The logistic regression classifier is a widely used machine learning model that predicts the group or category that an observation belongs to. When implementing this model, most people rely on some library or API: just hand over a dataset and out come the predictions. However, I'm not a fan of using black boxes without first understanding what's going on inside. In fact, lifting the hood on this classifier provides a segue to more complex models such as neural networks. Therefore, in this post, I'd like to explore the methodology behind logistic regression classifiers and walk through how to construct one from scratch.

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Iterators and generators are among my favorite programming tools—they're also some of the most powerful. These constructs enable us to write cleaner, more flexible and higher performance code; undoubtedly an invaluable addition to any programmer's toolbox. In addition, iterators and generators are an elegant means to work with large and potentially infinite data structures, coming in handy for data science. However, they can be some of the more perplexing concepts to grasp at first.

In this article, I'd like to deliver a gentle but in-depth introduction to iterators and generators in Python, although they're prevalent in other languages too. Nevertheless, in order to appreciate generators, we need to first have a good handle on iterators. And to understand iterators, we need to start with iterables.

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The Pokemon dataset is a listing of all Pokemon species as of mid-2016, containing data about their type and statistics. Considering how diverse Pokemon are, I was interested in analyzing this datset to learn how the game is balanced and to potentially identify the best Pokemon, if there exists one. Plus, it's a good excuse for me to practice exploratory data analysis with Python's open-source libraries: Pandas for data analysis and Seaborn for visualizations.

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