input: Write a Python function to train a linear regression model on IMDB data and predict the IMDB rating of a given movie.
  assert train_and_predict_linear_regression('imdb_data.csv', ['Grey Island', 2, 101, 1607800])==3.6
  assert train_and_predict_linear_regression('imdb_data.csv', ['New Movie', 5, 120, 2500000])==4.2
  assert train_and_predict_linear_regression('imdb_data.csv', ['Another Movie', 3, 95, 1800000])==3.8
output: def train_and_predict_linear_regression(file_path, movie_features):
      import pandas as pd
      from sklearn.linear_model import LinearRegression
  
      # Load the dataset
      data = pd.read_csv(file_path)
  
      # Assume the dataset has columns: 'title', 'rating', 'votes', 'duration', 'budget'
      # Drop the 'title' column as it is not needed for regression
      X = data.drop(columns=['title', 'rating'])
      y = data['rating']
  
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X, y)
  
      # Make a prediction for the given movie features
      features = movie_features[1:]  # Exclude the movie title
      prediction = model.predict([features])[0]
  
      # Round the prediction to one decimal place
      return round(prediction, 1)