How to Build and Evaluate Machine Learning models for Customer Churn Prediction | Part - 2 скачать в хорошем качестве

How to Build and Evaluate Machine Learning models for Customer Churn Prediction | Part - 2

This video explains the concepts behind various Machine Learning algorithms like Ensemble methods, Logistic Regression, Decision trees, SVM and Naive Bayes that are most commonly used for classification tasks. It demonstrates how encoding, data splitting and ML model building can be done with sklearn Python libraries. It also covers topics like Model selection techniques, and hyperparameters optimization and explains the evaluation metrics like accuracy, precision, recall, f1 score, and AUC scores. The dataset demonstrated in the project is imbalanced, so the concept of oversampling, undersampling and random sampling has also been explained. This video will give learners a comprehensive understanding of ML algorithms for predicting Customer Churn. The upcoming video will demonstrate automating this ML workflow using PyCaret AutoML Library. Check out ProjectPro for an end to end solved Machine Learning Projects: https://bit.ly/422Emyf Stay tuned!