Analyzing Automotive Evolution: Unveiling Trends, Patterns, and Influencing Factors through Advanced Data Mining

This article presents detailed information retrieval and data mining project aimed at researching models and try to realize it on selected dataset. This study focusing on the analysis of automotive data to glean insights into trends and patterns over time. Leveraging a diverse dataset encompassing various attributes of automobiles, including make, model, year, engine specifications, transmission type, and pricing, we employ a range of machine learning techniques to extract valuable knowledge. The primary objectives of this research are twofold: first, to explore the relationship between key attributes such as engine horsepower, cylinder count, and fuel efficiency, elucidating how technological advancements have influenced automotive performance over the years; second, to investigate the popularity of different vehicle styles across various periods, shedding light on consumer preferences and market trends. Using a diverse dataset encompassing attributes like make, model, year, engine specs, and pricing, various machine learning techniques such as Binary Independence Model, Probabilistic Models, and Naïve Bayesian Text Categorization are employed. The research aims to explore the relationship between engine attributes and fuel efficiency over time and understand consumer preferences in vehicle styles. Through rigorous analysis, the study provides valuable insights into the evolution of automotive technology and consumer trends, emphasizing the significance of data mining in informing decisions in the automotive sector. The project utilizes Natural Language Processing and text classification techniques, demonstrating efficacy in assessing news reliability with potential applications in media and journalism.