digits.ipynb

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      "name": "digits.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/div-yansh/Scikit-Example-Datasets/blob/master/digits.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "T6tXHnw25KcK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import SGDClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjfV9pbs5zVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "digits = datasets.load_digits()\n",
        "df = pd.DataFrame(digits.data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "681a7ea2-03ba-4148-9d65-cbe53165ccc3"
      },
      "source": [
        "len(digits.target_names)"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
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      "metadata": {
        "id": "kHO_kfJT69OJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target = pd.DataFrame(digits.target)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynslBicp8SwN",
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        "colab": {
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          "height": 224
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        "outputId": "ba35ae71-3eac-41ee-b0b9-577f6e493deb"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "e46c03c5-2a40-4b04-994b-b4fbf55a9ce7"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1797.0</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.0</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.0</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.303840</td>\n",
              "      <td>5.204786</td>\n",
              "      <td>11.835838</td>\n",
              "      <td>11.848080</td>\n",
              "      <td>5.781859</td>\n",
              "      <td>1.362270</td>\n",
              "      <td>0.129661</td>\n",
              "      <td>0.005565</td>\n",
              "      <td>1.993879</td>\n",
              "      <td>10.382304</td>\n",
              "      <td>11.979410</td>\n",
              "      <td>10.279354</td>\n",
              "      <td>8.175849</td>\n",
              "      <td>1.846411</td>\n",
              "      <td>0.107958</td>\n",
              "      <td>0.002782</td>\n",
              "      <td>2.601558</td>\n",
              "      <td>9.903172</td>\n",
              "      <td>6.992766</td>\n",
              "      <td>7.097941</td>\n",
              "      <td>7.806344</td>\n",
              "      <td>1.788536</td>\n",
              "      <td>0.050083</td>\n",
              "      <td>0.001113</td>\n",
              "      <td>2.469672</td>\n",
              "      <td>9.091263</td>\n",
              "      <td>8.821369</td>\n",
              "      <td>9.927101</td>\n",
              "      <td>7.551475</td>\n",
              "      <td>2.317752</td>\n",
              "      <td>0.002226</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.339455</td>\n",
              "      <td>7.667223</td>\n",
              "      <td>9.071786</td>\n",
              "      <td>10.301614</td>\n",
              "      <td>8.744018</td>\n",
              "      <td>2.909293</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.008904</td>\n",
              "      <td>1.583751</td>\n",
              "      <td>6.881469</td>\n",
              "      <td>7.228158</td>\n",
              "      <td>7.672231</td>\n",
              "      <td>8.236505</td>\n",
              "      <td>3.456316</td>\n",
              "      <td>0.027268</td>\n",
              "      <td>0.007234</td>\n",
              "      <td>0.704508</td>\n",
              "      <td>7.506956</td>\n",
              "      <td>9.539232</td>\n",
              "      <td>9.416249</td>\n",
              "      <td>8.758486</td>\n",
              "      <td>3.725097</td>\n",
              "      <td>0.206455</td>\n",
              "      <td>0.000556</td>\n",
              "      <td>0.279354</td>\n",
              "      <td>5.557596</td>\n",
              "      <td>12.089037</td>\n",
              "      <td>11.809126</td>\n",
              "      <td>6.764051</td>\n",
              "      <td>2.067891</td>\n",
              "      <td>0.364496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.907192</td>\n",
              "      <td>4.754826</td>\n",
              "      <td>4.248842</td>\n",
              "      <td>4.287388</td>\n",
              "      <td>5.666418</td>\n",
              "      <td>3.325775</td>\n",
              "      <td>1.037383</td>\n",
              "      <td>0.094222</td>\n",
              "      <td>3.196160</td>\n",
              "      <td>5.421456</td>\n",
              "      <td>3.977543</td>\n",
              "      <td>4.782681</td>\n",
              "      <td>6.052960</td>\n",
              "      <td>3.586321</td>\n",
              "      <td>0.827915</td>\n",
              "      <td>0.062368</td>\n",
              "      <td>3.576301</td>\n",
              "      <td>5.690767</td>\n",
              "      <td>5.802662</td>\n",
              "      <td>6.175729</td>\n",
              "      <td>6.197322</td>\n",
              "      <td>3.259870</td>\n",
              "      <td>0.438597</td>\n",
              "      <td>0.033352</td>\n",
              "      <td>3.146532</td>\n",
              "      <td>6.192038</td>\n",
