input: Write a python function to load a CSV file, split the data into training and test sets, train a linear regression model, and return the accuracy of the model.
  assert train_and_evaluate_model('orlando_housing.csv') >= 0.7
  assert train_and_evaluate_model('another_housing.csv') >= 0.6
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import r2_score
  
  def train_and_evaluate_model(csv_file):
      # Load the CSV file into a DataFrame
      df = pd.read_csv(csv_file)
      
      # Assuming the last column is the target and the rest are features
      X = df.iloc[:, :-1]
      y = df.iloc[:, -1]
      
      # Split the 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 a linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = model.predict(X_test)
      
      # Calculate the accuracy of the model
      accuracy = r2_score(y_test, y_pred)
      
      return accuracy