Price Prediction of Pre-Owned Automobiles Using Machine Learning-A Comprehensive Survey

Car sales are at an all-time high. Due of cost, developing nations embrace the practice of leasing new vehicles rather than buying them outright. Which causes the surge in secondhand cars market is raising beyond the expectations. Looking at the demand for goods, automobile vendors may implement this circumstance by proposing exorbitant rates. It is thus necessary for researchers to create a model that is capable of predicting a vehicle's price according to an examination of its highlights and consideration of the retail costs of rival automobiles. In this study, we employ the supervised learning technique Random Forest to forecast used automobile prices. There are various attributes in a car which has different data at different occasions, so only after thoroughly exploring all those data, the model was chosen. To train a Random Forest on the data, 500 Decision Trees were utilized. The model can accurately anticipate the price of cars by using the most linked features.