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We train different models and apply techniques gives us better evaluation metrics, and find out the best model which works the best for Parkinson's Prediction System
This project implements time series analysis techniques to capture temporal patterns and trends in stock price movements and gives a generalized model.
Achieved 99.46% accuracy by integrating Encoder-Decoder Architechture, CNN, early stopping, batch normalization, pooling, and dropout, outperforming traditional FCNN and RNN models
Human Stress Detection in and through Sleep by monitoring physiological data. The KNN model is working with an accuracy of 100% and random forest model is working with an accuracy of 99.35%.