input: Write a function to create a classification model in scikit-learn to determine customer churn using a dataset of customers. The function should load the dataset, select features and targets, split the data into training and testing sets, create a RandomForestClassifier model, train the model, and measure accuracy. If the provided dataset path is incorrect or the dataset is empty, the function should return False. Otherwise, it should return True if the model is successfully trained and evaluated.
  def customer_churn_model(dataset_path):
      try:
          import pandas as pd
          from sklearn.ensemble import RandomForestClassifier
          from sklearn.model_selection import train_test_split
          data = pd.read_csv(dataset_path)
          if data.empty:
              return False
          X = data.drop(['churn'], axis=1)
          y = data['churn']
          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)
          predictions = model.predict(X_test)
          print(model.score(X_test, y_test))
          return True
      except Exception as e:
          return False
  assert customer_churn_model("customer_data.csv")==True
  assert customer_churn_model("nonexistent_file.csv")==False
  assert customer_churn_model("empty_file.csv")==False
output: def customer_churn_model(dataset_path):
      try:
          import pandas as pd
          from sklearn.ensemble import RandomForestClassifier
          from sklearn.model_selection import train_test_split
          data = pd.read_csv(dataset_path)
          if data.empty:
              return False
          X = data.drop(['churn'], axis=1)
          y = data['churn']
          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)
          predictions = model.predict(X_test)
          print(model.score(X_test, y_test))
          return True
      except Exception as e:
          return False