Covid-19 Daily New Case Prediction

This project is a part of the assessment for SHRDC Data Science course

-- Project Status: [Completed]

Project Intro/Objective

The purpose of this project is to predict the number of daily new cases for covid-19

Methods Used

• Inferential Statistics
• Deep Learning
• Data Visualization
• Predictive Modeling

Technologies

• Python
• Pandas, Numpy, Sklearn
• Tensorflow, Tensorboard

Getting Started

1. Clone this repo (for help see this tutorial).
2. Raw Data is retrieved from [https://github.com/nkayfaith/covid19_prediction/tree/main/data] in this repository.
3. Data processing/transformation scripts are being kept [https://github.com/nkayfaith/covid19_prediction/tree/main/model]

Discussion, Analysis and Result

1. Model Architecture as follows:

A Sequential model with attributes of embedding_output=64, nodes=32, dropout=0.2, hidden_layer=2 and epochs = 100 with EarlyStopping

2. Training processes recorded as follows:

Process :

Loss :

MSE :

3. Performance of the model and the reports as follows:

Mean Absolute Percentage Error is recorded at :

Predicted vs Actual trend is recorded at :

4. Reviews

Reviews

• MAPE recorded at 0.0236%
• Graph shows low loss, low mse which indicates model is good
• The predicted vs Actual trend shows a good-fit

Credits

[https://github.com/MoH-Malaysia/covid19-public]