This project aims to analyze a loan approval dataset and build a model to predict the likelihood of loan approval. The dataset contains information about loan applicants, including their credit score, income, employment history, and loan history. The goal of the analysis is to identify patterns and trends in the data that can be used to predict loan approval and to improve loan underwriting and risk management processes.

This project includes Data Exploration,Data Cleaning,Feature Selection,Model Building and Model Evaluation.

This project is done using Python and common libraries like pandas, numpy, matplotlib, seaborn and sklearn.

Dataset link : https://www.kaggle.com/datasets/burak3ergun/loan-data-set

The data visualization is done in tableau and Insights is written in google docs. Have a look. https://public.tableau.com/app/profile/bi4250/viz/LoanAnalysisDashboard_16744010922460/Dashboard1

The insights is here too. https://docs.google.com/document/d/1pmgVpTnAkKdO2kVZnPQiekbZ6k63boFJzFy5xqdNj1o/edit?usp=sharing