How you can build a quick estimate of customer growth behavior using a set of simple assumptions with the help of Kwik-Look’s free web-tool

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Revenue does not magically occur by itself.

Revenue growth is a direct result of actions taken in your business. Do you know what growth hacks are required for your business’s year on year growth? Do you know how growth levers impact your top and bottom line?

This article outlines the design of a simple web-tool that I built for one my recent freelance engagements. (Look me up on Fiverr!) This tool is targeted for SME Start…

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…

Happy π Day everyone ! Come one , come all — gather “a-round” as I share how the Monte Carlo simulation method can be used to estimate the value of π

*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)

Since I’ve already written a number of other Medium articles on Monte Carlo simulations (…

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

Source : Dreamstime Stock Photos

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…

A 1920-s Ford Automotive Factory ( Aligned to the theme as the example portfolio later are all stocks from the American auto-industry)


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 ?

Image Source :


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 commercial mobile app called Reface (formerly Doublicat) which is completely no-code and easy to use but has limited options for what it can do
  • A Google Collab page that hosts a First Order Motion Model which allows for a bit more flexibility in choice / length of videos and images
  • Deep Face Lab 2.0, an open source software package for making Deep Fakes that provides more comprehensive customization over the creation…

A Structured Approach To Making A Data Driven Best Guess With XLRisk

Image Source : Geek Dad

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.


  1. Introduction
  2. Just Give Me The Number Already !
  3. The Problem With Single Point Estimates & Best-Worst Case Scenarios
  4. A Primer On Probability Distributions
  5. Monte Carlo Simulation Tools
  6. Modelling Uncertain Ranges
  7. Modelling Discrete Risk Events
  8. Modelling Dependencies / Correlations Between Uncertain Variables

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Hi ! I’m “Z”. I am big on sci-fi, tech and digital trends.

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