Data Science 101: Guide to Using Pipelines in Python

In my previous articles, I have talked in depth about steps to build a machine learning model using different tools such as Python and Alteryx. In short, it consists of the following steps. Sample: The process starts with data sampling, i.e., selecting the appropriate data set for modeling. Explore: This phase deals with understanding the data by exploring anticipated and unanticipated relationships between the variables and discover any abnormalities with the help of data visualization. Modify: Transform Data Create New Attributes and impute missing values.