algorithm chains and pipelines for many machine learning algorithmsthe particular representation of the data that you provide is very importantas we discussed in this starts with scaling the data and combining features by hand and goes all the way to learning features using unsupervised machine learningas we saw in consequentlymost machine learning applications require not only the application of single algorithmbut the chaining together of many different processing steps and machine learning models in this we will cover how to use the pipeline class to simplify the process of building chains of transformations and models in particularwe will see how we can combine pipeline and gridsearchcv to search over parameters for all processing steps at once as an example of the importance of chaining modelswe noticed that we can greatly improve the performance of kernel svm on the cancer dataset by using the min maxscaler for preprocessing here' code for splitting the datacomputing the minimum and maximumscaling the dataand training the svmin[ ]from sklearn svm import svc from sklearn datasets import load_breast_cancer from sklearn model_selection import train_test_split from sklearn preprocessing import minmaxscaler load and split the data cancer load_breast_cancer(x_trainx_testy_trainy_test train_test_splitcancer datacancer targetrandom_state= compute minimum and maximum on the training data scaler minmaxscaler(fit(x_train