About the dataset

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 (0-3) colored by fluoroscopy
thal: 3 = normal; 6 = fixed defect; 7 = reversible defect
target: 1 = Heart disease present, 0 = Heart disease not present

Evaluation Dataset

Load the evaluation data (name it as 'evaluation_data'). You can load the data using the below command.

evaluation_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.

Reference

This dataset is downloaded from UCI Machine Learning Repository -

https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Heart Disease

Challenge Starts

Challenge Ends