Premier League Points Prediction and Analysis
Project Overview This project aims to analyze and predict the performance of English Premier League (EPL) teams, focusing on the points needed to win the league. Using historical data from the past 14 seasons, the project investigates the relationship between various factors (such as wins, goals scored, and goals conceded) and the final points tally of EPL champions.
Features Data Visualization: The project includes various visualizations, such as stacked bar charts, to compare the points of EPL champions and runners-up across seasons. These visualizations help in understanding trends and differences in team performances over time.
Predictive Modeling: An XGBoost model is used to predict the points an EPL team is likely to achieve based on historical data. The model has been tuned for optimal performance, and key metrics such as Mean Squared Error (MSE) and R² Score are provided to assess its accuracy.
Statistical Insights: The project also offers insights into the correlations between different variables (like wins, goals scored, and goals conceded) and their impact on a team's chances of winning the league.
Technologies Used Python: The primary programming language used for data analysis and model building. Pandas: For data manipulation and preprocessing. Matplotlib/Seaborn: For creating visualizations. XGBoost: The machine learning algorithm used for predictive modeling. Getting Started To get started with this project:
Clone the repository to your local machine. Ensure you have Python installed along with the necessary libraries (pandas, matplotlib, seaborn, xgboost). Run the Jupyter Notebook or Python scripts to see the analysis and predictions
How to Use Visualize Trends: Use the provided visualizations to explore how the points difference between champions and runners-up has evolved over time. Predict Points: Leverage the XGBoost model to predict the potential points a team might achieve based on input features. Analyze Correlations: Understand the key factors that contribute to a team’s success in the league.
Conclusion This project provides a comprehensive toolset for analyzing and predicting EPL team performance. It highlights the critical factors that influence league success and offers a reliable method for estimating points totals.
Author This project was developed by Ajayi Ayomipo.