Sci-kit Learn - Credit Card Default Prediction - Logistic Regression.ipynb

Credit Card Default Prediction - Logistic Regression.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Python Sci-kit Learn: Machine Learning: Logistic Regression - Predicting Next Months Credit Card Default Payments**\n\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">**Note:** Citation:  Data Source:Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.. Datasource@ http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients</div>\n\n\nCreated by John Ryan 22th April 2017\n\n**Logistic Regression** \n\n\n__Overview__\n\nQuestion: How do we predict that a customer will default on next months credit card payment with the measurements provided?\n\nThe data set provides many categorical and continous variables that allow for the opportunity to implement Machine Learning models to accurately predict default payments. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default can be estimated using the binary result of classification - credible or not credible clients."
    },
    {
      "metadata": {
        "collapsed": true
      },
      "cell_type": "markdown",
      "source": "**Process of cleansing and transforming datasets to create a comprehensive data set which captures all possible hypothesis abd produces accurate predictive Models**\n\n**Data Exploration and Preperation**\n\n1.Variable Identification\n\n- Identify ID, Input and Target features\n\n- Identify categorical and numerical features\n\n- Identify columns with missing values\n\n2.Univariate Analysis\n\n3.Bi-variate Analysis\n\n4.Missing values treatment\n\n5.Outlier treatment\n\n**Feature-Engineering**\n\n6.Variable transformation\n\n - Logarithm\n        \n - Square/Cube root\n        \n - Binning\n \n - Seasonality effects due to periods\n \n 5.Scaling the data i.e standard, min-max\n        \n Note: decision tree models generally do not require features to be\n standardized or normalized, nor do they require categorical features to be binary-encoded.\n        \n 6.Dimensionality reduction does not focus on making predictions. Instead,\n  it tries to take a set of input data with a feature dimension D.\n  \n  - Principal Components Analysis (PCA)\n            \n  - Singular Value Decomposition (SVD)\n            \n  - Clustering\n   \n 7.Variable creation\n   \nIterate over last 4 until to refine the model\n\n 8.Feature -Importance\n \nNote: Set list of Hypothesis in line with the problem you are trying to solve\nSelect model for task at hand i.e classification, regression\n\n2. Data Modelling - Build Model\n\n  - Encode Labels for categorical variables - One hot encoder.\n    \n  - Split data into Training and Test sets.\n    \n  - First Model Gradient Boosting Machine/Random Forest to create bencemark solution\n   create additional models.\n    \n  - K-fold cross Validation vs predicted score, record error rates\n   \n3. Evaluate Model Performance Classification;\n\n    - Confusion Matrices\n    - Accuracy and prediction error \n    - Precision and Recall\n    - ROC curve and AUC\n    - The kappa statistic\n    - F-Measure\n    - KFold -Cross Validation\n    - Evaluate Model Performance Regression;\n      \nMultiple Linear Regression models: Understand how far away our predicted values are from\ntrue values, use metrics taking into account overall deviation; \n\n- Mean Squared Error\n- Root Mean Squared Logged Error \n- Mean Absolute Error(MAE) \n- R-Squared coefficient\n    \n4 Optimization- Model selection and parameter tuning pipelines\n\n- Logistic Regression and SVM use Stochastic Gradient Decent(SGD)\n- Decision Trees - Tuning tree depth and impurity\n- Naive Bayes - Changing the lambda parameter for naïve Bayes\n"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# First, we'll import pandas, a data processing and CSV file I/O library\nimport pandas as pd\n# We'll also import seaborn, a Python graphing library\n%matplotlib inline\nimport warnings # current version of seaborn generates a bunch of warnings that we'll ignore\nwarnings.filterwarnings(\"ignore\")\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nsns.set(style=\"white\", color_codes=True)",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Import the dataset using csv file \nloandata = pd.read_csv(\"C:/data/creditcard.csv\")",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#View the first 10 rows of the dataframe\nloandata.head(10)",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>LIMIT_BAL</th>\n      <th>SEX</th>\n      <th>EDUCATION</th>\n      <th>MARRIAGE</th>\n      <th>AGE</th>\n      <th>PAY_0</th>\n      <th>PAY_2</th>\n      <th>PAY_3</th>\n      <th>PAY_4</th>\n      <th>...