SUMMARY

Good experience implementing machine learning algorithms on text and image datasets.

Passionate about extracting insightful information from large datasets.

Experienced working with python libraries like Scikit-learn, pandas, numpy, Scrappy and BeautifulSoup,

EDUCATION

Master of Science in Computer Science, University of North Carolina at Charlotte, USA Dec 2017 GPA 4

Bachelor of Technology in Computer Engineering, GITAM University, India May 2016 GPA 3.8 TECHNICAL SKILLS

Programming Languages : Python, Java, Scala, PHP, MATLAB, C

Front-end : HTML, CSS, Bootstrap, Angular, JavaScript, JQuery, AJAX

Framework : Django, Spring Boot

Database : MySQL, MongoDB, SQLite

Big Data : Apache Spark, MapReduce

Tools : AWS, Git, npm, MySQL Workbench, Weka, Tableau

Platforms : MAC OS X, Linux, Windows

EXPERIENCE

Web Developer, CloudCruze, India March 2014 – July 2015

Designed and developed responsive, consistent and reusable server code using Django.

Collaborated with the front-end team to build a user-friendly interface for clients.

Assisted senior developers in testing and identifying bugs and other technical issues.

Technologies: Django, Bootstrap, HTML, CSS, JavaScript, JQuery

PROJECTS

Twitter Sentiment Analysis with Spark Nov 2017 – Dec 2017

Improved the accuracy of Naïve Bayes and K Nearest Neighbor by using normalized TF-IDF vectors for tweets.

Achieved an accuracy of 73.92% for Naïve Bayes and 75.56% for K Nearest Neighbor on 1.6 million tweets.

Technologies: Spark, Python

Document Search Engine with Hadoop Aug 2017 - Sep 2017

Developed a search engine to find the most relevant document in response to a search string (query) by computing scores using term frequency-inverse document frequency (TF-IDF).

Technologies: MapReduce, Java

Flight Performance analysis using GraphFrames on Apache Spark Jan 2017 - May2017

Performed analysis on flight delays using graph processing techniques such as page rank, motif finding, shortest path and breadth first search.

Used Zeppelin for visualization and execution of SQL queries on the DataFrames.

Technologies: Spark, Python, Zeppelin

Paintings Classifier Jan 2017 - May2017

Built a database of 15,000 paintings by scrapping data from Saatchiart website using Scrappy.

Performed discretization on the price attribute and classified into three intervals with high precision.

Technologies: Python, Weka, Scrappy

Image Classification Jan 2017 – May 2017

Implemented and compared the performance of different classification based algorithms on images from Caltech101.

Achieved an accuracy of 68% for KNN, 51% for SVM and 45 % for CNN after using PCA for feature selection.

Technologies: Python

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