq1CkNHz5cISEhCggIUGJionJyclwaHwCAmsalmfSQIUN07bXXau7cuQoLC7vst11t3bpVr776arkZ+JgxY/TRRx9p8eLFstvtGjFihPr3769PP/30svYHAEBN4FJIf/fdd1qyZImaNGly2QWcPHlS9913n15//XU9//zzjvb8/HzNnTtXCxYsUI8ePSRJ6enpiomJ0ebNm9WxY8fL3jcAACZz6XT3zTffrC+//LJSChg+fLjuuOMO9ezZ06k9KytLJSUlTu3NmzdXgwYNlJmZed7xioqKVFBQ4LQAAFATuTST/vvf/66kpCTt2LFDLVu2lJeXl9P2O++886LGWbhwob744gtt3bq13Lbs7Gx5e3srKCjIqT0sLEzZ2dnnHTM1NVXjx4+/qP0DAGAyl0I6MzNTn376qVasWFFum81m05kzZ351jMOHD+sPf/iD1qxZ4/QlHZcrJSVFY8eOdawXFBQoKiqq0sYHAKC6uHS6e+TIkbr//vt19OhRlZaWOi0XE9DSL6ezc3Nzdf3118vT01Oenp7asGGDZsyYIU9PT4WFham4uFh5eXlOj8vJyVF4ePh5x/Xx8VFgYKDTAgBATeTSTPr48eMaM2aMwsLCXN7xzTffrK+//tqp7aGHHlLz5s31pz/9SVFRUfLy8lJGRoYSExMlSbt379ahQ4f4fHAAwBXBpZDu37+/1q1bp8aNG7u84zp16qhly5ZObf7+/goJCXG0Jycna+zYsQoODlZgYKBGjhyp+Ph47uwGAFwRXArpa6+9VikpKdq0aZNatWpV7saxUaNGVUpxU6dOlYeHhxITE1VUVKSEhATNmjWrUsYGAMB0Lt/dHRAQoA0bNpT71iubzeZySK9fv95p3dfXV2lpaUpLS3NpPAAAajKXQnr//v2VXQcAADiHS3d3n82yLFmWVRm1AACAs7gc0m+++aZatWolPz8/+fn5qXXr1nrrrbcqszYAAK5oLp3ufuWVV/TMM89oxIgR6tSpkyRp06ZNeuSRR/TDDz9ozJgxlVokAABXIpdCeubMmZo9e7YefPBBR9udd96pFi1a6LnnniOkAQCoBC6d7j569KhuvPHGcu033nijjh49etlFAQAAF0O6SZMmeuedd8q1L1q0SE2bNr3sogAAgIunu8ePH6+BAwdq48aNjmvSn376qTIyMioMbwAAcOlcmkknJiZqy5Ytqlu3rpYtW6Zly5apbt26+vzzz3XXXXdVdo0AAFyRXJpJS1JcXJzefvvtyqwFAACcxaWZ9Mcff6xVq1aVa1+1alWF3zENAAAunUsh/dRTT1X4vdGWZempp5667KIAAICLIb1nzx7FxsaWa2/evLn27t172UUBAAAXQ9put+u7774r17537175+/tfdlEAAMDFkO7bt69Gjx6tffv2Odr27t2rxx9/XHfeeWelFQcAwJXMpZCePHmy/P391bx5c0VHRys6OloxMTEKCQnRSy+9VNk1AgBwRXLpLVh2u12fffaZ1qxZoy+//NLxLVhdunSp7PoAALhiufw+aZvNpltvvVW33nprZdYDAAD+j8shnZGRoYyMDOXm5qq0tNRp2xtvvHHZhQEAcKVz+bO7J0yYoHbt2ikiIkI2m62y6wIA4IrnUkjPmTNH8+bN0wMPPFDZ9QAAgP/j0t3dxcXFFX6fNAAAqDwuhfTQoUO1YMGCyq4FAACcxaXT3adOndJrr72mtWvXqnXr1vLy8nLa/sorr1RKcQAAXMlcCumvvvpKbdu2lSTt2LGjMusBAAD/x6WQXrduXWXXAQAAznFJId2/f/9f7WOz2bRkyRKXCwIAAL+4pJC22+1VVQcAADjHJYV0enp6VdUBAADO4dJbsAAAQNUjpAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABjKrSE9e/ZstW7dWoGBgQoMDFR8fLxWrFjh2H7q1CkNHz5cISEhCggIUGJionJyctxYMQAA1cetIV2/fn1NmjRJWVlZ2rZtm3r06KG+ffvqm2++kSSNGTNGy5cv1+LFi7VhwwZ9//336t+/vztLBgCg2ni6c+d9+vRxWp84caJmz56tzZs3q379+po7d64WLFigHj16SJLS09MVExOjzZs3q2PHju4oGQCAamPMNekzZ85o4cKFKiwsVHx8vLKyslRSUqKePXs6+jRv3lwNGjRQZmbmeccpKipSQUGB0wIAQE3k9pD++uuvFRAQIB8fHz3yyCNaunSpYmNjlZ2dLW9vbwUFBTn1DwsLU3Z29nnHS01Nld1udyxRUVFV/AwAAKgabg/pZs2aafv27dqyZYseffRRJSUlaefOnS6Pl5KSovz8fMdy+PDhSqwWAIDq49Zr0pLk7e2tJk2aSJLi4uK0detWTZ8+XQMHDlRxcbHy8vKcZtM5OTkKDw8/73g+Pj7y8fGp6rIBAKhybp9Jn6u0tFRFRUWKi4uTl5eXMjIyHNt2796tQ4cOKT4+3o0VAgBQPdw6k05JSVGvXr3UoEEDnThxQgsWLND69eu1atUq2e12JScna+zYsQoODlZgYKBGjhyp+Ph47uwGAFwR3BrSubm5evDBB3X06FHZ7Xa1bt1aq1at0i233CJJmjp1qjw8PJSYmKiioiIlJCRo1qxZ7iwZAIBq49aQnjt37gW3+/r6Ki0tTWlpadVUEQAA5jDumjQAAPgFIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEO5NaRTU1N1ww03qE6dOgoNDVW/fv20e/dupz6nTp3S8OHDFRISooCAACUmJionJ8dNFQMAUH3cGtIbNmzQ8OHDtXnzZq1Zs0YlJSW69dZbVVhY6OgzZswYLV++XIsXL9aGDRv0/fffq3///m6sGgCA6uHpzp2vXLnSaX3evHkKDQ1VVlaWunTpovz8fM2dO1cLFixQjx49JEnp6emKiYnR5s2b1bFjR3eUDQBAtTDqmnR+fr4kKTg4WJKUlZWlkpIS9ezZ09GnefPmatCggTIzMysco6ioSAUFBU4LAAA1kTEhXVpaqtGjR6tTp05q2bKlJCk7O1ve3t4KCgpy6hsWFqbs7OwKx0lNTZXdbncsUVFRVV06AABVwpiQHj58uHbs2KGFCxde1jgpKSnKz893LIcPH66kCgEAqF5uvSZdZsSIEfrwww+1ceNG1a9f39EeHh6u4uJi5eXlOc2mc3JyFB4eXuFYPj4+8vHxqeqSAQCocm6dSVuWpREjRmjp0qX65JNPFB0d7bQ9Li5OXl5eysjIcLTt3r1bhw4dUnx8fHWXCwBAtXLrTHr48OFasGCB3n//fdWpU8dxndlut8vPz092u13JyckaO3asgoODFRgYqJEjRyo+Pp47uwEAv3luDenZs2dLkrp16+bUnp6ersGDB0uSpk6dKg8PDyUmJqqoqEgJCQmaNWtWNVcKAED1c2tIW5b1q318fX2VlpamtLS0aqgIAABzGHN3NwAAcEZIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGMqtIb1x40b16dNHkZGRstlsWrZsmdN2y7L07LPPKiIiQn5+furZs6f27NnjnmIBAKhmbg3pwsJCtWnTRmlpaRVunzx5smbMmKE5c+Zoy5Yt8vf3V0JCgk6dOlXNlQIAUP083bnzXr16qVevXhVusyxL06ZN01/+8hf17dtXkvTmm28qLCxMy5Yt07333ludpQIAUO2MvSa9f/9+ZWdnq2fPno42u92uDh06KDMz87yPKyoqUkFBgdMCAEBNZGxIZ2dnS5LCwsKc2sPCwhzbKpKamiq73e5YoqKiqrROAACqirEh7aqUlBTl5+c7lsOHD7u7JAAAXGJsSIeHh0uScnJynNpzcnIc2yri4+OjwMBApwUAgJrI2JCOjo5WeHi4MjIyHG0FBQXasmWL4uPj3VgZAADVw613d588eVJ79+51rO/fv1/bt29XcHCwGjRooNGjR+v5559X06ZNFR0drWeeeUaRkZHq16+f+4oGAKCauDWkt23bpu7duzvWx44dK0lKSkrSvHnz9Mc//lGFhYUaNmyY8vLy1LlzZ61cuVK+vr7uKhkAgGrj1pDu1q2bLMs673abzaYJEyZowoQJ1VgVAABmMPaaNAAAVzpCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhiKkAQAwFCENAIChCGkAAAxFSAMAYChCGgAAQxHSAAAYipAGAMBQhDQAAIYipAEAMBQhDQCAoQhpAAAMRUgDAGAoQhoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhqoRIZ2WlqZGjRrJ19dXHTp00Oeff+7ukgAAqHLGh/SiRYs0duxYjRs3Tl988YXatGmjhIQE5ebmurs0AACqlPEh/corr+j3v/+9HnroIcXGxmrOnDmqXbu23njjDXeXBgBAlfJ0dwEXUlxcrKysLKWkpDjaPDw81LNnT2VmZlb4mKKiIhUVFTnW8/PzJUkFBQWXvP8zRT9f8mNgFld+7peD10zNx2sGl+pyXjN16tSRzWY7