</th>\n      <th>BILL_AMT4</th>\n      <th>BILL_AMT5</th>\n      <th>BILL_AMT6</th>\n      <th>PAY_AMT1</th>\n      <th>PAY_AMT2</th>\n      <th>PAY_AMT3</th>\n      <th>PAY_AMT4</th>\n      <th>PAY_AMT5</th>\n      <th>PAY_AMT6</th>\n      <th>default payment next month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20000</td>\n      <td>Female</td>\n      <td>university</td>\n      <td>married</td>\n      <td>24</td>\n      <td>2</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>689</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>120000</td>\n      <td>Female</td>\n      <td>university</td>\n      <td>single</td>\n      <td>26</td>\n      <td>-1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>3272</td>\n      <td>3455</td>\n      <td>3261</td>\n      <td>0</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>0</td>\n      <td>2000</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>90000</td>\n      <td>Female</td>\n      <td>university</td>\n      <td>single</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>14331</td>\n      <td>14948</td>\n      <td>15549</td>\n      <td>1518</td>\n      <td>1500</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>5000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>50000</td>\n      <td>Female</td>\n      <td>university</td>\n      <td>married</td>\n      <td>37</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>28314</td>\n      <td>28959</td>\n      <td>29547</td>\n      <td>2000</td>\n      <td>2019</td>\n      <td>1200</td>\n      <td>1100</td>\n      <td>1069</td>\n      <td>1000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>50000</td>\n      <td>Male</td>\n      <td>university</td>\n      <td>married</td>\n      <td>57</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>20940</td>\n      <td>19146</td>\n      <td>19131</td>\n      <td>2000</td>\n      <td>36681</td>\n      <td>10000</td>\n      <td>9000</td>\n      <td>689</td>\n      <td>679</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>50000</td>\n      <td>Male</td>\n      <td>graduate school</td>\n      <td>single</td>\n      <td>37</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>19394</td>\n      <td>19619</td>\n      <td>20024</td>\n      <td>2500</td>\n      <td>1815</td>\n      <td>657</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>800</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>500000</td>\n      <td>Male</td>\n      <td>graduate school</td>\n      <td>single</td>\n      <td>29</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>542653</td>\n      <td>483003</td>\n      <td>473944</td>\n      <td>55000</td>\n      <td>40000</td>\n      <td>38000</td>\n      <td>20239</td>\n      <td>13750</td>\n      <td>13770</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>100000</td>\n      <td>Female</td>\n      <td>university</td>\n      <td>single</td>\n      <td>23</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>221</td>\n      <td>-159</td>\n      <td>567</td>\n      <td>380</td>\n      <td>601</td>\n      <td>0</td>\n      <td>581</td>\n      <td>1687</td>\n      <td>1542</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>140000</td>\n      <td>Female</td>\n      <td>high school</td>\n      <td>married</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>...</td>\n      <td>12211</td>\n      <td>11793</td>\n      <td>3719</td>\n      <td>3329</td>\n      <td>0</td>\n      <td>432</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>20000</td>\n      <td>Male</td>\n      <td>high school</td>\n      <td>single</td>\n      <td>35</td>\n      <td>-2</td>\n      <td>-2</td>\n      <td>-2</td>\n      <td>-2</td>\n      <td>...</td>\n      <td>0</td>\n      <td>13007</td>\n      <td>13912</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>13007</td>\n      <td>1122</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 25 columns</p>\n</div>",
            "text/plain": "   ID  LIMIT_BAL     SEX        EDUCATION MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n0   1      20000  Female       university  married   24      2      2     -1   \n1   2     120000  Female       university   single   26     -1      2      0   \n2   3      90000  Female       university   single   34      0      0      0   \n3   4      50000  Female       university  married   37      0      0      0   \n4   5      50000    Male       university  married   57     -1      0     -1   \n5   6      50000    Male  graduate school   single   37      0      0      0   \n6   7     500000    Male  graduate school   single   29      0      0      0   \n7   8     100000  Female       university   single   23      0     -1     -1   \n8   9     140000  Female      high school  married   28      0      0      2   \n9  10      20000    Male      high school   single   35     -2     -2     -2   \n\n   PAY_4             ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  \\\n0     -1             ...                      0          0          0   \n1      0             ...                   3272       3455       3261   \n2      0             ...                  14331      14948      15549   \n3      0             ...                  28314      28959      29547   \n4      0             ...                  20940      19146      19131   \n5      0             ...                  19394      19619      20024   \n6      0             ...                 542653     483003     473944   \n7      0             ...                    221       -159        567   \n8      0             ...                  12211      11793       3719   \n9     -2             ...                      0      13007      13912   \n\n   PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n0         0       689         0         0         0         0   \n1         0      1000      1000      1000         0      2000   \n2      1518      1500      1000      1000      1000      5000   \n3      2000      2019      1200      1100      1069      1000   \n4      2000     36681     10000      9000       689       679   \n5      2500      1815       657      1000      1000       800   \n6     55000     40000     38000     20239     13750     13770   \n7       380       601         0       581      1687      1542   \n8      3329         0       432      1000      1000      1000   \n9         0         0         0     13007      1122         0   \n\n   default payment next month  \n0                           1  \n1                           1  \n2                           0  \n3                           0  \n4                           0  \n5                           0  \n6                           0  \n7                           0  \n8                           0  \n9                           0  \n\n[10 rows x 25 columns]"
