An interactive Machine Learning-powered web application that predicts whether a person is diabetic based on key health parameters. Built with Python, Scikit-learn, and Streamlit, this app aims to make early diabetes risk detection simple and accessible.

A Python-based news sentiment analysis using Hugging Face Sentiment Analysis Model and Dataset.

End-to-end Exploratory Data Analysis (EDA) project on Superstore Sales dataset including data cleaning, feature engineering, outlier handling, customer segmentation, and business insights using Python, Pandas, Matplotlib, and Seaborn.

This project aims to detect fake news using Python and machine learning. The model analyzes the textual content of online articles to classify them as FAKE or REAL based on linguistic and statistical patterns.

🚗 An End-to-End Machine Learning project to predict the market value of used cars using Random Forest Regression. Features extensive data cleaning, EDA with Seaborn/Matplotlib, and feature engineering to achieve accurate price estimations based on mileage, brand, and vehicle history.

In modern international business, conversations often shift between languages. Transcript AI bridges this gap by utilizing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to ensure no context is lost in translation. It doesn't just transcribe; it understands business intent.

Data science project analyzing Grammy Award trends (1959-2026). Includes a terminal search interface, Matplotlib visualizations, and a Random Forest classifier for era prediction.

📰 A Machine Learning based Article Recommendation System using Python, Scikit-learn, and NLP. Implemented TF-IDF and Cosine Similarity to analyze content and suggest similar reads. Includes automated data visualization. Part of my 30 Days of Project Building challenge. 🚀

A machine learning system that detects phishing URLs with ~97% accuracy using Random Forest. Includes feature visualization and confusion matrix analysis. 🛡️

This repository is meant to document my hands-on experience with supervised learning algorithms and techniques. It includes a variety of exercises, and experiments using different types of data and tools. Each file represents a step forward in building my machine learning skills.

An AI-driven study assistant that uses NLP (TF-IDF & Cosine Similarity) to explain complex topics and generate automated active-recall quizzes from custom study notes. Built with Python, Scikit-Learn, and Pandas.

Classify Redfish API logs with NLP model

End-to-end customer churn prediction using ML EDA, feature engineering, XGBoost, and Streamlit app

This project aims to predict customer churn using machine learning techniques. The primary goal is to build a predictive model that can determine whether a customer will churn (leave) based on their attributes.

Heart-Disease Prediction

An interactive data analytics web application built with Python + Streamlit that transforms raw datasets into visual dashboards, machine learning predictions, forecasts, and automated insights. Built using Python · Streamlit · scikit-learn · Plotly · statsmodels

A Python-based machine learning pipeline for global technology stocks. Predicts next-day closing prices using Random Forest Regression and classifies 5-year company performance using fundamental financial metrics like PE Ratio and ROE.

Employee attrition prediction using machine learning + insights

Comparative study of CNN and SVM models for facial emotion recognition on CK+ (CNN: 96%, SVM: 97%) and RAF-DB (CNN: 85%, SVM: 77%) datasets. Full data preprocessing pipeline in Python. Published in Springer 2024.