Projects

Description

This tutorial serves as both an analysis of relationship between crime and weather in New York and an introductory project for those aspiring to get into datascience. In our tutorial we dealt with problems like missing data and interpreting the data, all of which are invaluable in any datascience project. As a small spoiler we found that weather turned out to be a good predictor on crime frequency. Check it out here

Packages Used: Pandas, Numpy, Scikit Learn, Matplotlib, Seaborn, Sodapy

Description

This app was designed to help small/local bussinesses reduce costs by getting rid of external hardware required by services like google pay. It works by using QR codes to facilitate a peer to peer payment system. Additionally a scanner can only pay to the QR code owner which reduces the security risks that QR codes present. So getting into the technical details we split up the functionality between the front and back-end. On the back end we used Amazon Web Services as our back-end hosting provider, Express.js and the CapitalOne Nessie API to facilitate monetary management, MongoDB to store information on our user base, with it all powered by Node.js. On the front-end, we built an Android app using Android Studio and zxing to create the UI and the QR scanning and generation functionality of the app using android studio. Check it out here