Machine Learning vs Predictive Modelling

Differences Between Machine Learning and Predictive Modelling

Machine learning is an area of computer science which uses cognitive learning methods to program their systems without the need of being explicitly programmed. In other words, those machines are well known to grow better with experience.
Machine learning is related to other mathematical techniques and also with data mining which encompasses terms such as supervised and unsupervised learning.
Predictive modeling, on the other hand, is a mathematical technique which uses statistics for prediction. It aims to work upon the provided information to reach an end conclusion after an event has been triggered.

In a nutshell, when it comes to data analytics, machine learning is a methodology which is used to devise and generate complex algorithms and models which lend themselves to a prediction. This is popularly known as predictive analysis in commercial use which is used by researchers, engineers, data scientists and other analysts to make decisions and provide results and uncover the hidden insights by making use of historical learning.
In this post, we are going to study in detail about the differences.

Head to Head Comparison Between Machine Learning vs Predictive Modelling (Infographics)

Below is the top 8 Comparison between Machine Learning and Predictive Modelling:

Key Differences between Machine Learning and Predictive Modelling

Below are the lists of points, describe the key differences between Machine Learning and Predictive Modelling:

Machine learning is an AI technique where the algorithms are given data and are asked to process without a predetermined set of rules and regulations whereas Predictive analysis is the analysis of historical data as well as existing external data to find patterns and behaviors.
Machine learning algorithms are trained to learn from their past mistakes to improve future performance whereas predictive makes informed predictions based upon historical data about future events only
Machine learning is a new generation technology which works on better algorithms and massive amounts of data whereas predictive analysis are the study and not a particular technology which existed long before Machine learning came into existence. Alan Turing had already made used of this technique to decode the messages during world war II.
Related practices and learning techniques for machine learning include Supervised and unsupervised learning while for predictive analysis it is Descriptive analysis, Diagnostic analysis, Predictive analysis, Prescriptive analysis, etc.
Once our machine learning model is trained and tested for a relatively smaller dataset, then the same method can be applied to hidden data. The data effectively need not be biased as it would result in bad decision making. In the case of predictive analysis, data is useful when it is complete, accurate and substantial. Data quality needs to be taken care of when data is ingested initially. Organizations use this to predict forecasts, consumer behaviors and make rational decisions based on their findings. A success case will surely result in boosting business and firm’s revenues.