Preface
This book is about giving you basic statistical knowledge to work with machine learning using complex algorithms to classify data, such as the K-means method. You will use an included add-in for Excel to practice the concepts of grouping statistics without the need for a deep programming background in the R language or Python.
The book covers three topics of machine learning:
- Data segmentation
- Linear regression
- Forecasts with time series
Data segmentation has many practical applications because it allows applying different strategies depending on the segment data ranges. It has applications in marketing and inventory rotation to act accordingly to the location and season of the sales.
The linear regression statistical concepts in this book will help you to explore whether the variables that we are using are useful to build a predictive model.
The time series model helps to do a forecast depending on the different seasons of the year. It has applications in inventory planning to allocate the correct quantities of products and avoid stalled cash flow in the warehouses. The time series depends on statistical tests to see whether the present values depend on the past, so they are useful to forecast the future.
