Discover @supuna97 Open Source projects

Supun Nanayakkara (@supuna97)

supuna97

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Registered over 5 years ago
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JavaScript
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CSS
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Java
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Location 🇱🇰 Sri Lanka
Country Total Rank 149
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JavaScript
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CSS
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Java
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mobile_accessories_sales_server

Mobile accessories sales backend using spring boot - java

mobile_accessories_admin_web

Mobile accessories admin panel - Reactjs

expressjs-auth

Authentication REST APIs with NodeJS Express, JWT token and MYSQL.

express-image-upload

This is a simple express server that allows you to upload images to a local server project folder. It uses multer to handle the file uploads and stores the images in a folder called storage. It also uses the cors package to allow cross origin requests.

expressjs-passport-oauth2

This is a simple example of how to use passport with oauth2. It uses the google strategy to authenticate users.

express-email

This is a simple email sender using express and nodemailer

verify-commits

This is a guide to verify signed commits on Github.

expressjs_mvc

NodeJS Express with MVC without DB connection.

express_js_crud_operation_apis

Expressjs Rest APIs with MVC design pattern using mysql connection.

wordcount

knight-rider-circuit

Knight rider film car's circuit light system.

mobile_accessories_client_web

Mobile accessories client view - Reactjs

POS-Angular

Angular with type script for the admin portal

word_counter_tool

Free online word counter tool

supun-na97.github.io

divorce-prediction

The aim of this machine learning application is that it can use to predict divorce. The dataset is the Divorce Prediction dataset by Larxel from the website: Kaggle. There are few models tested when developing the application, including Tree-based models like Decision trees, Random forest, XGBoost and a binary classification model called Logistic Regression. In addition, there are some data pre-processing techniques also used to analyze and normalize the dataset. Then, after applying the normalized datasets to all the models and predicting, some model optimizing methods used to get more accuracy. To build the final application, I have used the Flask framework in Python to expose the model via a REST web API. And i have used Reactjs framework to view frontend. To run as an artefact, I have used the requirements.txt technique in Pip dependency management.

supun-na97

ReactJS-Crud-Application

express-multiple-file-upload