This repository contains a project that applies supervised machine learning techniques to predict whether a bank customer will accept a personal loan offer. The model is built using various classification algorithms based on customer demographic, financial, and account-related data.
Exploratory Data Analysis (EDA) to understand customer attributes affecting loan acceptance. Implements Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), and Random Forest for classification. Feature selection and engineering to improve model accuracy. Visualization using Matplotlib, Seaborn, and Plotly.