Washington DC-Baltimore Area
3K followers 500+ connections

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About

Experienced Data and Business professional with an understanding of People, Process, and…

Activity

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Experience & Education

  • Stanley Black & Decker, Inc.

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Licenses & Certifications

Volunteer Experience

  • Volunteer

    NGO

    - 2 years 5 months

    Education

    Recorded Voice for 'Talking Books Library' for the visually impaired students.

    http://www.arushi-india.org/talking-books-library-act.html

Courses

  • Applied Data Science

    IST 687

  • Big Data Analytics

    IST 718

  • Business Analytics

    SCM 651

  • Data Administration Concepts and Database Management

    IST 659

  • Data Warehousing

    IST 722

  • Information Systems Analysis

    IST 654

  • Introduction to Information Management

    IST 621

  • Management Principles for Information Professionals

    IST 614

  • Natural Language Processing (NLP)

    IST 664

Projects

  • Brooklyn Home Price Prediction

    -

    descriptionTechnologies used - Apache Spark (PySpark), Python, MLIB libraries

    • The dataset involved 230000 rows and 111 columns. Performed data wrangling and cleaning to get the required data. Used correlation and coefficients from a linear regression model to identify useful columns
    • Used PCA with 2 components to reduce the dimension space
    • Applied 4 different Linear regression models to predict the sale price.
    • Used RMSE as the parameter to select the best linear…

    descriptionTechnologies used - Apache Spark (PySpark), Python, MLIB libraries

    • The dataset involved 230000 rows and 111 columns. Performed data wrangling and cleaning to get the required data. Used correlation and coefficients from a linear regression model to identify useful columns
    • Used PCA with 2 components to reduce the dimension space
    • Applied 4 different Linear regression models to predict the sale price.
    • Used RMSE as the parameter to select the best linear regression model

  • Natural Language Processing (NLP) - Sentiment Analysis

    -

    Performed sentiment analysis on 10000+ celebrity tweets. Used techniques like bi-grams and Lemmatization to improve accuracy. Also, calculated the probability of positivity in the dataset using the Naïve Bayes model.

  • Data Warehousing

    -

    Created a data warehouse to restructure the online movie recommendation platform like Netflix. Performed Dimensional modeling, Designed SQL queries, ETL using SSIS and visualized data insights using Tableau

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