import pandas as pd
# 加载数据
data = pd.read_csv('student_data.csv')
# 数据清洗
data.dropna(inplace=True)
# 特征选择
features = data[['GPA', 'Attendance', 'CourseSelections']]
labels = data['Performance']
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from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
# 训练模型
model = LinearRegression()
model.fit(X_train, y_train)
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def generate_recommendation(student_data):
prediction = model.predict([student_data])
if prediction[0] > 80:
return "优秀"
elif prediction[0] > 60:
return "良好"
else:
return "需要改进"
# 示例调用
student_info = [3.5, 90, 5]
recommendation = generate_recommendation(student_info)
print(f"建议: {recommendation}")
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