What is Predictive Analytics: Definition, Concepts, Tools and More

What is Predictive Analytics?

Predictive analytics is a significant analytical approach used by many firms to assess risk, forecast future business trends, and predict when maintenance is required. Data scientists use historical data as their source and utilize various regression models and machine learning techniques to detect patterns and trends in the data.

The basic goal of predictive analytics is to forecast what will happen in the future with a high degree of certainty. This distinguishes predictive analytics from descriptive analytics, which assists analysts in analyzing what has previously occurred, and prescriptive analytics, which uses optimization techniques to detect optimal solutions to address the trends revealed by predictive analytics.

So far we have discussed what is predictive analytics, next, let us understand its examples.

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Examples of Predictive Analytics

Predictive analytics is used in a wide variety of ways by companies worldwide. Adopters from diverse industries such as banking, healthcare, commerce, hospitality, pharmaceuticals, automotive, aerospace, and manufacturing get benefitted from the technology.

Here are a few examples of how businesses are using predictive analytics:

• Customer Service

Businesses may better estimate demand by utilizing advanced and effective analytics and business intelligence. Consider a hotel company that wants to estimate how many people will stay in a certain area this weekend so that they can guarantee they have adequate employees and resources to meet demand.

• Higher Education

Predictive analytics applications in higher education include enrollment management, fundraising, recruiting, and retention. Predictive analytics offers a significant advantage in each of these areas by offering intelligent insights that would otherwise be neglected.

1. A prediction algorithm can rate each student and tell administrators ways to serve students during the duration of their enrollment using data from a student's high school years.
2. Models can give crucial information to fundraisers regarding the optimal times and strategies for reaching out to prospective and current donors.

• Supply Chain

Forecasting is an important concern in manufacturing because it guarantees that resources in a supply chain are used optimally. Inventory management and the shop floor, for example, are critical spokes of the supply chain wheel that require accurate forecasts to function.

Predictive modeling is frequently used to clean and improve the data utilized for such estimates. Modeling guarantees that additional data, including data from customer-facing activities, may be consumed by the system, resulting in a more accurate prediction.

• Insurance

Insurance firms evaluate policy applicants to assess the chance of having to pay out for a future claim based on the existing risk pool of comparable policyholders, as well as previous occurrences that resulted in payments. Actuaries frequently utilize models that compare attributes to data about previous policyholders and claims.

• Software Testing

Predictive analytics can help you enhance your operations throughout the full software testing life cycle.

Simplify the process of interpreting massive volumes of data generated during software testing by using that data to model outcomes. You can keep your release schedule on track by monitoring timelines and utilizing predictive modeling to estimate how delays will affect the project. By identifying these difficulties and their causes, you will be able to make course corrections in individual areas before the entire project is delayed.

Predictive analytics can assess your clients' moods by researching social media and spotting trends, allowing you to anticipate any reaction before it occurs.

So far we discussed what is Predictive analytics and its examples. Moving forward, lets understand what are its analytics tools.

Predictive Analytics Tools

Predictive analytics tools use data to help you predict the future. Instead, it informs you of the probability of various scenarios. Knowing these possibilities might assist you in planning various parts of your business.

Predictive analytics is a subset of data analysis. Descriptive analytics, which helps you determine what your data represents, is another part of data analytics. Diagnostic analytics identify the root reasons for what has occurred. Prescriptive analytics is more similar to predictive analytics. This provides you with actionable advice for making better selections.

In other words, predictive analytics lies between data mining, which searches for patterns, and prescriptive analytics, which instructs you what to do with this knowledge. Below is a list of the most popular Predictive Analytics Tools used in the industry.

• SAS Advanced Analytics

SAS is the global leader in analytics, with a plethora of various predictive analytics products offered. The list is so broad that it may be difficult to determine which tool(s) you will require for your specific needs. In addition, the firm does not give upfront pricing, making it difficult to compare prices. Nonetheless, with so many different tools available, chances are SAS offers just what you want.

• IBM SPSS

IBM SPSS (Statistical Package for the Social Sciences) is a data modeling and statistics-based analytics program. The software can handle both organized and unstructured data. To meet any security and mobility requirements, this software is offered in the cloud, on-premises, or via hybrid deployment.

• RapidMiner Studio

RapidMiner Studio blends data preparation and analysis with unique business implementation. You may use this code optimal application to automate reporting based on time intervals or to have events trigger changes in your visualizations. 

Using the platform's 60+ native integrations, you may import your own data sets and export them to other programs. Extensions provide you more functionality, for example, anomaly detection, text processing, and web mining, but they may cost more than the basic membership fee.

• TIBCO Spotfire

TIBCO Spotfire includes a variety of tools for working with large data sets. Spotfire is simple enough for anybody to utilize when it comes to predictive analytics. Spotfire includes a feature known as one-click predictions. These are pre-programmed methods for classifying and clustering data.

It also displays relationships and forecasts. Spotfire features an attractive data display. It is always constantly reading data and updating in real-time. It is simple to create your apps for use with the platform. Spotfire's machine learning algorithms get a greater in-depth understanding.

• H2O

If you're looking for an open-source predictive analytics solution, H2O should be at the top of your list. It provides quick performance, low cost, superior features, and great flexibility. The H2O dashboard provides excellent visualization of data insights. 

This tool, however, is designed for experienced data scientists rather than citizen data scientists. If you've invested in training, this may be a useful tool.

So far we discussed what is Predictive analytics, examples and its types in detail. Moving forward, let’s understand what are its techniques

Predictive Analytics Techniques

Predictive analytics incorporates a variety of data analysis approaches, including data mining, machine learning, and others. The following are the techniques used in predictive analytics:

• Decision Trees

A decision tree is an analytics methodology based on Machine Learning that uses data mining algorithms to forecast the potential risks and benefits of undertaking certain options. It is a visual chart that resembles an upside-down tree that depicts the prospective result of a decision. When used for analytics, it can solve all forms of classification problems and answer difficult issues.

• Neural Networks

Neural networks are biologically inspired data processing systems that use historical and present data to forecast future values. Their architecture allows them to identify complicated connections buried in data in a way that replicates the pattern detecting systems of the human brain.

They are widely used for image recognition and patient diagnosis and comprise many layers that accept data (input layer), compute predictions (hidden layer), and provide output (output layer) in the form of a single prediction.

• Text Analytics

Text Analytics is used when a company wants to anticipate a numerical number. It is built on approaches from statistics, machine learning, and linguistics. It assists in predicting the themes of a document and analyzes words used in the supplied form.

• Regression Model

A regression method is crucial for the organization when it comes to estimating a numerical number, such as how long it will take a target audience to return to an airline reservation before purchasing, or how much money someone would spend on vehicle payments over a specific length of time.

That was all about what is predictive analytics.

In this write-up what is predictive analytics we discussed everything about the predictive analysis tools, concepts, types, and different techniques? Although Predictive Analytics has received some criticism for the fact that machines or algorithms cannot foretell the future, it is now commonly applied in practically every industry, and with more and more data, we can forecast future outcomes with relative certainty. This allows businesses and institutions to make well-informed decisions. Because it has numerous applications in every industry conceivable, mastering Predictive Analytics tools is essential for anybody interested in a career in Data Science or Business Analytics in particular.

What is Predictive Analytics

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