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BreastCancerDataset.ipynb
BreastCancerDataset.ipynb
 
 
ColumnTransformerPipelinePerfectlyExplained.ipynb
ColumnTransformerPipelinePerfectlyExplained.ipynb
 
 
ColumnTransformer_FeatureEngineering.ipynb
ColumnTransformer_FeatureEngineering.ipynb
 
 
Datascience_Dataset_FeatureEngineering.ipynb
Datascience_Dataset_FeatureEngineering.ipynb
 
 
FeatureConstructionAndSplitting.ipynb
FeatureConstructionAndSplitting.ipynb
 
 
HandlingOutlier_usingZscore.ipynb
HandlingOutlier_usingZscore.ipynb
 
 
HandlingOutliers_usingIQR.ipynb
HandlingOutliers_usingIQR.ipynb
 
 
Handling_categorical_data.ipynb
Handling_categorical_data.ipynb
 
 
KNNImputer_HandlingMissingData.ipynb
KNNImputer_HandlingMissingData.ipynb
 
 
SklearnPipeline.ipynb
SklearnPipeline.ipynb
 
 
Titanic_dataset_prediction.ipynb
Titanic_dataset_prediction.ipynb
 
 
datasets.zip
datasets.zip
 
 
feature_scaling.ipynb
feature_scaling.ipynb
 
 
feature_scaling_normalization.ipynb
feature_scaling_normalization.ipynb
 
 
handling_missing_values.ipynb
handling_missing_values.ipynb
 
 

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This repo has .ipynb files which shows multiple types of feature transformation approach which we can apply in our dataset. Feature transformation comes under feature engineering step where we make our dataset 100% ready to feed into the model.

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