• Data Description, Data Auditing, Understanding the Business problems. • Converting business problem into Stats problem. • Data Cleaning and Preparing for Model building • Setting up the assumptions. • Testing the Assumptions (Hypothesis Testing- Anova, Chi Sq. Weight of evidence, Information Value). • Building the logistic regression model using variable from Correlation matrix. • Fine tuning of the model. • As per the business context setting the optimum Value for OCV. • Validating the Model using AUROC, concordance, discordance, decile analysis, Accuracy specificity and sensitivity. • Preparing the document about the Model and its Pros and CONs of the Model.