https://github.com/firaskahlaoui/customer-churn

This project aims to predict customer churn using machine learning techniques. By analyzing historical customer data, the model identifies patterns that indicate whether a customer is likely to leave. This can help businesses take proactive measures to retain customers and reduce churn rates.
https://github.com/firaskahlaoui/customer-churn

customer-churn-prediction flask-application jupyter-notebook python

Last synced: about 2 months ago
JSON representation

This project aims to predict customer churn using machine learning techniques. By analyzing historical customer data, the model identifies patterns that indicate whether a customer is likely to leave. This can help businesses take proactive measures to retain customers and reduce churn rates.

• Host: GitHub
• URL: https://github.com/firaskahlaoui/customer-churn
• Owner: FirasKahlaoui
• Created: 2024-06-02T20:27:59.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2024-06-23T20:56:19.000Z (7 months ago)
• Last Synced: 2024-06-23T21:48:49.949Z (7 months ago)
• Topics: customer-churn-prediction, flask-application, jupyter-notebook, python
• Language: Jupyter Notebook
• Homepage:
• Size: 5.27 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Customer Churn Prediction

This repository contains a machine learning model designed to predict customer churn. The model uses historical customer data to identify patterns and predict which customers are likely to leave. The project includes data preprocessing, feature engineering, model training, evaluation, and deployment scripts.

## Features

- **Data Preprocessing**: Cleaning and transforming raw data for analysis.

- **Feature Engineering**: Creating new features to improve model performance.

- **Model Training**: Using various machine learning algorithms to build predictive models.

- **Model Evaluation**: Assessing model accuracy and performance using metrics like precision, recall, and AUC-ROC.

- **Deployment**: Scripts and tools to deploy the model for real-time prediction.

## Installation

Clone the repository and install the required dependencies:

```bash

https://github.com/FirasKahlaoui/customer-churn.git

cd customer-churn

pip install -r requirements.txt