Image Classification Using Python Programming

After discussing three widely used ML algorithms (SVMs, KNNs, and decision trees), we will perform image classification using Python programming in this lesson.

Implementing the KNN algorithm using Python programming

KNN identifies the k-nearest neighbors in the training set for a given test data point (to be classified). KNN is a lazy learner approach because it stores the dataset and classifies it based on the majority classes of the neighboring data points when it receives new data. The five steps described below develop an image classifier using the KNN algorithm in Python programming.

Step 1: Importing the libraries

Sklearn, also known as scikit-learn, is a widely used Python package (library) that can implement the KNN algorithm for image classification. It’s an open-source Python library for ML, and it offers various in-built algorithms for classification, regression, clustering, and dimensionality reduction. Additionally, it contains in-built functions and modules for preprocessing data, choosing models, and evaluating results.

The following code imports the required libraries: