Highlights

• Energy Production in the United States

  Overview: Energy usage has been an interesting topic in the last century with the rise of automobiles, pollution, and environmental regulations. What this project looks at is a broad overview on what energy consumption and production has looked like from 1946 to 2016, and extrapolate potential trends from existing data.

  Skills: Data Exploration & Visualization, linear regression

  Tools: Python (numpy, pandas, matplotlib, scipy)

• Predicting the Outcome of Kickstarter Projects

  Overview: Kickstarter allows creators to transform ideas into actual products, but a lot of projects fail to meet expectations or get off the ground. What factors are responsible for dictating the success or failure of project, and what should creators do to bolster their odds?

  Skills: Data exploration & visualization, feature engineering & selection, machine Learning (logistic regression, random forest, gradient boosting)

  Tools: Python (numpy, pandas, matplotlib, seaborn, plotly, scipy, scikit-learn)

• Clustering With News Publications

  Overview: For the most part, people are free to choose what news outlets they read and follow. In the United States, there is a near-endless list of sites that people can choose from in order to get their daily news and over time, they develop preferences for sites that they are more attached to, and do their best to avoid. What I would like to examine in this project is if it is possible to differentiate from several different publications based on their works.

  Skills: Natural language processing, clustering (k-means, spectral, & affinity propagation), machine learning (logistic regression, random forest, gradient boosting)

  Tools: Python (numpy, pandas, matplotlib, plotly, scipy, scikit-learn, nltk, re, spacy)

• Sentiment Analysis using Airline Tweets

  Overview: Social media is a treasure trove of textual data, as it allows users all over the world to express themselves and share whatever they want to say, get attention, and even start movements. For this project, I will be evaluating what people think about the choices they have in their travels using publicly available Twitter mentions.

  Skill: Tweepy (Twitter API), natural language processing, clustering, machine learning (logistic regression, random forest, gradient boosting, neural networks)

  Tools: Python (numpy, pandas, plotly, scipy, scikit-learn, nltk, re, wordcloud)