There are so many machine learning algorithms out there, how do you choose the best one for your problem? This question is going to have a different response based on the application and the data. Is it classification, regression, supervised, unsupervised, natural language processing, time series? There are so many avenues to take but in this article I am going to focus on on algorithm that I particularly find very interesting, XGBoost.

What is XGBoost

XGBoost stands for extreme gradient boosting and is an open source library that provides an efficient and effective implementation of gradient boosting. …


Let’s say that you in reading through the sports section of your favorite newspaper or online publication and you come upon a strange article focused on a sunflower farmer in Arizona. This article has nothing to do with sports, so how could it have ended up in this section? You start to read the article out of curiosity and happen to see that a lot of the words in that article might be seen in an article about baseball.

“Field” appears quite a few times talking about the fields of sunflowers he harvests during the summer. “Diamondback” appears a…


If you are going to study data science, data analysis, machine learning, or any other discipline that builds models to make predictions on data, you are going to stumble upon the term “Bias-Variance Tradeoff”. Basically, it describes the tradeoff of a model learning “too much” or “too little” in the training process. If a model learns the training data too much, it will perform weakly on data is has not seen (the test data). …


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As humans with extremely complex brains, we make extremely complex decisions in a fraction of a second without even recognizing the process that happens behind that decision making. Simple things like reading your own handwriting may seem mundane but the amount of knowledge and processing power done by your brain makes these extremely hard to replicate in a computer program. Even decisions like whether you want to go to the grocery store now or later may take a little more time but still is done relatively quickly. …


When writing code it can seem inconsequential to add another if-else statement or loop because most of the code we write can terminate in seconds if not less. This is true for smaller projects that will be unused after their initial use. On a larger scale, certain code can be run billions of times making those inconsequential code snippets very detrimental to runtime. This is the study of time complexity or how long it takes for certain algorithms or tasks to run. …


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What if I told you that you didn’t need to pray to the fantasy gods in order to win your league? This may seem impossible but with the power of data science we can determine the players most likely to score the most points. The way that this can be done is using the statistics of players as the features to our models and their fantasy points as the target variable we are going to make predictions on. …


You must choose wisely

In order to have a prediction on an occurrence of an event or an outcome of that event, one needs data or knowledge on the domain of the event. If one does not have this information, a prediction or guess on any event will be completely random without evidence to back it up. This is how we as humans are able to have a good idea of what is going to happen in our lives next because we have so much data and evidence to base it off of. Computers work in the same exact way…


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So you want to enter the mysterious and wonderful world of coding. You want to enjoy the ability to create anything you can think of with the click of a few buttons. Maybe hack your way into any source of information to gain insight on what the government isn’t telling you. You could even create program that makes millions off the stock market. Well I am here to tell you that it is nothing like that. The beginning steps to coding are small, simple, and may be confusing. That is why I am providing this prologue to your…

Justin Fernandez

Soon to be data scientist! I also love shoes and my dogs!

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