input: Write a python function to train a RandomForestClassifier model on a given CSV file and return the accuracy.
  assert train_and_evaluate_model('arrhythmaiadata.csv') > 0.5
output: def train_and_evaluate_model(csv_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 the dataset
      data = pd.read_csv(csv_file_path)
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]  # Features
      y = data.iloc[:, -1]   # Target
  
      # 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)
  
      # Initialize the RandomForestClassifier
      model = RandomForestClassifier(n_estimators=100, random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy