This project provides a generalizable Python class, MLModel, that performs hyperparameter tuning using RandomizedSearchCV for various machine learning models. It demonstrates how to tune and evaluate models such as RandomForestClassifier and GradientBoostingClassifier using a dataset of customer churn.
Generalized ML Model Tuning: Easily adaptable to different machine learning models.
Hyperparameter Tuning: Utilizes RandomizedSearchCV for efficient hyperparameter search.
Model Evaluation: Evaluates model performance using metrics such as ROC AUC and accuracy.
Python 3.x
scikit-learn
pandas
Optional: Set up the hyperparameter search grid for your models. Other than that, run ML_Model with the respective dataset.