def categorize_students(data):
# 使用简单的机器学习模型对新生和老生进行分类
if 'year' in data and data['year'] == 'first':
return 'New Student'
else:
return 'Returning Student'
student_data = {'name': 'Alice', 'year': 'first'}
category = categorize_students(student_data)
print(f"Student Category: {category}")
]]>
import pandas as pd
def predict_tutoring_need(scores_df):
# 假设低于70分的学生需要辅导
scores_df['Tutoring Needed'] = scores_df['Score'].apply(lambda x: 'Yes' if x < 70 else 'No')
return scores_df
scores_data = {'Name': ['Alice', 'Bob'], 'Score': [85, 65]}
scores_df = pd.DataFrame(scores_data)
updated_scores = predict_tutoring_need(scores_df)
print(updated_scores)
]]>
def recommend_research_topics(student_interests, available_topics):
# 简单匹配学生的兴趣与可用课题
recommended_topics = []
for interest in student_interests:
if interest in available_topics:
recommended_topics.append(interest)
return recommended_topics
student_interests = ['Machine Learning', 'Data Science']
available_topics = ['Machine Learning', 'Robotics', 'Quantum Computing']
topics = recommend_research_topics(student_interests, available_topics)
print(f"Recommended Topics: {topics}")
]]>