This last-year main project is an Employee Leave Management System that automates leave application, approval, and scheduling. It uses Haar Cascade with OpenCV for face ID login, HTML, CSS, JavaScript for frontend, PHP for backend, and MySQL for database management. Employees can apply for planned or emergency leave, view remaining leave, and have schedules automatically adjusted. Emergency leave triggers notifications to other employees to cover shifts temporarily. Leave requests are reviewed by the department head, and approved requests update the schedule automatically.
Restaurant Table Booking Website – A third-year mini project built using MERN Stack that allows users to reserve tables, manage chair arrangements, and order and book menu items online. The system provides a seamless interface for both customers and restaurant staff to handle bookings, track availability, and manage orders efficiently.
This project detects human attributes such as Gender,Age Estimation,Mood,Facial Expression,Glasses,Beard,Hair colour,Eye colour,Head wear,Emotions detected,Confidence level from images that we uploaded.It uses various libraries such as os-for file handling and directory management,pillow-for image processing,streamlit-for building an interactive UI,google generative AI– for advanced attribute analysis.
A movie recommendation system built using Python that combines popularity-based and content-based filtering techniques. Analyzes movie data to recommend titles based on ratings, popularity, and genre similarity. Uses data science libraries like Pandas, NumPy, TF-IDF, and Cosine Similarity, with an interactive interface built using IPython widgets, leveraging public datasets from Kaggle.
A Telecom Customer Churn Prediction System built using Python and machine learning to identify customers likely to churn. The project involves data cleaning, exploratory analysis, feature encoding. Implemented and compared Random Forest and XGBoost models with hyperparameter tuning, achieving strong predictive performance. The final model was saved and deployed for churn prediction with probability scores to support data-driven decision-making.
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