# Importing Libraries¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

• " %matplotlib inline " makes life easy by returning output plots without needing to write plt.show() code everytime after each plot!

In [2]:
df = pd.read_csv('winequality-white.csv',sep=';')

Out[2]:
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
0 7.0 0.27 0.36 20.7 0.045 45.0 170.0 1.0010 3.00 0.45 8.8 6
1 6.3 0.30 0.34 1.6 0.049 14.0 132.0 0.9940 3.30 0.49 9.5 6
2 8.1 0.28 0.40 6.9 0.050 30.0 97.0 0.9951 3.26 0.44 10.1 6
3 7.2 0.23 0.32 8.5 0.058 47.0 186.0 0.9956 3.19 0.40 9.9 6
4 7.2 0.23 0.32 8.5 0.058 47.0 186.0 0.9956 3.19 0.40 9.9 6
• Original data is seperated by delimiter " ; " in given dataset
• " .head() " returns first five observations of the dataset

# Data Insights¶

In [3]:
df.shape

Out[3]:
(4898, 12)
• dataset comprises of 4898 observations and 12 chracteriestics
• out of which one is dependent variable and rest 11 are independent variables - physicochemical characteristics
In [4]:
df.columns.values

Out[4]:
array(['fixed acidity', 'volatile acidity', 'citric acid',
'residual sugar', 'chlorides', 'free sulfur dioxide',
'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',
'quality'], dtype=object)
• Label of each column
In [5]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4898 entries, 0 to 4897
Data columns (total 12 columns):
fixed acidity           4898 non-null float64
volatile acidity        4898 non-null float64
citric acid             4898 non-null float64
residual sugar          4898 non-null float64
chlorides               4898 non-null float64
free sulfur dioxide     4898 non-null float64
total sulfur dioxide    4898 non-null float64
density                 4898 non-null float64
pH                      4898 non-null float64
sulphates               4898 non-null float64
alcohol                 4898 non-null float64
quality                 4898 non-null int64
dtypes: float64(11), int64(1)
memory usage: 459.3 KB

• Data has only float and integer values
• No variable column has null/missing values

# Summary Statistics¶

In [6]:
df.describe()

Out[6]:
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
count 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000 4898.000000
mean 6.854788 0.278241 0.334192 6.391415 0.045772 35.308085 138.360657 0.994027 3.188267 0.489847 10.514267 5.877909
std 0.843868 0.100795 0.121020 5.072058 0.021848 17.007137 42.498065 0.002991 0.151001 0.114126 1.230621 0.885639
min 3.800000 0.080000 0.000000 0.600000 0.009000 2.000000 9.000000 0.987110 2.720000 0.220000 8.000000 3.000000
25% 6.300000 0.210000 0.270000 1.700000 0.036000 23.000000 108.000000 0.991723 3.090000 0.410000 9.500000 5.000000
50% 6.800000 0.260000 0.320000 5.200000 0.043000 34.000000 134.000000 0.993740 3.180000 0.470000 10.400000 6.000000
75% 7.300000 0.320000 0.390000 9.900000 0.050000 46.000000 167.000000 0.996100 3.280000 0.550000 11.400000 6.000000
max 14.200000 1.100000 1.660000 65.800000 0.346000 289.000000 440.000000 1.038980 3.820000 1.080000 14.200000 9.000000

# Key Observations -¶

• Mean value is less than median value of each column represented by 50%(50th percentile) in index column.
• Natably large differnece in 75th %tile and max values of predictors "residual sugar","free sulfur dioxide","total sulfur dioxide"
• Thus observations 1 and 2 suggests that there are extreme values-Outliers in our dataset

# Understanding Target variable¶

In [7]:
df.quality.unique()

Out[7]:
array([6, 5, 7, 8, 4, 3, 9], dtype=int64)
• Target variable/Dependent variable is discrete and categorical in nature.
• "quality" score scale ranges from 1 to 10;where 1 being poor and 10 being the best.
• 1,2 & 10 Quality ratings are not given by any obseravtion.Only scores obtained are between 3 to 9.
In [8]:
df.quality.value_counts()

Out[8]:
6    2198
5    1457
7     880
8     175
4     163
3      20
9       5
Name: quality, dtype: int64
• This tells us vote count of each quality score in descending order.
• "quality" has most values concentrated in the categories 5, 6 and 7.
• Only a few observations made for the categories 3 & 9

# To check missing values¶

In [9]:
sns.heatmap(df.isnull(),cbar=False,yticklabels=False,cmap = 'viridis')

Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x85cec50>
• Dataset has no missing values.
• If there were any, you would've noticed in figure represented by different colour shade on purple background.
• Do try it out with other dataset which has missing values,you'll see the difference.
• Ex.in titanic dataset,you will find "Age" and "Cabin" columns with differnt shades with this code.

# To check correlation¶

In [10]:
plt.figure(figsize=(6,4))
sns.heatmap(df.corr(),cmap='Blues',annot=False)

Out[10]:
<matplotlib.axes._subplots.AxesSubplot at 0xe793ac8>
• Dark shades represents positive correlation while lighter shades represents negative correlation.
• If you set annot=True, you'll get values by which features are correlated to each other in grid-cells
In [11]:
#Quality correlation matrix
k = 12 #number of variables for heatmap
cols = df.corr().nlargest(k, 'quality')['quality'].index
cm = df[cols].corr()
plt.figure(figsize=(10,6))
sns.heatmap(cm, annot=True, cmap = 'viridis')

Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0xe887f28>
• Here we can infer that "density" has strong positive correlation with "residual sugar" whereas it has strong negative correlation with "alcohol".
• "free sulphur dioxide" and "citric acid" has almost no correlation with "quality"
• Since correlation is zero we can infer there is no linear relationship between these two predictors.However it is safe to drop these features in case you're applying Linear Regression model to the dataset.

# To check Outliers¶

In [12]:
l = df.columns.values
number_of_columns=12
number_of_rows = len(l)-1/number_of_columns
plt.figure(figsize=(number_of_columns,5*number_of_rows))
for i in range(0,len(l)):
plt.subplot(number_of_rows + 1,number_of_columns,i+1)
sns.set_style('whitegrid')
sns.boxplot(df[l[i]],color='green',orient='v')
plt.tight_layout()


# To check distribution-Skewness¶

In [13]:
plt.figure(figsize=(2*number_of_columns,5*number_of_rows))
for i in range(0,len(l)):
plt.subplot(number_of_rows + 1,number_of_columns,i+1)
sns.distplot(df[l[i]],kde=True)

• "pH" column appears to be normally distributed
• remaining all independent variables are right skewed/positively skewed.