Intro.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import seaborn.apionly as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.multiclass import OutputCodeClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning workflow\n",
    "![alt text](figs/workflow.png)\n",
    "(image taken from [here](https://www.kaggle.com/mjbahmani/a-comprehensive-deep-learning-workflow-with-python))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task\n",
    "In this tutorial, we go through an introductory classification problem. It is called MNIST digit classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Acquisition\n",
    "There is a challenge on this dataset on Kaggle [here](https://www.kaggle.com/c/digit-recognizer). You should join the challenge, download the dataset and extract it into `data/`. The dataset is less than 20MB in size. It contains 42K training images and 20K validation images. Each image contains a single handwritten digit and is 28x28 pixels large."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data\n",
    "The data comes in csv format and we use pandas package to read it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/train.csv')\n",
    "test = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41970</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41971</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41972</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41973</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41974</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41975</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41976</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41977</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41978</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41979</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41980</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>253</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41981</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41982</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41983</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41984</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41985</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41986</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41987</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41988</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41989</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41990</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41991</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41992</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41993</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41994</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41995</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41996</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41997</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41998</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41999</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42000 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0          1       0       0       0       0       0       0       0       0   \n",
       "1          0       0       0       0       0       0       0       0       0   \n",
       "2          1       0       0       0       0       0       0       0       0   \n",
       "3          4       0       0       0       0       0       0       0       0   \n",
       "4          0       0       0       0       0       0       0       0       0   \n",
       "5          0       0       0       0       0       0       0       0       0   \n",
       "6          7       0       0       0       0       0       0       0       0   \n",
       "7          3       0       0       0       0       0       0       0       0   \n",
       "8          5       0       0       0       0       0       0       0       0   \n",
       "9          3       0       0       0       0       0       0       0       0   \n",
       "10         8       0       0       0       0       0       0       0       0   \n",
       "11         9       0       0       0       0       0       0       0       0   \n",
       "12         1       0       0       0       0       0       0       0       0   \n",
       "13         3       0       0       0       0       0       0       0       0   \n",
       "14         3       0       0       0       0       0       0       0       0   \n",
       "15         1       0       0       0       0       0       0       0       0   \n",
       "16         2       0       0       0       0       0       0       0       0   \n",
       "17         0       0       0       0       0       0       0       0       0   \n",
       "18         7       0       0       0       0       0       0       0       0   \n",
       "19         5       0       0       0       0       0       0       0       0   \n",
       "20         8       0       0       0       0       0       0       0       0   \n",
       "21         6       0       0       0       0       0       0       0       0   \n",
       "22         2       0       0       0       0       0       0       0       0   \n",
       "23         0       0       0       0       0       0       0       0       0   \n",
       "24         2       0       0       0       0       0       0       0       0   \n",
       "25         3       0       0       0       0       0       0       0       0   \n",
       "26         6       0       0       0       0       0       0       0       0   \n",
       "27         9       0       0       0       0       0       0       0       0   \n",
       "28         9       0       0       0       0       0       0       0       0   \n",
       "29         7       0       0       0       0       0       0       0       0   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "41970      2       0       0       0       0       0       0       0       0   \n",
       "41971      3       0       0       0       0       0       0       0       0   \n",
       "41972      4       0       0       0       0       0       0       0       0   \n",
       "41973      4       0       0       0       0       0       0       0       0   \n",
       "41974      3       0       0       0       0       0       0       0       0   \n",
       "41975      9       0       0       0       0       0       0       0       0   \n",
       "41976      2       0       0       0       0       0       0       0       0   \n",
       "41977      4       0       0       0       0       0       0       0       0   \n",
       "41978      4       0       0       0       0       0       0       0       0   \n",
       "41979      4       0       0       0       0       0       0       0       0   \n",
       "41980      7       0       0       0       0       0       0       0       0   \n",
       "41981      2       0       0       0       0       0       0       0       0   \n",
       "41982      8       0       0       0       0       0       0       0       0   \n",
       "41983      7       0       0       0       0       0       0       0       0   \n",
       "41984      3       0       0       0       0       0       0       0       0   \n",
       "41985      3       0       0       0       0       0       0       0       0   \n",
       "41986      0       0       0       0       0       0       0       0       0   \n",
       "41987      5       0       0       0       0       0       0       0       0   \n",
       "41988      0       0       0       0       0       0       0       0       0   \n",
       "41989      5       0       0       0       0       0       0       0       0   \n",
       "41990      3       0       0       0       0       0       0       0       0   \n",
       "41991      1       0       0       0       0       0       0       0       0   \n",
       "41992      9       0       0       0       0       0       0       0       0   \n",
       "41993      6       0       0       0       0       0       0       0       0   \n",
       "41994      4       0       0       0       0       0       0       0       0   \n",
       "41995      0       0       0       0       0       0       0       0       0   \n",
       "41996      1       0       0       0       0       0       0       0       0   \n",
       "41997      7       0       0       0       0       0       0       0       0   \n",
       "41998      6       0       0       0       0       0       0       0       0   \n",
       "41999      9       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "0           0    ...            0         0         0         0         0   \n",
       "1           0    ...            0         0         0         0         0   \n",
       "2           0    ...            0         0         0         0         0   \n",
       "3           0    ...            0         0         0         0         0   \n",
       "4           0    ...            0         0         0         0         0   \n",
       "5           0    ...            0         0         0         0         0   \n",
       "6           0    ...            0         0         0         0         0   \n",
       "7           0    ...            0         0         0         0         0   \n",
       "8           0    ...            0         0         0         0         0   \n",
       "9           0    ...            0         0         0         0         0   \n",
       "10          0    ...            0         0         0         0         0   \n",
       "11          0    ...            0         0         0         0         0   \n",
       "12          0    ...            0         0         0         0         0   \n",
       "13          0    ...            0         0         0         0         0   \n",
       "14          0    ...            0         0         0         0         0   \n",
       "15          0    ...            0         0         0         0         0   \n",
       "16          0    ...            0         0         0         0         0   \n",
       "17          0    ...            0         0         0         0         0   \n",
       "18          0    ...            0         0         0         0         0   \n",
       "19          0    ...            0         0         0         0         0   \n",
       "20          0    ...            0         0         0         0         0   \n",
       "21          0    ...            0         0         0         0         0   \n",
       "22          0    ...            0         0         0         0         0   \n",
       "23          0    ...            0         0         0         0         0   \n",
       "24          0    ...            0         0         0         0         0   \n",
       "25          0    ...            0         0         0         0         0   \n",
       "26          0    ...            0         0         0         0         0   \n",
       "27          0    ...            0         0         0         0         0   \n",
       "28          0    ...            0         0         0         0         0   \n",
       "29          0    ...            0         0         0         0         0   \n",
       "...       ...    ...          ...       ...       ...       ...       ...   \n",
       "41970       0    ...            0         0         0         0         0   \n",
       "41971       0    ...            0         0         0         0         0   \n",
       "41972       0    ...            0         0         0         0         0   \n",
