Stacked-LSTM-for-Covid-19-Outbreak-Prediction

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, it shows a four stacked LSTM network for early prediction of new Coronavirus dissease infections in some affected countries (India, USA, Czech Republic and Russia)which is based on real world data sets which are analyzed using various perspectives like day-wise number of confirmed cases, number of Cured cases, death cases. This attempthas been done to help the concerned authorities to get some early insights into the probable devastation likely to be effected by the deadly pandemic.

Some Information:

1. This repository contains the full datasets of all the four countries, which is uploaded in a folder called "Dataset Complete".
2. The "Model Evaluation" folder contains the code for testing the model, only.
3. The "Future Forecast Model" folder contains the code for Future prediction, from the results found in the code in "Model Evaluation" folder.This is done for better maintanance and understandability.
4. Note that in each folder I have organised the dataset according to my suitability, whose order may be different in the dataset uploaded in "Dataset Complete" folder. So dont let it confuse you ! Happy Learning! :)