import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn import metrics
from sklearn.metrics import classification_report, confusion_matrix



fruits = pd.read_csv('fruitscsv.csv')
print(fruits.head())
print(fruits.shape)

X = fruits[['mass','width','height','color_score']]
Y = fruits['fruit_label']

plt.scatter(X['height'], X['color_score'], c=Y, cmap=plt.cm.Paired)    #first
plt.show()

X_train,X_test,y_train,y_test=train_test_split(X,Y, test_size=0.2, random_state=30)

svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)

y_pred = svclassifier.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))