Supun Nanayakkara (@supuna97)
github

supuna97

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Top repositories

1

expressjs-auth

Authentication REST APIs with NodeJS Express, JWT token and MYSQL.
JavaScript
22
star
2

mobile_accessories_sales_server

Mobile accessories sales backend using spring boot - java
Java
19
star
3

mobile_accessories_admin_web

Mobile accessories admin panel - Reactjs
JavaScript
18
star
4

expressjs-passport-oauth2

This is a simple example of how to use passport with oauth2. It uses the google strategy to authenticate users.
JavaScript
12
star
5

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.
JavaScript
12
star
6

express_js_crud_operation_apis

Expressjs Rest APIs with MVC design pattern using mysql connection.
JavaScript
11
star
7

verify-commits

This is a guide to verify signed commits on Github.
10
star
8

express-email

This is a simple email sender using express and nodemailer
JavaScript
9
star
9

expressjs_mvc

NodeJS Express with MVC without DB connection.
JavaScript
8
star
10

wordcount

JavaScript
8
star
11

knight-rider-circuit

Knight rider film car's circuit light system.
JavaScript
7
star
12

mobile_accessories_client_web

Mobile accessories client view - Reactjs
CSS
7
star
13

POS-Angular

Angular with type script for the admin portal
TypeScript
7
star
14

word_counter_tool

Free online word counter tool
JavaScript
7
star
15

supun-na97.github.io

6
star
16

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.
HTML
6
star
17

supun-na97

4
star
18

ReactJS-Crud-Application

JavaScript
3
star
19

express-multiple-file-upload

TypeScript
1
star