Perform an end-to-end analysis putting into practice what you have learned so far. You will apply statistical or machine learning techniques and present your results to the class.
- Rank players by market value.
- Highlight the top players for their outstanding performances over a discrete season.
- Decide when to transfer a player.
- Decide the best replacement for a transferred player.
You might suggest your own outcomes. Check with instructional staff before committing to a new option.
- Ask interesting and thoughtful questions and find the data to answer them.
- Focus on improving in areas that are hard for you or learning more about something with which you feel comfortable.
- Apply the statistical and machine learning techniques we have learned.
- Create useful and clear graphs.
- Present your insights in a thoughtful, clear, and accurate way.
In this project, you will use the provided fifa23_players_dataset dataset.
Details about the dataset can be found here as well kaggle description
In case you want to find more information on how each column relates to a player, take a look at this website sofifa website
This data set includes:
- EA Sports FIFA 23 Game data:
- Player Name
- Club of the Player
- League
- Position
- Pace
- Shooting
- Passing
- Dribbling
- Defending
- Physical
- Transfermarkt extra info by player:
- Date of Birth
- Nationality
- Height
- Foot
- Day Joined the current club
- Day of Contract End
- Market Value of the Player
- ESPN FC data from the past 5 years performance of each player
- GS: Games Started
- SB: Games Substituted
- G: Goals Scored
- A: Assists
- SH: Shots
- SG: Shots on Goal
- FC: Fouls Committed
- FS: Fouls Suffered
- YC: Yellow Cards
- RC: Red Cards
- You must plan your project. Creating a Kanban or using Trello or a similar app for a digital board is mandatory.
- You CAN'T CODE until your project is planned.
- Create a
*.gitignore*file and include it in your repository. - As an optional you can include a linear regression as a way to answer question(s) on the data.
- A well-commented Jupyter notebook with your analysis.
- The final dataset after all cleaning and transformations.
- Repository with your workflow + documentation + code.
- Visual documentation of Kanban or Trello board link.
- Organize yourself (don't get lost!). Respect deadlines.
- Ask for help but don't forget that Google is your friend.
- Define a simple approach first. You never know how the data can betray you. 😉
- Document your work.
- Learn about the problem and what research has been done before you.
- Before making a graph, think about what you want to represent.