Estimation of the Price of Used Cars Using Machine Learning

Preview this chapter:

Estimation of the Price of Used Cars Using Machine Learning, Page 1 of 1

< Previous page | Next page > /docserver/preview/fulltext/9789815324099/chapter-17-1.gif

The manufacturer sets the price of new vehicles in the market, and taxes are an additional expense imposed by the government. Consumers can confidently purchase a new car, expecting a favourable return on their investment. However, with the rising cost of new automobiles, there is a growing trend worldwide for people to opt for purchasing pre-owned vehicles. As a result, there is an increasing need for a proficient and comprehensive system that can accurately predict the prices of preowned vehicles based on several features. This article demonstrates the predictive capability for older cars by using linear lasso regression, a machine-learning technique. This approach ensures a prediction accuracy of 83%, making it the most precise among three distinct algorithms. The data includes information on the model year, car model, engine type, and price. Over the past decade, one of the most significant advancements in technology has been machine learning, which has had a crucial impact on precise forecasting and reliability.