Fuel Consumption Prediction API

A FastAPI-based Machine Learning project that predicts vehicle fuel consumption using a regression model trained on the FuelConsumption dataset.

This project demonstrates the full pipeline — data preprocessing, model training, serialization with Joblib, and API deployment using FastAPI.

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Project Overview

This API allows users to input numeric vehicle parameters (like engine size, cylinders, or CO2 emissions) and get predicted fuel consumption values instantly.

FastAPI Docs View

Example Prediction

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Tech Stack

• FastAPI – Backend framework
• Scikit-learn – Model training
• Joblib – Model serialization
• Pydantic – Data validation
• Uvicorn – ASGI server for deployment

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Project Structure

├── api.py             # FastAPI app
├── model/
│   └── model.pkl          # Trained model
├── notebook.ipynb
├── data/
│   └── FuelConsumption.csv
├── requirements.txt
└── README.md

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API Endpoints

POST /predict/

Request Body

{
  "feature1": 5.3,
  "feature2": 120.5
}

Response

{
  "prediction": 7.84
}

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How to Run Locally

1️⃣ Clone the repo

git clone https://github.com/sameer-at-git/Fuel-Consumption-Prediction-API-using-Linear-Regression-and-FastAPI.git
cd Fuel-Consumption-Prediction-API-using-Linear-Regression-and-FastAPI

2️⃣ Create a virtual environment

python -m venv venv
source venv/bin/activate     # (Linux/Mac)
venv\Scripts\activate        # (Windows)

3️⃣ Install dependencies

pip install -r requirements.txt

4️⃣ Run the FastAPI app

uvicorn app.api:app --reload

Access the docs:
👉 http://127.0.0.1:8000/docs

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Model Training (Notebook)

The notebook notebook.ipynb covers:

• Dataset exploration
• Feature engineering
• Model training & evaluation
• Saving model using Joblib

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Deployment Ideas

• Hugging Face Spaces (with FastAPI)
• Render
• Railway.app
• Vercel (with ASGI adapter)

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Author

Md. Sameer Sayed
📧 mdsameersayed0@gmail.com
🌐 GitHub

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License

This project is released under the MIT License.