https://github.com/ibensusan/medical-data-visualizer

Medical Data Visualizer Project from FreeCodeCamp using Python
https://github.com/ibensusan/medical-data-visualizer

Last synced: 25 days ago
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Medical Data Visualizer Project from FreeCodeCamp using Python

• Host: GitHub
• URL: https://github.com/ibensusan/medical-data-visualizer
• Owner: iBensusan
• Created: 2024-09-25T12:18:38.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-28T09:20:51.000Z (3 months ago)
• Last Synced: 2024-10-18T07:02:30.623Z (3 months ago)
• Language: Python
• Size: 2.93 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Project: Medical Data Analysis and Visualization

This project demonstrates how to preprocess, analyze, and visualize medical examination data, focusing on BMI calculation, normalization of health indicators, and correlation analysis.

## Objectives:

1. **Data Preprocessing**:

    - Load and clean the dataset using pandas.

    - Calculate the Body Mass Index (BMI) and create a new column `overweight` to indicate individuals with a BMI greater than 25.

    - Normalize the `cholesterol` and `gluc` columns by adjusting the values to either `0` (normal) or `1` (above normal).

2. **Categorical Analysis**:

    - Transform the dataset into a long format using `pd.melt()` to facilitate categorical plotting.

    - Create a categorical plot to compare the distribution of health indicators (cholesterol, glucose, smoking, alcohol consumption, activity, and overweight) for individuals with and without cardiovascular disease.

3. **Outlier Removal**:

    - Filter the dataset to remove inconsistent and extreme values:

        - Ensure systolic blood pressure (`ap_hi`) is greater than or equal to diastolic blood pressure (`ap_lo`).

        - Remove outliers based on height and weight using the 2.5th and 97.5th percentiles.

4. **Correlation Analysis**:

    - Calculate a correlation matrix for numerical variables in the cleaned dataset.

    - Visualize the correlations using a heatmap, masking the upper triangle to improve readability.

## Tools and Libraries:

- **Pandas**: For data loading, manipulation, and cleaning.

- **NumPy**: For numerical operations and matrix calculations.

- **Matplotlib & Seaborn**: For data visualization, including categorical plots and heatmaps.

## Outcomes:

- A cleaned and preprocessed dataset ready for analysis.

- Visualization of the distribution of health indicators based on cardiovascular disease presence.

- Identification of key correlations between medical variables using a heatmap.

- Understanding of how cholesterol, glucose levels, smoking, alcohol consumption, activity, and BMI relate to cardiovascular disease.