From f9b2f71385af24c14f5e517d891d1d0574e5ddd6 Mon Sep 17 00:00:00 2001 From: elhadji Date: Thu, 13 Aug 2026 00:44:10 +0200 Subject: [PATCH 1/2] add python 3.12 as minimum version with the current requirement file --- churn/ReadMe.md | 8 ++++++++ revenueassurance/ReadMe.md | 9 +++++++++ serviceassurance/ReadMe.md | 8 ++++++++ starlink/README.md | 8 ++++++++ 4 files changed, 33 insertions(+) diff --git a/churn/ReadMe.md b/churn/ReadMe.md index 6539738..2fa61fc 100644 --- a/churn/ReadMe.md +++ b/churn/ReadMe.md @@ -9,6 +9,14 @@ This project delivers an end‑to‑end Telecom Customer Churn Prediction soluti Both models are trained on rich, synthetic telecom datasets—incorporating usage patterns, billing details, and support interactions—to predict which customers are at highest risk of churn. By identifying high‑risk segments in advance, operators can deploy targeted retention offers and optimize churn‑prevention campaigns, ultimately safeguarding revenue and improving customer lifetime value. +## Software-Requirement + +| Software | Version | +|---------------|---------| +| Python | 3.12 | + +> Tested with Python 3.12 as the minimum version. Python 3.13 and later 3.x releases may work but have not been validated. + ## Model Options Both the Balanced Random Forest and LightGBM classifiers are well‑suited for binary churn prediction, but they achieve this in different ways that are worth understanding: diff --git a/revenueassurance/ReadMe.md b/revenueassurance/ReadMe.md index 9b2a28f..ef67d0d 100644 --- a/revenueassurance/ReadMe.md +++ b/revenueassurance/ReadMe.md @@ -2,6 +2,7 @@ - [Revenue Assurance and Fraud Management (RAFM) with AI Assistance](#Revenue-Assurance-and-Fraud-Management-\(RAFM\)-with-AI-Assistance) - [Project Overview](#Project-Overview) + - [Software requirement](#Software-Requirement) - [Options](#Options) - [Data](#Data) - [Results](#Results) @@ -24,6 +25,14 @@ The models are trained on synthetic telecom data to predict fraud cases and iden Data-Set: https://huggingface.co/datasets/fenar/revenue_assurance +## Software-Requirement + +| Software | Version | +|---------------|---------| +| Python | 3.12 | + +> Tested with Python 3.12 as the minimum version. Python 3.13 and later 3.x releases may work but have not been validated. + ## Options (1) Revenue Assurance is a domain where traditional machine learning models, such as tree-based methods, often perform very well. Specifically, models like the `Balanced Random Forest` (01-xxx Worx) can be advantageous due to their inherent ability to handle class imbalance and interpretability.
[ Test Accuracy: 0.99962]
diff --git a/serviceassurance/ReadMe.md b/serviceassurance/ReadMe.md index b3bbe45..4fced12 100644 --- a/serviceassurance/ReadMe.md +++ b/serviceassurance/ReadMe.md @@ -7,6 +7,14 @@ This project aims to deliver a service assurance insights (net promoter score -N **🎥 Demo Video**: [Watch on YouTube](https://youtu.be/gFbtux0dGVA)
+## Software-Requirement + +| Software | Version | +|---------------|---------| +| Python | 3.12 | + +> Tested with Python 3.12 as the minimum version. Python 3.13 and later 3.x releases may work but have not been validated. + ## Data ![Service Assurance Data Structure](https://raw.githubusercontent.com/fenar/etc-ai-wrx/main/serviceassurance/data/svcass-datainsp.png)
diff --git a/starlink/README.md b/starlink/README.md index c58ef8c..0446cfd 100644 --- a/starlink/README.md +++ b/starlink/README.md @@ -12,6 +12,14 @@ To empower travelers with insights into Starlink's internet service quality at t +## Software-Requirement + +| Software | Version | +|---------------|---------| +| Python | 3.12 | + +> Tested with Python 3.12 as the minimum version. Python 3.13 and later 3.x releases may work but have not been validated. + ## Data ![Data Structure](https://raw.githubusercontent.com/tme-osx/TME-AIX/refs/heads/main/starlink/images/starlink-data.png)
DataSet Location: https://huggingface.co/datasets/fenar/starlink From d95e2642e68c2692e2b045d5dddb8b9b968a3420 Mon Sep 17 00:00:00 2001 From: elhadji Date: Thu, 13 Aug 2026 00:51:49 +0200 Subject: [PATCH 2/2] update Readme file with Python minimum version --- churn/ReadMe.md | 2 +- revenueassurance/ReadMe.md | 2 +- serviceassurance/ReadMe.md | 2 +- starlink/README.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/churn/ReadMe.md b/churn/ReadMe.md index 2fa61fc..ea8fa8f 100644 --- a/churn/ReadMe.md +++ b/churn/ReadMe.md @@ -9,7 +9,7 @@ This project delivers an end‑to‑end Telecom Customer Churn Prediction soluti Both models are trained on rich, synthetic telecom datasets—incorporating usage patterns, billing details, and support interactions—to predict which customers are at highest risk of churn. By identifying high‑risk segments in advance, operators can deploy targeted retention offers and optimize churn‑prevention campaigns, ultimately safeguarding revenue and improving customer lifetime value. -## Software-Requirement +## Software Requirement | Software | Version | |---------------|---------| diff --git a/revenueassurance/ReadMe.md b/revenueassurance/ReadMe.md index ef67d0d..9c6bbe5 100644 --- a/revenueassurance/ReadMe.md +++ b/revenueassurance/ReadMe.md @@ -25,7 +25,7 @@ The models are trained on synthetic telecom data to predict fraud cases and iden Data-Set: https://huggingface.co/datasets/fenar/revenue_assurance -## Software-Requirement +## Software Requirement | Software | Version | |---------------|---------| diff --git a/serviceassurance/ReadMe.md b/serviceassurance/ReadMe.md index 4fced12..876f0a7 100644 --- a/serviceassurance/ReadMe.md +++ b/serviceassurance/ReadMe.md @@ -7,7 +7,7 @@ This project aims to deliver a service assurance insights (net promoter score -N **🎥 Demo Video**: [Watch on YouTube](https://youtu.be/gFbtux0dGVA)
-## Software-Requirement +## Software Requirement | Software | Version | |---------------|---------| diff --git a/starlink/README.md b/starlink/README.md index 0446cfd..84419b8 100644 --- a/starlink/README.md +++ b/starlink/README.md @@ -12,7 +12,7 @@ To empower travelers with insights into Starlink's internet service quality at t -## Software-Requirement +## Software Requirement | Software | Version | |---------------|---------|