Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics withou
暫譯: 使用 Microsoft Excel 進行數據預測與分群：執行數據分組、線性預測及時間序列機器學習統計

Roque, Fernando

Data Forecasting and Segmentation Using Microsoft Excel: Perform data grouping, linear predictions, and time series machine learning statistics withou

商品描述

Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learning

Key Features

- Segment data, regression predictions, and time series forecasts without writing any code
- Group multiple variables with K-means using Excel plugin without programming
- Build, validate, and predict with a multiple linear regression model and time series forecasts

Book Description

Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises covered in this book use real-life data from Kaggle, such as demand for seasonal air tickets and credit card fraud detection.

You'll learn how to apply the grouping K-means algorithm, which helps you find segments of your data that are impossible to see with other analyses, such as business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you'll be able to detect outliers that could indicate possible fraud or a bad function in network packets.

By the end of this Microsoft Excel book, you'll be able to use the classification algorithm to group data with different variables. You'll also be able to train linear and time series models to perform predictions and forecasts based on past data.

What you will learn

- Understand why machine learning is important for classifying data segmentation
- Focus on basic statistics tests for regression variable dependency
- Test time series autocorrelation to build a useful forecast
- Use Excel add-ins to run K-means without programming
- Analyze segment outliers for possible data anomalies and fraud
- Build, train, and validate multiple regression models and time series forecasts

Who this book is for

This book is for data and business analysts as well as data science professionals. MIS, finance, and auditing professionals working with MS Excel will also find this book beneficial.

商品描述(中文翻譯)

執行時間序列預測、線性預測和數據分段，無需編寫代碼的 Excel 機器學習

主要特點

- 無需編寫任何代碼即可進行數據分段、回歸預測和時間序列預測
- 使用 Excel 插件通過 K-means 將多個變量分組，無需編程
- 構建、驗證和預測多元線性回歸模型和時間序列預測

書籍描述

《使用 Microsoft Excel 進行數據預測和分段》指導您通過基本統計來測試您的數據是否可以用於執行回歸預測和時間序列預測。本書中的練習使用來自 Kaggle 的真實數據，例如季節性機票需求和信用卡詐騙檢測。

您將學習如何應用分組 K-means 算法，這有助於您找到其他分析（如商業智能 (BI) 和樞紐分析）無法看到的數據分段。通過分析 K-means 返回的組，您將能夠檢測可能表明詐騙或網絡數據包中存在不良功能的異常值。

在這本 Microsoft Excel 書籍結束時，您將能夠使用分類算法對具有不同變量的數據進行分組。您還將能夠訓練線性和時間序列模型，以根據過去數據進行預測和預測。

您將學到的內容

- 理解為什麼機器學習對於分類數據分段很重要
- 專注於回歸變量依賴性的基本統計測試
- 測試時間序列自相關以構建有用的預測
- 使用 Excel 附加元件運行 K-means 而無需編程
- 分析分段異常值以查找可能的數據異常和詐騙
- 構建、訓練和驗證多元回歸模型和時間序列預測

本書適合誰

本書適合數據和商業分析師以及數據科學專業人士。從事 MS Excel 的 MIS、財務和審計專業人士也會發現本書有益。

作者簡介

Fernando Roque has 24 years of experience working with statistics for quality control and financial risk assessment of projects since planning, budgeting, and execution. Fernando works applying python k-means and time-series machine-learning algorithms using vegetable activity (NDVI) drones’ images to find the crop´s region with more resilience to droughts. He also applies time-series and k-means for supply chain management (logistics) and inventory planning for seasonal demand.

作者簡介(中文翻譯)

Fernando Roque 擁有 24 年的經驗，專注於統計學在品質控制和專案的財務風險評估方面，涵蓋規劃、預算編制和執行階段。Fernando 使用 Python 的 k-means 和時間序列機器學習演算法，應用於植物活動指數 (NDVI) 無人機影像，以找出對乾旱更具韌性的作物區域。他還將時間序列和 k-means 應用於供應鏈管理（物流）和季節性需求的庫存規劃。

目錄大綱

1. Understanding Data Segmentation
2. Applying Linear Regression
3. What is Time Series?
4. An Introduction to Data Grouping
5. Finding the Optimal Number of Single Variable Groups
6. Finding the Optimal Number of Multi-Variable Groups
7. Analyzing Outliers for Data Anomalies
8. Finding the Relationship between Variables
9. Building, Training, and Validating a Linear Model
10. Building, Training, and Validating a Multiple Regression Model
11. Testing Data for Time Series Compliance
12. Working with Time Series Using the Centered Moving Average and a Trending Component
13. Training, Validating, and Running the Model

目錄大綱(中文翻譯)

1. Understanding Data Segmentation

2. Applying Linear Regression

3. What is Time Series?

4. An Introduction to Data Grouping

5. Finding the Optimal Number of Single Variable Groups

6. Finding the Optimal Number of Multi-Variable Groups

7. Analyzing Outliers for Data Anomalies

8. Finding the Relationship between Variables

9. Building, Training, and Validating a Linear Model

10. Building, Training, and Validating a Multiple Regression Model

11. Testing Data for Time Series Compliance

12. Working with Time Series Using the Centered Moving Average and a Trending Component

13. Training, Validating, and Running the Model

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