Deep learning application for term deposit prediction on imbalanced dataset
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Updated
Sep 22, 2022 - Python
Deep learning application for term deposit prediction on imbalanced dataset
Project: Employee Attrition Predictor | SVM + SMOTE-ENN | Python | Scikit-learn
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.
ML approach to customer churn prediction in retail banking using Random Forest & Logistic Regression | Python · SMOTE-ENN · Scikit-learn | Top grade — FOM University 🏆
Customer bookings predictive model
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