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AI Transport Optimization System\n\nAn intelligent transport analytics and optimization system built using **Machine Learning + Streamlit Dashboard**.\n\n---\n\n## 🔥 Features\n\n- 📊 **Passenger Demand Prediction (ML Model)**\n- 🚦 **Traffic-Aware Decision System**\n- 🧠 **Smart Service Recommendations**\n- 🗺️ **Interactive Route Map**\n- 🔮 **What-If Simulation (Add Buses)**\n- 📈 **Demand Heatmaps \u0026 Trends**\n- ⚠️ **Overcrowding Detection**\n- 🔄 **Alternative Route Suggestions**\n\n---\n\n## 🧠 System Intelligence\n\nThe system automatically suggests actions like:\n\n- ➕ Add Bus\n- 🔁 Reroute\n- ⏱ Increase Frequency\n- ✅ Normal Operation\n\nBased on:\n- Passenger Load\n- Traffic Conditions\n- Delay\n- Peak Hours\n\n---\n\n## 📂 Dataset Features\n\n- Route \u0026 Stop Data\n- GPS Coordinates (Lat/Lon)\n- Passenger Demand\n- Traffic Levels\n- Delay \u0026 Speed\n- Load Factor\n- Bus Count\n- Travel Distance \u0026 Time\n- AI Recommendation Fields\n\n---\n\n## 🚀 Tech Stack\n\n- Python 🐍\n- Streamlit 🌐\n- Scikit-learn 🤖\n- Pandas 📊\n- Plotly 📈\n\n---\n\n##  ▶️ Run Locally \n\n```bash\n1️⃣ git clone https://github.com/prathmesh2507/CTC-HACKTHON.git \n2️⃣ cd CTC-HACKTHON\n3️⃣ pip install -r requirements.txt \n4️⃣ python -m streamlit run app.py\n```\n---\n## 🔗 Live Project:\nhttps://ctc-hackthon-dys4t4vucykonlztwhk3re.streamlit.app/\n\n---\n## 📸 Dashboard Preview\n![Dashboard](Screenshot_1.jpeg)\n![Dashboard](Screenshot_2.jpeg)\n## 📸 Flowchart Preview\n![Flowchart](Screenshot_3.jpeg)\n\n---\n## 💡 Future Improvements\n- Live Traffic API Integration\n- Real-time Bus GPS Tracking\n- Mobile App Integration\n- Smart Ticketing Data Integration\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprathmesh2507%2Fctc-hackthon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprathmesh2507%2Fctc-hackthon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprathmesh2507%2Fctc-hackthon/lists"}