• This repository contains the code and data for a project on England Premier League player's info and stats in 2020-2021 . The project uses the EPL_20_21.csv dataset, which can be downloaded from Kaggle. Link:https://www.kaggle.com/datasets/rajatrc1705/english-premier-league202021?select=EPL_20_21.csv

• This dataset is a collection of basic but crucial stats of the English Premier League 2020-21 season. The dataset has all the players that played in the EPL and their standard stats such as Goals, Assists, xG, xA, Passes Attempted, Pass Accuracy and more! The columns are:

• Position:Each player has a certain position, in which he plays regularly. The position in this dataset are, FW - Forward, MF - Midfield, DF - Defensive, GK - Goalkeeper

• Starts: The number of times the player was named in the starting 11 by the manager.

• MinS:The number of minutes played by the player.

• Goals:The number of Goals scored by the player.

• Assists:The number of times the player has assisted other player in scoring the goal.

• Passes_Attempted:The number of passes attempted by the player.

• Perc_Passes_Completed:The number of passes that the player accurately passed to his teammate.

• xG:Expected number of goals from the player in a match.

• xA:Expected number of assists from the player in a match.

• Yellow_Cards:The players get a yellow card from the referee for indiscipline, technical fouls, or other minor fouls.

• Red Cards:The players get a red card for accumulating 2 yellow cards in a single game, or for a major foul.

The following tools were used for this analysis:

• Python 3

• Pandas

• NumPy

• Matplotlib

• Seaborn

• Plotly

• Sklearn

• To run this project, you will need to have Python 3 installed on your machine. You can install the required libraries by running the following command:

• pip install pandas matplotlib seaborn numpy plotly

• To run the analysis, simply execute the notebook. The script will generate several visualizations that help illustrate analysis of data.

The analysis includes the following tasks:

• Data loading and cleaning
• Exploratory data analysis
• Feature engineering
• Correlation analysis
• detecting outliers
• building model
• evaluating model
• tuning model
• visualization results

The analysis includes visualizations using Matplotlib, Plotly and Seaborn.

• Contributions to this project are welcome. If you notice any errors or have ideas for additional analyses, please feel free to open an issue or submit a pull request.