angular-calendar

api_correios

api_electronjs

Aplicação de conhecimento da API node electronjs

api_movie_catalog

app_contactbook

bi_suicidio_brazil_1985_2015

This compiled dataset pulled from four other datasets linked by time and place, and was built to find signals correlated to increased suicide rates among different cohorts globally, across the socio-economic spectrum.

bithub

BTC + BitHub = An experiment in funding privacy OSS.

calculadora

controle_estoque_frontend

credit_card_customers

credit_risk

data_science_gold_price_1978_2020

Gold is a very essential commodity whose price varies continuously like any other commodity. It is interesting to know how the price varies according to time and place. The dataset contains gold prices from the year 1978 to 2020 in 24 different currencies.

electricity_production_by_source

face-api.js

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

facerecognitionauth

flutter-list-task

flutter_upload_image

gender-and-age-detection

german_credit_risk

Context The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below. Content It is almost impossible to understand the original dataset due to its complicated system of categories and symbols. Thus, I wrote a small Python script to convert it into a readable CSV file. Several columns are simply ignored, because in my opinion either they are not important or their descriptions are obscure. The selected attributes are: Age (numeric) Sex (text: male, female) Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled) Housing (text: own, rent, or free) Saving accounts (text - little, moderate, quite rich, rich) Checking account (numeric, in DM - Deutsch Mark) Credit amount (numeric, in DM) Duration (numeric, in month) Purpose (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others)

imc_flutter

Calculadora IMC em Flutter

javascriptaula

machine_learning_aula

Classroom tracking data project.

ml_python_mega_senna

Projeto que utiliza técnicas Web-Scraping e Machine Learning que usa históricos de jogos do mega-senna para projeções de padrões

moisesmember

movies

node-desktop-application

node_postgres_datawarehouse

node_suicidio

nodejs-dialogflow

Node.js client for Dialogflow: Design and integrate a conversational user interface into your applications and devices.

postgres_create_db_sales