{"push_id":2863038941,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"81cf6b9f6cccc59ff4601ab0b58475f110f05668","before":"a0dbf8e6d099f98de459f4496b836bd10c29b6b6","commits":[{"sha":"81cf6b9f6cccc59ff4601ab0b58475f110f05668","author":{"name":"Broto Bhattacharjee","email":"c29683ee6c5ed6b9c8c087ad22e3bef2d0416df4@gmail.com"},"message":"Text Classification using CNN and LSTM (RNN)\n\nProblem Statement :\r\nLifeLine Health Services is a top ranked Health care provider in USA with stellar credentials and provides high quality-care with focus on end-to-end Health care services. The Health Care Services range from basic medical diagnostics to critical emergency services.\r\nThe provider follows a ticketing system for all the telephonic calls received across all the departments. Calls to the provider can be for New Appointment, Cancellation, Lab Queries, Medical Refills, Insurance Related, General Doctor Advice etc. The Tickets have the details of Summary of the call and description of the calls written by various staff members with no standard text guidelines. These calls have been classified into 6 categories. Based on the call description you will have to train a model using categories\r\ncolumn as target attribute.\r\n\r\nData Set\r\nYou are provided with Data.csv which contains categories and converse column providing brief description of the call.\r\n\r\nEvaluations\r\nThe metrics we are interested in this problem are Accuracy and Recall for each category.\r\n\r\nNote\r\n1. Use Only the algorithms from AI & Decision Sciences Module(7321c). You can try various Neural network architectures (MLP, RNN, CNN).\r\n2. While running RNN, If you are using embedding layer it is advised to convert word vectors into vectors of unique Id.\r\n3. Please make sure that your systems have all required softwares and packages installed before coming to the class.","distinct":true,"url":"https://api.github.com/repos/bbroto06/DeepLearning/commits/81cf6b9f6cccc59ff4601ab0b58475f110f05668"}]}
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