Poorya Bagheri's repositories
kiosk-browser
Android Kiosk Browser App
advanced-terraform-3099246
This is a repo for the LinkedIn Learning course Advanced Terraform
aliyun-oss-php-sdk-laravel
A Laravel service provider for the AliCloud OSS SDK for PHP
Aliyun-oss-storage
阿里云OSS laravel storage Filesystem adapter, 打造Laravel最好的OSS Storage扩展.
AndroidDev2019Data
Exercise files for the course Android Development Essential Training: Manage Data on LinkedIn Learning
banks-reference-android
Reference Android code integrating TransferWise's services into a (Demo) Bank App
bnm-forex-scraper
It's a data extraction tool that mine currency exchange data from bank negara malaysia through api and other currency exchange through scraping, the purpose is to compare their rates.
devops-directive-terraform-course
Companion repo for complete Terraform course
docker-compose-laravel
A docker-compose workflow for local Laravel development
enlightn
Your performance & security consultant, an artisan command away.
Flutter-responsive-email-ui---Mobile-Tablet-and-Web
We redesign the outlook app also make it responsive so that you can run it everywhere on your phone, tab, or web.
flysystem-aliyun-oss
Flysystem adapter for the Aliyun OSS SDK
ifsc
:bank: IFSC Codes Repository
jsonschema-recipient-demo
A demo of parsing a JSON schema using jackson ObjectMapper and rendering it recursively
laravel-ecommerce
AvoRed an Open Source Laravel Shopping Cart
laravel-excel-docs
Laravel Excel Docs
laravel-medialibrary
Associate files with Eloquent models
learning-terraform-3087701
This repo is for the Linkedin Learning course: Learning Terraform
react-native-ssl-pinning
Examples of how to communicate over SSL in your React Native application
react-native-zendesk
React native wrapper for Zendesk SDK
react-router-example
Private Route, Public Route, and Restricted Route with React Router
splink
Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends
Stock-Market-Sentiment-Analysis
Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka
stocksight
Crowd-sourced stock analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
zingg
Scalable identity resolution, entity resolution, data mastering and deduplication using ML