Movie Nerd. Data Nerd.

An interactive dashboard built on JavaScript and Python (Flask) to show information about movies ratings and gender inequalities in the film industry.

Submitted for Monash University Bootcamp Project 3 (Group 7).

Prerequisites

API keys

To complete the ETL steps, the following APIs are used that need keys:

• Create a module named config.py in /ETL with a variable called omdb_api_key containing your OMDB API key
• Add a variable called geoapify_key and containing your GeoAPIfy key in config.py

No prerequisites are needed to run the dashboard itself.

Approach

• Get the data in CSV files for every collaborator to access
• Perform Data Exploration on all dataset to understand the data and extract initial information
• Extract, Transform and Load (ETL) data into a SQLite database
• Analyse data in Python (Jupyter notebooks used for Data Exploration) and in JavaScript
• Create and run a Flask Server to make the data in the database available through an API
• Create a dashboard (HTML, JavaScript and CSS) to visualise the data begind the analysis

How to run this project from scratch

The project contained in this repository is ready to run once the prerequesites are met.

However, if you would like to start with a blank sheet, the following steps can be followed

Preparation

• Delete movies_db.sqlite in /Server
• Delete all CSV files in /Datasets except actor.csv, character.csv and movies.csv

Steps (from /ETL directory)

1. Run ETL_omdb_to_csv.ipynb, make sure that omdb.csv is created in /Dataset
2. Run ETL_movies_to_db.ipynb, make sure that movies_db.sqlite is created in /Server
3. Run ETL_financials_to_db.ipynb
4. Run ETL_country_city_to_csv.ipynb, make sure that country_coordinates.csv is created in /Dataset
5. Run ETL_geodata_to_db.ipynb
6. Run ETL_check.ipynb and make sure that all tests are passed

How to run the dashboard

Launch the dashboard

To start the dashboard, simply open index.html in /docs in any major browser. The data will load automatically from the Movie Nerd API.

By default, the dashboard uses a version of the API (same code and same functions) that is hosted on PythonAnywhere so the Flask Server is not needed. To use the web-hosted API, make sure that the first lines of code in common.js in /docs/static/js are commented out as shown below:

//const api_base_url = 'http://127.0.0.1:5000/api/v1.0/'; // Use this for locally-run API (Flask Server must be running)
const api_base_url = 'https://spiderdwarf.pythonanywhere.com/api/v1.0/'; // Use this for web-hosted API

To use the local Flask Server instead, please follow the instructions in the next subsection.

Start Flask Server (optional)

1. Navigate to /Server
2. Start Flask by using the command python flask_app.py
3. Check your console to make sure Flask is running
4. Navigate to the address indicated in the console

See below for the expected output when running the Flask Server.

If the local Flask Server is used, make sure to change the first lines of code in common.js in /docs/static/js in the following way:

const api_base_url = 'http://127.0.0.1:5000/api/v1.0/'; // Use this for locally-run API (Flask Server must be running)
//const api_base_url = 'https://spiderdwarf.pythonanywhere.com/api/v1.0/'; // Use this for web-hosted API

File structure

Directories

• DataExploration contains files used early in the project to explore the data and functions to be used later
• Datasets contains all the CSV files extracted or created as part of the ETL process
• docs contains the HTML, JavaScript and CSS used to create the Dashboard
• ETL contains files to extract data from API and downloaded CSV files, transform the data and load them into the database.
• img contains the images used in this README
• Server contains the ETL Jupyter notebooks, the Flask code and the Python modules used in the Flask app, and the SQLite database

DataExploration directory

The files in the DataExploration directory were used at the very beginning of the project and serve various purposes:

1. Open and display the data from the different CSV files
2. Analyse some trends and display the data for qualitative analysis
3. Prepare prototypes of some functions to be used in the ETL process and in the Flask Server

These files are as self-explanatory as possible but they are secondary to the finished project and not required to run either of the ETL process files, Flask Server or the dasboard itself. They are provided for the sake of completeness only.

Dataset directory

• actor.csv is downloaded from the Movies and Actors database, by James Gaskin on data.world: https://data.world/jamesgaskin/movies (James Gaskin dataset)
• character.csv is downloaded from the James Gaskin dataset
• country_coordinates.csv is created as part of the ETL process
• movies.csv is downloaded from the James Gaskin dataset
• omdb_movies.csv is created as part of the ETL process
• omdb.csv is obtained by calling the OMDB API: http://www.omdbapi.com/?

docs directory

• /static/js contains all the JavaScript code for the dashboard
• /static/css contains all the CSS stylesheet for the dashboard
• index.html is the home page of the dashboard
• info_actors.html contains the actors visualisations
• info_movies.html contains the actors visualisations
• map.html contains geolocation visualisation

More information about the dashboard is provided in a dedicated section below.

ETL directory

More information about the files, notebooks and overall ETL process is given in a dedicated section below.

Server directory

The server side is composed of two parts:

• The ETL files used to extract the data from the data sources and populate the database
• The Flask code used to get data from the database and expose the API used by the dashboard

The Flask code uses the files shown in the diagram below:

• flask_server.py is the main code. To run flask locally, use the command python flask_server.py from within /Server
• The other python files are local modules used for different functions used in flask_server.py
• movies_db.sqlite is the database containing all the data used in the dashboard and exposed by the API

The ERD for the database is shown below:

The tables are more extensive but only the main columns used in the dashboard are shown here for simplicity. Due to the short timeline of this project, only little time has been given to the data engineering steps and the tables are not as normalised or optimised as they could be. This has been noted as an area of focus for future work.

