Bank Customer Churn Analysis

Project Overview

This project analyzes bank customer data to identify churn drivers and build a churn prediction model.

Objectives

• Analyze customer behavior and churn patterns
• Identify key churn factors
• Build machine learning model for churn prediction

Tools Used

Python, Pandas, Scikit-learn, Google Colab

Key Steps

• Data cleaning and preprocessing
• Exploratory churn analysis
• Categorical encoding
• Churn prediction model

Results

The churn prediction model achieved approximately 85% accuracy.

Dataset

Churn Modelling Dataset (Kaggle)