Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a55adae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas  as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "plt.rcParams['figure.figsize'] = [30,15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ca0b4680",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
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       "      <td>cellular</td>\n",
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       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
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       "      <td>1</td>\n",
       "      <td>339</td>\n",
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       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>apr</td>\n",
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       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>jun</td>\n",
       "      <td>199</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>226</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>5</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>747</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>23</td>\n",
       "      <td>feb</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>176</td>\n",
       "      <td>3</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>307</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>may</td>\n",
       "      <td>341</td>\n",
       "      <td>1</td>\n",
       "      <td>330</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>147</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>41</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>221</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>14</td>\n",
       "      <td>may</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>-88</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>apr</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age            job  marital  education default  balance housing loan  \\\n",
       "0   30     unemployed  married    primary      no     1787      no   no   \n",
       "1   33       services  married  secondary      no     4789     yes  yes   \n",
       "2   35     management   single   tertiary      no     1350     yes   no   \n",
       "3   30     management  married   tertiary      no     1476     yes  yes   \n",
       "4   59    blue-collar  married  secondary      no        0     yes   no   \n",
       "5   35     management   single   tertiary      no      747      no   no   \n",
       "6   36  self-employed  married   tertiary      no      307     yes   no   \n",
       "7   39     technician  married  secondary      no      147     yes   no   \n",
       "8   41   entrepreneur  married   tertiary      no      221     yes   no   \n",
       "9   43       services  married    primary      no      -88     yes  yes   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  cellular   19   oct        79         1     -1         0  unknown  no  \n",
       "1  cellular   11   may       220         1    339         4  failure  no  \n",
       "2  cellular   16   apr       185         1    330         1  failure  no  \n",
       "3   unknown    3   jun       199         4     -1         0  unknown  no  \n",
       "4   unknown    5   may       226         1     -1         0  unknown  no  \n",
       "5  cellular   23   feb       141         2    176         3  failure  no  \n",
       "6  cellular   14   may       341         1    330         2    other  no  \n",
       "7  cellular    6   may       151         2     -1         0  unknown  no  \n",
       "8   unknown   14   may        57         2     -1         0  unknown  no  \n",
       "9  cellular   17   apr       313         1    147         2  failure  no  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('bank.csv', delimiter=\";\")\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "95bcaffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choosing the columns which are needed\n",
    "df = df1[['job', 'marital', 'default', 'housing', 'loan', 'poutcome', 'y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "98a60e0d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>no</td>\n",
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       "      <th>3</th>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
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       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
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       "      <th>4</th>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
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       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>no</td>\n",
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      "text/plain": [
       "           job  marital default housing loan poutcome   y\n",
       "0   unemployed  married      no      no   no  unknown  no\n",
       "1     services  married      no     yes  yes  failure  no\n",
       "2   management   single      no     yes   no  failure  no\n",
       "3   management  married      no     yes  yes  unknown  no\n",
       "4  blue-collar  married      no     yes   no  unknown  no"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0372d8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding the dataframe\n",
    "# One hot encoding is the process of converting the categorical data variables to be provided to machine and deep learning algorithms which in turn improve predictions as well as classification accuracy of a model\n",
    "data = pd.get_dummies(df, columns =['job', 'marital', 'default', 'housing', 'loan', 'poutcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "13f2d470",
   "metadata": {},
   "outputs": [
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       "      <th>job_admin.</th>\n",
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       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    y  job_admin.  job_blue-collar  job_entrepreneur  job_housemaid  \\\n",
       "0  no           0                0                 0              0   \n",
       "1  no           0                0                 0              0   \n",
       "2  no           0                0                 0              0   \n",
       "3  no           0                0                 0              0   \n",
       "4  no           0                1                 0              0   \n",
       "\n",
       "   job_management  job_retired  job_self-employed  job_services  job_student  \\\n",
       "0               0            0                  0             0            0   \n",
       "1               0            0                  0             1            0   \n",
       "2               1            0                  0             0            0   \n",
       "3               1            0                  0             0            0   \n",
       "4               0            0                  0             0            0   \n",
       "\n",
       "   ...  default_no  default_yes  housing_no  housing_yes  loan_no  loan_yes  \\\n",
       "0  ...           1            0           1            0        1         0   \n",
       "1  ...           1            0           0            1        0         1   \n",
       "2  ...           1            0           0            1        1         0   \n",
       "3  ...           1            0           0            1        0         1   \n",
       "4  ...           1            0           0            1        1         0   \n",
       "\n",
       "   poutcome_failure  poutcome_other  poutcome_success  poutcome_unknown  \n",
       "0                 0               0                 0                 1  \n",
       "1                 1               0                 0                 0  \n",
       "2                 1               0                 0                 0  \n",
       "3                 0               0                 0                 1  \n",
       "4                 0               0                 0                 1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3eba110f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['y', 'job_admin.', 'job_blue-collar', 'job_entrepreneur',\n",
       "       'job_housemaid', 'job_management', 'job_retired', 'job_self-employed',\n",
       "       'job_services', 'job_student', 'job_technician', 'job_unemployed',\n",
       "       'job_unknown', 'marital_divorced', 'marital_married', 'marital_single',\n",
       "       'default_no', 'default_yes', 'housing_no', 'housing_yes', 'loan_no',\n",
       "       'loan_yes', 'poutcome_failure', 'poutcome_other', 'poutcome_success',\n",
       "       'poutcome_unknown'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ed16f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(data.columns[[12, 16, 18, 21, 24]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "110746c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,1:]\n",
    "y = data.iloc[:,0]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "135f08c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(solver='lbfgs',random_state=0)\n",
    "logreg.fit(X_train, y_train)\n",
    "y_pred = logreg.predict(X_test)\n",
    "print('Accuracy: {:.2f}'.format(logreg.score(X_test, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "13cbce63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y, logreg.predict(X))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(cm)\n",
    "ax.grid(False)\n",
    "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
    "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
    "ax.set_ylim(1.5, -0.5)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0f476e50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       0.89      1.00      0.94       993\n",
      "         yes       0.89      0.12      0.21       138\n",
      "\n",
      "    accuracy                           0.89      1131\n",
      "   macro avg       0.89      0.56      0.57      1131\n",
      "weighted avg       0.89      0.89      0.85      1131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b57c4f4b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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