Seatbelt detection using YOLOv5 ML model
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Updated
Sep 20, 2024 - Python
Seatbelt detection using YOLOv5 ML model
A real world crypto currencies streaming and analysing using kafka through snowflake.
An intelligent machine learning application that predicts student exam scores and provides personalized recommendations for academic improvement using advanced AI and data analytics.
Machine Learning as a Service for HEP
Telegram bot that uses an ML model to scan Binance/Bybit/MEXC futures for pump-like patterns and send alerts to subscribers
The Smart Crop Disease Detection System is a Django web app that uses machine learning to identify crop diseases from leaf images. It helps farmers detect diseases quickly and take action to protect their crops. The system features AWS S3 image storage, TensorFlow Lite integration, and a responsive front-end for easy use.
A Machine Learning model created using prebuild model. We need to feed the images to the model and it will predict if the same person is there else it will mark as unknown.
Animal Classification: A CNN-based image recognition model.
📊 ML Prediction App – Developed a Streamlit-based Machine Learning application that provides real-time predictions, data visualization, and an interactive learning experience for understanding ML concepts.
SugarSense : The Diabetes Prediction Application
Machine learning model for predicting the drone deployment for the smart agriculture use case. Developed in collaboration between University College Dublin (UCD) and Augmenta (acquired by CNH Industrial) for the EU MLSysOps project.
Video-based surgical skill assessment using 3D convolutional neural networks
Aplikasi ini adalah sebuah web sederhana untuk prediksi harga mobil menggunakan model Machine Learning yang telah dilatih sebelumnya.
A machine learning–powered web application that predicts salary based on years of experience using Linear Regression.
Machine learning model for anomaly detection on VM telemetry metrics. Developed by TU Delft for the EU MLSysOps project.
This project implements a production-ready customer churn prediction system built entirely on the Zerve platform. The system ingests telecom customer data, performs feature preprocessing and scaling, trains a Gradient Boosting classification model, and evaluates performance using business metrics including ROC-AUC, precision, recall,
Machine Learning Model for RF Fingerprinting using the Physical Layer Authentication approach. Developed by INRIA for the EU MLSysOps project.
A machine learning web application that predicts the likelihood of heart disease based on patient medical data using a K-Nearest Neighbors (KNN) classifier.
API service that serves the trained model and handles inference requests.
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