Wine Quality Analysis using clustering and classification technniques

1. WINE QUALITY
ANALYSIS
ANKIT HALDAR
ASHITHA VS
DEBARNIK BISWAS
KRISHNA BOLLOJULA

2. AGENDA
• OBJECTIVE
• DATA DESCRIPTION
• DATA ANALYSIS
• INSIGHTS FROM ANALYSIS

3. OBJECTIVE
• The dataset contains information about red
and white wine.
• The dataset has 1000 data points of each
wine with 13 characteristics(attributes).
• Wine quality is measured on 0(low)-10(high)
scale.
• Management want
– To understand the characteristics of these wines.
– How different ingredients affect the quality.

4. DATA DESCRIPTION
• The attributes are as follows
• fixed.acidity
• volatile.acidity
• citric.acid
• residual.sugar
• chlorides
• free.sulfur.dioxide
• total.sulfur.dioxide
• density
• pH
• sulphates
• alcohol
• quality
• wine_type

5. DATA ANALYSIS
• To understand the characteristics of the
wine and find ingredients contributing more
for quality, we have done the following
– Subset the data based on wine type and find
descriptive statistics.
– Subset the data based on good quality(7,8,9)
and bad quality(3,4,5,6) and find descriptive
statistics.
– Decision tree.
– Clustering the dataset.

6. DATA ANALYSIS
• Subset the data based on wine type and find
descriptive statistics.
• Subset the data based on good quality1(7,8,9)
and bad quality0(3,4,5,6) and found descriptive
statistics.

7. DATA ANALYSIS
• Subset the data based on wine quality and find
descriptive statistics.
• Volatile.acidity, citric.acid, chlorides are
showing some pattern. Alcohol being the best.

8. DATA ANALYSIS
• Decision tree
– Using rapid miner, we build a classification model
to find the ingredients which are important for
predicting red wine and white wine.
accuracy: 97.05% +/- 1.63% (mikro: 97.05%)
true Red Wine true White Wine class precision
pred. Red Wine 961 20 97.96%
pred. White Wine 39 980 96.17%
class recall 96.10% 98.00%
Ingredients
chlorides
sulphates
free.sulfur.dioxide
volatile.acidity
total.sulfur.dioxide

9. DATA ANALYSIS
• Decision tree
– Using rapid miner, we build a classification model
to find the ingredients which are important for
predicting the quality is high or low.
Ingredients
citric.acid
chlorides
volatile.acidity
density
alcohol
true 0 true 1
class
precision
pred. 0 1431 183 88.66%
pred. 1 211 175 45.34%
class recall 87.15% 48.88%

10. DATA ANALYSIS
• Clustering(Code is on next slide)
• The cluster divides the whole data based
on which wine it belongs.
• We can see there is significant difference
in all attributes.

11. DATA ANALYSIS

12. INSIGHTS FROM ANALYSIS
• Characteristics
– Red wine or white wine
– High or low quality
• Ingredients which contribute more for type
of the wine.
– Chlorides, sulphates, free.sulfur.dioxide, total
sulfur dioxide, volatile.acidity.
• Ingredients which contribute more for
quality of the wine.
– Alcohol(major), density.