Prediction of Used Car Prices using Machine Learning Techniques

Prediction of Used Car Prices using Machine Learning Techniques

1 Ajay S, 2Dr Shankaragowda B B

1Sem MCA Student, Department of MCA, BIET, Davanagere

2Associate Professor and HOD, Department of MCA, BIET, Davanagere

Abstract - The manufacturer sets the price of a new car in the industry, with the government incurring some additional expenditures in the form of taxes. Customers purchasing a new car may thus be sure that their investment will be worthwhile. However, due to rising new car prices and buyers' financial inability to purchase them, used car sales are increasing globally. As a result, a used car price prediction system that efficiently assesses the worthiness of the car utilizing a range of factors is required. The current system comprises a system in which a dealer decides on a price at random and the buyer has no knowledge of the car or its current worth. In reality, the seller has no clue what the car is worth or what price he should charge for it. To address this issue, we have devised a highly effective model. Regression algorithms are employed because they produce a continuous value rather than a classified value as an output. As a result, rather than predicting a car's price range, it will be feasible to estimate its real price. A user interface has also been created that takes input from any user and shows the price of a car based on the inputs.

Key Words: Used Car Price Prediction, Regression Algorithms, Machine Learning, Linear Regression, Ridge and Lasso Regression, Bayesian Ridge Regression, Decision Tree, Random Forest, XG Boost, Gradient Boosting.