       "41973       0    ...            0         0         0         0         0   \n",
       "41974       0    ...            0         0         0         0         0   \n",
       "41975       0    ...            0         0         0         0         0   \n",
       "41976       0    ...            0         0         0         0         0   \n",
       "41977       0    ...            0         0         0         0         0   \n",
       "41978       0    ...            0         0         0         0         0   \n",
       "41979       0    ...            0         0         0         0         0   \n",
       "41980       0    ...           27       253       110         0         0   \n",
       "41981       0    ...            0         0         0         0         0   \n",
       "41982       0    ...            0         0         0         0         0   \n",
       "41983       0    ...            0         0         0         0         0   \n",
       "41984       0    ...            0         0         0         0         0   \n",
       "41985       0    ...            0         0         0         0         0   \n",
       "41986       0    ...            0         0         0         0         0   \n",
       "41987       0    ...            0         0         0         0         0   \n",
       "41988       0    ...            0         0         0         0         0   \n",
       "41989       0    ...            0         0         0         0         0   \n",
       "41990       0    ...            0         0         0         0         0   \n",
       "41991       0    ...            0         0         0         0         0   \n",
       "41992       0    ...            0         0         0         0         0   \n",
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       "28            0         0         0         0         0  \n",
       "29            0         0         0         0         0  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "41970         0         0         0         0         0  \n",
       "41971         0         0         0         0         0  \n",
       "41972         0         0         0         0         0  \n",
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       "41989         0         0         0         0         0  \n",
       "41990         0         0         0         0         0  \n",
       "41991         0         0         0         0         0  \n",
       "41992         0         0         0         0         0  \n",
       "41993         0         0         0         0         0  \n",
       "41994         0         0         0         0         0  \n",
       "41995         0         0         0         0         0  \n",
       "41996         0         0         0         0         0  \n",
       "41997         0         0         0         0         0  \n",
       "41998         0         0         0         0         0  \n",
       "41999         0         0         0         0         0  \n",
       "\n",
       "[42000 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27970</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27971</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27972</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27973</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27974</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27975</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27976</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27977</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27978</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27979</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27980</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27981</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27982</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>28000 rows × 784 columns</p>\n",
       "</div>"
      ],
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       "22          0    ...            0         0         0         0         0   \n",
       "23          0    ...            0         0         0         0         0   \n",
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       "25          0    ...            0         0         0         0         0   \n",
       "26          0    ...            0         0         0         0         0   \n",
       "27          0    ...            0         0         0         0         0   \n",
       "28          0    ...            0         0         0         0         0   \n",
       "29          0    ...            0         0         0         0         0   \n",
       "...       ...    ...          ...       ...       ...       ...       ...   \n",
       "27970       0    ...            0         0         0         0         0   \n",
       "27971       0    ...            0         0         0         0         0   \n",
       "27972       0    ...            0         0         0         0         0   \n",
       "27973       0    ...            0         0         0         0         0   \n",
       "27974       0    ...            0         0         0         0         0   \n",
       "27975       0    ...            0         0         0         0         0   \n",
       "27976       0    ...            0         0         0         0         0   \n",
       "27977       0    ...            0         0         0         0         0   \n",
       "27978       0    ...            0         0         0         0         0   \n",
       "27979       0    ...            0         0         0         0         0   \n",
       "27980       0    ...            0         0         0         0         0   \n",
       "27981       0    ...            0         0         0         0         0   \n",
       "27982       0    ...            0         0         0         0         0   \n",
