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About Me

I have worked in Amdocs LLP. for 3 years as a software developer. My main role there was to develop and manage CRM applications. I have also worked extensively on Ordering Applications. Managing data for millions of users got me intrested in the field of data science. Now a days Im learning and deploying my skills to make most use of data available around us.

Apart from this im a big hackathon enthusiats I have participated in 6 hackathons uptill now.
Im a big fan of Friends(TV Show) too.

Education

   Georgia State University
Location: Atlanta, Georgia, United States
Year: 2019-Present
GPA: 4.0/4.0
Course Work: Big Data Programming, Data mining, Fundamentals of Data Science, Deep Learning , Advance Machine Learning, Statistical and Computational Analysis
    Lovely Professional University
Location: Jalandhar, Punjab, India
Year: 2012-2016
GPA: 8.26/10.0
Course Work:Design and Analysis of Data Structures, Artificial intelligence, Machine Learning, Scripting Languages, Advance Database Systems, Advance Software Engineering

Skills

Programming Languages

Python
90%
Spring MVC, Hibernate
80%
R language
70%
Apache Spark
65%
Android
50%

Databases

Oracle 12g
80%
MySQL
80%
SQLite
65%
Hive
50%
SQL Server
40%

Tools and Servers

Weblogic Server and Apache Tomcat
90%
Jupyter, Spyder, Knime and PyCharm
90%
Tableau
80%
Eclipse, Android Studio and VS Code
75%
Jenkins, SoapUI, Adobe Web Tools
70%

Work Experience

Georgia State University   Aug 2019 - Present

Graduate Research Assistant

Here my job is divided into two main areas one was the website management for the entire interdisciplinary program. The other one is related to one of the inner courses of this program homelessness lab.

As part of website management, I'm working on the website for this entire program where and integrating it with GSU's main webpage. For doing this, I'm using PHP with WordPress. Since the website also covers aspects of marketing and promotion and this program being a complex model, I'm making it as intuitive and simplistic as possible.

Homelessness lab is one of the labs of this program. Here I lead the students on the analytical part of student homelessness. From data collection, strategies until compiling it to represent it to the authorities without violating any privacy laws is our primary goal. .

Amdocs Development Center LLP. Aug 2016 - July 2019

Experienced Software Developer

  • Telecom. customer data-analysis to bring out relevant insights using Python, Tableau, Excel and other tools.
  • Implementation of Auto Ticket Resolution using Atom IQ, Power BI, Python where labeling, training and processing of UTS tickets were done. Tool helped to solve 42 % of the daily incoming tickets without human intervention.
  • Delivered more than 20 CR’s, Raised and Fixed more than 200 defects, implemented 5-7 toils helping client avoid P0 and P1 and improvise the code from performance and security point of view.
  • Many Client Site (Latin America) visits to explain the business growth with data visualizations and help get more business.

    • HEWLETT-PACKARD June 2015 - Aug 2015

    Web developoment Intern

  • Implemented online polling system using Android, PHP, WordPress, Oracle WebLogic for inhouse usage.
  • Worked in health and insurance domain for the areas of improvement and its feasibility study.

    • Key Projects

       Data Deduction and Key Analysis on Twitter Data in Apache Spark
    Description:
  • Analyzed and preprocessed Twitter data comparing run time speeds on simple python, parallel
    procesessing in python and Apache spark.
  • We analysed and ran our commands on 50GB of compressed data(~200GB uncompressed) to do that efficiently we used Azure HDinsights and clusters were made on Azure cloud.
  • Using Vader libraray from NLTK we did sentiment analysis on all tweets. Parity based sentiment analysis was done.
  • Implemented LDA to find the major topics of discussion in the tweets related to Pokemon GO.
  • Every task was done using Machine Learning library from spark.
    •    Food And Recipies Data Analysis from Food.com
    Description:
  • We Analysed data from Food.com and using Data Enginnering principle a new feature was added i.e. filter by cuisine. This was done by introducing a new column of cuisine. Cuisines were predicted using ingridients used to make the recipie. Random forest Classifier was used with the accuracy of 84%.
  • We also analyzed the calorific values and how people react to foods from different calorific values.
  • We also applied apriori algorithm to find the frequently bought items. That could also have been an addon feature on food.com
    •    Z-Cast Watch with Friends
    Description:
  • This was one of our hackathon project were we implemented a augumented reality movie theater where you can schedule and watch movies with friends.(Same as watch party in facebook now but we implented it earlier.)
  • We used Vibiquity and UbiSoft API's and gave a platforms just like netflix to display the data. the data was in form of movies, songs, games etc. We preprocessed data in java and then displayed it on a web page where we further extended the functionality of augumented reality.
  • This tool was made available across all the platforms TV, Phones, Webpages and tablets but just for android devices.
    •    Auto Ticket Resolution Tool
    Description:
  • This was one of the project that we worked as an SRE (Site-reliability Engineer) to eliminate toil.
  • Amdocs used to get more that 35k stuck orders daily due to multiple reasons. This tool was supposed to classify the stuck order into one ofthe labelled issues and apply the work around. if the confidence was above a threshold.
  • After this tool was in production 60% of the stuck order were getting resolved automatically.
    •    Machine Learning for Store Managers
    Description:
  • This is another intresting hackathon project where we utilized transaction API from NCR to train our model and and do predictive analytics.
  • We made a intuitive webpage for store managers where we predicted the short of stock of any product and sent notifications. This was done using SARIMAX time series analysis.
  • Other module was that it diplayed the frequently bought items and there quantity in stock.
  • We classified multiple production in the basis of their category and displayed some intresting facts which might be useful for the store manager.
  • Here machine learning alogrithms where implemented in pyhton which was later integrated with NodeJS fo visulizations.

    • Contact

    Email ID:  anitg.4@gmail.com
    Phone Number:  +1 404-343-5156
    Linkedin :  https://www.linkedin.com/in/anit-gupta
    Git Link :  https://github.com/gupta-anit