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Data Scientist? Yes. Researcher? Somewhat. Content creator? Sure, why not.

Using NLP and Granger causality to analyze the relationship between the sentiment of a written article and a stock’s price

Being able to accurately predict the stock market is like being able to see into the future. Stock market prediction refers to the act of attempting to determine the future value of a company’s stock that is traded on an exchange. However, being able to accurately predict the stock market is like being able to ride a purple unicorn; that is, it probably is not possible.

There are way too many factors to consider which can affect a stock’s price and building a model that consists of all these factors will likely produce poor predictions in the long run. Because…


A breakdown of kNN and how it works!

Machine learning (ML) algorithms are often categorized as either supervised or unsupervised, and this broadly refers to whether the dataset being used is labelled or not. Supervised ML algorithms apply what has been learned in the past to new data by using labelled examples to predict future outcomes. Essentially, the correct answer is known for these types of problems and the estimated model’s performance is judged based on whether or not the predicted output is correct.

In contrast, unsupervised ML algorithms refer to those developed when the information used to train the model is neither classified nor labelled. …


A break down of PCA, when to use it, and why it works

Machine learning (ML) is a subset of artificial intelligence (AI) and it provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The algorithms employed within ML are used to find patterns in data that generate insight and help make data-driven decisions and predictions. These types of algorithms are utilized every day to make critical decisions in medical diagnosis, stock trading, transportation, legal matters and much more. …


The theory and mathematics of DSGE models, and why DSGE models should be used for business

Economics is typically defined as the social science which studies the behaviour of people in terms of the choices made by individuals and societies in the face of scarcity. Scarcity here refers to the situation of having finite and limited resources, while also having limitless wants by agents within society. Because of this gap, all societies are tasked with answering three fundamental questions: What to produce? How to produce it? And for whom should this be produced?

These problems can be broken down and studied under two major categories: microeconomics and macroeconomics. Microeconomics focuses on disaggregated sectors within the economy…


Evaluating how recommender systems learn about users by comparing Instagram to TikTok

“Based on your activity, we think that you might like this.” That phrase is one of the most common phrases seen on almost every social media platform, and there is a good reason for it. The conversion rate on these platforms is what drives business. This conversion rate is essentially the number of persons who took some action on a post out of the total number of persons who saw the post. …


Stacking perceptrons is not appropriate for sequential tasks. Instead, use RNNs.

Artificial intelligence (AI) is bridging the gap between technology and humans by allowing machines to automatically learn things from data and become more ‘human-like’; thus, becoming more ‘intelligent’. In this case, intelligence can be considered to be the ability to process information which can be used to inform future decisions. This is ideal because humans can spontaneously put information together by recognizing old patterns, developing new connections, and perceiving something that they have learnt in a new light to develop new and effective processes. When combined with a machine’s computational power, tremendous results can be achieved.

The combination of automatic…


An explanation of how deep neural networks learn and adapt

Deep neural networks (DNNs) are essentially formed by having multiple connected perceptrons, where a perceptron is a single neuron. Think of an artificial neural network (ANN) as a system which contains a set of inputs that are fed along weighted paths. These inputs are then processed, and an output is produced to perform some task. Over time, the ANN ‘learns’, and different paths are developed. Various paths can have different weightings, and paths that are found to be more important (or produce more desirable results) are assigned higher weightings within the model than those which produce fewer desirable results.

Within…


Examining the fundamentals of deep learning models and neural networks

Since around November 2013, the term ‘deep learning’ started gaining popularity, especially within the data science community. This trend comes shortly after the ‘big data’ boom in 2010 and the ‘data science’ boom in 2011. The upticks in interest are not surprising because companies now realized that they needed individuals who were capable of deciphering insights from the information tsunami that was now present.

With data science now being referred to “almost everything that has something to do with data”, the process of data utilization evolved beyond data collection and analysis. Now, it was possible to use large sets of…


Examining how deep learning is derived from AI, and understanding its applications

Data science is revolutionizing many fields; from robotics to medicine, and everything in between. This revolution is partly due to advances in research, computing power, interests within the field, and the data science toolbox. Often, persons think of data science as extreme advances within artificial intelligence (AI); as in, eventually giving robots the ability to complete human-dominated tasks all on their own.

As much as this could be an aspect of data science, it is not all there is to data science. Rather, AI is part of the data science toolbox. …


An investigation into NLP using sentiment analysis to predict Exxon Mobil’s stock price movements

In late June 2020, I started a project to predict the stock market’s movement using Natural Language Processing (NLP). Stock market prediction refers to the act of attempting to determine the future value of a company’s stock that is traded on an exchange. Predicting the stock market, especially actual stock prices, turns out to be quite difficult, and this is so for several reasons. A major reason is that past performance is not necessarily a good indicator of future success. Simply put, what this means is that if a stock’s price increased by 2% two days ago, 4% yesterday, and…

Trist'n Joseph

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