Machine Learning vs Predictive Analytics: Know the difference

Artificial Intelligence (AI) has been trending in the technology realm for quite some time. AI is one such technology that has blessed us with improved computing and analysis of data, cloud-based services, and many such powerful applications. AI is helping to transform industries around the globe and enterprises are racing to adopt AI in their businesses to maximize their ROI.

Machine Learning and Predictive Analytics are two applications of AI that are quite often used interchangeably. They both can extract relevant insights through data processing at low operational costs. Although there is a strong relationship between the two, being centred around data processing, they are quite distinct concepts.

What is Machine Learning?

Machine Learning is an AI methodology where algorithms are given data which is then processed without predetermined rules. The algorithms use cognitive learning methods to program the systems, learning from their mistakes to improve future performance. More amount of data leads to better and accurate results as that refines its algorithm. 

Machine learning is of 2 types:

• Supervised, and
• Unsupervised machine learning

Supervised machine learning requires an operator to feed the training dataset to tell the machine what kind of output is desired. The fed data is labelled which gives information about the parameters of desired categories which the algorithm distinguishes. In unsupervised machine learning, no training data is provided. The algorithm analyzes data patterns or common elements from the data on its own.

What is Predictive Analytics?

Predictive Analytics is the analysis of both historical as well as existing external data to find patterns. It has existed long before AI and is an area of study rather than a specific technology. On the analytical spectrum, differentiating predictive analytics from closely related practice presents a better understanding of the field.

Main differences between Machine learning vs Predictive analytics:

• Predictive analytics is more static and less adaptive than machine learning as the pattern remains the same in its case.
• The statistical model for machine learning is updated automatically whereas, for predictive analytics, data scientists need to run the model manually many times.
• Machine learning is data-driven whereas predictive analytics is use case driven.
• Machine learning is a new generation technology that has better algorithms and massive amounts of data whereas predictive analytics is a study and not a specific technology.
• Machine learning plays a significant role in cybersecurity where predictive analytics is an indispensable asset in banks and Fintech companies.

Both these AI methodologies are increasingly being adopted by businesses due to their immense possibilities. It usually depends upon the businesses to decide whether to go for machine learning vs predictive analytics based on their project goals. No matter which methodology they chose, it is indisputable that implementing them can help businesses to tackle real-world problems in a better and efficient manner.