Tableau Projects

I designed a Tableau dashboard to analyze workforce demographics, retention, and attrition trends across departments. The insights help HR teams identify risk factors and implement data-driven retention strategies.

This dashboard visualises freelance job trends, earnings, and skill demand across platforms and regions. It demonstrates how Tableau can uncover market opportunities and guide workforce planning.

I created a Tableau dashboard that summarises player performance metrics, including goals, expected goals (xG), and win percentages. The project showcases how sports data can be transformed into actionable insights for coaches, analysts, and fans.

SQL Projects

I developed SQL queries to segment customers into spending tiers (low, middle, high) based on transaction history. This project highlights my ability to use SQL for business intelligence, customer profiling, and data-driven strategy

I built an SQL-based classification system to analyze patient symptom history and identify skin cancer risk categories such as squamous cell carcinoma, basal cell carcinoma, and melanoma. This project demonstrates how structured queries can support early detection and clinical decision-making

Machine Learning & Ai Modelling

I developed a machine learning model that predicts the likelihood of heart disease based on patient clinical data. Using algorithms like logistic regression, random forest, and XGBoost, the model achieved over 90% accuracy and is deployed as a live Streamlit app for real-time risk assessment.

I developed a logistic-regression baseline with PCA variants to classify health outcomes, achieving ~80% accuracy. The project highlights how dimensionality reduction affects performance, demonstrating clear trade-offs between model simplicity and predictive power.

I developed a machine learning model to predict hotel booking cancellations using features like lead time, room type, and booking channel. The XGBoost model achieved ~90% accuracy with a ROC-AUC of 0.96, enabling hotels to reduce lost revenue, improve occupancy planning, and optimize staffing