Built a full-stack web app that was secured and with role-based access.
Analyzed Starbucks' offers. Identified and investigated the offers that were wasted - unused or accidently used.
Built a recommendation engine using GloVe and deployed it using Flask.
Classified 25k+ real messages that were sent during disasters to send to an appropriate relief agency.
Containerized and deployed a Flask API to a Kubernetes cluster using Docker, AWS EKS, CodePipeline, and CodeBuild.
Built a backend in which different agents could log in securely and see the coffee menu with different details.
Wrote RESTful APIs with CRUD functionalities.
Built a database to power the API endpoints for searching, storing, and adding information.
Recommendation engine for IBM Watson Studio Platform.
Utilized EDA and ML models to understand why customers left us and to predict who will leave.
Deep dive into different methods for handling an imbalanced dataset.
Documenting my practices using Leetcode. Questions are organized by numbers and by topics.