Dataflow

Overview

The dataflow is shown in the diagram below:

• The ETL files are used to extract the data from the three main data sources and save it into the SQLite database
• The database, as well as the Flask code can be executed locally, but are also copied to PythonAnywhere to expose the API through the internet
• The API is generated by the Flask app and export the data from the database as JSON or GeoJSON files

Data engineering

ETL

We gathered data from three different open and free sources:

• Movies and Actors database, by James Gaskin on data.world: https://data.world/jamesgaskin/movies
• OMDB API: http://www.omdbapi.com/
• Geoapify: https://www.geoapify.com/

The James Gaskin's dataset includes information about 636 movies as well as their characters and the actors who play them. The James Gaskin's dataset was retrieved from data.world by writing an SQL query to get each table and download the result as a CSV file. The three files actor.csv, character.csv and movies.csv are saved in the Datasets directory.

Using the titles from the movies in the James Gaskin's dataset, queries are made to the OMDB API to retrieve additional information about the movies as well as data already present in the James Gaskin's dataset to cross-check the values. Because the call to the API to retrieve all 636 movies take some time, the data are added to a DataFrame and saved as a CSV file (Datasets/omdb.csv). The code used to perform these actions can be found in ETL_omdb_to_csv.ipynb.

The complete ETL process is shown, including the API used, the CSV files created and the tables in the database.

The complexity of the ETL is the result of the distribution of work. A system with redundancy was preferred over a more streamlined approach in order to give more independence to each team member with less risk for conflict. In a future version, the tables could be further normalised and the ETL process consolidated.

Flask and API

The API home page is shown below (the API may look different depending on the current version):

The API exposes various static and dynanmic routes that are detailed on the API home page.

Dashboard

Pages

The dashboard contains three pages:

1. The home page: index.html contains information about the movies return on investment (ROI), a tag cloud of words found in the movies summaries (filtered by genre) and the most successful directors (by IMDB ratings and gross revenue; also filtered by genre.)
2. The actors info page: info_actors.html contains information about the actors such as the Top 10 acors by rating and number of movies, the distribution of actors per rating and networth, a scatter plot of Net Worth vs IMDb Rating, and an overview of the wealth gap between the richest male and female actors.
3. The wealth map page: map.html shows a map with markers showing the average net worth of the actors born in different countries (see below)

The website CSS is based on Bootstrap.

JavaScript libraries

• D3.js
• Leaflet.js
• Plotly.js
• AnyChart.js

Dashboard codebase

An overview of the code behind the dashboard and how the different files call each other is shown below:

Data analysis

Research questions

Our main research question is: What Makes A Movie Succesful?

We define the success of a movie by two metrics:

1. IMDB ratings
2. Gross revenue

We look at different variables that can influence these metrics:

1. Actor wealth: do richer (i.e. potentially more succesful) actors act in movies with better ratings?
2. Directors: do certain directors get better IMDB ratings for their movies, or generate more revenue?
3. Budget: do movies with a higher budget get better reviews? Do they generate more revenue?

Analysis

We conducted various analyses on the cleaned and transformed dataset, including:

• Calculate the average rating for each actor by aggregating the ratings of the movies they appeared in.
• Calculate the wealth gap between male and female actors by comparing the net worth of male actors to female actors.
• Compare ratings to budget, gross revenue, and ROI to identify any trends or correlations.
• Identify the best director based on the average IMDb rating of their movies and the gross revenue.
• Analyse the relationship between budget, gross revenue and ROI, and movie ratings.

Data visualization

Visualizations are created first using Python libraries such as Matplotlib and eventually in JavaScript with Plotly, AnyChart and Leaflet. They are designed to effectively convey the insights and findings gained from the data analysis process; In a clear and concise manner. We use different types for different puproses:

• Bar charts: to compare the wealth of male and female actors, as well as the gross revenue and ratings from directors. This visualization helped us understand the differences in ratings between the two groups.
• Scatter plot: to compare the return on investment (ROI) and actors net worth to the average IMDB ratings.
• Word cloud: to display the most used words in the movies plots per genre.
• Leaflet (based on GeoJSON data): to provide insights into the global distribution of actors, and help identify any geographical patterns or trends. It can also be useful for making comparisons between regions and understanding the impact of location on the film industry.

Conclusion

Through this project, we successfully gathered and integrated data from different sources, performed data cleaning and transformation tasks, conducted data analysis, and created visualizations to gain insights about the movies and actors dataset. The project highlights the importance of data engineering in organizing and analyzing large datasets to extract valuable information.

The findings from this project can be used to make informed decisions in the film industry, such as casting choices, budget allocation, and identifying successful directors and actors.

Return on investment compared to IMDB ratings

We find only very weak or no correlation between revenue/budget and IMDB ratings.

Best directors

We find that across all genres, the directors getting the highest IMDB ratings differ from the ones generating the most gross revenues.

Actors networth compared to IMDB ratings

We find only a very weak correlation between an actor networth and their average IMDB ratings.

Gender Wealth Gap

We also looked at the Gender Wealth Gap (difference between the net worth of male and female actors)

• The wealth of the Top 100 richest male actors is compared to the wealth of the Top 100 richest female actors
• We find that the richest female actor (Reese Witherspoon) has a net worth of 50% of the richest male actor (Tom Cruise): 300M USD compared to 600M USD
• Reese Witherspoon is less rich than the Top 15 men
• The male actors' average net worth is $39,556,492
• The female actors' average net worth is $29,617,704 (75% of Male actors' net worth)

Limitations

• The sources for the net worth data in the James Gaskin dataset are unclear and may not be accurate
• The same goes for the movies budget and gross revenue and these data should be validated with alternate sources

Future work

• Better normalisation and structure for the tables in the database, make better use of foreign keys instead of repeating data