Machine Learning Techniques To Predict The Price Of Used Cars: Predictive Analytics in Retail Business
Chejarla Venkat Narayana, Chinta Lakshmi Likhitha, Syed Bademiya, Karre Kusumanjali
Abstract
It is generally known that, taking wise and challenging decisions is really a crucial task in every business. Taking improper decisions can cause huge loss and even lead to shutdown of business. To propose a novel solution for this challenge, this research work majorly focuses on one of the retail businesses i.e., used car sales business. The proposed research work shows that, the predictive analytical models will be a great add-on to business mainly for assisting the decision making process. Predictive Analytics is a process, where the businesses use statistical methods and technologies to analyze their historical data for delivering new insights and plan the future accordingly. The major objective of our paper is to build a prediction model i.e., a fair price mechanism to predict the cars selling price based on their features like the car model, the number of years that a car is old, the type of fuel it uses, the type of seller, the type of transmission and the number of kilometers that the car has driven so far. This paper will help to get an approximation about selling price of a used car based on its features and reduces the seller and consumer risk in business. The proposed model utilizes the machine learning algorithms and regression techniques of statistics like linear, decision tree and random forest regressions to achieve this task.