Team Machine

Future is here

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Inspiration

We believe Pega is not "just" for workflow applications, Pega can totally change the world with advance technologies like Data Science, Artificial Intelligence & Machine Learning

We believe future is AI and surely wants to contribute to future

What it does

Our Application fetches tweets based on hashtags from twitter and internally interacts with machine learning model wherein it'll identify the sentiment of the tweet.

How we built it

In the beginning of video they are 389 cases for “twitter case”. Here, cases are number of tweets in twitter

We created following rules for our project :

TwitterDataFlow : rule facilitate decision execution in real time

TwitterDS1(Data set) : Contains access details and search keywords( twitter hashtags)

TwitterAnalyzer : Contains Machine learning model which we created and lexicon which helps in processing of NLP. Best F-score value means good precision We have trained model based on the sentiment of the sentence

TwitterCase : Contains the case details, we can map multiple properties to a case

Challenges we ran into

Initially it was bit tough to create machine learning models, As we didn't knew basics of machine learning before doing this project but Pega helped us to learn real quick and it's fascinating to do NLP with Pega rather than other programming languages.

As we both have just 2 years experience and we don't know much about API's so initially it was difficult to understand about API connections between twitter and Pega application.

Accomplishments that we're proud of

Doing the new and different project which was rarely done using Pega makes us really proud and behind this project their was smart work & quality amount of time spent on learning new concepts.

Setting up a twitter API and connecting it with Pega was really good for learning and we spent most of our time training machine learning models for our tweets and their sentiment analysis, and we love to contribute AI and Data Science projects with pega in future.

What we learned

  1. Setting twitter API with pega
  2. Data flows 3.Text Analyzer
  3. Machine Learning models
  4. Simulations
  5. Natural Language Processing
  6. Adaptive & Predictive models
  7. Data Sets

The good part of doing this project was we get to learn ton of new concepts

What's next for Team Machine

Initially, when we got idea about this project we didn't thought we can do this using Pega because at that time we only believed Data Science/ Machine Learning projects can only be done using programming. However when we started data science course in Pega it was really easy and it made our work simple & on top of that the only thing where we spent most of the time was training machine learning models. We strongly feel that the idea of doing AI,Data Science,Machine Learning projects with Pega is really fascinating

We are planning to do many more machine learning, Data Science and AI projects in Pega like :

  1. Customer purchase pattern
  2. Credit Risk
  3. Share market analysis using monte carlo

Love to be part of Pega Family

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