input: Write a python function to read a CSV file, split the data into training and testing sets, create and train an Isolation Forest model, make predictions, and evaluate the model's accuracy.
  assert evaluate_model('dataset.csv') > 0  # Replace with actual accuracy comparison
  assert isinstance(evaluate_model('dataset.csv'), float)
output: def evaluate_model(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import IsolationForest
      from sklearn.metrics import accuracy_score
  
      # Read the CSV file
      data = pd.read_csv(csv_file)
      
      # Assuming the CSV file has a column named 'target' for labels
      X = data.drop(columns=['target'])
      y = data['target']
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      # Create and train the Isolation Forest model
      model = IsolationForest(contamination='auto', random_state=42)
      model.fit(X_train)
      
      # Make predictions
      y_pred_train = model.predict(X_train)
      y_pred_test = model.predict(X_test)
      
      # Convert predictions from {-1, 1} to {0, 1}
      y_pred_train = [1 if x == 1 else 0 for x in y_pred_train]
      y_pred_test = [1 if x == 1 else 0 for x in y_pred_test]
      
      # Evaluate the model's accuracy
      accuracy = accuracy_score(y_test, y_pred_test)
      
      return accuracy