📊 Bank Customer Churn Prediction (ML Web App)

🔗 Live Demo on Streamlit Cloud

This project is a machine learning web application that predicts whether a bank customer is likely to churn. The app is built using Streamlit and powered by a Random Forest classifier trained on customer data.

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🚀 Features

• Upload a CSV file containing customer data
• Predict churn using a trained ML model
• View predictions in a simple web interface
• Reusable .pkl model for fast inference
• Organized project structure with dataset included

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📂 Project Structure

bank-customer-churn-prediction/ │ ├── app.py # Streamlit app ├── random_forest_churn_model.pkl # Trained ML model ├── requirements.txt # Required Python libraries ├── README.md # Project documentation ├── streamlit_ui.png # Screenshot of the app ├── dataset/ # Folder containing data files │ ├── churn_data.xlsx # Dataset used for training │ └── sample_input.xlsx # Sample file for upload testing

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Model Overview

I trained multiple classification models including:

• Logistic Regression
• Support Vector Machine (SVM)
• Random Forest ✅

Random Forest gave the best performance:

• Accuracy: 85%
• Precision: 83%
• Recall: 81%

So it was selected for final deployment.

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🧰 Tech Stack

• Python
• Pandas, Scikit-learn
• Streamlit
• Joblib

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Dataset

The dataset includes customer demographic details, account activity, and churn labels. It's located in the dataset/ folder:

churn_data.xlsx – Used for training

sample_input.xlsx – Used for testing with the app