input: Write a python function to predict if a customer is likely to buy a product based on a RandomForestClassifier model.
  assert predict_customer_buying({'Age': [34], 'Gender': ['Male'], 'Homeowner': [1], 'Education': [1], 'Marital Status': [1], 'Employment': [2], 'Income': [50000]}) in [0, 1]
  assert predict_customer_buying({'Age': [25], 'Gender': ['Female'], 'Homeowner': [0], 'Education': [3], 'Marital Status': [0], 'Employment': [1], 'Income': [30000]}) in [0, 1]
  assert predict_customer_buying({'Age': [45], 'Gender': ['Male'], 'Homeowner': [1], 'Education': [2], 'Marital Status': [1], 'Employment': [3], 'Income': [80000]}) in [0, 1]
output: def predict_customer_buying(customer_data):
      import pandas as pd
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.preprocessing import LabelEncoder
  
      # Assuming the training of the model has already been done and we have the trained model
      # For simplicity, here we create a dummy model and fit it with dummy data.
      dummy_data = {
          'Age': [25, 34, 45],
          'Gender': ['Female', 'Male', 'Male'],
          'Homeowner': [0, 1, 1],
          'Education': [3, 1, 2],
          'Marital Status': [0, 1, 1],
          'Employment': [1, 2, 3],
          'Income': [30000, 50000, 80000],
          'Buy': [0, 1, 1]
      }
      df = pd.DataFrame(dummy_data)
  
      X = df.drop(columns=['Buy'])
      y = df['Buy']
  
      # Encode categorical variables
      label_encoders = {}
      for column in X.select_dtypes(include=['object']).columns:
          le = LabelEncoder()
          X[column] = le.fit_transform(X[column])
          label_encoders[column] = le
  
      model = RandomForestClassifier()
      model.fit(X, y)
  
      # Prepare customer_data for prediction
      customer_df = pd.DataFrame(customer_data)
  
      for column, le in label_encoders.items():
          customer_df[column] = le.transform(customer_df[column])
  
      prediction = model.predict(customer_df)
      return prediction[0]