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "loandata.describe()",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>LIMIT_BAL</th>\n      <th>AGE</th>\n      <th>PAY_0</th>\n      <th>PAY_2</th>\n      <th>PAY_3</th>\n      <th>PAY_4</th>\n      <th>PAY_5</th>\n      <th>PAY_6</th>\n      <th>BILL_AMT1</th>\n      <th>...</th>\n      <th>BILL_AMT4</th>\n      <th>BILL_AMT5</th>\n      <th>BILL_AMT6</th>\n      <th>PAY_AMT1</th>\n      <th>PAY_AMT2</th>\n      <th>PAY_AMT3</th>\n      <th>PAY_AMT4</th>\n      <th>PAY_AMT5</th>\n      <th>PAY_AMT6</th>\n      <th>default payment next month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>...</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>3.000000e+04</td>\n      <td>30000.00000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n      <td>30000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>15000.500000</td>\n      <td>167484.322667</td>\n      <td>35.485500</td>\n      <td>-0.016700</td>\n      <td>-0.133767</td>\n      <td>-0.166200</td>\n      <td>-0.220667</td>\n      <td>-0.266200</td>\n      <td>-0.291100</td>\n      <td>51223.330900</td>\n      <td>...</td>\n      <td>43262.948967</td>\n      <td>40311.400967</td>\n      <td>38871.760400</td>\n      <td>5663.580500</td>\n      <td>5.921163e+03</td>\n      <td>5225.68150</td>\n      <td>4826.076867</td>\n      <td>4799.387633</td>\n      <td>5215.502567</td>\n      <td>0.221200</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8660.398374</td>\n      <td>129747.661567</td>\n      <td>9.217904</td>\n      <td>1.123802</td>\n      <td>1.197186</td>\n      <td>1.196868</td>\n      <td>1.169139</td>\n      <td>1.133187</td>\n      <td>1.149988</td>\n      <td>73635.860576</td>\n      <td>...</td>\n      <td>64332.856134</td>\n      <td>60797.155770</td>\n      <td>59554.107537</td>\n      <td>16563.280354</td>\n      <td>2.304087e+04</td>\n      <td>17606.96147</td>\n      <td>15666.159744</td>\n      <td>15278.305679</td>\n      <td>17777.465775</td>\n      <td>0.415062</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>10000.000000</td>\n      <td>21.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-2.000000</td>\n      <td>-165580.000000</td>\n      <td>...</td>\n      <td>-170000.000000</td>\n      <td>-81334.000000</td>\n      <td>-339603.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.00000</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>7500.750000</td>\n      <td>50000.000000</td>\n      <td>28.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>-1.000000</td>\n      <td>3558.750000</td>\n      <td>...</td>\n      <td>2326.750000</td>\n      <td>1763.000000</td>\n      <td>1256.000000</td>\n      <td>1000.000000</td>\n      <td>8.330000e+02</td>\n      <td>390.00000</td>\n      <td>296.000000</td>\n      <td>252.500000</td>\n      <td>117.750000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>15000.500000</td>\n      <td>140000.000000</td>\n      <td>34.000000</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>22381.500000</td>\n      <td>...</td>\n      <td>19052.000000</td>\n      <td>18104.500000</td>\n      <td>17071.000000</td>\n      <td>2100.000000</td>\n      <td>2.009000e+03</td>\n      <td>1800.00000</td>\n      <td>1500.000000</td>\n      <td>1500.000000</td>\n      <td>1500.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>22500.250000</td>\n      <td>240000.000000</td>\n      <td>41.000000</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>67091.000000</td>\n      <td>...</td>\n      <td>54506.000000</td>\n      <td>50190.500000</td>\n      <td>49198.250000</td>\n      <td>5006.000000</td>\n      <td>5.000000e+03</td>\n      <td>4505.00000</td>\n      <td>4013.250000</td>\n      <td>4031.500000</td>\n      <td>4000.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>30000.000000</td>\n      <td>1000000.000000</td>\n      <td>79.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>964511.000000</td>\n      <td>...</td>\n      <td>891586.000000</td>\n      <td>927171.000000</td>\n      <td>961664.000000</td>\n      <td>873552.000000</td>\n      <td>1.684259e+06</td>\n      <td>896040.00000</td>\n      <td>621000.000000</td>\n      <td>426529.000000</td>\n      <td>528666.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 22 columns</p>\n</div>",
