一站式网上办事大厅

import pandas as pd

from sklearn.neighbors import NearestNeighbors

# 模拟学生数据

data = {

'student_id': [1, 2, 3, 4, 5],

'math_score': [80, 70, 90, 60, 85],

'english_score': [75, 65, 85, 70, 90],

'interest': ['science', 'arts', 'science', 'arts', 'science']

}

df = pd.DataFrame(data)

# 使用KNN进行相似学生匹配

model = NearestNeighbors(n_neighbors=2)

model.fit(df[['math_score', 'english_score']])

distances, indices = model.kneighbors(df[['math_score', 'english_score']])

print("相似学生索引:", indices)

print("距离:", distances)

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

# 模拟数据

data = {

'attendance': [90, 70, 85, 60, 95],

'assignment_score': [85, 65, 90, 55, 92],

'exam_score': [75, 60, 80, 50, 88],

'risk': [0, 1, 0, 1, 0] # 0表示无风险，1表示有风险

}

df = pd.DataFrame(data)

X = df[['attendance', 'assignment_score', 'exam_score']]

y = df['risk']

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)

y_pred = model.predict(X_test)

print("准确率:", accuracy_score(y_test, y_pred))

import speech_recognition as sr

r = sr.Recognizer()

with sr.Microphone() as source:

print("请说话...")

audio = r.listen(source)

try:

text = r.recognize_google(audio, language='zh-CN')

print("你说的是: " + text)

except sr.UnknownValueError:

print("无法识别你的语音")

except sr.RequestError as e:

print("请求错误; {0}".format(e))

import networkx as nx

# 构建知识点图

G = nx.DiGraph()

G.add_edges_from([

('数学基础', '微积分'),

('微积分', '线性代数'),

('线性代数', '机器学习'),

('机器学习', '深度学习')

])

# 计算从起点到终点的最短路径

path = nx.shortest_path(G, source='数学基础', target='深度学习')

print("学习路径:", path)