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

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.

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.

developed a machine learning model to predict the probability of a patient having heart disease or a heart attack using patient-specific medical data. A Logistic Regression model was chosen as the baseline in binary classification tasks, ensuring a clear interpretation of risk probabilities.

Something about ML, not all :)

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.

Heart-Disease Prediction

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.