Which Is Better For Machine Learning R or Python?

Machine learning is not a single science. It comprises a blend of fields such as analysis, recognition, prediction and decision making. There are several open-source tools available for machine learning out of which R and Python are the most demanded or rather the most popular ones. The main difference between the two languages has been seen in the fields of analysis and data science.

Both the languages provide open source tools and support from a wide variety of libraries for machine learning but because of the high degree of robustness provided by the python packages such as Scikit-learn built on numpy and Scipy, Python is preferred more for machine learning. According to a recent survey, Python had an increment in its popularity and use from 53% to 69% within two years.

Several machine learning courses aim at delivering courses dedicated to R and Python. The question as to whether an individual should learn both languages depends highly on the field of application and interest of an individual. Both languages have highly efficient ecosystems for machine learning tasks.

The difference in popularity and use is because of the comfort of an individual with the programming language, interest and application needs. Also, job opportunities can be one of the deciding factors whether an individual should learn Python or R for machine learning.

Provided below is a comparison of Python and R which could help an individual decide whether they need to learn both languages.


R was developed by the statisticians primarily for analysis. The programming language is based on the mathematical calculations comprising machine learning and hence forms a really important part of the statistics involved in the project. Thus, a project which is largely dependent on statistics should use R as a programming language.


  • Highly suitable for data analysis and visualization.
  • Support from the libraries
  • Highly robust
  • Highly suited for exploratory work


  • Scarcity of expertise in the language due to low learning rates.
  • The algorithms in R comes primarily from the third parties and hence, it is not very consistent to build the models.


Python came into existence in the ’80s. Today, it forms a core of the machine learning operations being performed by Google. It has extended its roots in the field of artificial intelligence as well and is being widely used in almost every possible domains whether technical or non-technical.


  • In contrast to R which provides support for only statistics, machine learning has extended beyond just statistics.
  • Python unlike R provides a smooth learning curve and is more consistent than R.
  • Huge support from libraries such as numpy, pandas, OpenCV, sklearn, etc.
  • Simplicity in the syntax making it easy to learn the language.
  • Highly robust models and boosting techniques.


  • Less support for statistical models due to the non-availability of suitable packages.
  • Multithreading is Pyhton is not generally preferred as it is difficult to implement.

From the above comparison, it can be seen that both the languages having their advantages and disadvantages. But the key point that differentiates them is the use and library support. R and Python in machine learning have succeeded in their way. One has left footprints in the field of analytics while the other has emerged victorious in the field of data science.


To choose the right language, the right strategy is needed. For a person stepping into the industry as a fresher, Python is preferred as compared to R because of its simple syntax and ease of learning.

Also, if an individual is looking for a career in the field of data science they should go for Python as the programming language and if they want to handle the huge data-related tasks such as analysis and prediction making, no doubt that R is a better choice.

R is closely related to analysis and Python is closely tied to huge tasks such as object detection, disease prediction, computer vision and so on. Hence, we can conclude by saying that an individual needs to rightly assess their needs before choosing one of them and should master only one trade.

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