Data Forecasting and Segmentation Using Microsoft Excel - Helion
Tytuł oryginału: Data Forecasting and Segmentation Using Microsoft Excel
ISBN: 9781803235264
stron: 324, Format: ebook
Data wydania: 2022-05-27
Księgarnia: Helion
Cena książki: 116,10 zł (poprzednio: 129,00 zł)
Oszczędzasz: 10% (-12,90 zł)
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.
Osoby które kupowały "Data Forecasting and Segmentation Using Microsoft Excel", wybierały także:
- Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
- Ruby on Rails. Ćwiczenia 18,75 zł, (3,00 zł -84%)
- Przywództwo w świecie VUCA. Jak być skutecznym liderem w niepewnym środowisku 58,64 zł, (12,90 zł -78%)
- Scrum. O zwinnym zarządzaniu projektami. Wydanie II rozszerzone 58,64 zł, (12,90 zł -78%)
- Od hierarchii do turkusu, czyli jak zarządzać w XXI wieku 58,64 zł, (12,90 zł -78%)
Spis treści
Data Forecasting and Segmentation Using Microsoft Excel. Perform data grouping, linear predictions, and time series machine learning statistics without using code eBook -- spis treści
- 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