fwfLYEeOHLEkWZ999plT+5NPPmm1b9++wseMGzfOksTCwsLCwmL8kp+ff8EcNHom7YqUlBSNHTvWsV5aWqoff/xRISEhF/5r5QpTUFCgqKgoHT58WIGBge4uBzUArxlcKl4zv65OnToX3G50SNetW1e1atVSTk6OU3tOTo7Cw8MrfIyPj498fHyc2oKCgqqqxBovMDCQ/3lwSXjN4FLxmnGd0TeOeXt7Ky4uThkZGY620tJSZWRkKD4+3o2VAQBQ9YyeSUvS2LFjlZSUpHbt2ql9+/aaNm2aCgsL9dBDD7m7NAAAqpTxIT1w4EAdO3ZMzz77rLKzs9W2bVutXLlSYWFh7i6tRvPx8dG4cePKXRoAzofXDC4Vr5nLZ7Msy3J3EQAAoDyjr0kDAHAlI6QBADAUIQ0AgKEIaQAADEVIX4E2btyoPn36KDIyUjabTcuWLXN3STBYamqqbrjhBtWpU0ehoaHq16+fdu/e7e6yYLDZs2erdevWjg8xiY+P14oVK9xdVo1ESF+BCgsL1aZNG6Wlpbm7FNQAGzZs0PDhw7V582atWbNGJSUluvXWW1VYWOju0mCo+vXra9KkScrKytK2bdvUo0cP9e3bV9988427S6txeAvWFc5ms2np0qXq16+fu0tBDXHs2DGFhoZqw4YN6tKli7vLQQ0RHBysKVOmKDk52d2l1CjGf5gJALOUff1rcHCwmytBTXDmzBktXrxYhYWFfJyzCwhpABettLRUo0ePVqdOndSyZUt3lwODff3114qPj9epU6cUEBCgpUuXKjY21t1l1TiENICLNnz4cO3YsUObNm1ydykwXLNmzbR9+3bl5+fr3XffVVJSkjZs2EBQXyJCGsBFGTFihD788ENt3LhR9evXd3c5MJy3t7eaNGkiSYqLi9PWrVs1ffp0vfrqq26urGYhpAFckGVZGjlypJYuXar169crOjra3SWhBiotLVVRUZG7y6hxCOkr0MmTJ7V3717H+v79+7V9+3YFBwerQYMGbqwMJho+fLgWLFig999/X3Xq1FF2drYkyW63y8/Pz83VwUQpKSnq1auXGjRooBMnTmjBggVav369Vq1a5e7SahzegnUFWr9+vbp3716uPSkpSfPmzav+gmA0m81WYXt6eroGDx5cvcWgRkhOTlZGRoaOHj0qu92u1q1b609/+pNuueUWd5dW4xDSAAAYik8cAwDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdLAb5jNZtOyZcvcXYYGDx7Md5YDLiCkgRpi8ODBstls5ZbbbrvN3aU5HDhwQDabTdu3b3dqnz59Op9mB7iAz+4GapDbbrtN6enpTm0+Pj5uqubi2e12d5cA1EjMpIEaxMfHR+Hh4U7LVVddJUnas2ePunTpIl9fX8XGxmrNmjVOj12/fr1sNpvy8vIcbdu3b5fNZtOBAwccbZ9++qm6deum2rVr66qrrlJCQoJ++uknSdLKlSvVuXNnBQUFKSQkRL1799a+ffscjy37hqzrrrtONptN3bp1k1T+dHdRUZFGjRql0NBQ+fr6qnPnztq6dWu5WjMyMtSuXTvVrl1bN954o3bv3l0ZhxGoMQhp4DegtLRU/fv3l7e3t7Zs2aI5c+boT3/60yWPs337dt18882KjY1VZmamNm3apD59+ujMmTOSpMLCQo0dO1bbtm1TRkaGPDw8dNddd6m0tFSS9Pnnn0uS1q5dq6NHj+q9996rcD9//OMftWTJEs2fP19ffPGFmjRpooSEBP34449O/Z5++mm9/PLL2rZtmzw9PTVkyJBLfk5AjWYBqBGSkpKsWrVqWf7+/k7LxIkTrVWrVlmenp7WkSNHHP1XrFhhSbKWLl1qWZZlrVu3zpJk/fTTT44+//73vy1J1v79+y3LsqxBgwZZnTp1uuiajh07Zkmyvv76a8uyLGv//v2WJOvf//53udr79u1rWZZlnTx50vLy8rL+8Y9/OLYXFxdbkZGR1uTJk51qXbt2raPPRx99ZEmyfv7554uuD6jpmEkDNUj37t21fft2p+WRRx7Rt99+q6ioKEVGRjr6xsfHX/L4ZTPp89mzZ48GDRqka665RoGBgWrUqJEk6dChQxe9j3379qmkpESdOnVytHl5eal9+/b69ttvnfq2bt3a8e+IiAhJUm5u7kXvC6jpuHEMqEH8/f3VpEkTlx7r4fHL3+TWWd9OW1JS4tTHz8/vgmP06dNHDRs21Ouvv67IyEiVlpaqZcuWKi4udqmmX+Pl5eX4d9n3WpedWgeuBMykgd+AmJgYHT58WEePHnW0bd682alPvXr1JMmpz7lvlWrdurUyMjIq3Mfx48e1e/du/eUvf9HNN9+smJgYxw1lZby9vSXJcQ27Io0bN5a3t7c+/fRTR1tJSYm2bt2q2NjYCzxL4MrDTBqoQYqKipSdne3U5unpqZ49e+raa69VUlKSpkyZooKCAj399NNO/Zo0aaKoqCg999xzmjhxov7zn//o5ZdfduqTkpKiVq1a6bHHHtMjjzwib29vrVu3TgMGDFBwcLBCQkL02muvKSIiQocOHdJTTz3l9PjQ0FD5+flp5cqVql+/vnx9fcu9/crf31+PPvqonnzySQUHB6tBgwaaPHmy/ve//yk5ObkSjxZQ8zGTBmqQlStXKiIiwmnp3LmzPDw8tHTpUv38889q3769hg4dqokTJzo91svLS//85z+1a9cutW7dWi+++KKef/55pz7XXnutVq9erS+//FLt27dXfHy83n//fXl6esrDw0MLFy5UVlaWWrZsqTFjxmjKlClOj/f09NSMGTP06quvKjIyUn379q3weUyaNEmJiYl64IEHdP3112vv3r1atWqV4+1kAH5hs86+QAUAAIzBTBoAAEMR0gAAGIqQBgDAUIQ0AACGIqQBADAUIQ0AgKEIaQAADEVIAwBgKEIaAABDEdIAABiKkAYAwFCENAAAhvp/284KpcRa6o4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Education\", y=\"Income\" , kind=\"bar\").set(title=\"Bar Plot of Education vs Income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that people who have Bachelors Degree earn more in average compared to those with Masters and Professional Degree. But, despite this they have a lower chance of getting Personal Loans. This shows that <mark>Education is factor independent of Income which is essential for predicting acceptance of Personal Loan</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CD Account vs Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For CD Account and Personal Loan:\n",
      "The Pearson Correlation Coefficient is 0.2747644224380305  with a P-value of 1.256809602856081e-47\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['CD Account'], df['Personal Loan'])\n",
    "print(\"For CD Account and Personal Loan:\\nThe Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As, P-value is <0.001 correlation of 0.27 between `CD Account` and `Personal Loan` is highly significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CD Account\n",
       "0    2542\n",
       "1     138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CD Account'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The disparity of entries between two types of CD Accounts is too large. So, it <mark>will distort the Bar plot</mark>. Therefore, we are using violin plot instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac85464e50>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"CD Account\", y=\"Personal Loan\", kind=\"violin\").set(title=\"Violin Plot of CD Account vs Personal Loan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CD Account  Personal Loan\n",
