Feature Scaling with Outliers: Data Pre-processing : Machine Learning Interview Questions

In this video I explain the importance of Feature Scaling in the presence of outliers . Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step. I explain the following methods: Robust Scaler, Power Transformer, Quantile Transformer If you like such content please subscribe to the channel here: https://www.youtube.com/c/RitheshSreenivasan?sub_confirmation=1 If you like to support me financially, It is totally optional and voluntary. Buy me a coffee here: https://www.buymeacoffee.com/rithesh Relevant Links: https://en.wikipedia.org/wiki/Feature_scaling https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html#sklearn.preprocessing.RobustScaler https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html#sklearn.preprocessing.PowerTransformer https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html#sklearn.preprocessing.QuantileTransformer https://machinelearningmastery.com/robust-scaler-transforms-for-machine-learning/ https://en.wikipedia.org/wiki/Power_transform