COVID-19 Prediction and Detection Using Machine Learning Algorithms: Catboost and Linear Regression
Issue:
Volume 10, Issue 5, September 2021
Pages:
208-215
Received:
13 September 2021
Accepted:
4 October 2021
Published:
12 October 2021
Abstract: A global pandemic COVID-19 has been rapidly spreading, and the predictions for infected rate shows how the cases will increase or decrease. Even though the number of people who get the corona vaccine is increasing, COVID-19 has been a serious worldwide problem. As machine learning and deep learning were implemented to predict COVID-19 in recent days, machine learning to predict the number of confirmed and death cases of COVID-19 was used. Prediction graphs of our proposed model play a crucial role for preventing more people getting infected. The project collected the number of daily infected cases in New York from March 21th 2020 to March 6th 2021. For precise results, the dataset in 6 different kinds of the machine learning methods was used. The methods were Decision Tree, Random Forest, Linear Regression, Gradient Boosting, XGboosting, and LGBM. RMSE and MAE values fluctuated from 9.95 to 68.85 and 5.99 to 58.76. The most accurate model was Linear Regression, RMSE and MAE with 9.96 and 5.99 for death cases and 597.61 and 346.04 for confirmed cases. Therefore, those prediction graph almost matched the same as the real number graph that the project drew with an actual dataset. The other dataset was about common COVID-19 symptoms, and the Catboost model listed from the most influential factor, breathing problem. Collecting data from other areas and specifying the patients’ features could have improved the quality of the research, though overall the result was successful.
Abstract: A global pandemic COVID-19 has been rapidly spreading, and the predictions for infected rate shows how the cases will increase or decrease. Even though the number of people who get the corona vaccine is increasing, COVID-19 has been a serious worldwide problem. As machine learning and deep learning were implemented to predict COVID-19 in recent day...
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Application of ARIMAX Model on Forecasting Nigeria’s GDP
Christogonus Ifeanyichukwu Ugoh,
Chinwendu Alice Uzuke,
Dominic Obioma Ugoh
Issue:
Volume 10, Issue 5, September 2021
Pages:
216-225
Received:
12 July 2021
Accepted:
21 July 2021
Published:
29 October 2021
Abstract: This paper proposes an appropriate ARIMAX model that is used to forecast the Nigeria’s GDP. The data used for the study is sourced from the World Bank for a period of 1990-2019. The ARIMA model is fitted on the residuals using Box-Jenkins approach. The Bayesian Information Criterion (BIC) is adopted to assess the adequacy of the models. The raw data satisfy the assumption of multicollinearity when export is eliminated and the residual series is stationary after the first differencing. This study shows that import is a significant exogenous variable for the GDP dynamics. The ARIMA (0,1,1) with BIC value of 35.253 is considered the appropriate model to be combined with the exogenous variable. The results showed that the ARIMAX (0,1,1) is more ideal and adequate for forecasting Nigeria’s GDP based on the Theil’s U forecast accuracy measures.
Abstract: This paper proposes an appropriate ARIMAX model that is used to forecast the Nigeria’s GDP. The data used for the study is sourced from the World Bank for a period of 1990-2019. The ARIMA model is fitted on the residuals using Box-Jenkins approach. The Bayesian Information Criterion (BIC) is adopted to assess the adequacy of the models. The raw dat...
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