       "0           0                2378\n",
       "            1                 164\n",
       "1           0                  82\n",
       "            1                  56\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['CD Account','Personal Loan']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By just observing the width of the plots at point 0 and 1 only we can see that <mark>most people don't have an CD Account and only a few among them have been granted loan, whereas for the minority who do have a CD Account, about almost **1/4<sup>th</sup> of them have been granted loan**</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CD Account vs Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac8559e210>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"CD Account\", y=\"Income\" , kind=\"bar\").set(title=\"Bar Plot of CD Account vs Income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>We can see that people who have a CD Account have a significantly higher income on average. This increase in income may contribute to the higher acceptance of Personal Loan request for those having a CD account.</mark>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education  CD Account\n",
       "1          0             984\n",
       "3          0             797\n",
       "2          0             761\n",
       "1          1              54\n",
       "2          1              47\n",
       "3          1              37\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Education','CD Account']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see that their is not much relation between CD Account and Education. So, <mark>CD account is only influenced by Income and not Education</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>So, even among the four key attribute i.e. Income, CCAvg, Education and CD Account, there two which have an even more significant impact on Personal Loan. They are **Income** and **Education**</mark> \n",
    "\n",
    "Besides these we can also see some other interesting relationships such as:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age vs Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Age and Experience:\n",
      "The Pearson Correlation Coefficient is 0.993959554212454  with a P-value of 0.0\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['Age'], df['Experience'])\n",
    "print(\"For Age and Experience:\\nThe Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of\", p_value)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>The correlation between Age and Experience is super highly positive and as the P-value is less than 0.001, it is highly significant</mark>. This means that we may use Experience in place of Age and vice-versa. This makes sense as people grow older they will have more work experience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Age vs Experience in Years'}, xlabel='Age', ylabel='Experience'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x=\"Age\", y=\"Experience\", title=\"Age vs Experience in Years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Personal Loan vs Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2acee9e49d0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Personal Loan\", y=\"Age\" , kind=\"bar\").set(title=\"Bar Plot of Personal Loan vs Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that <mark>Age and subsequently Experience has no effect on Personal Loan</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Home Ownership vs Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "metadata": {}
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac856a9150>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Home Ownership\", y=\"Personal Loan\", kind=\"violin\").set(title=\"Violin Plot of Home Ownership vs Personal Loan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Ownership  Personal Loan\n",
       "Home Mortage    0                1067\n",
       "Home Owner      0                 719\n",
       "Rent            0                 674\n",
       "Home Mortage    1                 108\n",
       "Home Owner      1                  63\n",
