Random Forest Regressor Approach for Predicting Resale Value of used Vehicles (RFRVP)

Nowadays, within last decade, car production has been growing substantially. In 2016, over than 70 million passenger cars have been produced. Different websites, like Cars24, CarDekho, OLX, and Quikr provide the platform to this buyers who are able to sell their cars, but determining the car's cost appears to be the most difficult choice ever. Algorithms for machine learning might be able to solve this problem. combining recent used-car sales data and machine learning techniques like supervised learning a fair price for the car could be predicted. To Forecast the selling cost of the old Cars, we used machine learning-based algorithms which includes Random Forest, Linear, Decision Tree, and Ridge Regression also we use sophisticated Python module Sklearn. The findings shows that Machine Learning algorithms has high accuracy for predicting if the data set is big. They give result with high precision which is independent of the dataset size. I'll also evaluate the prediction r2 scores of various models to see which one is the best.