Welcome to the Day-2 Afternoon Lab! We are super excited to work through tasks in linear regression and logistic regression, as well as familiarize you with the Iris dataset.

Iris Dataset

Let’s load the Iris dataset! Begin by importing the load_iris tool from sklearn. This is an easy loader scheme for the iris dataset.

from sklearn.datasets import load_iris

Then, we simply execute the following to load the data.

x,y = load_iris(return_X_y=True)

We use the return_X_y argument here so that, instead of dumping a large CSV, we get the neat-cleaned input and output values.

Woah! We talked about a lot of different ways of doing classification today! Let’s see what we can do about this for the Iris dataset!

Iris Dataset

Let’s load the Iris dataset! Begin by importing the load_iris tool from sklearn. This is an easy loader scheme for the iris dataset.

from sklearn.datasets import load_iris

Then, we simply execute the following to load the data.

x,y = load_iris(return_X_y=True)

We use the return_X_y argument here so that, instead of dumping a large CSV, we get the neat-cleaned input and output values.

Welcome to the Day-3 Morning Lab! We are glad for you to join us. Today, we are learning about how Pandas, a data manipulation tool, works, and working on cleaning some data of your own!

Iris Dataset

We are going to lead the Iris dataset from sklearn again. This time, however, we will load the full dataset and parse it ourselves (instead of using return_X_y.)

Let’s begin by importing the Iris dataset, as we expect.

Let’s run some clustering algorithms! We are still going to use the Iris data, because we are super familiar with it already. Loading it works the exactly in the same way; I will not repeat the notes but just copy the code and description from before here for your reference

Iris Dataset

Let’s load the Iris dataset! Begin by importing the load_iris tool from sklearn. This is an easy loader scheme for the iris dataset.

AIFS is a food systems institute at UC Davis.