锦中融合门户系统

def calculate_rank(students):

# 假设成绩占比60%，出勤率占比40%

for student in students:

student['score'] = student['grade'] * 0.6 + student['attendance'] * 0.4

# 按分数从高到低排序

sorted_students = sorted(students, key=lambda x: x['score'], reverse=True)

# 添加排名

for i, student in enumerate(sorted_students):

student['rank'] = i + 1

return sorted_students

# 示例数据

students = [

{'id': '001', 'name': '张三', 'grade': 85, 'attendance': 95},

{'id': '002', 'name': '李四', 'grade': 90, 'attendance': 80},

{'id': '003', 'name': '王五', 'grade': 75, 'attendance': 90},

]

# 计算并打印结果

ranked_students = calculate_rank(students)

for student in ranked_students:

print(f"学号: {student['id']}, 姓名: {student['name']}, 排名: {student['rank']}, 总分: {student['score']:.2f}")

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

# 假设我们有一组训练数据，包含学生的平时成绩、作业成绩、期中考试成绩等

data = {

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

'midterm_score': [75, 65, 85, 55, 80],

'final_score': [85, 70, 95, 65, 90]

}

df = pd.DataFrame(data)

# 特征和目标变量

X = df[['assignment_score', 'midterm_score']]

y = df['final_score']

# 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建线性回归模型

model = LinearRegression()

model.fit(X_train, y_train)

# 预测测试集

y_pred = model.predict(X_test)

# 评估模型

mse = mean_squared_error(y_test, y_pred)

print(f"均方误差: {mse:.2f}")

# 使用模型预测新学生的成绩

new_student = [[82, 78]]

predicted_final = model.predict(new_student)

print(f"预测最终成绩: {predicted_final[0]:.2f}")