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license: mit
language: en
pipeline_tag: tabular-regression
tags:
- regression
- sklearn
- demo
- active-users
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# 📊 Active Users Prediction Model (Simple Regression)
## 🧠 Overview
This project demonstrates a simple regression-based approach to estimate and predict active users in a Hugging Face Space.
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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## 🚀 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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## 🏗️ How It Works
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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## 📂 Project Structure
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## ⚙️ Model Details
- **Model Type:** Linear Regression
- **Library:** scikit-learn
- **Input Feature:** Timestamp
- **Output:** Estimated Active Users
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## 📈 Example Output
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## ⚠️ Limitations
- 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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## 🔮 Future Improvements
- 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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## 🧑💻 Author
Udyan Trivedi
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## 📜 License
MIT License |