       "27983       0    ...            0         0         0         0         0   \n",
       "27984       0    ...            0         0         0         0         0   \n",
       "27985       0    ...            0         0         0         0         0   \n",
       "27986       0    ...            0         0         0         0         0   \n",
       "27987       0    ...            0         0         0         0         0   \n",
       "27988       0    ...            0         0         0         0         0   \n",
       "27989       0    ...            0         0         0         0         0   \n",
       "27990       0    ...            0         0         0         0         0   \n",
       "27991       0    ...            0         0         0         0         0   \n",
       "27992       0    ...            0         0         0         0         0   \n",
       "27993       0    ...            0         0         0         0         0   \n",
       "27994       0    ...            0         0         0         0         0   \n",
       "27995       0    ...            0         0         0         0         0   \n",
       "27996       0    ...            0         0         0         0         0   \n",
       "27997       0    ...            0         0         0         0         0   \n",
       "27998       0    ...            0         0         0         0         0   \n",
       "27999       0    ...            0         0         0         0         0   \n",
       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0             0         0         0         0         0  \n",
       "1             0         0         0         0         0  \n",
       "2             0         0         0         0         0  \n",
       "3             0         0         0         0         0  \n",
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       "5             0         0         0         0         0  \n",
       "6             0         0         0         0         0  \n",
       "7             0         0         0         0         0  \n",
       "8             0         0         0         0         0  \n",
       "9             0         0         0         0         0  \n",
       "10            0         0         0         0         0  \n",
       "11            0         0         0         0         0  \n",
       "12            0         0         0         0         0  \n",
       "13            0         0         0         0         0  \n",
       "14            0         0         0         0         0  \n",
       "15            0         0         0         0         0  \n",
       "16            0         0         0         0         0  \n",
       "17            0         0         0         0         0  \n",
       "18            0         0         0         0         0  \n",
       "19            0         0         0         0         0  \n",
       "20            0         0         0         0         0  \n",
       "21            0         0         0         0         0  \n",
       "22            0         0         0         0         0  \n",
       "23            0         0         0         0         0  \n",
       "24            0         0         0         0         0  \n",
       "25            0         0         0         0         0  \n",
       "26            0         0         0         0         0  \n",
       "27            0         0         0         0         0  \n",
       "28            0         0         0         0         0  \n",
       "29            0         0         0         0         0  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "27970         0         0         0         0         0  \n",
       "27971         0         0         0         0         0  \n",
       "27972         0         0         0         0         0  \n",
       "27973         0         0         0         0         0  \n",
       "27974         0         0         0         0         0  \n",
       "27975         0         0         0         0         0  \n",
       "27976         0         0         0         0         0  \n",
       "27977         0         0         0         0         0  \n",
       "27978         0         0         0         0         0  \n",
       "27979         0         0         0         0         0  \n",
       "27980         0         0         0         0         0  \n",
       "27981         0         0         0         0         0  \n",
       "27982         0         0         0         0         0  \n",
       "27983         0         0         0         0         0  \n",
       "27984         0         0         0         0         0  \n",
       "27985         0         0         0         0         0  \n",
       "27986         0         0         0         0         0  \n",
       "27987         0         0         0         0         0  \n",
       "27988         0         0         0         0         0  \n",
       "27989         0         0         0         0         0  \n",
       "27990         0         0         0         0         0  \n",
       "27991         0         0         0         0         0  \n",
       "27992         0         0         0         0         0  \n",
       "27993         0         0         0         0         0  \n",
       "27994         0         0         0         0         0  \n",
       "27995         0         0         0         0         0  \n",
       "27996         0         0         0         0         0  \n",
       "27997         0         0         0         0         0  \n",
       "27998         0         0         0         0         0  \n",
       "27999         0         0         0         0         0  \n",
       "\n",
