input: Write a python function to train and evaluate a linear regression model on a given dataset. 
  assert train_and_evaluate_model('house_data.csv') > 0.7
  assert train_and_evaluate_model('another_house_data.csv') > 0.65
output: def train_and_evaluate_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import r2_score
      
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the dataset 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 the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Make predictions
      y_pred = model.predict(X_test)
      
      # Calculate the R^2 score
      r2 = r2_score(y_test, y_pred)
      
      return r2