Wine_Review

1. About

A predictive model to identify wines' quality through 10 features.

Dataset can be downloaded here.

The data consists of 10 fields:

• Points: the number of points WineEnthusiast rated the wine on a scale of 1-100 (though they say they only post reviews for wines that score >=80)

• Title: the title of the wine review, which often contains the vintage if you're interested in extracting that feature

• Variety: the type of grapes used to make the wine (ie Pinot Noir)

• Description: a few sentences from a sommelier describing the wine's taste, smell, look, feel, etc.

• Country: the country that the wine is from

• Province: the province or state that the wine is from

• Region 1: the wine growing area in a province or state (ie Napa)

• Region 2: sometimes there are more specific regions specified within a wine growing area (ie Rutherford inside the Napa Valley), but this value can sometimes be blank

• Winery: the winery that made the wine

• Designation: the vineyard within the winery where the grapes that made the wine are from

• Price: the cost for a bottle of the wine

• Taster Name: name of the person who tasted and reviewed the wine

• Taster Twitter Handle: Twitter handle for the person who tasted ane reviewed the wine

2. Data Preprocess

I used the winemag-data_first150k.csv which contains about 150k samples information.Data encoding and other preprocessing actions can be detailed checked in file data_preprocess.ipynb.

There were only 2 different actions I took in data preprocessing, however, there occured hug performance gap between validation model and test model, which may blame the feature importance is accounted for a big propertion durinhg model training.

Validation model

• In validation model, I drop the 'description' feature for the reason I think this contributes nothing to wine quality, which proves to be not proper.

• Then I merge 'region 1' and 'region 2' to 'region'.

Test model

• In test model, I encode 'description' to numeric type by the value of length of the description sentence.

• Then I merge 'province', 'region 1' and 'region 2' to 'region'.

Results shows that feature engineering is much better in test model, which contributes to it has about 15% higher prediction accuracy than validation model does.

3. Modeling

I chose Random Forest and XGBoost to validate this 5-classification task.

Randon Forest (ACC: 81.52%)

• Confusion Matrix:

• Feature importance:

XGBoost (ACC: 81.95%)

• Confusion Matrix:

• Feature importance:

License