Spam/Not Spam Mail Classifier Data

This dataset is designed to facilitate the development and evaluation of email filtering systems, specifically for spam detection. It consists of a collection of emails meticulously categorised into two primary classes: 'spam' and 'not spam'. The spam emails included are typically unsolicited and unwanted messages, often aiming to promote products or services, spread malware, or deceive recipients through malicious purposes. These may feature misleading subject lines, excessive use of advertisements, unauthorised links, or attempts to collect personal information. Conversely, the non-spam emails are genuine and legitimate messages, encompassing personal or professional communication, newsletters, or transaction receipts. The dataset features emails of varying lengths, languages, and writing styles, reflecting the inherent diversity of email communication, which helps in training algorithms that are robust against various spammer tactics and variations in legitimate email content.

The data file is typically provided in CSV format. Specific numbers for rows or records are not detailed in the sources. The dataset's structure involves a collection of email entries, each tagged with its corresponding classification.

This dataset is ideal for developing and evaluating email filtering and spam detection systems. It is highly suitable for various machine learning and natural language processing (NLP) tasks, including training algorithms for text classification, building predictive models for fraud detection related to email, and enhancing email client functionalities.

The geographic scope of the dataset is global. While a listing date of 05/06/2025 is noted, the specific time range for the data collection period is not provided. The dataset encompasses emails with diverse lengths, languages, and writing styles, aiding in broad applicability. Demographic scope is not applicable.

CCO

This dataset is well-suited for data scientists, machine learning engineers, and researchers focusing on email classification and security. It is also valuable for developers creating anti-spam solutions, and academic institutions conducting research in natural language processing or cybersecurity.

Original Data Source: Spam Mail Prediction Dataset