insurance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69cd2386",
   "metadata": {},
   "source": [
    "# # importing all the required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54844afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fca02413",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making a dataframe and loading the data to it\n",
    "df = pd.read_csv('bank-full.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b37f1659",
   "metadata": {},
   "source": [
    "# Pre-Processing Data Analysis (count of missing/ null values, redundant columns, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24e6cb35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will first find the NA values in the dataset\n",
    "# there is no NA value in the dataset\n",
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89a6a348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcab7442",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistics of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7bd6850",
   "metadata": {},
   "source": [
    "# Creating dummies \n",
    "convert categorical into numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f29bda55",
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
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       "      <td>yes</td>\n",
       "      <td>76</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   age default  balance housing loan  duration  campaign  pdays  previous   y\n",
       "0   58      no     2143     yes   no       261         1     -1         0  no\n",
       "1   44      no       29     yes   no       151         1     -1         0  no\n",
       "2   33      no        2     yes  yes        76         1     -1         0  no\n",
       "3   47      no     1506     yes   no        92         1     -1         0  no\n",
       "4   33      no        1      no   no       198         1     -1         0  no"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now in order to prepare the data for the machine learning models\n",
    "# we will have to drop some columns which are not really needed\n",
    "# in this case, we will drop job, contact, day, month, poutcome, education, job\n",
    "\n",
    "df1 = df.drop(['marital','contact','day','month','poutcome','education','job'], axis = 1)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bacd483f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>58</td>\n",
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       "      <th>1</th>\n",
       "      <td>44</td>\n",
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       "      <td>151</td>\n",
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       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1506</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance housing loan  duration  campaign  pdays  previous    y  \\\n",
       "0       58     2143     yes   no       261         1     -1         0   no   \n",
       "1       44       29     yes   no       151         1     -1         0   no   \n",
       "2       33        2     yes  yes        76         1     -1         0   no   \n",
       "3       47     1506     yes   no        92         1     -1         0   no   \n",
       "4       33        1      no   no       198         1     -1         0   no   \n",
       "...    ...      ...     ...  ...       ...       ...    ...       ...  ...   \n",
       "45206   51      825      no   no       977         3     -1         0  yes   \n",
       "45207   71     1729      no   no       456         2     -1         0  yes   \n",
       "45208   72     5715      no   no      1127         5    184         3  yes   \n",
       "45209   57      668      no   no       508         4     -1         0   no   \n",
       "45210   37     2971      no   no       361         2    188        11   no   \n",
       "\n",
       "       default  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "...        ...  \n",
       "45206        0  \n",
       "45207        0  \n",
       "45208        0  \n",
       "45209        0  \n",
       "45210        0  \n",
       "\n",
       "[45211 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we will do the one hot encoding, we will convert the categorical data into numerical data\n",
    "# we will do one by one in order to keep it simple\n",
    "#The default column have yes/no, we will convert it into (0 = no) and (1 = yes)\n",
    "\n",
    "default = df1.default.map(dict(yes=1, no=0))\n",
    "Final = pd.concat([df1.drop('default',axis = 1),default], axis = 1)\n",
    "Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c6d892d5",
   "metadata": {},
   "outputs": [
    {
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       "<p>45211 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "       age  balance loan  duration  campaign  pdays  previous    y  default  \\\n",
       "0       58     2143   no       261         1     -1         0   no        0   \n",
       "1       44       29   no       151         1     -1         0   no        0   \n",
       "2       33        2  yes        76         1     -1         0   no        0   \n",
       "3       47     1506   no        92         1     -1         0   no        0   \n",
       "4       33        1   no       198         1     -1         0   no        0   \n",
       "...    ...      ...  ...       ...       ...    ...       ...  ...      ...   \n",
       "45206   51      825   no       977         3     -1         0  yes        0   \n",
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       "45208   72     5715   no      1127         5    184         3  yes        0   \n",
       "45209   57      668   no       508         4     -1         0   no        0   \n",
       "45210   37     2971   no       361         2    188        11   no        0   \n",
       "\n",
       "       housing  \n",
       "0            1  \n",
       "1            1  \n",
       "2            1  \n",
       "3            1  \n",
       "4            0  \n",
       "...        ...  \n",
       "45206        0  \n",
       "45207        0  \n",
       "45208        0  \n",
       "45209        0  \n",
       "45210        0  \n",
       "\n",
       "[45211 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The Housing column also have Yes/No, we will do the same process as we have done with the default column\n",
    "\n",
    "housing = Final.housing.map(dict(yes=1, no=0))\n",
    "Final = pd.concat([Final.drop('housing',axis = 1),housing], axis = 1)\n",
    "Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a28babcd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "       age  balance  duration  campaign  pdays  previous    y  default  \\\n",
       "0       58     2143       261         1     -1         0   no        0   \n",
       "1       44       29       151         1     -1         0   no        0   \n",
       "2       33        2        76         1     -1         0   no        0   \n",
       "3       47     1506        92         1     -1         0   no        0   \n",
       "4       33        1       198         1     -1         0   no        0   \n",
       "...    ...      ...       ...       ...    ...       ...  ...      ...   \n",
       "45206   51      825       977         3     -1         0  yes        0   \n",
