Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By : Fernando Roque
Book Image

Data Forecasting and Segmentation Using Microsoft Excel

By: Fernando Roque

Overview of this book

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.
Table of Contents (19 chapters)
Part 1 – An Introduction to Machine Learning Functions
Part 2 – Grouping Data to Find Segments and Outliers
Part 3 – Simple and Multiple Linear Regression Analysis
Part 4 – Predicting Values with Time Series


Here are the answers to the preceding questions:

  1. The following are the two steps:
    1. Review the data chart and decide whether the past has an influence on the present.
    2. Confirm the influence using the Durbin-Watson test.
  2. The Durbin-Watson test uses the regression errors' fluctuation to see whether the past has an influence over present data.
  3. The distance from the medium moving average to the Y value. In this example, the Y value is the quarterly car sales.
  4. The season and the regression trend value.
  5. Here are two elements of time-series data:
    1. A season component
    2. A trend component