Projects
- Collaborated with a teammate to develop and train a decision forest and LSTM
neural network in Python using Tensorflow to recognize whether a given full name
is Brazilian or not
– Achieved an 0.842 accuracy with the decision forest and 0.905 accuracy with the
neural network on a test set of 12,000 names
Tools Used: Python, Tensorflow, Neural Networks, Decision Forest
– Extracted features from IMDb movie reviews using Python’s NLTK, Gensim, and
Scikit-learn libraries
– Implemented k-Means++ algorithm in Python to cluster IMDb movies based on
movie summaries and the content of each movie’s reviews in order to recommend
movies depending on previously liked movies
Tools Used: Python, NTLK, Gensim, Scikit-learn, k-Means++
– Collaborated with a team of 4 to develop and evaluate a linear regression model in
R for predicting daily bike rental counts using Boston bike share and weather data
pulled from 2011
– Identified outliers and significant predictors to optimize the model in R
Tools Used: R, Linear Regression
– Developed a web scraper in Python to pull NBA and college basketball data from
ESPN
– Conducted data preparation using Python and outlier detection using JMP
– Facilitated a team of 3 to develop and evaluate a linear regression model in JMP
for predicting professional basketball efficiency from college team and individual
statistics
Tools Used: Python, BeautifulSoup, JMP
