Machine Learning Modeling for Reconditioned Car Selling Price Prediction

Abstract

Almost 80% of the vehicles required for Bangladesh's road transportation industry are supplied by reconditioned cars. Using machine learning (ML) to predict car prices refers to using ML algorithms and techniques to make assumption about future car prices. 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 the ongoing cost of a car, such as its make and model, age, mileage, condition, and location. This data is then fed into the 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 data set and ensures that the model can then be used to make predictions on the future cost of an automobile based on these patterns and trends.