Developing a Classifier for Price Range of Wines using Data Mining

MIS772 Predictive Analytics (2019 T1) Individual Assignment A1-LP2 / Workshops M1T1-M1T4
Assignment A1-LP2: Classification
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Exceptional Meets expectations Issues noted Improve Unacceptable
Exec
Report
Discover
Relationships
Create
Models
Evaluate &
Improve
Provide
Solution
Research &
Extend
Brief
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MIS772 Predictive Analytics (2019 T1) Individual Assignment A1-LP2 / Workshops M1T1-M1T4
Executive summary (one page)
This project is uses the data mining method to develop a classifier which is used to determine the price range for
the new wines, by using the Rapid Miner data mining tool. Because, the American wine importers have asked the
user to develop a data mining method of classifying the imported wines based on price, variety, winery, country,
taster name, twitter handle, wine title, country, region, province and so on. The exporters are required to clean-up
and explore the wine tasting data to evaluate and develop a classifier, which determines the price range for new
wines. Moreover, it also wants to minimize the classification problems. Funding growth is considered as one of the
most common problem for the wine exporters. It is possible to ensure that the user are able to plan and manage
the funding appropriately for avoiding the problems, by developing data mining method for the provided wine data.
The main benefit of this project is to develop the classifier models to determine the price range for new wines.
The main objective of this project is to develop a data mining method for the provided wine data. The American
wine importers asked the user to develop a data mining method for classifying the imported wines based on the
following:
 Price
 Variety
 Winery
 Country
 Taster Name
 Twitter Handle
 Wine Title
 Region
 Province and More.
The Australian Wine importers want to clean-up and explore the wine tasting data, then evaluate and develop a
classifier to determine the price range for the new wines and also wishes to minimize the classification problems.
One of the most common problems of the wine exporters is funding growth. Once the export comes into the
equation, financing requirements becomes even more important and many wine businesses need support to
enable them to fulfil their export and export-related contracts. So, each province is introducing new measures all
favouring their domestic producers and they differ between provinces. By developing data mining method for the
provided wine data with detailed funding projections, it is possible to ensure that the user is able to plan and
manage your funding accordingly, to avoid problems.
Therefore, classifier is being developed to determine the price range for new wines by using the Rapid Miner data
mining tool. To develop the classifier, a new model is created for the provided wine data, by using the two main
methods of data mining namely- Random tree and decision tree. And, also it evaluates and validates the created
model.
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MIS772 Predictive Analytics (2019 T1) Individual Assignment A1-LP2 / Workshops M1T1-M1T4
Discovering Relationships and Data Transformation in RapidMiner (one page)
By analyzing the data, the relationship between the data attributes will be discovered. The provided wine data has
the following attributes such as price, variety, winery, country, taster name, twitter handle, wine title, country,
region, province and more. The weight relationship between the predictors and labels is illustrated below. Some
data is eliminated in the process. The predictor is selected as Prices and Winery as Labels in the proposed model,
to discover the relationship between the attributes. The below chart is used to display the relationship between the
price and points attributes is illustrated below.
The below chart is used to display the relationship between the price and taster name attributes, which is
illustrated below.
The below chart is used to display the Relationship between the price and variety attributes, which is illustrated
below.
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