       "[28000 rows x 784 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You can sample a subset of the table by `sample` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "40111      9       0       0       0       0       0       0       0       0   \n",
       "16204      7       0       0       0       0       0       0       0       0   \n",
       "38606      5       0       0       0       0       0       0       0       0   \n",
       "7851       4       0       0       0       0       0       0       0       0   \n",
       "6712       6       0       0       0       0       0       0       0       0   \n",
       "24634      4       0       0       0       0       0       0       0       0   \n",
       "39183      8       0       0       0       0       0       0       0       0   \n",
       "3394       3       0       0       0       0       0       0       0       0   \n",
       "25586      5       0       0       0       0       0       0       0       0   \n",
       "25715      7       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel8    ...     pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "40111       0    ...            0         0         0         0         0   \n",
       "16204       0    ...            0         0         0         0         0   \n",
       "38606       0    ...            0         0         0         0         0   \n",
       "7851        0    ...            0         0         0         0         0   \n",
       "6712        0    ...            0         0         0         0         0   \n",
       "24634       0    ...            0         0         0         0         0   \n",
       "39183       0    ...            0         0         0         0         0   \n",
       "3394        0    ...            0         0         0         0         0   \n",
       "25586       0    ...            0         0         0         0         0   \n",
       "25715       0    ...            0         0         0         0         0   \n",
       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "40111         0         0         0         0         0  \n",
       "16204         0         0         0         0         0  \n",
       "38606         0         0         0         0         0  \n",
       "7851          0         0         0         0         0  \n",
       "6712          0         0         0         0         0  \n",
       "24634         0         0         0         0         0  \n",
       "39183         0         0         0         0         0  \n",
       "3394          0         0         0         0         0  \n",
       "25586         0         0         0         0         0  \n",
       "25715         0         0         0         0         0  \n",
       "\n",
       "[10 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape = (42000, 785), test shape = (28000, 784)\n"
     ]
    }
   ],
   "source": [
    "print('train shape = {}, test shape = {}'.format(train.shape, test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what column names are there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['label', 'pixel0', 'pixel1', 'pixel2', 'pixel3', 'pixel4', 'pixel5',\n",
       "       'pixel6', 'pixel7', 'pixel8',\n",
       "       ...\n",
       "       'pixel774', 'pixel775', 'pixel776', 'pixel777', 'pixel778', 'pixel779',\n",
       "       'pixel780', 'pixel781', 'pixel782', 'pixel783'],\n",
       "      dtype='object', length=785)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What are the feature?\n",
    "- What is their type/range?\n",
    "- What is the output represented?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is your data clean?\n",
    "Generally, you might need to look for outliers or errors in the data. Data might have been entered incorrectly or measured incorrectly. Based on the dataset and the data type, you might want to look for different kinds of errors. Some of the most obvious erros could be:\n",
    "- Missing data\n",
    "- Data with inconsistent type e.g. string or negative number as pixel value\n",
    "- Data with value extremely far away from the rest of the data\n",
    "- Noisy data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see some statstics of the data columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.00000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.456643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219286</td>\n",
       "      <td>0.117095</td>\n",
       "      <td>0.059024</td>\n",
       "      <td>0.02019</td>\n",
       "      <td>0.017238</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.887730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.312890</td>\n",
       "      <td>4.633819</td>\n",
       "      <td>3.274488</td>\n",
       "      <td>1.75987</td>\n",
       "      <td>1.894498</td>\n",
       "      <td>0.414264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>253.00000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              label   pixel0   pixel1   pixel2   pixel3   pixel4   pixel5  \\\n",
       "count  42000.000000  42000.0  42000.0  42000.0  42000.0  42000.0  42000.0   \n",
       "mean       4.456643      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "std        2.887730      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "min        0.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "25%        2.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "50%        4.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "75%        7.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "max        9.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "        pixel6   pixel7   pixel8    ...         pixel774      pixel775  \\\n",
       "count  42000.0  42000.0  42000.0    ...     42000.000000  42000.000000   \n",
       "mean       0.0      0.0      0.0    ...         0.219286      0.117095   \n",
