The data science lifecycle is designed for big data issues and the data science projects. Generally, the data science project consists of seven steps which are problem definition, data collection, data preparation, data exploration, data modeling, model evaluation and model deployment.

The goal of this project is to go through the data science lifecycle steps in order to build a heart disease classification web application by using UCI heart disease dataset. This project uses Flask API to deploy the model and build the web application.

All libraries are available in Anaconda distribution of Python.

The dataset has 14 attributes:

• age: age in years.
• sex: sex (1 = male; 0 = female).
• cp: chest pain type (Value 0: typical angina; Value 1: atypical angina; Value 2: non-anginal pain; Value 3: asymptomatic).
• trestbps: resting blood pressure in mm Hg on admission to the hospital.
• chol: serum cholestoral in mg/dl.
• fbs: fasting blood sugar > 120 mg/dl (1 = true; 0 = false).
• restecg: resting electrocardiographic results (Value 0: normal; Value 1: having ST-T wave abnormality; Value 2: probable or definite left ventricular hypertrophy).
• 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 (Value 0: upsloping; Value 1: flat; Value 2: downsloping).
• ca: number of major vessels (0-3) colored by flourosopy.
• thal: thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect).
• target: heart disease (1 = no, 2 = yes).

• data.csv: the dataset file.
• Heart_Disease_Classification.ipynb: contains the code of data exploration, preparation and modeling.
• model.pkl: the classification model.
• heart_disease_app.py: Flask API that bind between the classification model and the web page.
• templates:
  • Heart Disease Classifier.html: a web page that contains a form for heart disease testing.

This [article] (https://medium.com/analytics-vidhya/the-lifecycle-to-build-a-web-app-for-prediction-from-scratch-bec1632b5f27) explaines the steps to build the heart disease classifier web App.