Final Scaling Techniques

Author: reddyprasade

Feature_Scaling/README.md

@@ -8,6 +8,19 @@
 * Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance.
 Another reason why feature scaling is applied is that gradient descent converges much faster with feature scaling than without it
 
+## Before Scaling
+1. Orginal Data Set Distribution![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/UnScaling%20Data.svg)
+## After Scaling
+1. Standard Scaler ![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/Standard%20Scaler%20Data.svg)
+2. Min_Max_scaler ![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/MinMaxScaler%20Data.svg)
+3. Robust Scaler ![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/RobustScaler%20Data.svg)
+4. MaxAbsScaler![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/MinMaxScaler%20Data.svg)
+5. PowerTransformer(Box Cox)![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/Power%20Transofmation%20Box%20Cox%20transforms.svg)
+6. PowerTransformer(Yeo-Johnson)![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/Yeo-Johnson.svg)
+7. Quantile_transform_uniform![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/QuantileTransformer%20(uniform).svg)
+8. Quantile_transform_Gaussian![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/QuantileTransformer%20(uniform).svg)
+9. Normalizer![](https://github.com/reddyprasade/Machine-Learning-with-Scikit-Learn-Python-3.x/blob/master/img/Feature%20Scaling/Normilizer.svg)
+
 
 ### Reference 
 1. [Wiki](https://en.wikipedia.org/wiki/Feature_scaling)