Deployment-of-ML-model-using-flask

The model is for predicting the possibility of a heart disease. The code has not been uploaded since this is part of an open competition. Will upload the code once the competition is over.

To run the code

pip install flask
Run python working-se.py
Go to http://127.0.0.1:5000/

There are 13 features which are considered to make the prediction -

slope_of_peak_exercise_st_segment (type: int): the slope of the peak exercise ST segment, an electrocardiography read out indicating quality of blood flow to the heart

thal : results of thallium stress test measuring blood flow to the heart, with possible values normal(0), fixed_defect(1), reversible_defect(2)

resting_blood_pressure (type: int): resting blood pressure

chest_pain_type (type: int): chest pain type (4 values)

num_major_vessels (type: int): number of major vessels (0-3) colored by flourosopy

fasting_blood_sugar_gt_120_mg_per_dl (type: binary): fasting blood sugar > 120 mg/dl

resting_ekg_results (type: int): resting electrocardiographic results (values 0,1,2)

serum_cholesterol_mg_per_dl (type: int): serum cholestoral in mg/dl

oldpeak_eq_st_depression (type: float): oldpeak = ST depression induced by exercise relative to rest, a measure of abnormality in electrocardiograms

sex (type: binary): 0: female, 1: male

age (type: int): age in years

max_heart_rate_achieved (type: int): maximum heart rate achieved (beats per minute)

exercise_induced_angina (type: binary): exercise-induced chest pain (0: False, 1: True)