DefaultDataSet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Default Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "default = pd.read_csv('/Users/chandrahasmakarandsoman/Downloads/Datasets/Default.csv', na_values='?').dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 default student      balance        income\n",
       "0           1      No      No   729.526495  44361.625074\n",
       "1           2      No     Yes   817.180407  12106.134700\n",
       "2           3      No      No  1073.549164  31767.138947\n",
       "3           4      No      No   529.250605  35704.493935\n",
       "4           5      No      No   785.655883  38463.495879"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>835.374886</td>\n",
       "      <td>33516.981876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>483.714985</td>\n",
       "      <td>13336.639563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>771.967729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>481.731105</td>\n",
       "      <td>21340.462903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>823.636973</td>\n",
       "      <td>34552.644802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1166.308386</td>\n",
       "      <td>43807.729272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>2654.322576</td>\n",
       "      <td>73554.233495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0       balance        income\n",
       "count  10000.00000  10000.000000  10000.000000\n",
       "mean    5000.50000    835.374886  33516.981876\n",
       "std     2886.89568    483.714985  13336.639563\n",
       "min        1.00000      0.000000    771.967729\n",
       "25%     2500.75000    481.731105  21340.462903\n",
       "50%     5000.50000    823.636973  34552.644802\n",
       "75%     7500.25000   1166.308386  43807.729272\n",
       "max    10000.00000   2654.322576  73554.233495"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting default_value = 1 if Default = Yes , else 0; \n",
    "Setting student_value = 1 if student = Yes , else 0; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "      <th>default_value</th>\n",
       "      <th>student_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138947</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493935</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495879</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 default student      balance        income  default_value  \\\n",
       "0           1      No      No   729.526495  44361.625074              0   \n",
       "1           2      No     Yes   817.180407  12106.134700              0   \n",
       "2           3      No      No  1073.549164  31767.138947              0   \n",
       "3           4      No      No   529.250605  35704.493935              0   \n",
       "4           5      No      No   785.655883  38463.495879              0   \n",
       "\n",
       "   student_value  \n",
       "0              0  \n",
       "1              1  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default['default_value']=default.default.factorize()[0]\n",
    "default['student_value']=default.student.factorize()[0]\n",
    "default.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividing the data set into people who defaulted and not defaulted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "default_yes = default[default.default_value == 1]\n",
    "default_no = default[default.default_value == 0].sample(frac=0.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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EbsMSY0PT2JuK8e4d5dmVihg0HJf3cmWiusaeVGzTd+Lr6bNZKe303L4BLGdjfT7rxX1p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\n3RgqkqRuvgA7LogY0TXo0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x255989c8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.scatter(x = default_yes.balance, y = default_yes.income, s=60, c='orange', marker='+', linewidths=1, alpha=.7)\n",
    "plt.scatter(x = default_no.balance, y = default_no.income, s=40,  marker='o', linewidths=1, edgecolors='lightblue',facecolors='white', alpha=.9)\n",
    "plt.ylabel('Income')\n",
    "plt.xlabel('Balance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eTJkyhSlTpjT0mG65mQ1W1RhnSauBq4o6Ec4CLk1Z6HdKOhW4D5gPLGnkD2mk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25598b1fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "gs = mpl.gridspec.GridSpec(1, 4)\n",
    "ax2 = plt.subplot(gs[0,0])\n",
    "ax3 = plt.subplot(gs[0,2])\n",
    "sns.boxplot('default', 'balance', data=default, orient='v',ax=ax2)\n",
    "sns.boxplot('default', 'income', data=default, orient='v',ax=ax3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression using Scikit learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression Intercept, B0 =  [-10.6513229]\n",
