Fuel Consumption Regression Models

This project explores multiple regression models to predict vehicle carbon dioxide emissions (CO2EMISSIONS) based on fuel consumption and vehicle-related features.

The notebook compares different approaches, including:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Random Forest Regression

Among the tested models, Random Forest Regression achieved the best performance.

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Project Overview

The goal of this project is to build predictive models that estimate CO2EMISSIONS using vehicle characteristics such as engine size, fuel type, vehicle class, and transmission type.

This notebook includes:

• Data preprocessing
• Categorical feature encoding
• Correlation analysis
• Train/test splitting
• Model training
• Model evaluation
• Model comparison

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Dataset

The dataset contains information about vehicle fuel consumption and emissions.
Important features used in this project include:

• ENGINESIZE
• CYLINDERS
• FUELTYPE
• VEHICLECLASS
• TRANSMISSION
• CO2EMISSIONS as the target variable

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Preprocessing

To prepare the data for machine learning:

• Categorical features were encoded using LabelEncoder
• Correlation analysis was performed using a heatmap
• The dataset was split into training and testing sets

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Models Used

1. Simple Linear Regression

A baseline model used to predict CO2EMISSIONS using a single feature.

2. Multiple Linear Regression

A model that uses several features to improve prediction accuracy.

3. Polynomial Regression

A 2nd-degree polynomial model was used to capture non-linear relationships in the data.

4. Random Forest Regression

An ensemble-based model that combines multiple decision trees.
This model produced the best score and performed better than the linear-based approaches.

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Evaluation Metrics

The models were evaluated using:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• R² Score

These metrics were used to compare the predictive performance of each model.

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Results

After comparing all models, Random Forest Regression gave the highest performance score on the test set.
This suggests that the relationship between the input features and CO2EMISSIONS is complex and non-linear.

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Technologies Used

• Python
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

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How to Run

1. Clone the repository:

git clone https://github.com/python-is-life2022/Fuel-Consumption-Regression-Models.git