# Import the neighbors classifier
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt

# Import LabelEncoder
from sklearn import preprocessing
#creating labelEncoder
le = preprocessing.LabelEncoder()

#********************************
# WE SHOULD PUT IN SOME DATA HERE
#********************************

# converting string labels into numbers
label=le.fit_transform(fish)
features=list(zip(weight,length))
model = KNeighborsClassifier(n_neighbors=5)

#********************************
# NOW LET US TRAIN OUR MODEL
#********************************

plt.scatter(weight, length, color="blue")

weight = float(input("Give a weight: "))
length = float(input("Give a length: "))

plt.scatter(weight, length, color="green")

#Predict Output
predicted= model.predict([[weight,length]])
print(fish[predicted[0]])
plt.savefig("fish.png")