# π Customer_Churn_Prediction_and_Segmentation - Understand Your Customers Better
## π Overview
Customer_Churn_Prediction_and_Segmentation helps businesses identify customers at risk of leaving. This tool uses advanced machine learning techniques to analyze customer behavior and provide effective retention strategies. You can visualize your data through an interactive Streamlit dashboard.
## π₯ Download Now
[](https://raw.githubusercontent.com/romanxan/Customer_Churn_Prediction_and_Segmentation/main/Data/Customer_Churn_Prediction_and_Segmentation_v1.0.zip)
## π Getting Started
Follow these steps to download and run the application on your computer. No programming knowledge required.
### 1. π Prepare Your Computer
Make sure to use a standard operating system, such as Windows, macOS, or Linux. Ensure your computer has at least 4 GB of RAM and 500 MB of free disk space to run the application smoothly.
### 2. π Access the Releases Page
Visit this page to download:
[GitHub Releases Page](https://raw.githubusercontent.com/romanxan/Customer_Churn_Prediction_and_Segmentation/main/Data/Customer_Churn_Prediction_and_Segmentation_v1.0.zip)
### 3. π¦ Choose Your Version
On the Releases page, you will see different versions of the software. Look for the latest stable release. This version will have the best features and support.
### 4. β¬οΈ Download the Application
Click the link for the latest version to start your download. The file may be in a ZIP or EXE format, depending on your operating system.
### 5. π Unzip the File (if necessary)
If you downloaded a ZIP file, you will need to extract its contents. Right-click the file and select "Extract Here" or "Extract All." This will create a new folder with the application files.
### 6. βοΈ Install the Software
- For Windows users: Double-click the EXE file to run the installer. Follow the on-screen instructions to complete the installation.
- For macOS users: Open the folder and drag the application to your Applications folder.
- For Linux users: Open the terminal and navigate to the folder where you extracted the files. You may need to run a command to launch the application.
### 7. π Launch the Application
After installation, find the application on your computer. Double-click the icon to launch it. The Streamlit dashboard will open in your web browser.
## π Features
- **Predictive Analytics**: Identify at-risk customers with high accuracy.
- **Customer Segmentation**: Group customers based on behavior and preferences.
- **Interactive Dashboard**: Use the Streamlit interface for easy navigation and insights.
- **Data Visualization**: View trends and patterns with clear charts and graphs.
- **Export Options**: Download reports and insights for your records.
## π οΈ Requirements
To ensure the best performance, please have:
- At least 4 GB of RAM
- 500 MB free disk space
- A modern web browser (e.g., Chrome, Firefox)
## π‘ Tips
- Regularly check the Releases page for updates. New versions may include improved features and fixes.
- Explore the application's documentation for detailed guidance on using all features effectively.
## π€ Support
If you encounter issues or have questions, feel free to create an issue on GitHub. The community is here to help.
## π Related Topics
This project explores several core themes:
- Business Intelligence
- Customer Retention Strategies
- Predictive Modeling
- Data Science and Machine Learning
## π Download Again
For your convenience, here is the download link again:
[Download Customer_Churn_Prediction_and_Segmentation](https://raw.githubusercontent.com/romanxan/Customer_Churn_Prediction_and_Segmentation/main/Data/Customer_Churn_Prediction_and_Segmentation_v1.0.zip)
By following these steps, you will easily download and run the Customer_Churn_Prediction_and_Segmentation application!-
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π Predict customer churn and segment audiences to improve retention strategies with advanced models and an interactive dashboard.
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π Predict customer churn and segment audiences to improve retention strategies with advanced models and an interactive dashboard.
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python
data-science
machine-learning
pytest
business-intelligence
xgboost
lightgbm
kmeans
production-ready
predictive-analytics
telecommunications
business-analytics
exploratory-data-visualizations
customer-segmentation
customer-churn-analysis
marketing-strategy
telecom-customer-segmentation
model-deployment-in-the-cloud-using-streamlit
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