Predictive Analysis involves predicting an event of the future or evoking a possible scenario from the past It is closely related to the disciplines of deep learning and data mining. Predictive Analytics solutions are used to extract, clean, deploy and analyze sets of data patterns that are used to predict specific outcomes. The predictive models are built using a set of traits on one facet, against the corresponding metrics to be recorded at specific time intervals. For example, predictive modeling could assess seasonal sales for a specific brand or product during certain months or weeks, based on figures from the previous cycle or year. Standard Predictive Analytics solutions involve specific techniques as part of best practices Define the project scope, input, problem statement, deliverables, efforts spent, and the output Data collection involving mining of data from multiple sources, including customer touch-points Creation of hypotheses and tests against the standard statistical models to validate assumptions Predict likely outcome or solution with an evaluation of different sources of data on likely events Deploying Predictive Analytics solutions for real-time analytics around daily business operations Continuously evaluate and monitor the systems to assess their behavior and degree of reliability There are various types of models leveraged by Predictive Analytics solutions: The linear relationship model for predictions relies on the regression equation as a function of linear variables that drive analysis. The model-fit is accomplished with an appropriate distribution of attributes to the set of possible outcomes. From a mathematical viewpoint, discrete ordered choice models are rather feasible and are mostly employed for hypothetical rather than real-time scenarios. As certain assumptions of multiple linear regression theory are no longer valid; as such, the discrete ordered models play a vital role in evaluating two or more alternatives. Advanced Capabilities Predictive Analytics can gauge trends with a time-series model, wherein data points are captured over time-periods, which are used to determine outcomes based on time-intervals. As standard regression techniques do not fit in a regular regression model, time-series forecasting is applied in certain scenarios. Predictive Analytics solutions enable calculation of probable success rates of machines and automated processes. The models gather information and generate output in the form of data patterns. The output data can answer questions about performance and subsequent customer feedback. The AIBridge ML team is trained to accomplish and deliver cutting-edge solutions in an agile environment, along with requisite expertise to build and deploy creative Predictive Analytics solutions. Related Posts 08Feb 2023 AIBridge ML attended HYSEA 30th edition annual summit & awards 2023 26Aug 2022 AIBridge ML conducted general health check-up camp 15Jul 2022 AIBridge ML conducted an eye check-up camp Latest Blogs 22Jun 2022 What are the differences between Machine Learning and Artificial Intelligence? 21Jun 2022 Artificial Intelligence in Manufacturing Industry 11Jan 2022 Document Automation and Its Benefits
Predictive Analysis involves predicting an event of the future or evoking a possible scenario from the past It is closely related to the disciplines of deep learning and data mining. Predictive Analytics solutions are used to extract, clean, deploy and analyze sets of data patterns that are used to predict specific outcomes. The predictive models are built using a set of traits on one facet, against the corresponding metrics to be recorded at specific time intervals. For example, predictive modeling could assess seasonal sales for a specific brand or product during certain months or weeks, based on figures from the previous cycle or year. Standard Predictive Analytics solutions involve specific techniques as part of best practices Define the project scope, input, problem statement, deliverables, efforts spent, and the output Data collection involving mining of data from multiple sources, including customer touch-points Creation of hypotheses and tests against the standard statistical models to validate assumptions Predict likely outcome or solution with an evaluation of different sources of data on likely events Deploying Predictive Analytics solutions for real-time analytics around daily business operations Continuously evaluate and monitor the systems to assess their behavior and degree of reliability There are various types of models leveraged by Predictive Analytics solutions: The linear relationship model for predictions relies on the regression equation as a function of linear variables that drive analysis. The model-fit is accomplished with an appropriate distribution of attributes to the set of possible outcomes. From a mathematical viewpoint, discrete ordered choice models are rather feasible and are mostly employed for hypothetical rather than real-time scenarios. As certain assumptions of multiple linear regression theory are no longer valid; as such, the discrete ordered models play a vital role in evaluating two or more alternatives. Advanced Capabilities Predictive Analytics can gauge trends with a time-series model, wherein data points are captured over time-periods, which are used to determine outcomes based on time-intervals. As standard regression techniques do not fit in a regular regression model, time-series forecasting is applied in certain scenarios. Predictive Analytics solutions enable calculation of probable success rates of machines and automated processes. The models gather information and generate output in the form of data patterns. The output data can answer questions about performance and subsequent customer feedback. The AIBridge ML team is trained to accomplish and deliver cutting-edge solutions in an agile environment, along with requisite expertise to build and deploy creative Predictive Analytics solutions.