Aditii7/Assignment logistic regression .ipynb

## Assignment logistic regression .ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import roc_auc_score\nimport matplotlib.pyplot as plt",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df=pd.read_csv(\"bank_data (1).csv\")",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.head()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "   age  default  balance  housing  loan  duration  campaign  pdays  previous  \\\n0   58        0     2143        1     0       261         1     -1         0   \n1   44        0       29        1     0       151         1     -1         0   \n2   33        0        2        1     1        76         1     -1         0   \n3   47        0     1506        1     0        92         1     -1         0   \n4   33        0        1        0     0       198         1     -1         0   \n\n   poutfailure  ...  johousemaid  jomanagement  joretired  joself.employed  \\\n0            0  ...            0             1          0                0   \n1            0  ...            0             0          0                0   \n2            0  ...            0             0          0                0   \n3            0  ...            0             0          0                0   \n4            0  ...            0             0          0                0   \n\n   joservices  jostudent  jotechnician  jounemployed  jounknown  y  \n0           0          0             0             0          0  0  \n1           0          0             1             0          0  0  \n2           0          0             0             0          0  0  \n3           0          0             0             0          0  0  \n4           0          0             0             0          1  0  \n\n[5 rows x 32 columns]",
            "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>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>poutfailure</th>\n      <th>...</th>\n      <th>johousemaid</th>\n      <th>jomanagement</th>\n      <th>joretired</th>\n      <th>joself.employed</th>\n      <th>joservices</th>\n      <th>jostudent</th>\n      <th>jotechnician</th>\n      <th>jounemployed</th>\n      <th>jounknown</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>0</td>\n      <td>2143</td>\n      <td>1</td>\n      <td>0</td>\n      <td>261</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>0</td>\n      <td>29</td>\n      <td>1</td>\n      <td>0</td>\n      <td>151</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>76</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>0</td>\n      <td>1506</td>\n      <td>1</td>\n      <td>0</td>\n      <td>92</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>198</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 32 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.isna().sum()",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "age                0\ndefault            0\nbalance            0\nhousing            0\nloan               0\nduration           0\ncampaign           0\npdays              0\nprevious           0\npoutfailure        0\npoutother          0\npoutsuccess        0\npoutunknown        0\ncon_cellular       0\ncon_telephone      0\ncon_unknown        0\ndivorced           0\nmarried            0\nsingle             0\njoadmin.           0\njoblue.collar      0\njoentrepreneur     0\njohousemaid        0\njomanagement       0\njoretired          0\njoself.employed    0\njoservices         0\njostudent          0\njotechnician       0\njounemployed       0\njounknown          0\ny                  0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df[[\"default\",\"housing\",\"loan\",\"y\"]]\ndf.head()\ndf.y",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "0        0\n1        0\n2        0\n3        0\n4        0\n        ..\n45206    1\n45207    1\n45208    1\n45209    0\n45210    0\nName: y, Length: 45211, dtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = df[[\"default\",\"housing\",\"loan\"]]\ny = df.y",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "classifier = LogisticRegression()\nclassifier.fit(x,y)",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "LogisticRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_pred = classifier.predict(x)\nprint(y_pred)",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[0 0 0 ... 0 0 0]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "confusion_matrix = confusion_matrix(y,y_pred)\nprint (confusion_matrix)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[[39922     0]\n [ 5289     0]]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(classification_report(y,y_pred))",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n           0       0.88      1.00      0.94     39922\n           1       0.00      0.00      0.00      5289\n\n    accuracy                           0.88     45211\n   macro avg       0.44      0.50      0.47     45211\nweighted avg       0.78      0.88      0.83     45211\n\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "C:\\Users\\91966\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n  _warn_prf(average, modifier, msg_start, len(result))\n",
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "\n\nfpr, tpr, thresholds = roc_curve(y, classifier.predict_proba (x)[:,1])\n\nauc = roc_auc_score(y, y_pred)\n\n\nplt.plot(fpr, tpr, color='red', label='logit model ( area  = %0.2f)'%auc)\nplt.plot([0, 1], [0, 1], 'k--')\nplt.xlabel('False Positive Rate or [1 - True Negative Rate]')\nplt.ylabel('True Positive Rate')\n",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "Text(0, 0.5, 'True Positive Rate')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "auc",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "0.5"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "gist": {
      "id": "",
      "data": {
        "description": "bankipynb",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}