William Kubin's Projects
In this project, I orchestrate a 3-tier php application on a Kubernetes cluster. The application consists of a frontend (php app), a backend (database management tool), and a database (mysql). I automated the launch of two AWS EC2 Instances as Master and Worker Nodes using Terraform and Ansible respectively.
App01
Example Apps to Demonstrate Argo CD
In this project, I implemented a continuous integration/continuous deployment (CI/CD) pipeline from GitHub to AWS EC2 Instance.
In this project, I implement AWS Infrastructure with an Infrastructure as Code (IaC) service CloudFormation
Docker AWS CI/CD Pipeline
In this project, I create a complete CI/CD pipeline. Please see the README file for more details
In this project, I create a complete CI/CD pipeline. Please see the README file for more details
This project is a continuation of Complete_CI_CD_Project_02. I add a deployment of the dockerized web app on Amazon ECS
In this project, I create infrastructure in AWS using Terraform (IaC) and use them to build a CI/CD pipeline for a java web app. I deploy the app on a tomcat server.
In this project, we analyze and predit the approval ratings of credit cards with respect to features including gender, age, debt, married, bank customer, industry, ethnicity, years employed, prior default, employed, credit score, driver's license, citizen, zipcode, income and approved (target feature) using a deep learning model (Artificial Neural Network (ANN)). We use precision, recall, f1-score to measure performance of our ANN model.
In this project, we analyze data on customers behavior with respect to their credit card usage. We employ KMeans clustering algorithm (an unsupervised machine learning model) to discover natural groupings in feature space in the data.
In this project, I analyze the approval ratings of credit cards with respect to features including gender, age, debt, married, bank customer, industry, ethnicity, years employed, prior default, employed, credit score, driver's license, citizen, zipcode, income and approved (target feature).
In this project, we apply various classification models (6) to the credit card data to examine their corresponding performance. The models include simple linear algorithms {Logistic Regression (LRG), Linear Discriminant Analysis (LDA)} and non-linear algorithms {K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Naive Bayes (GNB), Support Vector Machines (SVM)}
Detrended Fluctuation Analysis
A DevOps project using AWS services and Docker Containers
here is an eccommerce website
In this project, I deploy a springboot micro services app into an EKS Cluster using Jenkins pipeline and Helm
Terraform scripts to create Amazon EKS Infrastructure
Helm chart repository for example charts
Microservice fast food app
Fast food application project using micro-services
python package for DFA (Detrended Fluctuation Analysis) and related algorithms
this repository allows to build the AWS EKS infrastructure for the deployment of the full DevOps pipeline
This project provides a realistic forecast based on latest available data to reflect current state of the economy of Switzerland. The aim is to develop a model that can accurately predict the economic growth rate of Switzerland using the dataset that is available from dataseries.org website
geolocation app
Deployment of docker image of geolocation application to Kubernetes cluster using Helm
In this project, we utilize gradient boosting ensemble ML algorithm for structural predictive modeling of blood transfusion data.
This project illustrates the prediction of Heart Failure in people. The data used for the analysis has 299 observations with 13 variables namely age, anemia, creatinine phosphokinase, diabetes, ejection fraction, high blood pressure, platelets, serum creatinine, serum sodium, sex, smoking, time and the TARGET variable death event
In this project, we I use Kubernetes to implement a real-world project where Mongo Express UI is connected with MongoDB and the Mongo Express makes requests to the DataBase.