COVID-19-Trend-Prediction-using-Recurrent-Neural-Network

Predict whether the number of confirmed cases will increase or not with the use of RNN, LSTM & GRU.

Dataset

Observe the sequences of comfirmed cases during 2020-1-22 ~ 2020-4-12 from 185 countries.
The gloabl data of comfired cases please refer to 👉 https://ourworldindata.org/coronavirus

Execution & Overall Structure of system

1. Sequence Preprocessing : find high correlated countries & prepared segments for sequence modelling

   python3 Preprocess.py
   

2. RNN for trend prediction : training the model with Torch package.

   python3 RNN.py
   

3. Visualization on a world map by "pygal" packag

   python3 WorldMap.py
   

Sequence Preprocessing

1. Compute to correlation coefficient (CC) between 2 countries.
   

2. Add the pair of countries to set C if their CC higher than 0.7 (highly-correlated)
   

3. Generate the data pair (segment,lebel) for modelling from set C

RNN for Classification

• RNN with 2 layers & dropout
  In this project, dropout is adopted to aviod overfitting problem.

  

  

Visualization of prediction on a World Map