ML, AI Algorithms Boosts Predictive Analytics Models

By CIOReview | Monday, July 15, 2019

ML and AI-based algorithms can not only optimize the businesses but also equip them with new statistical patterns and trends which form the backbone of predictive analytics.

FREMONT, CA: Predictive analytics involves the usage of statistics, data mining, and modeling to make predictions over the upcoming trends and disruptions. The process requires a deep analysis of a large set of discrete data to gain an overview of the bigger picture. Artificial Intelligence (AI) and its subset Machine Learning (ML) are inherently designed to facilitate the above procedures, thereby emerging as the most sought out technology for predictive analytics.

Though 75 percent of the leaders consider growth as the leading source of value from analytics, only 60 percent of those have predictive analytics capabilities within their business processes. The primary factor that is limiting the businesses is the lack of clarity over the tools that need to be installed for best returns. ML and AI-based algorithms can not only optimize the businesses but also equip them with new statistical patterns and trends which form the backbone of predictive analytics.

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Predictive Analytics with ML and AI Algorithms

Predictive analytics is governed using predictive modeling, which is more of an approach than a process. ML and predictive analytics complement each other, which can be applied to understand and predict the behaviors of a customer or possible changes in the market. The predictive models can be classified under Regression models that predict a number and Classification models that predict class membership. The models are then equipped with AI algorithms that perform data mining and statistical analysis to determine the patterns and trends.

Here are some of the popular predictive models:

•    Decision Trees

Decision trees present a powerful format of multiple variable analyses. The algorithms identify various ways of segregating data into branch-like segments. The categorization of data into subsets is based on the types of input variables depicting someone’s path of decisions.

•    Regression

Regression is one of the widely adopted models in statistics. It analyses estimate relationships among variables, highlighting the critical patterns in diverse data sets and their relationship with each other.

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•    Neural Networks

Inspired by the operation of neurons in the human brain, neural networks, or artificial neural networks are a type of deep learning technologies. They are especially suited to analyze large data sets to solve complex pattern recognition problems.