Python Programming Lecture Series Part-11

Scikit Learn library (sklearn)

Welcome to Part-11 of our Python Programming Lecture Series. In this lecture, we will be focusing on the Scikit Learn library, also known as sklearn. Scikit Learn is a powerful machine learning library in Python, which provides simple and efficient tools for data analysis and modeling.

Scikit Learn is built on top of other popular Python libraries such as NumPy, SciPy, and Matplotlib, making it easy to integrate with your existing Python code. It offers a wide range of machine learning algorithms, including classification, regression, clustering, dimensionality reduction, and more.

Key Features of Scikit Learn:

• Simple and easy-to-use interface
• Highly optimized for performance
• Support for a wide range of machine learning algorithms
• Integration with other Python libraries
• Extensive documentation and community support

Getting Started with Scikit Learn:

To get started with Scikit Learn, you first need to install the library using pip:

pip install scikit-learn

Once you have installed Scikit Learn, you can import it in your Python code and start using its functionality. Here is a simple example of how to train a machine learning model using Scikit Learn:


from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load the Iris dataset
iris = datasets.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)

# Train a Random Forest classifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)

# Evaluate the model
accuracy = clf.score(X_test, y_test)
print(f"Accuracy: {accuracy}")


This is just a simple example to demonstrate how easy it is to use Scikit Learn for machine learning tasks. The library provides many more advanced features and algorithms that you can explore and experiment with.

That’s it for Part-11 of our Python Programming Lecture Series. Stay tuned for more tutorials and lectures on Python programming and machine learning!