One of the most intriguing questions is ‘What is life?’. I am not writing to answer this question but tell you something which might enthrall you similarly.

I had lived with this thought of existence that all species were created by the God. But as I grew up , I realised that my whole childhood and even adulthood is based on the wrong foundation. I lost my faith in almost every thing and pity those who still live in this illusions. So many generations had lived without realising that they were living under false roofs.

Humans crave for a meaningful…


K- Means Clustering is an unsupervised machine learning technique which is used to group similar data points together into a cluster. Now first question arises how similarity is measured between data points and is it the reliable algorithm.

Let us see below the algorithm to carry out the above task:

  1. Select number of clusters k you want to put data into. Don’t worry we have a cute way, Elbow method, of doing this which we will see later both in theory and implementation in python.
  2. Select randomly centroid for each cluster. …


‘Everything in python is an object’ is one of the line that every developers gets to hear. I have tried to explain four very important concepts in OOP in very simple terms with practical examples. I hope you people find it useful and easy to understand. So first let us start with the definitions.

Encapsulation is defined as the wrapping up of the essential data features and methods into single unit primarily class. Let us understand it with an example below:

We can see how different features like name, matches and scored and methods avg(), review() into a single class…


One of the most difficult challenges faced in understanding the relationship between time series variables is that it’s not easy to distinguish between the correlation and causation and correlation does not imply causality.

Before moving forward let us see first mathematical definition of Granger Causality(GC) followed by its intuitive explanation.

Definition
In other words x is Granger causal for y if x helps predict y at some stage in the future. Often you will have that x Granger causes y and y Granger causes x. This is the situation of feedback system.Now let us see how to carry out the process.1.Firstly we need to ensure that time series say x and y is stationary. If original time series is not stationary then you have to carry out some transformations to make them stationary.2.Then we have to check whether some linear relationship exists between the variables. For that we…

Ayush Yadav

Want to contribute in building an inclusive and confident society. Want quality education to reach everyone. Proponent of my own religion.

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