Hello everyone, in this meeting we will start our data analysis on the movie industry. The global cinematic industry earns several billions of dollars each and every year. A large percentage of the population still watches movies every month. Forecasters show that the revenue money and expected numbers of viewership will only go up by 2020. This is a good opportunity for the ugriders to create machine learning predictions and perform a sales/marketing analysis. Although, the number of people watching movies at home have become streamlined by technology such as Netflix. You can never replace the experience of watching movies on the big screen with your friends at the theaters. We will also be going over the following:

  • Wrap up the HR analytics dataset: If you want to showcase your work please email us back.
  • Prepare for Datafest: We want everyone to form teams, socialize, and answer questions about Datafest.
  • Schedule tentative plans: Polling for mock datafest date, do we want Sunday classes for beginners, and the recruitment of officers.

Our main focus this week is to explore the Human Resource Analytics dataset:

(1) IMDB 5000 Movie Dataset- https://www.kaggle.com/deepmatrix/imdb-5000-movie-dataset

If you need help getting started try answering these questions for your data analysis:

  • How could you determine popularity for a movie?
  • Which movie or actor would you recommend to a friend?
  • If you just wanted to make money… what is your targeted audience or genre?
  • Perform a sentiment or text analysis on the information given: Can you identify the difference between happiness(satisfcation) and excitement(hype)? Can you rank emotions and ratings?
  • Taken from kaggle, but will the number of human faces in movie poster correlate with the movie rating?

Optional Datasets:

(2) The Academy Awards, 1927-2015: https://www.kaggle.com/theacademy/academy-awards

  • Can you predict next academy award winner?
  • Can you predict who will not win not win an award?
  • Can you identify useful measures or metrics for determining how awards are presented?

(3) Movie Dataset: https://archive.ics.uci.edu/ml/datasets/Movie

  • What determines whether a movie gets a remake or not?
  • Which actor, movie, or studio is trending? In other words, determine popularity.
  • Can you identify TV tropes or recourring moments that keep appearing in cinematic histroy?