Machine Learning Modeling for Car Selling Price Prediction

Abstract

Forecasting car prices using machine learning (ML) refers to the use of ML algorithms and techniques to make assumptions of the future price of a car. This can be useful for a variety of purposes, such as helping car buyers and sellers make informed decisions, assisting car dealerships with inventory management, or providing insights for car manufacturers and other industry stakeholders. To predict car prices using ML, data is collected on a variety of factors that can affect ongoing costing of a car, such as its make and model, age, mileage, condition, and location. This data is then fed into XGBoost ML model, which uses statistical techniques to analyze the data and identify patterns and trends. The model performs 98% accurately in the tested portion of the dataset and ensures that the model can then be used to make predictions about the future cost of an automobile based on these patterns and trends.