Welcome to the FIFA Dataset Data Cleaning and Transformation project! This initiative focuses on refining and enhancing the FIFA dataset to ensure it is well-prepared for in-depth analysis. The project involves a comprehensive data cleaning process and transformation of key features to improve data quality and usability.
Here is a table outlining the features present in the dataset along with their descriptions:
| Column Name | Description |
|---|---|
| photoUrl | URL of the player's photo |
| LongName | Full name of the player |
| playerUrl | URL of the player's profile |
| Nationality | Nationality of the player |
| Positions | Player positions |
| Name | Player name |
| Age | Age of the player |
| ↓OVA | Overall rating of the player |
| POT | Potential rating of the player |
| Team & Contract | Information about the player's team and contract |
| ID | Player's unique identifier |
| Height | Player's height |
| Weight | Player's weight |
| foot | Dominant foot of the player |
| BOV | Best overall rating of the player |
| BP | Best position of the player |
| Growth | Growth potential of the player |
| Joined | Date when the player joined the team |
| Loan Date End | End date of the player's loan (if applicable) |
| Value | Market value of the player |
| Wage | Player's wage |
| Release Clause | Release clause amount for the player |
| Attacking | Overall attacking attribute |
| Crossing | Crossing attribute |
| Finishing | Finishing attribute |
| Heading Accuracy | Heading accuracy attribute |
| Short Passing | Short passing attribute |
| Volleys | Volleys attribute |
| Skill | Overall skill attribute |
| Dribbling | Dribbling attribute |
| Curve | Curve attribute |
| FK Accuracy | Free-kick accuracy attribute |
| Long Passing | Long passing attribute |
| Ball Control | Ball control attribute |
| Movement | Overall movement attribute |
| Acceleration | Acceleration attribute |
| Sprint Speed | Sprint speed attribute |
| Agility | Agility attribute |
| Reactions | Reactions attribute |
| Balance | Balance attribute |
| Power | Power attribute |
| Shot Power | Shot power attribute |
| Jumping | Jumping attribute |
| Stamina | Stamina attribute |
| Strength | Strength attribute |
| Long Shots | Long shots attribute |
| Mentality | Overall mentality attribute |
| Aggression | Aggression attribute |
| Interceptions | Interceptions attribute |
| Positioning | Positioning attribute |
| Vision | Vision attribute |
| Penalties | Penalties attribute |
| Composure | Composure attribute |
| Defending | Overall defending attribute |
| Marking | Marking attribute |
| Standing Tackle | Standing tackle attribute |
| Sliding Tackle | Sliding tackle attribute |
| Goalkeeping | Overall goalkeeping attribute |
| GK Diving | Goalkeeper diving attribute |
| GK Handling | Goalkeeper handling attribute |
| GK Kicking | Goalkeeper kicking attribute |
| GK Positioning | Goalkeeper positioning attribute |
| GK Reflexes | Goalkeeper reflexes attribute |
| Total Stats | Overall total statistics |
| Base Stats | Overall base statistics |
| W/F | Weak Foot rating |
| SM | Skill Moves rating |
| A/W | Attacking work rate |
| D/W | Defensive work rate |
| IR | International reputation |
| PAC | Pace attribute |
| SHO | Shooting attribute |
| PAS | Passing attribute |
| DRI | Dribbling attribute |
| DEF | Defensive attribute |
| PHY | Physicality attribute |
| Hits | Unknown attribute (requires further clarification) |
| Note: This table provides a comprehensive list of features present in the FIFA dataset. Feel free to refer to specific columns based on your analysis needs. |
A comprehensive check for missing values was conducted, and necessary values were filled, ensuring that the dataset is complete and ready for further analysis.
Data transformation steps significantly improved the usability and interpretability of the dataset, making it more convenient for analysts and researchers. In conclusion, data cleaning and transformation are foundational steps in any data analysis project. By addressing inconsistencies, refining data types, and enhancing data quality, we have set the stage for more meaningful and insightful analyses. The clean and structured dataset is now well-equipped for advanced analytics, visualizations, and modeling. This project not only improves the quality of our data but also provides a strong foundation for subsequent tasks, including exploratory data analysis, predictive modeling, and reporting. We look forward to leveraging this clean dataset to extract valuable insights and make informed decisions in the world of FIFA data analysis.
| Column Name | Description |
|---|---|
| LongName | Full name of the player |
| Nationality | Nationality of the player |
| Positions | Player positions |
| Name | Player name |
| Age | Age of the player |
| Overall Rating | Overall rating of the player |
| Potential | Potential rating of the player |
| ID | Player's unique identifier |
| Weight | Player's weight |
| foot | Dominant foot of the player |
| Best Overall | Best overall rating of the player |
| Best Position | Best position of the player |
| Growth | Growth potential of the player |
| Value | Market value of the player |
| Wage | Player's wage |
| Release Clause | Release clause amount for the player |
| ... (and so on) | ... (descriptions for other features) |
| Column Name | Description |
|---|---|
| CM | Central Midfielder |
| ST | Striker |
| RB | Right Back |
| GK | Goalkeeper |
| CB | Center Back |
| RWB | Right Wing Back |
| LB | Left Back |
| RM | Right Midfielder |
| LM | Left Midfielder |
| LW | Left Wing |
| CDM | Central Defensive Midfielder |
| CAM | Central Attacking Midfielder |
| LWB | Left Wing Back |
| CF | Center Forward |
| RW | Right Wing |
| Column Name | Description |
|---|---|
| Team | Player's team |
| Start Year | Start year of the player's contract |
| End Year | End year of the player's contract |
| Height | Player's height |
| Date | Date when the player joined the team |
| Year | Year of the player's contract |
| Month | Month of the player's contract |
| Day | Day of the player's contract |
| Hits | Unknown attribute (requires further clarification) |
The purpose of this project was to create a clean and structured dataset, enhancing its usability for advanced analytics, visualizations, and modeling in the domain of FIFA data analysis.
In conclusion, data cleaning and transformation are foundational steps in any data analysis project. By addressing inconsistencies, refining data types, and enhancing data quality, we have set the stage for more meaningful and insightful analyses. The resulting dataset, now comprising 18,852 entries, is well-equipped for advanced analytics, visualizations, and modeling.
This project not only improved the quality of our data but also provided a strong foundation for subsequent tasks, including exploratory data analysis, predictive modeling, and reporting. The clean and structured dataset is ready to be leveraged for extracting valuable insights and making informed decisions in the world of FIFA data analysis.
Feel free to explore, analyze, and derive meaningful insights from this enhanced FIFA dataset. Happy analyzing! You can copy and paste this content into your README.md file. Adjustments can be made according to your specific project details or preferences.