donateology

A -centralised P2P donation platform on blockchain that intelligently distribute resources on demand, ensuring transparency among transactions

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Inspiration

​From the Australia fire to the coronavirus outbreak, people are spreading their warmth by making donations and offering helps, in this 21th century of globalisation. Naturally we are thinking, maybe we can take another step forward, encouraging peer-to-peer individual donations using current technology, and that's why we proposed this Donateology ​ ​ ​

What it does

​Here we proposed a new project that aims to maximum the transparency of charity and donation by using the decentralized idea of blockchain and the data processing power of machine learning. Our online model provides a solution for everyone to be 120percent certain that their donations are directly verified by the receivers themselves. ​ ​ ​

How I built it

​Using multiple frameworks (React, Express, Flask, Truffle(Solidity), Web3js, mongoose, Google cloud) that each of our members specialized in. Mainly, we used javascript, in particular the react package, to build the frontend interface. We also Developed an Machine Learning ALgorithm after training linear model on google cloud server to simulate empidemc outbreaks. Deployed both tranditional database(mongodb) and decentralized ledger technology(Ethereum public blockchain). ​

Challenges I ran into

​The interaction with the blocckhain from React frame work as well as running multiple servers(Express, React, python ) in a way that functions together. Interaction with the express API to communicate with mongodb. ​

Accompliments that I'm proud of

​We have implemented and tested our machine learning algorithm via google cloud, which is a powerful virtual machine. We successfully implemented block-chain technology, allowing us to add new record to this donation changes. We also the front-end and back-end website source code within 24 hours, and during this time, we have learnt to implement several essential libraries such as Bootstrap and Flask. We also established API to connect between each modules within the project and spend hours unit testing and integrating-testing, which strengthens our skill in project managing as well as web-programming. ​ ​ ​

What's next for donateology

​First, we can deploy sophisticated machine learning to obtain a more accurate real world model, applying a dynamic schedule to mimic the chaning situation. Also, we aims to build connections with organization in need so that they can confirm the resources donated to utilize the blockchain tecchnology to make sure it goes to the right person.

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