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,0.2)
    X_train = train[['Fever','Body Pain','AGE','Runny Nose','Diff Breath']].to_numpy()
    X_test = test[['Fever','Body Pain','AGE','Runny Nose','Diff Breath']].to_numpy()
    
    Y_train = train[['Infection Prob']].to_numpy().reshape(736,)
    Y_test = test[['Infection Prob']].to_numpy().reshape(183,)
    
    clf = LogisticRegression()
    clf.fit(X_train,Y_train)

    # open a file, where you want to store the data
    file = open('model.pkl', 'wb')

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