Below is a collection of work that I have done, mostly Data Science related with machine learning, neural netowrks, an event driven FOREX trading app, and web development for HTML/CSS/Javascript knowledge. I have recently been bitten by the go gopher.
You can also check out my personal website.
Cardiovascular diseases (CVDs) or heart diseases are the most prevalent disease globally. According to the World Health Organization (WHO), heart disease is the primary cause of death around the world and is estimated to cause about 17.9 million deaths a year, with four out of five deaths due to heart attacks and strokes
This is notebook was created investigate heart disease using a heart disease dataset from UC Irvine Machine Learning Repository. I will be using the processed Cleveland dataset since it has already been recduced from the original 76 attributes to 14. The goal is to predict if a patient has heart disease or not based on 13 features by creating multiple supervised machine learning models. Before this is done the data will be cleaned to find any missing values and potentially scaled or transformed to improved the ML model.
This jupyter notebook has one dataset that has two different hotels from Portugal. This data will be analyzed to find any trends or patterns with guests booking into either hotel to try and find a way to minimize the amount of canceled bookings. A machine learning model will also be developed to attempt at predicting if a guest will cancel there booking before checking in.
There is a resort hotel in this dataset, found in the Algarve region of Portugal (southern Portugal), and a city hotel found in the captial Lisbon. Data was acquired directly from hotel's Property Managment System (PMS) SQL according to the paper which the data is originally from. The article is called, "Hotel Booking Demand Datasets", written by Nuno Antonio, Ana Almeida, and Luis Nunes for Data in Brief, Volume 22, February 2019. The artice can be found here.
Dataset can also be found on Kaggle.
Spotify’s Web API includes interesting Audio Analysis data for tracks played on Spotify and the intention is to use this low-level audio analysis for all the tracks in the entire Spotify Catalog. We are only using a sliver of the catalog that has been obtained from Kaggle’s dataset, “The Spotify Hit Predictor Dataset (1960-2019)”. Features (columns) of this dataset are listed in the Data Preparation section below in greater detail and used to develop a machine learning model to predict if a track will be a “hit” or “flop”, making this a binary classification challenge.
This notebook starts by using PRAW, "The Python Reddit API Wrapper" which will be used to download comments from the subreddit r/news/. PRAW can be used to create chat bots on reddit or just to scrap data from it to gain insights into online social media. I will use to Scapy to then perform natural language processing since this python library already have pretain models for this task. We will try to create an algorithm to detect online harassment, and in particular to flag if a comment has a high likihood of containing hate speech.
This dataset is a small portion of Yelp's businesses, reviews, and user data. It was originally put together for the Yelp Dataset Challenge which is a chance for students to conduct research or analysis on Yelp's data and share their discoveries. In the dataset you'll find information about businesses across 11 metropolitan areas in two countries.
More information can be found at Yelp.
Some dataset examples can be found on their GitHub.
Neural Network to detect if an image has a cactus in it or not. Dataset can be found here.
Predicting the image's classification using fashion MNIST dataset and the PyTorch Library for Neural Networks.
First neural network, very simple approach to classifying CIFAR-10 images.
This link has two small apps developed for interfacing with Oandas REST API. Specifically using Oandas V-20 REST API.
Oanda-first only has downloading historical data prices for 'EUR_USD' FOREX prices, and scaling two different currencies.
While Oanda-Event is an intro to an event driven software for streaming live prices from Oanda as well building on what was learned from Oanda-first to begin data collection for backtesting. Inspiration for the event driven software written in python comes from Quant Start. Warning, this is for educational purposes only.
This repo contains go code for querying Oanda's API for Forex price info. One could also set up a demo account to trade though right now it is only capable of sending GET requests to download JSON data, specifically OHLC data. It is also possible to connect to a local instance of PostgreSQL to save your OHLC data to a SQL database. There will be some additional configuration required to connect to your own instance of PostgreSQL.
I was looking for different ways to automate capturing a screenshot of my desktop. It would be possible to use python but using go may be faster since it is a compiled language. Then I will need to figure out how to capture portions of my screen instead of the entire thing incase I want to avoid capturing any timestamp from the operating system's clock. You can check out my blog post to learn more.
Using the gin webframework for go lang and PostgreSQL database for creating a simple RESTful API.
For starters I recommend following this tutorial from go lang for using gin web framework for creating a simple RESTful API.
This repo contains a small example of using PostgreSQL and the go programming language together.
The pgx driver is required for connecting to a local instance of PostgreSQL.
The sample data for this repo is from Microsoft's AdventureWorks OLTP sample database.
The AdventureWorks .bak files are backups for Microsoft SQL Server which we are replacing with PostgreSQL. In order to do this we can follow lorint repo's instructions for converting the files and inserting them into PostgreSQL tables.
From here we can execute raw SQL statements in our go programs.
This repo has a few examples for using go lang's tool present
A few things to note about this directory:
golangbasics.slide is from [Drashti Ved repo](https://github.com/drashtived03/goslides), along with the assets directory and Golang-Logo.jpg
the present.slide and legacy.slide is taken from go lang's present tool website and slightly modified
hello directory has a simple hello world script written in go and this is used in a few of the slide presentations
slide directory has one more example to show how to override the default stylesheet to modify pagenumber order & colour
presenting this directory requires Option 1, see below under Override Style
This repo contains an example of how one can query a local instance of Microsoft SQL using the Go programming language
The Montreal Exchange is the oldest exchange in Canada and has a very important history to Canada's economy. This is small web scraping module for acquiring TMX options data which is delayed by 15 minutes. It can be used to get options prices, both calls and puts for index and ETF options, equity options, currency options, and weekly options.
These prices are then displayed in a Pandas Dataframe for further analysis. This could include simple plots for visualizing the data or creating machine learning model or neural network to predict future prices. One could, for example, use Black-Scholes model to gain further insight.
Simple Calculator for using PyQt5 GUI library for python. PyQt is the the python port for Qt, which is a set of cross-platform C++ libraries that implement high-level APIs for accessing many aspects of modern desktop and mobile systems. In the src directory is a fbs project to create a stand alone python executable which will have matplotlib graphs within the app, though this is still a work in progress.
Learning CSS and Bootstrap.
Simple Example using Google Javascript API One form that asks user to enter two locations to find directions between the two points entered.