基于大数据分析平台的师范大学教学优化探索

import requests

def fetch_lms_data(api_url, token):

headers = {'Authorization': f'Bearer {token}'}

response = requests.get(api_url, headers=headers)

if response.status_code == 200:

return response.json()

else:

return None

# 示例调用

api_url = "https://example.edu/api/student_activity"

token = "your_token_here"

data = fetch_lms_data(api_url, token)

if data:

print("数据成功获取！")

else:

print("数据获取失败，请检查API连接。")

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import pandas as pd

def clean_data(raw_data):

df = pd.DataFrame(raw_data)

# 删除缺失值

df.dropna(inplace=True)

# 转换日期格式

df['timestamp'] = pd.to_datetime(df['timestamp'])

return df

cleaned_df = clean_data(data)

print(cleaned_df.head())

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from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

X = cleaned_df[['attendance', 'homework_completion']]

y = cleaned_df['performance']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LogisticRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print(predictions)

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import matplotlib.pyplot as plt

import seaborn as sns

sns.barplot(x='attendance', y='performance', data=cleaned_df)

plt.title('Attendance vs Performance')

plt.show()

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