Krishna Gera

Traditional productivity apps prioritize tasks without understanding the user's current mental and physical state. When users are tired, stressed, or overloaded, simply following a priority list may not be the best decision.

Moven combines task intelligence with wellness and emotional context to generate personalized next-step recommendations. Instead of only asking "What is most important?", it considers "What is realistically best for you right now?"

CSE students often have to choose specializations and career directions without a clear understanding of how their interests and existing skills align with different technology domains. Generic career advice does not account for an individual’s profile.

Nevio creates a personalized recommendation experience by combining structured student information with AI-driven reasoning. The platform helps students understand suitable career paths, specializations, and the reasoning behind those recommendations.

Students often have valuable skills but lack a simple way to discover peers who can teach them something they want to learn. At the same time, students with useful knowledge may not have an effective platform to share their skills with others.

Smart Skill Exchange connects students based on the skills they can offer and the skills they want to learn. The project transformed the initial concept into a working web platform where students can create skill profiles, discover relevant skills, and participate in peer-to-peer knowledge exchange.

Movie revenue is influenced by many interacting factors, making it difficult to estimate potential box-office performance using simple assumptions. Producers and analysts need a data-driven way to understand which movie characteristics may influence commercial performance.

The project uses a trained Random Forest regression model together with feature engineering and contextual multipliers for factors such as franchise, director, competition, language, and cast. A Flask-based interface converts user inputs into a prediction and presents the estimated revenue along with factor-level insights.

Artist information can become difficult to manage when stored as unstructured files or disconnected datasets. A centralized database and API layer are needed to make artist records easier to query, maintain, and extend.

Artist_DB uses a Flask backend with SQLAlchemy to provide structured API access to artist records stored in PostgreSQL. The system separates application logic from database operations, creating a cleaner foundation for future artist-management features.