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Wine Quality Dataset

1. Introduction

The Wine Quality Dataset involves predicting the quality of white wines on a scale given chemical measures of each wine.

The number of observations for each class is not balanced. There are 4,898 observations with 11 input variables and one output variable. The variable names are as follows:

Fixed acidity.
Volatile acidity.
Citric acid.
Residual sugar.
Free sulfur dioxide.
Total sulfur dioxide.
Quality (score between 0 and 10).

2. Loading the necessary libraries and datasets

import sys
assert sys.version_info >= (3, 5)

# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"

# Common imports
import numpy as np
import os
import pandas as pd

# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

#ML imports
from sklearn.linear_model import LinearRegression
## Loading the dataset from github repo
url = 'https://raw.githubusercontent.com/hargurjeet/MachineLearning/Wine-Quality-Dataset/winequality-white.csv'
df = pd.read_csv(url, delimiter= ';')