Assignment4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"E:/pythonbasics/bank.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
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       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
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       "      <td>1</td>\n",
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       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>20</td>\n",
       "      <td>apr</td>\n",
       "      <td>257</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>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>16</td>\n",
       "      <td>jun</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>aug</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>9</td>\n",
       "      <td>jul</td>\n",
       "      <td>628</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",
       "<p>11162 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital  education default  balance housing loan  \\\n",
       "0       59       admin.  married  secondary      no     2343     yes   no   \n",
       "1       56       admin.  married  secondary      no       45      no   no   \n",
       "2       41   technician  married  secondary      no     1270     yes   no   \n",
       "3       55     services  married  secondary      no     2476     yes   no   \n",
       "4       54       admin.  married   tertiary      no      184      no   no   \n",
       "...    ...          ...      ...        ...     ...      ...     ...  ...   \n",
       "11157   33  blue-collar   single    primary      no        1     yes   no   \n",
       "11158   39     services  married  secondary      no      733      no   no   \n",
       "11159   32   technician   single  secondary      no       29      no   no   \n",
       "11160   43   technician  married  secondary      no        0      no  yes   \n",
       "11161   34   technician  married  secondary      no        0      no   no   \n",
       "\n",
       "        contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "0       unknown    5   may      1042         1     -1         0  unknown   \n",
       "1       unknown    5   may      1467         1     -1         0  unknown   \n",
       "2       unknown    5   may      1389         1     -1         0  unknown   \n",
       "3       unknown    5   may       579         1     -1         0  unknown   \n",
       "4       unknown    5   may       673         2     -1         0  unknown   \n",
       "...         ...  ...   ...       ...       ...    ...       ...      ...   \n",
       "11157  cellular   20   apr       257         1     -1         0  unknown   \n",
       "11158   unknown   16   jun        83         4     -1         0  unknown   \n",
       "11159  cellular   19   aug       156         2     -1         0  unknown   \n",
       "11160  cellular    8   may         9         2    172         5  failure   \n",
       "11161  cellular    9   jul       628         1     -1         0  unknown   \n",
       "\n",
       "      deposit  \n",
       "0         yes  \n",
       "1         yes  \n",
       "2         yes  \n",
       "3         yes  \n",
       "4         yes  \n",
       "...       ...  \n",
       "11157      no  \n",
       "11158      no  \n",
       "11159      no  \n",
       "11160      no  \n",
       "11161      no  \n",
       "\n",
       "[11162 rows x 17 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          False\n",
       "job          False\n",
       "marital      False\n",
       "education    False\n",
       "default      False\n",
       "balance      False\n",
       "housing      False\n",
       "loan         False\n",
       "contact      False\n",
       "day          False\n",
       "month        False\n",
       "duration     False\n",
       "campaign     False\n",
       "pdays        False\n",
       "previous     False\n",
       "poutcome     False\n",
       "deposit      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing  import LabelEncoder\n",
    "al=LabelEncoder()\n",
    "data['job']=al.fit_transform(data['job'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<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",
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       "      <th>loan</th>\n",
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       "      <th>day</th>\n",
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       "      <th>duration</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
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       "      <td>no</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>56</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
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       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
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       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
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       "      <td>no</td>\n",
       "      <td>2476</td>\n",
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       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
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       "      <td>2</td>\n",
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       "      <td>yes</td>\n",
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       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>20</td>\n",
       "      <td>apr</td>\n",
       "      <td>257</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>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>16</td>\n",
       "      <td>jun</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>aug</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
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       "<p>11162 rows × 17 columns</p>\n",
       "</div>"
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      "text/plain": [
       "       age  job  marital  education default  balance housing loan   contact  \\\n",
       "0       59    0  married  secondary      no     2343     yes   no   unknown   \n",
       "1       56    0  married  secondary      no       45      no   no   unknown   \n",
