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The dataset used in this notebook contains a range of match, team and player statistics for the major European Football Leagues.

Football match data analysis and outcome prediction is a learning project as a part of Data Science course at Turing College. The main project goal is to research and find areas that could give a competitive advantage for betting company entering European football (soccer) market.

Based on the project goal and requirements, project is structured as follows:

• performing league-level analysis
• choosing two top leagues
• comparing and evaluating features between chosen leagues using hypothesis testing
• comparing and evaluating statistics between teams in chosen leagues using hypothesis testing
• applying basic ML algorithms to predict match outcome
• applying basic ML algorithms to predict goals that each team will score during the match

• SQLite is used to select, filter and merge data
• Various Python libraries, such as NumPy, Pandas, Seaborn are used for data manipulation, analysis and visualization
• Statsmodels package is used to perform inferential statistics
• Classification and regression algorithms featured by Scikit-learn library are used to predict machine learning targets

Learning @TuringCollege