Scikit Learn - Python

Scikit Learn is a free machine learning library in Python developed by a community of developers. It is commonly used for data analysis, data mining, and building predictive models. Scikit Learn is built on top of NumPy, SciPy, and Matplotlib libraries, and it provides a simple and consistent interface to a wide range of machine learning algorithms.

Installation

Scikit Learn can be installed using pip, a package manager for Python packages. To install Scikit Learn, run the following command:

!pip install -U scikit-learn

Features

Scikit Learn provides a variety of machine learning algorithms that can be used for various tasks, such as:

• Classification
• Regression
• Clustering
• Dimensionality reduction
• Model selection
• Preprocessing

It also includes datasets, such as Iris, Boston Housing, and Wine recognition, that can be used for testing and practicing different algorithms.

Example

Here is a simple example of how to use Scikit Learn for classification:

from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier

# Load dataset
iris = datasets.load_iris()

# Split data into features and labels
X = iris.data
y = iris.target

# Create KNN classifier object
knn = KNeighborsClassifier(n_neighbors=3)

# Train the classifier
knn.fit(X, y)

# Make predictions
prediction = knn.predict([[5.1, 3.5, 1.4, 0.2]])

# Print the predicted class
print(prediction)

This code loads the Iris dataset, splits the data into features and labels, creates a KNN classifier object with K=3, trains the classifier, and then makes a prediction.

Conclusion

Scikit Learn is a powerful library that brings machine learning capabilities to Python developers. With its simple and consistent interface, it provides a wide range of algorithms and tools for data analysis and predictive modeling.