input: Write a function to load data from a CSV file, split it into train and test sets, train a RandomForestClassifier model, and make predictions for the test set.
  assert len(load_and_train_model('stock_data.csv')) == len(pd.read_csv('stock_data.csv').iloc[:, -1].values) * 0.2
output: def load_and_train_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Load data from CSV file
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target and the rest are features
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
  
      # Split the data into train and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the RandomForestClassifier model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions for the test set
      y_pred = model.predict(X_test)
  
      return y_pred