In this data-driven world, predictive analytics put us on firm footing. It allows us to draw valuable insights and make rational decisions. Predictive analytics uses data, statistical algorithms and ML techniques to identify the future outcomes based on historical data. The aim is to see what the future would be like based on past data.

It is a process that uses data analysis, ML, AI and statistical models to identify patterns which predict future behaviour.

In short, predictive analytics is a technology to make predictions about what is unknown in the future.

There is wider interest observed in this field in the last five years. Another name for predictive analytics is advanced analytics. It has been linked to business intelligence.

If assesses risk, business trends and future maintenance requirements. In data science, they use various regression models and ML techniques to do this.

Predictive analytics lend a certainty to future, and this distinguishes itself from descriptive analytics.

It is useful in demand forecasting, production planning, insurance claims, software testing life cycle.

The following tools are well-entrenched in this digital age to do predictive analytics.

DataRobot

It is an automated ML platform used in predictive analysis. It enables data scientists to build predictive models. There are pre-built templates in it.

IBM Watson Studio

It is a comprehensive platform offering an array of tools for predictive analytics and data science. ML models construction and deployment could be done using its AutoAI feature. It has been integrated to IBM Cloud. It makes it scalable and flexible for all types of business — big, medium or small.

SAS Analytics

It has been around for quite some time. It keeps on evolving. It shows advanced analytics capabilities. It is useful in ML, model building and intelligence.

Tableau

We have already examined Tableau in detail in previous write-up. It is used for visualization. Its capacities have been expanded to cover predictive analytics. It has added features such as Explain Data and Ask Data. It makes available analytics to non-technical users. It has been integrated to cloud storage and ML libraries.

Google Cloud AI Platform

Google Cloud ML as well as cloud infrastructure. There is a suit of tools for Data Scientists. Model building becomes automatic. Being scalable, it is an ideal choice for organizations to leverage predictive analytics.

Some other predictive analytics tools are IBM SPSS (Statistical Package for Social Sciences), RapidMiner Studio, TIBCO Spotfire, H2O.

Some techniques of predictive analytics are decision trees, neural networks, text analytics, regression.