Suppose you work for a consumer finance company which specialises in lending various types of loans to urban customers. You have to use EDA to analyse the patterns present in the data. This will ensure that the applicants capable of repaying the loan are not rejected.

Analyzing customer dataset of current and previous applications for loan, as Target variable of 'Repayer' and 'Defaulter'. Aim is to find the factors to make decision of condition to be implied in order to reduce default rate.

A Flask-based web application that uses a machine learning model to assess the likelihood of a customer defaulting on a loan. The user inputs key financial and credit-related information, and the app returns a risk prediction (Low Risk / High Risk) in real-time.

Loan Default Risk Analysis Dashboard built using Python, MySQL, and Power BI. Includes a complete data pipeline: environment configuration, ETL scripts, data cleaning, database loading, and a fully interactive multi-page dashboard analyzing borrower demographics, credit behavior, and default risk patterns.

This repository describes my assignment in Data Science related to Exploratory Data Analysis in detecting the type of customers who are more likely to accept a loan with minimum defaulting rate.

This repository contains Python projects showcasing data analysis and visualization. 1. IMDB Movie Analysis: Analyzing movie trends, genres, and ratings. 2. Loan Default Analysis EDA: Exploring factors contributing to loan defaults.

This project involves building an interactive Power BI dashboard using synthetic loan data to analyze loan distribution, applicant demographics, and default trends. DAX measures are used to identify growth and risk patterns.

Analyze and understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.