A collection of the data science projects that I completed for academic, self learning and out-of-curiosity purposes.
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Supervised Learning: Finding Donors for CharityML: Testing out several different supervised learning algorithms to build a model that accurately predicts whether an individual makes more than $50,000, to identify likely donors for a fictional non-profit organisation.
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Supervised Learning: Classifying Flower Images with Neural Networks: Using a pre-trained classifier to build a new classifier that can identify 102 flower images. Additionally wrapping it all in a commandline application that let's users choose an algorithm, retrain it on a new image set and predict a class for a new image.
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Unsupervised Learning: Identify Customer Segments: Using unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns.