give-me-some-credit.ipynb

{"nbformat_minor": 0, "cells": [{"source": "# Conversion Logic Lunch & Learn - Predictive Modeling Part 1\nToday, we will go over common practices in predictive modeling - data exploration, visualization, modeling, and model validation.  \nWe will use the [Give Me Some Credit competition data](https://www.kaggle.com/c/GiveMeSomeCredit), for which the goal is to predict the default probability of each customer given some input variables.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 1, "cell_type": "code", "source": "%matplotlib inline", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"source": "Most popular and useful Python packages for predictive modeling are [Numpy/Scipy](http://www.scipy.org/), [Pandas](http://pandas.pydata.org/), and [Scikit-Learn](http://scikit-learn.org/stable/).", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "from __future__ import division\n\nfrom matplotlib import pyplot as plt\nfrom sklearn.cross_validation import cross_val_score, train_test_split, StratifiedKFold, LeaveOneOut\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import LinearSVC, SVC\nfrom sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, BaggingClassifier\nfrom sklearn.metrics import roc_curve, roc_auc_score, log_loss\n\nimport numpy as np\nimport pandas as pd", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## 1. Data Exploration", "cell_type": "markdown", "metadata": {}}, {"execution_count": 3, "cell_type": "code", "source": "trn = pd.read_csv('cs-training.csv', index_col=0)\ntst = pd.read_csv('cs-test.csv', index_col=0)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 4, "cell_type": "code", "source": "trn.head()", "outputs": [{"execution_count": 4, "output_type": "execute_result", "data": {"text/plain": "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n1                 1                              0.766127   45   \n2                 0                              0.957151   40   \n3                 0                              0.658180   38   \n4                 0                              0.233810   30   \n5                 0                              0.907239   49   \n\n   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n1                                     2   0.802982           9120   \n2                                     0   0.121876           2600   \n3                                     1   0.085113           3042   \n4                                     0   0.036050           3300   \n5                                     1   0.024926          63588   \n\n   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n1                               13                        0   \n2                                4                        0   \n3                                2                        1   \n4                                5                        0   \n5                                7                        0   \n\n   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n1                             6                                     0   \n2                             0                                     0   \n3                             0                                     0   \n4                             0                                     0   \n5                             1                                     0   \n\n   NumberOfDependents  \n1                   2  \n2                   1  \n3                   0  \n4                   0  \n5                   0  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SeriousDlqin2yrs</th>\n      <th>RevolvingUtilizationOfUnsecuredLines</th>\n      <th>age</th>\n      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n      <th>DebtRatio</th>\n      <th>MonthlyIncome</th>\n      <th>NumberOfOpenCreditLinesAndLoans</th>\n      <th>NumberOfTimes90DaysLate</th>\n      <th>NumberRealEstateLoansOrLines</th>\n      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n      <th>NumberOfDependents</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td> 1</td>\n      <td> 0.766127</td>\n      <td> 45</td>\n      <td> 2</td>\n      <td> 0.802982</td>\n      <td>  9120</td>\n      <td> 13</td>\n      <td> 0</td>\n      <td> 6</td>\n      <td> 0</td>\n      <td> 2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 0</td>\n      <td> 0.957151</td>\n      <td> 40</td>\n      <td> 0</td>\n      <td> 0.121876</td>\n      <td>  2600</td>\n      <td>  4</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 0</td>\n      <td> 0.658180</td>\n      <td> 38</td>\n      <td> 1</td>\n      <td> 0.085113</td>\n      <td>  3042</td>\n      <td>  2</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 0</td>\n      <td> 0.233810</td>\n      <td> 30</td>\n      <td> 0</td>\n      <td> 0.036050</td>\n      <td>  3300</td>\n      <td>  5</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td> 0</td>\n      <td> 0.907239</td>\n      <td> 49</td>\n      <td> 1</td>\n      <td> 0.024926</td>\n      <td> 63588</td>\n      <td>  7</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "tst.head()", "outputs": [{"execution_count": 5, "output_type": "execute_result", "data": {"text/plain": "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n1               NaN                              0.885519   43   \n2               NaN                              0.463295   57   \n3               NaN                              0.043275   59   \n4               NaN                              0.280308   38   \n5               NaN                              1.000000   27   \n\n   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n1                                     0   0.177513           5700   \n2                                     0   0.527237           9141   \n3                                     0   0.687648           5083   \n4                                     1   0.925961           3200   \n5                                     0   0.019917           3865   \n\n   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n1                                4                        0   \n2                               15                        0   \n3                               12                        0   \n4                                7                        0   \n5                                4                        0   \n\n   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n1                             0                                     0   \n2                             4                                     0   \n3                             1                                     0   \n4                             2                                     0   \n5                             0                                     0   \n\n   NumberOfDependents  \n1                   0  \n2                   2  \n3                   2  \n4                   0  \n5                   1  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SeriousDlqin2yrs</th>\n      <th>RevolvingUtilizationOfUnsecuredLines</th>\n      <th>age</th>\n      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n      <th>DebtRatio</th>\n      <th>MonthlyIncome</th>\n      <th>NumberOfOpenCreditLinesAndLoans</th>\n      <th>NumberOfTimes90DaysLate</th>\n      <th>NumberRealEstateLoansOrLines</th>\n      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n      <th>NumberOfDependents</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td> 0.885519</td>\n      <td> 43</td>\n      <td> 0</td>\n      <td> 0.177513</td>\n      <td> 5700</td>\n      <td>  4</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td> 0.463295</td>\n      <td> 57</td>\n      <td> 0</td>\n      <td> 0.527237</td>\n      <td> 9141</td>\n      <td> 15</td>\n      <td> 0</td>\n      <td> 4</td>\n      <td> 0</td>\n      <td> 2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td> 0.043275</td>\n      <td> 59</td>\n      <td> 0</td>\n      <td> 0.687648</td>\n      <td> 5083</td>\n      <td> 12</td>\n      <td> 0</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> 2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td> 0.280308</td>\n      <td> 38</td>\n      <td> 1</td>\n      <td> 0.925961</td>\n      <td> 3200</td>\n      <td>  7</td>\n      <td> 0</td>\n      <td> 2</td>\n      <td> 0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>NaN</td>\n      <td> 1.000000</td>\n      <td> 27</td>\n      <td> 0</td>\n      <td> 0.019917</td>\n      <td> 3865</td>\n      <td>  4</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "trn.shape, tst.shape", "outputs": [{"execution_count": 6, "output_type": "execute_result", "data": {"text/plain": "((150000, 11), (101503, 11))"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 7, "cell_type": "code", "source": "trn.describe()", "outputs": [{"execution_count": 7, "output_type": "execute_result", "data": {"text/plain": "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\ncount     150000.000000                         150000.000000  150000.000000   \nmean           0.066840                              6.048438      52.295207   \nstd            0.249746                            249.755371      14.771866   \nmin            0.000000                              0.000000       0.000000   \n25%            0.000000                              0.029867      41.000000   \n50%            0.000000                              0.154181      52.000000   \n75%            0.000000                              0.559046      63.000000   \nmax            1.000000                          50708.000000     109.000000   \n\n       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio   MonthlyIncome  \\\ncount                         150000.000000  150000.000000   120269.000000   \nmean                               0.421033     353.005076     6670.221237   \nstd                                4.192781    2037.818523    14384.674215   \nmin                                0.000000       0.000000        0.000000   \n25%                                0.000000       0.175074     3400.000000   \n50%                                0.000000       0.366508     5400.000000   \n75%                                0.000000       0.868254     8249.000000   \nmax                               98.000000  329664.000000  3008750.000000   \n\n       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\ncount                    150000.000000            150000.000000   \nmean                          8.452760                 0.265973   \nstd                           5.145951                 4.169304   \nmin                           0.000000                 0.000000   \n25%                           5.000000                 0.000000   \n50%                           8.000000                 0.000000   \n75%                          11.000000                 0.000000   \nmax                          58.000000                98.000000   \n\n       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\ncount                 150000.000000                         150000.000000   \nmean                       1.018240                              0.240387   \nstd                        1.129771                              4.155179   \nmin                        0.000000                              0.000000   \n25%                        0.000000                              