Predictive Analysis and Application of Various Machine Learning Algorithms to Forecast used Car Prices

India has been a huge market for secondhand market. The most popular second-hand items sold in India include used cars, furniture, electronic appliances, Mobile phones and etc. In general, a used car would cost lesser than a brand new car. Thus people prefer buying a used car. During the pandemic, the secondhand automobile industry in India has seen a sharp increase in popularity. Many people choose to purchase a used car rather than a brand-new one due to the difficulty of using public transit and the requirement for personal transportation. A used car was more cost-effective to purchase than a new one because of the economic uncertainty brought on by the pandemic and the increased prudence with which people approached their spending. Also, the pandemic’s effects on the supply chain and production delays made the used automobile market more alluring to customers. In this paper, the comparative analysis of multiple machine learning algorithms enabled us to identify the most effective method for determining the value of secondhand automobiles. Last but not least, providing an interactive web application to the owners of the cars where they can enter the details get an overview at the price. This study has processed the proposed dataset using popular Machine learning algorithms. Feature selection with Random Forest Regression yield the best performance with r$2_{-}$score = 0.9321 (accuracy of 93.2%) with MAE $\sim$= 70213.9685. This study aims to increase the reliability of prediction of used car prices. In particular, this study concentrates on the approach of selecting features. The precise selection of features definitely showed us a significant edge over previous works.