Used Car Price Prediction Using One Hot Encoding

• Published in: International Journal For Multidisciplinary Research Vol. 7; no. 2
• Main Authors: Sambyal, Rakesh, Kaushik, Rachit, Tyagi, Tushar, Sharma, Rudransh
• Format: Journal Article
• Language: English
• Published: 05.04.2025
• ISSN: 2582-2160; 2582-2160
• DOI: 10.36948/ijfmr.2025.v07i02.40641

Guessing the value of old cars is a topic of intense interest since it calls for distinctive effort from a subject-matter specialist. The manufacturer in the industry determines the cost of a new automobile, plus any additional taxes that the government must pay. Customers who purchase a new automobile are therefore certain that their financial commitment will be beneficial. However, old car sales are rising internationally as a outcome of new car price increases and consumers' financial inability to purchase them. Almost the previous ten years, the number of automobiles manufactured has constantly increased; in 2022, there will be over 80 million passenger cars produced. With the use of machine learning techniques like Extra Trees Regressor, Random Forest Regressor, and Regression Trees. we'll try to expend the model that predicts the cost of a old vehicle using past customer data and a specified set of characteristics. Regression algorithms are employed because they give clients continuous results as opposed to categorical final results. As a result, it will be feasible to forecast the exact cost of an automobile rather than just its price range. The user interface, which requests input from any user and shows a car's cost in response to that input, was likewise built using React js. The main aim of this exploration is to produce machine learning models that can directly predict an old car's cost based on its parameters so that customer or user may make best decisions.