0.000000   \n50%                        1.000000                              0.000000   \n75%                        2.000000                              0.000000   \nmax                       54.000000                             98.000000   \n\n       NumberOfDependents  \ncount       146076.000000  \nmean             0.757222  \nstd              1.115086  \nmin              0.000000  \n25%              0.000000  \n50%              0.000000  \n75%              1.000000  \nmax             20.000000  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SeriousDlqin2yrs</th>\n      <th>RevolvingUtilizationOfUnsecuredLines</th>\n      <th>age</th>\n      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n      <th>DebtRatio</th>\n      <th>MonthlyIncome</th>\n      <th>NumberOfOpenCreditLinesAndLoans</th>\n      <th>NumberOfTimes90DaysLate</th>\n      <th>NumberRealEstateLoansOrLines</th>\n      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n      <th>NumberOfDependents</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 150000.000000</td>\n      <td> 150000.000000</td>\n      <td> 150000.000000</td>\n      <td> 150000.000000</td>\n      <td> 150000.000000</td>\n      <td>  120269.000000</td>\n      <td> 150000.000000</td>\n      <td> 150000.000000</td>\n      <td> 150000.000000</td>\n      <td> 150000.000000</td>\n      <td> 146076.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>      0.066840</td>\n      <td>      6.048438</td>\n      <td>     52.295207</td>\n      <td>      0.421033</td>\n      <td>    353.005076</td>\n      <td>    6670.221237</td>\n      <td>      8.452760</td>\n      <td>      0.265973</td>\n      <td>      1.018240</td>\n      <td>      0.240387</td>\n      <td>      0.757222</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>      0.249746</td>\n      <td>    249.755371</td>\n      <td>     14.771866</td>\n      <td>      4.192781</td>\n      <td>   2037.818523</td>\n      <td>   14384.674215</td>\n      <td>      5.145951</td>\n      <td>      4.169304</td>\n      <td>      1.129771</td>\n      <td>      4.155179</td>\n      <td>      1.115086</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>       0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>      0.000000</td>\n      <td>      0.029867</td>\n      <td>     41.000000</td>\n      <td>      0.000000</td>\n      <td>      0.175074</td>\n      <td>    3400.000000</td>\n      <td>      5.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>      0.000000</td>\n      <td>      0.154181</td>\n      <td>     52.000000</td>\n      <td>      0.000000</td>\n      <td>      0.366508</td>\n      <td>    5400.000000</td>\n      <td>      8.000000</td>\n      <td>      0.000000</td>\n      <td>      1.000000</td>\n      <td>      0.000000</td>\n      <td>      0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>      0.000000</td>\n      <td>      0.559046</td>\n      <td>     63.000000</td>\n      <td>      0.000000</td>\n      <td>      0.868254</td>\n      <td>    8249.000000</td>\n      <td>     11.000000</td>\n      <td>      0.000000</td>\n      <td>      2.000000</td>\n      <td>      0.000000</td>\n      <td>      1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>      1.000000</td>\n      <td>  50708.000000</td>\n      <td>    109.000000</td>\n      <td>     98.000000</td>\n      <td> 329664.000000</td>\n      <td> 3008750.000000</td>\n      <td>     58.000000</td>\n      <td>     98.000000</td>\n      <td>     54.000000</td>\n      <td>     98.000000</td>\n      <td>     20.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "Note that MonthlyIncome and NumberOfDependents have missing values.  We will need to impute them to build a model.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 8, "cell_type": "code", "source": "_ = pd.tools.plotting.scatter_matrix(trn.ix[:, :5], alpha=0.2, figsize=(16, 16), diagonal='kde')", "outputs": [{"output_type": "display_data", "data": {"image/png": 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aVZu3PQvgYrjjqs3bzmku3zQxqW3Z76bxL7XP3rrjebtHDj/pxCVDdwDXd/ND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et23XcH5oov37RwvDTPP0G6+65KI/rWwrP9zRlH3sFacseqBec1vW2sZM0AAT35NK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\nP8ulDvsvS5ZxH9GyP29x13MWfW2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"text/plain": "<matplotlib.figure.Figure at 0x1086a78d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 9, "cell_type": "code", "source": "trn.NumberOfDependents.value_counts()", "outputs": [{"execution_count": 9, "output_type": "execute_result", "data": {"text/plain": "0     86902\n1     26316\n2     19522\n3      9483\n4      2862\n5       746\n6       158\n7        51\n8        24\n10        5\n9         5\n20        1\n13        1\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "trn.pivot_table(values='SeriousDlqin2yrs', index='NumberOfDependents')", "outputs": [{"execution_count": 10, "output_type": "execute_result", "data": {"text/plain": "NumberOfDependents\n0                     0.058629\n1                     0.073529\n2                     0.081139\n3                     0.088263\n4                     0.103774\n5                     0.091153\n6                     0.151899\n7                     0.098039\n8                     0.083333\n9                     0.000000\n10                    0.000000\n13                    0.000000\n20                    0.000000\nName: SeriousDlqin2yrs, dtype: float64"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": 11, "cell_type": "code", "source": "trn.plot(figsize=(16, 8), kind='scatter', y='SeriousDlqin2yrs', x='NumberOfDependents')", "outputs": [{"execution_count": 11, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10f61bf50>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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8si8tbkuwM5gzoZpMUU2mqCZTVJInxraesntZkl9M8ou99yOttZOT/O5itwUAAACbd8rx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\nACbn/wdXMHSsbLWxPgAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x10e191d10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## 2. Data Preparation", "cell_type": "markdown", "metadata": {}}, {"execution_count": 12, "cell_type": "code", "source": "trn.ix[pd.isnull(trn.MonthlyIncome), :].describe()", "outputs": [{"execution_count": 12, "output_type": "execute_result", "data": {"text/plain": "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines           age  \\\ncount      29731.000000                          29731.000000  29731.000000   \nmean           0.056137                              6.649421     56.362349   \nstd            0.230189                            217.814854     15.438786   \nmin            0.000000                              0.000000     21.000000   \n25%            0.000000                              0.016027     46.000000   \n50%            0.000000                              0.081697     57.000000   \n75%            0.000000                              0.440549     67.000000   \nmax            1.000000                          22198.000000    109.000000   \n\n       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\ncount                          29731.000000   29731.000000              0   \nmean                               0.579866    1673.396556            NaN   \nstd                                6.255361    4248.372895            NaN   \nmin                                0.000000       0.000000            NaN   \n25%                                0.000000     123.000000            NaN   \n50%                                0.000000    1159.000000            NaN   \n75%                                0.000000    2382.000000            NaN   \nmax                               98.000000  329664.000000            NaN   \n\n       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\ncount                     29731.000000             29731.000000   \nmean                          7.216071                 0.484612   \nstd                           4.842720                 6.250408   \nmin                           0.000000                 0.000000   \n25%                           4.000000                 0.000000   \n50%                           6.000000                 0.000000   \n75%                          10.000000                 0.000000   \nmax                          45.000000                98.000000   \n\n       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\ncount                  29731.000000                          29731.000000   \nmean                       0.871481                              0.452995   \nstd                        1.034291                              6.242076   \nmin                        0.000000                              0.000000   \n25%                        0.000000                              0.000000   \n50%                        1.000000                              0.000000   \n75%                        1.000000                              0.000000   \nmax                       23.000000                             98.000000   \n\n       NumberOfDependents  \ncount        25807.000000  \nmean             0.316310  \nstd              0.809944  \nmin              0.000000  \n25%              0.000000  \n50%              0.000000  \n75%              0.000000  \nmax              9.000000  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SeriousDlqin2yrs</th>\n      <th>RevolvingUtilizationOfUnsecuredLines</th>\n      <th>age</th>\n      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n      <th>DebtRatio</th>\n      <th>MonthlyIncome</th>\n      <th>NumberOfOpenCreditLinesAndLoans</th>\n      <th>NumberOfTimes90DaysLate</th>\n      <th>NumberRealEstateLoansOrLines</th>\n      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n      <th>NumberOfDependents</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 29731.000000</td>\n      <td> 29731.000000</td>\n      <td> 29731.000000</td>\n      <td> 29731.000000</td>\n      <td>  29731.000000</td>\n      <td>  0</td>\n      <td> 29731.000000</td>\n      <td> 29731.000000</td>\n      <td> 29731.000000</td>\n      <td> 29731.000000</td>\n      <td> 25807.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>     0.056137</td>\n      <td>     6.649421</td>\n      <td>    56.362349</td>\n      <td>     0.579866</td>\n      <td>   1673.396556</td>\n      <td>NaN</td>\n      <td>     7.216071</td>\n      <td>     0.484612</td>\n      <td>     0.871481</td>\n      <td>     0.452995</td>\n      <td>     0.316310</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>     0.230189</td>\n      <td>   217.814854</td>\n      <td>    15.438786</td>\n      <td>     6.255361</td>\n      <td>   4248.372895</td>\n      <td>NaN</td>\n      <td>     4.842720</td>\n      <td>     6.250408</td>\n      <td>     1.034291</td>\n      <td>     6.242076</td>\n      <td>     0.809944</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n      <td>    21.000000</td>\n      <td>     0.000000</td>\n      <td>      0.000000</td>\n      <td>NaN</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>     0.000000</td>\n      <td>     0.016027</td>\n      <td>    46.000000</td>\n      <td>     0.000000</td>\n      <td>    123.000000</td>\n      <td>NaN</td>\n      <td>     4.