---
license: mit
language: en
pipeline_tag: tabular-regression
tags:
- regression
- sklearn
- demo
- active-users
---

# 📊 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