Customer is leading Analytics company mainly dealing with Image Detection, Ammunition detection and the software are deployed in schools and universities where we often here that there are mass shootouts in the US Schools/Colleges. Basic need is to stop those shootouts.

Customer is world’s biggest telecommunication provider and customer wants to roll out the specific 5G customer who wants to churn from 4G to 5G. Predict customers who are going to churn and find out the factors that are leading to churn based on the customer feedback reports 

 This was basically a Q&A system where user can ask questions and service will respond based on trained data.

Technique applied - Used ChatterBotCorpusTrainer API to train the bot and google audio recognizer to sense audio question data & respond with correct answer 

Built machine learning model to predict positive & negative sentiment of tweets based on unigram word frequency features as well as other features like total words in documents, using Word2Vec model using genism library. Using Python script to download tweeter data. Pre-Processing was done using Python 

CPS must maintain persistent custom variables at the session level and subscriber level that can be used in custom policies (Policies Tab in PB) and CRD table evaluation. Session level variables will exist for the duration of the specific session and subscriber level variables will exist until all sessions are closed.