Data Camp Competition Entry to predict movie Gross
Imagine harnessing the power of data science to unveil the hidden potential of movies before they even hit the silver screen! As a data scientist at a forward-thinking cinema, you're at the forefront of an exhilarating challenge: crafting a cutting-edge system that doesn't just predict movie revenues, but reshapes the entire landscape of cinema profitability. This isn't just about numbers; it's about blending art with analytics to revolutionize how movies are marketed, chosen, and celebrated.
Your mission? To architect a predictive model that dives deep into the essence of a movie - from its title and running time to its genre, captivating description, and star-studded cast. And what better way to sprinkle some festive magic on this project than by focusing on a dataset brimming with Christmas movies? A highly-anticipated Christmas movie is due to launch soon, but the cinema has some doubts. It wants you to predict its success, so it can decide whether to go ahead with the screening or not. It's a unique opportunity to blend the cheer of the holiday season with the rigor of data science, creating insights that could guide the success of tomorrow's blockbusters. Ready to embark on this cinematic adventure?
We're providing you with a dataset of 788 Christmas movies, with the following columns:
christmas_movies.csv
Variable | Description |
---|---|
title |
the title of the movie |
release_year |
year the movie was released |
description |
short description of the movie |
type |
the type of production e.g. Movie, TV Episode |
rating |
the rating/certificate e.g. PG |
runtime |
the movie runtime in minutes |
imdb_rating |
the IMDB rating |
genre |
list of genres e.g. Comedy, Drama etc. |
director |
the director of the movie |
stars |
list of actors in the movie |
gross |
the domestic gross of the movie in US dollars (what we want to predict) |
You may also use an additional dataset of 1000 high-rated movies, with the following columns:
imdb_top1k.csv
Variable | Description |
---|---|
title |
the title of the movie |
release_year |
year the movie was released |
description |
short description of the movie |
type |
the type of production e.g. Movie, TV Episode |
rating |
the ratig/certificate e.g. PG |
runtime |
the movie runtime in minutes |
imdb_rating |
the IMDB rating |
genre |
list of genres e.g. Comedy, Drama etc. |
director |
the director of the movie |
stars |
list of actors in the movie |
gross |
the domestic gross of the movie in US dollars (what we want to predict) |
Finally you have access to a dataset of movie production budgets for over 6,000 movies, with the following columns:
movie_budgets.csv
Variable | Meaning |
---|---|
year |
year the movie was released |
date |
date the movie was released |
title |
title of the movie |
production budget |
production budget in US dollars |
Note: while you may augment the Christmas movies with the general movie data, the model should be developed to predict ratings of Christmas movies only.