       "Rent            1                  49\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Home Ownership','Personal Loan']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>We can see that people who rent are less likely to be approved for personal loans.</mark> Note that a number of home owners of all categories have mortgage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Personal Loan vs Mortgage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac854791d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 607.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Personal Loan\", y=\"Mortgage\", kind=\"bar\", hue=\"Personal Loan\").set(title=\"Bar Plot of Personal Loan vs Mortgage\", ylabel=\"Mortgage (in thousands)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph we can see that <mark>people who have been approved for Personal Loan have less Mortgage</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mortgage vs Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "sns.catplot(data=df, x=\"Income\", y=\"Mortgage\",aspect= 3).set(title=\"Income vs Mortgage (measured in thousands)\")\n",
    "# Set custom x-axis tick locations and labels\n",
    "plt.xticks(ticks=range(0, math.ceil(max(df['Income']))+1, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph we can tell that they have a curve-linear relation between Mortgage and Income. Mortgages seem to start from about 60k and initially as Income increases so does Mortgage but when we cross income threshold of about 85k the number of people who take Mortgages seems to decline significantly.\n",
    "So, <mark>the seemingly negative relation between Mortgage & Personal Loan may be due to the curve linear relation between Mortgage & Income where people of income >85k have less Mortgage</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender vs Personal Loan:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac889f6690>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Gender\", y=\"Personal Loan\", kind=\"violin\").set(title=\"Violin Plot of Gender vs Personal Loan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Personal Loan  Gender\n",
       "0              F         1226\n",
       "               M         1178\n",
       "1              M          118\n",
       "               F           88\n",
       "0              O           56\n",
       "1              O           14\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Personal Loan','Gender']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that 'Other genders' are a fraction of the population compared to males & females but in retrospect have <mark>'Other genders' higher chance of being approved. Males are also seen to have a slight edge over females when getting personal loans</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gender vs Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac890f9b90>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"Gender\", y=\"Income\", kind=\"bar\").set(title=\"Gender vs Income\",ylabel=\"Income(in thousands)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we postulate that <mark>the influence Gender had over Personal Loans may be largely due to their Income distribution</mark>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of EDA\n",
    "##### Two Key Factors:\n",
    "The two key factors which directly influence Personal Loan are:\n",
    "1. **Income**, which has a +ve relation with Personal Loan.\n",
    "2. **Education**, which also has a +ve relation with Personal Loan but is independent of Income.\n",
    "\n",
    "##### Other Indicators:\n",
    "1. **CCAvg**, which has +ve relation with Personal Loan mainly due to its curve-linear relation with Income.\n",
    "2. **CD Account**, which has +ve relation with Personal Loan mainly due to its +ve relation with Income.\n",
    "3. **Home Ownership**, where people with Rent are less likely to get Personal Loan.\n",
    "4. **Mortgage**, which has some what -ve relation with Personal Loan mainly due to its curve-linear relation with Income.\n",
    "5. **Gender**, where chances of getting Personal Loan is in descending order from Others, Males to Females, but its largely due to the subsequent income distribution between these genders. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Home Ownership</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Home Owner</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>45</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>15</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</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>35</td>\n",
       "      <td>M</td>\n",
       "      <td>9</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>M</td>\n",
       "      <td>8</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rent</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": [
