Have experience 1 yr experience in Flutter and currently working on ML and Game dev in Unreal 

github

Sabuj Ghosh • App Developer at GDSC IEM • Peerlist Profile

Computer Science Under-graduate who loves Solving Real World Problems.I love Algorithms and Data Structures. Enthusiasti

Flutter developmer

Machine Learning enthusiast and an eager problem solver

Businesses with the desideratum to burgeon face may predicaments . Some businesses fail because of improper choice of location for next branch location which in most cases are not profitable for them . Some face employee hoax in new unknown region of the branch.Our system aims to provide the business with the burgeoning view the intelligence to choose the best possible branch location which will be profitable for them . There will be a marketplace for the business owners where they can buy raw materials directly from the producer thus removing the intermediaries and drastically reducing the cost . There will be a blockchain system where the new branch's employees' data will be stored after the day of being hired .This will provide clarity and quick access to the credentials . The blockchain data can also be shared with other companies in case the employee joins another company only on their demand . We built the machine learning model with the help of the demographic data and the distance matrix of the places with some other parameters in mind . We used K-Means clustering to find the final result along with some preprocessing of the data . The blockchain is developed around 5ire and the data is stored in IPFS of Pinata . We used HardHat for deploying the blockchain and carrying out the transactions . We used MetaMask for the wallet . We also used Django for for deploying the Machine Learning model.We ran into challenges while deploying the machine learning model using Django. We also ran into issue while integrating the blockchain along with machine learning.We are proud of that we have been able to predict the most profitable location for the new branch with almost 80-85% accuracy. We are also proud of the fact that we have been able to store the data effortlessly in the IPFS with which we bring a huge lot of transparency and reducing the time of the verification to just few minutes.We learned much about the blockchain and how we can collect the data for using the machine learning model. We learnt about smart contracts and building the nft market place.We have planned to make a blockchain as a service where it will be used by the people in the hireable age. They can store their data or the credentials in the blockchain thus enhancing the transparency of the data . This blockchain service can also be used by other companies for hiring employees using this blockchain. This will reduce the time of verification drastically , from about 3-4 days to just few minutes.