ISSS608 2016 17T1 Group9 Proposal

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Proposal

Poster

Application

Report

 


Introduction

IMDB (Internet Movie Database) is an online database of information related to films from all over the world. The dataset taken into consideration has a list of 5,043 movies reviewed by users. The dataset contains 28 Variables spanning across 100 years in 66 countries.

Interactive tools have been developed to allow the user to explore the movies based on Genres. Dashboard has been developed to allow the user to explore the top movies, top actors, top directors, earnings and profits of the top movies. Treemap Explorations have been developed to allow the user to view the distribution of movies by Geography, Language, Income and Rating.

Motivation

The motivation behind this project is to cater to a movie enthusiast enabling him/her to explore the IMDB movie data set at a glance using various interactive visualisation techniques. Visualisation preferences of wide variety of users has been taken into consideration.

Review and Critic on past work

The dataset has been made available to us through Kaggle where a user has scraped 5000+ movies from IMDB website using a Python library called "scrapy". This data has been used with minor cleansing of the data. Reference has been taken from D3.js libraries to develop interactive sunburst diagrams. The work of Jason Davies on wordcloud has been used to create visualisations.

While a lot of predictive and exploratory models have been built for this dataset, there was limited past work which enables visualisation with interactivity for the user. Our project aims to leverage the interactivity of out tools to create visualisations that have not been created before.

Design Framework

To effectively build interactive exploratory visualisations of a movie datasets, we need to keep in mind that each users preference. While some users may want to explore movies by genre, others may want to look at movies by ratings. Some users may want to look at information on their favourite actors while other may have favourite directors whose data they would want to explore.

Challenges