Raghav Bhasin

Led a 10-member AGILE, cross-functional team to develop a course development tracking system, streamlining education workflow, Improved automation efficiency by integrating Katalon-Groovy, and failure-analysis tools, reducing test execution time by 30 %. Conducted rigorous QA testing and detailed documentation for a Link verification tool, ensuring 99 % accuracy in link validation. Extracted large datasets for quality analysis using Postman, employing data scraping techniques to information, and improve data accuracy

Led a team of 3 interns and conducted comprehensive manual testing of a new website, identifying and reporting over 100 bugs to the development team, resulting in a 30 % increase in overall website performance. • Developed automated testing script to find grammatical errors using Python language tool, improving error detection accuracy by 60 % and decreasing testing time by 50 %. • Collaborated with the AI team to enhance brand detection accuracy by implementing regularization techniques and creating a customized model, resulting in a 20 % improvement in overall brand logo recognition

Understood the business processes of the car loan direct sales associate to help identify challenges and the scope of the solution. • Challenges involved synergizing the customer database and the loan data, tracking the number of conversions, automating the calculation of commission to be paid to various channels • The proposed solution was to provide a unified platform for managing customer data, reporting relevant information like managing monthly commission rates, and highlighting timelines for loan repayment schedules. Briefly understood the tech stack. The backend was Java 11 and the User interface/Front end was designed on HTML5, CSS, and Angular12. • Documented the brief and enabled regular communication between the web app developer and DSA to maintain a continuous feedback loop

-Deep-dived into Python Data Analysis, NumPy arrays, and NLTK module. -While I was expected to only learn new concepts, I researched further on topics that would help me design a chatbot and created DocBot - a chatbot to answer questions on Covid-19. -Learn Data Cleaning methods which I later used in creating the Air-O-Grade. -Air-O-Grade: Using a dataset created a model named Air-O-Grade was used to predict air quality. -Analyses the concentration of harmful gases in the air for stakeholders like Government agencies & polluting industries so that they can take appropriate steps to control and reduce emissions and thereby improve air quality. Used Azure cloud computing pipeline to clean data and Decision tree, random forest & linear regression to create automated ML model. -My mentors were impressed with my dedication and hard work.