• api.ipynb Start here to view API calls and pandas dataframe
• notebooks/alina_eda.ipynb Individual notebook for exploratory data analysis
• requirements.txt
• .gitignore

Project Update: Due to lack of data within the Ticketmaster API, where the team wasn't able to get price related information we received approval to pivot over to an alternate Kaggle dataset on the gaming industry.

Check out the dataset on Kaggle.

1. Alina Tsui - Technical Lead
2. Oussama Fathi - Team Lead
3. Ye Morris - Data Analyst
4. Lofinda Beynis - Data Analyst
5. Shaina Smith - Data Analyst
6. Khadija Bangura- Coordinator/Analyst

This dataset looks at different video games and some trends in the gaming industry. It includes information like the game title, genre, platform, release year, developer, revenue, number of players, peak concurrent players, Metacritic score, and whether the game is popular in esports.

The dependent variable I would use is - Revenue (Millions $)- because it shows the revenue each game generated.

This variable is quantitative since it is made up of numeric values. Because of that, it could be used for regression or for looking at patterns in revenue.

Some of the independent variables are:

• Genre
• Platform
• Release Year
• Developer
• Players (Millions)
• Peak Concurrent Players
• Metacritic Score
• Esports Popularity

These variables can help explain possible differences in revenue between games.

The dataset has both types of variables.

• Categorical variables include genre, platform, developer, and esports popularity.
• Quantitative variables include release year, players, peak concurrent players, and Metacritic score.

There are more than 5 independent variables in this dataset. If revenue is the dependent variable, there are around 9 or 10 other columns that could be used as predictors depending on the analysis.

The dataset has over 1,000 rows and 11 columns. It already comes as a CSV file, so it is ready to use for analysis.