Analysis of Pandemic’s Impact on Healthcare Systems and Happiness Index

Team mates:

Sasank Chithirala - [email protected]

Rishik Reddy Musipatla - [email protected]

Bahirithi Karampudi - [email protected]

📌 Project Overview

This project explores the impact of the COVID-19 pandemic on global healthcare systems and the World Happiness Index using machine learning algorithms. The study aims to analyze changes in healthcare infrastructure, medical services, and overall well-being pre and post-pandemic. It integrates multiple datasets, applies various regression and classification models, and provides insights to improve healthcare resilience for future pandemics.

🔍 Key Objectives

• Investigate healthcare system changes before and after the pandemic.
• Analyze the correlation between COVID-19 impact and the Global Happiness Index.
• Use machine learning algorithms (classification, regression, and ensemble methods) to study patterns in healthcare services.
• Develop predictive models to understand factors influencing healthcare quality and happiness scores.

📂 Dataset Sources

The study integrates data from multiple sources:

1. COVID-19 Data Repository – Johns Hopkins University.
2. World Happiness Report – Kaggle dataset.
3. Vaccination and Healthcare Data – Our World in Data.

🏗️ Methodology

The project follows a structured approach:

1. Data Collection & Cleaning – Merging COVID-19, healthcare, and happiness index datasets.
2. Exploratory Data Analysis (EDA) – Understanding trends and key influencing factors.
3. Regression Analysis – Evaluating the relationship between GDP, healthcare spending, vaccination rates, and pandemic impact.
4. Classification Models – Decision Trees, Random Forest, Logistic Regression, Naïve Bayes, and Gradient Boosting.
5. Model Comparison – Identifying the most accurate model for predicting healthcare efficiency.
6. Visualization – Data trends using Seaborn, Matplotlib, and Plotly.

🚀 Technologies Used

• Programming Language: Python
• Libraries & Frameworks:
  • Pandas, NumPy – Data Processing
  • Matplotlib, Seaborn, Plotly – Data Visualization
  • Scikit-learn, TensorFlow – Machine Learning Models
  • PySpark, Spark MLlib – Big Data Analytics
• Cloud Platforms: Google Colab

📊 Key Findings

• Healthcare system strain: Countries with robust healthcare infrastructure showed higher resilience.
• Happiness Index: Countries with higher vaccination rates and economic stability had better happiness scores.
• Best Performing ML Model: Random Forest + Bagging Decision Tree outperformed other models in predicting healthcare resilience.