Modeling the Data
Data modeling, as the name suggests, refers to the process of creating a model that can define the data and can be used to draw conclusions and predictions for new data points. 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. For example, when trying to solve the churn prediction problem, which has a large number of features, feature selection can help in selecting the most relevant features. Those relevant features can then be used to train a model (in the model-building stage) to perform churn prediction.
Feature Selection
Before building our first machine learning model, we have to do some feature selection. Consider a scenario of churn prediction 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...
