Predictive Analytics: The Key Algorithm for Statistical Techniques, Data Mining, and Predictive Modeling

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As a subset of data analytics, predictive analytics looks to the past for clues about the future, using tools like statistical modeling and machine learning to extrapolate those clues. The science of predictive analytics can generate future insights with a significant degree of precision. With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future.

Predictive analytics has captured the support of a wide range of organizations, with a global market projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent between 2016 and 2022, according to a 2017 report issued by Zion Market Research.

What is Predictive Analytics?

Predictive Analytics is an analysis technique to predict future outcomes and performance by using statistics and modeling techniques. It observes current and historical data patterns to find out if those data patterns are likely to turn up again. Using predictive analytics techniques, you can gain insights to forecast activities, predict customers’ behaviors and trends and make informed decisions.

Types of Predictive Analytics Models

Decision trees, neural networks, and regression are the three most often utilized methods in predictive analytics. Details on each of these are provided below.

Decision Trees

Decision trees may be helpful if you want to understand how someone makes decisions. This model divides the data into groups according to factors such as price or market capitalization. It resembles a tree, complete with individual branches and leaves, as its name suggests. Branches represent the options, while individual leaves stand for a specific choice.

This is the simplest model because it is the most straightforward to comprehend and analyze. They are also incredibly helpful when you have to make a decision quickly.


The most often used model in statistical analysis is this one. Use it when there is a linear relationship between the inputs and you want to find patterns in vast data sets. The formula describing the relationship between all the information in the dataset is determined by this method. Regression can be used, for instance, to determine how the price and other important variables may influence a security’s performance.

Neural Networks

As a sort of this analytical technique, neural networks were created by modeling the functioning of the human brain. This model can handle complex data interactions using artificial intelligence and pattern recognition. Use it if you need to overcome several obstacles, such as when you have an excessive amount of data available, when you lack the necessary formula to help you identify a relationship between the inputs and outputs in your dataset, or when you need to make predictions rather than provide an explanation.

Why Do You Need Predictive Analytics?

According to the latest report, 52% of companies have embraced advanced and predictive analytics for business transformation. This innovative technology helps them with the following benefits:

  • Forecasting: It’s necessary in the case of manufacturing. It ensures the optimal use of resources in a supply chain. Predictive modeling ensures that the system can ingest more data from customer-facing operations to provide a more accurate prediction. It is often used to clean and optimize the data quality for such forecasts.
  • Credit: Credit Score uses this advanced analytics technique extensively. Suppose a consumer or business applies for credit. Then, the applicant’s credit history data and the borrower’s credit record data with similar characteristics are utilized to determine the risk that the applicant might fail to carry out any credit extension.
  • Underwriting: This analytics technique plays a crucial role in underwriting. Insurance firms investigate potential policyholders to determine the chance of having to pay out for a future claim based on the present risk pool of comparable policyholders and previous occurrences that have led to payouts. In addition, actuaries frequently utilize predictive models that consider traits compared to information in prior policyholders and claims.
  • Marketing: The person in this field observes consumers’ reactions to the overall economy before planning a new campaign. This technique of analytics is very helpful in this case to determine if the plan will work or not. In addition, it can help to understand whether consumers will prefer to purchase the new product.
  • Predictive maintenance and monitoring: Predictive modeling uses IoT data to predict equipment breakdowns. Manufacturers use sensor data to indicate when maintenance and repair work should be done to prevent problems by attaching sensors to factory floor machinery and mechatronic products, such as automobiles. In this way, predictive maintenance is being used extensively. This advanced analytics technique is essential for monitoring oil and gas pipelines, drilling rigs, windmill farms, and various other industrial IoT installations.

Predictive analytics implementation is not a walk in the park, but any company can do it if they are dedicated to the process and willing to put in the resources. Starting with a small pilot project in a crucial business area is a great way to control initial investment and shorten the time required to see a return on that investment. After a model is implemented, it typically needs little maintenance to keep churning out useful insights year after year.

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piyush gupta

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