https://github.com/coding-for-it/customer-churn-prediction
To identify customers who are likely to leave a business,so that the company can take action to retain them.
https://github.com/coding-for-it/customer-churn-prediction
logistic-regression matplotlib numpy pandas random-forest scikit seaborn streamlit-webapp
Last synced: 6 months ago
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To identify customers who are likely to leave a business,so that the company can take action to retain them.
- Host: GitHub
- URL: https://github.com/coding-for-it/customer-churn-prediction
- Owner: coding-for-it
- License: mit
- Created: 2025-07-15T10:24:57.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-07-15T18:10:46.000Z (6 months ago)
- Last Synced: 2025-07-16T00:11:51.861Z (6 months ago)
- Topics: logistic-regression, matplotlib, numpy, pandas, random-forest, scikit, seaborn, streamlit-webapp
- Language: Jupyter Notebook
- Homepage:
- Size: 2.85 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Customer Churn Prediction using Machine Learning
This project predicts whether a telecom customer will churn (leave the service) or stay, based on their service usage patterns and contract details. It is beginner-friendly and demonstrates a full ML workflow with deployment using **Streamlit** and **Docker**.
## Project Highlights
- Real-world customer churn dataset
- Cleaned, preprocessed, and encoded data
- Built and compared two models: **Logistic Regression** and **Random Forest**
- Simple and interactive **Streamlit web app**
- **Dockerized** for easy deployment
## What is Customer Churn?
**Customer churn** is when users stop using a service. Predicting churn helps companies take action in time to retain those users. In this project, we analyze features like tenure, monthly charges, gender, contract type, and internet service to predict churn.
## Technologies Used
- Python
- Pandas, NumPy for data manipulation
- Seaborn, Matplotlib for visualization
- Scikit-learn for ML models
- Streamlit for frontend UI
- Docker for containerized deployment