input: from typing import Tuple
  
  
  def analyze_data_and_build_model(file_path: str) -> Tuple[float, np.ndarray]:
      """ This function loads a dataset from a specified CSV file, selects features and targets 
      related to customer churn, splits the data into training and testing sets, 
      trains a RandomForestClassifier model, and then makes predictions on the test set. 
      Finally, it returns the accuracy score and the predictions made by the model.
      >>> analyze_data_and_build_model("customer_data.csv")
      (0.85, array([0, 1, 0, ..., 0, 1, 0]))
      """
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      import numpy as np
  
      # Load the dataset
      data = pd.read_csv(file_path)
  
      # Select features and target
      features = data.drop('Churn', axis=1)
      target = data['Churn']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
  
      # Train a RandomForestClassifier model
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      predictions = model.predict(X_test)
  
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, predictions)
  
      return accuracy, predictions
  