       "2       41    9  married  secondary      no     1270     yes   no   unknown   \n",
       "3       55    7  married  secondary      no     2476     yes   no   unknown   \n",
       "4       54    0  married   tertiary      no      184      no   no   unknown   \n",
       "...    ...  ...      ...        ...     ...      ...     ...  ...       ...   \n",
       "11157   33    1   single    primary      no        1     yes   no  cellular   \n",
       "11158   39    7  married  secondary      no      733      no   no   unknown   \n",
       "11159   32    9   single  secondary      no       29      no   no  cellular   \n",
       "11160   43    9  married  secondary      no        0      no  yes  cellular   \n",
       "11161   34    9  married  secondary      no        0      no   no  cellular   \n",
       "\n",
       "       day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0        5   may      1042         1     -1         0  unknown     yes  \n",
       "1        5   may      1467         1     -1         0  unknown     yes  \n",
       "2        5   may      1389         1     -1         0  unknown     yes  \n",
       "3        5   may       579         1     -1         0  unknown     yes  \n",
       "4        5   may       673         2     -1         0  unknown     yes  \n",
       "...    ...   ...       ...       ...    ...       ...      ...     ...  \n",
       "11157   20   apr       257         1     -1         0  unknown      no  \n",
       "11158   16   jun        83         4     -1         0  unknown      no  \n",
       "11159   19   aug       156         2     -1         0  unknown      no  \n",
       "11160    8   may         9         2    172         5  failure      no  \n",
       "11161    9   jul       628         1     -1         0  unknown      no  \n",
       "\n",
       "[11162 rows x 17 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['marital']=al.fit_transform(data['marital'])\n",
    "data['housing']=al.fit_transform(data['housing'])\n",
    "data['loan']=al.fit_transform(data['loan'])\n",
    "data['education']=al.fit_transform(data['education'])\n",
    "data['default']=al.fit_transform(data['default'])\n",
    "data['contact']=al.fit_transform(data['contact'])\n",
    "data['poutcome']=al.fit_transform(data['poutcome'])\n",
    "data['deposit']=al.fit_transform(data['deposit'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0       59    0        1          1        0     2343        1     0        2   \n",
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       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
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       "11161   34    9        1          1        0        0        0     0        0   \n",
       "\n",
       "       day month  duration  campaign  pdays  previous  poutcome  deposit  \n",
       "0        5   may      1042         1     -1         0         3        1  \n",
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       "...    ...   ...       ...       ...    ...       ...       ...      ...  \n",
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       "11160    8   may         9         2    172         5         0        0  \n",
       "11161    9   jul       628         1     -1         0         3        0  \n",
       "\n",
       "[11162 rows x 17 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11162, 17)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['month'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>9</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",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0       59    0        1          1        0     2343        1     0        2   \n",
       "1       56    0        1          1        0       45        0     0        2   \n",
       "2       41    9        1          1        0     1270        1     0        2   \n",
       "3       55    7        1          1        0     2476        1     0        2   \n",
       "4       54    0        1          2        0      184        0     0        2   \n",
       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
       "11157   33    1        2          0        0        1        1     0        0   \n",
       "11158   39    7        1          1        0      733        0     0        2   \n",
       "11159   32    9        2          1        0       29        0     0        0   \n",
       "11160   43    9        1          1        0        0        0     1        0   \n",
       "11161   34    9        1          1        0        0        0     0        0   \n",
       "\n",
       "       day  duration  campaign  pdays  previous  poutcome  deposit  \n",
       "0        5      1042         1     -1         0         3        1  \n",
       "1        5      1467         1     -1         0         3        1  \n",
       "2        5      1389         1     -1         0         3        1  \n",
       "3        5       579         1     -1         0         3        1  \n",
       "4        5       673         2     -1         0         3        1  \n",
       "...    ...       ...       ...    ...       ...       ...      ...  \n",
       "11157   20       257         1     -1         0         3        0  \n",
       "11158   16        83         4     -1         0         3        0  \n",
       "11159   19       156         2     -1         0         3        0  \n",
       "11160    8         9         2    172         5         0        0  \n",
       "11161    9       628         1     -1         0         3        0  \n",
       "\n",
       "[11162 rows x 16 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.iloc[:,0:15].values\n",
    "y=data.iloc[:,15:16].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 59,   0,   1, ...,  -1,   0,   3],\n",
       "       [ 56,   0,   1, ...,  -1,   0,   3],\n",
       "       [ 41,   9,   1, ...,  -1,   0,   3],\n",
       "       ...,\n",
       "       [ 32,   9,   2, ...,  -1,   0,   3],\n",
       "       [ 43,   9,   1, ..., 172,   5,   0],\n",
       "       [ 34,   9,   1, ...,  -1,   0,   3]], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [1],\n",
