My Projects
Diabetes Prediction Using Machine Learning and Scalable Analytics
Developed a system to predict diabetes using Streamlit that allows users to input health-related parameters and predicts the likelihood of diabetes using a pre-trained machine learning model.
Reddit ETL Pipeline
Developed an automated Reddit ETL pipeline using Apache Airflow, AWS Glue, Redshift, and Python to extract and analyze vendor-related discussions from Reddit. The pipeline pulls data via the Reddit API, performs sentiment analysis and topic tagging, and stores the results in Redshift for reporting. I built dashboards in Tableau to help compliance and procurement teams monitor vendor perception in real time, reducing manual review by 80% and enabling faster, data-driven decisions. This project demonstrates my ability to integrate cloud services, orchestrate complex workflows, and turn unstructured data into actionable business insights.
Football Database Management System
Football Database Management System (DBMS) developed to store, manage, and retrieve football-related data such as player details, team information, matches, statistics, and more. The system utilizes SQL (MySQL) for database creation and management, along with a Python-based command-line interface for interaction.
Detection of Phishing websites
This project focuses on detecting phishing websites using multiple machine learning models. Various algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, Support Vector Machines (SVM), and more, are used to classify websites as phishing or legitimate based on several features.
Land Mines Detection
This project applies machine learning techniques to classify landmines using environmental and sensor data from the UCI Machine Learning Repository. Several classification models, including Logistic Regression, Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines (SVM), were tested. The best-performing model, SVM with an RBF kernel, achieved an accuracy of 61.62%.
Hospital Length of Stay Prediction
This project is part of the Healthcare Analytics course, where we explore hospital inpatient data during the COVID-19 pandemic. The analysis includes preprocessing datasets, building predictive models to estimate hospital length of stay (HLOS), and conducting trials using both MLP and RNN models.
British Airways Dashboard
Developed an interactive dashboard using Tableau for British Airways Data.
Customer Analysis Dashboard
Developed an interactive dashboard using Tableau for Customer Sales Data.