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       "45209   57      668       508         4     -1         0   no        0   \n",
       "45210   37     2971       361         2    188        11   no        0   \n",
       "\n",
       "       housing  loan  \n",
       "0            1     0  \n",
       "1            1     0  \n",
       "2            1     1  \n",
       "3            1     0  \n",
       "4            0     0  \n",
       "...        ...   ...  \n",
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       "45207        0     0  \n",
       "45208        0     0  \n",
       "45209        0     0  \n",
       "45210        0     0  \n",
       "\n",
       "[45211 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The Loan column also have Yes/No, we will do the same process as we have done with the default column\n",
    "\n",
    "loan = Final.loan.map(dict(yes=1, no=0))\n",
    "Final = pd.concat([Final.drop('loan',axis = 1),loan], axis = 1)\n",
    "Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4de3b08",
   "metadata": {
    "scrolled": true
   },
   "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>balance</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2143</td>\n",
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       "    </tr>\n",
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       "      <th>45206</th>\n",
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       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>1729</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>5715</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  pdays  previous  default  housing  \\\n",
       "0       58     2143       261         1     -1         0        0        1   \n",
       "1       44       29       151         1     -1         0        0        1   \n",
       "2       33        2        76         1     -1         0        0        1   \n",
       "3       47     1506        92         1     -1         0        0        1   \n",
       "4       33        1       198         1     -1         0        0        0   \n",
       "...    ...      ...       ...       ...    ...       ...      ...      ...   \n",
       "45206   51      825       977         3     -1         0        0        0   \n",
       "45207   71     1729       456         2     -1         0        0        0   \n",
       "45208   72     5715      1127         5    184         3        0        0   \n",
       "45209   57      668       508         4     -1         0        0        0   \n",
       "45210   37     2971       361         2    188        11        0        0   \n",
       "\n",
       "       loan  y  \n",
       "0         0  0  \n",
       "1         0  0  \n",
       "2         1  0  \n",
       "3         0  0  \n",
       "4         0  0  \n",
       "...     ... ..  \n",
       "45206     0  1  \n",
       "45207     0  1  \n",
       "45208     0  1  \n",
       "45209     0  0  \n",
       "45210     0  0  \n",
       "\n",
       "[45211 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The y column also have Yes/No, we will do the same process as we have done with the default column\n",
    "\n",
    "dependent = Final.y.map(dict(yes=1, no=0))\n",
    "Final = pd.concat([Final.drop('y',axis = 1),dependent], axis = 1)\n",
    "Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c6065ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e13d8284",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               age   balance  duration  campaign     pdays  previous  \\\n",
      "age       1.000000  0.097783 -0.004648  0.004760 -0.023758  0.001288   \n",
      "balance   0.097783  1.000000  0.021560 -0.014578  0.003435  0.016674   \n",
      "duration -0.004648  0.021560  1.000000 -0.084570 -0.001565  0.001203   \n",
      "campaign  0.004760 -0.014578 -0.084570  1.000000 -0.088628 -0.032855   \n",
      "pdays    -0.023758  0.003435 -0.001565 -0.088628  1.000000  0.454820   \n",
      "previous  0.001288  0.016674  0.001203 -0.032855  0.454820  1.000000   \n",
      "default  -0.017879 -0.066745 -0.010021  0.016822 -0.029979 -0.018329   \n",
      "housing  -0.185513 -0.068768  0.005075 -0.023599  0.124178  0.037076   \n",
      "loan     -0.015655 -0.084350 -0.012412  0.009980 -0.022754 -0.011043   \n",
      "y         0.025155  0.052838  0.394521 -0.073172  0.103621  0.093236   \n",
      "\n",
      "           default   housing      loan         y  \n",
      "age      -0.017879 -0.185513 -0.015655  0.025155  \n",
      "balance  -0.066745 -0.068768 -0.084350  0.052838  \n",
      "duration -0.010021  0.005075 -0.012412  0.394521  \n",
      "campaign  0.016822 -0.023599  0.009980 -0.073172  \n",
      "pdays    -0.029979  0.124178 -0.022754  0.103621  \n",
      "previous -0.018329  0.037076 -0.011043  0.093236  \n",
      "default   1.000000 -0.006025  0.077234 -0.022419  \n",
      "housing  -0.006025  1.000000  0.041323 -0.139173  \n",
      "loan      0.077234  0.041323  1.000000 -0.068185  \n",
      "y        -0.022419 -0.139173 -0.068185  1.000000  \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = Final.corr();\n",
    "print(corr)\n",
    "sns.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ac48dea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c571f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f42b14f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we will go for dependent and independent variables\n",
    "# Dependent variable \"Y\"\n",
    "\n",
    "y = Final.y\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f7af02cf",
   "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>balance</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>2143</td>\n",
       "      <td>261</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>44</td>\n",
       "      <td>29</td>\n",
       "      <td>151</td>\n",
       "      <td>1</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>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1506</td>\n",
       "      <td>92</td>\n",
       "      <td>1</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>4</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  balance  duration  campaign  pdays  previous  default  housing  loan\n",
       "0   58     2143       261         1     -1         0        0        1     0\n",
       "1   44       29       151         1     -1         0        0        1     0\n",
       "2   33        2        76         1     -1         0        0        1     1\n",
       "3   47     1506        92         1     -1         0        0        1     0\n",
       "4   33        1       198         1     -1         0        0        0     0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Independent variable \"X\"\n",
    "\n",
    "x = Final.drop('y',axis = 1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6bdbc0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ecf494ff",
   "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>balance</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12096</th>\n",
       "      <td>51</td>\n",
       "      <td>2685</td>\n",
       "      <td>78</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42511</th>\n",
       "      <td>28</td>\n",
       "      <td>1705</td>\n",
       "      <td>412</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2731</th>\n",
