https://github.com/bhaveshbhakta/customer-churn-prediction-using-ann
Customer Churn Prediction
https://github.com/bhaveshbhakta/customer-churn-prediction-using-ann
ann artificial-neural-networks customer-churn-prediction deep-learning
Last synced: 7 months ago
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Customer Churn Prediction
- Host: GitHub
- URL: https://github.com/bhaveshbhakta/customer-churn-prediction-using-ann
- Owner: BhaveshBhakta
- Created: 2024-12-09T16:44:08.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-01-05T13:39:36.000Z (10 months ago)
- Last Synced: 2025-02-12T05:44:35.821Z (8 months ago)
- Topics: ann, artificial-neural-networks, customer-churn-prediction, deep-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 261 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Customer Churn Prediction Using ANN
This repository provides a machine learning-based approach to predicting customer churn using a dataset that includes various customer features. The project involves data preprocessing, model building, training, and evaluation, resulting in a predictive model for customer churn.
## Project Overview
The goal of this project is to predict whether a customer will leave a bank (churn) or stay. Using an Artificial Neural Network (ANN) model, the project explores how customer demographics, account information, and transaction history can influence customer retention.
Key Features:
- **Comprehensive EDA**: Visualized trends and relationships between customer features such as age, balance, and account tenure.
- **Data Preprocessing**: Handled missing values, encoded categorical data, and normalized numerical features for better model performance.
- **ANN Model**: Built and trained an ANN model with multiple layers, tuned the model's parameters for optimal performance.
- **Performance Metrics**: Achieved high accuracy and evaluated model performance on unseen data.
- **Actionable Insights**: Identified key factors contributing to customer churn, helping businesses focus retention strategies.
## Purpose and Applications
This project is designed to:
- Predict customer churn based on historical bank customer data.
- Provide actionable insights to help banks and businesses improve customer retention.
- Lay the groundwork for future improvements, such as incorporating more features or exploring other machine learning algorithms.
## Installation
### Clone the Repository:
```bash
git clone https://github.com/BhaveshBhakta/Customer-Churn-Prediction-using-ANN.git
```
### Navigate to the Project Folder:
```bash
cd Customer-Churn-Prediction-using-ANN
```
### Run the Jupyter Notebook or Python Script:
Open the `Churn_Prediction.ipynb` notebook
## Collaboration
Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.