input: Write a function to simulate a Kaggle competition and assess model accuracy by using a predefined Python script. The function should take the training data path as input, fit a model on the training data, make predictions on a test set, and return the accuracy score. If the input data is empty, return 'No data available'.
  assert simulate_kaggle_competition("data.csv")==0.85
  assert simulate_kaggle_competition("empty_data.csv")=='No data available'
  assert simulate_kaggle_competition("nonexistent_file.csv")=='No data available'
output: def simulate_kaggle_competition(train_data_path):
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.ensemble import RandomForestClassifier
          from sklearn.metrics import accuracy_score
          
          data = pd.read_csv(train_data_path)
          if data.empty:
              return 'No data available'
          
          X = data.drop('target', axis=1)
          y = data['target']
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          model = RandomForestClassifier()
          model.fit(X_train, y_train)
          y_pred = model.predict(X_test)
          
          return accuracy_score(y_test, y_pred)
      except Exception:
          return 'No data available'