Exploring Big Data: Trends & Patterns Analysis

Exploring Big Data: Trends & Patterns Analysis

Preetesh Sawant, Mitesh Yashwantrao, Kapil Pol, Suraj Jadhav, Prof. Gauri Bhosale

Dept. of Computer Engineering

Indala College of Engineering, Kalyan, India

Abstract - The COVID-19 pandemic has underscored the critical importance of data-driven decision-making in managing global health crises. With the availability of massive volumes of real-time data from official sources, there is a significant opportunity to apply big data analytics and machine learning techniques to derive meaningful insights. This project, titled "Exploring Big Data: Trends & Patterns Analysis", focuses on the analysis of real-time, country-wise COVID-19 data obtained from the official COVID-19 information portal. The project aims to explore various dimensions of the pandemic’s spread and impact across the globe, beyond just India, by leveraging advanced data analysis techniques.

The core of the project revolves around three major types of analyses: LSTM (Long Short-Term Memory) modeling, correlation analysis, and descriptive statistical analysis. The LSTM model, a form of recurrent neural network particularly suited for time-series forecasting, was employed to predict future case trends based on historical data. This predictive modeling helped in estimating the potential trajectory of the pandemic across different countries, thereby offering valuable insights into preparedness and resource planning

Key Words: COVID-19 pandemic, Trends & Patterns Analysis, LSTM model, preparedness, User Behavior, System Performance, neural network particularly, Data Protection, resource planning

, Resource Management, impact across the globe.