基于大模型优化的研究生管理信息系统设计与实现

import pandas as pd

# 加载数据

data = pd.read_csv('student_data.csv')

# 数据清洗

def clean_data(df):

df.dropna(inplace=True)

return df

cleaned_data = clean_data(data)

# 数据分析

def analyze_data(df):

publication_count = df['publications'].sum()

avg_grades = df['grades'].mean()

return publication_count, avg_grades

publications, grades = analyze_data(cleaned_data)

print(f"Total Publications: {publications}, Average Grades: {grades}")

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from sklearn.cluster import KMeans

# 特征选择

features = cleaned_data[['publications', 'grades']]

# 使用KMeans进行聚类

kmeans = KMeans(n_clusters=3)

clusters = kmeans.fit_predict(features)

cleaned_data['cluster'] = clusters

print(cleaned_data[['name', 'cluster']])

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