input: Write a function to create a classification model using a RandomForestClassifier from scikit-learn to determine customer churn based on a given dataset. The function should load the data, select features and targets, split the data into training and testing sets, train the model, and finally make predictions and measure accuracy. If the input data is empty, return False.
  assert customer_churn_model("customer_data.csv")==0.85
  assert customer_churn_model("empty_data.csv")==False
  assert customer_churn_model("another_customer_data.csv")==0.90
  assert customer_churn_model("non_existent_file.csv")==False
  assert customer_churn_model("customer_data_with_no_churn_column.csv")==False
output: def customer_churn_model(file_path):
      import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
      
      try:
          data = pd.read_csv(file_path)
          if data.empty:
              return False
          if 'Churn' not in data.columns:
              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(random_state=42)
          model.fit(X_train, y_train)
          y_pred = model.predict(X_test)
          
          return accuracy_score(y_test, y_pred)
      except:
          return False