Prediction of the presence of Heart disease in the patient using UCI Dataset
We will perform exploratory data analysis with python. Moreover, determine which factors lead to certain cardiovascular events by performing feature engineering on the dataset to get insight from the data.
We have used the following python packages to perform the visualization
- Pandas
- Matplotlib
- Seaborn
- Numpy
- Sklearn
The repository contains two files other than the readme file Heart_Disease_Prediction.ipynb: Jupyter Notebook file contains all the python code, documentation and visualization heart.csv: Our dataset file
Dataset contains the following features:
- age
- sex
- chest pain type (4 values)
- resting blood pressure
- serum cholestoral in mg/dl
- fasting blood sugar > 120 mg/dl
- resting electrocardiographic results (values 0,1,2)
- maximum heart rate achieved
- exercise induced angina
- oldpeak = ST depression induced by exercise relative to rest
- the slope of the peak exercise ST segment
- number of major vessels (0-3) colored by flourosopy
- thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
The dataset was posted on kaggle. It is avaiable to download here Heart Disease UCI Dataset