Image Classification Datasets
MNIST Dataset:
- The MNIST dataset is a collection of 70,000 handwritten digit images (0-9) used for image classification. Each image is 28×28 pixels, with 60,000 images for training and 10,000 for testing.
- It is a fundamental dataset for beginners in computer vision and deep learning.
Digits Dataset:
- Similar to MNIST, the Digits dataset contains images of handwritten digits (0-9) from the scikit-learn library.
- It includes 1,797 grayscale images of 8×8 pixels, used for classification tasks and algorithm comparisons in image recognition.
Fashion MNIST Dataset:
- Fashion MNIST is a dataset of 70,000 grayscale images of 10 fashion categories (e.g., T-shirts, trousers, bags, shoes).
- Each image is 28×28 pixels, intended as a more challenging drop-in replacement for the original MNIST dataset, promoting more advanced research in computer vision.
Chemical Analysis and Manufacturing Dataset
Wine Dataset
- The Wine dataset consists of 178 instances of Italian wines, classified into three types.
- Each instance is described by 13 chemical properties like alcohol content, malic acid, ash, and color intensity. It is widely used for classification and clustering in chemical and quality control analysis.
Text and Natural Language Processing Dataset
Spam Email Dataset
- The Spam Email dataset contains email messages labeled as spam or non-spam, used for spam detection. It includes features derived from the email content, such as word frequencies and the presence of certain keywords.
- This dataset is crucial for developing and testing email filtering algorithms.
Dataset for Classification
Classification is a type of supervised learning where the objective is to predict the categorical labels of new instances based on past observations. The goal is to learn a model from the training data that can predict the class label for unseen data accurately. Classification problems are common in many fields such as finance, healthcare, marketing, and more. In this article we will discuss some popular datasets used for classification.