### 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_<student id>.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.
        1. 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. 