Le dataset que nous allons utiliser ici est décrit dans : https://www.kaggle.com/raghupalem/bill_authentication
Il s'agit pour faire simple d'une procedure d'authentification à partir d'images.
import pandas as pa
bankdata = pa.read_csv('https://www.labri.fr/perso/zemmari/datasets/bill_authentication.csv')
bankdata.head()
X = bankdata.drop('Class', axis=1)
y = bankdata['Class']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, random_state=109)
from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)
y_pred = svclassifier.predict(X_test)
from sklearn import metrics
scores = metrics.accuracy_score(y_test, y_pred)
print('Accuracy: ','{:2.2%}'.format(scores))
cm = metrics.confusion_matrix(y_test, y_pred)
print(cm)
Et si on comparait avec un classifieur bayesien :
from sklearn.naive_bayes import GaussianNB
clsb = GaussianNB()
clsb.fit(X_train, y_train)
y_pred = clsb.predict(X_test)
scores = metrics.accuracy_score(y_test, y_pred)
print('Accuracy: ','{:2.2%}'.format(scores))