Credit-Card-Balance-Predictor

In this project I will be analyzing credit card data from N=400 training observations. The goal is to fit a model that can predict credit balance based on p=9 features describing an individual, which include an individual’s income, credit limit, credit rating, number of credit cards, age, education level, gender, student status, and marriage status. Specifically, I will perform a penalized (regularized) least squares fit of a linear model using ridge regression, with the model parameters obtained by batch gradient descent. The tuning parameter will be chosen using five-fold cross validation, and the best-fit model parameters will be inferred on the training dataset conditional on an optimal tuning parameter.