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.

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.

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.

📰 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. 🚀

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.

🚗 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.

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.

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

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.

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.

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

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

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.

Classify Redfish API logs with NLP model

This project applies Machine Learning techniques to predict the survival of Titanic passengers. It explores various data preprocessing, visualization, and model-building techniques to enhance predictive accuracy.

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.

Collection of data science and machine learning projects created during CodeAlpha internship — includes EDA, model training, evaluation and experimental datasets.