       "       ...,\n",
       "       [0],\n",
       "       [0],\n",
       "       [0]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing  import OneHotEncoder\n",
    "one=OneHotEncoder()\n",
    "z=one.fit_transform(x[:,1:2]).toarray()\n",
    "x=np.delete(x ,1,axis=1)\n",
    "x=np.concatenate((z,x),axis=1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   0.,   1., ...,  -1.,   0.,   3.],\n",
       "       [  1.,   0.,   1., ...,  -1.,   0.,   3.],\n",
       "       [  1.,   0.,   1., ...,  -1.,   0.,   3.],\n",
       "       ...,\n",
       "       [  1.,   0.,   1., ...,  -1.,   0.,   3.],\n",
       "       [  1.,   0.,   1., ..., 172.,   5.,   0.],\n",
       "       [  1.,   0.,   1., ...,  -1.,   0.,   3.]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       ...,\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>apr</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>jun</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>aug</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>9</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",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>jul</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0       59    0        1          1        0     2343        1     0        2   \n",
       "1       56    0        1          1        0       45        0     0        2   \n",
       "2       41    9        1          1        0     1270        1     0        2   \n",
       "3       55    7        1          1        0     2476        1     0        2   \n",
       "4       54    0        1          2        0      184        0     0        2   \n",
       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
       "11157   33    1        2          0        0        1        1     0        0   \n",
       "11158   39    7        1          1        0      733        0     0        2   \n",
       "11159   32    9        2          1        0       29        0     0        0   \n",
       "11160   43    9        1          1        0        0        0     1        0   \n",
       "11161   34    9        1          1        0        0        0     0        0   \n",
       "\n",
       "       day month  duration  campaign  pdays  previous  poutcome deposit  \n",
       "0        5   may      1042         1     -1         0         3     yes  \n",
       "1        5   may      1467         1     -1         0         3     yes  \n",
       "2        5   may      1389         1     -1         0         3     yes  \n",
       "3        5   may       579         1     -1         0         3     yes  \n",
       "4        5   may       673         2     -1         0         3     yes  \n",
       "...    ...   ...       ...       ...    ...       ...       ...     ...  \n",
       "11157   20   apr       257         1     -1         0         3      no  \n",
       "11158   16   jun        83         4     -1         0         3      no  \n",
       "11159   19   aug       156         2     -1         0         3      no  \n",
       "11160    8   may         9         2    172         5         0      no  \n",
       "11161    9   jul       628         1     -1         0         3      no  \n",
       "\n",
       "[11162 rows x 17 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2109c6f43c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['month'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>9</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",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0       59    0        1          1        0     2343        1     0        2   \n",
       "1       56    0        1          1        0       45        0     0        2   \n",
       "2       41    9        1          1        0     1270        1     0        2   \n",
       "3       55    7        1          1        0     2476        1     0        2   \n",
       "4       54    0        1          2        0      184        0     0        2   \n",
       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
       "11157   33    1        2          0        0        1        1     0        0   \n",
       "11158   39    7        1          1        0      733        0     0        2   \n",
       "11159   32    9        2          1        0       29        0     0        0   \n",
       "11160   43    9        1          1        0        0        0     1        0   \n",
       "11161   34    9        1          1        0        0        0     0        0   \n",
       "\n",
       "       day  duration  campaign  pdays  previous  poutcome deposit  \n",
       "0        5      1042         1     -1         0         3     yes  \n",
       "1        5      1467         1     -1         0         3     yes  \n",
       "2        5      1389         1     -1         0         3     yes  \n",
       "3        5       579         1     -1         0         3     yes  \n",
       "4        5       673         2     -1         0         3     yes  \n",
       "...    ...       ...       ...    ...       ...       ...     ...  \n",
       "11157   20       257         1     -1         0         3      no  \n",
       "11158   16        83         4     -1         0         3      no  \n",
       "11159   19       156         2     -1         0         3      no  \n",
       "11160    8         9         2    172         5         0      no  \n",
       "11161    9       628         1     -1         0         3      no  \n",
       "\n",
       "[11162 rows x 16 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['yes'],\n",
       "       ['yes'],\n",
       "       ['yes'],\n",
       "       ...,\n",
       "       ['no'],\n",
       "       ['no'],\n",
       "       ['no']], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[59, 0, 1, ..., 0, 3, 'yes'],\n",
       "       [56, 0, 1, ..., 0, 3, 'yes'],\n",
       "       [41, 9, 1, ..., 0, 3, 'yes'],\n",
       "       ...,\n",
       "       [32, 9, 2, ..., 0, 3, 'no'],\n",
       "       [43, 9, 1, ..., 5, 0, 'no'],\n",
       "       [34, 9, 1, ..., 0, 3, 'no']], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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