地球资源数据云——数据资源详情
餐厅订单
该数据集《Restaurant Orders》主要用于监督学习任务，数据形态以表格为主。 题目说明：A beginner - friendly synthetic dataset of 500 restaurant orders dataset. 任务类型：表格监督学习。 建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：restaurant_orders.csv。 Description: This synthetic dataset contains 500 restaurant order records for beginners to practice data analysis and visualization. Generated using Python’s faker library, it contains no real customer information. Columns: Order ID, Customer Name, Food Item, Category, Quantity, Price, Payment Method, Order Time. Use Cases: EDA, data cleaning, visualization, and SQL practice. Explore trends like top - selling items, busiest order times, and preferred payment methods.
摘要概览
该数据集《Restaurant Orders》主要用于监督学习任务，数据形态以表格为主。 题目说明：A beginner - friendly synthetic dataset of 500 restaurant orders dataset.
任务类型：表格监督学习。
建议流程：先做缺失值/异常值处理与特征编码，再比较逻辑回归、随机森林、XGBoost。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：restaurant_orders.csv。
Description: This synthetic dataset contains 500 restaurant order records for beginners to practice data analysis and visualization. Generated using Python’s faker library, it contains no real customer information.
Columns:
Order ID, Customer Name, Food Item, Category, Quantity, Price, Payment Method, Order Time.
Use Cases: EDA, data cleaning, visualization, and SQL practice. Explore trends like top - selling items, busiest order times, and preferred payment methods.