As part of our ongoing efforts to improve the effectiveness of our email spam detection system, we need to enhance our data collection process. Currently, we are facing limitations in acquiring diverse and real-time spam email data for training and testing purposes. This GitHub issue aims to address this challenge and explore potential solutions.
Goals:
Expand Data Sources: Investigate and integrate additional sources of spam email data to augment our existing dataset. This could include publicly available email datasets, collaborations with research organizations, or partnerships with email service providers.
Real-time Data Acquisition: Develop mechanisms to collect real-time spam email data in order to stay up-to-date with evolving spamming techniques. This may involve exploring APIs, scraping publicly available spam email repositories (if available), or implementing secure and ethical partnerships with organizations willing to share spam email data.
Data Diversity: Ensure that the collected dataset encompasses a wide range of spam email types, including various languages, content formats, and spamming techniques. This will help us train a more robust and comprehensive spam detection model.
Privacy and Compliance: Implement data collection procedures that adhere to privacy regulations and respect the confidentiality of users' personal information. Explore anonymization techniques or consider working with data sources that have already anonymized their datasets.
Tasks:
Research and evaluate existing sources of publicly available spam email datasets.
Investigate potential collaborations with research organizations or email service providers for data sharing.
Explore APIs or scraping methods to collect real-time spam email data.
Develop a data pipeline to efficiently process and integrate collected data into our training and testing processes.
Address privacy concerns and ensure compliance with relevant regulations throughout the data collection process.
Expected Outcome:
By enhancing our data collection for email spam detection, we aim to improve the accuracy and efficiency of our spam detection system. This will ultimately result in better protection for our users' inboxes and a more reliable email experience.
Contributors:
We welcome contributions from anyone interested in data collection, spam detection, or related fields. Please feel free to share your ideas, suggestions, or propose solutions to address this issue.
Note: Please refer to our code of conduct for guidelines on respectful and collaborative participation in this issue discussion.
As part of our ongoing efforts to improve the effectiveness of our email spam detection system, we need to enhance our data collection process. Currently, we are facing limitations in acquiring diverse and real-time spam email data for training and testing purposes. This GitHub issue aims to address this challenge and explore potential solutions.
Goals:
Tasks:
Expected Outcome:
By enhancing our data collection for email spam detection, we aim to improve the accuracy and efficiency of our spam detection system. This will ultimately result in better protection for our users' inboxes and a more reliable email experience.
Contributors: We welcome contributions from anyone interested in data collection, spam detection, or related fields. Please feel free to share your ideas, suggestions, or propose solutions to address this issue.
Note: Please refer to our code of conduct for guidelines on respectful and collaborative participation in this issue discussion.