Second Hand Price Prediction for Tesla Vehicles

TL;DR

This paper develops a machine learning-based system to predict second-hand Tesla vehicle prices, analyzing various algorithms to improve accuracy in a fluctuating market.

Contribution

It compares multiple machine learning techniques and implements a boosted decision tree regression model for Tesla price prediction, aiming for enhanced accuracy.

Findings

Boosted decision tree regression achieved promising results.

Different attributes significantly impact Tesla vehicle prices.

Future work includes exploring more sophisticated algorithms.

Abstract

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.