This project applies machine learning to predict the success of telemarketing campaigns. Using the Bank Marketing Dataset, the goal is to build a classification model that determines whether a client will subscribe to a term deposit ("yes" or "no"). Direct marketing campaigns are costly and time-consuming. This project demonstrates how data science can optimize these efforts by identifying the most likely prospects based on demographic data, economic indicators, and previous campaign history.

This project focuses on categorizing news articles into four distinct categories—World, Sports, Business, and Sci/Tech—using advanced Natural Language Processing (NLP) techniques. The project evaluates several transformer-based models, including BERT, DistilBERT, and ALBERT, to determine the most effective approach for text classification.

This project is a complete Machine Learning Web Application that predicts house prices based on property features.

The Clinic Management System is a Java-based desktop application designed to manage all core operations inside a medical clinic. The system applies Object-Oriented Programming (OOP) principles and structured entity relationships to ensure clean architecture and maintainability.

This project utilises machine learning to predict the survival outcomes of patients with cirrhosis. By analysing clinical and biochemical features, the system classifies patient status to assist in medical prognosis and risk assessment. Cirrhosis is a late-stage liver disease where healthy liver tissue is replaced with scar tissue. This project implements a data-driven approach to predict patient survival using a variety of classification algorithms, ensuring high reliability through rigorous preprocessing and evaluation.

This project utilizes machine learning and acoustic analysis to identify the gender (Male or Female) of a speaker based on their voice properties. The system analyzes frequency and spectral characteristics to build a robust classification model. Voice recognition technology is a key component of modern AI interfaces. This project demonstrates how specialized acoustic features—such as fundamental frequency and spectral flatness—can be used to distinguish between male and female voices with high precision.

This data science project provides a comprehensive, data-driven exploration of the COVID-19 pandemic. By integrating the Our World in Data (OWID) dataset with economic and mental health indicators, the study analyses how the virus spread, its seasonal patterns, and its far-reaching consequences on global society. The research focuses on answering critical questions about the pandemic's lifecycle and its side effects on human health and the global economy. It transitions from epidemiological tracking to socio-economic impact assessment.

This project automates the process of applying to jobs on LinkedIn by analysing job descriptions and dynamically aligning applications with relevant candidate skills and experience. Instead of manually reviewing and applying to each job, the system intelligently filters and targets opportunities that match predefined criteria. LinkedIn’s Easy Apply feature allows candidates to submit applications quickly without leaving the platform , but applying at scale can still be time-consuming and inefficient. This tool enhances that process by introducing automation, keyword extraction, and relevance matching.

This project focuses on bridging the communication gap between the deaf-mute community and the general public. It features an integrative real-time system capable of recognising multiple sign languages, providing text outputs, and incorporating advanced features like text-to-speech and spelling correction.

Medical Brain is an intelligent data pipeline designed to transform unstructured medical equipment service reports into a structured, searchable knowledge base. By leveraging Natural Language Processing (NLP), this project automates the extraction of critical technical data from engineer logs, helping maintenance teams track machine history and optimise repair workflows.