AliAmini93 / Telecom-Churn-Analysis

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

Date Created 2024-08-12 (3 months ago)

Commits 16 (last one 3 months ago)

Stargazers 17 (0 this week)

Watchers 1 (0 this week)

Forks 4

License unknown

Ranking

RepositoryStats indexes 584,353 repositories, of these AliAmini93/Telecom-Churn-Analysis is ranked #583,237 (0th percentile) for total stargazers, and #535,930 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #17,072/17,098.

AliAmini93/Telecom-Churn-Analysis is also tagged with popular topics, for these it's ranked: machine-learning (#7,919/7926), datascience (#165/165), feature-engineering (#113/113)

All Topics

shap xgboost datascience churn-prediction machine-learning feature-importance model-optimization feature-engineering business-intelligence

Star History

Github stargazers over time

Watcher History

Github watchers over time, collection started in '23

Recent Commit History

16 commits on the default branch (main) since jan '22

Yearly Commits

Commits to the default branch (main) per year

Issue History

No issues have been posted

Languages

The only known language in this repository is Jupyter Notebook

updated: 2024-11-05 @ 11:24am, id: 841546964 / R_kgDOMij81A