       "    Age Gender  Experience  Income  Family  CCAvg  Education  Mortgage  \\\n",
       "ID                                                                       \n",
       "1    25      M           1    49.0       4    1.6          1       0.0   \n",
       "2    45      M          19    34.0       3    1.5          1       0.0   \n",
       "3    39      M          15    11.0       1    1.0          1       0.0   \n",
       "4    35      M           9   100.0       1    2.7          2       0.0   \n",
       "5    35      M           8    45.0       4    1.0          2       0.0   \n",
       "\n",
       "   Home Ownership  Securities Account  CD Account  Online  CreditCard  \n",
       "ID                                                                     \n",
       "1      Home Owner                   1           0       0           0  \n",
       "2            Rent                   1           0       0           0  \n",
       "3            Rent                   0           0       0           0  \n",
       "4            Rent                   0           0       0           0  \n",
       "5            Rent                   0           0       0           1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.drop(columns=['Personal Loan', 'ZIP Code']) #Zip Code was deemed irrelevant and Personal Loan is the target variable\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25, 'M', 1, 49.0, 4, 1.6, 1, 0.0, 'Home Owner', 1, 0, 0, 0],\n",
       "       [45, 'M', 19, 34.0, 3, 1.5, 1, 0.0, 'Rent', 1, 0, 0, 0],\n",
       "       [39, 'M', 15, 11.0, 1, 1.0, 1, 0.0, 'Rent', 0, 0, 0, 0],\n",
       "       [35, 'M', 9, 100.0, 1, 2.7, 2, 0.0, 'Rent', 0, 0, 0, 0],\n",
       "       [35, 'M', 8, 45.0, 4, 1.0, 2, 0.0, 'Rent', 0, 0, 0, 1]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.values\n",
    "x[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoded Labels of Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values in x after Label Encoding:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[25, 1, 1, 49.0, 4, 1.6, 1, 0.0, 1, 1, 0, 0, 0],\n",
       "       [45, 1, 19, 34.0, 3, 1.5, 1, 0.0, 2, 1, 0, 0, 0],\n",
       "       [39, 1, 15, 11.0, 1, 1.0, 1, 0.0, 2, 0, 0, 0, 0],\n",
       "       [35, 1, 9, 100.0, 1, 2.7, 2, 0.0, 2, 0, 0, 0, 0],\n",
       "       [35, 1, 8, 45.0, 4, 1.0, 2, 0.0, 2, 0, 0, 0, 1]], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Gender\n",
    "le_gender = preprocessing.LabelEncoder()\n",
    "le_gender.fit(['M','F','O'])\n",
    "x[:,1] = le_gender.transform(x[:,1]) \n",
    "\n",
    "#For Home Ownership\n",
    "le_home_ownership = preprocessing.LabelEncoder()\n",
    "le_home_ownership.fit(['Home Owner','Home Mortage','Rent'])\n",
    "x[:,8] = le_home_ownership.transform(x[:,8])\n",
    "\n",
    "print(\"Values in x after Label Encoding:\")\n",
    "x[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `preprocessing.LabelEncoder()` encodes categorical values by assigning a unique integer to each category. The categories are sorted alphabetically and then assigned an integer value starting from 0. \n",
    "\n",
    "**For Gender:**\n",
    "<br>F = 0\n",
    "<br>M = 1\n",
    "<br>O = 2\n",
    "\n",
    "**For Home Ownership:**\n",
    "<br>Home Mortage = 0\n",
    "<br>Home Owner = 1\n",
    "<br>Rent = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading Target variable\n",
    "y = df['Personal Loan'].values\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data into Training & Testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (1876, 13) (1876,)\n",
      "Test set: (804, 13) (804,)\n"
     ]
    }
   ],
   "source": [
    "x_trainset, x_testset, y_trainset, y_testset = train_test_split(x, y, test_size=0.3, random_state=3)\n",
    "print ('Train set:', x_trainset.shape,  y_trainset.shape)\n",
    "print ('Test set:', x_testset.shape,  y_testset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "# Allows us to test parameters of classification algorithms and find the best one\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Confusion matrix plotting function\n",
    "def plot_confusion_matrix(y,y_predict, title=\"\"):\n",
    "    \"this function plots the confusion matrix\"\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    cm = confusion_matrix(y, y_predict)\n",
    "    ax= plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('{} Confusion Matrix'.format(title)); \n",
