The Differences Between Machine Learning And Predictive Analytics

The Differences Between Machine Learning And Predictive Analytics

From hosting annual AI summits to forming AI-based regulations, tech heads across sections seem to have woken up to the AI phenomenon. The world of AI is an ad-infinitum encapsulating and branching out in more than ways that the human mind can comprehend.

Companies have been shaping up for the AI-led future for quite some time by creating a work process that includes artificial intelligence innately. The arrival of ML and predictive analytics has been just the right vehicle to take the AI movement forward.


What is Predictive Analytics? 


Make-no-mistake, both ML and predictive analytics have their independent existence that demands individual explanations. Let us begin with the intricacies of Predictive Analytics. 

Predictive Analytics is the blend of statistical tools, mathematical modeling, and machine learning employed to obtain future trends from the historical and current data. This explains why machine learning and predictive analytics are used interchangeably.


What is Machine Learning?


Machine learning is the process of feeding structured and unstructured data to the system to analyze and make predictions. All of it happens with minimal human intervention. Here computer algorithms are at work, which fine-tunes itself based on the data fed into the system.

Machine learning is a subset of AI and is employed in combination with mathematical modelling for predictive analytics. This is where we draw the line between machine and predictive analytics.

How Does Predictive Analytics Work? 


Predictive Analytics is largely based on predictive modelling, where the predictive models are basically algorithms that are trained using various sets of data to perfect predictive accuracy. Predictive models can be further classified into two disinfect categories: Classification model and Regression models. 

The former deals with the prediction of class, while the latter covers the prediction for numbers use cases. Some of the elite institutes like Havard in their business review have stated that for a successful predictive analytics strategy, the following three things must be present.    

Data: It is the descriptive part of predictive analytics. Here, the relevant data is stored and accessible in a way that creates a visual appeal. Think of it as collecting all the data, and storing it in the backend only to be classified and segmented into the dash.

Statistics: This is the stage where we move from what are the important data-points in the data set to what is happening. Here, with the use of regression analysis to find the relation between two variables.

Assumption: The role of assumption is two-fold, for one, it gives a base of comparison between the past and future. All the predictive analysis that is done involves some form of assumption. Thus, it is crucial to take note of the assumptions for each scenario.

A Short Summary of the Above Difference Between Predictive And ML 


Though the term machine learning and predictive analytics may seem to be an overlapping concept, there are a couple of inherent differences that need to be understood. 

First and firstmost, predictive analytics is heavily dependent on statistics and human intervention alongside machine learning algorithms to create a predictive model. On the other hand, machine learning algorithms are pure work of learning programs for machines that use training data. 

Secondly, machine learning algorithms automatically update themselves as per the training data, while in the case of predictive analytics fresh data needs to be introduced for initiating updates in predictive models.

The Most Widely Used Predictive Models 


Effective predictive models are the outcome of robust predictive analytics software solutions. which has the trained algorithms to create new models. These algorithms dig deep into the data and gauge patterns or relationships to create a predictive model. There are three common forms of predictive models. 

Decision Trees:


Decision trees are a popular choice of methods opted for predictive modelling. In case of a decision tree, the algorithms work on the root data and segment them into branches based on specific conditions. The decision tree consists of nodes which is basically the data that has been tested for, it then branches out to create leaf indicating final results or decisions. 

Regression (Linear and Logistic)


As mentioned earlier in the importance of the statistics section, it is one of the best tools to establish the relationship between the variables. It becomes all the more useful when data is huge, and finding relations becomes tough. Regression analysis is among the few statistical tools that will never be rendered futile. 


Neural Networks

Creating a neural network is basically a way to mimic the cognitive abilities of the human brain, you have heard of neural networks while reading about AI. But in the case of predictive modeling, these neural networks come in real handy. They establish relationships between random data sets with unknown variables. On the surface, it helps figure out the link between complex data sets.   


Other Classifiers


Time Series Algorithms:


Time series algorithms are basically used to map the values of data sequentially over a period of time to predict a future value based on the time-related trends.  

Clustering Algorithms: 


Clustering Algorithms are basically used to group the homogeneous data type into a single category from a series of heterogeneous data sets. 

Outlier Detection Algorithms:

The outlier Algorithm is used to remove the noise that is unrelated to data elements from large data sets to arrive at a cleaner data sample.  

Ensemble Models: 


As the name suggests ensemble model basically refers to the use of multiple ML algorithms to enhance the accuracy of predictive models. 

Factor Analysis:


With Factor analysis, we can create models to find the variance and covariance between the given sample data and the hidden factors.

Naïve Bayes: 


Naive Bayes classifier helps to assign a class or category to a data set based on the features, all of it is done with the application of probability.  

Support Vector Machine:


SVM is based on a supervised form of ML, where associated learning algorithms are used to make sense of the data set, and find patterns.  


Applications of Predictive Analytics and Machine Learning


With tons of unstructured data being generated, the need for a technology-driven solution to manage and interpret data has given rise to the ubiquitous application of ML and PA. 

Some of the common applications of PA can be seen in the field of finance, consumer goods, heavy industries, health care, and many more such allied fields. On the other hand application of ML can be seen in the field of image & speech recognition, traffic prediction, self-driving cars. 

In this blog, we will cover three key areas where we see the use of both ML and PA. These examples highlight the importance of using both the solutions in tandem. 

Banking and Financial Services


The role of Predictive Analytics in the financial sector is to assess the risk associated with various forms of investments, be it money or share market. The use of ML in the banking realm is to mitigate fraud.  



Security-based applications showcase the importance of combined use of ML and PA by detecting potential security threats which can cost companies huge losses. Some of the security experts and institutes use the given technologies to detect loopholes and strengthen security standards. 



In the case of the retail sector Ml and PA are used to forecast the demand in the near future and create stocks accordingly by maneuvering production strategies. It also helps to gauge the consumer preference pattern and use predictive models to find some sort of relation with historical data of sales. 


Core Types of Machine Learning 


Supervised Learning


As the name suggests, supervised learning is a controlled process wherein there is a setup ready to get the desired outcome. The data introduced in the system here is labeled, meaning the data is structured in one form or the other.

For instance, you included a set of data regarding the number of students wearing a different colored uniform that represents a different school. So, the training data fed into the system reads as – if the student is wearing red then he is from Alphonso school.

Now, when the labeled data is fed into the system, and you enter a sample of different students with different colored uniforms, the system algorithms will predict accurately from which school they belong.

Unsupervised Learning


Unsupervised learning is the exact flip of the supervised form, here no labeled data is fed into the system. The computer algorithms have to make sense of the training data through patterns. Here, the data pushed into the system is not a wide sample size in terms of varying nature. For instance, you can see the unstructured for men & women, or apple & oranges.

Consider a situation where the input data consists of images of dogs and cats. Now, the data is not labeled, so the machine algorithms try to find a pattern that can be a differing factor between dogs and cats. This is how it gives a final output when enquired about which one is a cat and which of them is a dog.



One might argue that ML is the future and the labor-intensive approach of predictive analytics will be sidetracked as we move ahead. The flexibility of machine learning might force people to draw such a biased conclusion.

But the bottom line is that both branches of AI have the potential to shape the future of companies in their unique ways. To cut the long story short, the former is a means to end for the latter with inherent differences that you need to look for.