A deep learning, neural network classifier constructed using the Keras framework. Model hyperparameters were trained/optimized using a 5-fold cross-validation grid search appraoch in order to be able to predict whether an individual has diabetes or not using the Pima Indian Diabetes Database. Leveraged the visualization capabilities of TensorBoard in order to visualize the training process. Click here to see this repository.
I use Random Forest and Gradient Boosted Regressors in order to extrapolate the energy usage for household appliances and compare out-of-sample performances of the algorithmsClick here to see this repository.
I use a logistic regressor in order to predict fraudulent banking transactions in a highly unbalanced dataset. I used a PCA for dimensionality reduction and explore effects of feature selection on model performance as well. Click here to see this repository.
I explore building a sequence-to-sequence NLP solution using an Encoder-Decoder model built with the Keras framework that could predict the expanded form of a polynomial based on the input factored form. Click here to see this repository.
An exploration of the efficacy of clustering analyses on astronomical data (with observations from Spitzer and James Webb Space Telescopes) I perform for my M.S.c research. I hope to understand the mechnism of the evolution of stable, carbonaceous molecules in high-energy star-forming regions by analyzing key stratifications in emission properties from these molecules on various spatial and wavelength scales. To see this repository Click here.
I experiment with various advanced ensemble, regression techniques such as a Bagging Regressor, a Random Forest Regressor, and a Gradient Boosted Regressor in order to predict the selling price of a house based on property description features. Click here to view this repository.
I use a system of ordinary differential equations and a second-order Runge-Kutta integration scheme in order to numerically simulate the physical equation of state describing the interior structure of a stellar body. I solve the Lane-Emden equation for the solutions of order n=1.5, 3, 3.5, though this value can be altered to higher dimension for further exploration. Click here to view this repository.
I explore using Natural Language Processing techniques (punctuation removal, tokenization, stopword removal, lemmatization, and vectorization) to build a sample spam-trap to distinguish between ‘spam’ or ‘ham’ text messages. This classifier (I compare the performance of a Random Forest and Gradient Boosted Classifier in this context, and for my purposes find the Random Forest algorithm to be better suited. Click here to see this repository.
This repository contains a published Python package of my own creation which acts as an ETL pipeline that cleans and serves energy distribution data for any given object from the Sloan Digital Sky Survey database queried by the user using the object’s Celestial Coordinates (right ascension and declination). Though this package uses the celestial coordinates and SDSS database to query for data, these parameters can be easily varied in future releases. Click here to see this repository.
I use clustering (K-Means) in order to segment a consumer demographic based on Recency, Frequency and Monetary Value in order to improve quanitative marketing strategies of a business and to develop commercial strategies for a given company for which this data is available. Click here to see this repository.
I use dimensionality reduction techniques (PCA, Autoencoding) in order to analyse and predict the sentiment in a set of reviews from the Internet Movie Database (sentiments originally labelled by humans). I explore and compare the predictive performances of dimensionally-reduced input using Partial Component Analysis and Autoencoding. Click here to see this repository.
I explore multi-modal learning using a Convolutional Neural Network to predict housing prices based on numeric property descriptors as well as images of the interior/exterior of the house. I employ a grid search functionality in order to tune and assess effects of various hyperparameter combinations. Click here to see this repository.
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Published:
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Published:
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Undergraduate course, Western University, Earth Sciences, 2020
ES2213: The Dyanamic Earth
Undergraduate course, Western University, Earth Sciences, 2021
ES2232: Exploring the Planets