San Francisco Bay Area
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About

Senior Business Intelligence Developer with 6+ years of experience building data…

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Experience & Education

  • WebMD

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Licenses & Certifications

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Volunteer Experience

  • YUVA Unstoppable Graphic

    Volunteer

    YUVA Unstoppable

    Education

    I took Computer Sessions in government schools for underprivileged children to teach them some basic computer skills like MS Word, Writing Emails, Surfing Internet, etc.

Courses

  • APPLIED DATABASE MANAGEMENT

    INSY 5335

  • ADVANCED METHODS FOR ANALYTICS.

    BSTAT 5325

  • ANALYSIS AND DESIGN

    INSY 5341

  • APPLIED BUSINESS AND ECONOMICS DATA ANALYSIS

    ECON 5336

  • DATA SCIENCE: A PROGRAMMING APPROACH

    INSY 5378

  • DATA WAREHOUSING AND BUSINESS INTELLIGENCE

    INSY 5337

  • MANAGEMENT OF INFORMATION TECHNOLOGIES

    INSY 5375

  • PRINCIPLES OF BUSINESS DATA MINING

    INSY 5339

  • PROJECT MANAGEMENT

    INSY 5373

  • PYTHON PROGRAMMING

    INSY 5336

  • SELECTED TOPICS IN INFORMATION SYSTEMS

    INSY 5392

  • WEB AND SOCIAL ANALYTICS

    INSY 5377

Projects

  • TOPIC MODELLING AND TEXT ANALYSIS | PYTHON

    -

    • Text analysis was performed on blockchain data
    • Word clouds and frequency plot of top repeated words were created.
    • Topic modeling was performed using LDA and NMF techniques.
    • Used c_v and Umass Measure to find the optimum number of topics.

    See project

  • MOVIE GENRE PREDICTION BASED ON MOVIE POSTERS AND TEXT | MACHINE LEARNING ALGORITHMS, PYTHON

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    • Predicted the genre of a movie based on Posters (images) and Text.
    • For text analysis, movie descriptions were scrapped from TMDB.
    • After cleaning and pre-processing the scrapped text, Machine Learning Algorithms such as Naïve Bayes, SVM, etc. were used to predict movie genre.
    • For the second approach, movie posters were downloaded from a Kaggle dataset.
    • Keras and TensorFlow were used for image processing and predictions were made using neural networks.

    See project

  • PROBABILITY ANALYSIS WITH MAXIMUM LIKELIHOOD AND LOGISTIC REGRESSION | R

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    • Cleaned Data using VLOOKUP, Macros and Filters in Excel
    • Wrote Python script to merge two Excel files.
    • Wrote R code to perform Logistic regression and Maximum Likelihood test on the dataset.
    • Analyzed factors that might affect the customer’s decision to renew the service contract.
    • Provided Business Implications to increase the contract renewal rate.

    See project

  • PROBABILITY ANALYSIS WITH NON-LINEAR LEAST SQUARE AND HYPOTHESIS TESTING | R

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    • Analyzed various factors that might affect the Sales of a Company by applying nonlinear regression on sales data.
    • Analyzed diminishing return and interaction between different variables that might affect the sales.

    See project

  • TWITTER ANALYSIS ON US 2016 ELECTION | PYTHON, TABLEAU

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    • Wrote Python scripts using BeautifulSoup (python library) to scrape Tweets related to US 2016 Elections.
    • Wrote Python code to perform Sentiment Analysis on data.
    • Developed Word clouds and Frequency plots in python to analyze data.
    • Created Visualization Reports using Tableau.

    See project

  • DATABASE MANAGEMENT OF UNIVERSAL FURNITURE OUTLET (UFO) | SQL, MICROSOFT VISIO

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    • Created Information Systems for UFO to manage its operation.
    • Entities were identified and relationship matrix was formed.
    • Business Rules were created followed by ER Diagrams, 4NF relational schema and Data distortionary.
    • Created tables in Oracle and then executed queries using SQL.

    See project

  • PREDICTIVE CLASSIFICATION ON US ADULT INCOME

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    • Used VLOOKUP, Macros and Pivot table in Excel to clean and analyze data.
    • Wrote python code to apply Data mining Algorithms such as J48, Decision Table and Multilayer Perceptron
    • Predicted an individual’s income in one of two classes with 80% Accuracy.

    See project

  • AUTOMATED PASSENGER COUNTING SYSTEM (APCS) | RASPBERRY PI, JAVA, IOT

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    • APCS was developed to automatically count passengers traveling in a bus.
    • Developed using Java and Raspberry Pi.
    • The data collected can be used for better planning and improvement of services.
    • Project selected by University to be proposed to Government of Gujarat for application on Bus Rapid Transit System of Ahmedabad City as a Pilot Project.

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