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.
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Rank players by market value.
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Highlight the top players for their outstanding performances over a discrete season.
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Decide when to transfer a player.
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Decide the best replacement for a transferred player.
You might suggest your own outcomes. Check with instructional staff before committing to a new option.
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Ask interesting and thoughtful questions and find the data to answer them.
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Focus on improving in areas that are hard for you or learning more about something with which you feel comfortable.
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Apply the statistical and machine learning techniques we have learned.
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Create useful and clear graphs.
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Present your insights in a thoughtful, clear, and accurate way.
In this project, you will use the provided fifa21_male2.csv dataset.
Here details about the dataset can be found here as well.
This data set includes:
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EA Sports FIFA 19 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 -
Instagram and Facebook data by player:
- Number of followers on Instagram
- Number of likes on Facebook of the club in which the player has a contract
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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
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You CAN'T CODE until your project is planned.
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Create a
*.gitignore*
file and include it in your repository. -
You should include a linear regression question(s) on the data.
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Repository with your workflow + documentation + code. This repository must contain:
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README: it is mandatory to present the project.
- What made you decide to do this project?
- Objetive
- Used tools
- Workflow
- Results and conlusions (this is not so obligatory, it is a more personal decision)
⚠️ Readme will be the first thing that people will see from us. A person who knows nothing about this project should be able to read your readme and know what you have done.
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A SQL Database with the FIFA_MoneyBall, using the learned statements during the last weeks
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A well-commented Jupyter notebook with your analysis.
⚠️ Remember, you could use the markdown cells in jupyter!! -
The final dataset after all cleaning and transformations.
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To send us your work... as always a pull request where in the comments leave us the link to your repo.
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Organize yourself (don't get lost!). Respect deadlines.
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Ask for help but don't forget that Google is your friend.
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Define a simple approach first. You never know how the data can betray you. 😉
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Document your work.
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Learn about the problem and what research has been done before you.
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Before making a graph, think about what you want to represent.