The machine learns an algorithm which is capable of predicting future behaviour. There is software which is solely aimed at developing Data Science projects using Machine Learning processes.
One of these platforms is RapidMiner, a company which is solely and exclusively dedicated to this field and which, for the last seven years, has managed to stay at the top, leading its sector, and placed in the best position within the famous Gartner Group table.
Let’s go into the platform in a bit more detail.
The software has more than 1,500 algorithms enabling a huge number of Data Science tasks to be carried out. Predictions, process optimisations, classifications by similarity, search for relations amongst data or finding anomalies or faults are just some of the tasks which can be carried out with RapidMiner. All of this has a single focus – obtaining rapid, effective and efficient results for the business. Also, because of the graphic environment worked with, it becomes a simple, accessible platform without the need to write any codes.
Advantages of using RapidMiner
It is a simple tool which allows:
Users, other than the usual ones, can understand and work with the tool, widening the range of experts.
Acceleration of the development process, compared to the most commonly used tools, such as Python or R.
It can be used in Big Data or Small Data environments, simplifying decision making for businesses with the use of Data Science.
It is an intuitive, agile tool.
Simple creation of automated batches or pipelines for process execution.
The possibility of interaction with other tools, with Real Time response.
Inclusion of results from other applications.
There is other software relating to Machine Learning, but RapidMiner has two strong points which make it stand out from the competition:
Its community: highly active, easy to find help in any field and extremely well structured.
Simplicity and a broad capacity of utilities: You have a wide range of capabilities, both for the data cleaning process and for modelling and publishing the results.
At Mind we work with this tool because of all the benefits it brings to our Data Scientists and our products.