Methods and systems for predicting a proposed electronic mail (email) for an email campaign to be sent to a list of email addresses received from a sender is spam. A hard bounce rate is predicted for the list of email addresses based on hard bounce rates calculated for respective email addresses on the list. If predicted hard bounce rate exceeds a threshold, the proposed email is deemed spam and sender may be informed. If predicted hard bounce rate falls within range of the threshold, proposed email may be sent to less than all the email addresses on list. If an acceptable number of the sent emails are received, the email may be sent to remaining email addresses on list. Prior to sending proposed email, the list of email addresses may be compared to purchased lists of email addresses. If there is a match, the proposed email may be deemed spam.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. With respect to a service provider, which sends electronic mail (email) and stores data on the email transactions, a method for predicting that a proposed email from a sender is spam, comprising: receiving a proposed email from a sender; receiving a list of email addresses to be sent the proposed email; predicting a hard bounce rate for the list of email addresses; if the hard bounce rate exceeds a threshold, informing the sender that the proposed email is spam. 2. The method of claim 1, further comprising: if the hard bounce rate does not exceed the threshold, sending the proposed email. 3. The method of claim 1, further comprising: if the hard bounce rate falls between the threshold and a lower threshold, sending the email to some of the email addresses on the list of email addresses; determining if one or more of the sent emails hard bounces; and if the determined number of sent emails that hard bounce exceeds a hard bounce threshold, then informing the sender that the proposed email is spam. 4. The method of claim 3, further comprising: if the determined number of sent emails that hard bounce falls below the hard bounce threshold, sending the proposed email to email addresses on the list of email addresses to which the proposed email had not been previously sent. 5. The method of claim 1, wherein predicting the hard bounce rate for the list of email addresses comprises determining a predicted individual hard bounce rate for each email address on the list of email addresses; and predicting the hard bounce rate for the list of email addresses based on the individual hard bounce rates for each email address on the list of email addresses. 6. The method of claim 5, wherein determining the individual hard bounce rate for each email address on the list of email addresses comprises training a process to predict the individual hard bounce rate of each email address on the list of email addresses. 7. The method of claim 6 wherein training the process to predict the individual hard bounce for each email address on the list of email addresses comprises obtaining known past sending results for an email address, obtaining profile data for the email address, using the profile data to determine a profile data hard bounce rate, checking if the profile data hard bounce rate matches the known past sending results for the email address, and if the profile data hard bounce rate matches the known past sending results, then determining the process to predict the individual hard bounce rate to be trained. 8. The method of claim 7, wherein determining the individual hard bounce rate for each email address comprises running each email on the list of email addresses through the trained process to obtain the individual hard bounce rate for each email address on the list of email addresses. 9. The method of claim 7, wherein predicting the hard bounce rate for the list of email addresses comprises training a process to predict the hard bounce rate for the list of email addresses. 10. The method of claim 9, wherein training the process to predict the hard bounce rate for the list of email addresses, comprises: obtaining historic lists of email addresses from past email campaigns and a historic hard bounce rate associated respectively with each list, running each historic email address on the historic lists of email addresses through the process to predict historic individual hard bounce rates for each historic email address, combining the historic individual hard bounce rates into a bounce likelihood cumulative distribution, obtaining summary statistics from the bounce likelihood cumulative distribution, combining the summary statistics with the known bounce rates of the respective historic lists to induce a Random Forest model for predicting the hard bounce rate for the list of email addresses. 11. The method of claim 10, wherein determining the hard bounce rate for the list of email addresses comprises running the list of email addresses through the induced Random Forest model. 12. A method to stop spam before it is transmitted in an electronic mail (email) campaign, comprising: storing email data from previous email campaigns; receiving a list of email addresses for use in an email campaign or receiving an indication of a previously used list of email addresses for use in the email campaign; from the stored data, obtaining profile data for each email address in the list of email addresses or the previously used list of email addresses; calculating, respectively, a predicted hard bounce rate for each of the email addresses in the list of email addresses or the previously used list of email addresses, calculating, respectively, the predicted hard bounce rate for an email address by using the profile data of the email address; calculating an overall predicted hard bounce rate for the list of email addresses or the previously used list of email addresses based on the calculated predicted hard bounce rates for the email addresses; and if the overall predicted hard bounce rate exceeds a threshold value, then refraining from sending the email campaign. 13. The method of claim 12, further comprising: if the overall predicted hard bounce rate fails to exceed the threshold value, then sending the email campaign. 14. The method of claim 12, further comprising: if the overall predicted hard bounce rate falls within a range of the threshold value, then testing the list of email addresses or the previously used list of email addresses. 15. The method of claim 14, wherein testing the list of email addresses or the previously used list of email addresses comprises sending the email campaign to a less than all of the email addresses on the list of email addresses or the previously used list of email addresses, monitoring the email campaign for hard bounces.