svc.py

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('HealthData.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 13].values


#handling missing data

from sklearn.preprocessing import Imputer
imputer=Imputer(missing_values='NaN',strategy='mean',axis=0)
imputer=imputer.fit(X[:,11:13])
X[:,11:13]=imputer.transform(X[:,11:13])

#splitting dataset into training set and test set

from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.25)

#feature scaling

from sklearn.preprocessing import StandardScaler
sc_X=StandardScaler()
X_train=sc_X.fit_transform(X_train)

from sklearn.externals import joblib
# Save it
scaler_file = "standard_scalar.pkl"
joblib.dump(sc_X, scaler_file)

X_test=sc_X.transform(X_test)


#EXPLORING THE DATASET
import seaborn as sn

sn.countplot(x='num',data=dataset)
dataset.num.value_counts()



##SUPPORT VECTOR CLASSIFICATIONS

##checking for different kernels

from sklearn.svm import SVC
svc_scores = []
kernels = ['linear', 'poly', 'rbf', 'sigmoid']
for i in range(len(kernels)):
    svc_classifier = SVC(kernel = kernels[i])
    svc_classifier.fit(X_train, Y_train)
    svc_scores.append(svc_classifier.score(X_test, Y_test))

from matplotlib.cm import rainbow
##%matplotlib inline
colors = rainbow(np.linspace(0, 1, len(kernels)))
plt.bar(kernels, svc_scores, color = colors)
for i in range(len(kernels)):
    plt.text(i, svc_scores[i], svc_scores[i])
plt.xlabel('Kernels')
plt.ylabel('Scores')
plt.title('Support Vector Classifier scores for different kernels')


#dataset.describe()

# Fitting SVM to the Training set

classifier = SVC(kernel = 'rbf', random_state = 0 ,probability=True)
classifier.fit(X_train, Y_train)


from sklearn.externals import joblib
filename = 'svc_model.pkl'
joblib.dump(classifier,filename)
# Predicting the Test set results

y_pred = classifier.predict(X_test)

from sklearn.metrics import accuracy_score
accuracy_score(Y_test,y_pred)


# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_test, y_pred)

#Interpretation:

from sklearn.metrics import classification_report
print(classification_report(Y_test, y_pred))


#ROC
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
logit_roc_auc = roc_auc_score(Y_test, classifier.predict(X_test))
fpr, tpr, thresholds = roc_curve(Y_test, classifier.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.savefig('Log_ROC')
plt.show()

##PREDICTION FOR NEW DATASET

Newdataset = pd.read_csv('newdata.csv')
ynew=classifier.predict(Newdataset)