Udacity - Assignment 1 - not MNIST - update

My bad - used Linear Regression instead of Logistic Regression.

Updated answer to Problem 6

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import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model

data = train_dataset.reshape((train_dataset.shape[0], -1))

data_5000 = data[:5000]
label_5000 = train_labels[:5000]

model = linear_model.LogisticRegression()
model.fit(data_5000, label_5000)

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model.score(data_5000, label_5000)

0.94879999999999998

valid_data = valid_dataset.reshape((valid_dataset.shape[0], -1))
model.score(valid_data, valid_labels)

0.77329999999999999

test_data = test_dataset.reshape((test_dataset.shape[0], -1))
model.score(test_data, test_labels)

0.85729999999999995

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References:
http://nbviewer.jupyter.org/gist/justmarkham/6d5c061ca5aee67c4316471f8c2ae976