Bayesian-Predictive-System

Motivation

The proposed system aims to allow the client next month's default payment using Bayesian Logistic regression data analysis.

Knowledge Requirements:

1. Bayesian inference, Machine Learning, Statistics.
2. jags.model is used to create an object representing a Bayesian graphical model, specified with a BUGS-language description of the prior distribution, and a set of data.
3. Markov chains (conditional independence) and Monte Carlo techniques (estimation by simulation) to yield Markov chain Monte Carlo (MCMC) allows us to indirectly generate independent samples from a particular posterior distribution.

DataSet

The dataset I have chosen is the Default of credit card clients. The dataset is mainly aimed at the default payment of customers in Taiwan. The estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients.

I gathered this dataset from the UCI repository: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients

The data set contains 23 columns and a default payment binary response variable to be predicted. Some columns represent the demographics of the customer like gender, marriage, education, and age. The data set also provides information about the customer's previous payments, bill amounts and payment dues that helps in predicting the default payment.

• The columns PAY_0, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6 represents the history of past payments where PAY_0=the repayment status in September,...and PAY_6=the repayment status in April with the values ranging as -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above respectively.
• The columns BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6 represent the amount of bill statement in dollars.
• The columns PAY_AMT1,PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6 represent the number of previous payments in dollars from September to April.

Project Pipeline

1. Data Pre-processing(handling N/A values, Plotting individuals for better understanding and observations).
2. Feature engineering(Plotting the independent variable's behavior with respect to the dependent variable and observe by remove based on analysis, Co-Variat).
3. Bayesian modeling(jags.model and frequentist approaches).
4. Model observation (Convergence diagnosis Analysis, autocorrelation statistics and Examine convergence of the Markov chains using the Gelman-Brooks-Rubin diagnostic).
5. Models accuracy analysis(Deviation Information Criterion DIC metric).

Future Plans

Integrate with Big data for larger datasets and more samplings iterations(re-write JAGS).