以下是按照您提供的步骤编写的代码示例：
import pandas as pd
from apyori import apriori
# 读取 Excel 文件
df = pd.read_excel("your_file.xlsx")
# 显示数据预览
print(df.head())
# 获取交易项集
transactions = df.groupby("OrderID")["CategoryName"].apply(list)
# 进行关联规则挖掘
min_support = 0.1 # 最小支持度
min_confidence = 0.5 # 最小置信度
min_lift = 1.0 # 最小提升度
results = list(apriori(transactions, min_support=min_support, min_confidence=min_confidence, min_lift=min_lift))
# 提取并显示关联规则
rules_data = []
for result in results:
support = result.support
for item in result.items_ordered:
base_items = ', '.join(item.base_items)
add_items = ', '.join(item.added_items)
confidence = item.confidence
lift = item.lift
rules_data.append([support, confidence, lift, base_items, add_items])
rules_df = pd.DataFrame(rules_data, columns=["Support", "Confidence", "Lift", "Base Items", "Added Items"])
print(rules_df.tail(8))
请确保将 "your_file.xlsx" 替换为您的实际文件路径。此代码将根据给定的最小支持度、置信度和提升度执行 Apriori 关联规则挖掘，并从结果中提取支持度、置信度、提升度、基础项和附加项，存储在 DataFrame 中并显示最后8行的结果。
内容由零声教学AI助手提供，问题来源于学员提问
