mohdmusheer/ev-efficiency-api-g1 repository overview

📌 About the Project

EV Efficiency Predictor is a machine learning–based web API that predicts the efficiency category of an electric vehicle using its specifications such as battery capacity, range, charging time, speed, and safety features.

Github : https://github.com/rainerrodrigues/EV_Recommendation_System⁠

live :

🚀 Features

Predicts EV efficiency using real-world vehicle attributes

FastAPI-based REST API

Machine Learning model loaded with joblib

Input validation using Pydantic

Fully Dockerized for easy deployment

CORS enabled for frontend integration

🛠️ Tech Stack

Backend: FastAPI

Machine Learning: Scikit-learn

Model Loading: Joblib

Data Handling: Pandas

Deployment: Docker

▶️ How to Use the Project 1️⃣ Clone the Repository git clone cd EV_Recommendation_System

2️⃣ Build the Docker Image docker build -t ev-efficiency-api .

3️⃣ Run the Docker Container docker run -p 8000:8000 ev-efficiency-api

4️⃣ Open the API in Browser

Swagger UI (API Docs): 👉 http://localhost:8000/docs⁠

Home Page (UI): 👉 http://localhost:8000/⁠

5️⃣ Make a Prediction (Using Swagger UI)

Open /docs

Click on POST /predict

Click Try it out

Enter input like:

{ "manufacturer": "Tesla", "model": "Model 3", "type": "Sedan", "drive_type": "RWD", "fuel_type": "Electric", "color": "White", "fast_charging": "Yes", "country": "USA", "city": "San Francisco", "battery_kwh": 75, "range_km": 500, "charging_time_hr": 1.2, "release_year": 2024, "seats": 5, "acceleration_0_100_kmph": 3.3, "top_speed_kmph": 225, "warranty_years": 4, "cargo_space_liters": 425, "safety_rating": 5 }

Click Execute

6️⃣ Sample Output { "prediction": 1 }

1 → High Efficiency 0 → Low Efficiency

👥 Group Project Note

This project was developed as a group project, with collaborative contributions in:

Dataset cleaning and preprocessing

Feature engineering

Machine learning model training

Backend API development and Dockerization