Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them.

Project Description

• Implemented a machine learning framework to classify sleep stages using various algorithms including Random Forest (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Neural Networks (NN).
• Collected and pre-processed sleep data from various sources and applied feature engineering techniques to extract relevant information.
• Evaluated the performance of each algorithm and selected the most accurate model for sleep stage classification.

Knowledge Machine Learning, Random Forest, Support Vector Machines, k-Nearest Neighbors, Neural Networks, Sleep Stage Classification, Feature Engineering, Data Pre-processing

1. Sleep_Stage_Classification.ipynb - Code for Sleep Stage Classification
2. Report.pdf - Report
3. .mat files - Data and Features