Regression Project using R - Predictive Modeling of Sports Car Prices

Overview

This project focuses on predictive modeling for sports car prices, leveraging statistical analysis in RStudio. The goal is to develop robust linear regression models that effectively predict sports car prices based on their functional and technical attributes.

Motivation

With the growing demand for luxury vehicles, sports cars have become a significant segment in the automotive market. Consumers are increasingly interested in advanced features such as improved engines, acceleration times, and horsepower. This project aims to provide valuable insights into the factors influencing sports car prices, aiding car manufacturers, dealerships, and buyers in estimating the value of these high-end vehicles.

Dataset

The analysis utilizes the "Sports Car Prices" dataset sourced from Kaggle, which offers comprehensive information on various attributes of sports cars, including their prices. The dataset is publicly accessible at this link. By exploring this dataset, we seek to understand the relationships between specific car features and their impact on pricing.

Tools Used

The project relies on RStudio as the primary statistical software for conducting in-depth linear regression analyses. RStudio provides a robust environment for statistical computing and visualization, allowing for a comprehensive exploration of the dataset and effective model development.

Project Objectives

• Develop and evaluate linear regression models to predict sports car prices.
• Provide insights into the factors influencing sports car prices based on the model outcomes.

In the project, I applied various model building techniques, including:

• Full Model Fitting
• Generalized Linear Models (GLMs) Model Fitting
• Linear Regression Analysis
  • Linearity
  • Homoscedasticity
  • Autocorrelation
  • Normality
  • Residual Analysis
• Logarithmic Transformation
• Box-Cox Transformation

Conclusion

This project aims to contribute to the understanding of sports car pricing dynamics and provide a reliable predictive model for estimating the value of these high-performance vehicles. Feel free to explore the code and findings in this repository to gain insights into the relationships between sports car features and their market prices.

This project is derived from my own RMIT Master of Analytics assignment in the “Regression Analysis” course (2022). It has been slightly modified and refined to showcase my regression techniques using R.