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.

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 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.