!excelR/assignments/Gists/Logistic_Reg.ipynb

Logistic_Reg.ipynb

{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.406973Z",
          "start_time": "2023-02-05T10:48:13.392910Z"
        },
        "trusted": false
      },
      "id": "75f293e2",
      "cell_type": "code",
      "source": "import pandas as pd\nimport seaborn as sns\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport warnings\nwarnings.filterwarnings('ignore')",
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.521104Z",
          "start_time": "2023-02-05T10:48:13.410475Z"
        },
        "trusted": false
      },
      "id": "c24b98d1",
      "cell_type": "code",
      "source": "ds=pd.read_csv('bank-full.csv', sep=';')\nds.head()",
      "execution_count": 31,
      "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>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  \\\n0   58    management  married   tertiary      no     2143     yes   no   \n1   44    technician   single  secondary      no       29     yes   no   \n2   33  entrepreneur  married  secondary      no        2     yes  yes   \n3   47   blue-collar  married    unknown      no     1506     yes   no   \n4   33       unknown   single    unknown      no        1      no   no   \n\n   contact  day month  duration  campaign  pdays  previous poutcome   y  \n0  unknown    5   may       261         1     -1         0  unknown  no  \n1  unknown    5   may       151         1     -1         0  unknown  no  \n2  unknown    5   may        76         1     -1         0  unknown  no  \n3  unknown    5   may        92         1     -1         0  unknown  no  \n4  unknown    5   may       198         1     -1         0  unknown  no  "
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.537723Z",
          "start_time": "2023-02-05T10:48:13.522087Z"
        },
        "trusted": false
      },
      "id": "23d592c4",
      "cell_type": "code",
      "source": "#splitting dataset into 2 part one with numeric type and the other with categorical type\nds_num=ds.select_dtypes(['int64','float64'])\nds_cat=ds.select_dtypes(object)",
      "execution_count": 32,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.634570Z",
          "start_time": "2023-02-05T10:48:13.538787Z"
        },
        "trusted": false
      },
      "id": "429bcf92",
      "cell_type": "code",
      "source": "#converting categorical type into 0s and 1s using label encoder\nfrom sklearn.preprocessing import LabelEncoder\n\nfor col in ds_cat:\n    le=LabelEncoder()\n    ds_cat[col]=le.fit_transform(ds_cat[col])\n",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.658850Z",
          "start_time": "2023-02-05T10:48:13.635575Z"
        },
        "trusted": false
      },
      "id": "206602c6",
      "cell_type": "code",
      "source": "#concatenate both the parts\nds_new=pd.concat([ds_num,ds_cat],axis=1)\nds_new.head(10)",
      "execution_count": 34,
      "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>day</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</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>2143</td>\n      <td>5</td>\n      <td>261</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>29</td>\n      <td>5</td>\n      <td>151</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>2</td>\n      <td>5</td>\n      <td>76</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>1506</td>\n      <td>5</td>\n      <td>92</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>1</td>\n      <td>5</td>\n      <td>198</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>11</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>35</td>\n      <td>231</td>\n      <td>5</td>\n      <td>139</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>28</td>\n      <td>447</td>\n      <td>5</td>\n      <td>217</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>42</td>\n      <td>2</td>\n      <td>5</td>\n      <td>380</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>58</td>\n      <td>121</td>\n      <td>5</td>\n      <td>50</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</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      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>43</td>\n      <td>593</td>\n      <td>5</td>\n      <td>55</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age  balance  day  duration  campaign  pdays  previous  job  marital  \\\n0   58     2143    5       261         1     -1         0    4        1   \n1   44       29    5       151         1     -1         0    9        2   \n2   33        2    5        76         1     -1         0    2        1   \n3   47     1506    5        92         1     -1         0    1        1   \n4   33        1    5       198         1     -1         0   11        2   \n5   35      231    5       139         1     -1         0    4        1   \n6   28      447    5       217         1     -1         0    4        2   \n7   42        2    5       380         1     -1         0    2        0   \n8   58      121    5        50         1     -1         0    5        1   \n9   43      593    5        55         1     -1         0    9        2   \n\n   education  default  housing  loan  contact  month  poutcome  y  \n0          2        0        1     0        2      8         3  0  \n1          1        0        1     0        2      8         3  0  \n2          1        0        1     1        2      8         3  0  \n3          3        0        1     0        2      8         3  0  \n4          3        0        0     0        2      8         3  0  \n5          2        0        1     0        2      8         3  0  \n6          2        0        1     1        2      8         3  0  \n7          2        1        1     0        2      8         3  0  \n8          0        0        1     0        2      8         3  0  \n9          1        0        1     0        2      8         3  0  "
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.677087Z",
          "start_time": "2023-02-05T10:48:13.659840Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "e2c68bd0",
      "cell_type": "code",
      "source": "ds_new.tail(10)",
      "execution_count": 35,
      "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>day</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>poutcome</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>45201</th>\n      <td>53</td>\n      <td>583</td>\n      <td>17</td>\n      <td>226</td>\n      <td>1</td>\n      <td>184</td>\n      <td>4</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45202</th>\n      <td>34</td>\n      <td>557</td>\n      <td>17</td>\n      <td>224</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</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      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45203</th>\n      <td>23</td>\n      <td>113</td>\n      <td>17</td>\n      <td>266</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>8</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45204</th>\n      <td>73</td>\n      <td>2850</td>\n      <td>17</td>\n      <td>300</td>\n      <td>1</td>\n      <td>40</td>\n      <td>8</td>\n      <td>5</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>9</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45205</th>\n      <td>25</td>\n      <td>505</td>\n      <td>17</td>\n      <td>386</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45206</th>\n      <td>51</td>\n      <td>825</td>\n      <td>17</td>\n      <td>977</td>\n      <td>3</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45207</th>\n      <td>71</td>\n      <td>1729</td>\n      <td>17</td>\n      <td>456</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45208</th>\n      <td>72</td>\n      <td>5715</td>\n      <td>17</td>\n      <td>1127</td>\n      <td>5</td>\n      <td>184</td>\n      <td>3</td>\n      <td>5</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>9</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45209</th>\n      <td>57</td>\n      <td>668</td>\n      <td>17</td>\n      <td>508</td>\n      <td>4</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</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>9</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>45210</th>\n      <td>37</td>\n      <td>2971</td>\n      <td>17</td>\n      <td>361</td>\n      <td>2</td>\n      <td>188</td>\n      <td>11</td>\n      <td>2</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>9</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       age  balance  day  duration  campaign  pdays  previous  job  marital  \\\n45201   53      583   17       226         1    184         4    4        1   \n45202   34      557   17       224         1     -1         0    0        2   \n45203   23      113   17       266         1     -1         0    8        2   \n45204   73     2850   17       300         1     40         8    5        1   \n45205   25      505   17       386         2     -1         0    9        2   \n45206   51      825   17       977         3     -1         0    9        1   \n45207   71     1729   17       456         2     -1         0    5        0   \n45208   72     5715   17      1127         5    184         3    5        1   \n45209   57      668   17       508         4     -1         0    1        1   \n45210   37     2971   17       361         2    188        11    2        1   \n\n       education  default  housing  loan  contact  month  poutcome  y  \n45201          2        0        0     0        0      9         2  1  \n45202          1        0        0     0        0      9         3  1  \n45203          2        0        0     0        0      9         3  1  \n45204          1        0        0     0        0      9         0  1  \n45205          1        0        0     1        0      9         3  1  \n45206          2        0        0     0        0      9         3  1  \n45207          0        0        0     0        0      9         3  1  \n45208          1        0        0     0        0      9         2  1  \n45209          1        0        0     0        1      9         3  0  \n45210          1        0        0     0        0      9         1  0  "
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.697554Z",
          "start_time": "2023-02-05T10:48:13.677087Z"
        },
        "trusted": false
      },
      "id": "e0340f06",
      "cell_type": "code",
      "source": "ds_new.info()",
      "execution_count": 36,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 45211 entries, 0 to 45210\nData columns (total 17 columns):\n #   Column     Non-Null Count  Dtype\n---  ------     --------------  -----\n 0   age        45211 non-null  int64\n 1   balance    45211 non-null  int64\n 2   day        45211 non-null  int64\n 3   duration   45211 non-null  int64\n 4   campaign   45211 non-null  int64\n 5   pdays      45211 non-null  int64\n 6   previous   45211 non-null  int64\n 7   job        45211 non-null  int32\n 8   marital    45211 non-null  int32\n 9   education  45211 non-null  int32\n 10  default    45211 non-null  int32\n 11  housing    45211 non-null  int32\n 12  loan       45211 non-null  int32\n 13  contact    45211 non-null  int32\n 14  month      45211 non-null  int32\n 15  poutcome   45211 non-null  int32\n 16  y          45211 non-null  int32\ndtypes: int32(10), int64(7)\nmemory usage: 4.1 MB\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:13.712817Z",
          "start_time": "2023-02-05T10:48:13.697554Z"
        },
        "trusted": false
      },
      "id": "a7b495bb",
      "cell_type": "code",
      "source": "ds_new.isnull().sum()\n#all the columns are non null and numeric",
      "execution_count": 37,
      "outputs": [
        {
          "data": {
            "text/plain": "age          0\nbalance      0\nday          0\nduration     0\ncampaign     0\npdays        0\nprevious     0\njob          0\nmarital      0\neducation    0\ndefault      0\nhousing      0\nloan         0\ncontact      0\nmonth        0\npoutcome     0\ny            0\ndtype: int64"
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-26T10:34:07.482793Z",
          "start_time": "2023-01-26T10:34:07.467389Z"
        }
      },
      "id": "c53f2533",
      "cell_type": "markdown",
      "source": "## Visualizations"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:14.514624Z",
          "start_time": "2023-02-05T10:48:13.714771Z"
        },
        "trusted": false
      },
      "id": "b6cc46fa",
      "cell_type": "code",
      "source": "sns.heatmap(ds_new,cmap='BuGn')",
      "execution_count": 38,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 2 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:14.620385Z",
          "start_time": "2023-02-05T10:48:14.521537Z"
        },
        "trusted": false
      },
      "id": "aab954c5",
      "cell_type": "code",
      "source": "sns.countplot(x=\"y\",data=ds_new,palette=\"hls\") ",
      "execution_count": 39,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='y', ylabel='count'>"
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:14.723180Z",
          "start_time": "2023-02-05T10:48:14.622379Z"
        },
        "trusted": false
      },
      "id": "95a3062a",
      "cell_type": "code",
      "source": "sns.countplot(x=\"education\",data=ds_new,palette=\"hls\") ",
      "execution_count": 40,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='education', ylabel='count'>"
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:14.854745Z",
          "start_time": "2023-02-05T10:48:14.725163Z"
        },
        "trusted": false
      },
      "id": "d54ad3d4",
      "cell_type": "code",
      "source": "sns.countplot(x=\"job\",data=ds_new,palette=\"hls\") ",
      "execution_count": 41,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='job', ylabel='count'>"
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:14.953479Z",
          "start_time": "2023-02-05T10:48:14.856740Z"
        },
        "trusted": false
      },
      "id": "6dd0d4fa",
      "cell_type": "code",
      "source": "sns.countplot(x=\"marital\",data=ds_new,palette=\"hls\") ",
      "execution_count": 42,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='marital', ylabel='count'>"
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:15.058822Z",
          "start_time": "2023-02-05T10:48:14.954441Z"
        },
        "trusted": false
      },
      "id": "99b8ebf8",
      "cell_type": "code",
      "source": "sns.countplot(x=\"loan\",data=ds_new,palette=\"hls\") ",
      "execution_count": 43,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='loan', ylabel='count'>"
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:15.169966Z",
          "start_time": "2023-02-05T10:48:15.059858Z"
        },
        "trusted": false
      },
      "id": "bcbaba30",
      "cell_type": "code",
      "source": "sns.countplot(x=\"contact\",data=ds_new,palette=\"hls\") ",
      "execution_count": 44,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='contact', ylabel='count'>"
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:15.305549Z",
          "start_time": "2023-02-05T10:48:15.170969Z"
        },
        "trusted": false
      },
      "id": "2886e5b6",
      "cell_type": "code",
      "source": "sns.countplot(x=\"month\",data=ds_new,palette=\"hls\") ",
      "execution_count": 45,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:xlabel='month', ylabel='count'>"
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:15.967442Z",
          "start_time": "2023-02-05T10:48:15.306550Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "39ab4cdd",
