Popular GitHub Repositories for Telecom

Scripts for the "ITU-ML5G-PS-006: ML5G-PHY-Reinforcement learning: scheduling and resource allocation"

Developed BERT, LSTM, TFIDF, and Word2Vec models to analyze social media data, extracting service aspects and sentiments from a custom dataset. Provided actionable insights to telecom operators for customer satisfaction and competitive analysis.

Telecom Churn Prediction using Machine Learning models

Research internship 2023 at Telecom Paris

IMPLEMENTATION OF BACKPROPAGATION ARTIFICIAL NEURAL NETWORK ALGORITHM PREDICTING STOCK PRICE PT SMARTFREN TELECOM TBK

Predicts which telecom customers are likely to churn with 95% accuracy using real-world data features from usage, billing, and support data. Implements Sturges-based binning, one-hot encoding, stratified 80/20 train-test split, and a two-level ensemble pipeline with soft voting. Achieves 94.60% accuracy, 0.8968 AUC, 0.8675 precision, 0.7423 recall.

This repository is created to share the steps that were taken in making my Graduation Thesis for my Applied Statistics Diploma, the project is about creating a machine learning model to predict the churn in a telecom company, this repository includes: The dataset used in the project, the relevant code, and the theisis in pdf.

TinyML Signal Classifier: Lightweight CNN for embedded signal classification using TensorFlow Lite — optimized for IoT, telecom, and edge computing applications.

Training Machine learning models on Telecom Data to predict customer churn.

Machine learning project to predict customer churn for a telecom operator

I created this repo for a school project as a PFIEV telecom student at Ho Chi Minh university of Tech. Credits to the RetinaFace team, J. Deng, J. Guo, Y. Zhou et al.

This is a churn analysis and prediction project for orange telecom dataset. Alteryx designer cloud is used for workflow and data splitting and Alteryx AutoML cloud is used for prediction model. Tableau is used for visualization.

Telecom Customer Churn Analysis and prediction using Alteryx designer Cloud and Alteryx AutoML and EDA with visualization using tableau public.

Robust pipeline for predicting telecom customer churn using ML It includes data preprocessing, feature engineering, model training (MLPClassifier), hyperparameter tuning, and resampling (SMOTE & ENN). The pipeline also integrates model evaluation with metrics like accuracy and ROC-AUC, and provides insights using SHAP for interpretability.

? My Telecom Churn Analysis Repository Welcome to my Telecom Churn Analysis repository, where I fuse data science with strategic thinking to unveil the intricacies of customer churn in the telecommunication industry. ?? Leveraging advanced machine learning techniques, I embark on a journey to decode patterns in customer behavior

Classification task for machine laerning models to predict customer churn for a telecom company. Includes EDA, work plan, and model training/evaluation/comparison.

This project focuses on customer churn prediction for a telecommunications company, aiming to identify at-risk customers

SD 207: Statistical Machine Learning in practice