Logistic Regression Classifiers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models and Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import rcParams\n",
    "import seaborn as sb\n",
    "import scipy\n",
    "from scipy.stats import spearmanr\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn import preprocessing\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import  train_test_split\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "rcParams['figure.figsize']=7,4\n",
    "sb.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "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>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars=pd.read_csv('mtcars.csv')\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#select only those variables which satisfy the assumption of the model\n",
    "\n",
    "cars_data=cars.iloc[:,[5,11]]\n",
    "y=cars.iloc[:,9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#checking for indepenence b/w features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b49cf2b50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAEDCAYAAABtd+CqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b780b5c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#are predictor variables ordinal? ie a variable that can be grouped into a limited number of subcategories\n",
    "sb.regplot(x='drat',y='carb',data=cars,scatter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman Randk Correlation Coeff -0.125\n"
     ]
    }
   ],
   "source": [
    "drat=cars['drat']\n",
    "carb=cars['carb']\n",
    "spearmanr_coeff,p_value=spearmanr(drat,carb)\n",
    "print \"Spearman Randk Correlation Coeff %0.3f\" %(spearmanr_coeff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.852162</td>\n",
       "      <td>-0.847551</td>\n",
       "      <td>-0.776168</td>\n",
       "      <td>0.681172</td>\n",
       "      <td>-0.867659</td>\n",
       "      <td>0.418684</td>\n",
       "      <td>0.664039</td>\n",
       "      <td>0.599832</td>\n",
       "      <td>0.480285</td>\n",
       "      <td>-0.550925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <td>-0.852162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902033</td>\n",
       "      <td>0.832447</td>\n",
       "      <td>-0.699938</td>\n",
       "      <td>0.782496</td>\n",
       "      <td>-0.591242</td>\n",
       "      <td>-0.810812</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.492687</td>\n",
       "      <td>0.526988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disp</th>\n",
       "      <td>-0.847551</td>\n",
       "      <td>0.902033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790949</td>\n",
       "      <td>-0.710214</td>\n",
       "      <td>0.887980</td>\n",
       "      <td>-0.433698</td>\n",
       "      <td>-0.710416</td>\n",
       "      <td>-0.591227</td>\n",
       "      <td>-0.555569</td>\n",
       "      <td>0.394977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hp</th>\n",
       "      <td>-0.776168</td>\n",
       "      <td>0.832447</td>\n",
       "      <td>0.790949</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.448759</td>\n",
       "      <td>0.658748</td>\n",
       "      <td>-0.708223</td>\n",
       "      <td>-0.723097</td>\n",
       "      <td>-0.243204</td>\n",
       "      <td>-0.125704</td>\n",
       "      <td>0.749812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drat</th>\n",
       "      <td>0.681172</td>\n",
       "      <td>-0.699938</td>\n",
       "      <td>-0.710214</td>\n",
       "      <td>-0.448759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.712441</td>\n",
       "      <td>0.091205</td>\n",
       "      <td>0.440278</td>\n",
       "      <td>0.712711</td>\n",
       "      <td>0.699610</td>\n",
       "      <td>-0.090790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>-0.867659</td>\n",
       "      <td>0.782496</td>\n",
       "      <td>0.887980</td>\n",
       "      <td>0.658748</td>\n",
       "      <td>-0.712441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.174716</td>\n",
       "      <td>-0.554916</td>\n",
       "      <td>-0.692495</td>\n",
       "      <td>-0.583287</td>\n",
       "      <td>0.427606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qsec</th>\n",
       "      <td>0.418684</td>\n",
       "      <td>-0.591242</td>\n",
       "      <td>-0.433698</td>\n",
       "      <td>-0.708223</td>\n",
       "      <td>0.091205</td>\n",
       "      <td>-0.174716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.744535</td>\n",
       "      <td>-0.229861</td>\n",
       "      <td>-0.212682</td>\n",
       "      <td>-0.656249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vs</th>\n",
       "      <td>0.664039</td>\n",
       "      <td>-0.810812</td>\n",
       "      <td>-0.710416</td>\n",
       "      <td>-0.723097</td>\n",
       "      <td>0.440278</td>\n",
       "      <td>-0.554916</td>\n",
       "      <td>0.744535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168345</td>\n",
       "      <td>0.206023</td>\n",
       "      <td>-0.569607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>am</th>\n",
       "      <td>0.599832</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.591227</td>\n",
       "      <td>-0.243204</td>\n",
       "      <td>0.712711</td>\n",
       "      <td>-0.692495</td>\n",
