This article covers the Wedding Seating Plan problem which is a combinatorial puzzle of how best to assign guests to tables given that they may want to avoid or be seated with other guests to avoid.
Two in-depth example approaches are examined using a Brute Force Search and Simulated Annealing as well as a summary overview of other typical optimization approaches such as Genetic Algorithms , MILP , WalkSAT, etc
Weddings tend to be stressful events to plan for usually because of the many decisions that need to be made on dates, venue, catering and so on that lead…
*Yes I know it’s technically it’s Happy ‘Belated’ π Day since it’s past 3.14.2021 but my excuse is that I ran my simulation at too low a number of iterations :P and I was too lazy to wait for 22.7.2021 (Pi Approximation Day — look it up — it’s a real thing)
Fair warning — this is a bit of a geeky post but I am hoping my regular readers will indulge me for my “irrational” behavior (Geddit ?! Sorry more bad math puns incoming)
Ingredients : A cup of Factor Investing principles; A sprinkle of stock metrics freshly web scraped from Yahoo Finance ; A splash of your favorite data normalization and composite score aggregation method for flavouring, and seasoned to taste with a generous dollop of common sense
In this article, we examine a generic framework that can be used to select, normalize and aggregate factors that determine relative stock performance in order to build a stock ranking / screening tool that can be used to inform your stock portfolio construction.
Warning ! I am not an investment guru of any sort so…
Picture a scenario where you’re in a SME and have spent some time developing a custom spreadsheet based “calculator" tool. (E.g A mortgate calculator or a home repair quote estimator or medical screening tool or whatever).
Maybe you want to offer it out freely as long as users provide some contact details so you can build up a list of potential customer leads for your other service offerings or maybe you even want monetize this tool and wish to set up a ‘free trial duration’ on the tool before the user has to buy a paid version.
Now all you…
In this article, I briefly explain how Modern Portfolio Theory can be used together with Monte Carlo Simulations to estimate the optimum weights for a given portfolio of stocks to achieve the ‘best’ risk-reward trade-off
This is a continuation of my last post where I shared a python web app I developed that allows users to simulate future stock price movements using Geometric Brownian Motion (GBM) or Bootstrap Sampling.
However I left an open question at the end of the last article : How do we know we’ve selected the “best” weights for a given portfolio of stocks ?
I built a web app using Python Flask that allows you to simulate future stock price movements using a method called Monte Carlo simulations with the choice of two ‘flavours’ : Geometric Brownian Motion (GBM) and Bootstrapped Sampling.
While I may include some programming code below, this article is not going to be a ‘code-along’ tutorial and focuses more on the underlying theory behind GBM and Bootstrap Sampling.
However the native *.*py and a Jupyter Notebook version of the same code is available on this GitHub link below if you want to dig deeper.
Warning ! I am not…
In this post, I provide an introduction to Deep Fakes and briefly walk through of how I made my own ‘Deep Fake’ videos using three different methods :
A Structured Approach To Making A Data Driven Best Guess With XLRisk
In this installment, I explain how to use Monte Carlo Simulations to build a probabilistic estimate even if you don’t have all the relevant information , have to deal with uncertain variable inputs and are exposed to potential risks using an Excel Add In called XL Risk.
Happy Spreadsheet Day !
I’ve had the idea for this article for some time now but in honour of the upcoming Spreadsheet Day on 17th October , I thought I’d finally get around to writing it up.
If you are hardcore- you can also view this article itself in a Google Sheet ; )
Fair warning, this article is a bit of a rant and you will probably be rolling your eyes if you are already an experienced DevOps person or familar with Google Cloud App Engine
I will be detailing my journey down the Dev Ops “rabbit hole” and the troubleshooting I went through to deploy a simple Flask Web App online using Google’s App Engine along with any online resources (Stackoverflow posts or other tech blogs) I found useful as a n00b data science enthusiast.
Ultimately I didn’t manage to fix my problem as I ended up deploying NOT ON GOOGLE APP…
Hi ! I’m “Z”. I am big on sci-fi, tech and digital trends.