Data Analysis or sometimes referred to as exploratory data analysis (EDA) is one of the core components of data science. It is also the part on the majority of the time which makes it extremely important in the field of data science. This repository demonstrates Exploratory Data Analysis methods and techniques using Python. The purpose of the used Heart Disease dataset has been taken from Kaggle since it is one of the ideal dataset for performing EDA and taking a step towards the most amazing and interesting field of data science.
- Performed Data Cleaning and Data Manipulation.
- Performed Exploratory Data Analysis (EDA) using Pandas, NumPy, Matplotlib, Seaborn Libraries.
- Identify the risk of heart disease based on different attributes like- Age, FastingBS, MaxHR, etc.
(1) Age group 50-60 yrs have the higher no. of heart disease.
(2) Males are more likely to have Heart Disease in comparsion with females.
(3) With increasing age maximum heart rate decrease.