Software engineer with sharp skills and proven track record of producing results. Worked with startups in a fast paced environment with strict timelines, comfortable in doing multiple roles. Experience in project management, UI/UX designing apart from coding skills.
DUIT Technologies is a Singapore based startup. We create Electronic Business Cards for Corporates, Businesses and Startups similar to a paper visiting card, just digitized. Our ecards can be quickly shared with anyone over whatsapp, FB, email etc. Ecards facilitate direct business along with deep integration with e-commerce website, chatbots, CRM Tools and other platforms.
Worked on project called Duit Stocks (Algorithmic Trading Project) the objective was to achieve cumulative profit using data science and Machine Learning Algorithms.
The Objective was to create an app to order groceries online from nearby stores and to onboard multiple grocers. The main idea was to provide an online platform to local stores to get orders online.
Duit Stocks which was an Algorithmic Trading Project, the objective was to achieve positive cumulative profit using data science and Machine Learning Algorithms.
A basic chat app integrated with firebase notifications and using firestore for storing the data.
A flutter mobile application integrated with firebase, This is an app where user can sign-up and log in and Users can add items to the cart and order them. Users can also manage or add products to the database.
This project deals with a conference management system which is a web-based platform to organize paper submission and review. The user can register and publish his/her paper on the website.
Microsoft Azure Machine Learning Studio is a drag-and-drop tool you can use to rapidly build and deploy machine learning models on Azure.
Naive Bayes classifier to predict sentiment from thousands of Twitter tweets. Predict customer's sentiment (i.e.: whether their customers are happy or not).
Project-based course, build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces.