Most of the times we have a multiple feature with very medium to high magnitude or scaled values and this is obvious in the real-life data. For Example Annual Salary and the %of DA. Volume of Commodity and Exchange Rates.. and many more. Algorithms which are mostly dependent on the distance of the features which vary heavily in terms of scaling may not weigh all these in the same way. The parameter which has smaller values does not mean to be seen as less importance!!!

Hence, to make the predictions do not vary just because of the ‘X’ vars are…

Granger causality test is used to determine if one time series will be useful to forecast another variable by investigating causality between two variables in a time series. The method is a probabilistic account of causality; it uses observed data sets to find patterns of correlation. One good thing about time series vector autoregression (VAR) is that we could test ‘causality’ in some sense. This test is first proposed by Granger (1969), and therefore we refer to it as the Granger causality.

Simple Mechanism to define Granger Causality:

It is based on the idea that if X causes Y, then…

Source: Burst

Theoretically, every code you develop, it is very important to know which library is relevant enough to give you the best coding support. So I begin to analyze the very common and popular libraries which I might be using in my experiment of Sentiment Analysis with python (NLP)

When I first started learning NLP using Python, I was more curious to see how much a code can do related to Natural Language and how a code can replace human instincts to identify sentiment and pattern in the Natural Language communication.

“Cowards die many times before their deaths; The valiant never…

Feroz Kazi

AI & Machine Learning, Principal Data Scientist, Functional Analytics, Insights, Metrics, Dashboards, Researching in Forecasting Models Optimization and PCA

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