input: Write a python function to load a CSV file, split data into training and testing sets, train a Linear Regression model, and return the model score.
  assert load_and_train_model('sales_data.csv') > 0.7
  assert load_and_train_model('sales_data.csv') <= 1.0
output: def load_and_train_model(csv_file):
      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 data
      data = pd.read_csv(csv_file)
      
      # Assume the last column is the target variable and the rest are features
      X = data.iloc[:, :-1]
      y = data.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)
      
      # Predict on the test set
      y_pred = model.predict(X_test)
      
      # Calculate the model score
      score = r2_score(y_test, y_pred)
      
      return score