The SAS Data Science Blog
Advanced analytics from SAS data scientists
NeurIPS 2022 allowed researchers and practitioners to share progress and brainstorm new ideas for advancing machine learning and its related fields.
Time series data is widely used in various fields, such as finance, economics, and engineering. One of the key challenges when working with time series data is detecting level shifts. A level shift occurs when the time series’ mean and/or variance changes abruptly. These shifts can significantly impact the analysis and forecasting of the time series and must be detected and handled properly.
SAS' Kirk Swilley and Tom Sabo showcase how you can use perform text analysis on minimal structured narrative data to spot patterns of possible human trafficking.
SAS' Sylvia Kabisa shows you how an online media company might use SAS to offer targeted discounts through personalized pricing.
SAS' Marinela Profi and Sophia Rowland elaborate on IDC including SAS among the leading platform providers for Machine Learning Operations.
Anuja Nagpal and Yonglin Zhu of SAS R&D reveal how, MLPA – without any code and within a given timeframe – finds an effective pipeline for a data set after applying data preprocessing, feature engineering and modeling with hyperparameter tuning.