{
"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": [
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" rate_marriage age yrs_married children religious educ occupation \\\n",
"0 3 32 9.0 3 3 17 2 \n",
"1 3 27 13.0 3 1 14 3 \n",
"2 4 22 2.5 0 1 16 3 \n",
"3 4 37 16.5 4 3 16 5 \n",
"4 5 27 9.0 1 1 14 3 \n",
"\n",
" occupation_husb affairs \n",
"0 5 0.111111 \n",
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"2 5 1.400000 \n",
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},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
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"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def affair_check(x):\n",
" if x != 0:\n",
" return 1\n",
" else:\n",
" return 0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['Had_Affair'] = df['affairs'].apply(affair_check)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
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6366 rows × 10 columns
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" rate_marriage age yrs_married children religious educ occupation \\\n",
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"... ... ... ... ... ... ... ... \n",
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"\n",
" occupation_husb affairs Had_Affair \n",
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"... ... ... ... \n",
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"\n",
"[6366 rows x 10 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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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",
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},
"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"
},
{
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],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('age',data=df,hue='Had_Affair',palette='coolwarm')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('yrs_married',data=df,hue='Had_Affair',palette='coolwarm')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot('children',data=df,hue='Had_Affair',palette='coolwarm')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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},
"metadata": {},
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],
"source": [
"sns.factorplot('educ',data=df,hue='Had_Affair',palette='coolwarm')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"occ_dummies = pd.get_dummies(df['occupation'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hus_occ_dummies = pd.get_dummies(df['occupation_husb'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"execution_count": 17,
"metadata": {
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},
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"metadata": {
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},
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"execution_count": 25,
"metadata": {
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},
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{
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"execution_count": 25,
"metadata": {},
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"Y = df.Had_Affair\n",
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"metadata": {
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},
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"metadata": {
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"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": [
"\n",
"
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" | \n",
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" \n",
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" rate_marriage | \n",
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" 1 | \n",
" age | \n",
" [-0.0561916647628] | \n",
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" children | \n",
" [0.0182911397779] | \n",
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" religious | \n",
" [-0.36832290498] | \n",
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" educ | \n",
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" hocc2 | \n",
" [0.219634563784] | \n",
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" 12 | \n",
" hocc3 | \n",
" [0.323455986054] | \n",
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" hocc4 | \n",
" [0.189038509875] | \n",
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" \n",
" 14 | \n",
" hocc5 | \n",
" [0.212503714395] | \n",
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" \n",
" 15 | \n",
" hocc6 | \n",
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"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
}