004_comission/rm5wde7487/drill.md

source:

• https://www.kaggle.com/datasets/gregorut/videogamesales.

require:

• cleaned data provide on the unit Moodle page
• lecture 4 slide 52
• for documentation
  • standard academic report.

delivery:

• FOP_Assignment1_.zip

  • Your code
  • README
  • User Documentation
  • and Report
  • A signed and dated Declaration of Originality.

• two parts as

  • (a) coding part and

    • four main tasks
      1. Reading games sale data stored in the GamesSales.csv file.
      2. Displaying a menu asking the user what filters user want to apply to the data.
      • Add filters: User select a smaller portion (based on filters) of data to print or graph.
      • Print names: Prints the names and descriptions (attributes) of the filtered games (based on the filter).
      • Graph data: Graphs the filtered data (based on the filters).
      • Reset filters: Removes all the applied filters.
      • Exit: Exits the program.
      1. Displaying the data matched by the set filters as a graph or by printing to the terminal.
      • It is up to you as to 4 what type of graph you want to use to plot the data
        • (bar, line, histogram, etc)
      1. Reset filter by removing all the filters that has been applied.

  • (b) documentation and report

    • documentation
      • minimum of 2 pages long
        • should follow the structure of a standard academic report.
        • An overview of each of your program's features.
        • A guide on how to use your program.
        • A discussion of your code,
          • explaining the features you implemented,
          • how you implemented them and
          • why you implemented them the way you did.
    • report
      • will be a mini-paper that is 2-3 pages long
        • • References: see unit outlines for styling guide.
      • Such as, what games have the most entries?
      • What is the distribution of games (by rating) on sales data?
      • Which game has the most highest rating values and/or
        • any other interesting insights you discovered?

CSV fields

• Name: In market name of the game.
• Platform: The gaming platform for the game is designed (e.g., x360 PS5)
• Year: Year of release.
• Genre: Category based on recent games classification. (e.g., Sports)
• Publisher: Agency responsible for publication of the game.
• NA_Sales: Sales in North America (in millions).
• EU_Sales:. Sales in Europe (in millions).
• JP_Sales: Sales in Japan (in millions).
• Other_Sales: Sales in the rest of the world (in millions)
• Global_Score: Total worldwide sales.
• Critic_Score: Critic score given to each game.
• Critic_Count: Number critic contributed for the rating.
• User_Score: User rating for each game.
• User_Count: Number of users contributed to the rating.
• Developer: The name of developer.
• Raring: Overall market rating.