Yellow Cover of Feature Selection Through p-values with sklearn in Python. This template resources is from 365 Data Science.

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.

Hristina Hristova
Course Author

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.

Join 2M Students and Start Learning from the best

The 365 Data Science program is great for beginners. It gives in-depth knowledge and clears every concept. After every lecture, some exercises are given for students to complete for practice, and solutions also. I would highly recommend this program to everyone who wants to be a data scientist and brush up their skills.

harshita sharma Pakistan

The 365 Data Science course is outstanding, it has an outstanding interface and awesome courses from a basic to an advanced level. I really appreciate the quality of the material and the content of this Data Science course!! Thanks to the 365 Data Science team!

Rakesh Patel United States

The 365 Data Science program was excellent. It does meet my expectations 100%. I do thank the 365 Data Science Team for putting this great work together in one package & with a very reasonable price. Hands down, you are the best! Please, keep this great work up!

Amrouni France

Feature Selection Through p-values with sklearn in Python Template