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Data Camp Competition Entry to predict movie Gross

Competition Overview

Data Camp Competition Entry to predict movie Gross

Predicting Christmas Movie Grossings

📖 Background

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?

💾 The data

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.