Explainability

A multi-domain analysis of pre-trained AI models covering NLP, Computer Vision, and Tabular Data to evaluate key trade-offs between model size, accuracy, speed, and explainability. Includes a structured decision matrix and comparative insights that guide responsible AI model selection for real-world

Generative AI LLM Infrastructure: Training Process and Costs

This project explores the infrastructure required to train Generative AI Large Language Models (LLMs), explaining each stage of the training pipeline from data collection and preprocessing to model deployment and monitoring. The key resources needed large datasets, powerful computational hardware.

Understanding Neural Networks

This project demonstrates my understanding of neural network architecture and its key components through a visually engaging presentation. By experimenting with tools like Neural Network Playground, I explored how data flows through layers, how weights and activations influence learning.

Machine Learning Algorithms Visual Framework

This project presents a visual framework of ten key machine learning algorithms across supervised, unsupervised, and generative learning. It categorizes models by domain, tabular data, computer vision, NLP, and generative AI while explaining their purpose and real-world applications.

COVID-19 World Vaccine Monitor

This Tableau interactive project offers insights into COVID-19 vaccination coverage, doses administered, and rates worldwide, empowering informed decision-making and understanding of vaccination progress across regions and countries.

Data Professionals Survey Breakdown

This project compiled insights from data experts worldwide. It assesses career shifts, salaries, job satisfaction, and more, offering valuable insights into trends and challenges within the industry. Participants' anonymity were preserved, ensuring the project's credibility and usefulness.

Little Rock Crime Exploratory Data Analysis with Python

This Python project involves examining and understanding crime data specific to the Little Rock area. This process includes tasks such as data cleaning, summarization, visualization, and initial insights generation. Python libraries used are Pandas, Matplotlib, and Seaborn.

COVID-19 Hospital Analysis

In response to the global COVID-19 pandemic, I spearheaded the development of a comprehensive data dashboard using Power BI to analyze and visualize COVID-19 statistics for hospitals. The dashboard provided insights into total cases, confirmed cases, recoveries, and deaths,focusing on data from 2020

Interactive Dashboard of a Company's Beverage Sales in the US

This Tableau Project offers a detailed analysis of a US beverage company's sales performance. The dashboard showcases key financial metrics such as profit, units sold, and cost of goods sold, facilitating informed decision-making and strategic planning for the company's operations and growth.

Delta Assist

Delta Airlines' AI chatbot "Delta Assist" is designed to assist customers facing flight disruptions, such as cancellations, delays, and baggage issues. Instead of waiting on hold for a human agent, passengers can use the chatbot to reschedule flights, track baggage & check refund eligibility.

Dynamic Pricing ML Project

This project demonstrates how machine learning can be used to optimize ride pricing dynamically based on customer behavior, location, time of day, and ride preferences. Dataset: https://www.kaggle.com/datasets/arashnic/dynamic-pricing-datasetselect=dynamic_pricing.csv

Customer Churn Prediction with Machine Learning

This project aims to build a predictive model to identify customers likely to churn from a subscription-based telecom service. By analyzing customer behavior and service data, businesses can proactively retain customers and implement better retention strategies.

Credit Card Fraud Detection Using Machine Learning