input: Write a function to develop a ML model for predicting the winning team of football matches using data from the past 5 years of the Premier League. The function should load the data, select features and target, split the data into training and test sets, train the model, make predictions, and evaluate the model's performance.
  
  assert develop_football_model() == 'Model score: 0.85'
output: def develop_football_model():
      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
      data = pd.read_csv('premier_league_data.csv')
  
      # Select features and target
      features = ['home_team_strength', 'away_team_strength', 'home_team_form', 'away_team_form']
      target = 'winning_team'
  
      X = data[features]
      y = data[target]
  
      # Split data into training and test 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)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Evaluate the model's performance
      score = accuracy_score(y_test, y_pred)
  
      return f'Model score: {score:.2f}'