Subject: Data Science Programming Language: Python/Jupyter

Develop a loan repayment probability prediction model using python to identify the loan defaulters

Data Set: Assume(or create your own dataset) that you are given a data set with following fields

• Loanee Information: Demographic data like age, Identity proof etc.
• Loan Information: Disbursal details, loan to value ratio etc.
• Bureau data & history: Bureau score, number of active accounts, the status of other loans, credit history etc.

Idea is to ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimizing the default rates.

Expected output:

1. Write a Data Science Proposal for achieving the objective mentioned.

2. Perform exploratory analysis on the data.

3. Perform data wrangling / pre-processing.

4. Apply any 2 features engineering technique.

5. Plot top 10 features.

6. Identification of the performance parameters to be improved, for the given problem statement.

7. Design Machine learning models Logistic regression and Decision tree to predict.

8. Compare the performance of selected feature engineering techniques.

9. Compare the performance of the 2 classifiers Logistic regression and Decision tree to predict.

10. Present the conclusions/results in the format shared.