An Adaboost classifier to accurately predict whether an individual makes more than $50,000, and identify likely donors for a non-profit organisation.
Statistical and spatial data analysis, including visualizations, for the walkability of Melbourne suburbs.
Capstone project for The University of Melbourne.
Analyzing customer spending data using Unsupervised Learning techniques for discovering internal structure, patterns and knowledge.
Cross language information retrieval system (CLIR) which, given a query in German, searches text documents written in English using Natural Language Processing.
Designing and implementing a Convolutional Neural Network that learns to recognize sequences of digits using synthetic data generated by concatenating images from MNIST.
Analysis of the BRFSS-2013 data set using R, focusing on investigating the relationship between education and eating habits, sleep and mental health, and smoking, drinking and general health of a person.
Skills: R, Descriptive Statistics, ggplot, dplyr
3-way polarity (positive, negative, neutral) classification system for tweets, without using NLTK's sentiment analysis engine.
Skills: Python, NLP, Scikit-learn
Analysing the GSS (General Social Survey) dataset using R to infer if, in the year 2012, were men, of 18 years or above in the United States, more likely to oppose sex education in public schools than women.
Skills: R, Hypothesis Testing, ggplot, dplyr
Creating an optimized Q-Learning driving agent that will navigate a Smartcab through its environment towards a goal.
Skills: Python, PyGame, Q-Learning
Analysis of technology stocks, including change in price over time, daily returns, and stock behaviour prediction.
Skills: Python, Pandas, Seaborn, Financial Analysis
A model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.
Skills: Python, Scikit-learn, Decision Tree Regression, Model Complexity Analysis
Exploration of baseball data for the year 2001 using R to look at replacements for key players lost by the Oakland A's in 2001. Inspired by the book/movie: Moneyball.
Skills: R, Exploratory Data Analysis, ggplot, dplyr
Exploratory Data Analysis of the 911 calls dataset hosted on Kaggle. Demonstrates extraction of useful features from different variables.
Skills: Python, Pandas, Seaborn