husnamaliakkal/loan-default-predictor---exploratory-data-analysis-eda-

Lending companies often struggle with lending decisions due to applicants with insufficient credit history. This can lead to defaults. This case study performed Exploratory Data Analysis (EDA) on a loan application dataset to understand patterns and minimize loan defaults while ensuring deserving applicants are not rejected.

Loan Default Predictor - Exploratory Data Analysis

Lending companies often struggle with lending decisions due to applicants with insufficient credit history, leading to defaults. In this case study, I perform Exploratory Data Analysis (EDA) on a loan application dataset to understand patterns and minimize loan defaults while ensuring deserving applicants are not rejected.

Project Overview

The primary goal is to identify patterns indicating clients struggling with loan repayments. This insight informs actions such as loan denial, adjusting loan amounts, or offering loans to risky applicants at higher interest rates. The company aims to enhance portfolio management and risk assessment by understanding key variables influencing loan default.

Key Tasks

• Perform EDA on the loan application dataset.
• Identify patterns indicating clients struggling with loan repayments.
• Inform actions such as loan denial, adjusting loan amounts, or offering loans at higher interest rates.

Dataset

The dataset contains information about loan applicants, including their credit history, loan amount, loan term, income, employment status, and other relevant variables.

Methodology

• Data Cleaning: Clean and preprocess the dataset for analysis.
• Exploratory Data Analysis: Explore patterns and relationships in the dataset.
• Identifying Risk Factors: Identify key variables influencing loan default.
• Recommendations: Provide recommendations for portfolio management and risk assessment based on the analysis.

Conclusion

This repository contains EDA code and resources for making informed lending decisions and reducing default risks. By understanding key variables influencing loan default, lending companies can enhance portfolio management and risk assessment.