Moez-lab/CustomerChurn_Prediction
📊 A machine learning project to predict customer churn using classification models like Random Forest, Decision Tree, and XGBoost. Includes data preprocessing, SMOTE for class balancing, hyperparameter tuning, and model deployment using pickle.
No commits in the last 6 months.
Stars
2
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 01, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Moez-lab/CustomerChurn_Prediction"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
retentioneering/retentioneering-tools
Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web...
iterative/demo-bank-customer-churn
Demo DVC project training a classification model on tabular data
junfengn-ctrl/uplift-modeling-customer-retention
End-to-end uplift modeling pipeline for customer retention using T-Learner and X-Learner with...
gattsu001/Telecom-Churn-Predictor
Predicts which telecom customers are likely to churn with 95% accuracy using engineered features...
himanshu-03/Customer-Churn-Prediction
The Customer Churn table contains information on all 7,043 customers from a Telecommunications...