Feature selection using Python for tabular data

Name: Feature selection using Python for tabular data
Rating: 4.1 (12 reviews)

A Course by Kaggle grandmaster on Feature Selection : Machine Learning, Scikit Learn, Pandas, mlextend, clean your data

Created byRaju Kumar Mishra

Last updated 10/2025

English

What you'll learn

Feature Selection using Python machine learning packages Pandas, scikit-learn(sklearn), mlxtend
Learn the concept behind feature selection, detail discussion on feature selection method (filter, wrapper and embedded)
Filter methods selector like variance, F-Score, Mutual Information etc..
Wrapper Method : Exhaustive, Forward and Backward Selection
Embedded Method : Lasso Decision Tree, Random Forest, ExtraTree etc
Implemented with more than 15 Projects
Ready to use code in machine learning projects
Feature selection technique people used in Competitions.

Course content

5 sections • 40 lectures • 3h 41m total length

Introduction2:38
Explore feature selection for tabular data using Python, covering filter and embedded methods, mutual information, backward elimination, and random forest guidance with practical code examples.

Filter Method Introduction2:30
The lecture introduces filter methods for feature selection, scoring each column and selecting features based on mutual information with the target for fast processing of tabular data.
Variance For Feature Selection6:30
Project 1 : Variance for Feature selection on data for classification21:19
Project 2 : Variance for Feature selection on data for regression13:46
Apply variance-based feature selection to a Python regression dataset, scale features, and evaluate with mean absolute error. Compare thresholds to show how dropping low-variance features affects model performance.
Project 2 : Variance for Feature selection on data for regression part 23:22
Feature selection using F-Score12:50
Project 3 : Feature selection using F Score7:54
Feature Selection using Anova-F Score6:26
Project 4 : Feature selection using anova F-Score8:47
Mutual information to select features in a datasets with continuous target11:13
Project 5 : To select features from a dataset using Mutual Information8:32
Mutual Information to select feature from a dataset where target variable discre3:04
Project 6: Mutual information implementation on a dataset with discrete target10:29
Chi2 test method to select feature8:26
Project 7 : Implementation of chi25:04
Implement chi2 for feature selection in a Python tabular data workflow, build and train a random forest classifier, and assess model accuracy on training and testing data.

Introduction to wrapper methods0:42
Forward Feature Selection8:04
Project 8 : Implementation of forward feature selection using sklearn6:18
Project 9 : Implementation of forward feature selection using sklearn3:32
Forward Feature Selection in mlxtend1:57
Learn how forward feature selection in mlxtend builds feature subsets for tabular data, optimizing model accuracy in classification by evaluating sequentially added features.
Project 10 : Implementation of forward feature selection mlxtend5:09
Backward Feature Elimination0:54
Backward Feature Elimination sklearn0:43
Project 11 : Backward feature elimination implementation sklearn5:05
Project 12 : Backward feature elimination implementation3:03
In Project 12, implement backward feature elimination for a tabular dataset using Python, split data into training and testing sets, and iteratively remove features to evaluate classification accuracy.
Backward feature selection mlxtend1:57
Project 11 : Backward feature selection implementation6:12
Exhaustive feature selection3:59
Project 12 : Implementation of Exhaustive feature selection6:22

Introduction to Embedded Methods1:19
Tree based methods2:46
Explore tree-based methods for embedded feature selection using Python, including random forest, gradient boosting, and decision trees, and learn how to derive feature importance to select key attributes.
Project 13 : Implementation of Embedded Method using Decision Tree Classifier5:04
Project 14 : Implementation of Embedded Method using RandomForest Regressor4:01
Project 15 : Implementation of Embedded Method using Extremely randomized trees3:56
Introduction to Regularization Methods for feature selection4:46
Explore regularization methods for feature selection in Python for tabular data, focusing on lasso and elastic net, which shrink or drop coefficients to remove non-significant features and prevent overfitting.
Project 16 : Implementation of Lasso Regularization3:39
Project 17 : Implementation of Logistic Regression with Lasso Regularization6:06
Implement logistic regression with lasso regularization to perform feature selection on tabular data, fit on training data, predict on test data, and evaluate with a confusion matrix.
Benefits of Embedded Methods0:52
Explore the benefits of embedded feature selection, where selection occurs during model training, offering simplicity, no extra processing time, and often greater accuracy than filter methods.

Requirements

Familiarity with Python programming
Working knowledge of Jupyter Notebook
Working Knowledge of Pandas and Numpy
Working Knowledge of Machine learning Model Creation using sklearn
Understanding of Statistical methods like chisquare test

Description

Feature selection is one of most important activity in machine learning/Artificial Intelligence pipeline. We select all relevant features for machine learning algorithm and discard less relevant or not relevant features. Feature selection is also known as variable selection.This course will provide learner, detailed knowledge of feature selection. It is one of most detailed online course on feature selection.

Who is this course for ?

Data scientist who wants to create faster and more interpretable machine learning models.
Data analyst who wants to relation between two variables.
Data science aspirants who are preparing for data science interview.
Any One who wants to learn about feature selection process.
AI/ML software engineer who write code for machine learning.
Teachers who are teaching Machine Learning Models.

What will you learn ?

In this course, you are going to learn feature selection by doing. I have included more than 8 end to end small projects on feature selection methods. Each method has one project so that learner can understand the process fully. Code provided in throughout course is downloadable. You can download code and data and run by yourself to get confidence. Knowledge gain though this course is precious and can be used in We are going to learn following topics.

What is feature selection?

Different methods of feature selection.

Filter methods

Minimum variance method
F-Score using correlation for regression analysis data.
Anova F for classification analysis data
Mutual Information for regression and Classification analysis data.
Chi-Square Scores for categorical features and Target
All these methods implementation using sklearn

Wrapper Method

Forward selection of features.
Backward selection of features.
Exhaustive feature selection.
Implementation of each using sklearn and mlxtend.

Embedded Method

Introduction to Embedded Method for feature selection.
Using RandomForest
Using Extremely randomized trees to select features
Regularization based feature selection

So what are you waiting for? Join the course and get the knowledge of variable selection and apply it in your projects to get efficient and interpretable machine learning models.

Who this course is for:

For feature selection this course is for every one

Feature selection using Python for tabular data

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 3min

Feature Selection Introduction1 lecture • 2min

Filter Method15 lectures • 2hr 10min

Wrapper methods14 lectures • 54min

Embedded Methods for Feature Selection9 lectures • 32min

Requirements

Description

Who this course is for: