Estimating Used Car Prices with Artificial Neural Networks and Machine Learning

The use of machine learning methods and artificial neural networks (ANNs) to forecast used automobile prices is investigated in this paper. To predict resale prices, the suggested model takes into account variables such car age, mileage, brand, model, and condition. ANNs, Decision Trees, and Linear Regression are a few of the algorithms used to evaluate and create predictive models from past sales data. For on-site price evaluation at dealerships, the system can be installed on inexpensive hardware like Raspberry Pi, offering real-time assessments and enhancing market transparency. The model's ability to estimate used car prices properly is supported by the data, which provides buyers and sellers in the automobile market with a useful tool.