Predictive Analytics

Predictive analytics is a technique to identify the likelihood of future events based on previous data using data, statistical algorithms, and machine learning approaches. The objective is to go beyond understanding what has occurred and provide the most accurate prediction of what will occur in the future.

Predictive analytics gives insight into the future in addition to studying what occurred and why. Predictive models construct (or train) a model that may be used to forecast values for different or new data based on previously observed outcomes. Companies use predictive analytics to detect dangers and opportunities from these trends.

• Predictive analytics uses data, statistical algorithms, and machine learning approaches to identify the likelihood of future events based on previous data.
• Any industry may decrease risks, enhance processes, and boost revenue with the technique of predictive analytics.
• The Minitab, KNIME analytics, R, orange, and Microsoft Open are a few popular tools for predictive analytics.
• Business managers utilize predictive analytics, as the name indicates, to foresee the future by studying and discovering trends in previous data using machine learning and statistical models.

Predictive analytics is a subcategory of advanced analytics that helps businesses make sense of future outcomes or the consequences of a choice. It utilizes mining data, historical numbers, statistics, and raw, current data to foresee the future. Any industry may decrease risks, enhance processes, and boost revenue with predictive analytics.

In the 1940s, when governments began utilizing the first computers, predictive analytics emerged. In the middle of the 1970s, predictive analysis expanded to include the topic of why something occurred in addition to what occurred. Even though it has existed for decades, the time has finally arrived for it to become a mainstream notion.

A few years ago, predictive analytics was the exclusive domain of enterprise-level firms since they could afford and interpret massive amounts of data from many sources. However, expanding software as a service providers and customer relationship management (CRM) analytics enables even tiny businesses to access predictive analytics. A crucial part is separating irrelevant or deceptive data that might affect the conclusions.

Let us have a look at the well-known predictive analytics methods.

Decision Tree

It categorizes data according to input parameters, outlining the decision-making process. The branches of a decision tree indicate options, while the leaves reflect classifications or judgments. This approach analyses the data to identify the variable that separates it into the most different logical groups. They effectively handle missing values and aid in selecting variables.

Regression- Linear And Logistic

These are typical statistical techniques that estimate variable relationships. It identifies key patterns in big data sets. For example, it is typically used to assess the influence of factors such as a price on asset movement. Multiple regression predicts the outcome using two or more independent variable. Logistic regression predicts unknown discrete variables using known variables.

Neural Networks

It is used to represent complex interactions. They are potent and adaptable. Its ability to manage nonlinear data interactions, becoming increasingly prevalent, is the source of its power. Typically, they validate the outcomes of regression and decision trees. Using pattern recognition and AI, neural networks "model" parameters visually. When there is an abundance of training data, neural networks perform well.

Let us have a look at the following predictive analytics examples for a better understanding of the concept:

Example #1

In all organizations, missed payments, irrespective of the industry, disturb the financial goals. Hence, ABC organization decided to use predictive analytics to segregate unpaid invoices to determine who will and will not pay their debts on time. As a result, ABC may better manage cash flow by anticipating which people or companies would likely miss their next payment. They can also alleviate the situation by issuing notifications to possible late payers.

The system for predictive analytics examines corporate or individual demographics, items purchased/used, historical payment history, customer service records, and any recent unfavorable occurrences.

Once ABC determined who was likely to pay their bills late, payment reminders were sent to them. Predictive analytics can suggest the optimal day and time to send reminders, as well as the optimal method of contact (for example, text message, email, or phone call). ABC also provided clients with other payment choices, such as deferred payment plans.

Example #2

An article by Economic Times describes how predictive analytics can be used in the healthcare system. As a result of applying machine learning to vast amounts of historical data, medical practitioners may make judgments based on evidence. For example, based on previous and current patient admission rates, technology may give hospitals real-time evaluation and analysis of staff performance. Predictive analysis can also handle hospital bed shortages and thereby prevent the problem from happening in the first place.

The article also highlighted how utilizing AI yields quick and accurate patient information and therapy suggestions. It lightens the load on healthcare personnel and offers a bright and hopeful future in healthcare delivery. Across the healthcare value chain, one can see a complete shift from a labor-intensive to a technology-driven approach. Big data is beginning to enhance human knowledge, helping clinicians to make quicker decisions that result in positive patient outcomes.

Machine learning algorithms are designed for proposing treatment plans for present patients based on their historical data and outcome. In addition, they can be trained to recognize warning indicators before a condition becomes severe. Treatments are more successful when diagnostic accuracy is enhanced. Moreover, predictive analytics is a significant tool for analyzing how different patients respond to the same medication.

In today's fast-paced business climate, many entrepreneurs find predictive analysis advantageous. Following are the popular predictive analytics tools.

Microsoft R Open

It prioritizes statistical analysis and data science. This application provides broad functionality, an adaptable interface, and optional multithreading. It is compatible with Mac OS, Windows OS, and Linux, so you don't need to acquire new devices. In addition, Microsoft R Open is available for free download, use, and sharing.

KNIME Analytics

This platform is an open-source, enterprise-level data analysis platform. It has around 2000 modules, powerful algorithms, integrated tools for performing various data operations, rapid deployment, scalable interface, and intuitive features. These tools enable you to recognize new opportunities inside your data, get insights, and predict business outcomes.

The Minitab®

This Statistical Software offers visualization, analysis, forecasting, and improved analytics. As a result, everyone in an organization can access simple analytic tools, regardless of statistical experience or location. Since the 1960s, Minitab has aided organizations and institutions in identifying trends, resolving problems, and gaining valuable insights from data. In addition, Minitab's user-friendliness makes predictive analytics simpler than ever before.

Orange

It is a data visualization toolkit that is free source. Visual programming interface for qualitative exploratory data analysis like predictive analytics and interactive visualization. It provides a clean, open-source platform for all areas of science. The curriculum offers modules for most machine learning techniques, bioinformatics, text mining, and data analytics.

R

 It is a programming language for statistical computation and graphics, maintained by the R Core Team and the R Foundation for Statistical Computing. R is a language for data management, computing, and visual display used by data miners, bioinformaticians, and statisticians for data analysis and software creation. Users can also develop R packages as an open-source platform.

Predictive analytics identifies future results, whereas prescriptive analytics examines these possibilities and identifies further alternatives. Both types of analytics may assist any small firm in remaining competitive.

Business managers utilize predictive analytics, as the name indicates, to anticipate the future by studying and discovering trends in previous data using machine learning and statistical models. The insights from this data are used to make more informed decisions and enhance results. For instance, banks evaluate the data of mortgage applicants to determine whether they would be low- or high-risk customers. It's a sort of data analytics that incorporates information about available resources, potential scenarios, historical performance, present performance, and other aspects to recommend a course of action or plan.

Prescriptive analytics goes farther than predictive analytics by recommending various prescribed actions and the probable repercussions of each choice. In addition, it may offer the optimal course of action for each predetermined outcome. During each trip, a self-driving car, for example, does millions of calculations and drives autonomously. This is a demonstration of prescriptive analytics. Prescriptive analytics is an abstract kind of data analytics that enables businesses to investigate "what-if" scenarios and predict outcomes based on many inputs. Using prescriptive analytics, for instance, airlines determine the price of flight tickets depending on several potential criteria.

This has been a guide to predictive analytics and its definition. Here, we discuss methods, tools, examples, and predictive vs. prescriptive analysis. You can learn more from the following articles - 

• Probability Density Function
• Multicollinearity
• Descriptive Statistics