Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. In most of the functions in Machine Learning, the data that you work with is barely in a format for training the model with it’s the best performance. There are several steps in the process of training a machine learning model, like encoding, categorical variables, feature scaling, and normalization. The preprocessing package of Scikit-Learn provides all these functions that can be easily used as transformations.

In this article, I will take you through the implementation of Machine Learning Pipelines in a Machine Learning Project. First, I will transform the dataset according to our needs; then, I will move towards the implementation of the Machine Learning Pipelines.

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