Codes and data are updated in the new Zip "Codes and Data Updated.zip" which includes the novel new work of "Communication-Level Handover Supervision for Fog-Limited WDM-FSO Links: Physics Baselines, Channel-Proxy Audits, and Reproducible QoS Decisions" ____________________________________________________________________________________________________ fso_predictor is an open-source, command-line interface (CLI) package for end-to-end data processing, model training, and handover simulation in multi-user, Wavelength Division Multiplexing (WDM) Free-Space Optical (FSO) networks. Written in Python 3.10+, it encapsulates our entire research pipeline—data cleaning, feature engineering (with optional PCHIP interpolation), regression modeling (Random Forest, Gradient Boosting, KNN, Linear Regression, Bayesian Ridge, and optional Neural Nets), hyperparameter tuning via Particle Swarm Optimization (PSO), SHAP-based interpretability, and QoS-driven handover simulation—into a single, reproducible workflow. Key Features Modular Architecture: Separate subcommands for each stage (preprocess, train, tune, explain, predict, simulate, evaluate) driven entirely by human-readable TOML configuration files. Reproducibility: Locked dependencies, pre-commit hooks, CI/CD badges, Binder demos, and example workflows ensure bit-for-bit replication of our results. High Performance: Random Forest inference is up to 100× faster than comparable neural networks, enabling real-time handover decisions. Interpretability: SHAP summaries reveal that path range dominates link performance predictions—even across SISO and MIMO fog scenarios. Data Transparency: The accompanying 3 000-point FSO dataset is archived on Mendeley Data (doi:10.17632/8jmt7bjn8w.1). Flexible Installation: bash pip install -e .[dev] # Core + dev tools pip install "fso_predictor[interpret,pso,nn]" # Full feature set Extensible CLI: Easily plug in new regressors, interpolation schemes, or deployment scenarios without touching source code. Usage Example bash # 1. Preprocess raw link measurements fso_predictor preprocess --config configs/preprocess.toml # 2. Train Random Forest on Scenario 1 fso_predictor train --config configs/train_rf_sce1.toml # 3. Tune RF hyperparameters via PSO fso_predictor tune --config configs/tune_pso.toml # 4. Generate SHAP summary plots fso_predictor explain --config configs/shap.toml # 5. Simulate QoS-driven handover along a user mobility trace fso_predictor simulate --config configs/simulate.toml Citations & Links Zenodo DOI: 10.5281/zenodo.16759653 GitHub: https://github.com/AIBabyTeaching/PredictiveModellingRebirth License: MIT