input: Write a python function to predict the price of a car using linear regression.
  assert predict(15000, 'Ford', 'Focus', 5, 20000) == 14500
  assert predict(20000, 'Toyota', 'Camry', 3, 30000) == 18000  # Adjust this according to the model's expected output
  assert predict(12000, 'Honda', 'Civic', 6, 25000) == 13000  # Adjust this according to the model's expected output
output: def predict(price, make, model, age, mileage):
      # Example coefficients, these need to be determined from actual training data
      coef_price = 1.0
      coef_make = {'Ford': -0.1, 'Toyota': 0.2, 'Honda': 0.3}
      coef_model = {'Focus': -0.05, 'Camry': 0.1, 'Civic': 0.15}
      coef_age = -0.5
      coef_mileage = -0.0001
  
      # Make prediction using linear regression formula
      prediction = (coef_price * price +
                    coef_make.get(make, 0) +
                    coef_model.get(model, 0) +
                    coef_age * age +
                    coef_mileage * mileage)
      
      return int(prediction)