We have to predict the car price based upon the kilometres run, years of service, the number of previous owners, brand, fuel type, transmission type.

We are making the use of data available with price of the cars in the present market keeping all the important factors in mind and calculating an estimated price in the present date without asking help from a third party. Our model will calculate the estimated price will depend on the various factors and current news. We will make a model with the right amount of test data so that it wouldn’t depend on some bad reviews or results made by few customers.

Cars are something which mean so much to a person, being a car enthusiast myself. I thought if I were to buy a new car it would cost a lot from a showroom. On the other hand, we all know that a car loses at least 15% of its value the moment it is out of a showroom.

Realistically speaking second hand cars might look like a better deal in the long run if we can manage to find a great car for the value it is bought. It might seem easy for me to predict a car’s price as I am familiar with the car prices, the demand, liking and the safety reasons a car might have but to a normal person it might all confuse him and he would instead get a new one or a person who wants to buy a second vehicle who doesn’t have any prior knowledge of markets and how they should be priced will be taken advantage of.

So, we provide a model based on various factors so that a customer can enter all the details of the car and the model would predict an estimated value of the car.

If benefits us personally, the proposed framework for our model includes data cleaning, data pre-processing, applying classifiers on the data and finally comparing the results from the different classification models we used. So, by the end of this project if we have done it Successfully, we can say that we have got idea of them.

When we work on these sorts of data, we can make a model which can give us a good prediction on the price of the Car Price based on other variables. We are going to use Linear Regression and some other ML algorithms for this dataset and see if it gives us a good accuracy or not.