Machine Learning
Machine Learning
Prediction
I applied 3 regression models (OLS, Ridge, and Lasso) to marketing data to determine the most efficient ad spend to boost revenue.
Classification
I applied 4 classification algorithms to breast cancer tumor data to determine the best classifier of malignancy: Logistic Regression, Decision Tree, Naive Bayes, and Support Vector Machine (SVM).
Cluster Analysis
I applied the K-means algorithm to credit card spend data to find 4 distinct groups of customers that differ empirically and meaningfully from each other.
Deep Learning
Classification
Returning to the breast cancer dataset, I applied a Multilayer Perceptron (MLP) and sequential Deep Neural Network (DNN) to compare performance against metrics and thresholds important to medicine.
Sentiment Analysis
I compared the performance of Naive Bayes (NB) and a Recurrent Neural Network (RNN) on a consumer review data set from Yelp to determine sentiment.
Image Captioning
I applied pretrained Deep Learning models to generate captions for previously unseen photographs. Results were far below human capability; however, they were similar to state-of-the-art captions from the timeframe of the models publication in 2015.