Old cars price prediction from ebay.com

The data has been derived from the ebay website. It has following features dateCrawled : when this ad was first crawled, all field-values are taken from this date name : "name" of the car seller : private or dealer offerType price : the price on the ad to sell the car abtest vehicleType yearOfRegistration : at which year the car was first registered gearbox powerPS : power of the car in PS model kilometer : how many kilometers the car has driven monthOfRegistration : at which month the car was first registered fuelType brand notRepairedDamage : if the car has a damage which is not repaired yet dateCreated : the date for which the ad at ebay was created nrOfPictures : number of pictures in the ad (unfortunately this field contains everywhere a 0 and is thus useless (bug in crawler!) ) postalCode lastSeenOnline : when the crawler saw this ad last online

I have performed data cleaning, imputes the missing values and treated the outliers of the data which affect the accuracy scores. The following regression models were used to find the best accuracy score. Linear Regression SGD Regressor KNN regressor Ridge and Lasso regressor SVM regressor Decision tree and random forest

The R sqaure improved from .53 to .80

Link to the dataset : https://www.kaggle.com/orgesleka/used-cars-database