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This repo consists of the various practices and concepts that we come across in the domain of DS and ML

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Cross Validation.ipynb

Cross Validation.ipynb

 
 

Data Leakage.ipynb

Data Leakage.ipynb

 
 

Data Leakage.pdf

Data Leakage.pdf

 
 

Handling Categorical Values - Feature Engineering.ipynb

Handling Categorical Values - Feature Engineering.ipynb

 
 

Handling Categorical Values.pdf

Handling Categorical Values.pdf

 
 

Handling NaN - Feature Engineering.ipynb

Handling NaN - Feature Engineering.ipynb

 
 

Pipelines (How and why to use them).ipynb

Pipelines (How and why to use them).ipynb

 
 

Pipelines .ipynb

Pipelines .ipynb

 
 

README.md

README.md

 
 

XG Boost.pdf

XG Boost.pdf

 
 

XGBoost.ipynb

XGBoost.ipynb

 
 

xgb.png

xgb.png

 
 

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Machine-Learning-Tutorials

This repo consists of the various practices and concepts that we come across in the domain of DS and ML.

Author: Piyush Kumar

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This repo consists of the various practices and concepts that we come across in the domain of DS and ML

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data-science pipelines feature-engineering categorical-variables missing-values data-leakage

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