Let’s compare and contrast differential equations (DE) to data-driven approaches like machine learning (ML).
In a nutshell — albeit with caveats — they can be thought of different approaches to modelling various phenomena. Do you make the rules? Or should you let your juicy data learn the rules for you?
Both types of models absolutely drive the world around us. Let’s dig in.
The model behind weather predictions. It is a chaotic model — meaning predictions can be wildly off when using just slightly incorrect inputs. That’s why weather predictions are often wrong! Simulations are carried out with super-computers.
Property valuation is an imprecise science. Individual appraisers and valuers bring their own experience, metrics and skills to a job. Consistency is difficult, with UK and Australian-based studies suggesting valuations between two professionals can differ by up to 40%. Crikey!
Perhaps a well-trained machine could perform this task in place of a human, with greater consistency and accuracy.
Let’s prototype this idea and train some ML models using data about a house’s features, costs and neighbourhood profile to predict its value. Our target variable — property price — is numerical, hence the ML task is regression. …
That’s an annualised return of 234%. Compare that with:
This made Bitcoin’s return orders of magnitudes above equities, real estate and bonds. To see this better, we can standardise the above figures and compare how each of these assets would have grown $100.
A single dollar put into Bitcoin in 2010 could get you a house today. A hundred dollars and you could retire right now, whatever your age. …
Many are concerned about stock market performance surrounding the election. I am. Let’s take a data-driven deep dive (DDDD) into the situation.
Short answer: Democrats, but not because of Democratic leadership.
Between 1927–2015, the average excess market return under Democratic presidents is 10.7%, whereas under Republican presidents, it’s just -0.2% per year. According to Pastor et al., the difference of ~11% higher per year is “highly significant both economically and statistically”.
However, one should be very careful not to draw any erroneous causal relations between Democratic leadership and stock market performance. …
It’s well-known in HR that recruiting new employees is substantially more expensive than retaining existing talent. Experience is king and those who depart take with them valuable experience and knowledge of your organisation.
We’ll train some machine learning models in a Jupyter notebook using data about an employee’s position, happiness, performance, workload and tenure to predict whether they’re going to stay or leave.
Our target variable’s categorical, hence the ML task is classification. (For a numerical target, the task becomes regression.)
We’ll use a dataset from elitedatascience.com that simulates a large company with 14,249 past and present employees. …
Warning: This article contains massive spoilers.
The whole film is a big loop, starting on ‘the 14th’ at the Kiev Opera House and ending on that same day at the Soviet closed-city of Stalask-12 and coast of Vietnam. The story tracks four characters — Protagonist, Neil, Kat and Sator — traversing 3 weeks forward in time, then 3 weeks inverted.
During the forward-traversing weeks, Sator and Tenet (Protagonist, Neil, Ives and team) compete to get their hands on an elusive case of plutonium-241. Protagonist thinks Sator wants it to start World War III. Sator actually wants it in order to fully assemble the Algorithm, a doomsday device with the power to reverse entropy on a global scale. Its activation would instantly destroy the world. …