Neel Shah

Magikube, an open-source tool designed to simplify the lives of developers by automating infrastructure setup and application deployment. With a single command, Magikube orchestrates production-grade environments with precision, eliminating the need for manual configurations. Aimed at streamlining development, Magikube provides fine-grained control while addressing common hurdles like tech debt and complex debugging. It’s a modern solution for coders looking to balance speed and quality without sacrificing sanity. Perfect for teams and individuals alike, Magikube is the ultimate tool to focus on what truly matters: writing great code and seeing projects come to life with ease.

A collaboration platform for students of various universities to work on projects together. The platform will leverage their skills and abilities to help them find suitable partners to help them on their projects.

• Identified feature importance using techniques such as variable importance, lasso regression, and information gain on the Traffic Violations dataset with over 1 Million rows, and 40 attributes with highly cardinal data. • Executing algorithms such as SVM and kNN in parallel on a high-performance cluster leveraging 64/200 cores and working with bigmemory, biganalytics, biglm packages in R. • Attaining better accuracy over 93% using Ensemble methods such as cross-validation, boosting and, stacking.

• Executed multi-label classification on leaves with apple diseases on 18.6K Foliar images. • Incorporated image hashing and image augmentation techniques to clean data, performed data augmentation and sampling to handle imbalanced dataset classes. • Achieved 82% accuracy over validation and test sets. Implemented Efficientnet-B4 for better accuracy.

GPA: 3.89/4.00

GPA: 3.45/4.00