Driven engineering student passionate about artificial intelligence, machine learning, web development, and database management. Always eager to learn, grow, and tackle new challenges. Keen on using technology to solve real-world problems and contribute to the open-source community.
A Face and Emotion Detector using the LittleVGG model employs a simplified CNN architecture, detecting faces and predicting emotions from webcam feeds or uploaded images with a 60% accuracy rate. I…
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Predictive model forecasts client deposit likelihood by analyzing attributes like age, job, and marital status for tailored marketing. Data-driven decisions powered by advanced algorithms ensure tr…
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Utilizing MediaPipe for facial and body landmarks, we craft high-precision pose classification models, surpassing 90% accuracy. This innovation promises breakthroughs in human-computer interaction,…
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Developed Real-Time Sign Language Detection Model using Tensor Flow Object Detection, Deep Learning with OpenCV, and Python. Achieved prediction accuracy of over 95% for recognizing seven (07) ASL …
A highly accurate machine learning model leveraging Support Vector Machines (SVM) for voice-based gender recognition, achieving an impressive accuracy rate of 98.43%."
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Our OpenCV-powered system accurately recognizes handwritten digits from user-uploaded images. Trained on the MNIST dataset, it offers potential for efficient data entry and form processing.
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