Healthcare Data Analytics and Visualization in R 📊💻

This repository provides an in-depth exploration of statistical analysis and predictive modeling in the healthcare domain using R. The project is designed as a menu-driven program that focuses on analyzing and visualizing datasets related to healthcare trends, including COVID-19, cancer, and mental health.

Features

1. COVID-19 Data Visualization

• Interactive plots to analyze COVID-19 trends (e.g., cases, recoveries, fatalities).
• Regional and temporal trend comparisons.
• Use of R libraries like ggplot2 and plotly for rich visualizations.

2. Regression Techniques on Cancer Data

• Implementation of multiple regression methods (e.g., linear, polynomial, logistic) for predictive analysis.
• In-depth exploration of cancer-related datasets for pattern identification.
• Model evaluation using metrics such as R² and RMSE.

3. Refitting Methods on Mental Health Data

• Statistical techniques to analyze mental health datasets and trends.
• Application of refitting methods to improve predictive accuracy.
• Insights into correlations between mental health conditions and external variables (e.g., social, demographic factors).

4. Health Trend Analysis

• Comparative analysis of trends across all datasets.
• Extraction of meaningful insights for public health and policy-making.

Technologies Used

• R Programming: Core language for analysis and visualization.
• Dataset Sources: Publicly available datasets on COVID-19, cancer statistics, and mental health.