https://github.com/ashishpatel8736/emissions-predictor-using-mlr
CO2 Emissions Predictor is a machine learning project that uses a Multiple Linear Regression (MLR) model to predict the CO2 emissions of vehicles based on their specifications, such as engine size, cylinders, and fuel consumption.
https://github.com/ashishpatel8736/emissions-predictor-using-mlr
machine-learning multi-linear-regression python
Last synced: about 2 months ago
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CO2 Emissions Predictor is a machine learning project that uses a Multiple Linear Regression (MLR) model to predict the CO2 emissions of vehicles based on their specifications, such as engine size, cylinders, and fuel consumption.
- Host: GitHub
- URL: https://github.com/ashishpatel8736/emissions-predictor-using-mlr
- Owner: ashishpatel8736
- License: apache-2.0
- Created: 2024-11-21T19:18:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-28T14:20:08.000Z (over 1 year ago)
- Last Synced: 2025-04-03T08:13:58.179Z (about 1 year ago)
- Topics: machine-learning, multi-linear-regression, python
- Language: Jupyter Notebook
- Homepage: https://emissions-mlr.streamlit.app/
- Size: 396 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚗 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
```plaintext
📦 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
```bash
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:
```bash
pip install -r requirements.txt
```
### Step 3: Start the Application
Run the Streamlit app:
```bash
streamlit run app.py
```
### Step 4: Open your browser and go to:
```bash
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](LICENSE) file for details.
---
## 👤 Author
**Ashish Patel**
[](https://github.com/ashishpatel8736) | [](https://www.linkedin.com/in/ashishpatel8736)