            "text/plain": "                 ID       LIMIT_BAL           AGE         PAY_0         PAY_2  \\\ncount  30000.000000    30000.000000  30000.000000  30000.000000  30000.000000   \nmean   15000.500000   167484.322667     35.485500     -0.016700     -0.133767   \nstd     8660.398374   129747.661567      9.217904      1.123802      1.197186   \nmin        1.000000    10000.000000     21.000000     -2.000000     -2.000000   \n25%     7500.750000    50000.000000     28.000000     -1.000000     -1.000000   \n50%    15000.500000   140000.000000     34.000000      0.000000      0.000000   \n75%    22500.250000   240000.000000     41.000000      0.000000      0.000000   \nmax    30000.000000  1000000.000000     79.000000      8.000000      8.000000   \n\n              PAY_3         PAY_4         PAY_5         PAY_6      BILL_AMT1  \\\ncount  30000.000000  30000.000000  30000.000000  30000.000000   30000.000000   \nmean      -0.166200     -0.220667     -0.266200     -0.291100   51223.330900   \nstd        1.196868      1.169139      1.133187      1.149988   73635.860576   \nmin       -2.000000     -2.000000     -2.000000     -2.000000 -165580.000000   \n25%       -1.000000     -1.000000     -1.000000     -1.000000    3558.750000   \n50%        0.000000      0.000000      0.000000      0.000000   22381.500000   \n75%        0.000000      0.000000      0.000000      0.000000   67091.000000   \nmax        8.000000      8.000000      8.000000      8.000000  964511.000000   \n\n                  ...                  BILL_AMT4      BILL_AMT5  \\\ncount             ...               30000.000000   30000.000000   \nmean              ...               43262.948967   40311.400967   \nstd               ...               64332.856134   60797.155770   \nmin               ...             -170000.000000  -81334.000000   \n25%               ...                2326.750000    1763.000000   \n50%               ...               19052.000000   18104.500000   \n75%               ...               54506.000000   50190.500000   \nmax               ...              891586.000000  927171.000000   \n\n           BILL_AMT6       PAY_AMT1      PAY_AMT2      PAY_AMT3  \\\ncount   30000.000000   30000.000000  3.000000e+04   30000.00000   \nmean    38871.760400    5663.580500  5.921163e+03    5225.68150   \nstd     59554.107537   16563.280354  2.304087e+04   17606.96147   \nmin   -339603.000000       0.000000  0.000000e+00       0.00000   \n25%      1256.000000    1000.000000  8.330000e+02     390.00000   \n50%     17071.000000    2100.000000  2.009000e+03    1800.00000   \n75%     49198.250000    5006.000000  5.000000e+03    4505.00000   \nmax    961664.000000  873552.000000  1.684259e+06  896040.00000   \n\n            PAY_AMT4       PAY_AMT5       PAY_AMT6  default payment next month  \ncount   30000.000000   30000.000000   30000.000000                30000.000000  \nmean     4826.076867    4799.387633    5215.502567                    0.221200  \nstd     15666.159744   15278.305679   17777.465775                    0.415062  \nmin         0.000000       0.000000       0.000000                    0.000000  \n25%       296.000000     252.500000     117.750000                    0.000000  \n50%      1500.000000    1500.000000    1500.000000                    0.000000  \n75%      4013.250000    4031.500000    4000.000000                    0.000000  \nmax    621000.000000  426529.000000  528666.000000                    1.000000  \n\n[8 rows x 22 columns]"
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Data Munging, Preprocessing**\n\n**Checking for missing values in the data**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Data Munging\n#Checking for missing values in the data\nloandata.apply(lambda x: sum(x.isnull()),axis=0)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "ID                              0\nLIMIT_BAL                       0\nSEX                             0\nEDUCATION                     345\nMARRIAGE                       54\nAGE                             0\nPAY_0                           0\nPAY_2                           0\nPAY_3                           0\nPAY_4                           0\nPAY_5                           0\nPAY_6                           0\nBILL_AMT1                       0\nBILL_AMT2                       0\nBILL_AMT3                       0\nBILL_AMT4                       0\nBILL_AMT5                       0\nBILL_AMT6                       0\nPAY_AMT1                        0\nPAY_AMT2                        0\nPAY_AMT3                        0\nPAY_AMT4                        0\nPAY_AMT5                        0\nPAY_AMT6                        0\ndefault payment next month      0\ndtype: int64"
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#show the combined total number of missing values\nprint(\"\\nTotal number of NAN missing values: {0}\".format((loandata.shape[0] * loandata.shape[1]) - loandata.count().sum()))",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\nTotal number of NAN missing values: 399\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Display of the total Education value counts\nloandata['EDUCATION'].value_counts()",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "university         14030\ngraduate school    10585\nhigh school         4917\nothers               123\nName: EDUCATION, dtype: int64"
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Fill in the NaN values with the most common education type in the data\nloandata['EDUCATION'].fillna('university',inplace=True)",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Marriage type counts\nloandata['MARRIAGE'].value_counts()",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "single     15964\nmarried    13659\nothers       323\nName: MARRIAGE, dtype: int64"
