Objective: Study/observe the relationship between categories of the preexisting data in order to evaluate/predict new data into proper categories. Dif

Cross-Validation: Compares various forms/types of Machine Learning methods and gives insight regarding its actual performance. In Machine Learning,Est

One-Hot Encoding Categorical/Qualitative Data: Nominal -- has no order Ordinal -- has order One-Hot Encoding is encoding the categorical/qualitative

Main Purpose: Building a model that accurately predicts the test data (as opposed to the train data)Train/Test SplitTrain Data - used to train the mod

Reference ModelA prototype model that displays the most basic performance that becomes a reference for the prediction modelTypes: \- Classification =

Feature = Column or a Dimension of a DataFrameFeature Engineering = Combining/Restructuring the existing datasets to create a new featureScreen Shot 2

A process of reordering and restructuring data in a manner that is fit for analysis. An essential process that helps the user understand the data he/s