Predicting goodness points of a wine given its reviews
Predicting goodness points of a wine given its reviews.
Implemented Coordinate Descent Algorithm along with Lasso Regression for predicting the goodness points of a wine.
The data was scraped from WineEnthusiast during the week of June 15th, 2017 and can be found on the analytics website Kaggle https://www.kaggle.com/zynicide/wine-reviews. This dataset provides a variety of features, the points, description (review), variety, country, province etc. For this project we used the points and description features. Points are ratings within range 1-100. However, the dataset contains description for wines rated in range 80-100. Feature vectors are already extracted for each review.
| File | Description |
|---|---|
| trainData.txt | Training data matrix for predicting number of stars |
| trainLabels.txt | Training labels, list of the number of stars for each review |
| valData.txt | Validation data |
| valLabels.txt | Validation labels |
| testData.txt | Test data |
| featureTypes.txt | List of features used |
The data matrices are stored in Matrix Market Format, a format for sparse matrices. Each line is is a tuple of three elements for instance ID, feature ID, and feature value. Meta information for each feature is provided in featureTypes.txt. The features are strings of one, two, or three words (n-grams). The feature values correspond roughly to how often each word appears in the review. All rows have also been normalized.
Link for the Kaggle Competitionhttps://www.kaggle.com/c/hw1-wine-goodness-cse512-spr18/leaderboard