Hi, my name is Vishvesh Dumbre
I'm a Analytics Engineer.

Developed a robust real-time data streaming pipeline for stock market data leveraging Apache Kafka on AWS EC2. This solution efficiently ingests CSV files from local storage and streams the data to AWS S3 in real-time using Python and S3fs. Implemented AWS IAM for secure access management and utilized AWS Glue Crawler and Catalog to seamlessly catalog the data. Leveraged AWS Athena for sophisticated querying, enabling powerful data analysis and insights.

Leveraged Python, for optimizing data quality, leading clean data analysis. This improvement ensured that the underlying data was reliable and consistent, a critical factor for downstream analysis. Additionally, developed comprehensive Tableau dashboards that visualized key patterns, trends, and KPI's offering clear and actionable insights.

Designed a robust ETL data pipeline to streamline Twitter API data extraction, leveraging Python and Apache Airflow for effective orchestration. Deployed on AWS EC2 instances, to ensure scalable compute resources for processing. Data was stored in AWS S3, offering secure, scalable, and flexible storage with global accessibility. This architecture allowed for efficient management and extraction of Twitter data, providing a reliable platform for real-time analytics and insights.

Collaborated with a team of three to develop a mental health chatbot using RASA. Contributions included preprocessing data with Python to train an NLP model using the DIET intent classifier, achieving over 85% accuracy. Creating chatbot's frontend using React, Bootstrap, and JavaScript, with Django for the backend and SQLite for the database. The project was published in the International Research Journal of Engineering and Technology (IRJET).

By meticulously analyzing a comprehensive 2-year sales dataset, I developed dynamic and interactive dashboards that provided clear and actionable insights. These dashboards allowed for the visualization of sales trends, identification of key drivers affecting performance, and the formulation of strategic recommendations aimed at enhancing sales and optimizing profit margins.The actionable insights derived from this analysis led to strategic changes that ultimately resulted in increased sales and improved profitability for the store.

Performed an exploratory data analysis using real time S&P 500 data from the Fred API to examine fluctuations in the unemployment rate. By leveraging Python and the Pandas library, the data was meticulously analyzed to uncover significant trends and insights. These findings were then visualized using Matplotlib, highlighting key patterns and providing a clear understanding of the unemployment rate dynamics. This analysis revealed critical insights that could inform economic strategies and policy decisions.