input: Write a python function to train a RandomForestClassifier on a given dataset and make predictions.
  assert train_and_predict(data) == expected_predictions
output: def train_and_predict(data):
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      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(n_estimators=100, random_state=42)
      model.fit(X_train, y_train)
      
      predictions = model.predict(X_test)
      
      return predictions