Skip to content
#

telecom-churn-prediction

Here are 22 public repositories matching this topic...

This project involved analyzing 10,000 customer records, applying data preparation techniques, and training supervised machine learning models, achieving 94% accuracy. Model efficiency was further refined using cross-validation and hyperparameter tuning, ensuring reliability and performance

  • Updated Dec 14, 2023
  • Jupyter Notebook

Designing strategies to pull back potential churn customers of a telecom operator by building a model which can generalize well and can explain the variance in the behavior of different churn customer. Analysis being done on large dataset which has lot of scope for cleaning and choosing the right model for prediction.

  • Updated Apr 2, 2023
  • Jupyter Notebook

This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and data plan usage to forecast churn likelihood. Its insights aid telecom companies in proactively retaining customers and mitigating churn rates.

  • Updated Apr 25, 2024
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the telecom-churn-prediction topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the telecom-churn-prediction topic, visit your repo's landing page and select "manage topics."

Learn more