Solari – Finding Optimised Location Using Unsupervised Learning
Abstract
The present study aims to provide an optimal solution for ensuring an organized distribution of limited resources like, fuel, food and other necessities on time during natural calamities example flood by the organizations having limited number of helicopters, drones. Our project, SOLARI – Finding Optimized location using Unsupervised Learning (ML) is software providing an effective alternative to the existing ad hoc management scheme used by the government during natural calamities like floods. The domain of this study is Machine Learning and are based on Unsupervised ML Algorithm:K-means clustering. K-Mean’s algorithm proposed by J.B.MacQueen used in data mining and pattern recognition. The Software that is used in study are Python, Django[argon], Machine Learning supporting Libraries like (NumPy, pandas, sklearn, bcrypt, matplotlib etc.), HTML, CSS, Bootstrap, JavaScript, SQLite DB, Google maps API geopy, geocoder.The benefits of this study is that it helps the needy people stuck in affected areas will get necessary supplies. The algorithm gives an optimal solution for limited resources.
Authors
Srijal, Akanksha Singh,Deepti Shikha Ojha,Surbhi Tripathi Ms. Shweta Mayor Sabharwal