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Heart Attack Analysis using Python- A Case Study

When a heart attack occurs, the heart muscle that has lost blood supply begins to suffer injury. The amount of damage to the heart muscle depends on the size of the area supplied by the blocked artery and the time between injury and treatment. Heart muscle damaged by a heart attack heals by forming scar tissue.

In this session, we are taken a popular dataset from Kaggle, Heart_Attack_Analysis and will be analyzing this dataset based on the information provided. At the end we will get to many conclusions that for what are the reasons which can effect the heart attack from this Exploratory Data Analysis.

I also want to mention the course Data Analysis with Python: Zero to Pandas provided by Jovian that helped me with their lecture videos and an active forum.

<img src="3-D-illustration-of-Heart-Part-of-Human-Organic


List of all the python libraries that are required:

  • Library opendatasets will be required for downloading datasets from online sources.
  • Library pandas will be required to work with data in tabular representation.
  • Library numpy will be required to round the data in the correlation matrix.
  • Library warning will be required to ignore all warnings.
  • Library matplotlib, seaborn, plotly required for data visualization.

Downloading the Dataset

Importing jovian opendatasets to download our dataset from Kaggle.

!pip install jovian opendatasets --upgrade --quiet