https://github.com/mahdibehoftadeh/polynomial-regression-co2-emissions

A simple machine learning polynomial regression using a large dataset to learn and predict CO2 emission of a car by its built features like engine size and cylinders
https://github.com/mahdibehoftadeh/polynomial-regression-co2-emissions

machine-learning matplotlib numpy nural-network pandas polynomial-regression python scikit-learn

Last synced: 3 days ago
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A simple machine learning polynomial regression using a large dataset to learn and predict CO2 emission of a car by its built features like engine size and cylinders

• Host: GitHub
• URL: https://github.com/mahdibehoftadeh/polynomial-regression-co2-emissions
• Owner: MahdiBehoftadeh
• License: apache-2.0
• Created: 2024-08-02T21:09:09.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-02T21:25:28.000Z (3 months ago)
• Last Synced: 2024-10-17T12:45:56.682Z (20 days ago)
• Topics: machine-learning, matplotlib, numpy, nural-network, pandas, polynomial-regression, python, scikit-learn
• Language: Python
• Homepage: https://behoftadeh.ir
• Size: 129 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Car CO2 Emission Prediction - by Mahdi Behoftadeh

This project uses polynomial regression to predict CO2 emissions from car features. The code includes data loading, polynomial feature generation, model training, evaluation, and a user input loop for real-time predictions.

### Prerequisites

Make sure you have the following Python libraries installed:

- `numpy`

- `pandas`

- `matplotlib`

- `scikit-learn`

You can install them using pip:

```bash

pip install numpy pandas matplotlib scikit-learn

```

### Data

The dataset (`car_co2_emissions_data.csv`) in the same directory has the following columns:

- `ENGINE_SIZE`: Size of the car engine

- `CYLINDERS`: Number of cylinders in the car engine

- `CO2_EMISSIONS`: CO2 emissions of the car

### How It Works

1. **Load Data**: The script reads the dataset from `car_co2_emissions_data.csv`.

2. **Feature and Target Setup**: It selects `ENGINE_SIZE` and `CYLINDERS` as features and `CO2_EMISSIONS` as the target variable.

3. **Polynomial Features**: Generates polynomial features to capture interactions between the original features.

4. **Split Data**: Splits the data into training and testing sets.

5. **Train Model**: Trains a model to predict CO2 emissions.

6. **Evaluate Model**: Calculates and prints various performance metrics including Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, and R-squared.

7. **Plot Data**: Creates a scatter plot for training and test data along with a prediction line. (Helps us understand the accuracy of the model better)

8. **User Input**: Continuously prompts the user for engine size and number of cylinders, then predicts and displays the CO2 emissions.

### Running the Script

Run the script using Python:

```bash

python polynomial_regression_co2_emissions.py

```

Follow the prompts to input engine size and the number of cylinders for real-time CO2 emission predictions.