Java

PALAK BAID

Long Beach, CA ***** • adlms3@r.postjobfree.com • 562-***-****

https://www.linkedin.com/in/palakbaid https://github.com/palakbaid95 PROFILE

I am a graduate professional seeking full-time opportunities in the field of computer science. With my experience in coding projects, writing research papers, and being end-to-end responsible for an application, I will bring problem-solving, critical thinking, and teamwork to the workplace.

EDUCATION

Master of Science in Computer Science: California State University – Long Beach GPA – 3.5/4 August 2019 – May 2021 Bachelor of Engineering: Computer Science: Jaipur Engineering College and Research Centre GPA – 3.7/4 August 2013 – May 2017 SKILLS

Core Programming languages: Java, C, C++, Python, JavaScript Database: MySQL, PL/SQL, NoSql, Mongo DB

Web Technology: HTML, CSS

Framework and Platform: Eclipse, NetBeans, IntelliJ-Java, Spring MVC, Spring Boot, JUnit Networking: Putty, WinSCP

Scripting Language: Shell, JSON, BSON

Certificates: Copado Certified – Administrator, Developer, Flossom PROFESSIONAL EXPERIENCE

California State University, Long Beach

Instructional Student Assistant August 2020 – Present

• Developed Programming assignments and Study guides for 220+ students enabling them to master Python.

• Resolved student issues on programming errors, installation of libraries, and software on different platforms Accenture, Pune, Maharashtra

Application Development Analyst January 2019 – June 2019

• End to end responsible for maintenance application: Participated in designing the database, Tested application in Pre-live and Production environment, Resolved issues & maintained in JIRA, and Created Confluence documents for Knowledge Base

• Developed pages using HTML, CSS and JavaScript to improve the website.

• Ad-hoc support of Code Review(multi-threading, data structures, algorithms), Generation of SQL queries, and Client calls Application Development Associate May 2017 – December 2018

• Created SQL queries, Configured various software programs and proposed technical solutions

• Debugged & troubleshooted Applications and Maintained technical documentations

• Designed Website, Tested & Installed software for applications RESEARCH PAPERS & ACADEMIC PROJECTS

Title: Sentiment Analysis of Twitter Data: (Python, NLTK, Tweepy, Pandas, Matplotlib) April 2020

• Implemented NLTK classifier to predict the sentiment of the extracted tweets. Increased the accuracy from 61.10% to 83.50%. Accuracy was achieved by reducing the size of extracted features.

• Extracted live tweets using twitter APIs by establishing a connection between Python and the Twitter application using handshaking.

• Used corpus, the universal dataset for the classification of positive and negative sentiment in the dataset. Title: Weather Prediction: (Python, Jupyter-Notebook, NumPy, Panda, Matplotlib) February 2020

• Implemented Hidden Markov Model without using Scikit-learn tool to predict weather based on historical data.

• Used Pandas API to load files as Panda data frame for cleaning and transforming data in a desired fashion.

• Implemented transition and emission matrix from the dataset, calculated probability of the observations using the Viterbi algorithm, predicted the probable weather sequence depending on the input sequence if an umbrella was required or not. Title: Naïve Bayes Classifier: (Python, NumPy, Panda, Matplotlib) March 2020

• Implemented classifier based on Naïve Bayes technique without using the Scikit-learn tool to predict if the patient has diabetes. Achieved 73% accuracy.

• Used Pandas to load the train and test data as the Pandas data frame, implemented accuracy function to count estimate and generate Confusion Matrix to describe the performance of the classifier. Title: DDOS and its mitigation techniques April 2018

• Investigated various mitigation DDOS techniques

• https://www.ijcaonline.org/archives/volume180/number34/29269-201-***-**** Title: Sentiment Analysis of Live Tweets after Elections February 2018

• Developed R scripts to evaluate the tweets during and after the elections

• https://link.springer.com/chapter/10.1007/978-***-**-****-3_36 Title: Sentiment Analysis of Movie Reviews using Machine Learning Techniques December 2017

• Worked on WEKA Tool to analyze the movie reviews using techniques like Naïve Bayes, K-NN and Random Forest

• https://www.ijcaonline.org/archives/volume179/number7/28752-201-***-****

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