In this tutorial, we will walk through the process of performing supervised learning with Scikit-Learn in Python. Specifically, we will focus on classification tasks using the Scikit-Learn library.
First, make sure you have Scikit-Learn installed. You can install it using pip by running the following command in your terminal:
pip install -U scikit-learnNow, let’s get started with our classification task. We will use a popular dataset called the Iris dataset, which contains information about Iris flowers and their species.
To begin, import the necessary libraries:
<!DOCTYPE html>
<html>
<head>
<title>ML03: Supervised Learning With Scikit-Learn-Clasification</title>
</head>
<body>
</body>
</html>Next, load the Iris dataset and split it into training and testing sets:
<!DOCTYPE html>
<html>
<head>
<title>ML03: Supervised Learning With Scikit-Learn-Clasification</title>
</head>
<body>
</body>
</html>Now, let’s build a classification model using a machine learning algorithm. We will use the k-nearest neighbors algorithm for this task.
<!DOCTYPE html>
<html>
<head>
<title>ML03: Supervised Learning With Scikit-Learn-Clasification</title>
</head>
<body>
</body>
</html>Finally, evaluate the performance of our model on the test set:
<!DOCTYPE html>
<html>
<head>
<title>ML03: Supervised Learning With Scikit-Learn-Clasification</title>
</head>
<body>
</body>
</html>That’s it! You have successfully performed supervised learning with Scikit-Learn for a classification task. Feel free to experiment with different datasets and algorithms to further enhance your skills in machine learning.
I hope you found this tutorial helpful. Thank you for reading!
