A far-fetched dream or a probable reality?

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Photo by Bermix Studio on Unsplash

*Disclaimer: Any information and opinions found on this page are not to be considered as financial advice. You should do your own research before making any investment decisions.

January 3rd 2021 was the 12th birthday of Bitcoin since the inception of the genesis block. And there are good reasons to celebrate. The news of Bitcoin constantly breaching new highs is a welcoming narrative by all HODLers and bulls alike. …


That I would do differently in hindsight

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Photo by Charles Forerunner on Unsplash

9 months ago I was on a quest to find the right topic for my master thesis, then I stumbled upon this seemingly avant-garde topic rife with buzzwords —

“Data Quality Assessment and Predictive Analytics for Customer Journeys in a B2B Sector”

And of course, the proposal is not complete without catchphrases like #BigData #DataAnalytics #StatisticalModel #MachineLearning and bla bla bla…

Before we dive any deeper, let me make myself clear with a disclaimer. I am not a computer science nor IT major per se, but a post-grad in business and management science, with a strong affinity for data analytics. Therefore, my views are probably more oriented for people with similar backgrounds — those with some experience in programming (you know what an if...else


Precision, Recall, and F1-score in Python

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Photo by George Pagan III on Unsplash

“How accurate is your model?”

This is probably the most commonly asked question when one wants to know how a model performs or rather how accurate a classifier can actually predict an anticipated event.

While using accuracy to measure a classifier performance is not wrong, it may give you a false sense of optimism. A classifier with high accuracy tells you the overall performance of a model, but less so on a more localized level of the individual class it predicts.

What does that even mean? Or rather why accuracy alone is inadequate to evaluate the performance of a classifier? What other classification metrics out there can you use? In this article, we will explain how accuracy measures the classification results and what other metrics you can use to better gauge the performance of a classifier. …


The pythonic solution to balance the imbalanced

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Photo by 青 晨 on Unsplash

Imbalanced data is a very common occurrence in real-world domains, especially when the subject of interest for a decision-making system is a rare but important case. This can be a problem when a future decision is to be made based on insights from historical data. Inadequate data from the minority case can hamper the robustness of the new decision being made.

The case of imbalanced data exists almost to any real-life applications. For instance, the average customer churn rate for wireless carriers in the US is somewhere between 1–6%. …


Reimagining your surrounding with the new iPhone

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Photo by UNIBOA on Unsplash

You want to furnish your room with a new piece of furniture but unsure if it would look nice in your room? Prior to AR, you would probably have to measure the dimension and imagine how that piece of furniture would fit into your anticipated environment. Now, what if I tell you that you can just lift your iPhone to project an augmented version of the furniture right on that corner of your room to see if you like or not. Spot on, the era of Augmented Reality aka. …


How to select the right predictors using ML algorithms

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Edited by the author based on a photo by Markus Spiske on Unsplash

In the first series of this article, we discussed what feature selection is about and provided some walkthroughs using the statistical method. This article follow-ups on the original article by further explaining the other two common approaches in feature selection for Machine Learning (ML) — namely the wrapper and embedded methods. Explanations will be accompanied by sample coding in Python.

To recap, feature selection means to reduce the number of predictors used to train a ML model. The main goals are to improve the accuracy of the predictive performance (by reducing the number of redundant predictors), reduce calculation time (fewer predictors, less time needed to compute), and to improve the interpretability of the model (easier to study the dependency of predictors when the number is smaller). Filter method, which is based on statistical technique can be generally applied independently of the algorithms used for a ML model. …


A lesson of business model innovation and what you can learn from Apple

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Photo by Hugo Agut tugal on Unsplash

Under the shadow of the corona pandemic, Apple has staged its second major online event in 2020, after the WWDC in June. However, unlike any typical fall event for Apple, there was no iPhone announced at the “Time Flies” event this year. While we do get shiny hardware upgrades with the new Apple Watch and two variants of iPads, there is a subtle message at every turn of the event that could go easily unnoticed — A silent flex on strengthening Apple’s ecosystem and its springing service businesses.

This article will look at the new direction Apple has been quietly taking amid a global economic downturn due to the pandemic and geopolitical tension. The heated battlefield of digital subscription businesses with the likes of Netflix and Spotify also sees Apple’s repositioned strategy in leveraging its ecosystem in fending off competition. …


How to select the right predictors using statistical measures

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Photo by Maarten van den Heuvel on Unsplash

Too many cooks spoil the broth.

Even back in 1575, George Gascoigne already knew that a sumptuous bowl of broth can’t be achieved with too many cooks in the kitchen. The rigor of that proverb extends to modern days, yes, even in Machine Learning.

Have you ever wondered why the performance of your model hit a plateau no matter how you fine-tune those hyperparameters? Or even worse that you only see a mediocre improvement on performance after using the most accurate set of data you could ever find? …


A comprehensive guide to understanding Neuralink and Brain-Computer Interface (BCI)

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Illustration by Phonlamai Photo on Shutterstock

Elon Musk struck again!

Remember in 1996, Dolly the sheep became the first cloned mammal in the world. Some twenty years later, Gertrude the pig becomes the first animal who got a neural implant in her skull, which could potentially revolutionize how we communicate with computers and machines. “That’s one small step for piglet, one giant leap for mankind.”

In this article, we will explore the neurotechnology behind Neuralink, how Musk can potentially unveil a new world of bionic brains, what it means for us in the future, and of course not forgetting the possible ethical ramifications.

Understanding the jargons

Neurotechnology: an assembly of methods and instruments that enable a direct connection of technical components with the nervous system [1], so that people can understand the brain and various aspects of consciousness, thought, and higher order activities in the brain. …


Hint: Do science and numbers speak louder than your ego?

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Photo by Marie Jo on Pinterest

In early February 2020, I was catching a flight back from Singapore to Germany, after briefly attending my best friend’s wedding in Kuala Lumpur. I was constantly having a mask on at the airport, during the flight, and all the way until I touched down in Germany. While I was commuting on the train back home from the airport, I could see people throwing that stigmatized glance at me, it was not hard for me to guess that association between a mask, an Asian look, and the coronavirus.

Fast forward a few months later, masks are now compulsory in Germany when in public transport or in enclosed areas like supermarkets or shopping malls. The same rule applies to most parts of Europe and is now a common sight for people to stay masked in public. But across the Atlantic, people are getting more polarized over the debate — to mask or not to mask. …

About

Jack Tan

A Bitcoin aficionado, a tech enthusiast, an engineer, an entrepreneur wannabe, a world traveler. Find me at https://www.linkedin.com/in/jack-yee-tan-13221196/.

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