Explore My


Work Experience

Web Developer Intern

TRIX.edu


Mar 2020 – Dec 2020
Hyderabad, India

I developed responsive web applications, collaborating with academic teams to enhance the university's platform. I optimized UI, integrated new features, and ensured seamless database management. My work improved performance, security, and user experience while minimizing downtime.


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Microsoft Azure and PowerBI Analyst

ICT Academy


Oct 2022 – Feb 2023
Chennai, India

Experienced in Azure, Databricks, and ETL development, I specialize in data management and migration. Proficient in Power BI and SQL, I optimize pipelines and transform data. My expertise ensures scalability, security, and efficient cloud solutions.


Certificate

Data Engineering Intern

TRIX.edu


Mar 2021 – Dec 2021
Hyderabad, India

Optimized ETL pipelines using Azure Data Factory and Databricks for seamless data integration. Led MySQL to Azure SQL migration, improving performance and scalability. Processed diverse data formats and built Power BI dashboards for data-driven insights. Focused on performance tuning, secure data management, and compliance.


Report

Browse My

Projects




Project 1
Exploring Sentiment Towards a 10-Year Minimum Occupation Period in Prime Location Housing (PLH) Model
I conducted a text-based sentiment analysis on public reactions to Singapore's Prime Location Housing (PLH) Model, introduced to support middle-income families. Using BeautifulSoup and Scrapy, I extracted discussions from online platforms, then cleaned and processed the data by filtering spam, standardizing text, and handling slang/emojis. With TextBlob and NLP techniques (tokenization, POS tagging, stemming, and stop-word removal), I analyzed sentiment polarity and subjectivity. Insights revealed a largely positive public reception, influenced by extensive engagement, with a mix of neutral opinions providing balance. This project sharpened my skills in Python, Web Scraping, NLP, Data Cleaning, and Sentiment Analysis, offering real-world insights into policy reception.
Project 2
Adaptable Company Management System with MongoDB
As part of my Master's in Data Science at Sacred Heart University, USA, I developed an Adaptable Company Management System to streamline employee, department, and project management. Key features include secure authentication, dynamic employee management (CRUD operations), and automated leave tracking. The system integrates Flask and pymongo to interact with a MongoDB Atlas database, handling data retrieval, updates, and deletion through defined API routes. HTML templates are seamlessly rendered using Flask, ensuring a user-friendly interface. This project enhanced my skills in MongoDB, Flask, RESTful API design, and web development, strengthening my expertise in database-driven applications.
Project 3
Accurate prediction of student performance in online education during COVID using Machine Learning and Deep Learning Algorithms
For my Bachelor's final year project, I developed a machine learning and deep learning-based predictive analytics system to forecast student performance in online education during COVID-19. By integrating data from diverse platforms, the system provides actionable insights to help educators implement personalized interventions, enhancing adaptability in online learning. Skills: Machine Learning, Deep Learning, Python, MySQL, Data Analytics, Strategic Planning.
Project 4
Enterprise Data Migration and Analytics Pipeline | ICT Academy
Developed a scalable data migration and analytics pipeline using Azure, Databricks, and Power BI. Migrated legacy databases to Azure SQL, optimized ETL pipelines for real-time processing, and created interactive dashboards for actionable insights. Leveraged Azure Data Factory for integration, Databricks with PySpark for transformation, and Power BI for visualization. Ensured seamless migration with zero data loss, improved operational efficiency, and built a scalable architecture aligned with data governance standards.
Project 5
Northeastern United States traffic accident trends: a geospatial and statistical analysis using python
This project analyzes traffic accident trends in the northeastern U.S., focusing on Connecticut and neighboring states. It examines the impact of risky driving behaviors on accident outcomes and insurance costs using statistical modeling and geospatial analysis. Findings reveal Connecticut's higher rates of speeding and alcohol-impaired driving, contributing to above-average insurance premiums. Insights from this study can help policymakers implement targeted road safety improvements.