The world is growing day by day and also expectations of every people are also growing up. Out of all the expectation one of them is to buy a car. But all are not able to buy always a new car, so they will buy used one. But new person don't know about the market price for his or her dream car for old one. That is where we have need a platform which helps new people for car price prediction. In this paper we are coming up with that platform which is made using machine learning technology. Using supervised machine learning algorithms such as linear-regression, KNN, Random Forest, XG boost and Decision tree, let's try to build a statistical model which will be able to predict the price of a used car. For that previous consumer data and a given set of features will helps us. And we will also be comparing the prediction accuracy of these models to determine the optimal one. Keywords: Analysis, Research, Machine Learning, Random Forest, XG boost, Decision Tree, Linear Regression
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL
₹10000 (INR)
NON IEEE -2021