food_ml.py

from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingRegressor, AdaBoostClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import RobustScaler, MinMaxScaler
from sklearn.externals import joblib

import pickle
import numpy as np
import pandas as pd

def pipeline(url):
    df = pd.read_csv(url)
  
    labels = df.drop('class',1).to_numpy()
    target = df['class']

    models = [  LogisticRegression(penalty='l2',C=0.1,max_iter=1000), 
                RandomForestClassifier(n_estimators=100),
                GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0, loss='ls'),
                AdaBoostClassifier(n_estimators=100) ]

    for model in models:
        model.fit(labels, target)
        joblib.dump(model, '{}.pkl'.format(type(model).__name__))