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"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

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tanvirnwu/Feature-Selection--Python

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.gitignore

.gitignore

 
 

FS1 - Constant Features Removal.ipynb

FS1 - Constant Features Removal.ipynb

 
 

FS2 - Pearson Correlation.ipynb

FS2 - Pearson Correlation.ipynb

 
 

FS3 - Information Gain For Classification.ipynb

FS3 - Information Gain For Classification.ipynb

 
 

FS4 - Information Gain for Regression.ipynb

FS4 - Information Gain for Regression.ipynb

 
 

FS5 - Chi-square Test For Object Feature Selection.ipynb

FS5 - Chi-square Test For Object Feature Selection.ipynb

 
 

FS6 - Univariance Selection.ipynb

FS6 - Univariance Selection.ipynb

 
 

FS7 - Feature Importance.ipynb

FS7 - Feature Importance.ipynb

 
 

Housing.csv

Housing.csv

 
 

README.md

README.md

 
 

mobile_dataset.csv

mobile_dataset.csv

 
 

santander.zip

santander.zip

 
 

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Feature-Selection---Python

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"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

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python feature-selection housing feature-engineering constant feature chi-square information-gain chi-square-test pearson-correlation feature-importance pearson-correlation-coefficient housing-dataset features-removal univariance-selection univariance mobile-dataset

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