Machine Learning with Python

Machine learning is an art of enabling machines to learn from data and make predictions or decisions based on patterns without explicit programming. It has a wide range of applications, from recommendation systems to image recognition and natural language processing.

Scikit-learn, a popular machine learning library in Python, provides a vast array of tools for data preprocessing, model training, evaluation, and more. It serves as an excellent starting point for machine learning projects.

# Example: Using Scikit-learn for machine learning
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Build a k-nearest neighbors classifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# Make predictions on the test set
y_pred = knn.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(“Accuracy:”, accuracy)

Let’s embark on a journey to build a simple machine learning model using Scikit-learn. We’ll explore a classic example: the Iris dataset, which contains features of different iris flowers along with their corresponding species.

Example: