https://github.com/anuj-glitch/wine-variety-prediction

A predictive model for predicting the wine “variety”
https://github.com/anuj-glitch/wine-variety-prediction

Last synced: 3 months ago
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A predictive model for predicting the wine “variety”

• Host: GitHub
• URL: https://github.com/anuj-glitch/wine-variety-prediction
• Owner: anuj-glitch
• License: apache-2.0
• Created: 2020-05-16T06:15:26.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-05-16T07:24:57.000Z (about 5 years ago)
• Last Synced: 2025-01-07T05:15:48.901Z (5 months ago)
• Language: Jupyter Notebook
• Size: 4.03 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Wine-variety-prediction

A predictive model for predicting the wine “variety”

The training Dataset consist of over 28 varieties of wine and 82,657 reviews of different wines.

###The Data Description is as follows:###

*	user_name - user_name of the reviewer

*	country -The country that the wine is from.

*	review_title - The title of the wine review, which often contains the vintage.

*	review_description - A verbose review of the wine.

*	designation - The vineyard within the winery where the grapes that made the wine are from.

*	points - ratings given by the user. The ratings are between 0 -100.

*	price - The cost for a bottle of the wine

*	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

*	variety - The type of grapes used to make the wine. Dependent variable for task 2 of the assignment

Prediction of wine variety

* Model used : **LinearSVC ( sklearn.svm.LinearSVC )**  - LinearSVC is another (faster) implementation of Support Vector Classification

                                                      for the case of a linear kernel. it implements “one-vs-the-rest” multi-class 

                                                      strategy thus reducing the runtime significantly and also scale better to 

                                                      large numbers of samples. .

* Accuracy attained: **97.48 %**