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UCI-Heart-Disease

This is my Analysis From the UCI Heart Disease Compiled Data From Kaggle. A link to my dashboard visulisation can be found here.

UCI Heart Disease Dataset Analysis

Heart disease is one of the leading causes of death worldwide. Understanding the factors that contribute to heart disease can significantly improve preventive measures, early diagnosis, and treatment strategies. This project analyses cardiovascular data from the UCI Heart Disease dataset, aiming to uncover patterns and trends that can be interpreted and utilised effectively.

The dataset is compiled from multiple sources:

  • Cleveland
  • Hungary
  • Switzerland
  • VA Long Beach

Why This Dataset Is Important

Diverse Data Sources: It combines data from different regions, offering a broad perspective on heart disease.

Comprehensive Variables: Includes key cardiovascular measurements such as cholesterol, blood pressure, heart rate, Blood sugar levels and Chest Pain type.

Predictive Modeling: Helps in developing models to identify high-risk individuals for early intervention.

This project aims to enhance understanding and prevention of heart disease through data statistical analysis and insights.

Script

In this project, I utilised programming languages to analyse and visualise the UCI Heart Disease dataset. Below are the details of each program used:

MYSQL

Purpose: Data Preparation

Description: MySQL is a relational database management system (RDBMS). I used MySQL to store the raw data, clean it, and perform preliminary transformations. Functions like filters, aggregations, and joins were used ensuring that the data was in the best shape to use.

RStudio

Purpose: Statistical Analysis

Description: RStudio is a programming language focused on statistical computing and graphics. In this project, RStudio was used for Linear Regression and Clustering Analysis exploring patterns, trends and overall key findings from the data. Detailed statistical results and interpretations are presented in the script and Page 2 of my dashboard.

Tableau

Purpose: Data Visualization

Description: Tableau is a leading data visualization tool that helps creating interactive, shareable dashboards. I used Tableau to create compelling visualizations that illustrate the key findings from the data analysis.

Additional Notes

  • Please make sure to read the accompanying files that provide detailed instructions on how to use and run the code.

  • The dataset was last updated in March 2024 and is not expected to receive further updates.

  • If you encounter any issues or have general questions, feel free to reach out. Happy coding!

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Storing of code for the analysis of Heart Disease data from UCI repository

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