GitHub / miltiadiss / CEID_NE577-5G-Architectures-Technologies-Applications-and-Key-Performance-Indexes

This project involves predicting the downlink bitrate of mobile devices in 5G networks using machine learning (XGBoost Regressor) and deep learning (LSTM model). It includes data preprocessing, training and evaluation of the models, applying explainable AI (XAI) techniques such as SHAP, and optimizing feature selection based on XAI insights.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/miltiadiss%2FCEID_NE577-5G-Architectures-Technologies-Applications-and-Key-Performance-Indexes
PURL: pkg:github/miltiadiss/CEID_NE577-5G-Architectures-Technologies-Applications-and-Key-Performance-Indexes

Topics: deep-learning, explainable-ai, lstm-neural-network, shap, xgboost-regression

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• Readme
• Dependencies