Project Title: Optimum pricing for wine

Industry:

The wine industry is of USD 302.02 billion in 2017. It is pegged to grow at the rate of 5.8% year on year from 2017-23. Typically, there are two types of wine largely sold - the red and the white wine. The price of the wine is proportional to the quality of wine which is derived from various chemical factors

Business Objective:

The price of wine is directly proportional to the quality of wine, as given by various research papers. Predicting the wine quality will help in optimum pricing of the new wine in the market

Data Analysis Objective:

To determine the quality of the wine (Quality parameter 0-10) based on the chemical composition

Data Set:

The two datasets are related to red and white variants of the Portuguese “Vinho Verde” wine
Dataset link : https://drive.google.com/drive/folders/1kkgwAHnmMUwlNEsVEyHvEVaZkOU7ijmc?usp=sharing

Important Variables:

Input variables which affect the determination of the quality of wine (based on physicochemical tests) 1. fixed acidity 2. volatile acidity 3. citric acid 4. residual sugar 5. chlorides 6. free sulfur dioxide 7. total sulfur dioxide 8. density 9. pH 10. sulphates 11. alcohol

Tools:

  1. R
  2. R Studio
  3. MS Excel 2016

Methodology:

  1. Predictive Analytics using Correlation and Regression
  2. Clustering
  3. Sensitivity analysis

References

  1. “Wine Market by Colour (Red Wine, Rose Wine, White Wine and Others), By Product Type and By Distribution Channel: Global Industry Perspective, Comprehensive Analysis, and Forecast, 2017 - 2023”
  2. “Wine prices and quality ratings: A meta-regression analysis” by Edward Oczkowski and Hristos Doucouliagos
  3. https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/

Exploratory Data Analysis on White wine dataset

## reading the files
white_wine <- read.csv("winequality-white.csv", header = TRUE, sep = ";")

## summarising the variables for white wine
summary(white_wine)
##  fixed.acidity    volatile.acidity  citric.acid     residual.sugar  
##  Min.   : 3.800   Min.   :0.0800   Min.   :0.0000   Min.   : 0.600  
##  1st Qu.: 6.300   1st Qu.:0.2100   1st Qu.:0.2700   1st Qu.: 1.700  
##  Median : 6.800   Median :0.2600   Median :0.3200   Median : 5.200  
##  Mean   : 6.855   Mean   :0.2782   Mean   :0.3342   Mean   : 6.391  
##  3rd Qu.: 7.300   3rd Qu.:0.3200   3rd Qu.:0.3900   3rd Qu.: 9.900  
##  Max.   :14.200   Max.   :1.1000   Max.   :1.6600   Max.   :65.800  
##    chlorides       free.sulfur.dioxide total.sulfur.dioxide
##  Min.   :0.00900   Min.   :  2.00      Min.   :  9.0       
##  1st Qu.:0.03600   1st Qu.: 23.00      1st Qu.:108.0       
##  Median :0.04300   Median : 34.00      Median :134.0       
##  Mean   :0.04577   Mean   : 35.31      Mean   :138.4       
##  3rd Qu.:0.05000   3rd Qu.: 46.00      3rd Qu.:167.0       
##  Max.   :0.34600   Max.   :289.00      Max.   :440.0       
##     density             pH          sulphates         alcohol     
##  Min.   :0.9871   Min.   :2.720   Min.   :0.2200   Min.   : 8.00  
##  1st Qu.:0.9917   1st Qu.:3.090   1st Qu.:0.4100   1st Qu.: 9.50  
##  Median :0.9937   Median :3.180   Median :0.4700   Median :10.40  
##  Mean   :0.9940   Mean   :3.188   Mean   :0.4898   Mean   :10.51  
##  3rd Qu.:0.9961   3rd Qu.:3.280   3rd Qu.:0.5500   3rd Qu.:11.40  
##  Max.   :1.0390   Max.   :3.820   Max.   :1.0800   Max.   :14.20  
##     quality     
##  Min.   :3.000  
##  1st Qu.:5.000  
##  Median :6.000  
##  Mean   :5.878  
##  3rd Qu.:6.000  
##  Max.   :9.000

Exploratory Data Analysis on Red wine dataset

## reading the files
red_wine <- read.csv("winequality-red.csv", header = TRUE, sep = ";")

## summarising the variables for red wine
summary(red_wine)
##  fixed.acidity   volatile.acidity  citric.acid    residual.sugar  
##  Min.   : 4.60   Min.   :0.1200   Min.   :0.000   Min.   : 0.900  
##  1st Qu.: 7.10   1st Qu.:0.3900   1st Qu.:0.090   1st Qu.: 1.900  
##  Median : 7.90   Median :0.5200   Median :0.260   Median : 2.200  
##  Mean   : 8.32   Mean   :0.5278   Mean   :0.271   Mean   : 2.539  
##  3rd Qu.: 9.20   3rd Qu.:0.6400   3rd Qu.:0.420   3rd Qu.: 2.600  
##  Max.   :15.90   Max.   :1.5800   Max.   :1.000   Max.   :15.500  
##    chlorides       free.sulfur.dioxide total.sulfur.dioxide
##  Min.   :0.01200   Min.   : 1.00       Min.   :  6.00      
##  1st Qu.:0.07000   1st Qu.: 7.00       1st Qu.: 22.00      
##  Median :0.07900   Median :14.00       Median : 38.00      
##  Mean   :0.08747   Mean   :15.87       Mean   : 46.47      
##  3rd Qu.:0.09000   3rd Qu.:21.00       3rd Qu.: 62.00      
##  Max.   :0.61100   Max.   :72.00       Max.   :289.00      
##     density             pH          sulphates         alcohol     
##  Min.   :0.9901   Min.   :2.740   Min.   :0.3300   Min.   : 8.40  
##  1st Qu.:0.9956   1st Qu.:3.210   1st Qu.:0.5500   1st Qu.: 9.50  
##  Median :0.9968   Median :3.310   Median :0.6200   Median :10.20  
##  Mean   :0.9967   Mean   :3.311   Mean   :0.6581   Mean   :10.42  
##  3rd Qu.:0.9978   3rd Qu.:3.400   3rd Qu.:0.7300   3rd Qu.:11.10  
##  Max.   :1.0037   Max.   :4.010   Max.   :2.0000   Max.   :14.90  
##     quality     
##  Min.   :3.000  
##  1st Qu.:5.000  
##  Median :6.000  
##  Mean   :5.636  
##  3rd Qu.:6.000  
##  Max.   :8.000

Including Plots

## plotting the quality values of white wine
library(ggplot2)

ggplot(white_wine, aes(x = quality)) +
        geom_histogram(binwidth = 1)

## plotting the quality values of red wine
ggplot(red_wine, aes(x = quality)) +
        geom_histogram(binwidth = 1)