This project dives into football statistics with a special focus on goalscoring. It aims to analyze player performance and predict whether a given situation during a football match will result in a goal. The dataset used for this project is from Kaggle, containing detailed football event data.
The project is divided into three main phases:
- Data Analysis: Cleaning and exploring the dataset to extract insights related to goalscoring.
- Model Building: Using machine learning techniques to build a predictive model for goal outcomes.
- User Interface Development: Creating a user-friendly web interface where users can input their own data and test the predictive model.
- Data cleaning and exploratory data analysis (EDA) of football event data.
- Machine learning models including Gradient Boosting Classifier, Logistic Regression, and Soft Voting for prediction.
- Web application built with Flask, providing an interface for users to interact with the data and the prediction model.
- Python: Data cleaning, analysis, and machine learning.
- Flask: Backend framework to build the web application.
- HTML/CSS: Frontend for user interaction.
- Machine Learning: Classification and regression trees.
- Clone the repository.
- Install required dependencies from
requirements.txt. - Run the Flask application using
python app.py. - Access the web interface through your browser at
http://localhost:5000.
The dataset is sourced from Kaggle and includes detailed football event data such as:
- Time
- Player position
- Assist method
- Body part used to score, etc.
The model predicts the probability of a goal based on the input data, using factors such as the time of the match, assist method, and the player’s position.