This section provides a comprehensive guide to the code examples used in our Scikit-Tutorial series. Scikit-learn is a powerful Python library for machine learning and data mining. Whether you're a beginner or an experienced data scientist, this tutorial will help you get started with Scikit-learn.
Installation
Before you dive into the code, make sure you have Scikit-learn installed. You can install it using pip:
pip install scikit-learn
Getting Started
Here's a simple example of a classification model using Scikit-learn:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a Random Forest classifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)
# Train the classifier
clf.fit(X_train, y_train)
# Make predictions
predictions = clf.predict(X_test)
# Evaluate the model
print("Accuracy:", clf.score(X_test, y_test))
Further Reading
For more detailed examples and tutorials, check out our Scikit-Tutorial Series.
Images
Here's an image of a random forest classifier in action:
If you're looking to expand your knowledge on machine learning, consider exploring our Machine Learning Resources.