    "    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed']) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K nearest neighbor (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training model on best found parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2]})"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "              'p': [1,2]}\n",
    "\n",
    "KNN = KNeighborsClassifier()\n",
    "knn_cv = GridSearchCV(KNN, parameters, cv = 10)\n",
    "\n",
    "#Training Model\n",
    "knn_cv.fit(x_trainset, y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 2, 'p': 1}\n",
      "training accuracy : 0.9275145067698259\n",
      "test accuracy : 0.9216417910447762\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
    "print(\"training accuracy :\",knn_cv.best_score_)\n",
    "knnScore = knn_cv.best_estimator_.score(x_testset, y_testset)\n",
    "print(\"test accuracy :\",knnScore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = knn_cv.predict(x_testset)\n",
    "plot_confusion_matrix(y_testset,yhat, \"KNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>False negatives seems to be a big problem for this KNN model.</mark>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training model on best found parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "3240 fits failed out of a total of 6480.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "3240 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py\", line 1145, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py\", line 638, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 96, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of DecisionTreeClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:979: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.92802935 0.91951872 0.92484355 0.92375697 0.92963932 0.92110877\n",
      " 0.92537831 0.92430026 0.92003641 0.92110024 0.91737683 0.91791444\n",
      " 0.92324497 0.92004779 0.9248464  0.91791444 0.92377972 0.92216976\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.93602799 0.92270452 0.93869325 0.91950449 0.93869041 0.91951018\n",
      " 0.94029184 0.9280322  0.93976277 0.92536978 0.92963363 0.92271305\n",
      " 0.93713449 0.92911025 0.93069746 0.9333741  0.93710889 0.91844066\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94295995 0.92748891 0.94084367 0.92750313 0.94883377 0.93229036\n",
      " 0.94720958 0.93284503 0.94883946 0.92749175 0.94561952 0.92271021\n",
      " 0.94723234 0.92858687 0.9317812  0.92748891 0.94721527 0.93230459\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94988053 0.93977699 0.94776141 0.93338264 0.93868472 0.93122938\n",
      " 0.94882524 0.93337126 0.95095858 0.92378257 0.95309193 0.93658835\n",
      " 0.94670327 0.93390317 0.94776425 0.93232165 0.93871316 0.93336557\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94456423 0.940289   0.94348902 0.93337126 0.94883946 0.93016839\n",
      " 0.95042383 0.93337979 0.95203379 0.93230743 0.9482791  0.94297702\n",
      " 0.94669758 0.92963648 0.9498976  0.93336842 0.93713449 0.93124644\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.93658266 0.9312436  0.94084367 0.93123791 0.93496416 0.940289\n",
      " 0.94988338 0.93441233 0.95044658 0.93339402 0.94241666 0.93708613\n",
      " 0.94029184 0.93125213 0.95202241 0.93603368 0.94615713 0.93496416\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.95147628 0.92056832 0.94935715 0.93017408 0.95841962 0.92378257\n",
      " 0.94295426 0.92856127 0.93976562 0.93174707 0.9472238  0.93392309\n",
      " 0.94562806 0.92857549 0.94138696 0.93121516 0.94029184 0.93231027\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94295711 0.92325065 0.95362385 0.92484355 0.9514905  0.931218\n",
      " 0.9392337  0.93444931 0.94616282 0.93975993 0.94828763 0.940289\n",
      " 0.94028615 0.93228752 0.95042951 0.93763227 0.95362385 0.9328365\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94136989 0.92485778 0.94295142 0.94030038 0.9392337  0.93711458\n",
      " 0.94881955 0.94030038 0.95042951 0.93496132 0.94457276 0.9275202\n",
      " 0.94297133 0.9301627  0.93922801 0.93177551 0.94189612 0.93177551\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9205797  0.9243088  0.91951303 0.92218398 0.92484071 0.9205797\n",
      " 0.92002788 0.9291131  0.92271021 0.91897827 0.92270452 0.92271305\n",
      " 0.91897827 0.91897827 0.91843782 0.92536125 0.93176414 0.91950165\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9317812  0.92590454 0.93122369 0.9291131  0.9365599  0.92270167\n",
      " 0.93176698 0.92430595 0.93390317 0.92377404 0.93390602 0.92376835\n",
      " 0.93603937 0.92643645 0.92643645 0.92110877 0.93283366 0.93072306\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94191034 0.93496416 0.94082945 0.93071453 0.93444647 0.93177836\n",
