Machine learning model capable of predicting whether or not someone has heart disease or not based on their medical attributes

STEPS

1.Problem Definition Data source - https://archive.ics.uci.edu/ml/datasets/heart+disease

2.Main Features

a)Modelling

b)Experimentation

DATA DICTIONARY age - age in years

sex - (1 = male; 0 = female)

cp - chest pain type 0: Typical angina: chest pain related decrease blood supply to the heart 1: Atypical angina: chest pain not related to heart 2: Non-anginal pain: typically esophageal spasms (non heart related) 3: Asymptomatic: chest pain not showing signs of disease

trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern

chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern

fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes

restecg - resting electrocardiographic results 0: Nothing to note 1: ST-T Wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat 2: Possible or definite left ventricular hypertrophy Enlarged heart's main pumping chamber

thalach - maximum heart rate achieved

exang - exercise induced angina (1 = yes; 0 = no)

oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more

slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with excercise (uncommon) 1: Flatsloping: minimal change (typical healthy heart) 2: Downslopins: signs of unhealthy heart

ca - number of major vessels (0-3) colored by flourosopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots)

thal - thalium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversable defect: no proper blood movement when excercising

target - have disease or not (1=yes, 0=no) (= the predicted attribute)