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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.

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