Modeling the Data
Modeling the data not only includes building your machine learning model but also selecting important features/columns that will go into your model. This section will be divided into two parts: Feature Selection and Model building.
Feature Selection
Before building our first machine learning model, we have to do some feature selection. Imagine a scenario where you have a large number of columns and you want to perform prediction. Not all the features will have an impact on your prediction model. Having irrelevant features can reduce the accuracy of your model, especially when using algorithms such as linear and logistic regression.
The benefits of feature selection are as follows:
Reduces training time: Fewer columns mean less data, which in turn makes the algorithm run more quickly.
Reduces overfitting: Removing irrelevant columns makes your algorithm less prone to noise, thereby reducing overfitting.
Improves the accuracy: It improves the accuracy of your machine learning...
