input: Write a python function to load a CSV file into a pandas DataFrame, split the data into training and testing sets, train a RandomForestClassifier, and return the model's accuracy score. 
  assert round(load_and_train('data.csv'), 2) == 0.85
  assert round(load_and_train('data2.csv'), 2) == 0.90
  assert round(load_and_train('data3.csv'), 2) == 0.78
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import accuracy_score
  
  def load_and_train(file_path):
      # Load the CSV file into a pandas DataFrame
      df = pd.read_csv(file_path)
      
      # Assuming the last column is the target variable
      X = df.iloc[:, :-1]
      y = df.iloc[:, -1]
      
      # 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 a RandomForestClassifier
      clf = RandomForestClassifier(random_state=42)
      clf.fit(X_train, y_train)
      
      # Predict on the test set
      y_pred = clf.predict(X_test)
      
      # Calculate and return the model's accuracy score
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy