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Mario Albuquerque's Projects

alphalens icon alphalens

Performance analysis of predictive (alpha) stock factors

boston_housing_mlen icon boston_housing_mlen

Supervised learning regression project done as part of the Machine Learning Engineer Nanodegree at Udacity.

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bt - flexible backtesting for Python

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Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

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Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

datacamp_facebook_live_ny_resolution icon datacamp_facebook_live_ny_resolution

In this Facebook live code along session with Hugo Bowne-Anderson, you're going to check out Google trends data of keywords 'diet', 'gym' and 'finance' to see how they vary over time.

dog_breed_classifier icon dog_breed_classifier

Built an algorithm to identify canine breed given an image of a dog. If given image of a human, the algorithm identifies a resembling dog breed.

finding_donors_mlen icon finding_donors_mlen

Supervised learning binary classification project done as part of the Machine Learning Engineer Nanodegree at Udacity

machine_learning_capstone_project icon machine_learning_capstone_project

Capstone project for Udacity's Machine Learning Engineer Nanodegree: predicting deal probability of online advertisements using heterogeneous data types (numerical, categorical, text, and image).

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Materials for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

pyfolio icon pyfolio

Portfolio and risk analytics in Python

smartcab icon smartcab

Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.

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Shiny app that computes performance metrics for the top N S&P 500 members.

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