# 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.

## Customer segments

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.