地球资源数据云——数据资源详情

增强型披萨销售数据（2024–2025）

发布时间：2026-03-17 14:31:42资源ID：2031896703874273282资源类型：免费

该数据集《Enhanced Pizza Sales Data (2024–2025)》主要用于回归/预测任务，数据形态以时序/信号为主。 题目说明：Detailed sales data from a pizza business including toppings, size, quantity, an 任务类型：时序/信号回归/预测。 建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。 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：未检测到标准 CSV，可优先查看目录中的索引或说明文件。 This is a realistic and structured pizza sales dataset covering the time span from 2024 to 2025. Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you. What’s Inside? The dataset contains rich details from a pizza business including: Order Dates & Times Pizza Names & Categories (Veg, Non - Veg, Classic, Gourmet, etc.) Sizes (Small, Medium, Large, XL) Prices Order Quantities Customer Preferences & Trends It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau. Why Use This Dataset? This dataset is ideal for:

增强型披萨销售数据（2024–2025）

摘要概览

该数据集《Enhanced Pizza Sales Data (2024–2025)》主要用于回归/预测任务，数据形态以时序/信号为主。 题目说明：Detailed sales data from a pizza business including toppings, size, quantity, an

任务类型：时序/信号回归/预测。

建议流程：先做去噪和窗口化，再比较树模型与 1D CNN/LSTM 等时序基线。

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：未检测到标准 CSV，可优先查看目录中的索引或说明文件。

This is a realistic and structured pizza sales dataset covering the time span from 2024 to 2025. Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you.

What’s Inside? The dataset contains rich details from a pizza business including:

Order Dates & Times Pizza Names & Categories (Veg, Non - Veg, Classic, Gourmet, etc.) Sizes (Small, Medium, Large, XL) Prices Order Quantities Customer Preferences & Trends

It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau.

Why Use This Dataset? This dataset is ideal for: