Jupyter Notebook in Visual Studio Code

Evaluating a machine learning model - Scikit-Learn evaluation functions - 51

Evaluiating a machine leaning model - FINALLY using the scoring Paramter - 50

Regression model evaluation metrics - Part 2(Mean squared error) - 49

Regression model evaluation metrics - Part 2(Mean absolute error) - 48

Regression model evaluation metrics - Part 1(R^2) - 47

Evaluating a classification model with evaluation metrics - Part 4(Classsification Report) - 46

Evaluating a classification model with evaluation metrics - Part 3(Confusion Matrix) - 45

Evaluating a Classification model with evaluation metrics - Part 2(Area under ROC curve) - 44

Evaluating a Classification model with evaluating metrics - Part 1(Accuracy) - 43

Evaluating a machine learning model - Part 2 - 42

Evaluating a machine learning model - Part 1 - 41

Making predictions with a machine learning model - Part 2 - 40

Making predictions with machine learning models - Part 1 - 39

Fitting a machine learning model - 38

Choosing the right machine-learning model with scikit-learn - Part 2 - 37

Choosing the right Machine Learning Model with Scikit-Learn - Part 1 - 36

Getting our data ready - Filling in missing values - Part 2 - 35

Getting our data ready - Filling in missing values - Part 1 - 34

Getting our data ready - Converting non-numerical values into numerical values - 33

Getting our data ready - Splitting our data - 32

A typical Scikit-Learn workflow - 31

Introduction to Scikit-Learn - 30

Matplotlib project - 29

Customising and styling Matplotlib plots - 28