Introduction to Scikit-learn

Scikit-learn (imported as sklearn) is a leading open-source Python library for machine learning and data analysis.

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 the main reasons why Scikit-learn is a go-to library for machine learning in Python:

• Comprehensive algorithms — includes a wide range of supervised and unsupervised learning models
• Consistent API — uniform interface for model training and evaluation
• Data preprocessing — built-in tools for scaling, encoding, and feature transformation
• Model evaluation — ready-to-use metrics and validation utilities
• Seamless integration — works natively with NumPy arrays and Pandas DataFrames

You can install Scikit-learn using the following command:

Copy
pip install scikit-learn

After installing Scikit-learn, you can import it using the following command:

Copy
import sklearn

Copy
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