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Soccer Prediction Model

Introducing Soccer Predictor, a comprehensive project designed to scrape match statistics for over 700 players, manipulate and present the data dynamically, and predict match outcomes using machine learning.

Key Features:

  • Data Scraping: Engineered a comprehensive data scraping of match statistics for 700+ players using Python and pandas.
  • Backend: Dynamic manipulation and presentation of the scraped data through a Spring Boot application.
  • Database: Real-time data manipulation within a PostgreSQL database using SQL queries.
  • Machine Learning: Created a model to predict match outcomes by integrating data scraping with pandas and machine learning with scikit-learn.

Components:

  • Data Scraping: Technology: Python, pandas. This component scrapes match statistics for over 700 players and stores the data in a CSV file for further processing.
  • Backend: Technology: Spring Boot, Java. This component dynamically manipulates and presents the scraped data. It uses SQL queries to manage real-time data manipulation within a PostgreSQL database.
  • Machine Learning: Technology: Python, scikit-learn, pandas. This component creates a machine learning model to predict match outcomes based on the scraped data.

This project exemplifies my ability to integrate various technologies and techniques to create a powerful and versatile application, making it a valuable addition to my portfolio.