input: Write a python function to train a linear regression model on a given CSV file and return the predictions.
  assert train_and_predict('data.csv')[:5] == [your_expected_values_here]
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  
  def train_and_predict(file_path):
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assume 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
      predictions = model.predict(X_test)
      
      return list(predictions)