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jad2192/Census_Income_Model.ipynb

Created February 23, 2017 01:40
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Census_Income_Model.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt\n",
"import pandas as pd\n",
"import sklearn\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_df = pd.read_csv('/home/james/anaconda3/data/censusdata.csv',header=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the Census Income data set from UCI Machine learning repository.\n",
"https://archive.ics.uci.edu/ml/datasets/Adult\n",
"\n",
"The data is a mix of various continuous and categorical features to be described in the above link and the goal is predict whether the adult makes over $50,000 annually.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>native_country</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>State-gov</td>\n",
" <td>77516</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Never-married</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>2174</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>Self-emp-not-inc</td>\n",
" <td>83311</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>215646</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>Private</td>\n",
" <td>234721</td>\n",
" <td>11th</td>\n",
" <td>7</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Husband</td>\n",
" <td>Black</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>Private</td>\n",
" <td>338409</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Wife</td>\n",
" <td>Black</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>Cuba</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num \\\n",
"0 39 State-gov 77516 Bachelors 13 \n",
"1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
"2 38 Private 215646 HS-grad 9 \n",
"3 53 Private 234721 11th 7 \n",
"4 28 Private 338409 Bachelors 13 \n",
"\n",
" marital_status occupation relationship race sex \\\n",
"0 Never-married Adm-clerical Not-in-family White Male \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Divorced Handlers-cleaners Not-in-family White Male \n",
"3 Married-civ-spouse Handlers-cleaners Husband Black Male \n",
"4 Married-civ-spouse Prof-specialty Wife Black Female \n",
"\n",
" capital_gain capital_loss hours_per_week native_country label \n",
"0 2174 0 40 United-States <=50K \n",
"1 0 0 13 United-States <=50K \n",
"2 0 0 40 United-States <=50K \n",
"3 0 0 40 United-States <=50K \n",
"4 0 0 40 Cuba <=50K "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_df.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status',\n",
" 'occupation', 'relationship', 'race','sex', 'capital_gain', 'capital_loss', \n",
" 'hours_per_week', 'native_country', 'label']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"categorical_features = ['workclass', 'education', 'marital_status', 'occupation', 'relationship',\n",
" 'race', 'sex', 'native_country', 'label']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_df_dum = pd.get_dummies(data_df, columns = categorical_features, drop_first=1 )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the pandas' method get_dummies to do a 'one-hot-encoding' of the categorical features to use in the classification model. "
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th>education_num</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>workclass_ Federal-gov</th>\n",
" <th>workclass_ Local-gov</th>\n",
" <th>workclass_ Never-worked</th>\n",
" <th>workclass_ Private</th>\n",
" <th>...</th>\n",
" <th>native_country_ Puerto-Rico</th>\n",
" <th>native_country_ Scotland</th>\n",
" <th>native_country_ South</th>\n",
" <th>native_country_ Taiwan</th>\n",
" <th>native_country_ Thailand</th>\n",
" <th>native_country_ Trinadad&amp;Tobago</th>\n",
" <th>native_country_ United-States</th>\n",
" <th>native_country_ Vietnam</th>\n",
" <th>native_country_ Yugoslavia</th>\n",
" <th>label_ &gt;50K</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>77516</td>\n",
" <td>13</td>\n",
" <td>2174</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>83311</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>215646</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>234721</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>338409</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education_num capital_gain capital_loss hours_per_week \\\n",
"0 39 77516 13 2174 0 40 \n",
"1 50 83311 13 0 0 13 \n",
"2 38 215646 9 0 0 40 \n",
"3 53 234721 7 0 0 40 \n",
"4 28 338409 13 0 0 40 \n",
"\n",
" workclass_ Federal-gov workclass_ Local-gov workclass_ Never-worked \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"\n",
" workclass_ Private ... native_country_ Puerto-Rico \\\n",
"0 0.0 ... 0.0 \n",
"1 0.0 ... 0.0 \n",
"2 1.0 ... 0.0 \n",
"3 1.0 ... 0.0 \n",
"4 1.0 ... 0.0 \n",
"\n",
" native_country_ Scotland native_country_ South native_country_ Taiwan \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"\n",
