Predicting the price of an object given the historical data is one of the most common task of ML, usually achieved with the Linear Regression model. In this project the Linear Layer will be only the top of the iceberg of a model which combines the wideness of ML model and the deepness of DL model for NLP. The goal is to predict the price of a wine from its description (and variety).

Click this button to open a Workspace on FloydHub that will train this model.

In this notebook, we will build a classifier to correctly predict the price of a wine from its description. More in detail, we will combine the strength of ML and DL learning using a Wide & Deep Model, which provides really good performance for Regression and Recommendation tasks.

We will use the Kaggle's Wine Reviews dataset for training our model. The dataset contains 10 columns and 150k rows of wine reviews.

We will:

• Preprocess text data for NLP
• Build and train a Wide & Deep model using Keras and Tensorflow
• Evaluate our model on the test set
• Run the model on your own movie reviews!