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>     0.000000</td>\n      <td>     0.081697</td>\n      <td>    57.000000</td>\n      <td>     0.000000</td>\n      <td>   1159.000000</td>\n      <td>NaN</td>\n      <td>     6.000000</td>\n      <td>     0.000000</td>\n      <td>     1.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>     0.000000</td>\n      <td>     0.440549</td>\n      <td>    67.000000</td>\n      <td>     0.000000</td>\n      <td>   2382.000000</td>\n      <td>NaN</td>\n      <td>    10.000000</td>\n      <td>     0.000000</td>\n      <td>     1.000000</td>\n      <td>     0.000000</td>\n      <td>     0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>     1.000000</td>\n      <td> 22198.000000</td>\n      <td>   109.000000</td>\n      <td>    98.000000</td>\n      <td> 329664.000000</td>\n      <td>NaN</td>\n      <td>    45.000000</td>\n      <td>    98.000000</td>\n      <td>    23.000000</td>\n      <td>    98.000000</td>\n      <td>     9.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 13, "cell_type": "code", "source": "trn.ix[pd.isnull(trn.NumberOfDependents), :].describe()", "outputs": [{"execution_count": 13, "output_type": "execute_result", "data": {"text/plain": "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines          age  \\\ncount       3924.000000                           3924.000000  3924.000000   \nmean           0.045617                             10.745132    59.588940   \nstd            0.208679                            237.699246    18.634168   \nmin            0.000000                              0.000000    21.000000   \n25%            0.000000                              0.008474    48.000000   \n50%            0.000000                              0.047458    61.000000   \n75%            0.000000                              0.268155    74.000000   \nmax            1.000000                          10821.000000   109.000000   \n\n       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\ncount                           3924.000000    3924.000000              0   \nmean                               0.908257    1083.812181            NaN   \nstd                                8.679394    4186.731843            NaN   \nmin                                0.000000       0.000000            NaN   \n25%                                0.000000      21.000000            NaN   \n50%                                0.000000     358.000000            NaN   \n75%                                0.000000    1559.000000            NaN   \nmax                               98.000000  220516.000000            NaN   \n\n       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\ncount                      3924.000000              3924.000000   \nmean                          5.604230                 0.834608   \nstd                           4.096353                 8.679228   \nmin                           0.000000                 0.000000   \n25%                           3.000000                 0.000000   \n50%                           5.000000                 0.000000   \n75%                           8.000000                 0.000000   \nmax                          30.000000                98.000000   \n\n       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\ncount                   3924.000000                           3924.000000   \nmean                       0.590979                              0.812181   \nstd                        0.914455                              8.678008   \nmin                        0.000000                              0.000000   \n25%                        0.000000                              0.000000   \n50%                        0.000000                              0.000000   \n75%                        1.000000                              0.000000   \nmax                       15.000000                             98.000000   \n\n       NumberOfDependents  \ncount                   0  \nmean                  NaN  \nstd                   NaN  \nmin                   NaN  \n25%                   NaN  \n50%                   NaN  \n75%                   NaN  \nmax                   NaN  ", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>SeriousDlqin2yrs</th>\n      <th>RevolvingUtilizationOfUnsecuredLines</th>\n      <th>age</th>\n      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n      <th>DebtRatio</th>\n      <th>MonthlyIncome</th>\n      <th>NumberOfOpenCreditLinesAndLoans</th>\n      <th>NumberOfTimes90DaysLate</th>\n      <th>NumberRealEstateLoansOrLines</th>\n      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n      <th>NumberOfDependents</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td> 3924.000000</td>\n      <td>  3924.000000</td>\n      <td> 3924.000000</td>\n      <td> 3924.000000</td>\n      <td>   3924.000000</td>\n      <td>  0</td>\n      <td> 3924.000000</td>\n      <td> 3924.000000</td>\n      <td> 3924.000000</td>\n      <td> 3924.000000</td>\n      <td>  0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>    0.045617</td>\n      <td>    10.745132</td>\n      <td>   59.588940</td>\n      <td>    0.908257</td>\n      <td>   1083.812181</td>\n      <td>NaN</td>\n      <td>    5.604230</td>\n      <td>    0.834608</td>\n      <td>    0.590979</td>\n      <td>    0.812181</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>    0.208679</td>\n      <td>   237.699246</td>\n      <td>   18.634168</td>\n      <td>    8.679394</td>\n      <td>   4186.731843</td>\n      <td>NaN</td>\n      <td>    4.096353</td>\n      <td>    8.679228</td>\n      <td>    0.914455</td>\n      <td>    8.678008</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>    0.000000</td>\n      <td>     0.000000</td>\n      <td>   21.000000</td>\n      <td>    0.000000</td>\n      <td>      0.000000</td>\n      <td>NaN</td>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>    0.000000</td>\n      <td>     0.008474</td>\n      <td>   48.000000</td>\n      <td>    0.000000</td>\n      <td>     21.000000</td>\n      <td>NaN</td>\n      <td>    3.000000</td>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>    0.000000</td>\n      <td>     0.047458</td>\n      <td>   61.000000</td>\n      <td>    0.000000</td>\n      <td>    358.000000</td>\n      <td>NaN</td>\n      <td>    5.000000</td>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n      <td>    0.