TheBigScreen

Proposal

Project Presentation

Poster

Application

Research Paper

Problem and Motivation

Is it possible to predict how good a movie will be before it even screens? This is a subjective question. While some rely on movie critics and early reviews, others depend on instinct. However, we know reviews can take a long time to gather and human instinct is simply unreliable. Thousands of movies are produced every year and all of them our clamouring for the $11 we spend on movie tickets! Our group wants to know if we can predict which movies are worth you spending your money and time on.

Data

We are using the IMDB 5000 Movie Dataset from Kaggle. The Internet Movie Database (IMDB) is an online database of information related to films, television programs and video games [1]. Amongst its functions, IMDB allows users rate movies on a scale of 1 to 10.

The dataset contains the following variables, including but not limited to:

• movie title
• director name
• actors’ names and Facebook likes
• length of movie
• year
• gross earnings
• genres
• language
• country
• content rating
• budget
• IMDB rating

Related Work

Visualizations	Learning Points
Top 20 Most Profitable Movies Source: https://www.kaggle.com/param1/d/deepmatrix/imdb-5000-movie-dataset/the-money-makers	Simple and easy to read Not aesthetically pleasing Data points not properly explained e.g. why do some points have tails
Duration of Movie vs. IMDB Score Source: https://www.kaggle.com/benjaminlott/d/deepmatrix/imdb-5000-movie-dataset/imdb-5000-general-data-analysis	Colours are visually appealing Messy Visualization was so big that legend could not fit on the same window Tooltip tags are unformatted and messy
Age Ratings vs IMDB Score Source: https://www.kaggle.com/adhok93/d/deepmatrix/imdb-5000-movie-dataset/eda-with-plotly	Appropriate and informative use of boxplots to visualize continuous variable, IMDB scores Messy, especially when tooltip is displayed Unnecessary legend and use of colours No y-axis title

Technical Challenges

Challenges	Approach
Data Cleaning & Exploration	Collaborate in data cleaning and transformations
Use of Javascript and D3	Attend coding workshop in recess week Consult with Prakash
Implementing Interactive Visualizations	All team members to explore the use of various visualization tools e.g. Tableau, Power BI, Qlik Sense Explore how to implement interactivity and animation through online tutorials

Milestone and Schedule

Comments and Feedback