input: Write a python function to preprocess student profiles for logistic regression model.
  assert preprocess_student_profile({'GPA': 3.0, 'Major': 'Biology'}) == pd.DataFrame([[3.0, 0, 0, 1]], columns=['GPA', 'Computer Science', 'Business', 'Biology'])
  assert preprocess_student_profile({'GPA': 3.4, 'Major': 'Computer Science'}) == pd.DataFrame([[3.4, 1, 0, 0]], columns=['GPA', 'Computer Science', 'Business', 'Biology'])
  assert preprocess_student_profile({'GPA': 3.7, 'Major': 'Business'}) == pd.DataFrame([[3.7, 0, 1, 0]], columns=['GPA', 'Computer Science', 'Business', 'Biology'])
output: import pandas as pd
  
  def preprocess_student_profile(profile):
      # One-hot encoding for the 'Major' field
      major_encoding = {
          'Computer Science': [1, 0, 0],
          'Business': [0, 1, 0],
          'Biology': [0, 0, 1]
      }
      
      encoded_major = major_encoding.get(profile['Major'], [0, 0, 0])
      
      # Creating the DataFrame
      data = [[profile['GPA']] + encoded_major]
      columns = ['GPA', 'Computer Science', 'Business', 'Biology']
      
      return pd.DataFrame(data, columns=columns)