Loan Interest Rates Prediction

Predicting personalized loan interest rates using data-driven insights from the Bondora P2P Loans dataset.

Background

In the rapidly evolving fintech sector, efficient risk assessment and personalized loan offerings are crucial for enhancing customer satisfaction and maintaining profitability. This project entails analyzing peer-to-peer (P2P) loan data to identify the factors influencing loan interest rates and developing predictive models to inform loan term personalization.

Objectives

1. Import & preprocess the Bondora P2P Loans dataset.
2. Perform exploratory data analysis (EDA) to uncover key customer and loan patterns.
3. Visualize insights to support decision-making and risk profiling.
4. Develop predictive models to estimate loan interest rates based on borrower characteristics using both simple and multiple linear regression models.

Data

• Source: Bondora P2P Loans dataset (Kaggle): https://www.kaggle.com/datasets/marcobeyer/bondora-p2p-loans?select=LoanData.csv
• Features:

  Feature	Description
  VerificationType	Loan application data verification method
  Age	Borrower's age in years
  AppliedAmount	Requested loan amount
  Amount	Approved loan amount
  Interest	Actual interest rate
  LoanDuration	Loan term duration
  Education	Education level
  EmploymentDurationCurrentEmployer	Duration with current employer
  HomeOwnershipType	Homeownership status
  IncomeTotal	Total borrower income
  ExistingLiabilities	Number of existing liabilities
  RefinanceLiabilities	Total liabilities post-refinancing
  Rating	Bondora's internal borrower rating
  NoOfPreviousLoansBeforeLoan	Number of prior loans
  AmountOfPreviousLoansBeforeLoan	Value of previous loans

Environment Setup

To reproduce this project locally:

git clone https://github.com/shahab-ghafoor/Loan-Interest-Rates.git
cd Loan-Interest-Rates