Use random forest, gradient boosting, neural network, with SMOTE-ENN and random over-sampling
-
Updated
Feb 9, 2023 - Jupyter Notebook
Use random forest, gradient boosting, neural network, with SMOTE-ENN and random over-sampling
A binary classification task performed with machine learning in Python. The dataset's target distribution is heavily imbalanced. The model performance was evaluated with F1 scores.
Project: Employee Attrition Predictor | SVM + SMOTE-ENN | Python | Scikit-learn
Customer bookings predictive model
Deep learning application for term deposit prediction on imbalanced dataset
ML approach to customer churn prediction in retail banking using Random Forest & Logistic Regression | Python · SMOTE-ENN · Scikit-learn | Top grade — FOM University 🏆
Add a description, image, and links to the smote-enn topic page so that developers can more easily learn about it.
To associate your repository with the smote-enn topic, visit your repo's landing page and select "manage topics."