      "cell_type": "code",
      "source": "f, ax = plt.subplots(figsize=(8,4))\nsns.barplot(x='job',y='age' , data=ds_new);",
      "execution_count": 46,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 800x400 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:16.532245Z",
          "start_time": "2023-02-05T10:48:15.967442Z"
        },
        "trusted": false
      },
      "id": "d49d7f50",
      "cell_type": "code",
      "source": " f, ax = plt.subplots(figsize=(8,4))\nsns.barplot(x='education',y='age' , data=ds_new);",
      "execution_count": 47,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 800x400 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:17.020869Z",
          "start_time": "2023-02-05T10:48:16.535417Z"
        },
        "trusted": false
      },
      "id": "69af6bee",
      "cell_type": "code",
      "source": " f, ax = plt.subplots(figsize=(8,4))\nsns.barplot(x='marital',y='age' , data=ds_new);",
      "execution_count": 48,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 800x400 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:17.043829Z",
          "start_time": "2023-02-05T10:48:17.024229Z"
        },
        "trusted": false
      },
      "id": "5c5609fb",
      "cell_type": "code",
      "source": "#splitting data into x and y\nX=ds_new.iloc[:,0:16]\nY=ds_new['y']",
      "execution_count": 49,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:17.060998Z",
          "start_time": "2023-02-05T10:48:17.043829Z"
        },
        "trusted": false
      },
      "id": "7e3e6543",
      "cell_type": "code",
      "source": "X.head()",
      "execution_count": 50,
      "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>day</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>poutcome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>2143</td>\n      <td>5</td>\n      <td>261</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>29</td>\n      <td>5</td>\n      <td>151</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>2</td>\n      <td>5</td>\n      <td>76</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>1506</td>\n      <td>5</td>\n      <td>92</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>1</td>\n      <td>5</td>\n      <td>198</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>11</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   age  balance  day  duration  campaign  pdays  previous  job  marital  \\\n0   58     2143    5       261         1     -1         0    4        1   \n1   44       29    5       151         1     -1         0    9        2   \n2   33        2    5        76         1     -1         0    2        1   \n3   47     1506    5        92         1     -1         0    1        1   \n4   33        1    5       198         1     -1         0   11        2   \n\n   education  default  housing  loan  contact  month  poutcome  \n0          2        0        1     0        2      8         3  \n1          1        0        1     0        2      8         3  \n2          1        0        1     1        2      8         3  \n3          3        0        1     0        2      8         3  \n4          3        0        0     0        2      8         3  "
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:17.076651Z",
          "start_time": "2023-02-05T10:48:17.060998Z"
        },
        "trusted": false
      },
      "id": "446be912",
      "cell_type": "code",
      "source": "Y.head()",
      "execution_count": 51,
      "outputs": [
        {
          "data": {
            "text/plain": "0    0\n1    0\n2    0\n3    0\n4    0\nName: y, dtype: int32"
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "id": "3ef6657d",
      "cell_type": "markdown",
      "source": "## Model building"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:17.527108Z",
          "start_time": "2023-02-05T10:48:17.079597Z"
        },
        "trusted": false
      },
      "id": "b80a2d0b",
      "cell_type": "code",
      "source": "from sklearn.linear_model import LogisticRegression\nbank = LogisticRegression(class_weight={0:0.3,1:0.7})\nbank.fit(X,Y)",
      "execution_count": 52,
      "outputs": [
        {
          "data": {
            "text/plain": "LogisticRegression(class_weight={0: 0.3, 1: 0.7})"
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:17.542257Z",
          "start_time": "2023-02-05T10:48:17.529099Z"
        },
        "scrolled": true,
        "trusted": false
      },
      "id": "5ab8ea82",
      "cell_type": "code",
      "source": "bank.coef_",
      "execution_count": 53,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[-1.59853242e-02,  3.70362610e-05, -5.48329563e-03,\n         3.43696454e-03, -2.25871797e-01, -9.29286391e-04,\n         1.19054544e-01,  6.86826293e-03, -5.39065464e-02,\n         3.00925253e-03, -3.97230951e-03, -1.40874787e-01,\n        -4.15830038e-02, -1.57300492e-01, -1.11893270e-01,\n        -1.25172471e-01]])"
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:17.575545Z",
          "start_time": "2023-02-05T10:48:17.542257Z"
        },
        "trusted": false
      },
      "id": "75dce6a9",
      "cell_type": "code",
      "source": "# Predicted values\ny_pred = bank.predict(X)\nds_new[\"y_pred\"] = y_pred\nds_new  ",
      "execution_count": 54,
      "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>day</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>poutcome</th>\n      <th>y</th>\n      <th>y_pred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>2143</td>\n      <td>5</td>\n      <td>261</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>29</td>\n      <td>5</td>\n      <td>151</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>2</td>\n      <td>5</td>\n      <td>76</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>1506</td>\n      <td>5</td>\n      <td>92</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>1</td>\n      <td>5</td>\n      <td>198</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>11</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</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      <td>...</td>\n    </tr>\n    <tr>\n      <th>45206</th>\n      <td>51</td>\n      <td>825</td>\n      <td>17</td>\n      <td>977</td>\n      <td>3</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45207</th>\n      <td>71</td>\n      <td>1729</td>\n      <td>17</td>\n      <td>456</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>45208</th>\n      <td>72</td>\n      <td>5715</td>\n      <td>17</td>\n      <td>1127</td>\n      <td>5</td>\n      <td>184</td>\n      <td>3</td>\n      <td>5</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>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>45209</th>\n      <td>57</td>\n      <td>668</td>\n      <td>17</td>\n      <td>508</td>\n      <td>4</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</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>9</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>45210</th>\n      <td>37</td>\n      <td>2971</td>\n      <td>17</td>\n      <td>361</td>\n      <td>2</td>\n      <td>188</td>\n      <td>11</td>\n      <td>2</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>9</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>45211 rows × 18 columns</p>\n</div>",
            "text/plain": "       age  balance  day  duration  campaign  pdays  previous  job  marital  \\\n0       58     2143    5       261         1     -1         0    4        1   \n1       44       29    5       151         1     -1         0    9        2   \n2       33        2    5        76         1     -1         0    2        1   \n3       47     1506    5        92         1     -1         0    1        1   \n4       33        1    5       198         1     -1         0   11        2   \n...    ...      ...  ...       ...       ...    ...       ...  ...      ...   \n45206   51      825   17       977         3     -1         0    9        1   \n45207   71     1729   17       456         2     -1         0    5        0   \n45208   72     5715   17      1127         5    184         3    5        1   \n45209   57      668   17       508         4     -1         0    1        1   \n45210   37     2971   17       361         2    188        11    2        1   \n\n       education  default  housing  loan  contact  month  poutcome  y  y_pred  \n0              2        0        1     0        2      8         3  0       0  \n1              1        0        1     0        2      8         3  0       0  \n2              1        0        1     1        2      8         3  0       0  \n3              3        0        1     0        2      8         3  0       0  \n4              3        0        0     0        2      8         3  0       0  \n...          ...      ...      ...   ...      ...    ...       ... ..     ...  \n45206          2        0        0     0        0      9         3  1       1  \n45207          0        0        0     0        0      9         3  1       0  \n45208          1        0        0     0        0      9         2  1       1  \n45209          1        0        0     0        1      9         3  0       0  \n45210          1        0        0     0        0      9         1  0       1  \n\n[45211 rows x 18 columns]"
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:40.471561Z",
          "start_time": "2023-02-05T10:48:40.427228Z"
        },
        "trusted": false
      },
      "id": "b0e50cd0",
      "cell_type": "code",
      "source": "# probability values of the predicted values\ny_prob = pd.DataFrame(bank.predict_proba(X.iloc[:,:]))\nds_new = pd.concat([ds_new,y_prob],axis=1)\nds_new \n",
      "execution_count": 56,
      "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>day</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>poutcome</th>\n      <th>y</th>\n      <th>y_pred</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>2143</td>\n      <td>5</td>\n      <td>261</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.880446</td>\n      <td>0.119554</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>29</td>\n      <td>5</td>\n      <td>151</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.904805</td>\n      <td>0.095195</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>2</td>\n      <td>5</td>\n      <td>76</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.914549</td>\n      <td>0.085451</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>1506</td>\n      <td>5</td>\n      <td>92</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.920037</td>\n      <td>0.079963</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>1</td>\n      <td>5</td>\n      <td>198</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>11</td>\n      <td>2</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>8</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.852561</td>\n      <td>0.147439</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>45206</th>\n      <td>51</td>\n      <td>825</td>\n      <td>17</td>\n      <td>977</td>\n      <td>3</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.404277</td>\n      <td>0.595723</td>\n    </tr>\n    <tr>\n      <th>45207</th>\n      <td>71</td>\n      <td>1729</td>\n      <td>17</td>\n      <td>456</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.808907</td>\n      <td>0.191093</td>\n    </tr>\n    <tr>\n      <th>45208</th>\n      <td>72</td>\n      <td>5715</td>\n      <td>17</td>\n      <td>1127</td>\n      <td>5</td>\n      <td>184</td>\n      <td>3</td>\n      <td>5</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>9</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.359663</td>\n      <td>0.640337</td>\n    </tr>\n    <tr>\n      <th>45209</th>\n      <td>57</td>\n      <td>668</td>\n      <td>17</td>\n      <td>508</td>\n      <td>4</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>1</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>9</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.854099</td>\n      <td>0.145901</td>\n    </tr>\n    <tr>\n      <th>45210</th>\n      <td>37</td>\n      <td>2971</td>\n      <td>17</td>\n      <td>361</td>\n      <td>2</td>\n      <td>188</td>\n      <td>11</td>\n      <td>2</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>9</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.466831</td>\n      <td>0.533169</td>\n    </tr>\n  </tbody>\n</table>\n<p>45211 rows × 20 columns</p>\n</div>",
            "text/plain": "       age  balance  day  duration  campaign  pdays  previous  job  marital  \\\n0       58     2143    5       261         1     -1         0    4        1   \n1       44       29    5       151         1     -1         0    9        2   \n2       33        2    5        76         1     -1         0    2        1   \n3       47     1506    5        92         1     -1         0    1        1   \n4       33        1    5       198         1     -1         0   11        2   \n...    ...      ...  ...       ...       ...    ...       ...  ...      ...   \n45206   51      825   17       977         3     -1         0    9        1   \n45207   71     1729   17       456         2     -1         0    5        0   \n45208   72     5715   17      1127         5    184         3    5        1   \n45209   57      668   17       508         4     -1         0    1        1   \n45210   37     2971   17       361         2    188        11    2        1   \n\n       education  default  housing  loan  contact  month  poutcome  y  y_pred  \\\n0              2        0        1     0        2      8         3  0       0   \n1              1        0        1     0        2      8         3  0       0   \n2              1        0        1     1        2      8         3  0       0   \n3              3        0        1     0        2      8         3  0       0   \n4              3        0        0     0        2      8         3  0       0   \n...          ...      ...      ...   ...      ...    ...       ... ..     ...   \n45206          2        0        0     0        0      9         3  1       1   \n45207          0        0        0     0        0      9         3  1       0   \n45208          1        0        0     0        0      9         2  1       1   \n45209          1        0        0     0        1      9         3  0       0   \n45210          1        0        0     0        0      9         1  0       1   \n\n              0         1  \n0      0.880446  0.119554  \n1      0.904805  0.095195  \n2      0.914549  0.085451  \n3      0.920037  0.079963  \n4      0.852561  0.147439  \n...         ...       ...  \n45206  0.404277  0.595723  \n45207  0.808907  0.191093  \n45208  0.359663  0.640337  \n45209  0.854099  0.145901  \n45210  0.466831  0.533169  \n\n[45211 rows x 20 columns]"
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:44.985958Z",
          "start_time": "2023-02-05T10:48:44.971579Z"
        },
        "trusted": false
      },
      "id": "53440b00",
      "cell_type": "code",
      "source": "from sklearn.metrics import confusion_matrix\nconfusion_matrix = confusion_matrix(Y,y_pred)\nprint (confusion_matrix) ",
      "execution_count": 57,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[[38109  1813]\n [ 3551  1738]]\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:46.451743Z",
          "start_time": "2023-02-05T10:48:46.283145Z"
        },
        "trusted": false
      },
      "id": "5bd4f829",
      "cell_type": "code",
      "source": "# Show confusion matrix in a separate window\nimport matplotlib.pyplot as plt\nplt.matshow(confusion_matrix)\nplt.title('Confusion matrix')\nplt.colorbar()\nplt.ylabel('True label')\nplt.xlabel('Predicted label')\nplt.show()",
      "execution_count": 58,
      "outputs": [
        {
          "data": {
            "image/png": 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/RmBgIHbv3o3Y2FiUlZVBq9UCAHJycpCYmIjKykqLroWVHxGRjAg/DXixZhN+GvBSW1trthmNxlt+vslkQk5ODurr6xEZGSnuP3DgAHx8fNCvXz8kJyejsrJSfK+oqAhNTU1mi+xrtVqEhISIi+zn5+dDrVaLiQ8AIiIiro1w/UVMSEiImPgAIDo6GkajEUVFRRZ9j0x+dsJoNCIzM7NDv9xEt8LfJ9uR8p6fv7+/eH9NrVbf9DFnx48fR9euXaFSqfD8889j27ZtCA4OBgDExMRg06ZN2LdvH5YtW4ajR49i1KhR4u+HXq+HUqmEp6en2Tl/uci+Xq+Hj49Pm8/18fExi7l+aUZPT08olUqLF+vnVAc7YTQa8dprr2HWrFlQqVS2bg7JHH+fbMckdIFJsK5uMf10s6usrMysq/Bm/5aBgYEoLi5GTU0NtmzZgmnTpuHgwYMIDg4WuzIBICQkBEOGDEGvXr2wc+dOPP744zc85/WL7Le34P7txHQEKz8iIjvVOnqzdbtZ8lMqlbjvvvswZMgQZGVlYeDAgVi1alW7sX5+fujVqxdOnz4NANBoNGhsbER1dbVZ3C8X2ddoNLhw4UKbc1VVVZnFXF/hVVdXo6mpyeLF+pn8iIhkpAUKtKCLlZv1k9wFQbhht/fFixdRVlYGPz8/AEBYWBicnJzMFtmvqKhASUmJuMh+ZGQkDAYDjhw5IsYcPnwYBoPBLKakpAQVFRViTG5uLlQqFcLCwixqv6y7PVtaWnD+/Hm4u7vz+XS3UFtba/a/RNbg71PHCYKAy5cvQ6vVoksX6+sNWyxsPXfuXMTExMDf3x+XL19GTk4ODhw4AJ1Oh7q6OmRmZuKJJ56An58fzp49i7lz58Lb2xuPPfYYAECtViMpKQnp6eno1q0bvLy8kJGRgdDQUIwZMwYAEBQUhHHjxiE5ORlr164FAEyfPh2xsbEIDAwEAERFRSE4OBgJCQlYsmQJLl26hIyMDCQnJ1s8alXWya91qCx1HL8vkhJ/nzqurKwMPXr0sHUzbsuFCxeQkJCAiooKqNVqDBgwADqdDmPHjkVDQwOOHz+OTz75BDU1NfDz88PIkSOxefNmuLu7i+dYsWIFHB0dMXnyZDQ0NGD06NHIzs6Gg4ODGLNp0yakpqaKo0InTJiA1atXi+87ODhg586dSElJwbBhw+Di4oL4+HgsXbrU4muS9Tw/g8GAe+65Bz980xseXdmDS9J4rF+orZtAnUgzmpCHXaipqYFarb7t87TO89v2r75wc3e49QE3UX/ZhMcGnubC1nLV2tXp0bULPKyc9EnUylHhZOsmUGfyU3kh1a2Za/f8rFzYms/z44AXIiKyP7Ku/IiI7E3LbazN2fYcsr3bJRkmPyIiGZFmkjuTH7s9iYjI7rDyIyKSkdaJ6tadg5Ufkx8RkYyYBAVMgpWT3K08vjNgtycREdkdVn5ERDJyO09ib3sOdnsy+RERyUiL0AUtVo72bOFoTyY/IiI5YeUnDd7zIyIiu8PKj4hIRlpg/WjNFmmaImtMfkREMiLNPD92+vEbICIiu8PKj4hIRqRZ25N1D5MfEZGM8Hl+0mD6JyIiu8PKj4hIRtjtKQ0mPyIiGZFmkjuTH78BIiKyO6z8iIhkpEVQoMXaSe58pBGTHxGRnLRI0O3JSe5MfkREsiLNUx2Y/PgNEBGR3WHlR0QkIyYoYLJykrq1x3cGTH5ERDLCbk9p8BsgIiK7w8qPiEhGTLC+29IkTVNkjcmPiEhG2O0pDX4DRERkd1j5ERHJCBe2lgaTHxGRjAgSPM9P4FQHdnsSEZH9YeVHRCQj7PaUBpMfEZGM8KkO0mD6JyIiu8PKj4hIRvgkd2kw+RERyQi7PaXB5EdEJCMt6GL1w2j5MFve8yMioltYs2YNBgwYAA8PD3h4eCAyMhK7d+8W3xcEAZmZmdBqtXBxccGIESNw4sQJs3MYjUbMnDkT3t7ecHNzw4QJE1BeXm4WU11djYSEBKjVaqjVaiQkJKCmpsYs5ty5c4iLi4Obmxu8vb2RmpqKxsZGi6+JyY+ISEZMgkKSzRI9evTAm2++icLCQhQWFmLUqFGYOHGimOAWL16M5cuXY/Xq1Th69Cg0Gg3Gjh2Ly5cvi+dIS0vDtm3bkJOTg7y8PNTV1SE2NhYm08/LbMfHx6O4uBg6nQ46nQ7FxcVISEj4+dpNJowfPx719fXIy8tDTk4OtmzZgvT0dIu/R4UgCILFR90lamtroVarUf2fPvBwZx4naURrB9m6CdSJNAtNOIAvYTAY4OHhcdvnaf1799zXT0DV1cmqNhnrmrD2t1usapOXlxeWLFmCZ555BlqtFmlpaZgzZ8618xuN8PX1xaJFi/Dcc8/BYDCge/fu2LBhA6ZMmQIAOH/+PPz9/bFr1y5ER0ejtLQUwcHBKCgoQHh4OACgoKAAkZGR+Pe//43AwEDs3r0bsbGxKCsrg1arBQDk5OQgMTERlZWVFl0LMwYRkZ2qra0124xG4y2PMZlMyMnJQX19PSIjI3HmzBno9XpERUWJMSqVCsOHD8ehQ4cAAEVFRWhqajKL0Wq1CAkJEWPy8/OhVqvFxAcAERERUKvVZjEhISFi4gOA6OhoGI1GFBUVWXTtTH5ERDIi/PRII2s24acVXvz9/cX7a2q1GllZWTf83OPHj6Nr165QqVR4/vnnsW3bNgQHB0Ov1wMAfH19zeJ9fX3F9/R6PZRKJTw9PW8a4+Pj0+ZzfXx8zGKu/xxPT08olUoxpqM42pOISEZMUEjwMNtrx5eVlZl1FapUqhseExgYiOLiYtTU1GDLli2YNm0aDh48KL6vUJi3SRCENvuud31Me/G3E9MRrPyIiOxU6+jN1u1myU+pVOK+++7DkCFDkJWVhYEDB2LVqlXQaDQA0KbyqqysFKs0jUaDxsZGVFdX3zTmwoULbT63qqrKLOb6z6murkZTU1ObivBWmPyIiGSkRfh5ovvtb9a3QxAEGI1GBAQEQKPRYM+ePeJ7jY2NOHjwIIYOHQoACAsLg5OTk1lMRUUFSkpKxJjIyEgYDAYcOXJEjDl8+DAMBoNZTElJCSoqKsSY3NxcqFQqhIWFWdR+dnsSEclI6307a89hiblz5yImJgb+/v64fPkycnJycODAAeh0OigUCqSlpWHhwoXo27cv+vbti4ULF8LV1RXx8fEAALVajaSkJKSnp6Nbt27w8vJCRkYGQkNDMWbMGABAUFAQxo0bh+TkZKxduxYAMH36dMTGxiIwMBAAEBUVheDgYCQkJGDJkiW4dOkSMjIykJycbPGoVSY/IiK6qQsXLiAhIQEVFRVQq9UYMGAAdDodxo4dCwCYPXs2GhoakJKSgurqaoSHhyM3Nxfu7u7iOVasWAFHR0dMnjwZDQ0NGD16NLKzs+Hg4CDGbNq0CampqeKo0AkTJmD16tXi+w4ODti5cydSUlIwbNgwuLi4ID4+HkuXLrX4mjjPj+g6nOdHUpJ6nl/C/v+BsqvSqjY11jViw8jPrG6TnLHyIyKSkdtZoaW9c9g7lktERGR3WPkREcmILQa8dEZMfkREMtICCZ7nZ+Uk+c6AyY+ISEYEKKxOXgKTH+/5ERGR/WHlR0QkI62rtFh7DnvH5EdEJCMc8CINfgNERGR3WPkREckIuz2lweRHRCQjLRKM9uRUB3Z7EhGRHWLlR0QkI+z2lAaTHxGRjDD5SYPdnkREZHdY+RERyQgrP2kw+RERyQiTnzTY7UlERHbH5snv3XffRUBAAJydnREWFoZ//OMftm4SEdFdS8DPc/1udxNsfRF3AZsmv82bNyMtLQ3z5s3DsWPH8MgjjyAmJgbnzp2zZbOIiO5ard2e1m72zqbJb/ny5UhKSsKzzz6LoKAgrFy5Ev7+/lizZo0tm0VEdNdi8pOGzZJfY2MjioqKEBUVZbY/KioKhw4davcYo9GI2tpas42IiMhSNkt+P/74I0wmE3x9fc32+/r6Qq/Xt3tMVlYW1Gq1uPn7+/8aTSUiumuw8pOGzQe8KBTm/wiCILTZ1+rll1+GwWAQt7Kysl+jiUREdw0mP2nYbJ6ft7c3HBwc2lR5lZWVbarBViqVCiqV6tdoHhERdWI2q/yUSiXCwsKwZ88es/179uzB0KFDbdQqIqK7myAoJNnsnU1XeJk1axYSEhIwZMgQREZG4v3338e5c+fw/PPP27JZRER3LT7PTxo2TX5TpkzBxYsX8Ze//AUVFRUICQnBrl270KtXL1s2i4iIOjmbr+2ZkpKClJQUWzeDiEgWuLanNGye/IiIqOOkuGfHe353wVQHIiKiXxsrPyIiGWG3pzSY/IiIZITdntJgtycREdkdVn5ERDIiSNDtycqPyY+ISFYEAIKVT6Plw2yZ/IiIZKUFCii4wovVeM+PiIjsDis/IiIZ4WhPaTD5ERHJSIuggILz/KzGbk8iIrqprKwsPPjgg3B3d4ePjw8mTZqEU6dOmcUkJiZCoVCYbREREWYxRqMRM2fOhLe3N9zc3DBhwgSUl5ebxVRXVyMhIQFqtRpqtRoJCQmoqakxizl37hzi4uLg5uYGb29vpKamorGx0aJrYvIjIpIRQZBms8TBgwcxY8YMFBQUYM+ePWhubkZUVBTq6+vN4saNG4eKigpx27Vrl9n7aWlp2LZtG3JycpCXl4e6ujrExsbCZDKJMfHx8SguLoZOp4NOp0NxcTESEhLE900mE8aPH4/6+nrk5eUhJycHW7ZsQXp6ukXXxG5PIiIZscU9P51OZ/b6448/ho+PD4qKivDb3/5W3K9SqaDRaNo9h8FgwIcffogNGzZgzJgxAICNGzfC398fe/fuRXR0NEpLS6HT6VBQUIDw8HAAwLp16xAZGYlTp04hMDAQubm5OHnyJMrKyqDVagEAy5YtQ2JiIhYsWAAPD48OXRMrPyIiO1VbW2u2GY3GDh1nMBgAAF5eXmb7Dxw4AB8fH/Tr1w/JycmorKwU3ysqKkJTUxOioqLEfVqtFiEhITh06BAAID8/H2q1Wkx8ABAREQG1Wm0WExISIiY+AIiOjobRaERRUVGHr53Jj4hIRlorP2s3APD39xfvranVamRlZXXg8wXMmjULDz/8MEJCQsT9MTEx2LRpE/bt24dly5bh6NGjGDVqlJhQ9Xo9lEolPD09zc7n6+sLvV4vxvj4+LT5TB8fH7MYX19fs/c9PT2hVCrFmI5gtycRkYxIOdqzrKzMrJtQpVLd8tgXX3wR3377LfLy8sz2T5kyRfw5JCQEQ4YMQa9evbBz5048/vjjNzyfIAhQKH6+nl/+bE3MrbDyIyKyUx4eHmbbrZLfzJkzsWPHDuzfvx89evS4aayfnx969eqF06dPAwA0Gg0aGxtRXV1tFldZWSlWchqNBhcuXGhzrqqqKrOY6yu86upqNDU1takIb4bJj4hIRmwx2lMQBLz44ovYunUr9u3bh4CAgFsec/HiRZSVlcHPzw8AEBYWBicnJ+zZs0eMqaioQElJCYYOHQoAiIyMhMFgwJEjR8SYw4cPw2AwmMWUlJSgoqJCjMnNzYVKpUJYWFiHr4ndnkREMnIteVk72tOy+BkzZuDTTz/Fl19+CXd3d7HyUqvVcHFxQV1dHTIzM/HEE0/Az88PZ8+exdy5c+Ht7Y3HHntMjE1KSkJ6ejq6desGLy8vZGRkIDQ0VBz9GRQUhHHjxiE5ORlr164FAEyfPh2xsbEIDAwEAERFRSE4OBgJCQlYsmQJLl26hIyMDCQnJ3d4pCfAyo+IiG5hzZo1MBgMGDFiBPz8/MRt8+bNAAAHBwccP34cEydORL9+/TBt2jT069cP+fn5cHd3F8+zYsUKTJo0CZMnT8awYcPg6uqKr776Cg4ODmLMpk2bEBoaiqioKERFRWHAgAHYsGGD+L6DgwN27twJZ2dnDBs2DJMnT8akSZOwdOlSi65JIQjWPhzDdmpra6FWq1H9nz7wcGceJ2lEawfZugnUiTQLTTiAL2EwGCyqTK7X+vfuvg0vw8HV2ao2ma5cxXcJWVa3Sc7Y7UlEJCMCrH8en2wrHgkx+RERyQif6iAN9hUSEZHdYeVHRCQn7PeUBJMfEZGcSNDtCXZ7stuTiIjsDys/IiIZuZ0VWto7h71j8iMikhGO9pQGuz2JiMjusPIjIpITQWH9gBVWfkx+RERywnt+0mC3JxER2R1WfkREcsJJ7pJg8iMikhGO9pRGh5LfW2+91eETpqam3nZjiIioA1i5Wa1DyW/FihUdOplCoWDyIyKiu16Hkt+ZM2fudDuIiKgD2O0pjdse7dnY2IhTp06hublZyvYQEdHNCBJtds7i5HflyhUkJSXB1dUV/fv3x7lz5wBcu9f35ptvSt5AIiIiqVmc/F5++WX861//woEDB+Ds7CzuHzNmDDZv3ixp44iI6HoKiTb7ZvFUh+3bt2Pz5s2IiIiAQvHzFxgcHIz//ve/kjaOiIiuw3l+krC48quqqoKPj0+b/fX19WbJkIiI6G5lcfJ78MEHsXPnTvF1a8Jbt24dIiMjpWsZERG1xQEvkrC42zMrKwvjxo3DyZMn0dzcjFWrVuHEiRPIz8/HwYMH70QbiYioFZ/qIAmLK7+hQ4fin//8J65cuYJ7770Xubm58PX1RX5+PsLCwu5EG4mIiCR1W2t7hoaGYv369VK3hYiIboGPNJLGbSU/k8mEbdu2obS0FAqFAkFBQZg4cSIcHblONhHRHcXRnpKwOFuVlJRg4sSJ0Ov1CAwMBAD85z//Qffu3bFjxw6EhoZK3kgiIiIpWXzP79lnn0X//v1RXl6Ob775Bt988w3KysowYMAATJ8+/U60kYiIWrUOeLF2s3MWV37/+te/UFhYCE9PT3Gfp6cnFixYgAcffFDSxhERkTmFcG2z9hz2zuLKLzAwEBcuXGizv7KyEvfdd58kjSIiohvgPD9JdCj51dbWitvChQuRmpqKL774AuXl5SgvL8cXX3yBtLQ0LFq06E63l4iIyGod6va85557zJYuEwQBkydPFvcJP42bjYuLg8lkugPNJCIiAJzkLpEOJb/9+/ff6XYQEVFHcKqDJDqU/IYPH36n20FERPSrue1Z6VeuXMG5c+fQ2Nhotn/AgAFWN4qIiG6AlZ8kLE5+VVVVePrpp7F79+523+c9PyKiO4jJTxIWT3VIS0tDdXU1CgoK4OLiAp1Oh/Xr16Nv377YsWPHnWgjERGRpCyu/Pbt24cvv/wSDz74ILp06YJevXph7Nix8PDwQFZWFsaPH38n2klERABHe0rE4sqvvr5efJK7l5cXqqqqAFx70sM333wjbeuIiMhM6wov1m727rZWeDl16hQAYNCgQVi7di3+3//7f3jvvffg5+cneQOJiIikdlv3/CoqKgAA8+fPh06nQ8+ePfHWW29h4cKFkjeQiIh+wQbLm2VlZeHBBx+Eu7s7fHx8MGnSJLEIEpslCMjMzIRWq4WLiwtGjBiBEydOmMUYjUbMnDkT3t7ecHNzw4QJE1BeXm4WU11djYSEBKjVaqjVaiQkJKCmpsYs5ty5c4iLi4Obmxu8vb2RmpraZubBrVic/J566ikkJiYCAAYPHoyzZ8/i6NGjKCsrw5QpUyw9HRER3eUOHjyIGTNmoKCgAHv27EFzczOioqJQX18vxixevBjLly/H6tWrcfToUWg0GowdOxaXL18WY9LS0rBt2zbk5OQgLy8PdXV1iI2NNZslEB8fj+LiYuh0Ouh0OhQXFyMhIUF832QyYfz48aivr0deXh5ycnKwZcsWpKenW3RNCkGQ7zN9a2troVarUf2fPvBwtziPE7UrWjvI1k2gTqRZaMIBfAmDwQAPD4/bPk/r37uei95AFxdnq9rU0nAV5+b8L8rKyszapFKpoFKpbnl8VVUVfHx8cPDgQfz2t7+FIAjQarVIS0vDnDlzAFyr8nx9fbFo0SI899xzMBgM6N69OzZs2CAWSufPn4e/vz927dqF6OholJaWIjg4GAUFBQgPDwcAFBQUIDIyEv/+978RGBiI3bt3IzY2FmVlZdBqtQCAnJwcJCYmorKyssPfcYdGe86aNatDJwOA5cuXdziWiIgso4AEjzT66X/9/f3N9s+fPx+ZmZm3PN5gMAC4NugRAM6cOQO9Xo+oqCgxRqVSYfjw4Th06BCee+45FBUVoampySxGq9UiJCQEhw4dQnR0NPLz86FWq8XEBwARERFQq9U4dOgQAgMDkZ+fj5CQEDHxAUB0dDSMRiOKioowcuTIDn0HHUp+x44d69DJfrn49a/pd+GPwFGhtMlnU2dUbesGEN2YhFMd2qv8bnmoIGDWrFl4+OGHERISAgDQ6/UAAF9fX7NYX19f/PDDD2KMUqk0exZsa0zr8Xq9XpxN8Es+Pj5mMdd/jqenJ5RKpRjTEVzYmohITiRc4cXDw8PirtgXX3wR3377LfLy8tq8d30BJAjCLYui62Pai7+dmFvhjTIiIuqQmTNnYseOHdi/fz969Ogh7tdoNADQpvKqrKwUqzSNRoPGxkZUV1ffNKa9h6VXVVWZxVz/OdXV1WhqampTEd4Mkx8RkZzYYKqDIAh48cUXsXXrVuzbtw8BAQFm7wcEBECj0WDPnj3ivsbGRhw8eBBDhw4FAISFhcHJyckspqKiAiUlJWJMZGQkDAYDjhw5IsYcPnwYBoPBLKakpESccgcAubm5UKlUCAsL6/A13fZTHYiI6NcnxQotlh4/Y8YMfPrpp/jyyy/h7u4uVl5qtRouLi5QKBRIS0vDwoUL0bdvX/Tt2xcLFy6Eq6sr4uPjxdikpCSkp6ejW7du8PLyQkZGBkJDQzFmzBgAQFBQEMaNG4fk5GSsXbsWADB9+nTExsYiMDAQABAVFYXg4GAkJCRgyZIluHTpEjIyMpCcnGxRFy6THxER3dSaNWsAACNGjDDb//HHH4vzvmfPno2GhgakpKSguroa4eHhyM3Nhbu7uxi/YsUKODo6YvLkyWhoaMDo0aORnZ0NBwcHMWbTpk1ITU0VR4VOmDABq1evFt93cHDAzp07kZKSgmHDhsHFxQXx8fFYunSpRdfUKeb5jfacxtGeJBlTNUd7knSknufX+40F6OJs5Ty/q1dx9n/nWd0mObute34bNmzAsGHDoNVqxWGsK1euxJdffilp44iI6Do2uOfXGVmc/NasWYNZs2bh0UcfRU1NjbgszT333IOVK1dK3T4iIiLJWZz83n77baxbtw7z5s0z66cdMmQIjh8/LmnjiIjIHB9pJA2LB7ycOXMGgwcPbrNfpVKZLXJKRER3AB9mKwmLK7+AgAAUFxe32b97924EBwdL0SYiIqI7yuLK76WXXsKMGTNw9epVCIKAI0eO4LPPPkNWVhY++OCDO9FGIiJqJeHyZvbM4uT39NNPo7m5GbNnz8aVK1cQHx+P3/zmN1i1ahWmTp16J9pIREQ/scUk987otia5JycnIzk5GT/++CNaWlraXYWbiIjobmXVCi/e3t5StYOIiDqC3Z6SsDj5BQQE3PSxEd9//71VDSIiopuQYqoCk5/lyS8tLc3sdVNTE44dOwadToeXXnpJqnYREVF7WPlJwuLk98c//rHd/e+88w4KCwutbhAREdGdJtnz/GJiYrBlyxapTkdERO3h2p6SkOyRRl988QW8vLykOh0REbWDUx2kYXHyGzx4sNmAF0EQoNfrUVVVhXfffVfSxhEREd0JFie/SZMmmb3u0qULunfvjhEjRuD++++Xql1ERER3jEXJr7m5Gb1790Z0dDQ0Gs2dahMREd0IR3tKwqIBL46OjnjhhRdgNBrvVHuIiIjuOItHe4aHh+PYsWN3oi1ERHQLfJ6fNCy+55eSkoL09HSUl5cjLCwMbm5uZu8PGDBAssYREVE7mLys1uHk98wzz2DlypWYMmUKACA1NVV8T6FQQBAEKBQKmEwm6VtJREQkoQ4nv/Xr1+PNN9/EmTNn7mR7iIjoZjjgRRIdTn6CcO3b6tWr1x1rDBER3RwnuUvDogEvN3uaAxERkVxYNOClX79+t0yAly5dsqpBRER0E+z2lIRFye+1116DWq2+U20hIqJbYLenNCxKflOnToWPj8+dagsREd0KKz9JdPieH+/3ERFRZ2HxaE8iIrIhVn6S6HDya2lpuZPtICKiDuA9P2lI9iR3IiIiuZDsSe5ERPQrYLenJJj8iIjkhMlPEuz2JCIiu8PKj4hIRjjgRRpMfkREcsJuT0mw25OIiOwOKz8iIhlht6c0mPyIiOSE3Z6SYLcnERHZHSY/IiI5ESTaLPD1118jLi4OWq0WCoUC27dvN3s/MTERCoXCbIuIiDCLMRqNmDlzJry9veHm5oYJEyagvLzcLKa6uhoJCQlQq9VQq9VISEhATU2NWcy5c+cQFxcHNzc3eHt7IzU1FY2NjZZdEJj8iIhkRSHRZon6+noMHDgQq1evvmHMuHHjUFFRIW67du0yez8tLQ3btm1DTk4O8vLyUFdXh9jYWJhMJjEmPj4excXF0Ol00Ol0KC4uRkJCgvi+yWTC+PHjUV9fj7y8POTk5GDLli1IT0+38Ip4z4+ISF4kvOdXW1trtlulUkGlUrUJj4mJQUxMzE1PqVKpoNFo2n3PYDDgww8/xIYNGzBmzBgAwMaNG+Hv74+9e/ciOjoapaWl0Ol0KCgoQHh4OABg3bp1iIyMxKlTpxAYGIjc3FycPHkSZWVl0Gq1AIBly5YhMTERCxYsgIeHR4e/AlZ+RER2yt/fX+xiVKvVyMrKuu1zHThwAD4+PujXrx+Sk5NRWVkpvldUVISmpiZERUWJ+7RaLUJCQnDo0CEAQH5+PtRqtZj4ACAiIgJqtdosJiQkREx8ABAdHQ2j0YiioiKL2svKj4hIRqSc6lBWVmZWLbVX9XVETEwMnnzySfTq1QtnzpzBK6+8glGjRqGoqAgqlQp6vR5KpRKenp5mx/n6+kKv1wMA9Ho9fHx82pzbx8fHLMbX19fsfU9PTyiVSjGmo5j8iIjkRMJuTw8PD4u6Cm9kypQp4s8hISEYMmQIevXqhZ07d+Lxxx+/cTMEAQrFz3cgf/mzNTEdwW5PIiKSlJ+fH3r16oXTp08DADQaDRobG1FdXW0WV1lZKVZyGo0GFy5caHOuqqoqs5jrK7zq6mo0NTW1qQhvhcmPiEhufsVpDrfj4sWLKCsrg5+fHwAgLCwMTk5O2LNnjxhTUVGBkpISDB06FAAQGRkJg8GAI0eOiDGHDx+GwWAwiykpKUFFRYUYk5ubC5VKhbCwMIvayG5PIiIZscXyZnV1dfjuu+/E12fOnEFxcTG8vLzg5eWFzMxMPPHEE/Dz88PZs2cxd+5ceHt747HHHgMAqNVqJCUlIT09Hd26dYOXlxcyMjIQGhoqjv4MCgrCuHHjkJycjLVr1wIApk+fjtjYWAQGBgIAoqKiEBwcjISEBCxZsgSXLl1CRkYGkpOTLe6+ZfIjIqKbKiwsxMiRI8XXs2bNAgBMmzYNa9aswfHjx/HJJ5+gpqYGfn5+GDlyJDZv3gx3d3fxmBUrVsDR0RGTJ09GQ0MDRo8ejezsbDg4OIgxmzZtQmpqqjgqdMKECWZzCx0cHLBz506kpKRg2LBhcHFxQXx8PJYuXWrxNSkEQZDtKm+1tbVQq9UY7TkNjgqlrZtDnYTpuvsSRNZoFppwAF/CYDBYNbik9e9dSPJCOCidrWqTqfEqStbNtbpNcsbKj4hIRvhUB2lwwAsREdkdVn5ERHLCRxpJgsmPiEhG2O0pDSY/IiI5YeUnCd7zIyIiu8PKj4hITlj5SYLJj4hIRnjPTxrs9iQiIrvDyo+ISE7Y7SkJJj8iIhlRCAIUVq5Kae3xnQG7PYmIyO6w8iMikhN2e0rCppXf119/jbi4OGi1WigUCmzfvt2WzSEiuuu1jva0drN3Nk1+9fX1GDhwoNnzmoiIiO40m3Z7xsTEICYmpsPxRqMRRqNRfF1bW3snmkVEdPdit6ckZDXgJSsrC2q1Wtz8/f1t3SQiol8Vuz2lIavk9/LLL8NgMIhbWVmZrZtEREQyJKvRniqVCiqVytbNICKyHXZ7SkJWyY+IyN5xbU9pMPkREckJKz9J2DT51dXV4bvvvhNfnzlzBsXFxfDy8kLPnj1t2DIiIurMbJr8CgsLMXLkSPH1rFmzAADTpk1Ddna2jVpFRHR3Y7el9Wya/EaMGAGBC6wSEXWcIFzbrD2HnZPVVAciIiIpcMALEZGMcLSnNJj8iIjkhKM9JcFuTyIisjus/IiIZETRcm2z9hz2jsmPiEhO2O0pCXZ7EhGR3WHlR0QkIxztKQ0mPyIiOeEkd0mw25OIiOwOKz8iIhlht6c0mPyIiOSEoz0lweRHRCQjrPykwXt+RERkd1j5ERHJCUd7SoLJj4hIRtjtKQ12exIR0U19/fXXiIuLg1arhUKhwPbt283eFwQBmZmZ0Gq1cHFxwYgRI3DixAmzGKPRiJkzZ8Lb2xtubm6YMGECysvLzWKqq6uRkJAAtVoNtVqNhIQE1NTUmMWcO3cOcXFxcHNzg7e3N1JTU9HY2GjxNTH5ERHJiSDRZoH6+noMHDgQq1evbvf9xYsXY/ny5Vi9ejWOHj0KjUaDsWPH4vLly2JMWloatm3bhpycHOTl5aGurg6xsbEwmUxiTHx8PIqLi6HT6aDT6VBcXIyEhATxfZPJhPHjx6O+vh55eXnIycnBli1bkJ6ebtkFgd2eRESyImW3Z21trdl+lUoFlUrVJj4mJgYxMTHtnksQBKxcuRLz5s3D448/DgBYv349fH198emnn+K5556DwWDAhx9+iA0bNmDMmDEAgI0bN8Lf3x979+5FdHQ0SktLodPpUFBQgPDwcADAunXrEBkZiVOnTiEwMBC5ubk4efIkysrKoNVqAQDLli1DYmIiFixYAA8Pjw5/B6z8iIjslL+/v9jFqFarkZWVZfE5zpw5A71ej6ioKHGfSqXC8OHDcejQIQBAUVERmpqazGK0Wi1CQkLEmPz8fKjVajHxAUBERATUarVZTEhIiJj4ACA6OhpGoxFFRUUWtZuVHxGRnLQI1zZrzwGgrKzMrFpqr+q7Fb1eDwDw9fU12+/r64sffvhBjFEqlfD09GwT03q8Xq+Hj49Pm/P7+PiYxVz/OZ6enlAqlWJMRzH5ERHJiYQrvHh4eFjUVXgzCoXC/CMEoc2+Ns24Lqa9+NuJ6Qh2exIR0W3TaDQA0KbyqqysFKs0jUaDxsZGVFdX3zTmwoULbc5fVVVlFnP951RXV6OpqalNRXgrTH5ERDKiwM+DXm57k7A9AQEB0Gg02LNnj7ivsbERBw8exNChQwEAYWFhcHJyMoupqKhASUmJGBMZGQmDwYAjR46IMYcPH4bBYDCLKSkpQUVFhRiTm5sLlUqFsLAwi9rNbk8iIjmxwQovdXV1+O6778TXZ86cQXFxMby8vNCzZ0+kpaVh4cKF6Nu3L/r27YuFCxfC1dUV8fHxAAC1Wo2kpCSkp6ejW7du8PLyQkZGBkJDQ8XRn0FBQRg3bhySk5Oxdu1aAMD06dMRGxuLwMBAAEBUVBSCg4ORkJCAJUuW4NKlS8jIyEBycrLF3bdMfkREdFOFhYUYOXKk+HrWrFkAgGnTpiE7OxuzZ89GQ0MDUlJSUF1djfDwcOTm5sLd3V08ZsWKFXB0dMTkyZPR0NCA0aNHIzs7Gw4ODmLMpk2bkJqaKo4KnTBhgtncQgcHB+zcuRMpKSkYNmwYXFxcEB8fj6VLl1p8TQpBkO8ib7W1tVCr1RjtOQ2OCqWtm0OdhOm6+xJE1mgWmnAAX8JgMFg1uKT1793DozLh6OhsXZuaryJvX6bVbZIzVn5ERHLC5/lJgsmPiEhGFIIAhZUddtYe3xlwtCcREdkdVn5ERHLS8tNm7TnsHJMfEZGMsNtTGuz2JCIiu8PKj4hITjjaUxJMfkREcmKDFV46I3Z7EhGR3WHlR0QkI1I+yd2eMfkREckJuz0lwW5PIiKyO6z8iIhkRNFybbP2HPaOyY+ISE7Y7SkJdnsSEZHdYeVHRCQnnOQuCSY/IiIZ4dqe0mDyIyKSE97zkwTv+RERkd1h5UdEJCcCrH8eHws/Jj8iIjnhPT9psNuTiIjsDis/IiI5ESDBgBdJWiJrTH5ERHLC0Z6SYLcnERHZHVZ+RERy0gJAIcE57ByTHxGRjHC0pzTY7UlERHaHlR8RkZxwwIskmPyIiOSEyU8S7PYkIiK7w8qPiEhOWPlJgsmPiEhOONVBEkx+REQywqkO0uA9PyIisjus/IiI5IT3/CTB5EdEJCctAqCwMnm1MPmx25OIiOwOKz8iIjlht6ckZJ38hJ/+AZuFRhu3hDoTk9Bk6yZQJ9KMa79PgmQJR4Lkx6fZyjv5Xb58GQBwsOYzG7eEiOjmLl++DLVabetm3JbMzEy89tprZvt8fX2h1+sBXEvsr732Gt5//31UV1cjPDwc77zzDvr37y/GG41GZGRk4LPPPkNDQwNGjx6Nd999Fz169BBjqqurkZqaih07dgAAJkyYgLfffhv33HOP5Nck6+Sn1WpRVlYGd3d3KBTWzvrs3Gpra+Hv74+ysjJ4eHjYujkkc/x96jhBEHD58mVotVqpTmiTbs/+/ftj79694msHBwfx58WLF2P58uXIzs5Gv3798MYbb2Ds2LE4deoU3N3dAQBpaWn46quvkJOTg27duiE9PR2xsbEoKioSzxUfH4/y8nLodDoAwPTp05GQkICvvvrKmqttl6yTX5cuXcz+XwPdmoeHB/9YkWT4+9QxklZ8LQKs7ra8jdGejo6O0Gg0bfYLgoCVK1di3rx5ePzxxwEA69evh6+vLz799FM899xzMBgM+PDDD7FhwwaMGTMGALBx40b4+/tj7969iI6ORmlpKXQ6HQoKChAeHg4AWLduHSIjI3Hq1CkEBgZaccFtcbQnEZGdqq2tNduMRuMNY0+fPg2tVouAgABMnToV33//PQDgzJkz0Ov1iIqKEmNVKhWGDx+OQ4cOAQCKiorQ1NRkFqPVahESEiLG5OfnQ61Wi4kPACIiIqBWq8UYKTH5ERHJidAizQbA398farVa3LKystr9yPDwcHzyySf429/+hnXr1kGv12Po0KG4ePGieN/P19fX7Jhf3hPU6/VQKpXw9PS8aYyPj0+bz/bx8RFjpCTrbk/qOJVKhfnz50OlUtm6KdQJ8PfJhiS853f9Pdsb/XvGxMSIP4eGhiIyMhL33nsv1q9fj4iICABoM+5CEIRbjsW4Pqa9+I6c53aw8rMTKpUKmZmZ/GNFkuDvkw21CNJs+PmebevW0X9PNzc3hIaG4vTp0+J9wOurs8rKSrEa1Gg0aGxsRHV19U1jLly40Oazqqqq2lSVUmDyIyIiixiNRpSWlsLPzw8BAQHQaDTYs2eP+H5jYyMOHjyIoUOHAgDCwsLg5ORkFlNRUYGSkhIxJjIyEgaDAUeOHBFjDh8+DIPBIMZIid2eRERyYoOpDhkZGYiLi0PPnj1RWVmJN954A7W1tZg2bRoUCgXS0tKwcOFC9O3bF3379sXChQvh6uqK+Ph4ANdGuyYlJSE9PR3dunWDl5cXMjIyEBoaKo7+DAoKwrhx45CcnIy1a9cCuDbVITY2VvKRngCTHxGRvAiQIPlZFl5eXo7/+Z//wY8//oju3bsjIiICBQUF6NWrFwBg9uzZaGhoQEpKijjJPTc3V5zjBwArVqyAo6MjJk+eLE5yz87ONpsvuGnTJqSmpoqjQidMmIDVq1dbd603oBCkW3OHiIjukNraWqjVaozxew6OXZRWnau5pRF7K9bCYDDY7TxN3vMj2cnMzMSgQYPE14mJiZg0adKv3o6zZ89CoVCguLj4hjG9e/fGypUrO3zO7OxsSZZyUigU2L59u9XnobtQa7entZudY/IjSSQmJkKhUEChUMDJyQl9+vRBRkYG6uvr7/hnr1q1CtnZ2R2K7UjCIrqrtbRIs9k53vMjyYwbNw4ff/wxmpqa8I9//APPPvss6uvrsWbNmjaxTU1NcHJykuRz5bpYMBHZDis/koxKpYJGo4G/vz/i4+Px1FNPiV1vrV2VH330Efr06QOVSgVBEGAwGDB9+nT4+PjAw8MDo0aNwr/+9S+z87755pvw9fWFu7s7kpKScPXqVbP3r+/2bGlpwaJFi3DfffdBpVKhZ8+eWLBgAQAgICAAADB48GAoFAqMGDFCPO7jjz9GUFAQnJ2dcf/99+Pdd981+5wjR45g8ODBcHZ2xpAhQ3Ds2DGLv6Ply5cjNDQUbm5u8Pf3R0pKCurq6trEbd++Hf369YOzszPGjh2LsrIys/e/+uorhIWFwdnZGX369MFrr72G5uZmi9tDMsRuT0kw+dEd4+Ligqamn5+N99133+Hzzz/Hli1bxG7H8ePHQ6/XY9euXSgqKsIDDzyA0aNH49KlSwCAzz//HPPnz8eCBQtQWFgIPz+/Nknpei+//DIWLVqEV155BSdPnsSnn34qTpJtnUO0d+9eVFRUYOvWrQCuLaA7b948LFiwAKWlpVi4cCFeeeUVrF+/HgBQX18vDrkuKipCZmYmMjIyLP5OunTpgrfeegslJSVYv3499u3bh9mzZ5vFXLlyBQsWLMD69evxz3/+E7W1tZg6dar4/t/+9jf8/ve/R2pqKk6ePIm1a9ciOztbTPDUyTH5SYLdnnRHHDlyBJ9++ilGjx4t7mtsbMSGDRvQvXt3AMC+fftw/PhxVFZWiitLLF26FNu3b8cXX3yB6dOnY+XKlXjmmWfw7LPPAgDeeOMN7N27t0311+ry5ctYtWoVVq9ejWnTpgEA7r33Xjz88MMAIH52t27dzFaof/3117Fs2TJxVfqAgAAxsUybNg2bNm2CyWTCRx99BFdXV/Tv3x/l5eV44YUXLPpe0tLSxJ8DAgLw+uuv44UXXjBL6E1NTVi9erW4wO/69esRFBSEI0eO4KGHHsKCBQvw5z//Wby+Pn364PXXX8fs2bMxf/58i9pDZK+Y/Egyf/3rX9G1a1c0NzejqakJEydOxNtvvy2+36tXLzH5ANdWeq+rq0O3bt3MztPQ0ID//ve/AIDS0lI8//zzZu9HRkZi//797bahtLQURqPRLOneSlVVFcrKypCUlITk5GRxf3Nzs3g/sbS0FAMHDoSrq6tZOyy1f/9+LFy4ECdPnkRtbS2am5tx9epV1NfXw83NDcC1R8cMGTJEPOb+++/HPffcg9LSUjz00EMoKirC0aNHzSo9k8mEq1ev4sqVK2ZtpE7IRo806myY/EgyI0eOxJo1a+Dk5AStVttmQEvrH/dWLS0t8PPzw4EDB9qc63aH+7u4uFh8TMtPI9/WrVtn9jgV4OcHdkoxHfaHH37Ao48+iueffx6vv/46vLy8kJeXh6SkJLPuYaD9BX5b97W0tOC1114Tq9RfcnZ2trqddHcThBYIgnWjNa09vjNg8iPJuLm54b777utw/AMPPAC9Xg9HR0f07t273ZigoCAUFBTgD3/4g7ivoKDghufs27cvXFxc8Pe//13sKv0lpfLa5GCTySTu8/X1xW9+8xt8//33eOqpp9o9b3BwMDZs2ICGhgYxwd6sHe0pLCxEc3Mzli1bhi5drt1u//zzz9vENTc3o7CwEA899BAA4NSpU6ipqcH9998P4Nr3durUKYu+ayIyx+RHNjNmzBhERkZi0qRJWLRoEQIDA3H+/Hns2rULkyZNwpAhQ/DHP/4R06ZNw5AhQ/Dwww9j06ZNOHHiBPr06dPuOZ2dnTFnzhzMnj0bSqUSw4YNQ1VVFU6cOIGkpCT4+PjAxcUFOp0OPXr0gLOzM9RqNTIzM5GamgoPDw/ExMTAaDSisLAQ1dXVmDVrFuLj4zFv3jwkJSXhf//3f3H27FksXbrUouu999570dzcjLfffhtxcXH45z//iffee69NnJOTE2bOnIm33noLTk5OePHFFxERESEmw1dffRWxsbHw9/fHk08+iS5duuDbb7/F8ePH8cYbb1j+D0HyIgjWd1tywAtHe5LtKBQK7Nq1C7/97W/xzDPPoF+/fpg6dSrOnj0rjs6cMmUKXn31VcyZMwdhYWH44YcfbjnI5JVXXkF6ejpeffVVBAUFYcqUKaisrARw7X7aW2+9hbVr10Kr1WLixIkAgGeffRYffPABsrOzERoaiuHDhyM7O1ucGtG1a1d89dVXOHnyJAYPHox58+Zh0aJFFl3voEGDsHz5cixatAghISHYtGlTuw8PdXV1xZw5cxAfH4/IyEi4uLggJydHfD86Ohp//etfsWfPHjz44IOIiIjA8uXLxXUWqZPjaE9JcG1PIiIZaF3bc7T7U3BUWLm2p9CIv1/exLU9iYiI7Anv+RERyYkgwVQHdvgx+RERyYnQ0gJBwakO1mK3JxER2R1WfkREcsJuT0kw+RERyUmLACiY/KzFbk8iIrI7rPyIiOREEABYOWCFlR+THxGRnAgtAgQruz25tgm7PYmIyA6x8iMikhOhBdZ3e3KeH5MfEZGMsNtTGuz2JCIiu8PKj4hIRpoFo9Xdls1okqg18sXkR0QkA0qlEhqNBnn6XZKcT6PRQKm07tFIcsbn+RERycTVq1fR2NgoybmUSiWcnZ0lOZccMfkREZHd4YAXIiKyO0x+RERkd5j8iIjI7jD5ERGR3WHyIyIiu8PkR0REdofJj4iI7M7/B4pvOEVbW1WvAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 480x480 with 2 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:49.989837Z",
          "start_time": "2023-02-05T10:48:49.836331Z"
        },
        "trusted": false
      },
      "id": "1bcd8122",
      "cell_type": "code",
      "source": "# to represent the confusion matrix using heat map\ndf_cm=pd.DataFrame(confusion_matrix,index=[i for i in ['0','1']],\n                   columns=[i for i in ['predict 0','predict 1']])\nplt.figure(figsize=(7,5))\nsns.heatmap(df_cm,annot=True\n           )",
      "execution_count": 59,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 700x500 with 2 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:51.394886Z",
          "start_time": "2023-02-05T10:48:51.382183Z"
        },
        "trusted": false
      },
      "id": "bf7bda65",
      "cell_type": "code",
      "source": "ds_new.y.value_counts()",
      "execution_count": 60,
      "outputs": [
        {
          "data": {
            "text/plain": "0    39922\n1     5289\nName: y, dtype: int64"
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:52.492909Z",
          "start_time": "2023-02-05T10:48:52.470220Z"
        },
        "trusted": false
      },
      "id": "989dae22",
      "cell_type": "code",
      "source": "# using cross tab metho d to represent confusion matrix\npd.crosstab(Y,y_pred)",
      "execution_count": 61,
      "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>col_0</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>38109</td>\n      <td>1813</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3551</td>\n      <td>1738</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "col_0      0     1\ny                 \n0      38109  1813\n1       3551  1738"
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:53.416348Z",
          "start_time": "2023-02-05T10:48:53.404793Z"
        },
        "trusted": false
      },
      "id": "7d9e2058",
      "cell_type": "code",
      "source": "# model fitted accurecy\naccuracy = sum(Y==y_pred)/ds_new.shape[0]\naccuracy ",
      "execution_count": 62,
      "outputs": [
        {
          "data": {
            "text/plain": "0.8813563070934065"
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:54.419969Z",
          "start_time": "2023-02-05T10:48:54.363586Z"
        },
        "trusted": false
      },
      "id": "98b939a9",
      "cell_type": "code",
      "source": "# model summary\nfrom sklearn.metrics import classification_report \nprint (classification_report (Y, y_pred))  ",
      "execution_count": 63,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n           0       0.91      0.95      0.93     39922\n           1       0.49      0.33      0.39      5289\n\n    accuracy                           0.88     45211\n   macro avg       0.70      0.64      0.66     45211\nweighted avg       0.87      0.88      0.87     45211\n\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:55.388865Z",
          "start_time": "2023-02-05T10:48:55.351229Z"
        },
        "trusted": false
      },
      "id": "3adb29e9",
      "cell_type": "code",
      "source": "# find the roc score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import roc_curve\nLogit_roc_score=roc_auc_score(Y,bank.predict(X))\nLogit_roc_score  ",
      "execution_count": 64,
      "outputs": [
        {
          "data": {
            "text/plain": "0.6415964927221619"
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:56.429758Z",
          "start_time": "2023-02-05T10:48:56.290139Z"
        },
        "trusted": false
      },
      "id": "cecb38d7",
      "cell_type": "code",
      "source": "#  visualizing the ROC curve\nfpr, tpr, thresholds = roc_curve(Y,bank.predict_proba(X)[:,1]) \nplt.plot(fpr, tpr, label='Logistic Regression (area=%0.2f)'% Logit_roc_score)\nplt.plot([0, 1], [0, 1],'r--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic')\nplt.legend(loc=\"lower right\")\nplt.show()   ",
      "execution_count": 65,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 640x480 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:57.773692Z",
          "start_time": "2023-02-05T10:48:57.745984Z"
        },
        "trusted": false
      },
      "id": "2105c3dc",
      "cell_type": "code",
      "source": "y_prob1 = pd.DataFrame(bank.predict_proba(X)[:,1]) \ny_prob1 ",
      "execution_count": 66,
