Let's dive deeper into how linear regression works.

Linear regression follows a general formula:

Where \(\hat{y}\) is the predicted value, \(n\) is the number of features, \(x_i\) is the \(i^{th}\) feature value and \(\theta_n\) is the \(n^{th}\) model parameter. This …

Recently, I was traveling around New Zealand, and noticed in the Maori language they use letters back to back a lot like in the original Maori name for Stratford ("whakaahurangi"). So as any normal person does, I thought, well what town has the most repeated letters, and the idea for …

Recently after looking for a different flavour of database apart from MySQL (which is what I am personally use to), I had always heard about MongoDB. So after some investigation, I found that MongoDB has a platform MongoDB University to familiarize yourself with their product.

I completed their very first …

Following last 2 weeks’ posts (Python for the Finance Industry & Portfolio Balancing with Historical Stock Data), we now know how to extract historical records on stock information from the ASX through an API, present it in a graph using matplotlib, and how to balance a portfolio using randomly generated portfolios …

Following last weeks' post (Python for the Finance Industry). This post is to demonstrate a method of determining an optimized portfolio based on historical stock price data.

First of all while attempting to tackle this problem, I stumbled across many very informative articles in which based on what I learned …

This is the first post in a series of posts dedicated for demonstrating how Python can be applied in the finance industry. Personally, the first thing that comes to mind when I think of the finance industry is the stock market. For fellow Australians, our main stock exchange is the …