Evolving the StarCraftII Build Order Meta

2017-10-22 | : python, tensorflow, rnn, EDA, Data Munging, Deep Learning, flask, d3.js

Introduction

Google's Artificial Intelligence research group, DeepMind recently released a python API, pySC2 for the popular Real Time Strategy (RTS) computer game, StarCraftII. After successfully conquering the boardgame, Go, with their AlphaGo program, DeepMind has set their sights on the next big challenge for AI systems in attempting to train these systems to learn how to compete with world champions in the increadibly complex world of StarCraft. In this post, we use the pySC2 API to collect gameplay data from replays of human played games with the aim of discoverying some macro elements of the game, such as the different technology progression trees players use. Ultimately, the hope is to build up some intuition on what data is availible from the pySC2 API and how it might be useful in building systems capable of playing the game, but that work will be outside the scope of...

Recurrent Neural Network Based Subreddit Recommender System

2017-01-07 | : python, tensorflow, rnn, bokeh, EDA, Data Munging, Deep Learning, Recommender Systems

Introduction

As part of a project course in my second semester, we were tasked with building a system of our chosing that encorporated or showcased any of the Computational Intelligence techniques we learned about in class. For our project, we decided to investigate the application of Recurrent Neural Networks to the task of building a Subreddit recommender system for Reddit users. In this post, I outline some of the implementation details of the final system. A minimal webapp for the final model can be interacted with here, The final research paper for the project can be found here and my collaboraters on the project are Barbara Garza and Suren Oganesian. The github repo for the project can be found here with this jupyter notebook being here.

spez

Model Hypothesis

The goal of the project is to utilize the sequence prediction power of RNN's to...

Sklearn Compatable AdaBoost from Scratch and Predicting Customer Churn

2016-12-15 | : python, sklearn, seaborn, matplotlib, EDA, numpy, pandas, Data Munging, Machine Learning

MAI-IML Exercise 4: Adaboost from Scratch and Predicting Customer Churn

Abstract

In this work, we develop a custom adaboost classifier compatible with the sklearn package and test it on a dataset from a telecommunication company requiring the correct classification of custumers likely to "churn", or quit their services, for use in developing investment plans to retain these high risk customers.

Adaboost from Scratch

Defined below is an sklearn compatable estimator utilizing the adaboost algorithm to perform binary classification. The input parameters for this estimator is the number of weak learners (which are decision tree stubs on a single, randomly selected feature) to train and aggregate to produce the final classifier. An optional weight distribution can also be passed to the classifier, which defaults to uniform if not set. This custom estimator will later be utilized to develop a classifier capable of predicting customer churn from labelled customer data.

Tesla Supercharger Network Exploratory Data Analysis

2016-06-21 | : Data Munging, Data Analysis, Complex Networks, Tesla, Beaker Notebook, EDA, python, R, d3.js

Network

Introduction

With the recent unveiling of Tesla's Model 3 and pre-orders approaching 400,000, the internet has been buzzing with Tesla discussions and analysis. One of Tesla's key differentiators from other mass market Electric Vehicals (EVs) is its Super Charger (SC) network that provides 170 miles of range in 30 minutes source. With Elon Musk stating plans to double the size of the SC network by the end of 2017, a large amount of planning, resources and investment are being allocated to this network expansion.

An analysis exploring Tesla's Supercharger network was performed using beaker notebooks and is currently the top rated notebook published on the platform.

The full analysis can be viewed here.

Fanduel MLB Exploratory Data Analysis

2015-10-15 | : openopt, Data Munging, Data Analysis, SQL, EDA, R, dplyr, ggplot2, ggplot2, DFS

Introduction

The Daily Fantasy Sports (DFS) industry has exploded in popularity in recent years, largely due to the exponential growth of users playing on industry titans such as Fanduel and DraftKings. These platforms allow users to gamble real money by selecting a set of players known as a roster from a sport, with rules constraining the total salary and specific player position types required for a selected roster.Each player in the roster can accumulate points based on their performance in the sport in the upcoming game that day.The user then enters this roster into contests against other users who have also entered their rosters, with the aim of selecting the roster that accumulates the most points based on the competitions set rule for point accumulation. The user or subset of users with the top accumulated points at the end of the competition win the pot of money entered...