Machine Learning - Implementation of Data Scaling Using Python

In this video we are going to learn about how we will get the data and observe the distribution of the data and its information and then understand this distribution we will visualize this distribution using histogram, boxplot and then we have performed mix max scaler and standard scaler using dataset. And then we have compared our results with the actual dataset graph, which clearly signifies we are not changing the data but we are just scaling the data. Data scaling is a data preprocessing technique that steps the data before passing it into any machine learning model. And it can be done using Normalization and Standardization data. 

Related Articles: 

https://www.geeksforgeeks.org/ml-feature-scaling-part-1/ 

https://www.geeksforgeeks.org/ml-feature-scaling-part-2/ 

https://www.geeksforgeeks.org/feature-scaling-part-3/ 

https://www.geeksforgeeks.org/data-pre-processing-wit-sklearn-using-standard-and-minmax-scaler/