1. Analysis of wine quality
Aadhish Chopra
Abhilekh Das
Gopal Bhutada
Parichay Jain
Presented By:

2. Steps:
●Data Exploration
●Data Cleaning
●Examining Relationship
●Modeling and Prediction

3. Data Exploration
Dataset Source Link: https://archive.ics.uci.edu/ml/datasets/Wine+Quality
Predictors (Variables) in dataset: fixed acidity, volatile acidity, citric acid,
residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH,
sulphates, alcohol
Output variable:quality

4. Understanding Data

5. Understanding Data

6. Understanding Data

7. Exploratory Analysis

8. Data Cleaning
The cleaning of the data is done in three steps here
Imputation of missing values
Removal of outliers
Scaling of all the Quantitative variables

9. Removing Outliers
Boxplot from Original Data Boxplot after removing outliers

10. Scaling the Variables

11. Examining Relationship
Correlation between the variables
● We try to find out the relation
between various attributes and with
respect to our output variable quality
● Correlation factor lies between -1 to
+1
● Chart along-with indicates the
measure of correlation between
various attributes.

12. Regression
Divide data into train and test data
Train data using regression model
Based on the output of regression analysis
we find out the parameters which has
statistical importance over the quality of
wine and are not by random chance
Model analysis various combinations and
finally concludes the one with minimum
RSE, better adjusted R-squared value and
F-statistics

13. Regression
Interpretation from Regression

14. Prediction
Based on the train data we try to predict the quality
in the test data that is we apply our model on test
data

15. Thank You!
Questions?