https://github.com/riya2624/heart-disease-diagnostic-analysis

Analyzed heart disease diagnostic data through an ETL process, ensuring data accuracy and readiness for analysis using Python (Pandas, NumPy, Matplotlib, Seaborn). Conducted comprehensive exploratory data analysis (EDA) to uncover trends related to heart disease rates by gender and age. Developed interactive dashboards with Power BI and Tableau
https://github.com/riya2624/heart-disease-diagnostic-analysis

dashboard dax-query eda machine-learning matplotlib numpy pandas python seaborn

Last synced: 3 days ago
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Analyzed heart disease diagnostic data through an ETL process, ensuring data accuracy and readiness for analysis using Python (Pandas, NumPy, Matplotlib, Seaborn). Conducted comprehensive exploratory data analysis (EDA) to uncover trends related to heart disease rates by gender and age. Developed interactive dashboards with Power BI and Tableau

• Host: GitHub
• URL: https://github.com/riya2624/heart-disease-diagnostic-analysis
• Owner: Riya2624
• Created: 2024-12-29T05:59:44.000Z (5 days ago)
• Default Branch: main
• Last Pushed: 2024-12-29T06:26:00.000Z (5 days ago)
• Last Synced: 2024-12-29T07:17:35.685Z (5 days ago)
• Topics: dashboard, dax-query, eda, machine-learning, matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.73 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Heart-Disease-Diagnostic-Analysis

## Project Overview

This project involves analyzing heart disease diagnostic data through a complete ETL (Extract, Transform, Load) process. The goal is to ensure data accuracy and readiness for analysis, conduct comprehensive exploratory data analysis (EDA), and develop interactive dashboards for visualization and actionable insights.

---

## Key Features

- **ETL Process**: Cleaned and transformed raw diagnostic data using Python libraries (Pandas, NumPy).

- **Exploratory Data Analysis**: Uncovered trends related to heart disease rates by gender and age, visualized using Matplotlib and Seaborn.

- **Interactive Dashboards**: Created insightful dashboards using Power BI and Tableau to visualize key findings and attributes.

---

## Tools & Technologies

- **Programming Languages**: Python  

- **Libraries**: Pandas, NumPy, Matplotlib, Seaborn  

- **Visualization Tools**: Tableau, Power BI  

---

## Workflow

1. **Data Extraction**: Loaded raw heart disease diagnostic data into the environment.

2. **Data Transformation**:

   - Handled missing values and outliers.

   - Standardized data formats for consistency.

3. **Exploratory Data Analysis**:

   - Performed statistical analysis and visualized trends.

   - Analyzed rates of heart disease by gender and age groups.

4. **Dashboard Development**:

   - Built interactive dashboards showcasing:

     - Heart disease rates by demographic attributes.

     - Comparative visualizations of key metrics.

---

## Results

- Identified significant patterns, such as higher prevalence of heart disease in specific age and gender groups.

- Provided actionable insights for health preparedness and resource allocation.

---

## Installation & Usage

1. Clone the repository:  

   ```bash

   git clone (https://github.com/Riya2624/Heart-Disease-Diagnostic-Analysis

2.Install required libraries:

   pip install -r requirements.txt

3.Run the Python scripts for ETL and EDA:

   Heart Disease Dignosis.ipynb

4.Access Tableau or Power BI dashboards via provided links or local files.

   "C:\Users\riya\Downloads\Heart Disease Analysis dashboard1.pbix"