For project 1, I used the New York City Yellow Taxicab Data to predict Trip prices based on some other attributes. The data was collected between 2008 and 2010 using a mechanical GPS reader in the cab meter. The data is reported by a trip for the 19 000 trips. The data has 26 variables: a starting point, ending point, trip time, distance traveled, and several other features for each trip. The description of the full list of features can be retrieved here
For project 2, I used the well-known housing prices dataset compiled by Dean De Cock for use in data science education. The dataset has 79 explanatory features describing most aspects of residential homes in Iowa. The goal of the project was to predict the final price of each home based on the remaining 78 variables in the dataset. In other words, to minimize the difference between the real price and the price estimated by our models.