Bank Churn Prediction — Machine Learning Project

This project predicts whether a bank customer will churn (leave the bank) using machine learning model.
The dataset used is the Bank Churn Kaggle dataset.

Project Overview

This ML project includes:

• Loading and cleaning the data
• Exploratory Data Analysis (EDA)
• Removing unnecessary columns
• Encoding categorical variables
• Splitting the dataset
• Training and testing the model
• Evaluating results

I have received an accuracy for 83.2% by this model.

Project Structure

│── churn_predictor.py # Main ML code │── data.csv # Kaggle dataset │── README.md # Documentation

Technologies Used

Python Pandas NumPy Matplotlib / Seaborn Scikit-Learn Jupyter / VS Code

Model Details

The model pipeline includes: Cleaning missing data One-hot encoding (pd.get_dummies()) Train-test split Training ML model (Logistic Regression / Random Forest) Accuracy & performance evaluation

Dataset

10,000 customers Features including age, credit score, balance, tenure, geography, etc.

Future Improvements

Hyperparameter tuning XGBoost / LightGBM models Deployment with Flask / FastAPI

Author

Rishi Kumar Machine Learning & AI enthusiast Github: Rishiii57