Feat of arms
Dog breed classifier
I built an algorithm capable of identifying canine breed given an image of a dog. If the given image features a human, the algorithm identifies a resembling dog breed. In this project, I leverage the power of Convolutional Neural Networks (CNN) for the dog breed classifier.
Train a Smartcab to drive
I 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.
I reviewed unstructured data to understand the patterns and natural categories that the data fits into. Multiple algorithms were compared both empirically and theoretically. I made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.
Finding donors to Charity
Factors that affect the likelihood of charity donations were investigated based on real census data. I developed a naive classifier, trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. The best model was selected based on accuracy, a modified F-scoring metric, and algorithm efficiency.
Predicting housing prices
I built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools and identified the best price that a client can sell their house utilizing machine learning.
Titanic survival exploration
In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. I explored a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive. To complete this project, I implemented several conditional predictions.