One of my newest projects is this website which I built from scratch with HTML, CSS, Python and Flask.

Unleash data-driven marketing with this customer segmentation project powered by K-Means clustering. We take raw customer data (like age, income, and spending behavior), clean it, visualize it, and group similar customers into clusters that actually make sense.

Tennis_Detection_Project

This project is a data-driven dashboard designed to help Santander Bank analyze customer churn and implement an early warning system to identify at-risk customers. The app integrates machine learning models for churn prediction and customer segmentation, providing actionable insights for retention strategies.

Repository to generate single-tree, bi-directional graph of the world's airports and their destinations. (Result used in COSC1 Lab4, as of Winter 2023)

DataChain 🔗 Process and curate unstructured data using local ML models and LLM calls

A machine learning app using Streamlit

A repo about tools and recipes for data analysis or management.

Anomaly detection in transactions means identifying unusual or unexpected patterns within transactions or related activities. These patterns, known as anomalies or outliers, deviate significantly from the expected norm and could indicate irregular or fraudulent behaviour.

Gain the career-building Python skills you need to succeed as a data scientist. No prior coding experience required.

Loan approval prediction means using credit history data of the loan applicants and algorithms to build an intelligent system that can determine loan approvals.

This repository hosts a packaged machine learning model designed for predicting passenger survival based on the Titanic dataset.

Anomaly detection in transactions means identifying unusual or unexpected patterns within transactions or related activities. These patterns, known as anomalies or outliers, deviate significantly from the expected norm and could indicate irregular or fraudulent behaviour.