In this project, i have used a model named XGBoost which is one of the most used model for a industrial and heavy model training. Mainly for this project i have used classification version of xgboost because the data seems to let you use classification model. There is a option to use regressor model + classification model and this combination is pretty powerful.
In model training, it is better to first clean up the data. That's were i have created cleaning() function which help to find is there any missig value inside the features(columns) of the data. After finishing cleaning project, i created a new function name heart_attack() which just simply from the smoking, drinking and weight it find the heart attack rate of chance. Third function created is a model() which is the main model training function. I just fit x and y where x is a weight, smoking, alcohol intake and y is a rate of heart attack which taken from heart_attack() function. Then fitting and training is done.
