AWS-Business-Analysis-and-Prediction

Build machine learning-powered business intelligence analyses using Amazon QuickSight

Amazon QuickSight ML Insights uses AWS-proven ML and natural language capabilities to help you gain deeper insights from your data. These powerful, out-of-the-box features make it easy to discover hidden trends and outliers, identify key business drivers, and perform powerful what-if analysis and forecasting with no technical or ML experience.

Steps

1.Set up and import data into Amazon QuickSight

The NYC Taxi data set is in an S3 bucket. To import S3 data into Amazon QuickSight, use a manifest file.

2.Create an ML-powered visual to forecast the future demand for taxis

After you import the data set into Amazon QuickSight SPICE, you can start creating analyses and visuals. Your goal is to create an ML-powered visual to forecast the future demand for taxis. For more information, see Forecasting and Creating What-If Scenarios with Amazon Quicksight.

3.Generate an ML-powered insight to detect anomalies in the data set

In Amazon QuickSight, you can add insights, autonarratives, and ML-powered anomaly detection to your analyses without ML expertise or knowledge. Amazon QuickSight generates suggested insights and autonarratives automatically, but for ML-powered anomaly detection, you need to perform additional steps. For more information, see Using ML-Powered Anomaly Detection.