https://github.com/kumar-o/customer-churn-prediction
🔍 Predict customer churn using a synthetic dataset with advanced models and metrics to enhance business retention strategies and decision-making.
https://github.com/kumar-o/customer-churn-prediction
churn-prediction customer-churn-prediction customer-survival-analysis data-science factorization-machines flask-application gridsearchcv libsvm machine-learning model-evaluation numpy pipelines roc-auc scikit-learn seaborn smote survival-analysis xgboost4j
Last synced: 5 days ago
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🔍 Predict customer churn using a synthetic dataset with advanced models and metrics to enhance business retention strategies and decision-making.
- Host: GitHub
- URL: https://github.com/kumar-o/customer-churn-prediction
- Owner: kumar-O
- License: mit
- Created: 2025-09-30T07:03:30.000Z (10 days ago)
- Default Branch: main
- Last Pushed: 2025-09-30T16:00:23.000Z (10 days ago)
- Last Synced: 2025-09-30T18:05:02.743Z (10 days ago)
- Topics: churn-prediction, customer-churn-prediction, customer-survival-analysis, data-science, factorization-machines, flask-application, gridsearchcv, libsvm, machine-learning, model-evaluation, numpy, pipelines, roc-auc, scikit-learn, seaborn, smote, survival-analysis, xgboost4j
- Language: Python
- Size: 311 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🎉 Customer-Churn-Prediction - Predict Customer Churn Simply
[](https://github.com/kumar-O/Customer-Churn-Prediction/releases)
## 🚀 Getting Started
Welcome to the Customer Churn Prediction software. This tool helps businesses understand why customers leave. You can easily download and run the application without any technical knowledge. Follow these steps to get started.
## 📥 Download & Install
To begin, visit this page to download the software: [Releases Page](https://github.com/kumar-O/Customer-Churn-Prediction/releases).
1. Click on the link to go to the Releases page.
2. You will see a list of available versions. Look for the latest version.
3. Find the appropriate file for your system. For Windows, it might be "CustomerChurnPrediction.exe." For macOS, look for "CustomerChurnPrediction.dmg."
4. Click on the download link next to the file you need.
5. Once the download finishes, locate the file in your downloads folder.
## ⚙️ System Requirements
Before running the application, check these requirements:
- **Operating System:** Windows 10 or later, macOS 10.12 or later.
- **RAM:** At least 4GB is recommended.
- **Disk Space:** Minimum 200MB available for installation.
## 📊 How to Use the Application
After installing the application, follow these steps to analyze customer churn:
1. **Launch the Software:**
- Locate the downloaded file and double-click it to open.
2. **Input Data:**
- You will see an option to upload your dataset. This dataset should include customer information, like usage data and demographic details.
3. **Select Model Type:**
- Choose which model you want to use: Logistic Regression, Random Forest, or Gradient Boosting. Each model has its advantages depending on your data.
4. **Generate Reports:**
- Click the "Run Analysis" button. The software will process your data and provide outputs. You will receive metrics, charts, and reports regarding customer churn.
5. **Interpreting Results:**
- Review the results to understand which factors contribute to churn. The software will also highlight key features that impact customer retention.
## 📈 Features of This Software
- **Data Generation:** Automatically generate synthetic datasets for initial testing.
- **Feature Engineering:** Prepare your data for analysis with ease.
- **Multiple Models:** Compare results from Logistic Regression, Random Forest, and Gradient Boosting.
- **Hyperparameter Tuning:** Automatically fine-tune model settings for better performance.
- **Detailed Reports:** Receive charts and metrics that summarize your findings.
- **User-Friendly Interface:** Designed for easy navigation.
## 📚 About Customer Churn Prediction
This project uses Python to help businesses predict customer churn using synthetic datasets. The software applies various machine learning models and enhances performance through hyperparameter tuning and threshold optimization. This helps you get the most accurate predictions.
## 🛠️ Troubleshooting
If you encounter issues while using the software:
- **File Not Opening:** Ensure you downloaded the correct version for your operating system.
- **Error Messages:** Check if your dataset format aligns with the application’s requirements.
- **Slow Performance:** Make sure your computer meets the recommended system requirements.
## 🌟 Key Topics Covered
This tool encompasses several important topics:
- **Classification**: Learn how to classify customer actions.
- **Customer Analytics**: Gain insights into customer behaviors.
- **Machine Learning**: Understand fundamental concepts in predictive modeling.
- **Feature Importance**: Discover which variables influence customer decisions.
## 👍 Contributing
If you would like to contribute to the project, find the guidelines in the repository. Your suggestions and improvements are welcome!
## 🔗 Useful Links
- [Releases Page](https://github.com/kumar-O/Customer-Churn-Prediction/releases)
- [Documentation](https://github.com/kumar-O/Customer-Churn-Prediction/wiki)
- [Issues Page](https://github.com/kumar-O/Customer-Churn-Prediction/issues)
For additional questions or support, feel free to reach out through the Issues page on GitHub. Happy predicting!