Heart Disease Prediction
Heart disease describes a range of conditions that affects heart. The diagnosis of heart disease is a challenging task, which can offer automated prediction about the heart condition of patient so that further treatment can be made effective. It is difficult to identify heart disease because of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and many other factors. Due to such constraints, scientists have turned towards modern approaches like Machine Learning for predicting the disease.
The dataset is taken from Cleveland Heart Disease dataset from the UCI Repository and the same is also available at Kaggle. The dataset consists of 303 individuals data and there are 14 columns in the dataset.
The code is implemented in Google Colaboratory with the help of Python.
Libraries used - Pandas, Matplotlib, Numpy, Seaborn and Sklearn.