Description

A car price prediction has been a high interest research area, as it requires noticeable effort and knowledge of the field expert. This paper aims to build a model to predict used cars reasonable prices based on multiple aspects, including vehicle name, vehicle mileage, and year of manufacturing, fuel consumption, transmission(Manual or Automatic), fuel type, and also prices. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively price prediction based on the information that users input. Various regression methods including linear regression, Random Forest regression, were applied in the project to achieve the highest accuracy. Before the actual start of model-building, this project visualized the data to understand the dataset better. The dataset was divided and modified to fit the regression, thus ensure the performance of the regression.