Scikit-Learn is a powerful Python library for machine learning. It provides a wide range of algorithms and tools for data analysis and modeling. Whether you are a beginner or an experienced data scientist, Scikit-Learn can be a valuable asset in your toolkit.
Quick Start
Here's a simple example to get you started with Scikit-Learn:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split the dataset 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 KNN classifier
knn = KNeighborsClassifier(n_neighbors=3)
# Train the classifier
knn.fit(X_train, y_train)
# Predict the labels of the test set
y_pred = knn.predict(X_test)
# Evaluate the classifier
print("Accuracy:", knn.score(X_test, y_test))
Key Features
- Algorithms: Scikit-Learn provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction.
- Data Preprocessing: Tools for handling missing values, feature scaling, and encoding categorical variables.
- Model Selection: Functions for splitting data into training and test sets, cross-validation, and hyperparameter tuning.
- Evaluation: Metrics for assessing model performance, such as accuracy, precision, recall, and F1 score.
More Resources
For more information and detailed documentation, please visit our Scikit-Learn Documentation.
Useful Links
If you're looking to dive deeper into machine learning, consider exploring our Machine Learning Tutorials.