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real-time-prediction

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The project uses computer vision and machine learning to translate sign language gestures into text, MediaPipe detects handprints, and Random Forest Classifier predicts characters in real time. It recognizes all 26 letters of the English alphabet, providing an intuitive interface to improve communication for people who need to use sign language.

  • Updated Dec 26, 2024
  • Python

This project centers around two primary aspects: the development of a model for the recognition of American Sign Language (ASL) gestures utilizing a dataset, and the real-time prediction of gestures via a camera feed, accompanied by the conversion of these predictions into spoken language.

  • Updated Aug 24, 2023
  • Python

Built and deployed a Flask-based machine learning system to predict loan default risk using customer demographics and financial indicators. Applied advanced ensemble models like XGBoost and LightGBM to achieve ~99% accuracy. Designed a full-stack solution with real-time prediction capabilities, enabling faster, smarter loan decisions in banking.

  • Updated May 24, 2025
  • Python

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