This project aims to identify football players who offer great potential at a lower cost based on FIFA ratings. Utilizing data from the popular FIFA video game series, the project focuses on analyzing player attributes, potential ratings, and market values to discover undervalued talents. This can be particularly useful for football managers, scouts, and enthusiasts who are interested in finding cost-effective players with high potential.
- Data Extraction and Preparation: Extract and clean data from the FIFA dataset.
- Player Analysis: Analyze player attributes and potential ratings to assess their true value.
- Value Identification: Identify players who are undervalued based on their potential compared to their market value.
- Visualization: Create visualizations to illustrate the findings and make the data more accessible.
- Data Collection: Import FIFA dataset containing player information such as overall rating, potential, and market value.
- Data Cleaning: Process the dataset to handle missing values, inconsistencies, and irrelevant data.
- Feature Selection: Select relevant features that influence a player's potential and market value.
- Analysis: Perform statistical analysis and machine learning techniques to evaluate players.
- Visualization: Develop charts and graphs to represent the findings.
- Python: Programming language used for data manipulation and analysis.
- Pandas: Library for data manipulation and analysis.
- NumPy: Library for numerical operations.
- Matplotlib/Seaborn: Libraries for data visualization.
- Jupyter Notebook: Interactive computing environment for developing and presenting the project.
- Clone the Repository:
git clone https://github.com/yourusername/cheaper-but-great-players-based-on-fifa-potential.git
- Install Dependencies:
pip install -r requirements.txt
- Run the Notebook: Open the Jupyter Notebook and run the cells to see the analysis.
Contributions are welcome! If you have any suggestions, enhancements, or bug fixes, feel free to open an issue or submit a pull request.