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This is the code repository for Data Forecasting and Segmentation Using Microsoft Excel, published by Packt.
Learn how to design and build cloud-based data transformation pipelines using AWS
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
le = LabelEncoder()
for i in bin _ cols:
churn _ data[i] = le.fit _ transform(churn _ data[i])
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
| Chapter | Software required | OS required |
|---|---|---|
| 1-13 | Microsoft Excel | Any OS |
| 1-13 | Jupyter notebook environment | Any OS |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Hands-On Machine Learning with Microsoft Excel 2019 [Packt] [Amazon]
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Forecasting Time Series Data with Facebook Prophet [Packt] [Amazon]
Fernando Roque has 24 years of experience in working with statistics for quality control and financial risk assessment of projects after planning, budgeting, and execution. In his work, Fernando applies Python k-means and time series machine learning algorithms, using Normalized Difference Vegetation Index (NDVI) drone images to find crop regions with more resilience to droughts. He also applies time series and k-means algorithms for supply chain management (logistics) and inventory planning for seasonal demand.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.