              "      <td>5.882936</td>\n",
              "      <td>6.152093</td>\n",
              "      <td>5.872556</td>\n",
              "      <td>3.686456</td>\n",
              "      <td>0.047140</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.480372</td>\n",
              "      <td>6.324687</td>\n",
              "      <td>6.268391</td>\n",
              "      <td>5.933490</td>\n",
              "      <td>5.870648</td>\n",
              "      <td>3.537283</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.145185</td>\n",
              "      <td>2.981816</td>\n",
              "      <td>6.537955</td>\n",
              "      <td>6.441378</td>\n",
              "      <td>6.259511</td>\n",
              "      <td>5.695527</td>\n",
              "      <td>4.330951</td>\n",
              "      <td>0.307356</td>\n",
              "      <td>0.204223</td>\n",
              "      <td>1.746153</td>\n",
              "      <td>5.644496</td>\n",
              "      <td>5.226948</td>\n",
              "      <td>5.302048</td>\n",
              "      <td>6.031154</td>\n",
              "      <td>4.919406</td>\n",
              "      <td>0.984401</td>\n",
              "      <td>0.023590</td>\n",
              "      <td>0.934302</td>\n",
              "      <td>5.103019</td>\n",
              "      <td>4.374694</td>\n",
              "      <td>4.933947</td>\n",
              "      <td>5.900623</td>\n",
              "      <td>4.090548</td>\n",
              "      <td>1.860122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           0            1            2   ...           61           62           63\n",
              "count  1797.0  1797.000000  1797.000000  ...  1797.000000  1797.000000  1797.000000\n",
              "mean      0.0     0.303840     5.204786  ...     6.764051     2.067891     0.364496\n",
              "std       0.0     0.907192     4.754826  ...     5.900623     4.090548     1.860122\n",
              "min       0.0     0.000000     0.000000  ...     0.000000     0.000000     0.000000\n",
              "25%       0.0     0.000000     1.000000  ...     0.000000     0.000000     0.000000\n",
              "50%       0.0     0.000000     4.000000  ...     6.000000     0.000000     0.000000\n",
              "75%       0.0     0.000000     9.000000  ...    12.000000     2.000000     0.000000\n",
              "max       0.0     8.000000    16.000000  ...    16.000000    16.000000    16.000000\n",
              "\n",
              "[8 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G29yT2t28dm4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "3ff53a1a-6816-4680-90d2-53473a7b3940"
      },
      "source": [
        "df1 = df / 16\n",
        "df1.describe()"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1797.0</td>\n",
              "      <td>1797.00000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.0</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.0</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "      <td>1797.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.01899</td>\n",
              "      <td>0.325299</td>\n",
              "      <td>0.739740</td>\n",
              "      <td>0.740505</td>\n",
              "      <td>0.361366</td>\n",
              "      <td>0.085142</td>\n",
              "      <td>0.008104</td>\n",
              "      <td>0.000348</td>\n",
              "      <td>0.124617</td>\n",
              "      <td>0.648894</td>\n",
              "      <td>0.748713</td>\n",
              "      <td>0.642460</td>\n",
              "      <td>0.510991</td>\n",
              "      <td>0.115401</td>\n",
              "      <td>0.006747</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>0.162597</td>\n",
              "      <td>0.618948</td>\n",
              "      <td>0.437048</td>\n",
              "      <td>0.443621</td>\n",
              "      <td>0.487896</td>\n",
              "      <td>0.111784</td>\n",
              "      <td>0.003130</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.154354</td>\n",
              "      <td>0.568204</td>\n",
              "      <td>0.551336</td>\n",
              "      <td>0.620444</td>\n",
              "      <td>0.471967</td>\n",
              "      <td>0.144859</td>\n",
              "      <td>0.000139</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.146216</td>\n",
              "      <td>0.479201</td>\n",
              "      <td>0.566987</td>\n",
              "      <td>0.643851</td>\n",
              "      <td>0.546501</td>\n",
              "      <td>0.181831</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000556</td>\n",
              "      <td>0.098984</td>\n",
              "      <td>0.430092</td>\n",
              "      <td>0.451760</td>\n",
              "      <td>0.479514</td>\n",
              "      <td>0.514782</td>\n",
              "      <td>0.216020</td>\n",
              "      <td>0.001704</td>\n",
              "      <td>0.000452</td>\n",
              "      <td>0.044032</td>\n",
              "      <td>0.469185</td>\n",
              "      <td>0.596202</td>\n",
              "      <td>0.588516</td>\n",
              "      <td>0.547405</td>\n",
              "      <td>0.232819</td>\n",
              "      <td>0.012903</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.017460</td>\n",
