Design and Implementation of Stock Market Prediction System for Used Cars in Nigeria

Abstract

Stock market prediction of commodities have undergone changes from the traditional to modern methods of using machine learning.  Hence, the objective of this study was to design and implement a stock market price prediction system for used cars in Nigeria using machine learning techniques, the extra tree algorithm and support vector machine (SVM). The dataset used included such attributes as fuel type, number of doors, number of cylinders, drive wheel and price amongst others. The model was designed by training and testing using pre-processed data. Python programming language was used in the implementation. The results obtained for the mean square error and the R-squared showed high accuracy and therefore made the model ideal for car price prediction in the automobile Nigerian market.