Pinned Repositories
AIOCoppingEngine
Free AIO bot for the culture
best-resume-ever
:necktie: :briefcase: Build fast :rocket: and easy multiple beautiful resumes and create your best CV ever! Made with Vue and LESS.
ebay-views-gen
generates fake views on items for you to increase activity . discord integration version too
mesh-scraper
NikeKorea-EarlyLink-Crawl-
lecs시절 얼리링크 Crawl(현재 GS)
Off-White-Monitor
Monitors off---white.com for new product releases and restocks
ShopifyAutoCheckout
Auto Checkout for Shopify using HTTP Requests. Coded in Node JS
ShopifyBot
Uses selenium to automatically navigate shopify websites, add item to cart and checkout
SupremeRestocks
Script that monitors supremenewyork for items that have restocked.
wm-pricer
Check WalMart's local store prices
achenxu's Repositories
achenxu/SupremeRestocks
Script that monitors supremenewyork for items that have restocked.
achenxu/best-resume-ever
:necktie: :briefcase: Build fast :rocket: and easy multiple beautiful resumes and create your best CV ever! Made with Vue and LESS.
achenxu/wedding-website
Our Wedding Website 👫
achenxu/100daysofcode-with-python-course
Course materials and handouts for #100DaysOfCode in Python course
achenxu/BlackScholesCalculator
A Java implementation of the Black-Scholes Option Pricing Model, using closing stock prices from Yahoo! Finance. Created while studying for SOA Exam MFE.
achenxu/Cracker
python script to find facebook accounts that have password is the same as phone number
achenxu/flight-schedules
A custom ML model that finds the optimal schedule of flights such that delayed flight compensation is minimized. Won 2nd place in AoCMM 2018 competing with 120+ teams.
achenxu/house-price-prediction
Predicting house prices using Linear Regression and GBR
achenxu/info343-final-project
Info 343 final project / Trivia Quiz
achenxu/instagram-nodejs
Simple library for auth, get followers, search by hashtags and locations, like posts, follow, get user feed of instagram with nodejs
achenxu/License-plate-recognition-using-opencv
The video is passed and the open cv is used to detect the vehicle and then the number plate and give the output
achenxu/LicensePlateDetector-deployment-flask
Deployment of ML model using flask
achenxu/mailanes
E-mail delivery system
achenxu/matcha
My own online dating website
achenxu/matcha-1
Dating website to find your best match
achenxu/nikeAPI-Py
achenxu/opensupports
OpenSupports is an open source ticket system
achenxu/paginas-amarillas-scrapper
A CLI tool for scraping paginasamarillas.com.ar
achenxu/panama-asamblea
achenxu/predicting-Paid-amount-for-Claims-Data
Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.
achenxu/shepherd
Guide your users through a tour of your app
achenxu/Spent
Budget tracking and money management web app
achenxu/stockx-future-releases
search stockx future releases page for profitable sneakers
achenxu/Stockx_Scraper
Scrapes StockX and outputs the user's sale history into a CSV file. Very useful come tax season
achenxu/StockXSneakers
An experiment with the StockX API, hmmm this could lead to something...
achenxu/the-price-is-right
Guess the price of the top sneakers on StockX (using the StockX API).
achenxu/Traffic-Rule-Violation-Detection-System
achenxu/Vehicle-Number-Plate-Reading
Read Vehicle Number Plate and store the data in a CSV file with date and time.
achenxu/Walmart-Stock-Price-Prediction-using-Recurrent-Neural-Network
achenxu/WebScraping-Google-Reviews-Python-Selenium-