Bank Churn Prediction System

Project Overview

This project aims to predict whether a bank customer will exit or stay based on various factors. It utilizes machine learning techniques to analyze customer data and provide insights into customer churn.

Preview Image

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Table of Contents

• Project Overview
• Features
• Setup
  • Prerequisites
  • Running the System
• Usage
• Contribution

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Features

• Accurate customer churn prediction.
• User-friendly input interface.
• Machine learning model with high accuracy.
• Data visualization for insights.
• Customizable parameters for prediction.

Setup

Prerequisites

Before running the system, ensure you have the following installed:

• Jupyter Notebook
• Python 3.x
• Required Python libraries (scikit-learn, pandas, numpy, matplotlib, ipywidgets)

Running the System

1. Clone this repository to your local machine:

   git clone https://github.com/prathmeshborate/Bank-Churn-Prediction.git

2. Open Jupyter Notebook:

   jupyter notebook

3. Navigate to the project folder and open the Untitled1.ipynb notebook.

4. Follow the instructions in the notebook to run the Bank Churn Prediction system.

Usage

1. Launch the Jupyter Notebook and open the project's notebook.
2. Use the provided user-friendly input interface to enter customer data.
3. Click the "Predict" button to obtain the churn prediction result.
4. Review the prediction and insights generated by the system.

Contribution

Contributions to this project are welcome! If you'd like to add features, fix bugs, or improve the game, please follow these steps:

1. Fork the project.
2. Create a new branch with a descriptive name: git checkout -b feature-branch.
3. Make your changes and commit them: git commit -m 'Add feature'.
4. Push to the branch: git push origin feature-branch.
5. Create a pull request on the GitHub repository.

Thank you for considering contributing to this project!

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Happy Predicting!