projects
- Detection of Parkinson’s Diseases using machine learning with EEG signal
project brief :Parkinson's disease is a central nervous system disorder that affects movement, primarily impacting individuals aged 45-65. It progresses with age, and while there's no cure, medication and therapies can manage symptoms. EEG tests help detect neural abnormalities, while machine learning can assess the patient's emotional state, identifying whether they are happy, sad, or neutral.
- Classification of Neurological Disorder using Machine learning algorithms
project brief: Epilepsy is a neurological disorder, and data extracted from Kaggle is classified using various signal processing techniques and machine learning algorithms such as Logistic Regression, Naive Bayes, Stochastic Gradient Descent, Random Forest, and Decision Tree. Among these, Decision Trees achieve the highest accuracy at 96.3%, while Logistic Regression shows the lowest performance. Researchers often use platforms like Google Colab, MATLAB, and Python for processing datasets, including EDF files and signal data. A major challenge in this project is data loss, which causes variability in preprocessing results each time.
- Train reservation system using backend development
This project report details the development of an online railway ticket booking system, designed to allow users to search train schedules, book or cancel tickets, and view booking details online. It aims to reduce long queues at reservation counters and provide a more efficient alternative to call center support. The system uses a central database to manage train and booking information, while administrators can access and manage train details and bookings through the platform.