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>    0.000000</td>\n      <td>     0.268155</td>\n      <td>   74.000000</td>\n      <td>    0.000000</td>\n      <td>   1559.000000</td>\n      <td>NaN</td>\n      <td>    8.000000</td>\n      <td>    0.000000</td>\n      <td>    1.000000</td>\n      <td>    0.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>    1.000000</td>\n      <td> 10821.000000</td>\n      <td>  109.000000</td>\n      <td>   98.000000</td>\n      <td> 220516.000000</td>\n      <td>NaN</td>\n      <td>   30.000000</td>\n      <td>   98.000000</td>\n      <td>   15.000000</td>\n      <td>   98.000000</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 14, "cell_type": "code", "source": "df = pd.concat([trn, tst], axis=0)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 15, "cell_type": "code", "source": "n_trn = trn.shape[0]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 16, "cell_type": "code", "source": "df.shape", "outputs": [{"execution_count": 16, "output_type": "execute_result", "data": {"text/plain": "(251503, 11)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 17, "cell_type": "code", "source": "df.fillna(0, inplace=True)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"source": "## 3. Modeling", "cell_type": "markdown", "metadata": {}}, {"source": "### 3.1. Logistic Regression", "cell_type": "markdown", "metadata": {}}, {"execution_count": 18, "cell_type": "code", "source": "lr = LogisticRegression(C=0.1, random_state=42)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 19, "cell_type": "code", "source": "X_trn = df.ix[:n_trn, 1:].values\ny_trn = df.ix[:n_trn, 'SeriousDlqin2yrs'].values", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 20, "cell_type": "code", "source": "X_tst = df.ix[n_trn:, 1:].values", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 21, "cell_type": "code", "source": "lr.fit(X_trn, y_trn)", "outputs": [{"execution_count": 21, "output_type": "execute_result", "data": {"text/plain": "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr',\n          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n          verbose=0)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 22, "cell_type": "code", "source": "p_trn = lr.predict_proba(X_trn)[:, 1]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 23, "cell_type": "code", "source": "roc_auc_score(y_trn, p_trn)", "outputs": [{"execution_count": 23, "output_type": "execute_result", "data": {"text/plain": "0.69838074085805757"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 24, "cell_type": "code", "source": "fpr, tpr, _ = roc_curve(y_trn, p_trn)\n\nplt.figure(figsize=(16, 8))\nplt.plot(fpr, tpr)\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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JwEaEV3jnAQf2/voywu67rwbqpsgfiTKkJCXGkir1wbucSoVZVgomK8dZWW8BzAV2IBwF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\n/Bvf2huDyd7xXwAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x108cccc10>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 25, "cell_type": "code", "source": "X_valtrn, X_valtst, y_valtrn, y_valtst = train_test_split(X_trn, y_trn, test_size=.2, random_state=42)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 26, "cell_type": "code", "source": "X_valtrn.shape, X_valtst.shape", "outputs": [{"execution_count": 26, "output_type": "execute_result", "data": {"text/plain": "((120000, 10), (30000, 10))"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 27, "cell_type": "code", "source": "lr.fit(X_valtrn, y_valtrn)", "outputs": [{"execution_count": 27, "output_type": "execute_result", "data": {"text/plain": "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr',\n          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n          verbose=0)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 28, "cell_type": "code", "source": "p_valtrn = lr.predict_proba(X_valtrn)[:, 1]\np_valtst = lr.predict_proba(X_valtst)[:, 1]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 29, "cell_type": "code", "source": "auc_valtrn = roc_auc_score(y_valtrn, p_valtrn)\nauc_valtst = roc_auc_score(y_valtst, p_valtst)\nprint('Validation Training AUC: {:.4f}'.format(auc_valtrn))\nprint('Validation Test AUC: {:.4f}'.format(auc_valtst))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Validation Training AUC: 0.6990\nValidation Test AUC: 0.6926\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 30, "cell_type": "code", "source": "fpr_valtrn, tpr_valtrn, _ = roc_curve(y_valtrn, p_valtrn)\nfpr_valtst, tpr_valtst, _ = roc_curve(y_valtst, p_valtst)\n\nplt.figure(figsize=(16, 8))\nplt.plot(fpr_valtrn, tpr_valtrn, label='Validation Training ({:.4f})'.format(auc_valtrn))\nplt.plot(fpr_valtst, tpr_valtst, label='Validation Test ({:.4f})'.format(auc_valtst))\nplt.plot([0, 1], [0, 1], 'k--')\nplt.legend(loc=\"lower right\")\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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eBF4Flubi4Y6u1/bD59bAyEQsMxIXOk/ErT2dAgzGddX9m/+R0bgg9yzwEnBlpXXX9dec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\nSC3a29sPB7ra2tq+EjoWEemf9vb2DYBvtLe3f6uRhWqzTPfdH3hlhPs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"text/plain": "<matplotlib.figure.Figure at 0x10f5fb3d0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## 4. Feature Engineering", "cell_type": "markdown", "metadata": {}}, {"source": "### 4.1. Feature Transformation", "cell_type": "markdown", "metadata": {}}, {"source": "Many modeling algorithms including Logistic Regression works best if input variables follow the normal (or Gaussian) distribution.  