| --- |
| license: mit |
| language: en |
| pipeline_tag: tabular-regression |
| tags: |
| - regression |
| - sklearn |
| - demo |
| - active-users |
| --- |
| |
| # 📊 Active Users Prediction Model (Simple Regression) |
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| ## 🧠 Overview |
| This project demonstrates a simple regression-based approach to estimate and predict active users in a Hugging Face Space. |
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| Since Hugging Face does not provide direct access to real-time active user metrics, this model uses request counts as a proxy signal and applies a regression model to estimate user activity trends. |
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|
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| ## 🚀 Features |
| - Tracks incoming requests as a proxy for user activity |
| - Logs usage data over time |
| - Trains a Linear Regression model on historical data |
| - Predicts current active users based on timestamp |
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| --- |
|
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| ## 🏗️ How It Works |
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| 1. Each user interaction increases a counter |
| 2. Data is stored in a CSV file (`usage.csv`) |
| 3. The model is trained on: |
| - Time (timestamp) |
| - Active user count |
| 4. The model predicts active users for the current time |
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| --- |
|
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| ## 📂 Project Structure |
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| ## ⚙️ Model Details |
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| - **Model Type:** Linear Regression |
| - **Library:** scikit-learn |
| - **Input Feature:** Timestamp |
| - **Output:** Estimated Active Users |
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| --- |
|
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| ## 📈 Example Output |
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| --- |
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| ## ⚠️ Limitations |
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| - This is an approximation, not real user tracking |
| - Counts requests, not unique users |
| - No session or IP-based filtering |
| - Model retrains on each request (not optimized) |
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| --- |
|
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| ## 🔮 Future Improvements |
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| - Use a database instead of CSV |
| - Track unique users via sessions/IP |
| - Add time-based features (hour, day) |
| - Use advanced models (Random Forest, LSTM) |
| - Deploy with AWS for scalability |
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| --- |
|
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| ## 🧑💻 Author |
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| Udyan Trivedi |
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| --- |
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| ## 📜 License |
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| MIT License |
