Now let's create a proper pipeline with the **final estimator**. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the `.predict()` method.  

To train a predictor (model), you need `y` to be encoded. This is done **separately** from the pipeline we build for `X`. Remember that `LabelEncoder` is used for encoding the **target**.

Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.

Learn the Machine Learning concepts and the ML project workflow.

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

Challenge: Creating a Complete ML Pipeline

Solution

Challenge: Creating a Complete ML Pipeline

Solution