Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learning
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This book guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises 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 to find segments of your data that are impossible to see with other analyses. 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, you'll be able to use the classification algorithm to group data with different variable and train linear and time series models to perform predictions and forecasts based on past data.
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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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