input: Write a python function to split data into training and testing sets, build a linear regression model, and evaluate its performance.
  assert evaluate_model('data.csv')>0.6
  assert evaluate_model('another_data.csv')>0.7
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 evaluate_model(file_path):
      # Load the data
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target variable and the rest are features
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # 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)
      
      # Build the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Predict on the test set
      y_pred = model.predict(X_test)
      
      # Evaluate the model performance using R^2 score
      r2 = r2_score(y_test, y_pred)
      
      return r2