| --- |
| title: Mumbai Crowd Predictor |
| emoji: 🚆 |
| colorFrom: blue |
| colorTo: purple |
| sdk: docker |
| pinned: false |
| --- |
| # 🚆 Mumbai Local Train — Crowd Density Predictor (Advanced Edition) |
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| AI-powered crowd prediction system for Mumbai Local Trains with a full-stack FastAPI + HTML/CSS/JS deployment. |
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| --- |
|
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| ## 🚀 Features |
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| - **AI Prediction** — RandomForest model with 17 engineered features (~97% accuracy) |
| - **Hourly Forecast** — Full day crowd pattern for any route |
| - **Station Heatmap** — Visual heatmap of crowd across stations × hours |
| - **Risk Scoring** — 0–100 risk score with reasoning and travel tips |
| - **Beautiful UI** — Dark glassmorphism design, animated charts, responsive |
|
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| --- |
|
|
| ## 🗂️ Project Structure |
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|
| ``` |
| mumbai_train_app/ |
| ├── main.py # FastAPI application (entry point) |
| ├── requirements.txt |
| ├── src/ |
| │ ├── model.py # RandomForest + feature engineering |
| │ └── risk_engine.py # Risk scoring, explanations, tips |
| ├── data/ |
| │ └── raw/ |
| │ └── mumbai_local_crowd_data.csv |
| ├── static/ |
| │ ├── css/style.css # Full dark-mode glassmorphism CSS |
| │ └── js/app.js # Frontend JavaScript |
| └── templates/ |
| └── index.html # Main HTML template |
| ``` |
|
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| --- |
|
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| ## ⚙️ Setup & Run |
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| ### 1. Install dependencies |
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
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| ### 2. Run the server |
| ```bash |
| python main.py |
| # OR with uvicorn directly: |
| uvicorn main:app --host 0.0.0.0 --port 8000 --reload |
| ``` |
|
|
| ### 3. Open in browser |
| ``` |
| http://localhost:8000 |
| ``` |
|
|
| --- |
|
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| ## 🧠 ML Model Details |
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| | Feature | Description | |
| |---------|-------------| |
| | `hour` | Hour of travel (5–22) | |
| | `hour_weight` | Peak-hour weight (empirical) | |
| | `day_num` | 0=Monday → 6=Sunday | |
| | `is_weekday/weekend` | Binary flags | |
| | `is_morning_peak` | 7–9 AM flag | |
| | `is_evening_peak` | 5–7 PM flag | |
| | `morning_southbound` | Directional rush interaction | |
| | `evening_northbound` | Directional rush interaction | |
| | `line_num` | Western/Central/Harbour encoded | |
| | `dir_num` | Northbound/Southbound encoded | |
| | `station_enc / dest_enc` | Label-encoded stations | |
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| **Algorithm**: Random Forest Classifier (200 trees, balanced class weights) |
| **Accuracy**: ~97% on training data |
|
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| --- |
|
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| ## 📡 API Endpoints |
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| | Method | Path | Description | |
| |--------|------|-------------| |
| | GET | `/` | Web UI | |
| | GET | `/api/lines` | All train lines | |
| | GET | `/api/stations/{line}` | Stations for a line | |
| | GET | `/api/stats` | Dataset + model stats | |
| | POST | `/api/predict` | Single prediction | |
| | POST | `/api/hourly-forecast` | Full day forecast | |
| | GET | `/api/heatmap/{line}` | Station × hour heatmap | |
|
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| --- |
|
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| ## 🛤️ Train Lines Covered |
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| - **Western Line**: Churchgate → Virar (12 stations) |
| - **Central Line**: CSMT → Kalyan (9 stations) |
| - **Harbour Line**: CSMT → Panvel (8 stations) |
|
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| --- |
|
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| ## 👨💻 Tech Stack |
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| - **Backend**: FastAPI + Uvicorn |
| - **ML**: scikit-learn RandomForest, Pandas, NumPy, Joblib |
| - **Frontend**: Pure HTML5 + CSS3 + Vanilla JS |
| - **Design**: Dark glassmorphism, CSS animations, responsive |
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|
