Introduction to Scikit-learn

Scikit-learn (also known as sklearn) is one of the most popular open-source Python libraries for machine learning.
It provides efficient tools for:

• Classification
• Regression
• Clustering
• Dimensionality reduction
• Model selection
• Data preprocessing

Built on top of NumPy, SciPy, and Matplotlib, Scikit-learn is designed to be simple, efficient, and accessible for both beginners and professionals.

Here are some key reasons why Scikit-learn is a go-to library for ML:

• Comprehensive Algorithms – Includes a wide variety of supervised and unsupervised learning methods.
• Easy-to-Use API – Consistent interface across models.
• Preprocessing Tools – Built-in utilities for scaling, encoding, and transforming data.
• Model Evaluation – Ready-to-use metrics and validation tools.
• Integration – Works seamlessly with NumPy arrays and Pandas DataFrames.

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# Install scikit-learn in Jupyter Lite
import piplite
await piplite.install('scikit-learn')

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load dataset
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    iris.data, iris.target, test_size=0.2, random_state=42
)

# Create and train model
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

# Evaluate
accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy:.2f}")

This example shows how little code is needed to:

1. Load a dataset
2. Split it into training and testing sets
3. Train a machine learning model
4. Evaluate its performance

In the next lesson, we’ll explore the Machine Learning Workflow and understand the main steps from data preparation to model deployment.