Biological and Medical Datasets

Iris Dataset

• The Iris dataset is a classic dataset in the field of machine learning, consisting of 150 observations of iris flowers.
• Each observation has four features (sepal length, sepal width, petal length, petal width) and belongs to one of three species: Setosa, Versicolour, or Virginica. It is commonly used for classification tasks and visualizations.

Breast Cancer Wisconsin Dataset

• Breast Cancer Wisconsin Dataset contains features computed from breast cancer biopsy images, aiming to predict whether a tumor is benign or malignant. It includes 569 instances with 30 features such as radius, texture, perimeter, and area of the nuclei.
• It is widely used in the medical field for diagnostic purposes.

Heart Disease Dataset

• The Heart Disease dataset contains various patient attributes to predict the presence of heart disease. It includes features like age, sex, chest pain type, resting blood pressure, and cholesterol levels, with a total of 303 instances.
• This dataset is essential for developing models to diagnose cardiovascular conditions.

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.