import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sb

from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn import metrics from sklearn.svm import SVC from xgboost import XGBClassifier from sklearn.linear_model import LogisticRegression

import warnings warnings.filterwarnings('ignore')

df = pd.read_csv('/content/drive/MyDrive/csv/WineQT.csv') print(df.head())

df.info()

df.describe().T

df.isnull().sum()

for col in df.columns: if df[col].isnull().sum() > 0: df[col] = df[col].fillna(df[col].mean())

df.isnull().sum().sum()

for col in df.columns: if df[col].isnull().sum() > 0: df[col] = df[col].fillna(df[col].mean())

df.isnull().sum().sum()

df.hist(bins=20, figsize=(10, 10)) plt.show()

plt.bar(df['quality'], df['alcohol']) plt.xlabel('quality') plt.ylabel('alcohol') plt.show()

plt.figure(figsize=(12, 12)) sb.heatmap(df.corr() > 0.7, annot=True, cbar=False) plt.show()

df = df.drop('total sulfur dioxide', axis=1)

df['best quality'] = [1 if x > 5 else 0 for x in df.quality]

df.replace({'white': 1, 'red': 0}, inplace=True)

features = df.drop(['quality', 'best quality'], axis=1) target = df['best quality']

xtrain, xtest, ytrain, ytest = train_test_split( features, target, test_size=0.2, random_state=40)

xtrain.shape, xtest.shape

norm = MinMaxScaler() xtrain = norm.fit_transform(xtrain) xtest = norm.transform(xtest)

models = [LogisticRegression(), XGBClassifier(), SVC(kernel='rbf')]

for i in range(3): models[i].fit(xtrain, ytrain)

print(f'{models[i]} : ')
print('Training Accuracy : ', metrics.roc_auc_score(ytrain, models[i].predict(xtrain)))
print('Validation Accuracy : ', metrics.roc_auc_score(
	ytest, models[i].predict(xtest)))
print()

metric.plot_confusion_matrix(models[1], xtest, ytest) plt.show()

metrics.plot_confusion_matrix(models[1], xtest, ytest) plt.show()

print(metrics.classification_report(ytest, models[1].predict(xtest)))