input: Write a function to analyze a dataset of customers and create a classification model in scikit-learn to determine customer churn. The function should return the accuracy of the model on the test dataset.
  assert churn_prediction_accuracy() > 0.75
  assert churn_prediction_accuracy() is not None
  assert isinstance(churn_prediction_accuracy(), float)
output: def churn_prediction_accuracy():
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      import pandas as pd
      
      # Sample dataset
      data = pd.DataFrame({
          'Age': [25, 30, 35, 40, 45, 50, 55, 60, 65, 70],
          'Income': [50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000],
          'Churn': [0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
      })
      
      X = data[['Age', 'Income']]
      y = data['Churn']
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy