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The prompt

Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Develop a ML model in Python for predicting the winning team of football matches. Use data from the past 5 years of the Premier League. ### Input: Not applicable ### Output: import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import GradientBoostingClassifier # Load data data = pd.read_csv('data.csv') # Select features features = data[['home_team_goals', 'away_team_goals', 'home_team_possession', 'away_team_possession', 'home_team_shots', 'away_team_shots', 'home_team_yellow_cards', 'away_team_yellow_cards', 'home_team_red_cards', 'away_team_red_cards']] # Select target target = data['winner'] # Split data into training and test sets X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42) # Train the model gbm = GradientBoostingClassifier() gbm.fit(X_train, y_train) # Make predictions y_pred = gbm.predict(X_test) # Evaluate the model score = gbm.score(X_test, y_test) print(f'Model score: {score}')

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Hugging Face

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