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Based on infrared satellite images, the existence of a methane plume was predicted. Transfer Learning for PyTorch Classification CNNs was combined with traditional methods on tabular data. This is a fork of a group project of my DSB Master's Degree at HEC Paris in cooperation with McKinsey QuantumBlack. (mckinsey.com/capabilities/quantumblack/)
Example project built as a tutorial on how to monitor the emissions and energy consumption of a Python application, using AWS CloudWatch to increase the visibility of these statistics using Custom Metrics and Dashboards.
This repository contains the test data and code used for the paper titled "Physics-Based Machine Learning Framework for Predicting NOx Emissions from Compression-Ignition Engine Powered Vehicles" that is submitted to the Applied Energy Journal