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Projects

Nexus-AI
Nexus is an AI-powered SAAS application that quickly finds and retrieves insights from your documents. Using advanced vector search, reranking, and RAG, it intelligently locates relevant information, making document navigation fast and effortless.
TypeScript
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Youtube-Analytics-Dashboard
This open-source project provides a comprehensive YouTube Analytics Dashboard built with Python and Streamlit. The YouTube Analytics Dashboard allows users to interactively explore and visualize data related to YouTube video performance and audience engagement.
Jupyter Notebook
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Personal-NextJS-Portfolio-Website
This is an updated version (V2) of my portfolio, built with Next.js, Tailwind, Drizzle, and Vercel for optimized performance and scalability.
TypeScript
Personal-Portfolio-Website
This website was created to display my technology portfolio, featuring the projects I've undertaken in the fields of Artificial Intelligence and Data Analytics. The site is hosted on Netlify, ensuring it's fully accessible across various devices, and its interactive elements are powered by JavaScript.
CSS
VeggieLens
This project aims to develop Deep Learning models for predicting vegetable classes based on uploaded images and selected models (CNN 31x31 and CNN 128x128) using Docker, Tensorflow-Serving, and Render. Automated testing is conducted via PyTest and GitLab.
HTML
Car-Price-Predictor-Website
This project aims to develop a ML web application that can predict car prices, given a set of input car feature data with regression modelling. This project involves the use of the DevOps process, and the '100,000 UK Cars' dataset from Kaggle, which contained extensive data on car model, car registration year, mileage, tax and more.
HTML
RNN-Next-Word-Predictor
To build a next-word predictor using RNN models, given a sequence of words with Tensorflow and Keras, utilizing a variety of models involving LSTMs, GRUs, and Bi-Directional RNNs.
HTML
Vegetable-CNN-Image-Classification
Leveraging on Tensorflow and Keras Libraries, this projects aims to accurately classify grayscaled vegetable images, for sizes 31 x 31 px and 128 x 128 px with Convolutional Neural Network models.
HTML