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Practice

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.


Why Use Scikit-learn?​

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

  • 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.

Example: Training a Simple Model​

You can install Scikit-learn using the following command:

pip install scikit-learn

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

import sklearn

Example: Training a Simple Model​

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

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