Create a classifier to predict the wine color from wine quality attributes using this dataset: http://archive.ics.uci.edu/ml/datasets/Wine+Quality

The data is in the database we've been using

• host='training.c1erymiua9dx.us-east-1.rds.amazonaws.com'
• database='training'
• port=5432
• user='dot_student'
• password='qgis'
• table name = 'winequality'

Query for the data and create a numpy array

Split the data into features (x) and target (y, the last column in the table)

Remember you can cast the results into an numpy array and then slice out what you want

Create a decision tree with the data

Run 10-fold cross validation on the model

If you have time, calculate the feature importance and graph based on the code in the slides from last class

Use this tip for getting the column names from your cursor object