Here our input variables are all positive with long tails.  Let's see how to deal with non-normal input variables.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 31, "cell_type": "code", "source": "df.MonthlyIncome.hist(bins=30)", "outputs": [{"execution_count": 31, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10886ac10>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x108875090>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 32, "cell_type": "code", "source": "df.MonthlyIncome.apply(np.log1p).hist(bins=30)", "outputs": [{"execution_count": 32, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10913fd90>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x110872c50>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 33, "cell_type": "code", "source": "X_trn = np.log1p(X_trn)\nX_tst = np.log1p(X_tst)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 34, "cell_type": "code", "source": "X_valtrn, X_valtst, y_valtrn, y_valtst = train_test_split(X_trn, y_trn, test_size=.2, random_state=42)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 35, "cell_type": "code", "source": "lr.fit(X_valtrn, y_valtrn)", "outputs": [{"execution_count": 35, "output_type": "execute_result", "data": {"text/plain": "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr',\n          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n          verbose=0)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 36, "cell_type": "code", "source": "p_valtrn = lr.predict_proba(X_valtrn)[:, 1]\np_valtst = lr.predict_proba(X_valtst)[:, 1]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 37, "cell_type": "code", "source": "auc_valtrn = roc_auc_score(y_valtrn, p_valtrn)\nauc_valtst = roc_auc_score(y_valtst, p_valtst)\nprint('Validation Training AUC: {:.4f}'.format(auc_valtrn))\nprint('Validation Test AUC: {:.4f}'.format(auc_valtst))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Validation Training AUC: 0.8345\nValidation Test AUC: 0.8302\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### 4.2. Feature Creation", "cell_type": "markdown", "metadata": {}}, {"execution_count": 38, "cell_type": "code", "source": "df['Debt'] = df.DebtRatio * df.MonthlyIncome.apply(lambda x: 1. if x == 0. else x)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 39, "cell_type": "code", "source": "df.Debt.describe()", "outputs": [{"execution_count": 39, "output_type": "execute_result", "data": {"text/plain": "count    251503.000000\nmean       2030.304078\nstd        3837.276816\nmin           0.000000\n25%         528.000000\n50%        1547.793630\n75%        2793.745277\nmax      478450.559160\nName: Debt, dtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 40, "cell_type": "code", "source": "df.Debt.fillna(0, inplace=True)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 41, "cell_type": "code", "source": "X_trn = np.log1p(df.ix[:n_trn, 1:].values)\nX_tst = np.log1p(df.ix[n_trn:, 1:].values)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 42, "cell_type": "code", "source": "X_valtrn, X_valtst, y_valtrn, y_valtst = train_test_split(X_trn, y_trn, test_size=.2, random_state=42)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 43, "cell_type": "code", "source": "lr.fit(X_valtrn, y_valtrn)", "outputs": [{"execution_count": 43, "output_type": "execute_result", "data": {"text/plain": "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr',\n          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n          verbose=0)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 44, "cell_type": "code", "source": "p_valtrn_lr = lr.predict_proba(X_valtrn)[:, 1]\np_valtst_lr = lr.predict_proba(X_valtst)[:, 1]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 45, "cell_type": "code", "source": "auc_valtrn_lr = roc_auc_score(y_valtrn, p_valtrn_lr)\nauc_valtst_lr = roc_auc_score(y_valtst, p_valtst_lr)\nprint('Validation Training AUC: {:.4f}'.format(auc_valtrn_lr))\nprint('Validation Test AUC: {:.4f}'.format(auc_valtst_lr))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Validation Training AUC: 0.8314\nValidation Test AUC: 0.8259\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## 3. Modeling - Cont.", "cell_type": "markdown", "metadata": {}}, {"source": "### 3.2. Support Vector Machine (SVM)", "cell_type": "markdown", "metadata": {}}, {"execution_count": 46, "cell_type": "code", "source": "svm = LinearSVC(C=0.1, random_state=42)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 47, "cell_type": "code", "source": "svm.fit(X_valtrn, y_valtrn)", "outputs": [{"execution_count": 47, "output_type": "execute_result", "data": {"text/plain": "LinearSVC(C=0.1, class_weight=None, dual=True, fit_intercept=True,\n     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n     verbose=0)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 49, "cell_type": "code", "source": "p_valtrn_svm = svm.decision_function(X_valtrn)\np_valtst_svm = svm.decision_function(X_valtst)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 50, "cell_type": "code", "source": "auc_valtrn_svm = roc_auc_score(y_valtrn, p_valtrn_svm)\nauc_valtst_svm = roc_auc_score(y_valtst, p_valtst_svm)\nprint('Validation Training AUC: {:.4f}'.format(auc_valtrn_svm))\nprint('Validation Test AUC: {:.4f}'.format(auc_valtst_svm))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Validation Training AUC: 0.8445\nValidation Test AUC: 0.8395\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### 3.3. Random Forest", "cell_type": "markdown", "metadata": {}}, {"execution_count": 61, "cell_type": "code", "source": "rf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=42)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 62, "cell_type": "code", "source": "rf.fit(X_valtrn, y_valtrn)", "outputs": [{"execution_count": 62, "output_type": "execute_result", "data": {"text/plain": "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n            max_depth=10, max_features='auto', max_leaf_nodes=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, n_estimators=50, n_jobs=1,\n            oob_score=False, random_state=42, verbose=0, warm_start=False)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 63, "cell_type": "code", "source": "p_valtrn_rf = rf.predict_proba(X_valtrn)[:, 1]\np_valtst_rf = rf.predict_proba(X_valtst)[:, 1]", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 64, "cell_type": "code", "source": "auc_valtrn_rf = roc_auc_score(y_valtrn, p_valtrn_rf)\nauc_valtst_rf = roc_auc_score(y_valtst, p_valtst_rf)\nprint('Validation Training AUC: {:.4f}'.format(auc_valtrn_rf))\nprint('Validation Test AUC: {:.4f}'.format(auc_valtst_rf))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Validation Training AUC: 0.8902\nValidation Test AUC: 0.8619\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "### 3.4. Gradient Boosting Machine (GBM)", "cell_type": "markdown", "metadata": {}}, {"execution_count": 79, "cell_type": "code", "source": "gbm = GradientBoostingClassifier(n_estimators=100, max_depth=4, learning_rate=0.1, random_state=42)", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 80, "cell_type": "code", "source": "gbm.fit(X_valtrn, y_valtrn)", "outputs": [{"execution_count": 80, "output_type": "execute_result", "data": {"text/plain": "GradientBoostingClassifier(init=None, learning_rate=0.1, loss='deviance',\n              max_depth=4, max_features=None, max_leaf_nodes=None,\n              min_samples_leaf=1, min_samples_split=2,\n              min_weight_fraction_leaf=0.0, n_estimators=100,\n              random_state=42, subsample=1.0, verbose=0, warm_start=False)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 81, "cell_type": "code", "source": "p_valtrn_gbm = gbm.predict_proba(X_valtrn)[:, 1]\np_valtst_gbm = gbm.predict_proba(X_valtst)[:, 1]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 82, "cell_type": "code", "source": "auc_valtrn_gbm = roc_auc_score(y_valtrn, p_valtrn_gbm)\nauc_valtst_gbm = roc_auc_score(y_valtst, p_valtst_gbm)\nprint('Validation Training AUC: {:.4f}'.format(auc_valtrn_gbm))\nprint('Validation Test AUC: {:.4f}'.format(auc_valtst_gbm))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Validation Training AUC: 0.8741\nValidation Test AUC: 0.8634\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "## 5. Ensemble", "cell_type": "markdown", "metadata": {}}, {"execution_count": 83, "cell_type": "code", "source": "fpr_lr, tpr_lr, _ = roc_curve(y_valtst, p_valtst_lr)\nfpr_svm, tpr_svm, _ = roc_curve(y_valtst, p_valtst_svm)\nfpr_rf, tpr_rf, _ = roc_curve(y_valtst, p_valtst_rf)\nfpr_gbm, tpr_gbm, _ = roc_curve(y_valtst, p_valtst_gbm)\n\nplt.figure(figsize=(16, 8))\nplt.plot(fpr_lr, tpr_lr, label='LR ({:.4f})'.format(auc_valtst_lr))\nplt.plot(fpr_svm, tpr_svm, label='SVM ({:.4f})'.format(auc_valtst_svm))\nplt.plot(fpr_rf, tpr_rf, label='RF ({:.4f})'.format(auc_valtst_rf))\nplt.plot(fpr_gbm, tpr_gbm, label='GBM ({:.4f})'.format(auc_valtst_gbm))\nplt.plot([0, 1], [0, 1], 'k--')\nplt.legend(loc=\"lower right\")\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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FWnPrMoeSW/N5EZJwnoB8ru7J7gHAB9w/fwdZ+rrSPf56XX3o0YHEEIglvMAXgbnI0lsf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\nuoaLVDMzMzMzM+saLlLNzMzMzMysa/w3VD98PaJpmy8AAAAASUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x10bee8910>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 90, "cell_type": "code", "source": "p_valtst_esb = .3 * p_valtst_rf + .7 * p_valtst_gbm", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 91, "cell_type": "code", "source": "auc_valtst_esb = roc_auc_score(y_valtst, p_valtst_esb)\nprint('Validation Test AUC: {:.4f}'.format(auc_valtst_esb))", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Validation Test AUC: 0.8636\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 78, "cell_type": "code", "source": "fpr_lr, tpr_lr, _ = roc_curve(y_valtst, p_valtst_lr)\nfpr_svm, tpr_svm, _ = roc_curve(y_valtst, p_valtst_svm)\nfpr_rf, tpr_rf, _ = roc_curve(y_valtst, p_valtst_rf)\nfpr_gbm, tpr_gbm, _ = roc_curve(y_valtst, p_valtst_gbm)\nfpr_esb, tpr_esb, _ = roc_curve(y_valtst, p_valtst_esb)\n\nplt.figure(figsize=(16, 8))\nplt.plot(fpr_lr, tpr_lr, label='LR ({:.4f})'.format(auc_valtst_lr))\nplt.plot(fpr_svm, tpr_svm, label='SVM ({:.4f})'.format(auc_valtst_svm))\nplt.plot(fpr_rf, tpr_rf, label='RF ({:.4f})'.format(auc_valtst_rf))\nplt.plot(fpr_gbm, tpr_gbm, label='GBM ({:.4f})'.format(auc_valtst_gbm))\nplt.plot(fpr_esb, tpr_esb, label='RF + GBM ({:.4f})'.format(auc_valtst_esb))\nplt.plot([0, 1], [0, 1], 'k--')\nplt.legend(loc=\"lower right\")\nplt.show()", "outputs": [{"output_type": "display_data", "data": {"image/png": 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CSFgZtyuQdFK2F1kOFKacl0DpgGfbw4nMk0HX7hi2M7HLfvfze4/2nJO5dLeh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\n1my81ZnL3wJ2ovQB0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