Predictive Analytics: A Comprehensive Introduction

Last Updated on December 5, 2022

Predictive Analytics

Companies are currently flooded with data, which can range from log files to photos and video and is stored in various data repositories across an organisation. Data scientists employ deep learning and machine learning algorithms to detect patterns and forecast future events in order to obtain insights from this data. Neural networks, decision trees, and logistic and linear regression models are a few of these statistical methods. Some of these modelling methods build on earlier prediction learnings to derive new predictive insights.

What is Predictive Analytics?

Predictive analytics uses both current and historical data to forecast activity, behaviour, and trends. Statistical analytic methods, data queries, and machine learning algorithms are applied to data sets to develop predictive models that provide a score or numerical value to the likelihood that a specific action or event will occur.

It is a crucial discipline in the field of data analytics, which is an umbrella term for the application of quantitative methods and subject-matter expertise to extract meaning from data and provide basic insights into a company, the environment, health care, scientific research, and other fields of study. 

Predictive Analytics Models

These models are made to analyse historical data, identify patterns, spot trends, and then utilise that knowledge to forecast upcoming developments. Clustering, classification, and time series models are common predictive analytics models.

Clustering models

Models for clustering are a type of unsupervised learning. They classify data according to comparable qualities. The approach, for instance, can be used by an online store to categorise clients into groups that have similar characteristics and then create marketing plans for each group. K-means, mean-shift, density-based spatial clustering of applications with noise (DBSCAN), expectation-maximization (EM) clustering with Gaussian Mixture Models (GMM), and hierarchical clustering are examples of common clustering techniques.

Classification models

The category of supervised machine learning models includes classification models. These models describe relationships within a particular dataset and categorise data based on past data. This paradigm, for instance, might be applied to the segmentation process by grouping customers or prospects. As an alternative, it can also be used to provide binary responses to questions, such as true or false or yes or no; common applications for this include fraud detection and credit risk assessment. Logistic regression, decision trees, random forests, neural networks, and Naive Bayes are examples of categorization models.

Time series models

Time series models employ different data inputs at predetermined time intervals, such as daily, weekly, monthly, etc. When analysing data for seasonality, trends, and cyclical behaviour, it is common practice to display the dependent variable over time. This can help identify the requirement for particular model types and transformations. Time series models include the autoregressive (AR), moving average (MA), ARMA, and ARIMA types. A call centre, for instance, might use a time series model to predict how many calls it will get per hour at various times of the day.

Predictive Analytics Techniques

Data mining, machine learning, and other data analysis techniques are all incorporated into predictive analytics. Predictive analytics uses the approaches listed below:

Decision Trees

A decision tree is a machine learning-based analytics technique that forecasts the prospective risks and advantages of performing particular actions using data mining techniques. It is a graphic representation of the potential outcomes of a decision that looks like an upside-down tree. It can address complex questions and resolve all kinds of categorization challenges when utilised for analytics.

Neural Networks

By replicating how the human brain functions, neural networks were created as a type of predictive analytics. Artificial intelligence and pattern recognition are used in this model to handle complex data interactions. Use it when you have a number of challenges to overcome, such as when you have an excessive amount of data, when you lack the formula you need to assist you to identify a relationship between the inputs and outputs in your dataset, or when you need to make predictions rather than provide explanations.


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 inputs in the dataset is determined by this method. Regression can be used, for instance, to determine how a security’s performance may be influenced by the price and other important variables.

Predictive Analytics Tools

 Predictive analytics tools assist you in making future predictions with the use of data. Instead, it gives you information about the likelihood of alternative events. Understanding these alternatives could help you plan different aspects of your organisation.

SAS Advanced Analytics

The market leader in analytics, SAS offers a wide range of different predictive analytics products. It could be challenging to decide which tool(s) you’ll need for your particular needs because the list is so extensive. Additionally, it is challenging to compare prices because the company does not provide upfront pricing. But given the variety of tools available, it’s likely that SAS has what you need.


An analytics programme that uses statistics and data modelling is called IBM SPSS (Statistical Package for the Social Sciences). Both structured and unstructured data can be handled by the software. This software is available in the cloud, on-premises, or through a hybrid deployment to satisfy any security and mobility requirements.


H2O ought to be at the top of your list if you’re seeking an open-source predictive analytics solution. It offers rapid performance, affordable pricing, top-notch features, and excellent adaptability. Excellent data insights are visualised in the H2O dashboard.

But rather than being created for amateur data scientists, this tool is for seasoned data scientists. This can be a helpful tool if you’ve invested in training.

RapidMiner Studio

RapidMiner Studio combines business implementation with data preparation and analysis. To automate reporting based on time intervals or to have events cause changes to your visualisations, you can use this code-optimal programme.

You can import your own data sets and export them to different programmes using the platform’s more than 60 inbuilt integrations. Anomaly detection, text processing, and web mining are just a few of the additional features that extensions offer you, but they may cost more than the basic membership fee.

TIBCO Spotfire

There are many capabilities in TIBCO Spotfire for handling huge data volumes. When it comes to predictive analytics, Spotfire is easy enough for everyone to use. One-click prediction is a function available in Spotfire. These techniques for grouping and classifying data have already been programmed.

Relationships and forecasts are also displayed. Spotfire offers a lovely data presentation. It continuously updates in real-time while reading data. Making your own apps to utilise the platform is easy. The machine learning algorithms used by Spotfire gain deeper knowledge.

Why Predictive Analytics is Important

Predictive analytics is being used by businesses to find new opportunities and address challenging issues. Typical uses include:

Optimizing marketing campaigns

Predictive analytics is employed to forecast customer behaviour or purchases and to encourage cross-selling opportunities. Predictive models assist firms in luring in, keeping, and expanding their most valuable clients.

Improving operations

Predictive models are often used by businesses to forecast inventory and manage resources. Predictive analytics is used by airlines to determine ticket prices. In order to maximise occupancy and boost income, hotels make an effort to anticipate the number of guests for any particular night. Organizations are able to work more effectively thanks to predictive analytics.

Reducing risk

Credit ratings are a well-known use of predictive analytics that are used to determine a buyer’s propensity to default on transactions. A predictive model’s calculation of a person’s creditworthiness yields a number known as a credit score. The usage of insurance claims and collections falls under the category of risk.

Detecting fraud

Combining several analytics techniques helps increase pattern recognition and deter illicit activity. High-performance behavioural analytics monitors all network activity in real-time to look for anomalies that could point to fraud, zero-day vulnerabilities, or advanced persistent threats as cybersecurity concerns escalate.


In the field of healthcare, predictive analytics is used to track specific infections like sepsis as well as to identify and manage the care of people with chronic illnesses. To better understand the detection and management of sepsis, Geisinger Health mined medical information using predictive analytics. Based on the medical records of more than 10,000 patients who had previously been given a sepsis diagnosis, Geisinger developed a predictive model. Impressive predictions of patients with a high rate of survival were made by the model.

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