Car Resale Price Predictor Using Machine Learning Algorithms

EasyChair Preprint 9646

Abstract

In the industry, the manufacturer sets the price of a new car, with taxes adding some additional costs for the government. Therefore, prospective car buyers can rest assured that the money they spend will be well spent.However, used car sales are rising worldwide because of the rising cost of new cars and the financial inability of customers to purchase them. As a result, a Used Car Price Prediction system that uses a variety of features to accurately assess a vehicle's worth is urgently required. The Existing System uses a method in which the seller chooses a price at random, and the buyer is unaware of the car's current value. In fact, the seller is also unaware of the vehicle's current value or the appropriate selling price. To conquer this issue, we have fostered a model which will be profoundly viable. We use regression algorithms because they give us an output that is continuous rather than categorized. As a result, it will be possible to predict a vehicle's actual price rather than its price range. Additionally, a user interface has been developed that takes input from any user and displays a car's price based on that input.

Keyphrases: Data correlation with python, Frontend & model Implementation, data analysis, data preprocessing

@booklet{EasyChair:9646,
  author    = {Namrata Sharma and Sakshi Mogha and Jasprit Singh and Gautam Singhal},
  title     = {Car Resale Price Predictor Using Machine Learning Algorithms},
  howpublished = {EasyChair Preprint 9646},
  year      = {EasyChair, 2023}}