This project uses different machine learning classifiers to predict if a person is having heart disease using 9 different parameters. This project uses pandas profiling for data analysis and pycaret library for automating a lot of machine learning workflows.
heart.csv - Dataset containing the following columns. It has 918 rows of data. columns: ['Age', 'Sex', 'ChestPainType', 'RestingBP', 'Cholesterol', 'FastingBS', 'RestingECG', 'MaxHR', 'ExerciseAngina', 'Oldpeak', 'ST_Slope', 'HeartDisease']
pandas profiling.ipynb - Contains code for installing the pandas profiling library and the code for generating the report
report.html - The report generated by pandas profiling.ipynb, it has a detailed analysis of all of the parameters.
pycaret-model.ipynb - Different models have been tested by using pycaret library, this data has been tested for 18 models and further 3 models were fine tuned and the best model is selected. This file shows different ways pycaret module can be used. Various plots have been plotted to show the acuracy, precision and feature importnace etc.,