🚗 Emissions Predictor Using Multiple Linear Regression (MLR)

Emissions Predictor Using MLR is a machine learning application that uses a Multiple Linear Regression (MLR) model to predict carbon dioxide emissions (g/km) of a vehicle based on multiple features like engine size, fuel consumption, and other vehicle characteristics. This project demonstrates how to build, train, evaluate, and deploy a regression model using Python and Streamlit.

---

🌟 Features

• Multiple Linear Regression (MLR) Model: Accurate prediction of CO2 emissions using multiple features.
• Interactive User Interface: Built using Streamlit for an easy-to-use experience.
• Real-Time Predictions: Adjust features like engine size and fuel consumption to get instant CO2 emission predictions.
• Data Visualization: Displays visualizations to understand the relationships between vehicle features and CO2 emissions.

---

🛠️ Tech Stack

• Python: Core programming language.
• Streamlit: Framework for creating the interactive web application.
• Scikit-learn: Machine learning library used for training the MLR model.
• Matplotlib: For data visualization.
• Pandas: For data manipulation and analysis.

---

🚀 How It Works

1. Input Features: Use the sliders to select features like engine size and fuel consumption.
2. Real-Time Prediction: The app instantly predicts the CO2 emissions based on the input.
3. Visualize Data: See scatterplots and other visualizations to understand the relationship between features and emissions.

---

📂 Repository Structure

📦 Emissions-Predictor-using-MLR

├──  app.py 
├──  mlr_model.pkl 
├──  ridge_tuned_model.pkl
├──  scaler.pkl
├──  CO2 Emissions Predictor using MLR.ipynb
├──  README.md                 
├──  vehicle_data.csv 
├──  requirements.txt           
├──  LICENSE                   
├──  banner_md.jpeg             
├──  icons8-github-50.png

---

🖥️ Installation and Usage

Prerequisites

• Python 3.8 or higher installed on your machine.

Setup Instructions

Step 1: Clone the Repository

git clone https://github.com/ashishpatel8736/Emissions-Predictor-using-MLR.git
   cd CO2-Emissions-Predictor

Step 2: Install Dependencies

Ensure you have Python installed. Run the following to install the required libraries:

pip install -r requirements.txt

Step 3: Start the Application

Run the Streamlit app:

streamlit run app.py

Step 4: Open your browser and go to:

http://localhost:8501

📊 Sample Data

Here is an example of the dataset used for training the SLR model:

Engine Size (L)	Fuel Consumption (L/100km)	CO2 Emissions (g/km)
1.5	6.5	145
2.0	7.0	185
3.0	8.5	250
4.0	9.0	320
5.0	10.5	400

---

🎯 Future Enhancements

• Add support for more input features, such as vehicle weight or fuel type.
• Implement model optimization techniques like cross-validation for better accuracy.
• Add support for uploading custom datasets.
• Provide downloadable results and summary reports.

---

🤝 Contributing

Contributions are welcome! If you'd like to contribute, please:

1. Fork the repository.
2. Create a feature branch.
3. Submit a pull request.

🙌 Acknowledgements

• Scikit-learn for providing robust machine learning tools.
• Streamlit for enabling easy deployment of ML apps.
• Pandas and Matplotlib for data manipulation and visualization.

---

🛡️ License

This project is licensed under the MIT License - see the LICENSE file for details.

---

👤 Author

Ashish Patel
|