       "      <td>-0.229861</td>\n",
       "      <td>0.168345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.794059</td>\n",
       "      <td>0.057534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gear</th>\n",
       "      <td>0.480285</td>\n",
       "      <td>-0.492687</td>\n",
       "      <td>-0.555569</td>\n",
       "      <td>-0.125704</td>\n",
       "      <td>0.699610</td>\n",
       "      <td>-0.583287</td>\n",
       "      <td>-0.212682</td>\n",
       "      <td>0.206023</td>\n",
       "      <td>0.794059</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carb</th>\n",
       "      <td>-0.550925</td>\n",
       "      <td>0.526988</td>\n",
       "      <td>0.394977</td>\n",
       "      <td>0.749812</td>\n",
       "      <td>-0.090790</td>\n",
       "      <td>0.427606</td>\n",
       "      <td>-0.656249</td>\n",
       "      <td>-0.569607</td>\n",
       "      <td>0.057534</td>\n",
       "      <td>0.274073</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mpg       cyl      disp        hp      drat        wt      qsec  \\\n",
       "mpg   1.000000 -0.852162 -0.847551 -0.776168  0.681172 -0.867659  0.418684   \n",
       "cyl  -0.852162  1.000000  0.902033  0.832447 -0.699938  0.782496 -0.591242   \n",
       "disp -0.847551  0.902033  1.000000  0.790949 -0.710214  0.887980 -0.433698   \n",
       "hp   -0.776168  0.832447  0.790949  1.000000 -0.448759  0.658748 -0.708223   \n",
       "drat  0.681172 -0.699938 -0.710214 -0.448759  1.000000 -0.712441  0.091205   \n",
       "wt   -0.867659  0.782496  0.887980  0.658748 -0.712441  1.000000 -0.174716   \n",
       "qsec  0.418684 -0.591242 -0.433698 -0.708223  0.091205 -0.174716  1.000000   \n",
       "vs    0.664039 -0.810812 -0.710416 -0.723097  0.440278 -0.554916  0.744535   \n",
       "am    0.599832 -0.522607 -0.591227 -0.243204  0.712711 -0.692495 -0.229861   \n",
       "gear  0.480285 -0.492687 -0.555569 -0.125704  0.699610 -0.583287 -0.212682   \n",
       "carb -0.550925  0.526988  0.394977  0.749812 -0.090790  0.427606 -0.656249   \n",
       "\n",
       "            vs        am      gear      carb  \n",
       "mpg   0.664039  0.599832  0.480285 -0.550925  \n",
       "cyl  -0.810812 -0.522607 -0.492687  0.526988  \n",
       "disp -0.710416 -0.591227 -0.555569  0.394977  \n",
       "hp   -0.723097 -0.243204 -0.125704  0.749812  \n",
       "drat  0.440278  0.712711  0.699610 -0.090790  \n",
       "wt   -0.554916 -0.692495 -0.583287  0.427606  \n",
       "qsec  0.744535 -0.229861 -0.212682 -0.656249  \n",
       "vs    1.000000  0.168345  0.206023 -0.569607  \n",
       "am    0.168345  1.000000  0.794059  0.057534  \n",
       "gear  0.206023  0.794059  1.000000  0.274073  \n",
       "carb -0.569607  0.057534  0.274073  1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model    0\n",
       "mpg      0\n",
       "cyl      0\n",
       "disp     0\n",
       "hp       0\n",
       "drat     0\n",
       "wt       0\n",
       "qsec     0\n",
       "vs       0\n",
       "am       0\n",
       "gear     0\n",
       "carb     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the missing values\n",
    "cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b49b98590>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAEDCAYAAABOLLF/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b49f9ed90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking id target variable is ordinal\n",
    "sb.countplot(x='am',data=cars,palette='hls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32 entries, 0 to 31\n",
      "Data columns (total 12 columns):\n",
      "model    32 non-null object\n",
      "mpg      32 non-null float64\n",
      "cyl      32 non-null int64\n",
      "disp     32 non-null float64\n",
      "hp       32 non-null int64\n",
      "drat     32 non-null float64\n",
      "wt       32 non-null float64\n",
      "qsec     32 non-null float64\n",
      "vs       32 non-null int64\n",
      "am       32 non-null int64\n",
      "gear     32 non-null int64\n",
      "carb     32 non-null int64\n",
      "dtypes: float64(5), int64(6), object(1)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#datset sufficiency (50 obs for each predictor) ie 100 reqd in this case\n",
    "cars.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "x_scale=scale(cars_data)\n",
    "print type(x_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.57659448  0.74696708]\n",
      " [ 0.57659448  0.74696708]\n",
      " [ 0.48158406 -1.14010764]\n",
      " [-0.98157639 -1.14010764]\n",
      " [-0.84856181 -0.51108274]]\n",
      "   drat  carb\n",
      "0  3.90     4\n",
      "1  3.90     4\n",
      "2  3.85     1\n",
      "3  3.08     1\n",
      "4  3.15     2\n"
     ]
    }
   ],
   "source": [
    "print x_scale[:5]\n",
    "print cars_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8125\n"
     ]
    }
   ],
   "source": [
    "LogReg=LogisticRegression()\n",
    "LogReg.fit(x_scale,y)\n",
    "print LogReg.score(x_scale,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.79      0.83        19\n",
      "          1       0.73      0.85      0.79        13\n",
      "\n",
      "avg / total       0.82      0.81      0.81        32\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=LogReg.predict(x_scale)\n",
    "print metrics.classification_report(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}