{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Data Imports\n", "import numpy as np\n", "import pandas as pd\n", "from pandas import Series,DataFrame\n", "\n", "# Math\n", "import math\n", "\n", "# Plot imports\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_style('whitegrid')\n", "%matplotlib inline\n", "\n", "# Machine Learning Imports\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.cross_validation import train_test_split\n", "\n", "# For evaluating our ML results\n", "from sklearn import metrics\n", "\n", "# Dataset Import\n", "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = sm.datasets.fair.load_pandas().data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " rate_marriage age yrs_married children religious \\\n", "Had_Affair \n", "0 4.329701 28.390679 7.989335 1.238813 2.504521 \n", "1 3.647345 30.537019 11.152460 1.728933 2.261568 \n", "\n", " educ occupation occupation_husb affairs \n", "Had_Affair \n", "0 14.322977 3.405286 3.833758 0.000000 \n", "1 13.972236 3.463712 3.884559 2.187243 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby('Had_Affair').mean()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAZMAAAFhCAYAAAC1RkdzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAG89JREFUeJzt3X+0XWV95/H3QZKATkJnhJDJKN6Mkq+oi7HcKDVoIBX5\n", "kSoomRFrrUpHaBUZoa7BemW5qg3gaIWY1rIcooLVjgiXWlz80FW0gGkreMbRMtAvxSaUMWIMv4IU\n", 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" ], "text/plain": [ " rate_marriage age yrs_married children religious educ affairs occ1 \\\n", "0 3 32 9.0 3 3 17 0.111111 0 \n", "1 3 27 13.0 3 1 14 3.230769 0 \n", "2 4 22 2.5 0 1 16 1.400000 0 \n", "3 4 37 16.5 4 3 16 0.727273 0 \n", "4 5 27 9.0 1 1 14 4.666666 0 \n", "\n", " occ2 occ3 occ4 occ5 occ6 hocc1 hocc2 hocc3 hocc4 hocc5 hocc6 \n", "0 1 0 0 0 0 0 0 0 0 1 0 \n", "1 0 1 0 0 0 0 0 0 1 0 0 \n", "2 0 1 0 0 0 0 0 0 0 1 0 \n", "3 0 0 0 1 0 0 0 0 0 1 0 \n", "4 0 1 0 0 0 0 0 0 1 0 0 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6361 0\n", "6362 0\n", "6363 0\n", "6364 0\n", "6365 0\n", "Name: Had_Affair, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y = df.Had_Affair\n", "\n", "Y.tail()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = X.drop('affairs',axis=1)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " rate_marriage age yrs_married children religious educ occ2 occ3 \\\n", "0 3 32 9.0 3 3 17 1 0 \n", "1 3 27 13.0 3 1 14 0 1 \n", "2 4 22 2.5 0 1 16 0 1 \n", "3 4 37 16.5 4 3 16 0 0 \n", "4 5 27 9.0 1 1 14 0 1 \n", "\n", " occ4 occ5 occ6 hocc2 hocc3 hocc4 hocc5 hocc6 \n", "0 0 0 0 0 0 0 1 0 \n", "1 0 0 0 0 0 1 0 0 \n", "2 0 0 0 0 0 0 1 0 \n", "3 0 1 0 0 0 0 1 0 \n", "4 0 0 0 0 0 1 0 0 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 1\n", "2 1\n", "3 1\n", "4 1\n", "Name: Had_Affair, dtype: int64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y.head()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, ..., 0, 0, 0], dtype=int64)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y = np.ravel(Y)\n", "\n", "Y" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.72588752748978946" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_model = LogisticRegression()\n", "\n", "log_model.fit(X,Y)\n", "\n", "log_model.score(X,Y)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.32249450204209867" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y.mean()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coeff_df = DataFrame(zip(X.columns,np.transpose(log_model.coef_)))" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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01
0rate_marriage[-0.69751024616]
1age[-0.0561916647628]
2yrs_married[0.10377681236]
3children[0.0182911397779]
4religious[-0.36832290498]
5educ[0.00890195753587]
6occ2[0.296111837283]
7occ3[0.606052332054]
8occ4[0.343259482442]
9occ5[0.94006396288]
10occ6[0.919085673732]
11hocc2[0.219634563784]
12hocc3[0.323455986054]
13hocc4[0.189038509875]
14hocc5[0.212503714395]
15hocc6[0.212868942457]
\n", "
" ], "text/plain": [ " 0 1\n", "0 rate_marriage [-0.69751024616]\n", "1 age [-0.0561916647628]\n", "2 yrs_married [0.10377681236]\n", "3 children [0.0182911397779]\n", "4 religious [-0.36832290498]\n", "5 educ [0.00890195753587]\n", "6 occ2 [0.296111837283]\n", "7 occ3 [0.606052332054]\n", "8 occ4 [0.343259482442]\n", "9 occ5 [0.94006396288]\n", "10 occ6 [0.919085673732]\n", "11 hocc2 [0.219634563784]\n", "12 hocc3 [0.323455986054]\n", "13 hocc4 [0.189038509875]\n", "14 hocc5 [0.212503714395]\n", "15 hocc6 [0.212868942457]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coeff_df" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train,X_test,Y_train,Y_test = train_test_split(X,Y)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_model2 = LogisticRegression()\n", "\n", "log_model2.fit(X_train,Y_train)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class_predict = log_model2.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.722361809045\n" ] } ], "source": [ "print metrics.accuracy_score(Y_test,class_predict)" ] }, { "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": "2.7.7" } }, "nbformat": 4, "nbformat_minor": 0 }