image_output_1.png
from sklearn.preprocessing import StandardScaler

import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')

df.shape

missing_val_count_by_column = df.isnull().sum()

df.fillna(df.mean(), inplace=True)

df['type'] = pd.get_dummies(df['type'], drop_first=True)

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(df.drop('quality', axis=1))