      "Regression coefficient, B1 =  [[ 0.00549891]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "import numpy\n",
    "logisticRegr = LogisticRegression(solver='newton-cg')\n",
    "X=default['balance'].values.reshape(-1,1)\n",
    "y=default['default_value'].values.reshape(-1,1)\n",
    "logisticRegr.fit(X, numpy.ravel(y))\n",
    "print(\"Regression Intercept, B0 = \",logisticRegr.intercept_)\n",
    "print(\"Regression coefficient, B1 = \",logisticRegr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression using Logit function from Statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.079823\n",
      "         Iterations 10\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-10.651331</td>\n",
       "      <td>0.361169</td>\n",
       "      <td>-29.491287</td>\n",
       "      <td>3.723665e-191</td>\n",
       "      <td>-11.359208</td>\n",
       "      <td>-9.943453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.005499</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>24.952404</td>\n",
       "      <td>2.010855e-137</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.005931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Coef.  Std.Err.          z          P>|z|     [0.025    0.975]\n",
       "const   -10.651331  0.361169 -29.491287  3.723665e-191 -11.359208 -9.943453\n",
       "balance   0.005499  0.000220  24.952404  2.010855e-137   0.005067  0.005931"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "X = sm.add_constant(default.balance)\n",
    "est = smf.Logit(y, X).fit()\n",
    "est.summary2().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.145434\n",
      "         Iterations 7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-3.504128</td>\n",
       "      <td>0.070713</td>\n",
       "      <td>-49.554094</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.642723</td>\n",
       "      <td>-3.365532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student_value</th>\n",
       "      <td>0.404887</td>\n",
       "      <td>0.115019</td>\n",
       "      <td>3.520177</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.179454</td>\n",
       "      <td>0.630320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Coef.  Std.Err.          z     P>|z|    [0.025    0.975]\n",
       "const         -3.504128  0.070713 -49.554094  0.000000 -3.642723 -3.365532\n",
       "student_value  0.404887  0.115019   3.520177  0.000431  0.179454  0.630320"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "X = sm.add_constant(default.student_value)\n",
    "est = smf.Logit(y, X).fit()\n",
    "est.summary2().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.078577\n",
      "         Iterations 10\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coef.</th>\n",
       "      <th>Std.Err.</th>\n",
       "      <th>z</th>\n",
       "      <th>P&gt;|z|</th>\n",
       "      <th>[0.025</th>\n",
       "      <th>0.975]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-10.869045</td>\n",
       "      <td>0.492273</td>\n",
       "      <td>-22.079320</td>\n",
       "      <td>4.995499e-108</td>\n",
       "      <td>-11.833882</td>\n",
       "      <td>-9.904209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.005737</td>\n",
       "      <td>0.000232</td>\n",
       "      <td>24.736506</td>\n",
       "      <td>4.331521e-135</td>\n",
       "      <td>0.005282</td>\n",
       "      <td>0.006191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.369808</td>\n",
       "      <td>7.115254e-01</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student_value</th>\n",
       "      <td>-0.646776</td>\n",
       "      <td>0.236257</td>\n",
       "      <td>-2.737595</td>\n",
       "      <td>6.189022e-03</td>\n",
       "      <td>-1.109831</td>\n",
       "      <td>-0.183721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Coef.  Std.Err.          z          P>|z|     [0.025  \\\n",
       "const         -10.869045  0.492273 -22.079320  4.995499e-108 -11.833882   \n",
       "balance         0.005737  0.000232  24.736506  4.331521e-135   0.005282   \n",
       "income          0.000003  0.000008   0.369808   7.115254e-01  -0.000013   \n",
       "student_value  -0.646776  0.236257  -2.737595   6.189022e-03  -1.109831   \n",
       "\n",
       "                 0.975]  \n",
       "const         -9.904209  \n",
       "balance        0.006191  \n",
       "income         0.000019  \n",
       "student_value -0.183721  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "X = sm.add_constant(default[['balance','income','student_value']])\n",
