7. Chi-Square Clearly Explained | Feature Selection Using Chi-Square | Chi-Square by hand |

#chisquaretest #statistics #featureselection #chi2 #datapreprocessing #machinelearning #python #scikitlearn #selectkbest #modelperformance #correlatedfeatures #datacleansing #datawrangling
Welcome to our video on feature selection using Chi-Square! In this tutorial, we'll be exploring a technique for identifying and selecting features that are highly correlated with the target variable. This method can be particularly useful when working with datasets that contain many irrelevant or redundant features.
We'll be demonstrating how to apply this technique in Python using scikit-learn. We'll start by loading and preparing the dataset, then we'll use scikit-learn's Chi2 method to calculate the Chi-Square statistic for each feature. Next, we'll use the SelectKBest method to select the top performing features based on their Chi-Square score. Finally, we'll evaluate the impact of feature selection on the model's performance.
By the end of this video, you'll have a solid understanding of how to use Chi-Square to select relevant features and improve the performance of your machine learning models.

[@bariselmas4809]

This channel should have millions of subscribers!

[@ParthGupta-my9ox]

Underrated channel man!!

[@sohampavaskar4991]

Best video. Amazing and clear explanation.

[@farrugiamarc0]

Well done. Very well presented and explained.

[@ParthGupta-my9ox]

This deserves a subscribe.

[@ozan4702]

Excellent content!

[@user-uc9wp8mx3j]

Well Explained Vishnu. Really appreciable

[@learnwithvichu]

Thanks a lot...

[@ajaykryadav]

too good

[@learnwithvichu]

Thanks a lot....

[@hitendrasingh01]

good explanation

[@vijaybudhewar7014]

Very good man..Thanks for making this easy

[@borhanmukto7770]

great presentation! Please upload other statistical supervised models, Thanks!!!

[@bellion166]

Thanks for the intuition! Helped a lot during my 0th review today!

[@learnwithvichu]

Happy to hear that 🙂.

[@datadoctor10]

Thanks so much. Wondering why you have very little views…. Your channel deserves to get more subscribers

[@learnwithvichu]

Thanks a lot 😊....

[@69nukeee]

Nicely explained, great job Vishnu!

[@learnwithvichu]

Glad you liked it!

[@kseniiafilippova8540]

Thank you!

[@krishipathak474]

thank you
vishnu

[@thomascotter4141]

so the features with higher chi values are the ones you should be using in your ml model?

[@learnwithvichu]

Exactly... Because, if the chi value is high which indicates that there is a huge difference between observed and expected values.

[@danielyesiduribe2905]

Hello, what technique would work for a situation where my independent variables are numerical and my target variable is categorical?

[@learnwithvichu]

Forget about dependent and independent variables.
Simply consider like this "You want to find out the relationship between numeric and categorical variables".
Then you can use ANOVA.
I hope it is clear.. check out my video on ANOVA for better understanding and subscribe for more such a contents

[@danielyesiduribe2905]

thank you very much, I discovered your channel and I really like your content.@@learnwithvichu

[@TheKumarAshwin]

bro have small doubt, The expected values table is calculated assuming there is no relationship between gender and results?

[@learnwithvichu]

Yes, if there is no relationship between the variables, how the expected values would look like.
Then we can compare the observed values against expected values.

[@siddharth4251]

after wasting almost hour finally get you and feel satisfied....keep on doing such wonderful work.....nice presentation with animation and excellent explanation..
you earned one subscription bro!! thanks

[@learnwithvichu]

Thank you so much 😀