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Data Forecasting and Segmentation Using Microsoft Excel. Perform data grouping, linear predictions, and time series machine learning statistics without using code

(ebook) (audiobook) (audiobook) Książka w języku angielskim
Data Forecasting and Segmentation Using Microsoft Excel. Perform data grouping, linear predictions, and time series machine learning statistics without using code Fernando Roque - okladka książki

Data Forecasting and Segmentation Using Microsoft Excel. Perform data grouping, linear predictions, and time series machine learning statistics without using code Fernando Roque - okladka książki

Data Forecasting and Segmentation Using Microsoft Excel. Perform data grouping, linear predictions, and time series machine learning statistics without using code Fernando Roque - audiobook MP3

Data Forecasting and Segmentation Using Microsoft Excel. Perform data grouping, linear predictions, and time series machine learning statistics without using code Fernando Roque - audiobook CD

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324
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Do przechowalni

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.

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O autorze książki

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.

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