/English_Premier_League_Exploratory_Data_Analysis_in_Python

I will be using DataFrames to examine the dataset in different ways, along with line plots, bar plots, and scatter plots.

Primary LanguageJupyter Notebook

English Premier League Exploratory Data Analysis (EDA) in Python

Project Overview

This project conducts an exploratory data analysis of the English Premier League (EPL), focusing on uncovering trends and insights through data visualization and manipulation. The analysis provides a deeper understanding of the league's performance metrics, player statistics, team dynamics, and other critical aspects.

Dataset

The dataset used for this analysis contains key information about EPL matches, including:

  • Teams
  • Players
  • Match dates
  • Goals scored
  • Other performance metrics

Objectives

The goal is to explore the dataset and answer questions such as:

  • Which team has the highest win percentage?
  • How do player statistics influence match outcomes?
  • What are the trends in goal scoring over different seasons?

Features

  • Data Cleaning & Preprocessing: Handling missing values, data types conversion, and ensuring the data is ready for analysis.
  • Data Exploration & Visualization:
    • Analyzing team and player performance.
    • Plotting trends in goals, assists, and win/loss records.
    • Identifying standout players and teams over time.
  • Statistical Insights: Uncovering correlations between variables and their influence on match outcomes.

Tools & Libraries

  • Pandas: For data manipulation and cleaning.
  • Matplotlib & Seaborn: For creating visualizations (line plots, bar charts, scatter plots, etc.).
  • NumPy: For numerical operations.

Key Visualizations

  • Win/Loss Trends: Line plots showing each team's performance across seasons.
  • Player Performance: Bar charts and scatter plots illustrating player contributions like goals, assists, and appearances.
  • Team Comparisons: Visual comparisons between top-performing teams and their statistics.

How to Run the Project

  1. Clone the repository: 
    git clone https://github.com/farahmoataz90/English_Premier_League_Exploratory_Data_Analysis_in_Python.git
cd English_Premier_League_Exploratory_Data_Analysis_in_Python
  2. Install Dependencies:
    Install the required Python libraries by running: 
    pip install -r requirements.txt
  3. Run the Jupyter Notebook:
    Open the notebook in Jupyter and run the cells to perform the analysis: 
    jupyter notebook notebook.ipynb

Results & Insights

  • Top performing teams and players over different EPL seasons.
  • Statistical correlations between player contributions and match outcomes.
  • Visualized trends in scoring, team formations, and win/loss patterns.

Contributing

Feel free to contribute by submitting a pull request, reporting issues, or suggesting enhancements.