Hello everyone and welcome to the support vector machines project overview lecture and this lecture
we're just going to be doing an overview of the support vector machines project notebook.
Let's go ahead and jump to that notebook now.
OK so here we are at the project notebook for this support vector machines project we're going to be
working with the famous Iris flower dataset the iris floured data center or Fisher's Iris data set as
it's also known is a data set introduced by Sir Ronald Fisher in about 1936 as an example of sacrament
analysis for classification.
This dataset consists of 50 samples from each of three species of iris flowers Iris Tosa Iris virginica
and Iris first color.
So 150 total samples and there were four features that were measured from each sample.
The length and width of the seat wheels and pedals and centimeters and here you can see a picture of
the different flower types.
Iris the Tosa Iris versicolor and Iris virginica.
And so this iris data set contains measurements of 150 iris flowers from the three different species
.
It's a really famous dataset and a lot of times to say students first dataset when encountering classification
of machine learning we're going to go ahead and use this dataset in order to not focus so much on exploratory
data analysis but focus a little more on just learning the syntax for support vector machines but more
importantly the grid search Zilker.
You'll go ahead and get the data a little bit of exploratory data analysis not so much essentially just
a pair plot and a Katie plot then you'll go ahead and do a tree and test it.
They'll train your model.
You'll evaluate the model and then you'll go ahead and practice a grid search on your model but you'll
notice that your model actually perform quite well and your grid search actually won't be able to if
you scroll all the way down to improve it you should still get the same results from the grid search
.
But we'll go ahead and run a grid search anyways for you to practice on.
OK.
Hope you enjoy the project should be pretty straightforward and simple but most importantly this is
just to understand the syntax of grid search and support vector machines.
You really have enough practice as far as importing models and running them.
You don't need to do too much exploratory data analysis for this dataset.
Okay thanks everyone and I'll see you at the next lecture or you go over the solutions to this project
.