SIA enables users to get hidden insights from their data. It eases the woes of data analysts, data scientists, and business professionals by simplifying their tasks. It empowers data analysts to efficiently carry out data preparation task. SIA enhances the predictive power of ML model allowing data scientists to focus on deriving business insights. It gives an insight-driven approach that enables professionals to improve business processes.
SIA allows you to import structured and unstructured data into the system from different data sources. Types of data you can upload in SIA can be in the form of spreadsheets, CSV files, and many others.
Data cleansing helps you focus more on the areas where data needs more attention. Cleaning involves removing data that might distort the analysis. SIA allows you to easily remove the unwanted data, and sort and standardize the format.
SIA helps to quickly identify variances and standardize the format for continuous integration, deployment along with stringent data security and privacy, preparing dynamic data for further analysis, and automating data recognition as well as structure.
Feature engineering helps to increase the predictive power of the learning algorithm. SIA enables users to identify business values required to create intelligence by carrying data operations in HDFS.
Classification feature provided by SIA deals with defining sets of observations and adding new observations in respective stacks depending on the parameters which allows users to identify which category the new set of data belongs to.
Empowering predictive analytics with artificial intelligence, users will be able to view prescriptive insights. And based on that, they will be able to forecast desired parameters. SIA will enable users to select previous models to run algorithms on current datasets to get similar results.
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