input: Write a python function to clean a dataset by removing specific columns, normalizing a specific column, and handling missing values.
  assert clean_dataset('dataset.csv', 'Unnamed', 'column', 'price') == 'cleaned_dataset.csv'
output: import pandas as pd
  from sklearn.preprocessing import MinMaxScaler
  
  def clean_dataset(file_path, column_to_remove, column_to_normalize, column_to_fillna):
      # Load the dataset
      df = pd.read_csv(file_path)
      
      # Remove specified column
      if column_to_remove in df.columns:
          df.drop(column_to_remove, axis=1, inplace=True)
      
      # Normalize the specified column
      if column_to_normalize in df.columns:
          scaler = MinMaxScaler()
          df[column_to_normalize] = scaler.fit_transform(df[[column_to_normalize]])
      
      # Handle missing values in the specified column
      if column_to_fillna in df.columns:
          df[column_to_fillna].fillna(df[column_to_fillna].mean(), inplace=True)
      
      # Save the cleaned dataset
      cleaned_file_path = 'cleaned_dataset.csv'
      df.to_csv(cleaned_file_path, index=False)
      
      return cleaned_file_path