Second Hand Price Prediction for Tesla Vehicles
This paper provides a price prediction system for potential buyers and sellers of used Tesla vehicles, which is an incremental contribution to the used car market.
This paper addresses the problem of predicting second-hand prices for Tesla vehicles using machine learning techniques. The study implemented boosted decision tree regression to predict prices based on attributes like model, production year, miles driven, and battery type.
The Tesla vehicles became very popular in the car industry as it was affordable in the consumer market and it left no carbon footprint. Due to the large decline in the stock prices of Tesla Inc. at the beginning of 2019, Tesla owners started selling their vehicles in the used car market. These used car prices depended on attributes such as the model of the vehicle, year of production, miles driven, and the battery used for the vehicle. Prices were different for a specific vehicle in different months. In this paper, it is discussed how a machine learning technique is being implemented in order to develop a second-hand Teslavehicle price prediction system. To reach this goal, different machine learning techniques such as decision trees, support vector machine (SVM), random forest, and deep learning were investigated and finally was implemented with boosted decision tree regression. I the future, it is intended to use a more sophisticated algorithm for better accuracy.