Logistic Regression’s roots date back to the 19th century when Belgian Mathematician, Pierre François Verhulst proposed the Logistic Function/Logistic Growth in a series of three papers for modelling population growth. It was later applied for modelling autocatalysis in chemistry by Friedrich Wilhelm Ostwald in 1883. Around 200 years later, Logistic Regression is now one of the most widely utilised statistical models in various fields including machine learning, economics, medical, etc.

1.0. Geez! Some Mathematical Definitions 😞

In brief, a logistic regression model uses the logistic function:

to squeeze the output of a linear equation between 0 to 1. …


Having pursued my undergraduate degree in software engineering and currently pursuing my postgraduate degree in data science, I am sometimes puzzled by the fact that several people consider data science and software engineering to be two mutually exclusive fields. Some of my fellow college mates believe that thriving as a data scientist requires business acumen, analysis, communication and visualisation as the necessary skillset.

Whilst it may be correct from certain points of view, I believe otherwise. Throughout my studies, I have come across several experiences where I had the golden opportunity to merge both worlds to create beautiful yet complex…

Akbar Husnoo

Akbar is a versatile aspiring data scientist with a flair for data analysis, statistical predictive modelling, machine learning and persuasive story-telling.

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