NakerTheFirst / MPG

Mileage Per Gallon (MPG) dataset based LSM linear regression model

Mileage Per Gallon

This project demonstrates the development of a linear regression model to predict fuel consumption using various car attributes from the Auto MPG dataset. An answer to a question - what decides how far a car will go?

Features

The project includes data preprocessing, exploratory data analysis, and the implementation of a linear regression model using the following features:

• Cylinders
• Displacement
• Horsepower
• Weight
• Acceleration
• Model year
• Origin

Installation

To run this project, you need Python installed along with the following libraries:

• NumPy
• pandas
• matplotlib
• seaborn
• scikit-learn

You can install these packages using pip:

pip install numpy pandas matplotlib seaborn scikit-learn

Usage

To use this project:

1. Ensure you have the auto-mpg.data file in your working directory. This file contains the dataset used for modeling.
2. Run the script via the command line:

python main.py

Functionalities

Data Preprocessing

• Reads car data from a file.
• Converts MPG (Miles per Gallon) to liters per 100 km.
• Normalises selected numerical features.
• Feature engineers categorical origin variable into Europe, USA or Asia via one-hot encoding.

Visualisation

• Provides scatter plots and histograms to explore data relationships and distribution.
• Offers correlation heatmaps to understand feature interactions.

Model Training and Evaluation

• Splits the data into training and testing sets.
• Trains a linear regression model and evaluates its performance using mean squared error (MSE).
• Compares the accuracy of different regression methods: LSM, Lasso, Ridge, ElasticNet.
• Outputs the parameter values for optimal LSM-based linear regression method.

A full, detailed report of the process in Polish can be read in report.pdf