Machine Learning has become the most-talked-about topic these days, what is so different that Machine learning does when compared to traditional software applications?
In the traditional software applications, we developed systems and logic for existing rules and we kept updating the systems as rules changed. But, change is the norm in business and technology. The main feature of machine learning is that it allows you to continually learn from data and predict the future. This powerful set of algorithms and models are being used across industries to improve processes and gain insights into patterns and anomalies within data.

So, what…


I came across a fascinating topic called “violin plot” when dealing with box and whisker plots. As a violinist, I was immediately intrigued by the topic. This article is an attempt to introduce my fellow analysts and machine learning aficionados to this really interesting and helpful plot. I hope you find it enjoyable.

A box plot corresponds to actual data points, it depicts the distribution of quantitative data in such a way that it is easy to compare variables or levels of a categorical variable. …


This post will walk through an introductory example of STL analysis using the NASA turbofan Jet Engine dataset.

Seasonal and Trend Decomposition Using Loess (STL) is an acronym for Seasonal and Trend Decomposition Using Loess. This is a statistical method for breaking down Time Series data into three components: seasonality, trend, and residual. STL extracts smooth estimates of the three components using LOESS (locally estimated scatterplot smoothing). One of the major goals of decomposition is to evaluate seasonal impacts so that seasonally adjusted data may be created and presented. A seasonally adjusted value eliminates the seasonal influence from a measurement…


Anomaly detection is a crucial idea that has been examined and examined in a variety of fields. This article attempts to present a well-organized overview of anomaly detection.

What is Anomaly Detection?

The discovery of patterns in data that do not conform to expected behaviour is known as anomaly detection. In layman’s terms, it is a technique used to identify unusual patterns that do not conform to normal patterns. These items are often referred to as anomalies, outliers, peculiarities or contaminants in different application domains.

Credit card fraud, cyber-intrusion, terrorist action, system failure, and other factors might cause data anomalies…


I was into my final year at college when I stumbled upon this interesting book with an even more interesting cover picture of a wilted and dried rose. My romantic soul assumed it to be a tragic novel on couples parting their ways, but it was a stunning revelation on how pesticides & herbicides which were seen as a marvel back in the 1960s had later turned out to be a disaster to humans and the environment around.


Compassion, a quality that seems to be one that mankind needs the most was perhaps instilled in me by a story that I read when I was only 8. I was a lone child and often was all alone at home as both my parents were working. Books and the TV were my companions. My mother used to buy me a lot of books which had wonderful stories and beautiful illustrations in vibrant colours. I loved to read them and look at the pictures. The story that stands out in my memory after all these years is one ofthe…

Sivashankari Vaitheswaran

Techie/ Avid Reader/ Music Lover

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