Hello World for Google

hello-world.py

# Code example for:
# Hello World - Machine Learning Recipes #1 - Google Developers
# https://www.youtube.com/watch?v=cKxRvEZd3Mw

from sklearn import tree

# Bumpy = 0, Smooth = 1
features = [[140, 1], [130, 1], [150, 0], [170, 0]]
# Apple = 0, Orange = 1
labels = [0, 0, 1, 1]

clf = tree.DecisionTreeClassifier()
clf = clf.fit(features, labels)

print clf.predict([[160, 0]])

Excellent work, Now change the above code to handle the following input:

import numpy as np
import urllib2
from sklearn.utils import shuffle
def load_heart():
    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/heart/heart.dat"
    y = np.genfromtxt(urllib2.urlopen(url), delimiter=" ", dtype=None, usecols=[12])
    x = np.loadtxt(urllib2.urlopen(url), delimiter=" ", usecols=range(0, 12))

    features_names = np.array(
        ['age', 'sex', 'chest pain type(4 values)', 'resting blood pressure', 
        'serum cholestoral in mg/dl', 'fasting blood sugar > 120 mg/dl', 
        'resting electrocardiographic results  (values 0,1,2)', 
        'maximum heart rate achieved', 
        'exercise induced angina',
        'oldpeak = ST depression induced by exercise relative to rest', 
        'the slope of the peak exercise ST segment',
        'the slope of the peak exercise ST segment'
        ])
    x, y = shuffle(x, y, random_state=13)
    return x, y, features_names

x represents the feature training rows. y represents the labels. Do this, and I'll guide you again.