      " 0.93871885 0.92854989 0.94296279 0.92750882 0.94989191 0.92802935\n",
      " 0.92910172 0.93497269 0.94242804 0.93603368 0.93871032 0.93498692\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.9488167  0.93230174 0.93498407 0.93603368 0.94296564 0.93494709\n",
      " 0.94135567 0.93230743 0.94187905 0.93388042 0.94347764 0.94081522\n",
      " 0.94137274 0.9296251  0.94029469 0.93710035 0.94720674 0.92697406\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.93762942 0.93549039 0.9472494  0.92909034 0.93816987 0.92909034\n",
      " 0.94775287 0.93392024 0.94029184 0.94240812 0.94561952 0.94297133\n",
      " 0.93975424 0.92910456 0.94297702 0.92961941 0.93229321 0.92697406\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94563944 0.93177551 0.9504096  0.92536409 0.93709182 0.92644499\n",
      " 0.94988622 0.93655706 0.940289   0.93443509 0.9466862  0.93335988\n",
      " 0.95469337 0.93070884 0.95095005 0.93760382 0.94562237 0.93123791\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94669189 0.92748037 0.95095574 0.93547616 0.94668904 0.93387757\n",
      " 0.95041529 0.92965923 0.94296279 0.93176698 0.93974002 0.93283081\n",
      " 0.94402662 0.9259017  0.94988622 0.94135852 0.94988907 0.93229321\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94297133 0.93335135 0.94987769 0.93761236 0.94349755 0.93495847\n",
      " 0.95041529 0.93443224 0.95307771 0.92962794 0.94882524 0.93763796\n",
      " 0.94030891 0.92961372 0.95044089 0.93494709 0.94507339 0.93230174\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.94082945 0.92750597 0.93870748 0.93710889 0.95044658 0.93177551\n",
      " 0.95361531 0.93175845 0.9498976  0.93495278 0.95735579 0.93547616\n",
      " 0.94244226 0.93231312 0.94030322 0.91842644 0.94192172 0.92750313]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         'max_features': ['auto', 'sqrt'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']})"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import sklearn.tree as tree_plot\n",
    "\n",
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "     'splitter': ['best', 'random'],\n",
    "     'max_depth': [2*n for n in range(1,10)],\n",
    "     'max_features': ['auto', 'sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "tree_cv = GridSearchCV(tree, parameters, cv = 10)\n",
    "\n",
    "#Training Model\n",
    "tree_cv.fit(x_trainset,y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 14, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'splitter': 'best'}\n",
      "training accuracy : 0.9584196154283763\n",
      "test accuracy: 0.9477611940298507\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
    "print(\"training accuracy :\",tree_cv.best_score_)\n",
    "\n",
    "treeScore = tree_cv.best_estimator_.score(x_testset, y_testset)\n",
    "print(\"test accuracy:\",treeScore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Predicting\n",
    "yhat = tree_cv.predict(x_testset)\n",
    "plot_confusion_matrix(y_testset, yhat, \"Decision Tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>Here, problem of false negative is substantially lower compared to KNN but the problem of false positive seems to have risen.</mark>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training model on best found parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;sigmoid&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;sigmoid&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(), n_jobs=-1,\n",
       "             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'kernel': ('linear', 'sigmoid')})"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machine classification algorithm\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "parameters = {'kernel':('linear','sigmoid'),\n",
    "              'C': np.logspace(-3, 3, 5),\n",
    "              'gamma':np.logspace(-3, 3, 5)}\n",
    "svm = SVC()\n",
    "\n",
    "svm_cv = GridSearchCV(svm, parameters, cv = 10, n_jobs=-1) #Here n_jobs=-1 was specified to utilize multiple cores of the pc as it was taking too long to train the model\n",
    "#Training Model\n",
    "svm_cv.fit(x_trainset, y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 31.622776601683793, 'gamma': 0.001, 'kernel': 'linear'}\n",
      "training accuracy : 0.950418136306747\n",
      "test accuracy: 0.9502487562189055\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
    "print(\"training accuracy :\",svm_cv.best_score_)\n",
    "svmScore = svm_cv.best_estimator_.score(x_testset, y_testset)\n",
