This is the code used for End-to-End Example: Using Logistic Regression for predicting Diabetes.
import pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltfrom sklearn.linear_model import LogisticRegressionfrom sklearn.externals import joblibdiabetesDF = 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)/stdstestData = (testData - means)/stds# np.mean(trainData, axis=0) => check that new means equal 0# np.std(trainData, axis=0) => check that new stds equal 1diabetesCheck = 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'] = labelsfeatures['importance'] = coefffeatures.sort_values(by=['importance'], ascending=True, inplace=True)features['positive'] = features['importance'] > 0features.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 samplesampleDataFeatures = np.asarray(sampleData.drop('Outcome',1))sampleDataFeatures = (sampleDataFeatures - means)/stds# predictpredictionProbability = diabetesLoadedModel.predict_proba(sampleDataFeatures)prediction = diabetesLoadedModel.predict(sampleDataFeatures)print('Probability:', predictionProbability)print('prediction:', prediction)