Football is one of the most popular sports worldwide, winning a football match has become one of the most crucial aspect of football clubs.

• Primary Source of our data: Football API - https://www.football-data.org/

  Secondary Sources: Kaggle and Github, http://football-data.co.uk/data.php

• For Data Cleaning and Transformation

  -‘tibble’, ‘purr’

  -packages in ‘tidyverse’ including the ‘dplyr’, ‘ggplot’, and ‘stringr’

• Our goal is to predict the outcome of matches using the historic match results and pre-match odds of winning a game

• Visualize the data in R using ggplot2 to understand the relationships amongst the variables and the distributions
• Examine the wins for home and away matches and evaluate the performance of teams
• Explore and Plot correlation coefficient between different variables and choose the best factors for model training
• Predict the wins for home and away teams using different machine learning models such as Logistic regression, Support Vector Machine, and KNN.

• We will be using AUC to identify the precision and recall curve
• We will use a confusion matrix to evaluate our results and evaluate models using metrics such as Accuracy, Sensitivity, Specificity