metode.py

# import joblib
# import pandas as pd

# # preprocessing
# def normalisasi(x):
#     # # import data test
#     # cols = ['age','sex','BP','cholestrol']
#     df = pd.DataFrame([x],columns=cols)
#     # data_test = pd.read_csv('model/data_test2.csv')
#     # data_test = data_test.drop(data_test.columns[0],axis=1)
#     # # memasukkan data kedalam data test
#     # data_test = data_test.append(other=df,ignore_index=True)
#     # # return data_test yang sudah dinormalisasi
#     # return joblib.load('model/norm.sav').fit_transform(data_test)

# # normal
# def normal(x):
#     cols = ['age','sex','BP','cholestrol']
#     df = pd.DataFrame([x],columns=cols)
#     data_test = pd.read_csv('model/data_test2.csv')
#     data_test = data_test.drop(data_test.columns[0],axis=1)
#     # memasukkan data kedalam data test
#     data_test = data_test.append(other=df,ignore_index=True)
#     # return data_test yang sudah dinormalisasi
#     return (data_test)

# # metode with normalization
# def knn(x):
    
#     return joblib.load('model/modelKNN11.pkl').predict(x)

# def bagging(x):
#     return joblib.load('model/bagginggaussian.pkl').predict(x)

# def randomforest(x):
#     return joblib.load('model/randomforest.pkl').predict(x)

# # metode without normalization
# def knn_no_norm(x):
#     return joblib.load('model/modelKNN11_1.pkl').predict(x)

# def bagging_no_norm(x):
#     return joblib.load('model/bagginggaussian_1.pkl').predict(x)

# def randomforest_no_norm(x):
#     return joblib.load('model/randomforest_1.pkl').predict(x)


# # print(normalisasi([50,1,120,200]))