This project display used cars in many different brands to pay. The number of used cars in the website is 1219 cars, I create data frame for them and each car has 9 features and the prediction of this data is car's price.
I choice used cars in the project in order to use machine learning to predict car's price. Assign fair price for used car has a big issue depend on their features after usr it. So, I tried to collect as much as I can of features to give a good chance for ML to allocate a best price for car.
The kind of modeling that match with my data is supervised model because it includes label 'car_price' and use regression type because the target is number. Additionally, it can be used recommender system in this data .