Image Classification Project

The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning algorithms. It is one of the most widely used datasets for machine learning research.

The CIFAR-10 dataset consists of 60000 32x32 colored images in 10 classes, with 6000 images per class. There are 50,000 training images and 10,000 test images.

Description

• We are Building a classifier for classifying 10,000 different images into ten unique classes that include the images of ten different animals such as dogs, horses, cats, and so on using the CIFAR-10 Dataset.

• For classification purposes, we have used a Supervised Learning Algorithm i.e., Random Forest

• For reducing the dimensionality, we are using principal component analysis (PCA)

Technology Used

• CIFAR-10 Dataset
• Supervised Learning
• Principal Component Analysis (PCA)
• Random Forest
• Classification Report