This is the code used for End-to-End Example: Using Logistic Regression for predicting Diabetes.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt 
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
 
diabetesDF = pd.read_csv('diabetes.csv')
print(diabetesDF.head())
print(diabetesDF.info())
 
dfTrain = diabetesDF[:650]
dfTest = diabetesDF[650:750]
dfCheck = diabetesDF[750:] 
 
trainLabel = np.asarray(dfTrain['Outcome'])
trainData = np.asarray(dfTrain.drop('Outcome',1))
testLabel = np.asarray(dfTest['Outcome'])
testData = np.asarray(dfTest.drop('Outcome',1))
 
means = np.mean(trainData, axis=0)
stds = np.std(trainData, axis=0)
 
trainData = (trainData - means)/stds
testData = (testData - means)/stds
 
# np.mean(trainData, axis=0) => check that new means equal 0
# np.std(trainData, axis=0) => check that new stds equal 1
 
diabetesCheck = LogisticRegression()
diabetesCheck.fit(trainData, trainLabel)
 
accuracy = diabetesCheck.score(testData, testLabel)
print("accuracy = ", accuracy * 100, "%")
 
coeff = list(diabetesCheck.coef_[0])
labels = list(dfTrain.drop('Outcome',1).columns)
 
features = pd.DataFrame()
features['Features'] = labels
features['importance'] = coeff
features.sort_values(by=['importance'], ascending=True, inplace=True)
features['positive'] = features['importance'] > 0
features.set_index('Features', inplace=True)
features.importance.plot(kind='barh', figsize=(11, 6),color = features.positive.map({True: 'blue', False: 'red'}))
plt.xlabel('Importance')
 
joblib.dump([diabetesCheck, means, stds], 'diabeteseModel.pkl')
 
diabetesLoadedModel, means, stds = joblib.load('diabeteseModel.pkl')
accuracyModel = diabetesLoadedModel.score(testData, testLabel)
print("accuracy = ",accuracyModel * 100,"%")
 
sampleData = dfCheck[:1]
 
# prepare sample  
sampleDataFeatures = np.asarray(sampleData.drop('Outcome',1))
sampleDataFeatures = (sampleDataFeatures - means)/stds
 
# predict 
predictionProbability = diabetesLoadedModel.predict_proba(sampleDataFeatures)
prediction = diabetesLoadedModel.predict(sampleDataFeatures)
print('Probability:', predictionProbability)
print('prediction:', prediction)