Java Developer Data

Palak Patel

**** **** ******, *** ***, Sacramento, CA 916-***-****

ac80of@r.postjobfree.com https://github.com/Palakpatel67 https://www.linkedin.com/in/palak-patel67/ Summary: Experienced professional pursuing Master’s Degree in Computer Science from California State University, Sacramento with a background in Java and Python.

Education:

Master of Science, Computer Science, California State University, Sacramento GPA: 3.6 Aug 2017 – Dec 2019 Bachelors of Engineering, Computer Engineering, Gujarat Technological University, India GPA: 3.7 Aug 2013 – June 2017 Technical Skills:

Languages: Java, Node.js, Python, C++

Web and Cloud Technologies: HTML5, CSS3, JavaScript, Amazon Web Services Databases: SQL, PL/SQL, MySQL, Oracle

Industry tools: Eclipse, IntelliJ, Android Studio, Anaconda, Visual Studio Professional Experience:

Emerging Technology Intern at Celgene Corp, Summit, NJ May 2018 – August 2018

• Worked on Celgene’s project ‘Celexa-Clinical trial process’ that Use Alexa Skill for patient routine monitoring.

• Technologies used : Amazon Web Services Alexa skill NodeJS

• Used AWS’s Lambda function, Alexa skills kit and Amazon developer console to build skill.

• Using Node.js to handle responses from lambda function when user needs data via Echo Dot. Junior JAVA Developer at Maxgen Technologies, India July 2016 – April 2017

• Worked with the development team to enhance the existing product named ‘Online Real Estate’ – a product used to handle real estate market in city, developed in Java JSF and Spring(MVC)

• The objective of the application is to provide platform for buyer & seller to collaborate online.

• Used HTML, JAVA, JSP for front end, Tomcat as application server, JDBC for database connectivity and MySql as database.

• Prepared unit test cases and reviewing test results. Academics:

Distributed Computing - Mobile Learning Application, California State University, Sacramento, CA January 2018 – May 2018

• The application will provide a collaborative platform for administrator, Students and will be used for enrollment of students, managing subjects, classes and grades and keeping track of their upcoming schedules.

• Mobile Learning Application is an Android application that communicates with a server using RESTful API. The business layer of the application is in .NET and hosted on Amazon EC2. Managing the database layer in Microsoft SQL Server Management Studio 2014 and will be hosted on Amazon Relational Database Service(RDS). Integrating TensorFlow in Android Application, California State University, Sacramento October 2017 – November 2017

• Implemented a multi-class classification problem on android application which helped in detecting the digits drawn by the user.

• Used the MNIST handwritten digits classification dataset to train the model with TensorFlow.

• Integrated the pre-trained tensor model in android application which demonstrated the portability features of TensorFlow. Yelp Data Analysis (Python, Python libraries-pandas, matplotlib, scikit-learn) January 2019 – February 2019

• Accurately predicted a business's stars rating using all the reviews of that business and review count.

• Executed all steps of data analysis process including Mining, Cleaning, Analysis, Accuracy Checking and Visualization. Implemented Machine Learning Algorithms such as K-means, Agglomerative, KNN, SVM and NN with Tensorflow with accuracy of 91%. Network Intrusion Detector, California State University, Sacramento January 2019 – Present

• Software to detect network intrusions protects a computer network from unauthorized users, including perhaps insiders. This project aims to build a Network Intrusion Detector, a predictive model capable of distinguishing between bad connections, called intrusions or attacks, and good normal connections. It is Binary Classification Problem. This database contains a wide variety of intrusions simulated in a military network environment.

• To detect the network intrusions and predict where it is bad or good connection used different Scikit-learn Models, Neural Network and CNN. Got the best accuracy of 99% with CNN.

Sentiment Analysis of Movie Reviews – Neural Network January 2018 – May 2018

• Implemented a binary sentiment classifier on IMDB dataset to predict whether a review is positive or negative. Used IMDB movie reviews dataset to predict whether given movie review is positive or negative.

• Using Neural Network gained accuracy up to 87% in movie reviews classification. Certification and Publication:

• DataCamp – Introduction to Python for Data Science, Importing & Cleaning data, Data Manipulation & Visualization

• Research Paper : Signature verification using Artificial Intelligence

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