Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataset: Example_bank_data.csv\n",
    "\n",
    "The data is based on the marketing campaign efforts of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).\n",
    "\n",
    "Source: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# this part not be needed after the latests updates of the library\n",
    "from scipy import stats\n",
    "stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the ‘Example_bank_data.csv’ dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>no</td>\n",
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       "      <td>274</td>\n",
       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>686</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>157</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>513</td>\n",
       "      <td>204</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>514</td>\n",
       "      <td>806</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>515</td>\n",
       "      <td>290</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>516</td>\n",
       "      <td>473</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>517</td>\n",
       "      <td>142</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>518 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  duration    y\n",
       "0             0       117   no\n",
       "1             1       274  yes\n",
       "2             2       167   no\n",
       "3             3       686  yes\n",
       "4             4       157   no\n",
       "..          ...       ...  ...\n",
       "513         513       204   no\n",
       "514         514       806  yes\n",
       "515         515       290   no\n",
       "516         516       473  yes\n",
       "517         517       142   no\n",
       "\n",
       "[518 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('Example_bank_data.csv')\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know whether the bank marketing strategy was successful, so we need to transform the outcome variable into 0s and 1s in order to perform a logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
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       "      <td>686</td>\n",
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       "      <th>4</th>\n",
       "      <td>157</td>\n",
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       "      <th>...</th>\n",
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       "      <th>513</th>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>806</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
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       "      <th>516</th>\n",
       "      <td>473</td>\n",
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       "<p>518 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     duration  y\n",
       "0         117  0\n",
       "1         274  1\n",
       "2         167  0\n",
       "3         686  1\n",
       "4         157  0\n",
       "..        ... ..\n",
       "513       204  0\n",
       "514       806  1\n",
       "515       290  0\n",
       "516       473  1\n",
       "517       142  0\n",
       "\n",
       "[518 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = raw_data.copy()\n",
    "data = data.drop(['Unnamed: 0'], axis = 1)\n",
    "\n",
    "# We use the map function to change any 'yes' values to 1 and 'no' values to 0. \n",
    "data['y'] = data['y'].map({'yes':1, 'no':0})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>518.000000</td>\n",
       "      <td>518.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>382.177606</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>344.295990</td>\n",
       "      <td>0.500483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>155.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>266.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>482.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2653.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          duration           y\n",
       "count   518.000000  518.000000\n",
       "mean    382.177606    0.500000\n",
       "std     344.295990    0.500483\n",
       "min       9.000000    0.000000\n",
       "25%     155.000000    0.000000\n",
       "50%     266.500000    0.500000\n",
       "75%     482.750000    1.000000\n",
       "max    2653.000000    1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Declare the dependent and independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['y']\n",
    "x1 = data['duration']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.546118\n",
      "         Iterations 7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   518</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>   516</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 26 Oct 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.2121</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>11:24:02</td>     <th>  Log-Likelihood:    </th> <td> -282.89</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -359.05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>5.387e-35</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>    <td>   -1.7001</td> <td>    0.192</td> <td>   -8.863</td> <td> 0.000</td> <td>   -2.076</td> <td>   -1.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>duration</th> <td>    0.0051</td> <td>    0.001</td> <td>    9.159</td> <td> 0.000</td> <td>    0.004</td> <td>    0.006</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  518\n",
       "Model:                          Logit   Df Residuals:                      516\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Wed, 26 Oct 2022   Pseudo R-squ.:                  0.2121\n",
       "Time:                        11:24:02   Log-Likelihood:                -282.89\n",
       "converged:                       True   LL-Null:                       -359.05\n",
       "Covariance Type:            nonrobust   LLR p-value:                 5.387e-35\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.7001      0.192     -8.863      0.000      -2.076      -1.324\n",
       "duration       0.0051      0.001      9.159      0.000       0.004       0.006\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = sm.add_constant(x1)\n",
    "reg_log = sm.Logit(y,x)\n",
    "results_log = reg_log.fit()\n",
    "\n",
    "# Get the regression summary\n",
    "results_log.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of x1 (Duration, no constant) and y (Subscribed)\n",
    "plt.scatter(x1,y,color = 'C0')\n",
    "\n",
    "# labels\n",
    "plt.xlabel('Duration', fontsize = 20)\n",
    "plt.ylabel('Subscription', fontsize = 20)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}