Month: December 2017

How to Start Jupyter Notebook From Anaconda Prompt

/ RP / 2 Comments

Jupyter Notebook can be started using many ways, most common ones are-

  1. From the Windows or Mac search interface. Type “Jupyter Notebook” and it should show you to application to start
  2. From Anaconda prompt by typing “jupyter notebook” at the anaconda prompt
  3. For high graphics display such as with plotly package, you are advised to start the jupyter notebook using the following command- “jupyter notebook –NotebookApp.iopub_data_rate_limit=1e10”
Jupyter Notebook Start from Anaconda

Jupyter Notebook Start from Anaconda for High Resolution Graphics

otherwise you can get an error message similar to the one shown below-

IOPub data rate exceeded. The notebook server will temporarily stop sending output to the client in order to avoid crashing it. To change this limit, set the config variable `--NotebookApp.iopub_data_rate_limit`.

Python Error Message for High Graphics Images

Cheers!

Markov Chains and Markov Model

/ RP / Leave a comment

Markov chains or Markov models are statistical sequential models leveraging probability concepts to predict outcome. This model is named after a Russian mathematician Andrey Markov.  The main guiding principle for Markov chains is that the probability of certain events to happen in the future depend on past events. They can be broken down into different categories-

  • First order Markov model- probability of next event depends on the current event
  • Second order Markov model- probability of the next event depends on the current and the previous event
  • A Hidden Markov Model ( HMM) is where the previous states from which the current state is generated are hidden or unobserved or unknown.

and so on…

Markov models have been proven to be very effective in sequential modeling such as-

  • Speech recognition
  • Handwriting recognition
  • Stock market forecasting
  • Online sales attribution to channels

etc.

Here is a pictorial depiction of HMM-

 

MARKOV

This link has a very good visual explanation of the Markov Models and guiding principles.

R has an in built package called “ChannelAttribution” for solving online multi channel attribution. This package has also an excellent explanation of the Markov Model and working example.

Python also has a library to build Markov models in Python.

Cheers!