Application of Data Mining to Predict Car Sales Using K-Means Clustering Method
DOI:
https://doi.org/10.62181/nb8vt960Keywords:
Data Mining, Car Sales Prediction, K-Means Clustering MethodAbstract
The automotive industry is one of the key sectors in the economy. Accurate car sales prediction is crucial for automotive businesses to develop effective marketing and production strategies. Data mining, with its various techniques, offers solutions to assist automotive companies in predicting car sales. One popular data mining technique for predicting car sales is K-Means Clustering. This technique groups car sales data based on characteristics such as car model, price, sales region, and other factors. The clustering results can be used to identify sales patterns and trends, which can then be used to predict future sales. This paper discusses the application of K-Means Clustering for car sales prediction. It explains the steps involved in applying K-Means Clustering, its advantages and disadvantages, and provides an example of its application.
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Copyright (c) 2024 Antonia Desya, Dimas Ilham Pratama, Muchammad Vico Airlangga, Andi Diah Kuswanto (Author)
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