Link: https://colab.research.google.com/#scrollTo=MIma91dkHO0E&uniqifier=1
This repository contains Python code for analyzing heart disease data using the Pandas library and visualizing the results with Seaborn. The code reads a CSV file containing heart disease data, performs data analysis and visualization, and creates various plots to gain insights into the dataset.
- Python
- NumPy
- Pandas
- Seaborn
You can install them using pip:
pip install python numpy pandas seabornThe code reads the heart disease data from a CSV file named heart.csv. Make sure you have this file in the same directory as the script.
The code includes the following sections:
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Data Loading: The heart disease data is loaded from the CSV file using Pandas.
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Gender vs Count: A distribution plot is created to visualize the count of gender (sex) in the dataset.
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Age vs Count: A distribution plot is created to visualize the count of age in the dataset.
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Resting BP, Cholesterol, Resting ECG, and Heart Disease vs Count: Distribution plots are created for each of these variables to visualize their counts.
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Pie Charts: Pie charts are used to compare the ratios for sex, resting BP, chest pain type, resting ECG, and heart disease.
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Violin Plots: Violin plots are used to analyze the variation and comparison of age, sex, and heart disease.
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Heat Map: A correlation matrix and a corresponding heat map are generated to visualize the relationships between different variables in the dataset.
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Pair Plot: A pair plot is created to show scatterplots between different pairs of variables in the dataset.
You can run the code by simply executing the Python script. Make sure to have the required libraries installed and the heart.csv file in the same directory.
python your_script_name.py
Feel free to contribute to this project by opening issues or creating pull requests.
This project is Licensed under the MIT License - see the LICENSE.md file for details.
