Personal Financial Analyzer with Machine Learning is a full-stack web application developed using Python Flask, MySQL, and scikit-learn that transforms personal financial management through intelligent automation and predictive analytics. The system enables users to upload bank statements via CSV or manually enter transactions, which are then automatically categorized using a Multinomial Naive Bayes classifier with TF-IDF vectorization, achieving 92% accuracy across thirty distinct spending categories. A Linear Regression forecasting engine analyzes historical spending patterns to generate three-month expense predictions with confidence intervals, helping users anticipate future financial obligations.

This project involved a comprehensive data mining investigation for an international confectionery organisation, analysing 2,000 transactions across five countries (England, Ireland, Northern Ireland, Scotland, Wales) from 2021 to 2025. Using Python, I conducted end-to-end data analysis including data cleaning, exploratory data analysis, and time series forecasting. Key achievements included identifying seasonal profit patterns (with November showing £98,972 above trend and August £57,944 below trend), evaluating regional performance across eight product categories, and developing a monthly forecasting model that achieved 94% accuracy (6.1% MAPE). The analysis provided actionable recommendations for inventory planning, marketing budget allocation, and regional investment strategies, demonstrating practical application of data science techniques to drive business decision-making.

A fully responsive 15-page e-commerce website developed for NOVI, a modern jewelry brand. The site features distinct gender-based collections (women, men, unisex) with dynamic product filtering, sorting capabilities, and dedicated pages for new arrivals and sale items. Key functionality includes user authentication through signup/login pages, comprehensive FAQ section with interactive accordions, and essential policy pages (shipping, returns, payment methods, terms). Built with HTML, CSS, and JavaScript, the design incorporates premium gradient themes, animated elements, hover effects, and high-quality imagery from Unsplash. The project demonstrates frontend development skills including responsive grid layouts, form validation, and interactive UI components.