prediction.pynb

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
%matplotlib inline

#Loading dataset
wine = pd.read_csv('../input/winequality-red.csv')
wine.head()



#Now seperate the dataset as response variable and feature variabes
X = wine.drop('quality', axis = 1)
y = wine['quality']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)




#Applying Standard scaling to get optimized result
sc = StandardScaler()



X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)

svc2 = SVC(C = 1.2, gamma =  0.9, kernel= 'rbf')
svc2.fit(X_train, y_train)
pred_svc2 = svc2.predict(X_test)
print(classification_report(y_test, pred_svc2))