              "      <td>0.347350</td>\n",
              "      <td>0.755565</td>\n",
              "      <td>0.738070</td>\n",
              "      <td>0.422753</td>\n",
              "      <td>0.129243</td>\n",
              "      <td>0.022781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05670</td>\n",
              "      <td>0.297177</td>\n",
              "      <td>0.265553</td>\n",
              "      <td>0.267962</td>\n",
              "      <td>0.354151</td>\n",
              "      <td>0.207861</td>\n",
              "      <td>0.064836</td>\n",
              "      <td>0.005889</td>\n",
              "      <td>0.199760</td>\n",
              "      <td>0.338841</td>\n",
              "      <td>0.248596</td>\n",
              "      <td>0.298918</td>\n",
              "      <td>0.378310</td>\n",
              "      <td>0.224145</td>\n",
              "      <td>0.051745</td>\n",
              "      <td>0.003898</td>\n",
              "      <td>0.223519</td>\n",
              "      <td>0.355673</td>\n",
              "      <td>0.362666</td>\n",
              "      <td>0.385983</td>\n",
              "      <td>0.387333</td>\n",
              "      <td>0.203742</td>\n",
              "      <td>0.027412</td>\n",
              "      <td>0.002084</td>\n",
              "      <td>0.196658</td>\n",
              "      <td>0.387002</td>\n",
              "      <td>0.367684</td>\n",
              "      <td>0.384506</td>\n",
              "      <td>0.367035</td>\n",
              "      <td>0.230403</td>\n",
              "      <td>0.002946</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.217523</td>\n",
              "      <td>0.395293</td>\n",
              "      <td>0.391774</td>\n",
              "      <td>0.370843</td>\n",
              "      <td>0.366915</td>\n",
              "      <td>0.221080</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009074</td>\n",
              "      <td>0.186364</td>\n",
              "      <td>0.408622</td>\n",
              "      <td>0.402586</td>\n",
              "      <td>0.391219</td>\n",
              "      <td>0.355970</td>\n",
              "      <td>0.270684</td>\n",
              "      <td>0.019210</td>\n",
              "      <td>0.012764</td>\n",
              "      <td>0.109135</td>\n",
              "      <td>0.352781</td>\n",
              "      <td>0.326684</td>\n",
              "      <td>0.331378</td>\n",
              "      <td>0.376947</td>\n",
              "      <td>0.307463</td>\n",
              "      <td>0.061525</td>\n",
              "      <td>0.001474</td>\n",
              "      <td>0.058394</td>\n",
              "      <td>0.318939</td>\n",
              "      <td>0.273418</td>\n",
              "      <td>0.308372</td>\n",
              "      <td>0.368789</td>\n",
              "      <td>0.255659</td>\n",
              "      <td>0.116258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.312500</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.312500</td>\n",
              "      <td>0.312500</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.687500</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.687500</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.687500</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.687500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.437500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.812500</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.562500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           0           1            2   ...           61           62           63\n",
              "count  1797.0  1797.00000  1797.000000  ...  1797.000000  1797.000000  1797.000000\n",
              "mean      0.0     0.01899     0.325299  ...     0.422753     0.129243     0.022781\n",
              "std       0.0     0.05670     0.297177  ...     0.368789     0.255659     0.116258\n",
              "min       0.0     0.00000     0.000000  ...     0.000000     0.000000     0.000000\n",
              "25%       0.0     0.00000     0.062500  ...     0.000000     0.000000     0.000000\n",
              "50%       0.0     0.00000     0.250000  ...     0.375000     0.000000     0.000000\n",
              "75%       0.0     0.00000     0.562500  ...     0.750000     0.125000     0.000000\n",
              "max       0.0     0.50000     1.000000  ...     1.000000     1.000000     1.000000\n",
              "\n",
              "[8 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mALIqfFx8oNG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img = df1.iloc[0, :]\n",
        "img = np.asarray(img)\n",
        "img = img.reshape(8, 8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bk0TKOxB9EzQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "beacf288-dcd9-4f93-8328-ba1516da714a"
      },
      "source": [
        "plt.imshow(img, cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ9RAMkQNJEPUQDJEDSTzP9Sir9UysSZhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RZs4VZV9uMz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "95034d23-58fd-4e41-c3f7-e2e0ee22a344"
      },
      "source": [
        "plt.gray()\n",
        "plt.matshow(digits.images[0])\n",
