Application of Principal Component Analysis to Vehicle Sales: Case Study at IBM Company

Principal Component Analysis (PCA) is a fundamental statistical technique used fordimensionality reduction and data transformation. It is providing an overview of PCA's principles, methodology, and applications. PCA aims to capture the most important information in high-dimensional data by transforming it into a new coordinate system defined by its principal components. These components are linear combinations of the original variables, ordered by the amount of variance they explain. The data has been taken from original data from (IBM) company on car sales. According to a real-world example, sales of cars are based on (car type, sales in thousands, 4-year resale value, price in thousands, engine size, horsepower, wheelbase, width and length, curb weight, fuel capacity, fuel economy, and log-transformed sale), these data used in SPSS program for this purpose.The result indicates that the first and second components (Wheelbase, Engine size, Price in thousands, Horsepower, and Sales in thousands) factors together represent the total variances % (75.8(45%) in the model, which uses appropriate sampling and contains two components in total with up to 12 variables added.It is recommended that drivers should have automobiles that are more comfortable for divers and with (high-quality horsepower Wheelbase, Engine size, Price in thousands, Horsepower, and Sales in thousands) of cars.