diabetes.py

#!/usr/bin/env python3

#Diabetes Prediction Using Support Vector Machine
import pickle
import os
import sys
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import train_test_split

#For training
def train():
    dataset = pd.read_csv('pima.csv')
    X = dataset[['F','D','E','B','C']]
    Y = dataset[['I']]
    
    #train test split
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 101)
    
    from sklearn.svm import SVC
    model = SVC(kernel='linear')
    svc=model.fit(X_train,Y_train)
    
    #Save Model As Pickle File
    with open('svc.pkl','wb') as m:
        pickle.dump(svc,m)
    test(X_test,Y_test)

#Test accuracy of the model
def test(X_test,Y_test):
    with open('svc.pkl','rb') as mod:
        p=pickle.load(mod)
    
    pre=p.predict(X_test)
    print (accuracy_score(Y_test,pre)) #Prints the accuracy of the model


def find_data_file(filename):
    if getattr(sys, "frozen", False):
        # The application is frozen.
        datadir = os.path.dirname(sys.executable)
    else:
        # The application is not frozen.
        datadir = os.path.dirname(__file__)

    return os.path.join(datadir, filename)


def check_input(data) ->int :
    df=pd.DataFrame(data=data,index=[0])
    with open(find_data_file('svc.pkl'),'rb') as model:
        p=pickle.load(model)
    op=p.predict(df)
    return op[0]
if __name__=='__main__':
    train()