"Forward-thinking engineer with hands-on experience in implementing production-level trditional machine learning models, deep learning models and large language models"
As an Machine learning engineer and AI enthusiast, I turn data into smart solutions. With a Master's in Data Science and AI, I've worked across various industries like tech, retail, and finance. I love tackling data challenges, from Excel to advanced Machine Learning. My passion is helping businesses make data-driven decisions leveraging the capabilities of new generations of AI models like large language models
Decided to do some experiments on multi agent language model, this my own implementation of autogen. AutoGen provides a multi-agent conversation framework as a high-level abstraction. It is an open-source library for enabling next-generation LLM applications with multi-agent collaborations, teachability and personalization. With this framework, users can build LLM workflows
I implemented a simple streamlit chat app where you can upload your PDF and ask extract informations from your pdf based on prompt. I implemeted this app using langchain and Open AI embeddings.
I designed an intuitive, interactive, and engaging analytics app that provides real-time data visualization for an organization's periodic employee satisfaction survey. This app helps in monitoring and interpreting employee satisfaction metrics.
I utilized data from the Nigeria Interbank Settlement System to extract valuable insights regarding consumer purchasing patterns and motivations. For the analysis, I employed Power BI to examine the data specifically for the month of January 2023.
This retail analytics dashboard is designed to investigate the revenue of a pharmaceutical chain across 25 different branches.
This report presents my findings on customer retention versus churn within the HubPay dataset.
This analysis aims to employ advanced natural language processing (NLP) techniques to thoroughly explore and understand the content of a large collection of tweets written in Dutch
The analysis was conducted using two datasets: daily price and volume data for Bitcoin and transaction data for Tether. These datasets were combined to produce a dataset containing daily Bitcoin prices and daily USDT transaction volumes for further analysis..