Predicting IMDb Movie Ratings using Supervised Machine Learning

Predicting IMDb Movie Ratings using Supervised Machine Learning

I mean, the title of this post includes ‘Supervised Machine Learning’ and I’ve only been in the program for three weeks, so it seems like Metis is holding up their end of the bargain. Anyway, I’ll try to make a post about who I am for those interested, but for now, let’s take a look at how I used supervised machine learning to predict IMDb movie ratings.

The 12-week immersive program will turn me from ‘data novice’ into a full-fledged data scientist. I mean, the title of this post includes ‘Supervised Machine Learning’ and I’ve only been in the program for three weeks, so it seems like Metis is holding up their end of the bargain. Anyway, I’ll try to make a post about who I am for those interested, but for now, let’s take a look at how I used supervised machine learning to predict IMDb movie ratings.

Background:

During my musical career, the question was always, “how good is this song?” and never, “how much money will this song make?” Maybe that’s why we were your typical starving artists… Regardless, I took that concept and applied it to movies for this model. The idea is that artists in the movie industry can utilize this model to predict how well a movie will be received by viewers, thus, focusing on IMDb rating as the target, rather than Metacritic’s rating system or Rotten Tomatoes’s Tomatometer.

In its entirety, this project explored a few critical skills required of a data scientist:

  • Web scraping (requests, HTML, Beautiful Soup)
  • EDA (pandas, numpy)
  • Linear regression (scikit-learn)
  • Data visualization (seaborn, matplotlib)

data-science beautifulsoup machine-learning artificial-intelligence web-scraping

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