From both Programming Languages R and Python: Which is best for Machine Learning?

R has the steepest learning curve, so it becomes necessary to learn to code. It is a low - level language, so simple procedures can take longer codes. On the other hand, Python is known for its simplicity.

Data Handling

R computations are limited to the amount of RAM on 32 - bit PC

Graphical Capabilities

Graphical Capabilities of R is advanced

Advancement in tools

Both the languages are open in nature and contributions. So in the latest developments, there are more chances of error.

Speed

R slow and it is designed to so to make data analysis and statistics easier. But this makes life on a computer more difficult. We need to define how implementations work. Also, R is poorly written.

Job scope

Python and R are good for start-ups and companies looking for cost efficiencies.

Customer Service support

None of these have this facility. In the time of any trouble, you are on your own.

Let us Discuss some pros and cons of both Python and R separately

Python Pros

Free availability and stability
Easy integration with extensible using C and Java
Supports multiple Systems and Platforms
Easy to learn even for a novice developer
Ample of resourced available

Python Cons

A comparatively smaller pool of Python Developers
Software performance
Not Good for Mobile Development
Database access Limitations
Slower speed than C or C++

R Pros

Comprehensive Statistical Analysis Package. New ideas mostly appear in R
Open Source. Anyone can use it
Suitable for GNU/Linux and Microsoft Windows. It also has cross platforms which can run on many operating systems.
Anyone can do bug fixing and code enhancements

R cons

Quality of some Packages is not Good
If something doesn�??t work, there is no one to whom we can complain
People devote their own time developing it
R can consume all the memory because of its memory management

Source: HOB

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