Diligent Computer Science Graduate from the University of North Carolina, Charlotte. My interests are in the field of Full-stack Software Development, Web Design, Mobile Application Development, Data Science and Machine Learning. I am excited about making outstanding apps that enhances the lives of those around me. Prior to Graduate school, I worked as a Software Developer at Aspire Systems, India. My Primary role was Designing, Developing and Deploying Web E-Commerce Applications.
Worked as a full stack developer across various projects using Angular 4 and Java
Developed E-Commerce website for various countries across the globe for the Client Samsung
Worked as a full-stack developer for an in-house project called Deliverypedia - project management system which can keep track of Statement of Work (SOW), Milestones Achieved, etc. The restful web-service layer was developed in Java while the frontend was developed in Angular JS. Extensively used Hibernate in the data access layer.
A web-based application based on MVC architecture that keeps track of multiple conference proceedings. This automated system helps the researcher, conference chair and the reviewers in their respective activities with ease.
Developed web-based software that makes Alumni and Student interactions easy. Reunion with alumni and hiring opportunities can be made using the web interface.
Project is to classify the repayment ability of loan applicants based on various financial metrics other than credit history. Dataset for this project was massive and lot of preprocessing was done before employing ml models. Fuzzy C-Means clustering, and Adaptive network-based fuzzy inference systems were used to classify borrowers into various risk categories. Multiple custom activation functions were created to improve accuracy.
The main aim of the project is to distinguish if a tweet talks about a real disaster or not. This is for a competition hosted by Kaggle and the dataset consisted of 10,000 hand classified tweets. Various models were employed like a Naive Bayes classifier, SVM, LSTM and BERT models. Also used complex visualization tools such as Scatter text for EDA.
The goal of the project is to predict how food consumption and health factors influence the COVID-19 fatality rates around the world. Data were scaled and Unsupervised learning (Clustering) was performed to see how different countries are grouped based on food consumption, health factors, and COVID death rates.
Built recommender systems in python for good read books dataset. Implemented multifarious systems in both collaborative and content-based approaches. Further, the algorithms were executed in a distributed manner using spark ML on the AWS EMR cloud.
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