Predictive Analytics VS Machine Learning

machine_learning

Nowadays, with the enormous amount of data we generate in the era of digital transformation (Big Data), new technologies are emerging, not always well known, which are difficult to place in the panorama of tools to which we are accustomed. The challenge in these cases is always the same: to know what we can expect from each of these technological approaches and when they are the solution to the problems at hand.

One of the most common confusing cases is when we talk about Predictive Analytics and Machine Learning. Both concepts share similarities and the same objective, but some nuances differentiate them. In this article, we will try to provide some light and better understand what each of them consists of, and their possible applications within the industry.

Machine Learning

Machine Learning (ML) is a discipline of Artificial Intelligence (AI) that arises as a result of a paradigm shift. The focus shifts from programmatic to autonomous learning (let the machine learn from the data), and comes hand in hand with an increase in both computing power and the amount of data available (and accelerated even more as a result of the emergence of the Internet of Things, or IoT).

ML encompasses a wide variety of methods that help machines learn, always through data and without the need to program them for it. This variety of methods can be broken down into three main groups: supervised learning (training with labelled data, i.e., we know in advance how we want them to serve as an example to the ML solution), unsupervised learning (machines look for patterns through the similarities they find in the data) and reinforcement learning (the machine learns in a specific environment through trial and error cycles).

This range of approaches allows us to tackle a wide variety of problems and therefore it has caused ML to be mandatory as a tool for all companies that wish to compete successfully in their sector, as well as to optimise any area of their operations.

Some examples of ML applied to specific problems are:

  • Product classification: One of the great applications of ML solutions to the industrial sector, as it allows to automate two very important processes such as quality control and the separation of products according to type. Through artificial vision techniques, it is possible to you obtain the type of product we have and in turn be able to detect if it has any defect. A great example of this can be found on any production conveyor belt where a wide variety of products arrive, for which it is necessary to make sure that they do not have any visible defects and then classify them for later shipment or sale.

  • Recommenders: Widely used by those companies that provide services or sell products directly to the end customer (Netflix, Inditex, Telefónica, El Corte Inglés and countless others), their main objective is to personalise their offer. Such is the importance that companies give to this use case that, in communities of professionals like Kaggle, companies launch competitions with important prizes for those who help them solve these types of problems. In the following link you will go into more detail about the recommenders: Recommendation Systems
Competition organised by H&M on Kaggle
Competition organised by H&M on Kaggle
  • Diagnosis through medical images: Through Deep Learning techniques (a branch of ML that uses neural networks) we can be able to detect and diagnose a wide variety of diseases through images, coulding be X-ray plates, ultrasound scans or simple photographs. This type of techniques, which are quite new and in full expansion today, will have a huge social impact in the coming years.

Predictive Analytics

Predictive analysis (PA, henceforth) has been with us for decades and can be defined as a mathematical tool that mainly applies statistical and modelling techniques intending to anticipate the temporal evolution of some indicators of interest from historical data.

Some of the PA’s best-known techniques are Naïve Bayes, classification and regression trees, ordinary least squares, and logistic regression. Some examples of PA use are:

  • Sales Forecast: Applying somo PA algorithms (e.g. regressions) can obtain excellent results that help companies to know the estimated sales and thus improve the organisation of the available stock.
  • Market Price Prediction: Applying complex mathematical techniques, such as Fourier series or different types of regression, about time series with stock exchange prices, we can make predictions of their evolution. This use case can also be applied to all types of prices, or even other interest ratios such as unemployment or detected cases of COVID-19.
Unemployment prediction

Conclusions

For all the above, and although the objective with which they are applied is very similar, using the terms Machine Learning and Predictive Analysis interchangeably is not correct, since there are relevant differences. By way of summary:

  • The main use of Machine Learning is to design algorithms and models that improve the results of their predictions, while predictive models are an advanced form of basic descriptive analysis.For this reason, Predictive Analytics could be classified as a subset or application of Machine Learning.

  • Machine Learning is a technology while Predictive Analytics is a study and not a technology as such.

  • While Machine Learning is able to learn from its past mistakes, Predictive Analytics comes down to prediction based on historical data.

Finally, although ML and PA are different concepts, these complement each other very well. The combination of the different mathematical techniques of the PA, together with the possibility of learning of the machines generated by the ML lead to very important and interesting results for any type of industry.

Want to know more about the differences between predictive analytics and Machine Learning?

Leave a Reply

Your email address will not be published. Required fields are marked *