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 to build a predictive mode for Tesla's Supercharger network expansion was performed using beaker notebooks.
The full analysis can be viewed here.
The interactive network data visualization can be viewed here
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.
Identifying Fraud from Enron Emails
Objective
Using the Enron email corpus data to extract and engineer model features, we will attempt to develop a classifier able to identify a "Person of Interest" (PoI) that may have been involved or had an impact on the fraud that occured within the Enron scandal. A list of known PoI has been hand generated from this USATODAY article by the friendly folks at Udacity, who define a PoI as individuals who were indicted, reached a settlement or plea deal with the government, or testified in exchange for prosecution immunity. We will use these PoI labels with the Enron email corpus data to develop the classifier.
Data Structure
The dataset used in this analysis was generated by Udacity and is a dictionary with each person's name in the dataset being the key to each data dictionary. The data dictionaries have the following features:
...
Continue Reading...
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...
Continue Reading...
Data Munging and Analyzing Barcelona OSM Data
The XML data for the city boundary of Barcelona was downloaded from OSM to clean and transform into a json encodable structure to allow loading into MongoDB, providing storage and artbitrary querying to enable further data analysis of the Barcelona OSM dataset. I chose the Barcelona dataset because I just recently spent a few weeks there and noticed some pretty cool urban planning features of the city (ie. Avinguda Diagonal) that could be cool to explore further.
Inspired by the "Diagonality" of Barcelona streets and to give the scope of the project a little more focus, I will be attempting to analyze the Barcelona OSM data and see if I can generate a measure for the "degree of diagionality" for Barcelona and possibly compare this measure with other cities. Not knowing ahead of time the difficulty of this, I may eventually have to...
Continue Reading...