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Fill in the NaN values with the most common  type in the data\nloandata['MARRIAGE'].fillna('single', inplace=True)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Checking for missing values in the data after missing value treatment\nloandata.apply(lambda x: sum(x.isnull()),axis=0)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "ID                            0\nLIMIT_BAL                     0\nSEX                           0\nEDUCATION                     0\nMARRIAGE                      0\nAGE                           0\nPAY_0                         0\nPAY_2                         0\nPAY_3                         0\nPAY_4                         0\nPAY_5                         0\nPAY_6                         0\nBILL_AMT1                     0\nBILL_AMT2                     0\nBILL_AMT3                     0\nBILL_AMT4                     0\nBILL_AMT5                     0\nBILL_AMT6                     0\nPAY_AMT1                      0\nPAY_AMT2                      0\nPAY_AMT3                      0\nPAY_AMT4                      0\nPAY_AMT5                      0\nPAY_AMT6                      0\ndefault payment next month    0\ndtype: int64"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Value Counts of the target output variable**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "loandata['default payment next month'].value_counts()",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "0    23364\n1     6636\nName: default payment next month, dtype: int64"
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#A look at the target variable using a bar plot\nimport seaborn as sns\nsns.countplot(x=\"default payment next month\", data=loandata)\nsns.plt.show()",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0MAAAUP/qFP6ZM2dq06ZN2rVrlwICApSQkKAhQ4b4ejYAAFDP6vy5u8OGDdOwYcN8OQsA\nAPCxn/21vAAAoOki/AAAWITwAwBgEcIPAIBFCD8AABYh/AAAWITwAwBgEcIPAIBFCD8AABYh/AAA\nWMTn4d+9e7fi4+MlSf/9738VFxencePGacGCBd5tcnJydN9992nMmDHaunWrJKmyslLTpk3T2LFj\nNXnyZJ08eVKSVFRUpAceeEBxcXFatmyZr8cHAKBZ8Wn4V6xYoblz58rj8UiSUlNTlZSUpFWrVqmm\npka5ubk6duyYMjMzlZ2drRUrVigtLU0ej0dr1qxRt27dlJWVpZEjRyojI0OSlJKSopdeekmrV69W\ncXGxSkpKfHkKAAA0Kz4Nf5cuXZSenu69vXfvXkVGRkqSoqOjlZ+fr+LiYkVERCggIEBOp1OhoaEq\nKSlRYWGhoqOjvdvu2LFDLpdLHo9HISEhkqQBAwYoPz/fl6cAAECz4tPwDx06VP7+/t7bxhjvz0FB\nQXK5XHK73QoODvaut27d2rvudDq925aVldVaO38dAADUTYNe3Neixf8dzu12q02bNnI6nXK5XBdc\nd7vd3rXg4GDvg4XvbwsAAOqmQcPfvXt37dq1S5K0bds2RUREqFevXiosLFRVVZXKysp04MABhYWF\nqW/fvsrLy5Mk5eXlKTIyUk6nUw6HQ6WlpTLGaPv27YqIiGjIUwAAoEkLaMiDzZo1S08//bQ8Ho+6\ndu2qYcOGyc/PT/Hx8YqLi5MxRklJSXI4HIqNjdWsWbMUFxcnh8OhtLQ0SdKCBQs0Y8YM1dTUKCoq\nSr17927IUwAAoEnzefivvfZarV27VpIUGhqqzMzMH2wTExOjmJiYWmstW7bUyy+//INte/furezs\nbN8MCwBAM8cH+AAAYBHCDwCARQg/AAAWIfwAAFiE8AMAYBHCDwCARQg/AAAWIfwAAFiE8AMAYBHC\nDwCARQg/AAAWIfwAAFiE8AMAYBHCDwCARQg/AAAWIfwAAFiE8AMAYBHCDwCARQg/AAAWIfwAAFiE\n8AMAYBHCDwCARQg/AAAWIfwAAFiE8AMAYBHCDwCARQg/AAAWIfwAAFiE8AMAYBHCDwCARQg/AAAW\nIfwAAFiE8AMAYBHCDwCARQg/AAAWIfwAAFgkoLEHAICfq7q6WgcPHmzsMYBfLTQ0VP7+/g16TMIP\noMk5ePCgpv+/p9SqfVBjjwL8YmdOuvXypMXq2rVrgx6X8ANoklq1D1LQFcGNPQbQ5PAePwAAFiH8\nAABYhPADAGARwg8AgEUIPwAAFiH8AABYhPADAGARwg8AgEUIPwAAFiH8AABYhPADAGARwg8AgEUI\nPwAAFiH8AABYhPADAGCRgMY46OjRo+V0OiVJISEhSkxM1OzZs9WiRQuFhYVp/vz5kqScnBxlZ2cr\nMDBQiYmJGjhwoCorKzVz5kwdP35cTqdTS5YsUfv27RvjNAAAaHIaPPxVVVWSpJUrV3rXHn30USUl\nJSkyMlLz589Xbm6ubrzxRmVmZmr9+vWqqKhQbGysoqKitGbNGnXr1k1Tp07Vxo0blZGRoeTk5IY+\nDQAAmqQGf6m/pKRE5eXlmjRpkiZMmKDdu3fr008/VWRkpCQpOjpa+fn5Ki4uVkREhAICAuR0OhUa\nGqqSkhIVFhYqOjrau+1HH33U0KcAAECT1eDP+Fu2bKlJkyYpJiZGBw8e1MMPPyxjjPf3QUFBcrlc\ncrvdCg4O9q63bt3au37ubYJz2wIAgLpp8PCHhoaqS5cu3p/btWunTz/91Pt7t9utNm3ayOl01or6\n+etut9u7dv6DAwAAcHEN/lL/3/72Ny1ZskSSdOTIEblcLkVFRamgoECStG3bNkVERKhXr14qLCxU\nVVWVysrKdODAAYWFhalv377Ky8uTJOXl5XnfIgAAAD+twZ/x33///ZozZ47i4uLUokULLVmyRO3a\ntdPcuXPl8XjUtWtXDRs2TH5+foqPj1dcXJyMMUpKSpLD4VBsbKxmzZqluLg4ORwOpaWlNfQpAADQ\nZDV4+AMDA/Xiiy/+YD0zM/MHazExMYqJiam11rJlS7388ss+mw8AgOaMD/ABAMAihB8AAIsQfgAA\nLEL4AQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEHAMAihB8A\nAIsQfgAALEL4AQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEH\nAMAihB8AAIsQfgAALEL4AQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEHAMAihB8AAIsQfgAALEL4\nAQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEHAMAihB8AAIsQ\nfgAALEL4AQCwCOEHAMAihB8AAIsQfgAALEL4AQCwCOEHAMAiAY09wC9hjFFKSor27dsnh8OhZ599\nVp07d27ssQAAuOQ1yWf8ubm5qqqq0tq1a/XEE08oNTW1sUcCAKBJaJLhLyws1O233y5J6tOnj/bs\n2dPIEwEA0DQ0yZf6XS6XgoODvbcDAgJUU1OjFi1++DimurpakvTNN9/U+xxHjhxR2fFSVZ05Xe/7\nBhpKZfkpHTlyRJdddlljj1JnR44c0elDJ1RVVtHYowC/WMWpcp/9b+9c88418HxNMvxOp1Nut9t7\n+8eiL0lHjx6VJI0dO7ZBZgOaookT/9HYIwBWmrhpok/3f/ToUXXp0qXWWpMM/0033aQtW7Zo2LBh\nKioqUrdu3X502549eyorK0sdO3aUv79/A04JAEDjqK6u1tGjR9WzZ88f/M7PGGMaYaZf5fyr+iUp\nNTVVv/nNbxp5KgAALn1NMvwAAOCXaZJX9QMAgF+G8AMAYBHCDwCARQg/AAAWIfy4JBljNH/+fI0Z\nM0YJCQkqLS1t7JEAq+zevVvx8fGNPQZ8oEn+O340f+d/H8Pu3buVmpqqjIyMxh4LsMKKFSv09ttv\nKygoqLFHgQ/wjB+XJL6PAWg8Xbp0UXp6emOPAR8h/Lgk/dj3MQDwvaFDh/JJp80Y4ccl6ed8HwMA\noO74f1Jckm666Sbl5eVJ0k9+HwMA3+CDXZsnLu7DJWno0KH68MMPNWbMGEn/+z4GAA3Lz8+vsUeA\nD/BZ/QAAWISX+gEAsAjhBwDAIoQfAACLEH4AACxC+AEAsAjhBwDAIoQf8KE5c+Zow4YNF90mJydH\ngwYN0gsvvPCr9p+QkPCLZmxMOTk52rhxo8/2/+qrr6qwsNBn+z+fy+XSlClTJEmHDx/WoEGDGuS4\nwM9F+IFG9s9//lOLFi3SzJkzf9V+CgoK6mmihvPxxx+rqqrKZ/svKChosO94+O6771RSUuK9zYff\n4FLFJ/cB9Sw1NVVbt27VlVdeqZqaGt1yyy2SpA0bNmjlypUyxqhHjx6aN2+e3njjDRUXF2vBggVK\nTk5WeXm5/vznP6uyslIVFRVatGiRIiMjFR8fr2nTpqlfv346fPiw4uPj9f7773uPuWjRIknSgw8+\nqOzs7Frz3HrrrRo4cKD27t0rp9OpF198Uddcc43effddvfnmm7WOdeWVV2r8+PHasmWLJGnXrl16\n/fXX9fDDD+tPf/qTjDEqLS3VnXfeqeDgYOXm5kqS3njjDXXo0EEffPCBXnnlFVVXVyskJEQLFy5U\n27ZtNWjQII0cOVLbt29XRUWFnnvuOZ06dUrvv/++du7cqY4dOyoqKso785w5c+R0OrV3714dOXJE\nU6ZM0ejRo1VeXq5nnnlG+/fvV01NjR5++GHdddddWrJkiU6cOKHnn39ef//735WVlaUxY8Zoz549\nmjt3rpYtW6awsDDv/uPj49W9e3fl5+erqqpKycnJyszM1BdffKGEhARNmDBBFRUVmjt3rvbt26cW\nLVpo4sSJGjVqlNavX68PPvhAp06dUmlpqQYMGKB58+bp2Wef1bfffqvHHntMs2fPVkVFhZ544gl9\n/vnnatu2rdLT09W2bVvf/JcO+DkMgHqzadMmk5CQYKqrq83x48dNVFSUWb9+vdm/f7+Ji4szlZWV\nxhhj0tLSzGuvvWaMMWbcuHFm165dpqamxkyYMMGcPHnSGGPMW2+9ZRITE73bFBQUGGOMOXTokBk0\naJAxxpjZs2eb9evXG2OMuf766y840/XXX282bNhgjDEmMzPTJCYm/uSxduzYYYwxZs6cOWbjxo1m\n586dJiIiwnzzzTfmzJkz5sYbbzQ5OTneGVauXGmOHz9uRo4caU6fPm2MMWbt2rUmOTnZGGPMHXfc\nYVauXOmd4bHHHvvB/OebPXu2d5t9+/aZm2++2RhjzIsvvmgyMzONMcaUlZWZ3//+96a0tNRUVFSY\nu+66y/zjH/8wd9xxhyktLa31Z/t948aNM6mpqcYYY1599VVz5513msrKSnP48GHTr18/Y4wxzz33\nnFm0aJExxpgTJ06YwYMHm3379pl169aZO+64w5SXl5szZ86Y3/72t+bzzz+v9fdy6NAhEx4ebj75\n5BNjjDGPPfaYycrKuuDfD9DQeMYP1KOCggLdeeedatGihTp06KCBAwdKknbu3Kn//Oc/evDBB2WM\n0dmzZ9WjRw/v/Ywx8vPz06uvvqotW7boyy+/VEFBQb18NWrLli01cuRISdKoUaOUlpZ20WPdd999\nevvtt9WnTx/t2LFDCxYs0Mcff6ywsDB16tRJktS+fXv1799fknTttdfq1KlTKi4u1tdff62EhAQZ\nY1RTU6N27dp55xgwYIAkKSwsTP/6179+cu5zrwB069ZNp0+fliTl5+ersrJSb731liTpzJkz+ve/\n/62QkBAtXrxYY8aM0dNPP62QkBDvfsyPfCp5dHS0d/4+ffrI4XDommuuUVlZmaT//Z0tXrzYe75D\nhgxRQUGBgoKC1LdvX7Vq1UqS1LlzZ506dUqtW7eutf9OnTqpZ8+e3nM+efLkT54z0BAIP1CP/Pz8\nar2nfO6rhKurqzV8+HAlJydL+l+wqqura923vLxc999/v0aNGqV+/frp+uuvV1ZWlne/5wJ29uzZ\nnz3TOTU1NQoMDLzosYYNG6alS5dq06ZN+u1vf6vAwEBJ8v7nOd9/UFJdXa2IiAhlZGRIkqqqqmp9\ntfJll132g3O5mHPbn6+mpkYvvPCCbrjhBknS8ePHvQ8uDhw4oMsvv1x79+79yX1//3wu9ADr+zPW\n1NR4/+wdDsdFt/3+Put6zkBD4OI+oB7deuut2rRpk6qqqnTq1Clt375dknTzzTcrNzdXJ06ckDFG\n8+fP15tvvlnrvgcPHpS/v78SExPVv39/bdu2zfsgon379tq/f78k/eiz5YCAgAteyHbmzBlt3bpV\nkrRu3TrdfvvtFz1Wy5YtFR0draVLl+ree++t87n36dNHRUVFOnjwoCQpPT1dzz///EXv4+/vL4/H\n85P7PhfN/v37a/Xq1ZKkb7/9Vvfcc4+++uorHTlyRK+88orWrl2rzz77TNu2bZP0vz+Tn/tA6dyx\nbrnlFu8rCydOnNDmzZu912tcSEBAQK0Hc4QelyrCD9SjwYMHq1+/fhoxYoSmTJmi6667TpIUHh6u\nKVOmaPz48RoxYoSMMXrkkUck/d8z8vDwcIWHh+t3v/udRo8eraCgIH311VeSpD/84Q9avXq1Ro8e\n/aNXwZ+7gO5Cv9+0aZPuueceffjhh0pOTr7osSTprrvuUnBwsHr37n3BY13oivUrrrhCixcv1uOP\nP6577rlHn332mebMmfOj20vSbbfdptdff13vvffeBX///eNNmTJFFRUVGjFihCZOnKgnn3xSnTt3\n1rx58/TQQw8pJCREKSkpSklJkcvl0u23366UlBQVFRX95PwXOtZ3332nESNGKCEhQY8++qj3lYYL\nbX/55Zfrqquu0vjx43/yGEBj4mt5gWYuPDy81j8z+ynV1dVaunSprrjiCk2YMMF3gwFoFLzHDzRz\nP/eZ5/33368OHTrotdde89FEABoTz/gBALAI7/EDAGARwg8AgEUIPwAAFiH8AABYhPADAGCR/w9V\nelaN5xXWjAAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x66aad68>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Univariate Analysis - Continous Variables**\n\nAnalysis of central tendency and spread of the variables."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Step A: Central Tendency; Mean, Meadian, Mode, Min & Max**\n(out of scope for this example)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Step B: Measures of Dispersion; Range, Quartile, IQR, Variance, Standard Deviation, Skewness and Kurtosis**\n(out of scope for this example)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**1. Violin, Swarm Plots**"
    },
    {
      "metadata": {
        "collapsed": false
      },
      "cell_type": "markdown",
      "source": "**2. Boxplots & Outlier Analysis**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Boxplot for applicant income sorted by Education type\nloandata.boxplot(column='LIMIT_BAL', by = 'EDUCATION')\nplt.show()",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hgYiIiGQxJBAREZEshgQiIiKSpWzpClDDce4GoqY3fPhwnDp1qsn306dPH2Rm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\n",
            "text/plain": "<matplotlib.figure.Figure at 0xb8c99b0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Boxplot for applicant Income sorted by Gender Type\nloandata.boxplot(column='LIMIT_BAL', by = 'SEX')\nplt.show()",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ESJLatWsnt9vteZJ69OjRnm289NJLhATgGrVw4cLGLgHAJbjgg4sDBw6Uj4+P5+fT7060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\nQAAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0xf3b3f60>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "loandata.boxplot(column='LIMIT_BAL', by = 'MARRIAGE')\nplt.show()",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S09OVkZGhrVu3qqioSFu2bKmT/gBXO0YSgKvY+fPn1aVLF0kXD7AODg6aOHGiad3OnTvr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\ng4ODbr/9dj300EOaO3euNm7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            "text/plain": "<matplotlib.figure.Figure at 0xb914ba8>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**3.Histograms**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Boxplot for Loan Amounts Issued to Applicants\nloandata.boxplot(column='LIMIT_BAL')\nplt.show()",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0xef3d080>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "#Limit Balance Histogram\nloandata['LIMIT_BAL'].hist(bins = 50)\nplt.show()",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l8XjU1dWlxx9/XKZpRo47nU75/X4FAgGlp6dH2tPS0nTp0qVbnzUAALhhNxX6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\nj9lJ4iMAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0xb8c9c18>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Transforming the data to treat outliers (Logging)**"
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "#import numpy library for the computation of np.log and create resulting histogram\nimport numpy as np\nloandata['LIMIT_BAL_log'] = np.log(loandata['LIMIT_BAL'])\nloandata['LIMIT_BAL_log'].hist(bins=20)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0xedb5eb8>"
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G5HA4tG/fPi1YsOBmjAsAgJQ1aXi//PLLWrBggZ5++mn96U9/0j/8wz/o+9//vh588EFt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\ndyuK5m82AuIAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0xef3db00>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "#Boxplot for Loan balances logged results in less outliers and a more balanced distribution\nloandata.boxplot(column='LIMIT_BAL_log')\nplt.show()",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0xf4f6320>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# One way we can extend this plot is adding a layer of individual points on top of\n# it through Seaborn's striplot\n# \n# We'll use jitter=True so that all the points don't fall in single vertical lines\n# above the species\nax = sns.boxplot(x=\"EDUCATION\", y=\"LIMIT_BAL\", data=loandata)\nax = sns.stripplot(x=\"EDUCATION\", y=\"LIMIT_BAL\", data=loandata, jitter=True, edgecolor=\"gray\");",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HM1TqKuiqQ2gM2aW17AhpVrCMjHbvWvf0f2cTbAqq9Zwmg4kYqyR9TcUSGUG/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\nQgghRED/DzDVLcIEUU6bAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0xecf47f0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Bi-variate Analysis - Continous Variables**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "loandata.plot(kind=\"scatter\", x=\"BILL_AMT3\", y=\"PAY_AMT3\");",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/Rt+6jBAtmKYZ7x2SmRwb0OMUEZHTszx43HLLLcybNw+AaDSK3W5nz549FBUV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\n",
            "text/plain": "<matplotlib.figure.Figure at 0xecf4d30>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Univariate Analysis - Categorical Variables**\n\nFrequency table distribution analysis;\n"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#creating a pivot table to assess the Credit History of Applicants\n#Step 1: Create a Frequency Table for Credit History, how many applicants Yes (0.1) vs. No (0.0)\nfreqtable = loandata['EDUCATION'].value_counts(ascending = True)\nfreqtable",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "others               123\nhigh school         4917\ngraduate school    10585\nuniversity         14375\nName: EDUCATION, dtype: int64"
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Stacked Chart by Education Type\nstack1 = pd.crosstab(loandata['EDUCATION'], loandata['default payment next month'])\nstack1.plot(kind = 'bar', stacked=True, color=['red','green'], grid = False)\nplt.show()",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y20cffeQ/BypJCxYsUPfu3dWzZ0+tWbNGklRYWKgBAwaoV69e6tu3r/Ly8iRJW7duVY8e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\nzP8HuZhY/3pj6dAAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0xbcf9860>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Data Munging\n#Checking for missing values in the data\nloandata.apply(lambda x: sum(x.isnull()),axis=0)",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "ID                            0\nLIMIT_BAL                     0\nSEX                           0\nEDUCATION                     0\nMARRIAGE                      0\nAGE                           0\nPAY_0                         0\nPAY_2                         0\nPAY_3                         0\nPAY_4                         0\nPAY_5                         0\nPAY_6                         0\nBILL_AMT1                     0\nBILL_AMT2                     0\nBILL_AMT3                     0\nBILL_AMT4                     0\nBILL_AMT5                     0\nBILL_AMT6                     0\nPAY_AMT1                      0\nPAY_AMT2                      0\nPAY_AMT3                      0\nPAY_AMT4                      0\nPAY_AMT5                      0\nPAY_AMT6                      0\ndefault payment next month    0\nLIMIT_BAL_log                 0\ndtype: int64"
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#show number of missing values\nprint(\"\\nNumber of NAN missing values : {0}\".format((loandata.shape[0] * loandata.shape[1]) - loandata.count().sum()))",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\nNumber of NAN missing values : 0\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Building a predictive model: Logistic Regression**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Label encoder\nfrom sklearn.preprocessing import LabelEncoder\n\nfor feature in loandata.columns:\n    if loandata[feature].dtype=='object':\n        le = LabelEncoder()\n        loandata[feature] = le.fit_transform(loandata[feature])\nloandata.tail(5)",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>LIMIT_BAL</th>\n      <th>SEX</th>\n      <th>EDUCATION</th>\n      <th>MARRIAGE</th>\n      <th>AGE</th>\n      <th>PAY_0</th>\n      <th>PAY_2</th>\n      <th>PAY_3</th>\n      <th>PAY_4</th>\n      <th>...</th>\n      <th>BILL_AMT5</th>\n      <th>BILL_AMT6</th>\n      <th>PAY_AMT1</th>\n      <th>PAY_AMT2</th>\n      <th>PAY_AMT3</th>\n      <th>PAY_AMT4</th>\n      <th>PAY_AMT5</th>\n      <th>PAY_AMT6</th>\n      <th>default payment next month</th>\n      <th>LIMIT_BAL_log</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>29995</th>\n      <td>29996</td>\n      <td>220000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>39</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>31237</td>\n      <td>15980</td>\n      <td>8500</td>\n      <td>20000</td>\n      <td>5003</td>\n      <td>3047</td>\n      <td>5000</td>\n      <td>1000</td>\n      <td>0</td>\n      <td>12.301383</td>\n    </tr>\n    <tr>\n      <th>29996</th>\n      <td>29997</td>\n      <td>150000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>43</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>...