Hi, I'm Soham Hait.

Built a feature-rich frontend using Next.js, TypeScript, and Tailwind CSS, leveraging shadcn components and lucide-react icons to create a clean and modular UI. Designed a scalable backend with Node.js, managing API endpoints for task, user, and project operations. Integrated JWT-based authentication and bcrypt password hashing for secure login functionality. Employed MongoDB as the primary database to store and retrieve user data, notes, and organizational structures efficiently.

Developed an interactive front-end using Next.js, TypeScript, and Tailwind CSS, enabling users to browse movies, view details, and book tickets seamlessly. Built a high-performance backend using Node.js and Express, integrating a RESTful API to handle client requests efficiently. Utilized MongoDB for secure storage of user data, employing bcrypt and JWT-based authentications to encrypt sensitive information such as passwords.

Deployed and managed a containerized web application using Docker and Apache on an Azure Virtual Machine, ensuring scalable and efficient hosting. Configured the Azure VM environment to optimize performance and leverage features such as secure networking, automated backups, and resource monitoring. Performed all setup, configuration, and deployment tasks through the command-line interface (CLI) of the Azure Linux Virtual Machine.

Utilized pandas, numpy, scikit-learn, seaborn, and matplotlib libraries within Jupyter Notebook, leveraging Kaggle datasets for comprehensive data analysis and machine learning workflows. Conducted exploratory data analysis and built machine learning models to predict house prices using key real estate features and market trends. Performed data preprocessing, visualization, and classification modeling on the Iris dataset to accurately predict iris flower species based on morphological characteristics.

Built a feature-rich frontend using Next.js, TypeScript, and Tailwind CSS, leveraging shadcn components and lucide-react icons to create a clean and modular UI. Designed a scalable backend with Node.js, managing API endpoints for task, user, and project operations. Integrated JWT-based authentication and bcrypt password hashing for secure login functionality. Employed MongoDB as the primary database to store and retrieve user data, notes, and organizational structures efficiently.

Developed an interactive front-end using Next.js, TypeScript, and Tailwind CSS, enabling users to browse movies, view details, and book tickets seamlessly. Built a high-performance backend using Node.js and Express, integrating a RESTful API to handle client requests efficiently. Utilized MongoDB for secure storage of user data, employing bcrypt and JWT-based authentications to encrypt sensitive information such as passwords.

Deployed and managed a containerized web application using Docker and Apache on an Azure Virtual Machine, ensuring scalable and efficient hosting. Configured the Azure VM environment to optimize performance and leverage features such as secure networking, automated backups, and resource monitoring. Performed all setup, configuration, and deployment tasks through the command-line interface (CLI) of the Azure Linux Virtual Machine.

Utilized pandas, numpy, scikit-learn, seaborn, and matplotlib libraries within Jupyter Notebook, leveraging Kaggle datasets for comprehensive data analysis and machine learning workflows. Conducted exploratory data analysis and built machine learning models to predict house prices using key real estate features and market trends. Performed data preprocessing, visualization, and classification modeling on the Iris dataset to accurately predict iris flower species based on morphological characteristics.