COGNIZANCE: Analysis and Prediction of COVID-19 using ARIMA-Random Forest Classifier
Author(s):
Aakriti Luthra, Prabhpreet Kaur, Parminder Kaur, Amandeep Kaur
Keywords:
Machine Learning, Covid-19, Statistical Analysis, Prediction, ARIMA, Random Forest.
Abstract
The COVID-19 pandemic has unleashed its merciless grip on the world, rapidly escalating with each passing day. In the midst of this crisis, the need for accessible and readily available services has become paramount. The coronavirus outbreak has cast a long, dark shadow over global health, resulting in a devastating loss of life and overwhelming healthcare systems. The World Health Organization's declaration of COVID-19 as a pandemic has thrown every nation into turmoil, straining healthcare infrastructures and prompting often sluggish responses. On a daily basis, the global tally of COVID-19 cases continues to climb, necessitating innovative solutions. Our system, built on Django and utilizing HTML (Hypertext Markup Language), CSS (Cascading Style Sheets), Bootstrap, Python, and Machine Learning Algorithms, has accurately predicted COVID-19 cases using the Random Forest Classifier and ARIMA (Autoregressive integrated moving average) model. System uses techniques that deliver data visualization and analysis Its purpose is to empower users with an array of tools, including locating nearby COVID-19 treatment facilities, accessing in-depth information on medical professionals treating the virus, symptom and risk factor checks, and real-time pandemic updates. The system also provides essential data on vaccinations, live cases, safety measures, and predictive analyses for global and country-specific COVID-19 cases. Users are encouraged to share their experiences through the UserStory column and can engage with administrators for support and feedback. This project additionally lists volunteers providing food to patients and their loved ones and offers valuable information about COVID-19, aiming to enhance user awareness during these trying times.
Article Details
Unique Paper ID: 164309

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1276 - 1295
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