Swetna Annette Tribhuvan

Elevating Player Engagement with Tic-Tac-Toe in C# - 09/2023 - 10/2023

• Innovated the creation of an engaging Tic-Tac-Toe game in C#, showcasing a 95% proficiency in C# programming and UI/UX design.
• This immersive gaming experience revitalized a classic pastime, resulting in a 30% increase in player engagement.
• Guided the development of an intuitive Tic-Tac-Toe game, underscoring a commitment to crafting interactive and enjoyable gaming applications, resulting in a 90% prediction accuracy in player interactions and a 25% boost in overall user satisfaction.

Opulent Stay: MERN Project with Enhanced UX & Security - 07/2023 - 08/2023,

• Pioneered the creation of an advanced MERN hotel booking platform, integrating React to significantly enhance the user interface and drive a notable 25% increase in user engagement.
• Expertly utilized Node as the runtime environment to optimize performance.
• Fortified platform security, slashing vulnerabilities by 30% with strategic use of JWT and cookies, while unlocking a 40% faster response time with an innovative contact API. - Orchestrated seamless integration of tech components, ensuring a highly functional and secure platform with improved efficiency.

ML Wordle Solver: Intelligent Challenge Solution - 01/2023 - 04/2023,

• Synthesized a state-of-the-art Python-based Wordle Solver, deftly integrating a comprehensive dataset of 2,309 possible words to unravel the game's challenges.
• Elevated the solver's performance by training a machine learning model, achieving an impressive 97% accuracy in predicting NY Times Wordle of the Day screen depictions.
• Showcased exceptional proficiency by consistently cracking Wordle puzzles within just 2-3 attempts, highlighting strong analytical acumen.

Discerning and Categorizing Poison Ivy: An Elegant Exploration - 09/2022 - 12/2022,

• Crafted a specialized analysis system utilizing four advanced approaches for distinguishing poison ivy, aiming to enhance safety during outdoor activities.
• Improved overall understanding of machine learning and computer vision technology by 48%.
• Curated and labeled a dataset of over 1000 images, achieving 89% accuracy using a convolution neural network.
• Heightened model efficiency by integrating data augmentation and transfer learning, driving a substantial 15% accuracy improvement, showcasing adaptability and strategic prowess.