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Atul Kumar's Projects

chapel icon chapel

a Productive Parallel Programming Language

chhatrawas icon chhatrawas

An Android application created in HackNITP 2019 event for hostel management. It got the first Position in the event! It was designed and written from scratch in less than 30 hours with my three other teammates. This app does what it was built for so don't judge us for not using Industry levels design principal like MVVM or MVP 😜

ds-algo icon ds-algo

Make your first PR! ~ A beginner friendly repository made specifically for open source beginners. Add any program under any language (it can be anything from a hello-world program to a complex data structure algorithm) or update the existing one. Just make sure you add the program under the correct language directory. Happy codingImplementation of specific algorithms and data structure used and problem based on that algorithms(C++/JAVA/PYTHON)

easyupipayment-android icon easyupipayment-android

πŸ“±Android Library to implement UPI Payment integration easily in Android App πŸ’³πŸ’Έ

maang-1 icon maang-1

πŸ† Winning Project of (Smart India Hackathon 2019) for the problem statement KK4.

mifos-mobile icon mifos-mobile

Repository for the Mifos Mobile Banking App for clients

neurolab-android icon neurolab-android

NeuroLab Android https://github.com/fossasia/neurolab-android/raw/apk/neurolab-dev-debug.apk

open-event-attendee-android icon open-event-attendee-android

Open Event Attendee Android General App https://github.com/fossasia/open-event-android/blob/apk/open-event-dev-app-playStore-debug.apk

open-event-organizer-android icon open-event-organizer-android

Open Event Mobile App for Organizers and Entry Managers https://play.google.com/store/apps/details?id=com.eventyay.organizer

pslab-android icon pslab-android

PSLab Android App https://play.google.com/store/apps/details?id=io.pslab

susi_android icon susi_android

SUSI.AI Android App https://play.google.com/apps/testing/ai.susi

taxifare icon taxifare

An app to predict the fare of NYC taxis and compare them with live fares of Uber and Lyft taxi providers. It uses Neural Network trained on 50 million taxi trips to predict the fare. It uses Uber API and Lyft API to provide live fares and directly book a ride through Uber and Lyft apps and uses Mapbox API to plot a map between source and destination. TaxiFare utilizes Django framework to connect the Python training code and Android.

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