</td>\n      <td>5190</td>\n      <td>0</td>\n      <td>1837</td>\n      <td>3526</td>\n      <td>8998</td>\n      <td>129</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11.918391</td>\n    </tr>\n    <tr>\n      <th>29997</th>\n      <td>29998</td>\n      <td>30000</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>37</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n      <td>-1</td>\n      <td>...</td>\n      <td>20582</td>\n      <td>19357</td>\n      <td>0</td>\n      <td>0</td>\n      <td>22000</td>\n      <td>4200</td>\n      <td>2000</td>\n      <td>3100</td>\n      <td>1</td>\n      <td>10.308953</td>\n    </tr>\n    <tr>\n      <th>29998</th>\n      <td>29999</td>\n      <td>80000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>41</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>11855</td>\n      <td>48944</td>\n      <td>85900</td>\n      <td>3409</td>\n      <td>1178</td>\n      <td>1926</td>\n      <td>52964</td>\n      <td>1804</td>\n      <td>1</td>\n      <td>11.289782</td>\n    </tr>\n    <tr>\n      <th>29999</th>\n      <td>30000</td>\n      <td>50000</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n      <td>46</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>32428</td>\n      <td>15313</td>\n      <td>2078</td>\n      <td>1800</td>\n      <td>1430</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>10.819778</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>",
            "text/plain": "          ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n29995  29996     220000    1          1         0   39      0      0      0   \n29996  29997     150000    1          1         2   43     -1     -1     -1   \n29997  29998      30000    1          3         2   37      4      3      2   \n29998  29999      80000    1          1         0   41      1     -1      0   \n29999  30000      50000    1          3         0   46      0      0      0   \n\n       PAY_4      ...        BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n29995      0      ...            31237      15980      8500     20000   \n29996     -1      ...             5190          0      1837      3526   \n29997     -1      ...            20582      19357         0         0   \n29998      0      ...            11855      48944     85900      3409   \n29999      0      ...            32428      15313      2078      1800   \n\n       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \\\n29995      5003      3047      5000      1000                           0   \n29996      8998       129         0         0                           0   \n29997     22000      4200      2000      3100                           1   \n29998      1178      1926     52964      1804                           1   \n29999      1430      1000      1000      1000                           1   \n\n       LIMIT_BAL_log  \n29995      12.301383  \n29996      11.918391  \n29997      10.308953  \n29998      11.289782  \n29999      10.819778  \n\n[5 rows x 26 columns]"
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Import models from scikit learn module:\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.cross_validation import KFold   #For K-fold cross validation\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import metrics\n\n#Generic function for making a classification model and accessing performance:\ndef classification_model(model, data, predictors, outcome):\n    #Fit the model:\n    model.fit(data[predictors],data[outcome])\n  \n    #Make predictions on training set:\n    predictions = model.predict(data[predictors])\n  \n    #Print accuracy\n    accuracy = metrics.accuracy_score(predictions,data[outcome])\n    print \"Accuracy : %s\" % \"{0:.3%}\".format(accuracy)\n\n    #Perform k-fold cross-validation with 5 folds\n    kf = KFold(data.shape[0], n_folds=5)\n    error = []\n    for train, test in kf:\n        train_predictors = (data[predictors].iloc[train,:])# Filter training data\n    \n    # The target we're using to train the algorithm.\n    train_target = data[outcome].iloc[train]\n    \n    # Training the algorithm using the predictors and target.\n    model.fit(train_predictors, train_target)\n    \n    #Record error from each cross-validation run\n    error.append(model.score(data[predictors].iloc[test,:], data[outcome].iloc[test]))\n \n    print \"Cross-Validation Score : %s\" % \"{0:.3%}\".format(np.mean(error))\n\n    #Fit the model again so that it can be refered outside the function:\n    model.fit(data[predictors],data[outcome]) ",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Create Logistic Regression Model**"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Model 1: Logistic Regression Model - using Education\nimport numpy as np\noutcome_var = 'default payment next month'\nmodel = LogisticRegression()\npredictor_var = ['EDUCATION']\nclassification_model(model,loandata, predictor_var, outcome_var)",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Accuracy : 77.880%\nCross-Validation Score : 78.900%\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Model 2: Logistic Regression - using additional variables\npredictor_var = ['EDUCATION', 'SEX', 'MARRIAGE']\nclassification_model(model,loandata, predictor_var, outcome_var)",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Accuracy : 77.880%\nCross-Validation Score : 78.900%\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Reference: https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/"
    }
  ],
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        "description": "Sci-kit Learn - Credit Card Default Prediction - Logistic Regression.ipynb",
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