myTraining.py

import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
import pickle

def data_split(data,ratio):
    np.random.seed(42)
    shuffled=np.random.permutation(len(data))
    test_set_size=int(len(data)*ratio)
    test_indices=shuffled[:test_set_size]
    train_indices=shuffled[test_set_size:]
    return data.iloc[train_indices],data.iloc[test_indices]


if __name__=='__main__':

        #Read the data
        df=pd.read_csv("Data.csv")
        train,test=data_split(df,0.3)
        X_train=train[['Fever','BodyPain','Age','RunnyNose','DiffBreath']].to_numpy()
        X_test=test[['Fever','BodyPain','RunnyNose','DiffBreath']].to_numpy()

        Y_train=train[['InfectionProb']].to_numpy().reshape(1750,)
        Y_test=test[['InfectionProb']].to_numpy().reshape(749,)
        
        clf=LogisticRegression()
        clf.fit(X_train,Y_train)
       
       
        # open a file, where you ant to store the data
        file = open("model.pkl", 'wb')

        # dump information to that file
        pickle.dump(clf, file)
        file.close()