GOALS:

1. to develop a strategy for the bank managers that help them in making decisions on loan approval to potential customers.
2. to decide which attributes should be considered by the bank managers to access and predict the creditability of the potential customer.

IMPORTANCE:

The approval of a loan application can be a life changing decision for credit seeking customers. Importantly analyzing credit worthiness of potential clients becomes more than just a decision point as a worthy customer should not be denied of getting a mortgage. The following analysis of the dataset and the methodologies help loan officers to make the right decisions in approving loan to the worthy customers.

LANGUAGES USED:

SQL, SAS, R, Python.

TABLE OF CONTENTS:

1. Data Preparation
   1. Attribute Types
   2. Descriptive Statistics
   3. Correlation and Determination of Variables
2. Predictive Modelling
   1. Decision Tree
   2. Naïve Bayes
   3. Comparison and Re-evaluation of the Selected Variables
3. Conclusions and Recommendations
4. Appendix: Output Tables

COWORKER:

L. S. Naupada