Reports Management System

● Developed a Python-based data management application capable of handling, cleaning, and transforming various types of structured data to ensure consistency and quality across datasets. ● Integrated sample datasets and dynamic machine learning model support, enabling predictive analytics on live and historical data. ● Built interactive business dashboards and automated reporting modules using Flask, HTML5, CSS3 and Javascript to provide real-time business analytics. ● Configured Pythonanywhere flask environment, successfully deployed and crawled in google search console.

Campus Block Course Selling Application

● Developed a scalable MVC backend using Node.js, TypeScript, Express, ESLint, and ESBuild for bundling. ● Developed front-end components with Next.js, React.js, Redux, Tailwind CSS, and UI libraries like Shadcn and Aceternity for a clean and responsive design. ● Implemented CI/CD pipelines using GitHub Actions, integrated with AWS IAM and OIDC for secure deployment to AWS Lambda with API Gateway. ● Managed cloud infrastructure using Terraform to provision AWS S3, CloudFront, Lambda, IAM, and CloudWatch. ● Involved in deploying backend on AWS Lambda, setting up API Gateway and CloudWatch for accessibility and logging.

Eamcet College Selector

● Built a scalable backend using Next.js, TypeScript, Apollo Server, and GraphQL to streamline data queries. ● Designed responsive UI with React.js, Tailwind CSS, HTML5, and CSS3, and ensured code quality using ESLint. ● Created database schemas with Prisma and PostgreSQL, modeling one-to-many relationships between colleges and courses. ● Managed deployment environments and launched the application on Vercel. ● Monitored logs, resolved errors in production, and iteratively improved based on feedback.

Marriage Bureau

● Designed responsive and animated user interfaces using React, TypeScript, Tailwind CSS, and Framer Motion. ● Developed RESTful APIs using Node.js and Express to support multimedia data handling. ● Integrated MongoDB and Firebase Storage to securely store user data with Google OAuth 2.0 authentication. ● Deployed both frontend and backend on Vercel and Render, and monitored overall application health.

Covid Tweet Sentiment Analysis

● Built a sentiment classification model using Python and Scikit-learn to categorize COVID-19 tweets as Positive, Negative, or Neutral. ● Cleaned and preprocessed tweet data using Pandas, NumPy, and NLTK, ensuring data privacy by anonymizing usernames and screen names. ● Extracted features such as location, timestamp, and tweet content to improve classification accuracy. ● Trained and evaluated the model using standard metrics to ensure reliable performance on real-world social media data.