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 load the dataset, select features, select targets, split the data into training and testing sets, create a RandomForestClassifier model, train the model, and then make predictions and measure accuracy.
  assert churn_prediction().startswith("\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\n")
  assert churn_prediction().endswith("print(model.score(X_test, y_test))\n")
output: def churn_prediction():
      import pandas as pd
      import numpy as np
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.model_selection import train_test_split
      
      data = pd.read_csv('customer_data.csv')
      features = data.drop('churn', axis=1)
      target = data['churn']
      
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
      
      model = RandomForestClassifier(random_state=42)
      model.fit(X_train, y_train)
      
      print(model.score(X_test, y_test))