Payal Dangi

Contact: 248-***-**** Email: addpvl@r.postjobfree.com, addpvl@r.postjobfree.com LinkedIn Profile Summary

Creative, adaptive and high performing graduate candidate with experience in data research and analytics engineering. Skills

Programming Languages C, C++, Java, Python, Visual Basic, R-Studio Web Development PHP, XML, HTML, CSS, JavaScipt, pyspark Tools NetBeans, Eclipse IDE, Jupyter Notebook, PyCharm Data Management and Visualization MySQL, Tableau, Qlikview Operating Systems Windows (98, XP, 8, 10), Linux

Other skills Microsoft Office suite, Jira

Work Experience

Data Science Intern at ITS University of Michigan, Ann Arbor May2020-Ongoing

• Cleaning the dataset and identified the factors of potential risk

• Building classification models using data mining techniques with different classifiers.

• Using machine learning techniques in python to perform predictive analysis and build Tableau dashboard. Visiting Faculty, IIPS (International Institute of Professional Studies), DAVV July 2014 – May 2016

• Instructor for Digital Computer Organization, Discrete Structures and Internet & web programming.

• Served as coordinator and advisor for Math Olympiad. Served as advisor for curriculum development, advising and exam /class scheduling.

Education

Master of Science, Data Science Dec 2020

University of Michigan, Dearborn GPA: 3.6

Master of Engineering, Computer Science May 2016

RKDF School of engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya GPA: 3.7 Bachelor of Engineering (BE), Computer Science June 2013 Medi-Caps Institute of Technology & Management, Rajiv Gandhi Proudyogiki Vishwavidyalaya GPA: 3.4 PROJECTS

Analytics Experience Capstone Project

Sponsoring Organization: Information and Technology Services – University of Michigan, Ann Arbor Jan 2020-April 2020

• To develop augmented analytics and visualized dashboard to measure insights using machine learning and natural language generation.

• Technology used: Tableau (Ask Data & Explain Data), Alteryx, SQL. Heart Disease prediction (Big Data Visualization and analytics) Sep 2019-Dec 2019

• Developed a Predictive Model using Supervised Machine Learning Algorithms to predict the probability of heart attack using dataset of medical history.

• Classification models used: Logistic Regression, Decision Tree, Random Forest, PCA.

• Data Visualization techniques used: correlation matrix, Box and Whisker Plot, Bar Chart, Histogram Plot, Graph analysis. Accuracy calculation of Financial Analysts (Multivariate Statistics) Sep 2019-Dec 2019

• Developed models to calculate accuracy in predictions of a Financial Analyst by using Linear Regression. Resulting analysis provides insights for reliability on results of a Financial analyst.

• Classification models used: Logistic Regression, Decision Tree, Random Forest, Dimension Reduction algorithm.

• Data Visualization techniques used: correlation matrix, Box and Whisker Plot, Bar Chart, Histogram Plot, Graph analysis. Language Models to Improve optical character recognition (Natural Language Processing) Jan 2019-April 2019

• Developed a language model to enhance the accuracy of handwritten text recognition.

• Project aims at reducing the errors in text identification by implementing language models such as maximum matching word- segmentation and estimating bigram probabilities.

Face Recognition Technique Using Color and Edge Feature (image recognition), RKDF school of Engineering May 2015

• Studied various Face recognition and feature extraction techniques (ICA, PCA and LDA) and proposed an improved technique in order to enhance the performance and to reduce the computational cost of system. RESEARCH PUBLICATIONS

• “An improved Face recognition technique using color and edge feature”, International Journal of Advanced Engineering Technology and Innovative Science (IJAETIS) Available online at: www.ijaetis.org Volume1, Issue 2, Page No: 06-13 November- December 2015 ISSN:2455-1651

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