Car Price Predictor: Unlocking Insights for Used Car Buyers and Seller

Abstract

The growing market for old cars is causing an evolutionary shift in the automotive sector. The difficult part of the purchasing and selling method for old cars is the rating of these vehicles because buyers look into more affordable and environmentally friendly modes of transportation. This paper offers a thorough analysis of the use of machine learning techniques to estimate used automobile prices. Several factors including mileage and other relevant characteristics which impact the nature of used vehicle costs are gathered and examined as part of our methodology. Our machine learning algorithms seek to understand the complex correlations among these qualities and used car sale prices. It is done by utilising an extensive database that includes an extensive variety of cars. To improve the capacity of the model for detecting minute trends in the data, methods such as feature engineering are used. Pre-processing the dataset properly ensures that the prediction models are stable by handling values that are lacking, anomalies, and variables that are categorical. The machine learning-powered predictive algorithms that have been built are useful resources for customers as well as sellers in the marketplace for used cars. Our strategy enhances accountability, effectiveness, and sound choice-making in the ever-changing used car market by offering precise and data-driven automobile pricing calculations.