AInalytics delivers predictive analytics using machine learning, deep learning models. It is the process of using data to forecast future outcomes. The process uses data analysis, machine learning, artificial intelligence, and statistical models to find patterns that might predict future behavior.
AInalytics help organizations to hardness their dataflows to deliver predictive analytics which uses data to solve complex questions. The machine learning process uses data analytics, artificial intelligence and statistical models to find patterns that might predict future behaviors. The organization’s data is trained and tested from historical or real-time events to forecast trends and behaviors in seconds, days, or years.
In general, there are two types of predictive analytics models: classification and regression models. Classification models attempt to put data objects (such as customers or potential outcomes) into one category or another. For instance, if a retailer has a lot of data on different types of customers, they may try to predict what types of customers will be receptive to market emails. Regression models try to predict continuous data, such as how much revenue that customer will generate during their relationship with the company.