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codelibrary

:gem:Collection of algorithms and data structures

gin-boilerplate

The fastest way to deploy a restful api's with Gin Framework with a structured project that defaults to PostgreSQL database and JWT authentication middleware stored in Redis

gitignore

A collection of useful .gitignore templates

gnirehtet

Gnirehtet provides reverse tethering for Android

license-super-resolution

A License Plate Image Reconstruction Project in Tensorflow2

lordlro

Config files for my GitHub profile.

multiple-ssh

Manage multiple ssh keys on 1 machine

sales-data-prediction-system

The specific problem in this project is about the time-series data trend prediction. The specific application scenario is in e-commerce. You are given a real dataset obtained from a real-world e-commerce application where there were 1000 products and 31490 customers (i.e., buyers) who bought these products. Of these 1000 products there are 100 key products (popular products). Also these 1000 products are in 15 categories. The specific data are given in the seven tables and the specific details of these tables are given below. The time window of this dataset is in 118 days with data documentation for each day. Hence, the time unit is one day where the timeline goes from the 0-th day to the 117-th day (17 weeks less one day in total). Now you are asked to do the sale quantity prediction for the 100 key products for each day between the 118-th day and the 146-th day (29 days).

topic-classification

uniranking

Webapp for Vietnam university rankings