Feature Selection Through p-values with sklearn in Python Template
The following Feature Selection Through p-values with sklearn in Python template shows how to solve a multiple linear regression problem using the machine learning package sklearn. Based on the p-value of each feature, we can determine whether it is useful or irrelevant. Download and unzip the .zip file in a new folder. Inside the folder you will find a .csv and a .ipynb file. The first one contains the database and the second one contains the Python code. Open the .ipynb file using Jupyter notebook. Some other related topics are Feature selection through standardization with sklearn in Python.
Who is it for
This is an open-access Python template in .ipynb format that will be useful for anyone who wants to work as a Data Analyst, Data Scientist, Business Analyst, Statistician, Software Engineer, and anyone who works with Python.
How it can help you
More features don't necessarily give you better results. Problems can occur whenever independent variables are correlated with each other and don't bring new information to the table which can lead to the so-called curse of dimensionality. What is important is to have few but meaningful features. This template can be used whenever you need to remove the irrelevant features. In this example, this is done via examining the p-values of each feature.