As a data scientist, you may very frequently encounter categorical variable in your dataset like location, car model, gender, etc. You cannot directly use them in our machine learning algorithm as these algorithms only understand numbers. There are various techniques to convert these categorical features to numerical features but that is not the focus of this post, this post is about how to implement these techniques in python. I will talk a little bit about these techniques but won’t go into too much depth, I will emphasise more on various ways how you can implement this technique in python.

Due to the flourish of internet and accessibility of technology incredible platforms like social media, forums, etc have been created for knowledge sharing. Exchanging ideas is not confined to a geographical area. Due to this volume and variety of content is generated in the form of images, video, text, etc. The amount of information is so much that it’s unmanageable to perceive it in bounded time, in such times area of text analytics has got the attention of people in the field of linguistics, business, etc. The goal of the post is to summarize few of the visual text analytical techniques that could help you in your initial phase of text mining or help you create a new feature for creating machine learning model. I will describe few online and offline tools that you could use to help you get started. By offline tools, I mean using python based software packages to created visualization and text pre-processing. Online tools will be web-browser based applications to which just have to paste the text or upload the text file to visualise the results.