       "      <td>57</td>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
       "      <td>6</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>29474</th>\n",
       "      <td>34</td>\n",
       "      <td>2467</td>\n",
       "      <td>368</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>53</td>\n",
       "      <td>592</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  pdays  previous  default  housing  \\\n",
       "12096   51     2685        78         2     -1         0        0        0   \n",
       "42511   28     1705       412         2     -1         0        0        0   \n",
       "2731    57        7        65         6     -1         0        0        1   \n",
       "29474   34     2467       368         1     -1         0        0        0   \n",
       "2569    53      592       151         1     -1         0        0        1   \n",
       "\n",
       "       loan  \n",
       "12096     0  \n",
       "42511     0  \n",
       "2731      0  \n",
       "29474     0  \n",
       "2569      0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5547521a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>balance</th>\n",
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       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
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       "  </thead>\n",
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       "      <td>50</td>\n",
       "      <td>1458</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>65</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>264</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  pdays  previous  default  housing  \\\n",
       "28011   50     1458        92         1     -1         0        0        0   \n",
       "42021   65    11240       149         1     -1         0        0        1   \n",
       "41714   25     1185       290         1    114         1        0        0   \n",
       "4532    34      371        77         1     -1         0        0        0   \n",
       "14977   38        0       264         5     -1         0        0        1   \n",
       "\n",
       "       loan  \n",
       "28011     0  \n",
       "42021     0  \n",
       "41714     0  \n",
       "4532      0  \n",
       "14977     0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "faf36629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12096    0\n",
       "42511    1\n",
       "2731     0\n",
       "29474    0\n",
       "2569     0\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9402c84a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28011    0\n",
       "42021    0\n",
       "41714    1\n",
       "4532     0\n",
       "14977    0\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "53b73e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "Model = LinearRegression()\n",
    "Model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a44783fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
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       "      <th>default</th>\n",
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       "      <td>50</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>264</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  pdays  previous  default  housing  \\\n",
       "28011   50     1458        92         1     -1         0        0        0   \n",
       "42021   65    11240       149         1     -1         0        0        1   \n",
       "41714   25     1185       290         1    114         1        0        0   \n",
       "4532    34      371        77         1     -1         0        0        0   \n",
       "14977   38        0       264         5     -1         0        0        1   \n",
       "\n",
       "       loan  \n",
       "28011     0  \n",
       "42021     0  \n",
       "41714     0  \n",
       "4532      0  \n",
       "14977     0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8ee46c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08882112, 0.04521405, 0.22600337, ..., 0.13781303, 0.00381834,\n",
       "       0.07244684])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a02bab84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2211125301413589"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f8a82de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ea3ab72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "746ff7cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: 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": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0416df4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28011</th>\n",
       "      <td>50</td>\n",
       "      <td>1458</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42021</th>\n",
       "      <td>65</td>\n",
       "      <td>11240</td>\n",
       "      <td>149</td>\n",
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       "      <th>41714</th>\n",
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       "      <td>1185</td>\n",
       "      <td>290</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>34</td>\n",
       "      <td>371</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>264</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  pdays  previous  default  housing  \\\n",
       "28011   50     1458        92         1     -1         0        0        0   \n",
       "42021   65    11240       149         1     -1         0        0        1   \n",
       "41714   25     1185       290         1    114         1        0        0   \n",
       "4532    34      371        77         1     -1         0        0        0   \n",
       "14977   38        0       264         5     -1         0        0        1   \n",
       "\n",
       "       loan  \n",
       "28011     0  \n",
       "42021     0  \n",
       "41714     0  \n",
       "4532      0  \n",
       "14977     0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "74f2455d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "51177063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[80,3000,140,5,2,1,1,1,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9da0b4a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7806e4f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.91070852, 0.08929148],\n",
       "       [0.98132382, 0.01867618],\n",
       "       [0.59484789, 0.40515211],\n",
       "       ...,\n",
       "       [0.92722239, 0.07277761],\n",
       "       [0.96565339, 0.03434661],\n",
       "       [0.87640633, 0.12359367]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8ceff890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is   :   \n",
      "0.885891198584697\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Accuracy is   :   \")\n",
    "print(metrics.accuracy_score(y_test,y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "52720542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.885891198584697"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbaecb04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d443174",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67189ef1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d28ded8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f87b866",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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