Join Kumaran Ponnambalam for an in-depth discussion in this video Using Jupyter Notebook, part of Text Analytics and Predictions with Python Essential Training.
- [Instructor] All examples in this course are built using the Jupyter Notebook Python distribution. If you do not have it already on your setup, you can download the same using the Anaconda distribution. In this case we would be using Python 3.7 version for this course. You can go to Anaconda.com/distribution and download the Python 3.7 version of Anaconda for your specific operating system. I will be using the 64 bit graphical installer for Windows because that is my operating system use in this case. Once you install Anaconda, it is going to show up in your menu. From here you can execute the Jupyter Notebook. In order for the Jupyter Notebook to use your code, download the code for this course into your directory. Then you can go to the Jupyter Notebook and change it to launch from that directory. You can do so by doing the following. Go to Jupyter Notebook, right click, More, Open File Location. Right click on Jupyter Notebook and look at properties. By default, it is going to launch from the directory called User Profile. You can change this to your own data tree in which you have downloaded your code. In my case, I have downloaded into users: (mumbles) desktop code. Apply and save. Now when you launch the Jupyter Notebook, it is going to launch from this specific directory. And it opens the browser for you with that specific directory. In this directory, you can see examples for various modules and also the source files that are used for text content.
- Generating a word cloud
- Determining the sentiments of customers
- K-means clustering of text
- Predicting the classification of text documents
- Predictive text