input: Write a python function to load multiple CSV files, calculate the mean of 'Accuracy' and 'F1-Score' for each file, and return a summary dataframe.
  assert summary_dataframe(['logistic_regression.csv', 'linear_regression.csv', 'knn.csv', 'dtree.csv', 'svm.csv']).loc[0, 'Algorithm'] == 'Logistic Regression'
  assert summary_dataframe(['logistic_regression.csv', 'linear_regression.csv', 'knn.csv', 'dtree.csv', 'svm.csv']).loc[1, 'Accuracy'] > 0
  assert 'F1-Score' in summary_dataframe(['logistic_regression.csv', 'linear_regression.csv', 'knn.csv', 'dtree.csv', 'svm.csv']).columns
output: import pandas as pd
  
  def summary_dataframe(file_list):
      summary_data = []
      
      for file in file_list:
          df = pd.read_csv(file)
          mean_accuracy = df['Accuracy'].mean()
          mean_f1_score = df['F1-Score'].mean()
          algorithm_name = file.replace('.csv', '').replace('_', ' ').title()
          
          summary_data.append({'Algorithm': algorithm_name, 'Accuracy': mean_accuracy, 'F1-Score': mean_f1_score})
          
      summary_df = pd.DataFrame(summary_data)
      return summary_df