Check the ticket database and bring up the ticket that has best/closet match for the problem in the new ticket and best expert who can solve the issue from the team so that the request of the issue can approach him directly for quick resolution implemented by using word embedding,vector representation. Used NLTK, Gensim libraries in Python for implementing the above.

Responsibilities:

• Working on various Natural Language Processing techniques to analyze the data and get insights to
• understand trends better.
• Worked on Integration of Hive with Pyhton.
• Worked on development of various NLP models (TF-IDF,Word embedding ,Logistic Regression)

Created a chatbot that enables the engineers/customers to identify the relevant past issues from Isolar
Centralized knowledge database(based on Jira) and resolve the customer issues and queries in an expedited manner.
The conversational in nature, lookup similar past issues from knowledge database and suggest solutions, cognitive capability, Manage tickets for issues.
Responsibilities:
• NLP and Machine learning based solution retrival from Jira knowledge base
• Neural Network based prediction and information retrival
• Creat django services and host model inferences in IIS server
• Batch Jobs to train the models on regular basis with new data coming from jira
• Data stroing and retriving using MSSql
• Used Python, pandas, numpy, cx_oracle, Nltk, Gensim, Pyodbc, Tensorflow, Django, MSSQL, Jira software, Microsoft LUIS

Created video classification for detecting, tracking and counting car objects in the video from real time streaming.Used YOLO (You Only Look Once) real-time object detection system with deep learning.
Responsibilities:
• Detecting car objects in the video using Yolo deep learning model.
• Classifying the cars as Incar and Outcar based on the directional flow using computer vision
• (Opencv).
• Counting the number of incoming cars and outgoing cars.
• Tracking the car objects in the video using Dlib.
• Real time streaming from camera or vlc using Opencv.
• Implemented the above in Python using TensorFlow, keras, Dlib, OpenCV, CUDA