model.py

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
#from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score


diabetes = pd.read_csv('diabetes.csv')

features = ['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']
label = 'Diabetic'
X, y = diabetes[features].values, diabetes[label].values

# Split data 70%-30% into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)

print ('Training cases: %d\nTest cases: %d' % (X_train.shape[0], X_test.shape[0]))

reg = 0.1
model = RandomForestClassifier(n_estimators=100).fit(X_train, y_train)
#model = LogisticRegression(C=1/reg, solver="liblinear").fit(X_train, y_train)
print (model)


predictions = model.predict(X_test)
y_scores = model.predict_proba(X_test)
cm = confusion_matrix(y_test, predictions)
print ('Confusion Matrix:\n',cm, '\n')
print('Accuracy:', accuracy_score(y_test, predictions))
print("Overall Precision:",precision_score(y_test, predictions))
print("Overall Recall:",recall_score(y_test, predictions))
auc = roc_auc_score(y_test,y_scores[:,1])
print('\nAUC: ' + str(auc))

# calculate ROC curve
#fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])

# plot ROC curve
#fig = plt.figure(figsize=(6, 6))
# Plot the diagonal 50% line
#plt.plot([0, 1], [0, 1], 'k--')
# Plot the FPR and TPR achieved by our model
#plt.plot(fpr, tpr)
#plt.xlabel('False Positive Rate')
#plt.ylabel('True Positive Rate')
#plt.title('ROC Curve')
#plt.show()



# Saving model to disk
pickle.dump(model, open('model.pkl','wb'))

# Loading model to compare the results
model = pickle.load(open('model.pkl','rb'))
print(model.predict([[5, 114,101,43,70,36.5,0.079,38]]))