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Predict Wine Preferences of Customers using Wine Dataset

In this machine learning project, you will build predictive models to identify wine preferences of people using physiochemical properties of wines and help restaurants recommend the right quality of wine to a customer.
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What will you learn

  • Use of outlier detection techniques
  • Use of feature selection methods
  • Application of Lasso Model, out of sample validation using Lasso model
  • Application of elastic net regression, to build a binary classification
  • Cross-validation results and result interpretation

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Prerequisites

  • Language used: R

Project Description

Wine tasting is a unique profession, it is usually difficult to predict what the customer would like, based on the past preferences, hence in this machine learning project before recommending any particular variety of wine to the customer if we can identify their preferences using data mining processing from the physiochemical properties of the wines, it would be easier for the restaurant to recommend wines. This machine learning project example can be taken to other similar products that can help in target marketing by modeling consumer tastes from niche markets.
Wine dataset  is considered for this R machine learning project, with white and red vinho verde samples (from Portugal)

Instructors

 
Pradeepta