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:

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:

python polynomial_regression_co2_emissions.py

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