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Predict Wine Preferences using Outlier Detection, Lasso Model and Elastic Nets

In this project, we will build predictive models using penalized linear methods.

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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.


  • 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 project before recommending any product to the customer if we can recognize their preferences using data mining processing from the physiochemical properties of the wines, it would be easier for the restaurant to recommend wines. this wine example can be taken to any other products with a similar problem.