" native_country_ Thailand native_country_ Trinadad&Tobago \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"\n",
" native_country_ United-States native_country_ Vietnam \\\n",
"0 1.0 0.0 \n",
"1 1.0 0.0 \n",
"2 1.0 0.0 \n",
"3 1.0 0.0 \n",
"4 0.0 0.0 \n",
"\n",
" native_country_ Yugoslavia label_ >50K \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"\n",
"[5 rows x 101 columns]"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df_dum.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = data_df_dum.ix[:,:100].as_matrix()\n",
"Y = data_df_dum['label_ >50K'].as_matrix()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.33, random_state=5 )"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clf_1 = GradientBoostingClassifier(learning_rate=0.009, n_estimators=1200, subsample=0.95, verbose=1,\n",
" max_depth=6)\n",
"clf_2 = RandomForestClassifier(n_estimators=500,verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Iter Train Loss OOB Improve Remaining Time \n",
" 1 1.0966 0.0077 3.39m\n",
" 2 1.0883 0.0079 3.42m\n",
" 3 1.0783 0.0077 3.36m\n",
" 4 1.0722 0.0074 3.37m\n",
" 5 1.0652 0.0077 3.34m\n",
" 6 1.0585 0.0073 3.35m\n",
" 7 1.0519 0.0064 3.32m\n",
" 8 1.0445 0.0065 3.32m\n",
" 9 1.0394 0.0061 3.31m\n",
" 10 1.0330 0.0058 3.31m\n",
" 20 0.9723 0.0046 3.27m\n",
" 30 0.9252 0.0039 3.23m\n",
" 40 0.8870 0.0035 3.21m\n",
" 50 0.8493 0.0032 3.18m\n",
" 60 0.8220 0.0026 3.15m\n",
" 70 0.7944 0.0026 3.12m\n",
" 80 0.7741 0.0020 3.09m\n",
" 90 0.7561 0.0018 3.06m\n",
" 100 0.7357 0.0016 3.04m\n",
" 200 0.6282 0.0007 2.79m\n",
" 300 0.5797 0.0002 2.50m\n",
" 400 0.5538 0.0002 2.18m\n",
" 500 0.5334 0.0001 1.88m\n",
" 600 0.5174 0.0001 1.58m\n",
" 700 0.5040 0.0000 1.30m\n",
" 800 0.4923 -0.0000 1.02m\n",
" 900 0.4838 -0.0001 45.49s\n",
" 1000 0.4747 -0.0000 29.95s\n"
]
},
{
"data": {
"text/plain": [
"GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
" learning_rate=0.009, loss='deviance', max_depth=6,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=1200, presort='auto', random_state=None,\n",
" subsample=0.95, verbose=1, warm_start=False)"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_1.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The error rate on the hold out set is : 12.58 %\n"
]
}
],
"source": [
"print('The error rate on the hold out set is :', \n",
" round(100 - 100 * accuracy_score(y_test, clf_1.predict(X_test)),2), '%')"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed: 8.6s finished\n"
]
},
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=500, n_jobs=1, oob_score=False, random_state=None,\n",
" verbose=1, warm_start=False)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_2.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The error rate on the hold out set is : 14.41 %\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed: 0.8s finished\n"
]
}
],
"source": [
"print('The error rate on the hold out set is :', \n",
" round(100 - 100 * accuracy_score(y_test, clf_2.predict(X_test)),2), '%')"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"confusion_grad = pd.DataFrame(confusion_matrix(y_test, clf_1.predict(X_test)))\n",
"confusion_grad.columns = ['Predicted: Under 50K', 'Predicted: Over 50K']\n",
"confusion_grad.index = ['Actual: Under 50K', 'Actual: Over 50K']"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Predicted: Under 50K</th>\n",
" <th>Predicted: Over 50K</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Actual: Under 50K</th>\n",
" <td>7736</td>\n",
" <td>421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Actual: Over 50K</th>\n",
" <td>931</td>\n",
" <td>1658</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Predicted: Under 50K Predicted: Over 50K\n",
"Actual: Under 50K 7736 421\n",
"Actual: Over 50K 931 1658"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"confusion_grad"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 500 out of 500 | elapsed: 0.8s finished\n"
]
}
],
"source": [
"confusion_rf = pd.DataFrame(confusion_matrix(y_test, clf_2.predict(X_test)))\n",
"confusion_rf.columns = ['Predicted: Under 50K', 'Predicted: Over 50K']\n",
"confusion_rf.index = ['Actual: Under 50K', 'Actual: Over 50K']"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Predicted: Under 50K</th>\n",
" <th>Predicted: Over 50K</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Actual: Under 50K</th>\n",
" <td>7596</td>\n",
" <td>561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Actual: Over 50K</th>\n",
" <td>988</td>\n",
" <td>1601</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Predicted: Under 50K Predicted: Over 50K\n",
"Actual: Under 50K 7596 561\n",
"Actual: Over 50K 988 1601"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"confusion_rf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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