       "std        0.0      0.0      0.0    ...         6.312890      4.633819   \n",
       "min        0.0      0.0      0.0    ...         0.000000      0.000000   \n",
       "25%        0.0      0.0      0.0    ...         0.000000      0.000000   \n",
       "50%        0.0      0.0      0.0    ...         0.000000      0.000000   \n",
       "75%        0.0      0.0      0.0    ...         0.000000      0.000000   \n",
       "max        0.0      0.0      0.0    ...       254.000000    254.000000   \n",
       "\n",
       "           pixel776     pixel777      pixel778      pixel779  pixel780  \\\n",
       "count  42000.000000  42000.00000  42000.000000  42000.000000   42000.0   \n",
       "mean       0.059024      0.02019      0.017238      0.002857       0.0   \n",
       "std        3.274488      1.75987      1.894498      0.414264       0.0   \n",
       "min        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "25%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "50%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "75%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "max      253.000000    253.00000    254.000000     62.000000       0.0   \n",
       "\n",
       "       pixel781  pixel782  pixel783  \n",
       "count   42000.0   42000.0   42000.0  \n",
       "mean        0.0       0.0       0.0  \n",
       "std         0.0       0.0       0.0  \n",
       "min         0.0       0.0       0.0  \n",
       "25%         0.0       0.0       0.0  \n",
       "50%         0.0       0.0       0.0  \n",
       "75%         0.0       0.0       0.0  \n",
       "max         0.0       0.0       0.0  \n",
       "\n",
       "[8 rows x 785 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you saw in the data tables, for training data, the first column is the true label for the image and the rest of the columns show the value of each of 784 (= 28x28) pixels. And for test data, there is just pixel values (the label of images will be predicted by you)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From tables to features and labels\n",
    "\n",
    "In this dataset, there is no errors in the data and we can move on. Let's split the table into features and labels for each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (42000, 784), Y_train sahpe: (42000,), X_test shape: (28000, 784)\n",
      "X_train type: <class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "Y_train = train[\"label\"]\n",
    "X_train = train.drop(labels=\"label\",axis=1)\n",
    "X_test = test\n",
    "print('X_train shape: {}, Y_train sahpe: {}, X_test shape: {}'.format(X_train.shape, Y_train.shape, X_test.shape))\n",
    "print('X_train type: {}'.format(type(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now reshape the pixels to their original 28x28 format (we also change the data format to numpy array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (42000, 28, 28), Y_train sahpe: (42000,), X_test shape: (28000, 28, 28)\n",
      "X_train type: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "X_train, Y_train = X_train.values.reshape(-1, 28,28), Y_train.values\n",
    "X_test = X_test.values.reshape(-1,28,28)\n",
    "# See how they are shaped now\n",
    "print('X_train shape: {}, Y_train sahpe: {}, X_test shape: {}'.format(X_train.shape, Y_train.shape, X_test.shape))\n",
    "print('X_train type: {}'.format(type(X_train)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A function for plotting images in a grid\n",
    "def plotImages(images, labels=None, nrows=2, ncols=3, shuffle=False):\n",
    "    i = 0\n",
    "    fig, ax = plt.subplots(nrows,ncols,sharex=True,sharey=True)\n",
    "    for row in range(nrows):\n",
    "        for col in range(ncols):\n",
    "            idx = i\n",
    "            if shuffle:\n",
    "                idx = random.randint(0, len(images)-1)\n",
    "            elif i >= len(images):\n",
    "                break\n",
    "            ax[row,col].imshow(images[idx], cmap='gray')\n",
    "            if labels is not None:\n",
    "                ax[row,col].set_title(\"True label :{}\".format(labels[idx]))\n",
    "            i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a few images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5Nskbo9dbkZxKclH0b8vCpysiItmkuULfAWCwmR0H4KsABpA8DsBQANPM7CgA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\nhDp0EZFAqEMXEQmEOnQRkUD8P9GMhHTDnIAyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb3ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImages(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the frequency of the labels in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb3e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(Y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or in a more fancy way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saeidnaderi/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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e/6nt72Dn6ytJXjlmDa2O32rfl3ckuSzJY8euodVxXqvhzjH+Hpyq2kV7vMm/A89lcqPh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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3f1ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data pre-processing\n",
    "Based on what model you are going to use, you might need to process your data in some way. Here are some examples:\n",
    "\n",
    "- If you are using SVM, you need some kind of feature for your samples. Raw pixel values is not useful there (maybe PCA is helpful)\n",
    "- Does your model put any specific constraint on its inputs? e.g. for some models such as most of neural networks, the input should be in [0,1] and you need to normalize your features\n",