      "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>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.119554</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.095195</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.085451</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.079963</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.147439</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>45206</th>\n      <td>0.595723</td>\n    </tr>\n    <tr>\n      <th>45207</th>\n      <td>0.191093</td>\n    </tr>\n    <tr>\n      <th>45208</th>\n      <td>0.640337</td>\n    </tr>\n    <tr>\n      <th>45209</th>\n      <td>0.145901</td>\n    </tr>\n    <tr>\n      <th>45210</th>\n      <td>0.533169</td>\n    </tr>\n  </tbody>\n</table>\n<p>45211 rows × 1 columns</p>\n</div>",
            "text/plain": "              0\n0      0.119554\n1      0.095195\n2      0.085451\n3      0.079963\n4      0.147439\n...         ...\n45206  0.595723\n45207  0.191093\n45208  0.640337\n45209  0.145901\n45210  0.533169\n\n[45211 rows x 1 columns]"
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:48:58.951897Z",
          "start_time": "2023-02-05T10:48:58.939427Z"
        },
        "trusted": false
      },
      "id": "cc422dc1",
      "cell_type": "code",
      "source": "# calculate the fpr and tpr\nprint(fpr)\n   \nprint(tpr)",
      "execution_count": 67,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[0.00000000e+00 2.50488452e-05 7.51465357e-05 ... 9.97319774e-01\n 9.97319774e-01 1.00000000e+00]\n[0.         0.         0.         ... 0.99981093 1.         1.        ]\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:50:19.971690Z",
          "start_time": "2023-02-05T10:49:00.034382Z"
        },
        "trusted": false
      },
      "id": "cd47c578",
      "cell_type": "code",
      "source": "# to find the allthreshold values\n\nfrom sklearn.metrics import accuracy_score\naccuracy_ls = []\nfor thres in thresholds:\n    y_pred = np.where(bank.predict_proba(X)[:,1]>thres,1,0)\n    accuracy_ls.append(accuracy_score(Y, y_pred, normalize=True))\n    \naccuracy_ls = pd.concat([pd.Series(thresholds), pd.Series(accuracy_ls)],\n                        axis=1)\naccuracy_ls.columns = ['thresholds', 'accuracy']\naccuracy_ls.sort_values(by='accuracy', ascending=False, inplace=True)\naccuracy_ls  ",
      "execution_count": 68,
      "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>thresholds</th>\n      <th>accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>787</th>\n      <td>6.943775e-01</td>\n      <td>0.887970</td>\n    </tr>\n    <tr>\n      <th>790</th>\n      <td>6.941064e-01</td>\n      <td>0.887970</td>\n    </tr>\n    <tr>\n      <th>788</th>\n      <td>6.941665e-01</td>\n      <td>0.887970</td>\n    </tr>\n    <tr>\n      <th>791</th>\n      <td>6.939780e-01</td>\n      <td>0.887948</td>\n    </tr>\n    <tr>\n      <th>789</th>\n      <td>6.941526e-01</td>\n      <td>0.887948</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>7680</th>\n      <td>5.404328e-03</td>\n      <td>0.127204</td>\n    </tr>\n    <tr>\n      <th>7681</th>\n      <td>5.366677e-03</td>\n      <td>0.127181</td>\n    </tr>\n    <tr>\n      <th>7682</th>\n      <td>4.702356e-04</td>\n      <td>0.119351</td>\n    </tr>\n    <tr>\n      <th>7683</th>\n      <td>4.676763e-04</td>\n      <td>0.119329</td>\n    </tr>\n    <tr>\n      <th>7684</th>\n      <td>1.037094e-07</td>\n      <td>0.117007</td>\n    </tr>\n  </tbody>\n</table>\n<p>7685 rows × 2 columns</p>\n</div>",
            "text/plain": "        thresholds  accuracy\n787   6.943775e-01  0.887970\n790   6.941064e-01  0.887970\n788   6.941665e-01  0.887970\n791   6.939780e-01  0.887948\n789   6.941526e-01  0.887948\n...            ...       ...\n7680  5.404328e-03  0.127204\n7681  5.366677e-03  0.127181\n7682  4.702356e-04  0.119351\n7683  4.676763e-04  0.119329\n7684  1.037094e-07  0.117007\n\n[7685 rows x 2 columns]"
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:50:19.998280Z",
          "start_time": "2023-02-05T10:50:19.974397Z"
        },
        "trusted": false
      },
      "id": "91cfc7e6",
      "cell_type": "code",
      "source": "# to find the best fited threshold\n\nfrom numpy import argmax\nJ = tpr - fpr\nix = argmax(J)\nbest_thresh = thresholds[ix]\nprint('Best Threshold=%f' % (best_thresh)) ",
      "execution_count": 69,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Best Threshold=0.245213\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:51:30.975218Z",
          "start_time": "2023-02-05T10:51:30.896443Z"
        },
        "trusted": false
      },
      "id": "639f5664",
      "cell_type": "code",
      "source": "# model-2 (after finding the best fitted threshold )\n\nthreshold =0.245137\npreds = np.where(bank.predict_proba(X)[:,1] > threshold, 1, 0)\nprint(classification_report(Y,preds))   ",
      "execution_count": 70,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n           0       0.95      0.77      0.85     39922\n           1       0.30      0.72      0.42      5289\n\n    accuracy                           0.77     45211\n   macro avg       0.63      0.75      0.64     45211\nweighted avg       0.88      0.77      0.80     45211\n\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:51:32.019968Z",
          "start_time": "2023-02-05T10:51:31.998799Z"
        },
        "trusted": false
      },
      "id": "c5a07b36",
      "cell_type": "code",
      "source": "from sklearn.metrics import confusion_matrix\nconfusion_matrix1 = confusion_matrix(Y,preds)\nprint (confusion_matrix1) ",
      "execution_count": 71,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[[30805  9117]\n [ 1460  3829]]\n"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:51:33.705733Z",
          "start_time": "2023-02-05T10:51:33.545374Z"
        },
        "trusted": false
      },
      "id": "ac3f600f",
      "cell_type": "code",
      "source": "# Show confusion matrix in a separate window\nimport matplotlib.pyplot as plt\nplt.matshow(confusion_matrix1)\nplt.title('Confusion matrix')\nplt.colorbar()\nplt.ylabel('True label')\nplt.xlabel('Predicted label')\nplt.show()",
      "execution_count": 72,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 480x480 with 2 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:51:34.835047Z",
          "start_time": "2023-02-05T10:51:34.678080Z"
        },
        "trusted": false
      },
      "id": "1e75abea",
      "cell_type": "code",
      "source": "# to represent the confusion matrix using heat map\ndf_cm=pd.DataFrame(confusion_matrix1,index=[i for i in ['0','1']],\n                   columns=[i for i in ['predict 0','predict 1']])\nplt.figure(figsize=(7,5))\nsns.heatmap(df_cm,annot=True\n           )",
      "execution_count": 73,
      "outputs": [
        {
          "data": {
            "text/plain": "<AxesSubplot:>"
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 700x500 with 2 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-02-05T10:51:35.815570Z",
          "start_time": "2023-02-05T10:51:35.783045Z"
        },
        "trusted": false
      },
      "id": "0884b701",
      "cell_type": "code",
      "source": "# using cross tab metho d to represent confusion matrix\npd.crosstab(Y,preds)",
      "execution_count": 74,
      "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>col_0</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>y</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>30805</td>\n      <td>9117</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1460</td>\n      <td>3829</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "col_0      0     1\ny                 \n0      30805  9117\n1       1460  3829"
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "3eb60108",
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "!excelR/assignments/Gists/Logistic_Reg.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}