The dataset is collected from various web resources in order to explore the used cars market and try to build a model that effectively predicts the price of the car based on its parameters (both numerical and categorical)
The data is scraped in Belarus (western Europe) on the 2nd of December 2019, so the dataset is pretty fresh and relevant.
I'd like to thank my dear friend Vasily Kachalko for technical advice during data parsing.
I've always been amazed by the amount of time people spend discussing prices of heavily used cars, how they age and hold or loose value. And if you are going to sell your car, you have to spend some time finding similar cars in the catalog and trying to discover trends and figure out the fair price. And, more importantly, you have to withstand the stress of decision making regarding the price!
So the idea for me was to collect the data and build an effective model as quickly as possible, use the most effective tools available and explore the decision making of the model thoroughly.
