The Spambase dataset from the UCI Machine Learning Repository is a well-known dataset used in machine learning and data science, particularly for email classification tasks. It’s commonly used to classify emails as spam or non-spam based on a variety of features.
Key Features of the Spambase Dataset:
- Purpose: The dataset is designed to distinguish between spam and non-spam emails.
- Number of Instances: 4,601 emails.
- Number of Features: 57 continuous variables and 1 binary class label (spam or not spam).
- The features represent statistical properties of the emails, such as:
- The frequency of specific words and characters (e.g., “free,” “money,” “business”).
- Capitalization patterns (e.g., percentage of capital letters in the email).
- Other structural characteristics.
- The features represent statistical properties of the emails, such as:
Class Label:
- 1: Indicates the email is spam.
- 0: Indicates the email is not spam.
Common Use:
- The dataset is widely used for training and evaluating classification models like:
- Logistic Regression
- Decision Trees
- Support Vector Machines
- Naive Bayes
- Neural Networks