Machine learning (ML) has become a buzzword in the technology community. Though machine learning is now a part of many business conversations, it is often misunderstood and misinterpreted. People get confused between different data science concepts. For instance, I came across a popular discussion on Quora on the difference between predictive analytics and machine learning. It is actually not surprising for people to get confused considering the complex technology machine learning is.
Read more: What is Cluster Analysis in Machine Learning
In this blog on machine learning vs predictive analytics, we are going to understand what each of these technologies means and what is the difference between them (if any).
Download our in-depth guide on big data analytics which covers everything from meaning to trends in this field:
What is Machine Learning?
Machine learning is a technology used to enable computers to analyze a set of data and learn from the insights gathered. By using complex algorithms an artificial neural network is simulated that enables machines to classify, interpret and understand data and then use the insights for solving problems or making predictions. Once programmed a machine learning algorithm improves and enriches itself based on the data fed to it.
Popular examples of machine learning include classification models, chatbots, recommendation engines, etc. It is a new branch of programming and is considered an evolving technology. There are many use cases of machine learning in business and there are more to come.
Read More: 5 Trends in Machine Learning in 2017
What is Predictive Analytics?
Predictive analytics refers to the field of data science that involves making predictions about future events. By using different statistical techniques like linear regression an algorithm analyzes past data to make predictions about the future events. A common example of predictive analytics is credit scoring. Based on the financial behavior and economic conditions of an individual, predictions are made about his capability of paying his dues in future. A predictive analytics model is based on 3 key elements:
Historical Data - To make predictions about future, a machine needs past data. Predictions are made about the future based on the trends in this data. Thus, accuracy and depth of data work as the primary influencer for predictions.
Statistical Modelling - Many complex statistical algorithms are used to make sense out of past data and make predictions about the future. Regression analysis, in particular, linear regression is the most common approach used to understand correlations between different variable factors to the one fixed factor.
Assumptions - Predictive analytics is based on the simple assumption that future trends in data are based on past trends and the level of correlation between varied factors will continue to stay true in future, just like in present.
Read More: 8 Ways to Leverage Predictive Analytics for Business
Predictive Analytics vs Machine Learning:
As a matter of fact, we cannot logically differentiate between the two fields. Predictive analytics is an application of machine learning. Machine learning is used to enable a program to analyze data, understand correlations and make use of insights to solve problems and/or enrich data. Thus, machine learning is the core principle behind predictive analytics. You can consider predictive analytics as a subset of machine learning.