    "est = smf.Logit(y, X).fit()\n",
    "est.summary2().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.462</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>         <td>y</td>              <td>AIC:</td>         <td>1579.5448</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2018-03-10 13:03</td>       <td>BIC:</td>         <td>1608.3862</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>        <td>10000</td>       <td>Log-Likelihood:</td>    <td>-785.77</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>3</td>            <td>LL-Null:</td>        <td>-1460.3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>          <td>9996</td>         <td>LLR p-value:</td>    <td>3.2575e-292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>         <td>1.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>No. Iterations:</td>        <td>10.0000</td>             <td></td>               <td></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>-10.8690</td>  <td>0.4923</td>  <td>-22.0793</td> <td>0.0000</td> <td>-11.8339</td> <td>-9.9042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>balance</th>        <td>0.0057</td>   <td>0.0002</td>   <td>24.7365</td> <td>0.0000</td>  <td>0.0053</td>  <td>0.0062</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>income</th>         <td>0.0000</td>   <td>0.0000</td>   <td>0.3698</td>  <td>0.7115</td>  <td>-0.0000</td> <td>0.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>student_value</th>  <td>-0.6468</td>  <td>0.2363</td>   <td>-2.7376</td> <td>0.0062</td>  <td>-1.1098</td> <td>-0.1837</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                          Results: Logit\n",
       "==================================================================\n",
       "Model:              Logit            Pseudo R-squared: 0.462      \n",
       "Dependent Variable: y                AIC:              1579.5448  \n",
       "Date:               2018-03-10 13:03 BIC:              1608.3862  \n",
       "No. Observations:   10000            Log-Likelihood:   -785.77    \n",
       "Df Model:           3                LL-Null:          -1460.3    \n",
       "Df Residuals:       9996             LLR p-value:      3.2575e-292\n",
       "Converged:          1.0000           Scale:            1.0000     \n",
       "No. Iterations:     10.0000                                       \n",
       "------------------------------------------------------------------\n",
       "                 Coef.   Std.Err.    z     P>|z|   [0.025   0.975]\n",
       "------------------------------------------------------------------\n",
       "const           -10.8690   0.4923 -22.0793 0.0000 -11.8339 -9.9042\n",
       "balance           0.0057   0.0002  24.7365 0.0000   0.0053  0.0062\n",
       "income            0.0000   0.0000   0.3698 0.7115  -0.0000  0.0000\n",
       "student_value    -0.6468   0.2363  -2.7376 0.0062  -1.1098 -0.1837\n",
       "==================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.summary2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LDA Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      "\n",
      "[[9645  254]\n",
      " [  22   79]]\n",
      "\n",
      "\n",
      "Classification Report \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0     0.9743    0.9977    0.9859      9667\n",
      "          1     0.7822    0.2372    0.3641       333\n",
      "\n",
      "avg / total     0.9679    0.9724    0.9652     10000\n",
      "\n",
      "\n",
      "\n",
      "Precision =  0.782178217822\n",
      "Recall =  0.237237237237\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Paaru\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_score, recall_score\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "X = sm.add_constant(default[['balance','income','student_value']])\n",
    "y = numpy.ravel(default.default_value.values.reshape(-1,1), order='C')\n",
    "pred = lda.fit(X, y).predict(X)\n",
    "print(\"Confusion Matrix \\n\")\n",
    "print(confusion_matrix(y, pred).T)\n",
    "print(\"\\n\")\n",
    "print(\"Classification Report \\n\")\n",
    "print(classification_report(y, pred, digits=4))\n",
    "print(\"\\n\")\n",
    "print(\"Precision = \", precision_score(y,pred))\n",
    "print(\"Recall = \", recall_score(y,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manual Verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision =  0.7821782178217822\n",
      "Recall =  0.23723723723723725\n"
     ]
    }
   ],
   "source": [
    "Precision = 79/(79+22)\n",
    "Recall = 79/(254+79)\n",
    "print(\"Precision = \", Precision)\n",
    "print(\"Recall = \", Recall)"
   ]
  }
 ],
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