{'fraction_non_alphanumeric': 0.01848249027237354, 'fraction_numerical': 0.0, 'mean_word_length': 5.638709677419355, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Approach for Clustering and Classification of Alumni Employment Information

Data-driven approach has proven wonders to the different sectors of industry. Similar approach can be worked out to enhance the employment rate in the current scenario. Educational institutions have alumni data regarding their job roles, departments, companies, etc. Using this data and applying different clustering and classifying techniques, useful insights can be obtained such as the sectors which have highest intake, departments where most of the students from a particular institution are working, etc. The information would also be used by the institutions in designing the curriculum and academic planning. Current students can use the classified data for their preparations. This paper presents a classification model for an Alumni job role data, mentioning the different clustering techniques such as Agglomerative Clustering, Machine Learning algorithms for data classification such as K-Nearest neighbors, Support Vector Machine, etc.