# 导入必要的库
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# 模拟学生学习数据
data = {
'HoursStudied': [1, 2, 3, 4, 5],
'TestScores': [50, 60, 70, 80, 90]
}
df = pd.DataFrame(data)
# 分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(
df[['HoursStudied']], df['TestScores'], test_size=0.2, random_state=42)
# 训练模型
model = RandomForestClassifier()
model.fit(X_train, y_train)
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# 预测新数据
new_data = pd.DataFrame({'HoursStudied': [2.5]})
prediction = model.predict(new_data)
print(f"Predicted Test Score: {prediction[0]}")
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本文通过对话展示了如何利用Python和机器学习构建智慧校园系统，重点介绍了AI在学生学习数据分析和个性化建议中的应用。