    "print(\"test accuracy:\", svmScore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=svm_cv.predict(x_testset)\n",
    "plot_confusion_matrix(y_testset, yhat, \"SVM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model has by far <mark>the best accuracy score of 95.02% but is a little worse compared to the Decision Tree model as the true positive is a little lower. It also took an exceptionally long time to train this model i.e. about 30 minutes, this is with optimization for best performance.</mark>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training model on best found parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\Users\\hakul\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
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       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
       "                         'solver': ['lbfgs']})"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression classification algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\n",
    "logreg_cv = GridSearchCV(lr, parameters, cv = 10)\n",
    "\n",
    "#Training model\n",
    "logreg_cv.fit(x_trainset, y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "training accuracy : 0.9482876322676072\n",
      "test accuracy: 0.9415422885572139\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \", logreg_cv.best_params_)\n",
    "print(\"training accuracy :\", logreg_cv.best_score_)\n",
    "logregScore = logreg_cv.best_estimator_.score(x_testset, y_testset)  #Accuracy check with best parameters\n",
    "print(\"test accuracy:\", logregScore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=logreg_cv.predict(x_testset)\n",
    "plot_confusion_matrix(y_testset, yhat, \"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<mark>This model has the highest false positives but is still better than the KNN model due to its lower false negative and higher true positives.</mark>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Test Performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.941542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.950249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.947761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K Near Neighbor</td>\n",
       "      <td>0.921642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Test Performance\n",
       "0  Logistic Regression          0.941542\n",
       "1                  SVM          0.950249\n",
       "2        Decision Tree          0.947761\n",
       "3      K Near Neighbor          0.921642"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_performance = {\n",
    "  \"Model\": ['Logistic Regression', 'SVM', 'Decision Tree', 'K Near Neighbor'],\n",
    "  \"Test Performance\": [logregScore, svmScore, treeScore, knnScore]\n",
    "} \n",
    "\n",
    "ml_df = pd.DataFrame(ml_performance)\n",
    "ml_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that the <mark>**SVM model** has the **best accuracy** of 95.02% but as it takes a very **long time to train**, i.e. about 30 min in my case, and **has less true positives** compared to the second best model i.e. Decision Tree model with 94.77% accuracy. I have decided that the **Decision Tree model is better overall**.</mark>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Export & Import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loan_approval_prediction_model.joblib']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "best_model = tree_cv.best_estimator_\n",
    "# Save the model\n",
    "dump(best_model, 'loan_approval_prediction_model.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import load\n",
    "\n",
    "# Load the model\n",
    "loaded_decision_tree_model = load('loan_approval_prediction_model.joblib')\n",
    "\n",
    "# Use loaded model to make predictions\n",
    "predictions = loaded_decision_tree_model.predict([[1,0,0,120.0,0,0,3.4,3,30.0,0,1,0,0]]) #Inputted best values of key attributes based on EDA\n",
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, when I inputted best values for key attributes like. **Income, Home Ownership, CCAvg, Education, Mortgage & CD Account** and worst values for other irrelevant attributes, based on the Exploratory Data Analysis done before, it is predicated that loan application will be approved. <mark>This shows that the relationship discovered between various factors were correct and the trained model was working accurately.</mark>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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