Medical AI & Healthcare Skin-Lesion-MSc

End-to-end deep learning system for automated skin cancer diagnosis. Classifies 7 lesion types using ResNet50 & EfficientNet-B3 on 43,000+ dermatology images (HAM10000 & ISIC 2020), with Explainable AI (Grad-CAM, LIME, SHAP) for clinical interpretability. Achieved 90%+ validation accuracy on Google

Breast-Cancer-Prediction-Using-Machine-Learning-Algorithms

Comparative ML study for early breast cancer diagnosis. Benchmarks Logistic Regression, SVM, Random Forest, and XGBoost classifiers on the Wisconsin Breast Cancer dataset to identify the most reliable algorithm for medical screening.

Analysis-of-a-Financial-Risk

Financial risk assessment analysis identifying key risk indicators across portfolios. Uses statistical modeling and visualization to quantify exposure and support data-driven risk mitigation strategies.

Bitcoinprediction,

Cryptocurrency price forecasting model built on historical Bitcoin OHLCV data. Explores trend patterns using machine learning and time-series techniques to predict short-term price movements.

Gold-Prediction

Time-series forecasting model predicting gold commodity prices using historical market data. Applies regression and ensemble methods to identify price trends and support investment decision-making.

Car-price-prediction

Regression model estimating used car market prices based on 20+ vehicle attributes (brand, mileage, year, fuel type). Achieved R² = 0.92 after hyperparameter tuning; includes a stakeholder-ready pricing dashboard.

Campaingdata-SQL

SQL-driven marketing campaign analytics project. Queries and analyses multi-channel campaign performance data to identify top-converting channels, optimise budget allocation, and measure ROI across 5 campaigns.

Market_campaign

Machine learning model predicting customer response to marketing campaigns. Uses demographic and behavioral data to segment audiences and maximise campaign conversion rates.

TS-ACADEMY-CAPSTONE-PROJECTS

Final capstone project from TS Academy's Data Science program. Demonstrates end-to-end data science workflow: problem framing, data cleaning, EDA, modeling, and storytelling with results.

TS-ACADEMY-CAPSTONE-PROJECT

Collaborative capstone project applying supervised ML techniques to a real-world dataset. Covers feature engineering, model selection, validation, and presentation of actionable insights.

Fitness-model-prediction

Predictive model estimating fitness outcomes (e.g., calorie burn, performance metrics) based on user activity data. Applies regression and classification techniques to health and wellness datasets.

Customer-churn-model-deployment

end-to-end churn prediction system for subscription businesses. Identifies 12 key churn drivers, achieves 25% improvement in forecast accuracy, and deploys a real-time Power BI monitoring dashboard with automated intervention triggers.

California-Housing

Regression analysis on the California housing dataset predicting median home values based on location, income, and housing attributes. Includes EDA, feature scaling, and model performance comparison.

SPE (Oil & Gas)

Data analysis project focused on the oil and gas sector. Applies analytics techniques to industry datasets — covering production metrics, operational efficiency, or reservoir data relevant to the SPE (Society of Petroleum Engineers) domain.

Analysis-Of-PQ-Exercise-ID

Data analysis exercise solving structured analytical problems, likely covering pivot queries, data aggregation, or statistical reasoning challenges.

Machine-learning

Foundational machine learning notebook series covering core algorithms: linear regression, decision trees, KNN, clustering, and model evaluation. A solid reference for ML fundamentals.

Data-Analysis

Exploratory data analysis (EDA) project showcasing data cleaning, statistical summaries, and visualisation techniques using Pandas, Matplotlib, and Seaborn.