Edit on 28/02/24: Finished the data analysis portion using Matplotlib and NumPy. Here are the findings

• Chance, Community Chest, and Jail are the most landed on properties
• Other than brown and blue properties, all other properties have roughly the same proportion of landings
• Green properties make the most profit, and brown the least

Edit on 27/02/24: I am revamping this project as it is outdated and I have a better understanding of Data Analysis in Python. I will no longer using OpenPyxl, but instead moving into Matplotlib for my EDA. I have remade the simulation portion of the project. Here are my future goals:

• Plot the most frequented squares
• Plot the most frequented group of squares - colour of property, type
• Construct a ROI plot based on the following conditions:
  • If someone has not landed on the property yet, it will be bought
  • Every subsequent landing by the buyer on the property will force the player to improve on the house (+1 house or hotel)
  • Every player has infinite money
• Summarise the above 3 points to make an optimal playing strategy

Created a simulation for a monopoly game to figure out which properties are most landed on. I adjusted the way I calculated some of the values: the chance/community chest values are the entire chance/community chest percentage, not the specific property. Meaning, the final value is the overall chance of landing on any of the community chests.