Big Data predictive analytics is a section of data mining that deals with extracting information from data and applying it to foretell trends and behavior patterns. Usually, the unknown event of interest is in the future; however, big data predictive analytics can be used in any variety of unknown whether it is placed in the past, present or future. For instance, identifying suspects after a crime has been committed, or credit card fraud as it happens. The essence of big data predictive analytics relies on capturing partnerships between explanatory variables and the envisioned variables from past experiences and applying them to foretell the hidden result. It is best to observe that the accuracy and simplicity of results will depend greatly on the amount of data analysis and the quality of assumptions.
Extensively, the term big data predictive analytics is used to denote predictive modeling, scoring data with predictive models, and predicting. However, a lot of people consider it as equivalent to related analytical disciplines, which include decision modeling and descriptive modeling. These disciplines also entail robust data analysis, and are commonly used in business for segmentation and for making decisions but have several applications and the statistical techniques.
Here are the types of big data predictive analytics
Decision models describe the link between all the elements of a decision — the identified data including the outcome of predictive models, the decision, and the forecast outcome of the decision — so as to predict the outcome of decisions involving several variables. These models can be considered in optimization, increasing certain outcomes while reducing others. Decision models are often used to improve decision logic or a set of business limits that will bring about the desired action for every buyer.
Descriptive models quantify relationships in data in such a way that is often accustomed to distinguish buyers or prospective customers into groups. As opposed to predictive models that concentrate on forecasting a single buyer behavior. Descriptive models identify numerous relationships between customers or products. Unlike predictive models, these models do not rank customers by their possibility of taking a particular action. Instead, descriptive models can be applied, for instance, to categorize buyers by their product preferences and life stage. Descriptive modeling tools can be used to develop further models that can pose a significant number of personalized agents and make predictions.
Predictive models are models of the relation between the specific efficiency of a unit in a sample and one or more identified characteristics or advantages of the unit. The purpose of the model is to analyze the occurrence that a similar unit in another sample will exhibit the specific performance. This category entails models in many areas, such as marketing, where they seek out great data models to respond to questions concerning customer performance, or fraud detection models.
There are a lot of tools obtainable in the marketplace that helps with the execution of big data predictive analytics. These range from those that demand little user sophistication to those that are made for the expert practitioner. The distinction between these tools is usually in the level of choices and heavy data lifting allowed.
Here are some notable open source big data predictive analytic tools include:
- Apache Mahou
- GNU Octave
Some notable commercial big data predictive analytic tools include:
- Axibase Time Series Database
- Angoss Knowledge STUDIO
- Alpine Data Labs
- Revolution Analytics
- Oracle Advanced Analytics
- BIRT Analytics