All Machine Learning Algorithms using the European Soccer Database

1. Clustering:

Exercise: Identify clusters of similar players based on their attributes.

• Task: Use K-Means or hierarchical clustering to group players by their performance metrics (e.g., passing accuracy, finishing, dribbling).
• Goal: Discover patterns in player attributes (such as attacking and defending skills) and identify player roles (e.g., defenders vs attackers) from the clusters.
• Key Skills: Preprocessing (scaling features), choosing the number of clusters (Elbow method, silhouette score).

Example Steps:

• Load the player attributes data.
• Select relevant numeric columns like crossing, finishing, short_passing, dribbling.
• Use KMeans or AgglomerativeClustering from sklearn.
• Visualize the clusters using PCA or t-SNE.

2. Regression:

Exercise: Predict a player's potential or overall rating.

• Task: Train a regression model to predict the overall_rating or potential of a player based on other features like finishing, crossing, dribbling, etc.
• Goal: Use linear regression, Ridge regression, or Decision Trees (for non-linear relations) to predict these continuous variables.
• Key Skills: Feature selection, handling multi-collinearity, regularization.

Example Steps:

• Load player attributes, selecting relevant predictors.
• Use train_test_split to divide the data.
• Fit a Ridge or Decision Tree regressor, tuning hyperparameters (like alpha for Ridge or max_depth for Decision Trees).

3. Support Vector Machines (SVM):

Exercise: Classify whether a player is an "attacker" or "defender".

• Task: Use SVM with a linear or RBF kernel to classify players into offensive and defensive roles based on their skill ratings.
• Goal: Explore how different features like finishing, marking, tackling, etc., impact the classification of a player’s role.
• Key Skills: Hyperparameter tuning (C, gamma), feature scaling.

Example Steps:

• Create binary labels for attackers (e.g., finishing > heading_accuracy) and defenders (tackling > dribbling).
• Use StandardScaler to preprocess data.
• Train an SVM model with GridSearchCV for hyperparameter tuning.

4. Decision Trees:

Exercise: Predict the outcome of a match (win, loss, or draw).

• Task: Use Decision Trees or Random Forest to predict match outcomes based on features like team ratings, home/away status, and past performance.
• Goal: Analyze the importance of each feature in predicting the match outcome.
• Key Skills: Overfitting control (max depth, min samples), feature importance analysis.

Example Steps:

• Use match data (team attributes, home/away team, match history).
• Predict outcomes as win, loss, or draw (multi-class classification).
• Train a Decision Tree classifier, and evaluate performance using cross-validation.