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achenxu's Projects

shopifybot icon shopifybot

Uses selenium to automatically navigate shopify websites, add item to cart and checkout

shubi icon shubi

All in one shoe purchasing program.

simpleplatformgame icon simpleplatformgame

A platform game created using Python. The game resembles Mario where the player has to complete different levels by defeating/avoiding monsters and jumping off obstacles. Enjoy!

sjs_drops icon sjs_drops

Script using requests module to register accounts to Slam Jam Socialism raffles.

slack-chatbot-database-watson icon slack-chatbot-database-watson

Code to build a Slackbot to create and search Db2 or PostgreSQL database entries for events and conferences. The Slackbot is backed by the IBM Watson Assistant service. An older version uses Botkit to integrate Slack and Watson Assistant/Conversation.

smspva icon smspva

A Python class to help working with http://smspva.com/

sneaker-notify icon sneaker-notify

Sneaker/Restock/Monitor Notify via Twitter coded in Python using Scrapy.

sneakerbottutorials icon sneakerbottutorials

SneakerBot Youtube Tutorials created as a suppliment to my "Creating a Sneaker Bot in Python" Youtube Series

sneakers_project icon sneakers_project

Using Selenium, Neha scraped data about 35 top selling sneakers of Nike and Adidas from stockx.com. She used this data to draw insights about sneaker resales.

sneaks-api icon sneaks-api

A StockX, FlightClub, Goat, and Stadium Goods API all in one. This sneaker API allows users to search sneakers and track and compare prices while providing additional info such as product links and images

snkrs-moniter icon snkrs-moniter

Keep Track of Sneaker Releases (Not Done, I well be changing the application into an Electron App)

socrates icon socrates

An anonymous, real-time Q&A facilitating application

southy icon southy

Automatic check-in for Southwest flights

speed-estimation-of-vehicles-with-plate-detection icon speed-estimation-of-vehicles-with-plate-detection

The main objective of this project is to identify overspeed vehicles, using Deep Learning and Machine Learning Algorithms. After acquisition of series of images from the video, trucks are detected using Haar Cascade Classifier. The model for the classifier is trained using lots of positive and negative images to make an XML file. This is followed by tracking down the vehicles and estimating their speeds with the help of their respective locations, ppm (pixels per meter) and fps (frames per second). Now, the cropped images of the identified trucks are sent for License Plate detection. The CCA (Connected Component Analysis) assists in Number Plate detection and Characters Segmentation. The SVC model is trained using characters images (20X20) and to increase the accuracy, 4 cross fold validation (Machine Learning) is also done. This model aids in recognizing the segmented characters. After recognition, the calculated speed of the trucks is fed into an excel sheet along with their license plate numbers. These trucks are also assigned some IDs to generate a systematized database.

spent icon spent

Budget tracking and money management web app

splash-party icon splash-party

Run multiple headless browsers on Adidas.com waiting room

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