input: Write a function to read a CSV file, split the data into training and testing sets, train a linear regression model, and make predictions.
  assert len(y_pred)==len(y_test)
  assert isinstance(y_pred[0], float)
  assert len(X_train) > 0
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  
  def read_and_train(csv_file):
      # Read the CSV file into a DataFrame
      df = pd.read_csv(csv_file)
      
      # Assuming the last column is the target variable and rest are features
      X = df.iloc[:, :-1].values
      y = df.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)
      
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Make predictions
      y_pred = model.predict(X_test)
      
      return X_train, X_test, y_train, y_test, y_pred