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| title: Uber Driver Recommendation System | |
| emoji: 🚖 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: "4.31.0" | |
| python_version: "3.10" | |
| app_file: app.py | |
| pinned: false | |
| # 🚖 Uber Driver Recommendation System | |
| An intelligent, lightweight ML-powered recommendation system that suggests the best ride options for drivers by optimizing earnings, efficiency, and ride quality. | |
| --- | |
| ## 🎯 Overview | |
| This project simulates a real-world ride allocation system (like Uber/Ola) where multiple ride requests are ranked and recommended to drivers using a machine learning model. | |
| The system focuses on: | |
| - Maximizing driver earnings 💰 | |
| - Minimizing idle time ⏱️ | |
| - Improving ride efficiency 🚗 | |
| --- | |
| ## 🧠 How It Works | |
| 1. Synthetic ride data is generated | |
| 2. Feature engineering calculates efficiency metrics | |
| 3. A Random Forest model predicts a reward score | |
| 4. Ride options are ranked based on score | |
| 5. Top recommendations are displayed with explanations | |
| --- | |
| ## ⚙️ Features | |
| - ✅ ML-based ride scoring engine | |
| - ✅ Real-time recommendation simulation | |
| - ✅ Explainable AI ("Why this ride?") | |
| - ✅ Clean and minimal Gradio UI | |
| - ✅ Fast and lightweight (HF Spaces ready) | |
| --- | |
| ## 📊 Input Parameters | |
| - Pickup Distance (km) | |
| - Trip Distance (km) | |
| - Fare (₹) | |
| - Surge Multiplier | |
| --- | |
| ## 📤 Output | |
| - Top 3 recommended rides | |
| - AI-generated score | |
| - Key reasons (high fare, low pickup, surge, etc.) | |
| --- | |
| ## 🚀 Run Locally | |
| ```bash | |
| pip install -r requirements.txt | |
| python app.py | |