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README.md

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-license: mit
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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.
+
+---
+
+## 🚀 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
+
+---
+
+## 🏗️ 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
+
+---
+
+## 📂 Project Structure
+
+
+
+---
+
+## ⚙️ Model Details
+
+- **Model Type:** Linear Regression
+- **Library:** scikit-learn
+- **Input Feature:** Timestamp
+- **Output:** Estimated Active Users
+
+---
+
+## 📈 Example Output
+
+
+---
+
+## ⚠️ 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)
+
+---
+
+## 🔮 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
+
+---
+
+## 🧑‍💻 Author
+
+Udyan Trivedi
+
+---
+
+## 📜 License
+
+MIT License