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.

🚗 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.

📰 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. 🚀

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.

Heart-Disease Prediction

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

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

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.

Classify Redfish API logs with NLP model

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

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.

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

An automated machine learning experimentation engine that builds, evaluates, and compares multiple ML models through a structured pipeline to identify the most effective model for a dataset.