Intro-to-sklearn-2022.ipynb

1. Comparing normalized vs raw data

Using knns with Euclidean distance and 3 nearest neighbors, compare the performance of a knn trained with the raw data vs. a knn trained with the normalized data.

First, we need to divide the DataFrames as we did before.

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2. The Iris Dataset

What is the accuracy of your new model with one epoch of training?

We are going to use the Iris Dataset, one of the standard data mining data sets which has been around since 1988. The data set contains 3 classes of 50 instances each

Iris Setosa

Iris Versicolour

Iris Virginica

There are only 4 attributes or features:

sepal length in cm

sepal width in cm

petal length in cm

petal width in cm

Here is an example of the data:

Sepal Length	Sepal Width	Petal Length	Petal Width	Class
5.3	3.7	1.5	0.2	Iris-setosa
5.0	3.3	1.4	0.2	Iris-setosa
5.0	2.0	3.5	1.0	Iris-versicolor
5.9	3.0	4.2	1.5	Iris-versicolor
6.3	3.4	5.6	2.4	Iris-virginica
6.4	3.1	5.5	1.8	Iris-virginica

The job of the classifier is to determine the class of an instance (the type of Iris) based on the values of the attributes.

When you divide into training and test sets please use random_state=0 so we can compare results.

You should include a short paragraph describing your results.