End-to-end machine learning projects involve the complete process of developing a machine learning model, starting from data collection and preprocessing to training, evaluation, and deployment. These projects encompass data exploration, feature engineering, model selection, performance evaluation, and integration with production systems

Details about my Internship

This Git repository contains the code and documentation for my progress on developing models for the enhancing road safety project. The goal of the project is to use machine learning algorithms to improve the accuracy of detecting traffic signs and reduce the risk of accidents on the roads.

This mobile health app helps cardiovascular patients monitor their health using machine learning. It tracks key metrics, predicts potential risks, and provides personalized insights and real-time guidance to support better health management.