input: Write a function to develop a machine learning model in Python for predicting the winning team of football matches using data from the past 5 years of the Premier League. The function should return the model score after evaluation.
  assert predict_winner()==0.75
  assert predict_winner()==0.72
  assert predict_winner()==0.78
  assert predict_winner()==0.76
  assert predict_winner()==0.73
output: 
  def predict_winner():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Load data (assuming data is available in a CSV file)
      data = pd.read_csv('premier_league_data.csv')
  
      # Preprocess data
      X = data.drop('winning_team', axis=1)
      y = data['winning_team']
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Evaluate the model
      y_pred = model.predict(X_test)
      score = accuracy_score(y_test, y_pred)
  
      return score
  