I have designed and developed a live chat system with features such as team and agent management, queueing conversations, agent assignment rules, multilingual support, and analytics. The technology stack used includes Python, Flask, Socket, MongoDB, AWS, S3, and Google Translator.I have also designed a feedback feature for the data management and processing module, where agents can request feedback on resolved and unresolved queries. This feature was developed using Python, Flask, MongoDB, AWS, and S3.In addition, I have developed a customer engagement feature for the data management and processing module, which allows for configuring and setting multiple customer engagement rules and engaging with customers accordingly.Finally, I have designed a lazy loader for the chat history of the live chat system, which helps improve the chat's performance and user experience.using Python, Flask, MongoDB, AWS EC2, S3, Redis, RabbitMQ, Docker, Jenkins, Celery, Nginx, Git, and Bash.

1. Created a Python-based framework for calculating financial ratios and scores, including valuation, peer median, and peer averages, and generating alerts based on historical data.2. Developed a REST API using Flask, MySQL, and MongoDB and deployed it on AWS EC2 instances to deliver data to clients.3. Designed a Google Alert bot to fetch news articles and deliver them to clients daily.4. Implemented a sentiment analysis model using Python, pandas, NumPy, NLP, Text-blob, Selenium, BeautifulSoup, Plotly, and Matplotlib.

Designed and implemented bots that scraped and cleaned data from websites such as Swiggy and Airbnb using Selenium and BeautifulSoup.