My name is Daniel Jaouen and I am a student in Lambda School's part time Data Science program. I am excited to have this opportunity to learn Data Science and I look forward to updating all of you on my progress as I progress through the course.
Our first assignment was to help get us a broad overview of the associated libraries (i.e., pandas, numpy, and matplotlib). For this assignment, I chose the following data set: https://www.kaggle.com/rush4ratio/video-game-sales-with-ratings As a long-time gamer, this data set piqued my interest. In particular, I would like to (down the road) predict a game's global sales based on user and critic ratings.
However, for now, I just cleaned up the user rating feature (by removing 'NA' and 'tbd' values from the data frame), converted the column to numeric by using pandas'
to_numeric function, and then classified the data into "high", "medium", and "low" score rankings. See the image below.
Even though it's still early in the course, I've already learned a lot through the first lecture as well as the precourse work. Anyway, I hope you enjoyed this post and I will see you next time!