def predict_banknote(data: BankNote):
data = data.dict()
variance = data['variance']
skewness = data['skewness']
curtosis = data['curtosis']
entropy = data['entropy']
prediction = classifier.predict([[variance, skewness, curtosis, entropy]])
if (prediction[0] > 0.5):
prediction = "Fake note"
else:
prediction = "Its a Bank note"
return {'prediction': prediction}
async def predict(data: BankNote):
data = data.dict()
variance = data['variance']
skewness = data['skewness']
curtosis = data['curtosis']
entropy = data['entropy']
predictedValue = model.predict_proba(
[[variance, skewness, curtosis, entropy]])[:, 1][0]
if predictedValue < 0.5:
prediction = "Fake Note"
else:
prediction = "Authentic Bank Note"
return {'prediction': prediction}
def predict_banknote(data:BankNote):
data = data.dict()
print(data)
variance = data['variance']
skewness = data['skewness']
curtosis = data['curtosis']
entropy = data['entropy']
print(classifier.predict([[variance,skewness,curtosis,entropy]]))
prediction = classifier.predict([[variance,skewness,curtosis,entropy]])
if(prediction[0]>0.5):
prediction = 'Fake Note'
else:
prediction = 'Genuine Bank Note'
return {'prediction':prediction}
def pred_bank(
data: BankNote
): #capture the i/p features needed from the class object and assign to an object data
data = data.dict()
print(data)
variance = data["variance"]
print(variance)
skewness = data["skewness"]
curtosis = data['curtosis']
entropy = data["entropy"]
print(classifier.predict([[variance, skewness, curtosis, entropy]]))
prediction = classifier.predict([[variance, skewness, curtosis, entropy]])
if prediction[0] > 0.5:
prediction = "Fake_Note"
else:
prediction = "Genuine Note"
return {"prediction": prediction}
