About the Data

This database contains 14 attributes. The "target" variable refers to the presence of heart disease in the patient (0 = not present, 1 = present).

To load the training data in your jupyter notebook, use the below command:

import pandas as pd

heart_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/Heart_Disease/Training_set_heart.csv" )

Data Description

age: Age in years
sex: 1 = male, 0 = female
cp: Chest pain type
trestbps: Resting blood pressure (in mm Hg on admission to the hospital)
chol: serum cholesterol in mg/dl
fbs: fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
restecg: Resting electrocardiographic results
thalach: Maximum heart rate achieved
exang: Exercise induced angina (1 = yes; 0 = no)
oldpeak: ST depression induced by exercise relative to rest
slope: The slope of the peak exercise ST segment
ca: Number of major vessels (0-4) colored by fluoroscopy
thal: 0 = null, 1 = fixed defect found, 2 = blood flow is normal, 3 = reversible defect found
target: 1 = Heart disease present, 0 = Heart disease not present

Saving Prediction File & Sample Submission

You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548

Sample submission: You should submit a CSV file with a header row and the sample submission can be found below.

prediction

0

0

1

0

1

1

.

.

Etc.

Note that the header name should be prediction else it will throw an evaluation error. A sample submission file can be found here

Load the test data (name it as test_data). You can load the data using the below command.

test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/Heart_Disease/Testing_set_heart.csv')

Here the target column is deliberately not there as you need to predict it

Heart Disease Prediction