Wine Classification

In this project you'll be analyzing another set of data from the UC Irvine Machine Learning Repository.

Your goal is to classify wines grown in the same region of Italy, but from different cultivars (cultivated varities) of grape. The data consists of a class (1, 2, or 3) followed by 13 features as described in the wine.names file.

Follow the directions in each cell to complete the project.

In [37]:
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%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import pandas as pd

In [1]:
# Load the training data from 'data/wine_train.csv' into
# a pandas dataframe.

In [54]:
# Display the first few rows from the dataframe
# to ensure proper loading

In [56]:
# Use the dataframe.count method to ensure
# all 148 records were loaded

In [61]:
# Declare placeholders and variables for your TensorFlow model here (if applicable)

In [35]:
# Define your TensorFlow or scikit-learn model here

In [ ]:
# Train the model in this cell

In [ ]:
# Run the trained model on 'data/wine_test.csv' here.
# Be sure to print out the accuracy!