🚗 EV Efficiency Recommendation System

An end-to-end Machine Learning solution designed to predict Electric Vehicle (EV) efficiency. This project integrates a Scikit-learn classifier with a FastAPI backend and a responsive frontend, all encapsulated within a Docker container for seamless deployment.

🚀 Live Demo

🌐 Web App: View Live Deployment on Render
📑 API Documentation: Swagger UI (Local)

---

✨ Key Features

• 🔮 Predictive Intelligence: Classifies EV efficiency as High or Low.
• ⚡ Comprehensive Data Inputs: Processes battery, range, charging, and safety ratings.
• 🌐 Interactive UI: Clean interface for manual data entry and instant results.
• 🚀 Production-Ready API: Powered by FastAPI for high-performance handling.
• 🐳 Containerized Workflow: Fully Dockerized to ensure cross-platform consistency.

---

🛠 Tech Stack

Category	Tools
Language	Python 3.11
Machine Learning	Scikit-learn, Pandas, Joblib
Backend	FastAPI, Uvicorn
Frontend	HTML5, CSS3, JavaScript
DevOps	Docker, Docker Hub, Render

---

📦 Getting Started (Docker)

To run this project locally without installing Python dependencies:

1. Pull the Image

docker pull mohdmusheer/ev-efficiency-api-g1

2. Run the container

docker run -d -p 8000:8000 mohdmusheer/ev-efficiency-api-g1

3. Access the app

Web UI: http://localhost:8000 API Docs: http://localhost:8000/docs

📂 Project Structure

.
├── api/
│   └── api.py          # FastAPI backend logic
├── model/
│   └── ev_efficiency_classifier.pkl  # Trained ML Model
├── UI/
│   └── index.html      # Frontend interface
├── requirements.txt    # Python dependencies
├── Dockerfile          # Container configuration
└── README.md           # Documentation

🔗 API Usage

POST /predict

Send a JSON payload to receive an efficiency prediction.

vehicle = {
  "battery_kwh": 75.0,
  "range_km": 500.0,
  "charging_time_hr": 1.0,
  "fast_charging": 1,
  "release_year": 2024,
  "seats": 5,
  "price_usd": 45000,
  "acceleration_0_100_kmph": 3.3,
  "top_speed_kmph": 225,
  "warranty_years": 4,
  "cargo_space_liters": 425,
  "safety_rating": 5.0,
  "type": "Sedan",
  "drive_type": "AWD",
  "fuel_type": "Electric",
  "country": "USA"
}

Response:

1 → High Efficiency

0 → Low Efficiency

🎯 Target Use Cases

1. Academic Portfolios: Demonstrating end-to-end ML deployment.
2. Engineering Interviews: Highlights full-stack and hardware-adjacent software skills.
3. DevOps Practice: Template for CI/CD and containerization workflows.