7 Major Areas of SAS Innovations For Machine Learning
SAS is the market leader in data analytics; they provide data analytics tool that is used in data science machine learning and Business Intelligence applications. SAS software use SAS programming to allow management of a variety of data from different sources. SAS programming language is a fourth generation computer programming language used for statistical analysis of raw data.
SAS provides a range of machine learning algorithms for classification, predictions, model creations, assessment, comparison, etc. Machine learning automates analytical model building for faster response and insights. Following are seven major areas of SAS innovations for machine learning.
Internet of Things Analytics
There is a vast variety of data streaming from a different type of devices when machines communicate with each other. This diversity of data adds complexity to the entire process of data analytics in real time. Machine learning algorithms implemented by SAS programming can automatically create models that continuously learn and evolve. SAS IoT analytics leverages data from a wide range of devices for generating early warnings by predicting faults, failing devices, maintenance requirements and accidents along with personalized recommendations.
Big Data Analytics
SAS has rendered customized products for Hadoop ecosystem to expand the reach of their customers to big data in Hadoop clusters. SAS software supports applications of machine learning algorithms to develop and deploy models for operationalized analytics. SAS programming enables distributed processing for statistics, text mining, data mining and machine learning directly in Hadoop environments. With the in-memory structure of SAS software, algorithms operate on Big Data in Hadoop cluster directly at very high speeds, and this allows for results to be returned quickly.
Data from social media, comments, blogs, chats, messages, etc. is unstructured and most of the times contain informal or mixed language. Natural language Processing (NLP) algorithms implemented via SAS programming derive patterns and meaning from language to establish rules that can be leveraged across the platform for both structured and unstructured data. SAS provides sentiment analysis studio to create rule-based analysis models using NLP capabilities for higher precision.
To retain customer loyalty SAS provides a way of smart marketing strategies backed by predictive analysis and machine learning. For live predictions and recommendations for customer personalization feedback based reinforcement learning algorithms can be very useful. SAS’s recommendation engine includes best features from logistic regression classifier and naïve bayes classifier to analyze customer’s behavioral data and generate highly personalized recommendations.
SAS provides solutions for detection and proactive protection from fraud via advanced analytics and supervised and unsupervised machine learning. SAS’ proprietary Self-Organizing Neural Network Arboretum (SONNA) modeling technique improves model performance by capturing account behavior through a collection of optimized neural networks. Fraud detection performance improves as models become more robust by detecting changes in customer behavior over time.
SAS cybersecurity approach is based on analysis of real-time streaming data to identify suspicious device behavior on a network. SAS makes use of semi-supervised machine learning algorithm to develop initial detection models that focus on specific statistical anomalies. Through unsupervised machine learning, SAS cybersecurity automatically assigns a risk score to each device based on multiple behavioral attributes when compared to its peer-group behavior and historical measures.
Business rules encode the policies and procedures of business operation, as the business grows new rules are generated, and slowly the system becomes too big and complicated to be handled manually. Supervised machine learning models are commonly used for business rule generation based on either historical data or managed data. Decision tree models also called Classification And Regression Trees (CART), allows finding all possible consequence of a decision for rule creation. Analytical scorecard model is used mostly in credit scoring and retail marketing type fields. Market basket model works by analyzing conditions frequently occurring together and their resulting action to produce set of rules.