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Content: Machine Learning, KNN concept, Euclidean distance, Data preprocessing, Scaling the data, Performing train-test split, Applying KNeighbors Classifier, Predicting Y_pred based on X_test, Evaluation using Confusion Matrix, Accuracy score, Recall value & Precision, Underfitting & Overfitting, Measures to overcome Underfitting & Overfitting
Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.