Apply the K-Nearest Neighbors algorithm effectively to classify the renowned iris dataset, analyzing attributes such as sepal and petal dimensions. Understand crucial techniques and evaluation metrics to enhance predictive accuracy with machine learning.
Key Insights
- The article leverages the iris dataset, containing measurements on sepal length and width plus petal length and width, demonstrating the practical application of the K-Nearest Neighbors algorithm for classification.
- Essential Python libraries including NumPy, pandas, and sklearn's train-test split and K-Nearest Neighbors classifier are used for data preprocessing and model training.
- A classification report is employed to assess model performance, providing precision and recall metrics to evaluate the accuracy and effectiveness of the classification approach.
Note: These materials offer prospective students a preview of how our classes are structured. Students enrolled in this course will receive access to the full set of materials, including video lectures, project-based assignments, and instructor feedback.
We're going to now look at applying K&N, the K-Nearest Neighbors Algorithm, to a more real dataset. We're going to use the famous iris dataset from sklearn. And the iris dataset is a set of flowers, irises, with their sepal length and width and their petal length and width.
And, you know, you don't need to know a lot about flowers to do this, fortunately. But we can plot these, we can feed the sepal length, sepal width, petal length, and petal width data to a K-Nearest Neighbors Algorithm. And it will look at, hey, which one was closest among all four directions to, you know, what are the nearest neighbors to that particular new flower.
And we'll find that this has surprisingly good accuracy. All right. So here are our imports.
Here are the things we'll need. NumPy and pandas. We'll be showing you some images to help visualize this.
And we do need to load the iris data in. And they give us a function called load iris that we can use for that. We'll also have, you know, our more typical train test split and the K-Nearest Neighbors classifier model instantiation.
And we'll also be using a classification report, which is going to show us precision and recall, as well as some other interesting bits of information to see how we did. The other code we are giving you is our loading Google Drive block. Let's run both of those.
This may take a minute if it's the first time running it, as it is for me. And we'll also want to grab Google Drive. So you'll run this block as well.
And once you've imported everything and loaded Google Drive, we'll dive into, you know, what these flowers are, what data we have to work with.