A simple pipeline

We will first import a dataset known as Iris, which is already available in scikit-learn's sample dataset library (http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html). The dataset consists of four features and has 150 rows. We will be developing the following steps in a pipeline to train our model using the Iris dataset. The problem statement is to predict the species of anIris data using four different features:

In this pipeline, we will use aMinMaxScaler method to scale the input data and logistic regression to predict the species of the Iris. The model will then be evaluated based on the accuracy measure:

1. The first step is to import various libraries from scikit-learn that will provide methods to accomplish our task. We have learn about all this in previous chapters. The only addition is the Pipeline method from sklearn.pipeline. This will provide us with necessary methods needed to create an ML pipeline:

from sklearn.datasets import load_iris...