Pragatindu Yajnashree Parida

Final-year engineering student with expertise in cloud computing, full-stack web development, and machine learning. Proficient in JavaScript, HTML, CSS, C, C++, Java, Python and frameworks like React, Angular, and Vue.js. Experienced with the implementation of machine learning pipelines with scikit-learn, TensorFlow, and PyTorch, and skilled in backend technologies including MySQL, PostgreSQL, and MongoDB for efficient data management. Adept in integrating MERN Stack, cloud analytics, and advanced Java to deliver scalable solutions. Adaptable, dedicated to excellence, and committed to driving innovation within organization.

• Participant in IEEE event 2022
• Participant in TECHNOFAIR 2023
• 1st Runners Up in Alliance One Event 2023
• Volunteered For Alliance Literary Festival 2022
• 3rd place in 5km Marathon 2023

1. Cyclone Intensity Estimation Based on Deep Learning Using INSAT 3D Images :

• Developed a system to estimate cyclone intensity using deep learning models trained on INSAT 3D satellite images.
• Implemented front-end for user interaction and back-end with Flask and PostgreSQL.
• Integrated PyTorch for machine learning pipeline to analyze and predict cyclone intensity.

2. Blood Bank Management System Using Advanced Java :

• Designed and implemented a blood bank management system using Advanced Java.
• Used JDBC for database connectivity with MySQL, and Apache Tomcat for deploying the application.
• Created a responsive user interface with HTML, CSS, and Bootstrap for effective user interaction and data management.

3. Visualization of Bank Marketing Dataset Using D3 :

• Created a data visualization project for analyzing a bank marketing dataset.
• Collected data from various sources and built a data warehouse.
• Implemented interactive visualizations using D3.js JavaScript library and Managed backend data with MySQL, ensuring efficient data retrieval and visualization.

4. Implementation of PCA and LDA on Heart Disease DataSet using Python :

• Implemented Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on a heart disease dataset using Python.
• Utilized NumPy and pandas for data manipulation, scikit-learn for machine learning algorithms, and matplotlib for data visualization. Analyzed and visualized data to derive insights and patterns related to heart disease diagnosis.

5. E-Learning Web Page :

• Developed an interactive e-learning web page using HTML, CSS, and JavaScript for the front-end interface.
• Implemented Python for backend operations, including data fetching from MySQL databases and Created a user-friendly platform for accessing educational content and managing user interactions.

• Machine Learning Regression, University of Washington-October 2023
• Networking in Google Cloud Specialization, Google Cloud-November 2023
• Developing Front-End Apps with React, IBM-March 2024

• Cyclone Intensity Estimation Based on Deep Learning Using INSAT 3D Images
  - Published with other co-authors.
• Big Data Analytics Application in Cloud Computing
  - Published under faculty supervision.
• Cloud Security and Deep Learning Approach
  - Currently working on this paper under faculty supervision..
• High Performance Computing
  - Currently working on this paper under faculty supervision..

• Institute of Electrical and Electronics Engineers
• Project Management Institute
• Society of Human Resource Management