The Boston Housing Market dataset is ubiquitous but imperfect: with problems like small size, inconsistent definitions and incorrect coordinates. However, it is still a very rich dataset containing informative geographical information, powerful socioeconomic indicators, and continuous levels of Nitrogen Oxides (NOx). This project explores the effect of developing low income neighbourhoods on NOx. The project won the first place prize in the 24-hour AI Hack 2021. The links below are for those interested to delve further into the analysis.
The aim of this project is to explore novel avenues in the Boston Housing Dataset…
The Boston Housing Market dataset has incorrect latitudes and longitudes. The corrected dataset can be found here.
The Boston Housing Market dataset is ubiquitous. Despite this, the latitude and longitude values are erroneous. This article is fairly short, aiming to: a) bring people’s attention to the problem, b) provide a link for the corrected dataset, and c) describe the methodology used for correcting the dataset.
A quick plot of the dwellings shows the problem with the latitudes and longitudes, as shown in Figure 1.
Neural Networks are ubiquitous due to their ability to capture non-linear relationships in data very well. The article intends to explain popular neural network structures succinctly and in simple terms. The aim is to provide an intuition for how they work, but more importantly, why their structures might be useful for different problems. Each section has links to resources for in-depth explanations of the topics.
A neural network is a function that takes an input tensor X (with i rows and…
If you’re a beginner in Data Science, then you’ve likely come across List Comprehensions (cool one-line for loops). Have you wondered what these are? Why they are so ubiquitous and when they should be used?
In this article, I’m going to briefly explain List Comprehensions with examples to make them digestible. I’ll be specifically highlighting how they differ with for-loops and whether they are more efficient or not.
This is the second of a four-part series on using one-line statements to shorten code. The previous article in this series looked at Ternary Operators (cool one-line if statements). …
If you are a beginner in Data Science, it is likely that you’ve come across Ternary Operators (cool one-line if-statements). Have you wondered what they are, why they are ubiquitous and when they should be used?
In this article, I’m going to briefly explain ternary operators with examples to make them digestible. This is the first of a four-part series on using one-line statements to shorten code (i.e. List Comprehensions, Lambda Functions, etc.). The final article in the series will teach you how to combine these different methods to shorten your code substantially. …
I have seen a lot of people reporting that training neural networks with validation_split causes overfitting, and thus many opt to use train_test_split() instead. However, unlike validation_split, train_test_split() does not directly allow the user to ‘see’ how the network is training, and thus aspiring data scientists tend to solely rely on test accuracy to guide neural network architecture design. This is like shooting in the dark.
This article will address why many people overfit when using validation_split, and how to prevent it. The key differences between validation_split and train_test_split() will also be discussed. …
Have you ever wondered how successful people — not necessarily the millionaires of the world, but even those in your immediate circle — juggle multiple business and people while maintaining a work-life balance, all the time appearing cool and calm?
The key is organisation.
At the end of the day, Millionaires aren’t supernatural beings… they are just like you and me.
However, here are a few things that make them different to everyone else:
Example: Naval Ravikant has used the analogy: “People find a mountain and spend most their lives…
Being 100% efficient is something we all dream of, but rarely (or likely never) achieve.
I’ve tried so many ways of managing my day: from planning it to every minute, to having no plan and have my brain do what it pleases.
Back in school, I had something like this:
If you’ve ever been to London, or any other part of the UK, then chances are you’ll have noticed that the British do things… differently.
They drive on the other side of the road, they use miles per hour instead of kilometers per hour, they overuse the word ‘Sorry’, etc. But for those of you who are eagle-eyed, you may have also noticed that pedestrian crossings in the UK are quite elaborate, unlike in the rest of mainland Europe or North America.
For starters, many of the pedestrian crossings have warning signs for pedestrians:
However, this hardly captures the differences…
This article is aimed at students in high school or at university who struggle with understanding chain rule. I try to explain a concept in an intuitive yet different way, while building foundations for the concept of the ‘total derivative’ which will help you in the future. That said though, if you are just interested in Maths, then this article is also for you!
This is the first part of a Calculus series, where I will explain the concepts of Chain Rule for univariate problems and multivariate problems (after introducing Partial Differentiation). …