The evolution of foreign ministries followed from the desire of rulers and their ministers to maintain a continuous flow of diplomatic business in which cross relationships between diplomatic partners, between internal sources of political influence and between differing issues could be carefully followed and controlled. International relations is by definition the study of countries' interactions with other countries. Since at least the time of the ancient period, these relationships have been directed, promoted, and negotiated by way of diplomacy and diplomats. Unsurprisingly then, scholars have devoted considerable attention to the subject of envoys and their activities. As with all research efforts, data is key to progress. However data on diplomacy, whether qualitative or quantitative, can be difficult to obtain and difficult to work with. The reasons for this paucity are well rehearsed. For one thing, diplomatic records are often covered by countries' secrecy laws, and may be made available for public consumption on a haphazard schedule, if at all. Simultaneously though, governments often release files in overwhelming numbers but without careful curation, making it impossible for scholars to keep up with the flow of information available to them. On the one hand, the sheer size of the data collection makes it difficult to catalog and work with, especially if one wishes to move between aggregate analysis and the inspection of individual cases. On the other hand, finding more than one example of a particular phenomenon and applying the scientific method to it can be daunting. All told, there is an obvious need for a classification of Diplomatic Missions Data using Machine Learning. The TF-IDF(Term Frequency-Inverse Document Frequency) algorithm that has been used for the project easily helps to classify the diplomat data into the necessary categories.