    "- There might be unuseful or redundant features in the dataset and removing them is quite useful. This is called feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(X_train.reshape(-1, 784))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1f594ba8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E+AzwGYDFixffxCG/sbmczfP501c4PpVGrd5BfXqKt54/yrm2xVxubOPh/U9V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/a/o8oqRQovhvFcwavsTr9CccznBconQbmC6X2rKHWLvAEKKDBLEQjiWpL/U/NLdoYP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19B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\nG5tuYSsMGxsbG5tu8f8BlbSsCiCgLqIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1beab978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_PCA = pca.transform(X_train.reshape(-1, 784))\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "plt.scatter(X_PCA[:,0], X_PCA[:, 1], color=[colors[i] for i in Y_train])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply learning\n",
    "Now that we have the data is clean and ready, it's time to think about what model to use and how to train it.\n",
    "\n",
    "A simple model for classification task is k-nearest neighbors (KNN). This model classifies a test point x based as the mode of k nearest neighbors of x among the training points. So, for training the model, you just store all the training points in the memory but, for training, you need to look into all training points to find the nearest neighbors. Check out [here](http://vision.stanford.edu/teaching/cs231n-demos/knn/) for a simple demo of KNN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation set and testing the model\n",
    "**How well good is the trained model?**\n",
    "Since we do not have true labels for test data, how do we know how good the model will perform on test data? (Note that even if we had the labels, they shouldn't affect the training process in any way. Don't do it! It's cheating!) \"Validation set\" solves this problem for us. It is nothing but a small portion of training set which we put aside and don't look at while training our model and training will be done on the rest of it.\n",
    "\n",
    "**Hyper-parameter:**\n",
    "A question that comes to mind is that what \"k\" should be? It is called a \"hyper-parameter\" and most of the models have some hyper-parameters. Another place where we need validation sets is here, to choose hyper=parameters. We train the model with a specific hyper-parameter and then test the performance on the validation set and if it was not satisfactory, we change the hyper-parameter and repeat the process.\n",
    "\n",
    "**Cross-validation**\n",
    "A more effective way of testing the model performance is cross-validation. For more information, [here](https://www.cs.cmu.edu/~schneide/tut5/node42.html) and/or whatch [this](https://www.youtube.com/watch?v=sFO2ff-gTh0)\n",
    "\n",
    "Now that we need a validation set, let's provide ourselves one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Randomly permute the dataset\n",
    "perm = np.random.permutation(len(X_train))\n",
    "X_train, Y_train = X_train[perm], Y_train[perm]\n",
    "\n",
    "# Put 2K training samples aside as the validation set remove them from training set\n",
    "X_valid, Y_valid = X_train[-2000:], Y_train[-2000:]\n",
    "X_train, Y_train = X_train[:-2000], Y_train[:-2000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use scikit-learn for fitting a KNN model to our dataset. scikit-learn is a machine learning package containing a wide variety of models and tools. Check out the list of supervised learning models and tools it has [here](http://scikit-learn.org/stable/supervised_learning.html)\n",
    "\n",
    "Let's train the model and predict validation set labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors=10, n_jobs=-1) # n_jobs sets the number of parallel jobs. -1 means max possible.\n",
    "# KNN expects a feature vector for each sample. We reshape the training data back to the flattened form\n",
    "neigh.fit(X_train.reshape(-1, 784), Y_train)\n",
    "Y_pred = neigh.predict(X_valid.reshape(-1, 784))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a few random validation samples and the predicted labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c564be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImages(X_valid, Y_pred, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also measure the the model accuracy on the validation set i.e. the percentage of the correctly labelled validation samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy = 96.39999999999999%\n"
     ]
    }
   ],
   "source": [
    "print(\"KNN accuracy = {}%\".format(np.mean(Y_valid == Y_pred)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try another k value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del neigh # delete the previous KNN to free up memory\n",
    "neigh = KNeighborsClassifier(n_neighbors=3, n_jobs=-1) # n_jobs sets the number of parallel jobs. -1 means max possible.\n",
    "# KNN expects a feature vector for each sample. We reshape the training data back to the flattened form\n",
    "neigh.fit(X_train.reshape(-1, 784), Y_train)\n",
    "Y_pred = neigh.predict(X_valid.reshape(-1, 784))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print(\"KNN accuracy = {}%\".format(np.mean(Y_valid == Y_pred)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del neigh # delete the KNN model to free up memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the workflow again:\n",
    "![alt text](figs/workflow.png)\n",
    "Did we missed something?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to do \"multilabel\" classification?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Excercise**: Apply multiclass SVM from sklearn to this dataset.\n",
    "\n",
    "NOTES:\n",
    "- You should apply PCA first\n",
    "- You can use `LinearSVC` class.\n",
    "- Google is your friend!"
   ]
  }
 ],
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