Data Analytics. Predictive Analytics

Predictive analytics is an area of analytics sphere which predicts future results based on existing and available data, statistical modelling, data retrieval technologies, and machine learning.

When we talk about predictive analytics, we tend to imagine that it predicts future results based on the available data, statistical modelling, data retrieval technologies and machine learning. It is a common practice of IT companies to use this type of analytics to, for example, identify potential risks and prospective opportunities.

Let’s take a look at the predictive analysis history. Powerful, fast, and – what is most significant – relatively inexpensive computers have made it possible to exploit the full potential of predictive analytics.

Its history dates back to the 1940s. At those times, the government shown great interest in linear and computer modelling. «Monte Carlo model» or manual analysis was used during the nuclear weapon development.

With the development of computers in the 1950s, nonlinear programming and computer heuristics continued to develop, and R. Johnson invented a hard disk drive (HDD) that would later form the basis of other innovative solutions: magnetic disks and database management systems (DBMS).

With the rise of the stock market, the forecasting analyst was used in the 1970s and 1980s to predict the stock price. Edgar F. Cod, an outstanding scientist, has laid the theoretical foundation for relational databases and their management systems, including the Application Programming Interface (API) and the Structured Query Language (SQL).

By the 1990s and 2000s, extensive databases were being used to personalize and optimize digital ways of working with customers and to promote marketing.

1. Classification models


Such models are used to analyze historical data (data are grouped and sorted by category). In enterprises, such models are used to solve complex problems and to find new potential opportunities, to approve applications and to determine the probability of non-payment, to detect fraudulent transactions.

2. Clustering models


In this version, the data are divided into groups based on common criteria. Such data can be sorted into hard and soft clusters. Rigid clustering is a direct categorization, while mild clustering includes data admissibility. Used in marketing to plan strategy.

3. Predictive models


When we need to use a number of input variables to predict the future quantitative value of objects using historical numerical data, this category should be used. It will ensure the highest universality within a multitude of industries. For example, based on past experience in retail stores, it is possible to forecast the expected number of visitors or sales for any of the following weeks and to plan accordingly.

4. Anomaly detection models


These models are particularly popular in production as well as in the financial sphere. They help to identify possible fraudulent activities or inefficiencies.
It is quite obvious that anomaly detection models define in one or more data sets those data that are outside the norm and based on this help to draw conclusions.

5. Time models


Anomalous data are used in time models, it’s significant that the primary entrance criteria is time. This is used to provide valuable insights for future periods. They have the advantage of being able to measure changes in specific indicators over time, considering the selected variables, such as weather or previous sales. They are usually applied in combination with several forecasts, allowing better prediction of development or more effective strategy for future action.

Thus, we see that a forecasting analyst can be useful both in the production sphere and in the sociocultural and continuous development thereof can greatly facilitate the work of many.

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