waicongtam / assignment-portfolio

This repository is showcase of the codes of my assignments. All the assignments I consider worth sharing will be updated here right after the late penalty has reached 50%.

Assignment Portfolio

by Waicong Charles Tam

Introduction

Hi! I'm Waicong Tam (Charles), a current Master of Information Technology student in Monash University. This is a repository that works as my personal portfolio. I will update the assignment that I consider worth showcasing here. Usually I will upload the assignment after it had been graded and I had organised it well.

What are the units (subjects) that have been uploaded?

FIT9131 - Programming foundations in Java

This is a unit I took when I was in my first semester here. I achieved semester top in this unit, and I have so much enthusiasm in this unit! Hopefully I had made progress and improvement from the first assignment to the other! This is a unit about basic programming skills and principles in Java. The first assignment is a chess game, the other one is a Grand Prix simulator. Personally I love the second one so much since I am a huge Formula One fan!

FIT5202 - Data processing for big data

This unit is about processing data using Apache Spark and the basics of different machine learning algorithms. It is one of my favourite units.

Assignment 1

The first assignment is about wrangling and explanatory analysis on data, it is composed of two parts.
Part A is about analysis of the words in two books. I discovered the numeric distribution and compare the top words of the two books.
Part B is about the analysis of the crime records in the state of South Australia. Data wrangling and visualisation techniques are implememnted in this part. I analysed crimes in different suburbs, in different years and in other aspects.

Assignment 2

This assignment is about applying classification machine learning algorithms on weather data to make prediction. At the last part of the assignment, I tried out different methods to improve the accuracy of the model including parameter tuning, feature scaling, using a larger training set and using more feature. I made a amateur analysis on those methods. Overall this is a very fun and interesting project.

Hope you enjoy it!