What is Predictive Analytics? : Step-By-Step Process with REAL-TIME Examples

• In this article you will learn:
• 1.Introduction.
• 2.Predictive analytics: what are they?
• 3.Examples of Predictive Analytics.
• 4.Predictive Analytics Tools.
• 5.Predictive Analytics Techniques.
• 6.Conclusion.

Introduction:

Google Trends indicates that over the past five years interest in predictive analytics has steadily grown. Business intelligence and predictive analytics commonly referred to as advanced analytics are becoming more and more related. But are the two related and if so what benefits can firms expect from combining their business intelligence and predictive analytics efforts?

Predictive analytics: what are they?

A lot of businesses employ predictive analytics as a crucial analytical technique to evaluate risks estimate market trends and anticipate maintenance needs. To find patterns and trends in data data scientists take historical data as their source and employ various regression models and machine learning approaches.To accurately anticipate what will happen in the future is the fundamental aim of predictive analytics. This separates predictive analytics from prescriptive analytics which use optimization techniques to find the best solutions to address trends revealed by a predictive analytics and descriptive analytics which aids analysts in assessing what has already happened.

Various Predictive Analytics Examples:

A wide range of applications for predictive analytics are employed by businesses worldwide. Technology benefits adopters from a variety of industries, including finance, healthcare, commerce, hospitality, pharmaceuticals, automotive, aerospace and manufacturing.Following are some instances of how companies are utilising predictive analytics:

Consumer Assistance:

By using effective and sophisticated analytics as well as business information businesses may more accurately predict demand. Consider a hotel chain that needs to determine how many guests will be staying in a certain location this weekend in order to ensure that they have enough staff and resources to fulfil demand.

University Education:

Applications for predictive analytics in higher education include recruitment, retention, fundraising and enrollment management. Each of these sectors benefits significantly from predictive analytics since it offers insightful information that would otherwise be overlooked.

1. Using information from the student’s high school years a prediction algorithm can grade each student and inform administrators of the best methods to serve them throughout their enrollment.

2. Models may provide critical information to fundraisers on the best times and approaches for contacting both existing and potential donors.

Supply Chain:

Supply Chain Forecasting is a key topic in manufacturing because it ensures that resources in a supply chain are utilised as efficiently as possible. For example a shop floor and inventory management are crucial spokes of the supply chain wheel that require precise estimates to function.Data that is used for these estimations is constantly cleaned and improved using predictive modelling. When a system is modelled it is guaranteed that more data including data from customer-facing actions will be ingested leading to a more precise prediction.

The term “insurance”:

Insurance companies review policy applicants to determine the likelihood that they would be required to make payments for future claims based on an existing risk pool of similar policyholders as well as prior occurrences that led to the payments. Actuaries commonly make use of models that contrast characteristics with information on past policyholders and claims.

Software Evaluation:

Predictive analytics can assist to improve operations across the whole software testing life cycle.Simplify the process of analysing large amounts of data collected during software testing by applying that data to model results. Can keep a release schedule on pace by tracking deadlines and using predictive modelling to forecast how delays will impact the project. By recognising these complexes and their causes we will be able to correct course in particular areas before the entire project is delayed.Predictive analytics may measure the moods of clients by investigating social media and finding trends allowing any reaction to be predicted before it occurs.

Tools for Predictive Analytics:

Predictive analytics tools employ data to forecast the future. Instead it informs on the likelihood of certain outcomes. Knowing about these possibilities may help in the planning of many aspects of a firm.Data analysis is a subset of predictive analytics. Another component of data analytics is descriptive analytics, which aids in determining what data represents. Diagnostic analytics identifies the underlying causes of what has transpired. Prescriptive analytics resembles predictive analytics more. This gives concrete suggestions for making better choices.In other words predictive analytics sits between between data mining which looks for patterns and prescriptive analytics which tells you what to do with this knowledge. The following is a list of the most well-known Predictive Analytics Tools utilised in an industry:

SAS Advanced Analytics (SAS):

SAS is a worldwide analytics company offering a wide range of predictive analytics technologies. The list is so extensive that determining which tool(s) will be required for a certain purpose may be difficult. Furthermore the organisation does not provide upfront pricing making price comparison difficult. Nonetheless with so many different tools accessible chances are SAS has just what you’re looking for.

IBM SPSS Statistics:

IBM SPSS is a tool for data modelling and statistics-based analytics. The programme can deal with both structured and unstructured data. This software is available in the cloud on-premises or as a hybrid solution to fulfil any security and mobility needs.

RapidMiner Studio:

• RapidMiner Studio combines data preparation and analysis with a one-of-a-kind business implementation. This code may be used in an ideal application to automate reporting based on time intervals or to have events trigger changes in visuals.
• You may import and export your own data sets using the platform’s 60+ native connectors. Extensions provide additional capabilities, such as anomaly detection, text processing and web mining although they may cost more than the standard membership price.

TIBCO Spotfire:

• TIBCO Spotfire is a software application developed by TIBCO.
• TIBCO Spotfire comes with a number of tools for working with massive data volumes. When it comes to predictive analytics Spotfire is easy enough for anybody to use. Spotfire has a function called as one-click predictions. These are pre-programmed data classification and clustering procedures.
• It also shows correlations and projections. Spotfire has an appealing data presentation. It is continually reading data and updating in real time. It is easy to design apps for usage with a platform. Spotfire’s machine learning algorithms get a better understanding.

H2O:

• H2O should be at the top of your list if you’re seeking for an open-source predictive analytics solution. It offers speedy performance, low cost, exceptional features and a high degree of flexibility. The H2O dashboard provides an outstanding visual representation of data insights.
• This tool on the other hand is intended for seasoned data scientists rather than citizen data scientists. If you invest in training this might be a beneficial tool.

Techniques for Predictive Analytics:

Predictive analytics encompasses a wide range of data analysis methodologies such as data mining machine learning and others. Predictive analytics employs the following techniques:

Trees of Decision:

A decision tree is an analytics approach based on Machine Learning that forecasts probable risks and rewards of pursuing particular alternatives using data mining algorithms. It is a visual chart that resembles an upside-down tree that displays the potential outcome of a decision. When used for analytics, it can solve all types of categorization difficulties and provide answers to complicated questions.

Neural Networks:

Neural networks are biologically inspired data processing systems that estimate future values using past and current data. Their architecture enables them to find complex relationships hidden in data in a way that mimics the pattern detection mechanisms of the human brain:

• They are commonly used for image recognition and patient diagnosis, and they include multiple layers that take data (input layer) calculate predictions (hidden layer) and output (output layer) in the form of a single prediction.
• Text Analytics is a term used to describe the study of text.
• When a corporation wants to predict a numerical figure it uses text analytics. It is based on statistical machine learning and language methodologies. It aids with the prediction of document themes and analyses words used in a specified form.

Modeling of Regression:

When it comes to projecting numerical numbers such as how long it would take a target audience to return to an airline reservation before purchasing or how much money someone would spend on auto payments over a given amount of time a regression approach is critical for a company.

Conclusion:

Are all aspects of predictive analytics—including tools, ideas, types and different techniques—discussed here? Predictive Analytics is used often in almost every business and with more and more data it is able to estimate future events with a certain degree of accuracy, despite some criticism that computers or algorithms cannot predict the future. This enables organisations and enterprises to take well-informed decisions.