🏥 Health Center Churn Analysis

This project analyzes patient churn at a community health center to identify key risk factors and support data-driven retention strategies.

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🔍 Problem Statement

Many patients stop visiting the clinic over time, affecting care continuity and planning. This project identifies churn patterns and builds a dashboard to support targeted actions.

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📊 Tools & Libraries

• Python: pandas, seaborn, matplotlib (for EDA)
• Jupyter Notebook: For data exploration and documentation
• Tableau Public: For final dashboard visualization

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🧠 Key Steps

1. Data cleaning and preprocessing
2. Exploratory Data Analysis (EDA)
3. Feature engineering
4. Churn risk factor identification
5. Dashboard creation in Tableau

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📈 Sample Insights (optional)

• Patients with infrequent visits over 6 months are more likely to churn.
• Certain demographics (e.g. age 60+) have higher retention with consistent follow-ups.

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📁 Files

• Health_Center_Churn_Analysis.ipynb: Jupyter notebook with full analysis
• .ipynb_checkpoints/: Auto-generated by Jupyter (can be ignored)

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📊 Interactive Dashboard

Click the image below to explore the live dashboard on Tableau Public:

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📌 Future Improvements

• Incorporate ML model for churn prediction
• Automate reporting
• Integrate real-time data from a health system

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🤝 Let's Connect

If you find this project useful or want to collaborate, feel free to connect on LinkedIn.