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Spambase datasets
Text datasets of emails of different formats for training a neural network to identify spam and classify messages
Case
Machine Learning
Enables computer systems to automatically learn from data and make predictions
Safety
обучение алгоритмов распознавать ситуации, способные причинить вред
NLP
The ability of a system to understand, analyze and interpret human's languages
Data Collection
Gathering data for subsequent annotation
Cases’ description
Content and format:
Collection metrics:
.csv file with message text
jpg/png screenshots
jpg/png screenshots
10,000 messages
15 days
15 days
The dataset consists of a wide range of spam messages, including promotional offers, fraudulent schemes and phishing attempts
Language:
English
SMS spam in English
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Content and format:
.csv file with message text (title, text, type)
jpg/png screenshots
jpg/png screenshots
Collection metrics:
Languages:
15,000 messages
20 days
20 days
English, Spanish, French, German, Polish, Czech
Email spam in European languages
The dataset consists of a set of emails divided into two main classes: “spam" and “not spam". E-mails with a length of 50 to 7,500 characters are written in different languages, designed in colloquial and official speech styles
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Content and format:
.csv file with message text
jpg/png screenshots
jpg/png screenshots
Collection metrics:
10 000 messages
12 days
12 days
Language:
Russian
Russian spam SMS
The dataset contains examples of unsolicited text messages, which includes promotional mailings, viral links, microfinance offers and other fraudulent schemes
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AI solutions for your business
-01-
LLM training to recognize different spam formats, generate, rewrite and perform any other actions on request based on spam texts
-02-
Spam protection in chat applications: NLP to improve the spam filtering system in chats and prevent unwanted messages, advertisements or malicious links, as well as to increase protection and security
-04-
Phishing Protection: Classification for recognizing phishing emails and preventing users from interacting with them
-03-
Preventing text spam in comments: NLP for detecting and blocking spam in comments and ensuring safety and comfort when using mobile applications
-05-
Optimization of marketing campaigns: Classification for automatic filtering of unwanted or fraudulent requests from users and improving the quality and accuracy of marketing campaigns
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THE FINAL COST OF THE PROJECT IS INFLUENCED BY:
Scope of work
Markup complexity
Timing
Markup quality
Our data quality guarantee is 95%. When ordering markup with quality above 95%, we offer enterprise solutions
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