input: Write a python function to load a CSV file, create a linear regression model using scikit-learn, fit it to the data, and make predictions.
  assert isinstance(load_and_predict('apple_stock.csv'), np.ndarray)
  assert len(load_and_predict('apple_stock.csv')) > 0
output: def load_and_predict(csv_file):
      import numpy as np
      import pandas as pd
      from sklearn.linear_model import LinearRegression
  
      # Load the CSV file
      data = pd.read_csv(csv_file)
      
      # Assume that the CSV has columns 'X' for features and 'y' for target
      X = data[['X']].values  # features (reshaped to 2D array if needed)
      y = data['y'].values    # target
      
      # Create the linear regression model
      model = LinearRegression()
      
      # Fit the model
      model.fit(X, y)
      
      # Make predictions
      predictions = model.predict(X)
      
      return predictions