Wine ml.txt

import pandas as pd
wine = pd.read_csv('',sep=';')
wine.head()	wine.shape	wine.data	wine.target

	#wine.isnull().sum()
	#wrangle  = wine[wine['sulphur-content'].notnull()]
#the quality is range 1 to 8, to categorizing it into just 3 way, that is bad, okay and good quality
	wineBad = wine[wine['quality']<3]
	wineOkay = wine[wine['quality'].between(3,6)]
	wineGood = wine[wine['quality']>6]

	new_dataset = pd.append(wineBad,wineOkay,wineGood)

x = wine.drop['quality']
y = wine['quality']

from sklearn.model_selection import train_test_split
x-train,x-test,y-train,y-test = train_test_split(x,y,test_size,random_size=4)

x-train-mod = x-train.ravel() or reshape
y-train-mod
x-test-mod
y-test-mod  	all reshape(-1,1) or ravel()

from sklearn import svm
svm_model = svm.SVC()
svm_model.fit(x-train,y-train)
	#testing
y_predicted = svm_model.predict(y-test-mod)

	#for testing
from sklearn.metrices import accuracy_score, confusion_matrix, classification_report
print(accuracy_score(y_test-mod,y_predicted)
similar for confusion matrix and classification report

	#for  own data
import numpy as np
datasa = np.array[(1,2,5,64,45,2)]

predict = svm_model.predict([datsa])