        "plt.show()"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAECCAYAAADXWsr9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMdPGjgCB0vr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "45dddd44-09ed-452a-938c-b54c22c0d7f6"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df1, target, test_size=0.3, shuffle=True)\n",
        "y_train.shape, X_train.shape"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1257, 1), (1257, 64))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfEBrlHlCZF8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "81e89cbd-762f-4d8e-c244-0a15c04cc2dc"
      },
      "source": [
        "lm = LogisticRegression(max_iter=200)\n",
        "lm.fit(X_train, y_train)"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=200,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9zTSX6fC9Jx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e6d37e9-575d-43d0-a43d-ce4e34ad71ef"
      },
      "source": [
        "lm.score(X_train, y_train)"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9896579156722355"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0wTENxHDoe4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "45c63361-b2ae-48f8-d4f0-20faaa5eabb6"
      },
      "source": [
        "lm.score(X_test, y_test)"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9648148148148148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dF5l8R2iD9cs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sgd = SGDClassifier(max_iter=2000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bjk8ZjMEs3-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "a415cc8a-075d-49b1-8d0a-0f4aa9256f5e"
      },
      "source": [
        "sgd.fit(X_train, y_train)"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
              "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
              "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
              "              max_iter=2000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
              "              power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n",
              "              validation_fraction=0.1, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1iNY44IEw7D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b8bf9986-b07e-4fb7-def3-04627aafb6a2"
      },
      "source": [
        "sgd.score(X_train, y_train)"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9856801909307876"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mwsLilPE06R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0f5a7b7e-5f8b-4019-b18f-f24a7d23bb0d"
      },
      "source": [
        "sgd.score(X_test, y_test)"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9629629629629629"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGd2scONE-_N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict = pd.DataFrame(lm.predict(df))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJnGfaRTFomq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2634f50d-4edf-46ca-b313-99620325e4ae"
      },
      "source": [
        "images = [random.randint(0, target.shape[0]) for i in range(10)]\n",
        "images"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[797, 111, 950, 488, 246, 922, 197, 750, 752, 336]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYRiwyanGJ4V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b6c53ff6-4e97-42b7-cf1d-3295f2220957"
      },
      "source": [
        "for i in images:\n",
        "    img = digits.images[i]\n",
        "    plt.imshow(img)\n",
        "    plt.xlabel(\"Predicted Digit: {}\".format(target.iloc[i]))\n",
        "    plt.ylabel(\"Actual Digit: {}\".format(predict.iloc[i]))\n",
        "    plt.show()"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pph3HWcCVkk7LywcDZxbsfyRwIWnu2WuBCcC+3QQjaRHwJOkK5bmImNTNcWY2urrpq/I1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\nxGFmpTlxmFlp/w9+eOINpw0UcQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zKNae/TmwHsU4llFJugt4mOIK5cksgxDRTN0Mq/+8pOuAoWUcj7J9UYfjF0n6W2ALiicx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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xs97S6lLl3cCRwGhSU/Lal38ZcEqrk9YmrO5r3cx6W6vxOKYCUyUdGBE/afeEeZa3rwAv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\nhTlxmFlhThxmVpgTh5kV5sRhZoX9f7EVUHx2J22iAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5kt4XET9t9YR5lbdvkiby2UfSVsDOEfHDwYdrZmXQShvHDpLWrGxIWkvSSU32Pxe4Btgg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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7VRxS8JR3Axs27vhLWi3XJPolaRTwC+C4iPhdU2xnR8TGkSZGejtwT04akNpY\n7JyP35JUM3pUqUn69S2KaddE/UXNyCPdb3mQ1PO3OaGuQrqUgtQt/8ZIHezuk/TBHIskbd3u8xbw\nXOO+UfQzmVJEzI2IjSJiTP53WgRsM1yTBjhx1MmppL/WDZNIVe+ZpCceHYuIZ0lfrq9JmkO6F7ED\nLBtJ62MtDjsSeB3wBS0f5WqjAYo6D3htvsl4MXBIrn28mnTjt29cs0iXGXOAq3hxE/UppGbkzWNs\nXATcHxF3Nu33JLBdLnMX4It5+4HAYfnzziPdc2iMdvZFVtxk4I+SLsrrjcmU/ki65/GVQZy7Z7kB\nmJVOada8v8Qgu7orDeJzW0R8r2nbExExcrAx2uA4cVgt5ZrWk8B7Ij/uzdudOGrAicPMCvM9DjMr\nzInDzApz4jCzwpw4zKwwJw4zK8yJw8wK+1/68kL8OuWFMgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MrAd00o7jNy223djugDzgTr/rZtbb2o3H8QpgI2A1SRNY3jV+bVKDsHb6dqNv263ezHpL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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AkLS2pG+0Wf9U4DLSYD6Qxh2dMugIzax2OkkcezR3k4+IvwLtZqtfLyLOIY9WHhHPkwYu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\nicPMCnPiMLPCnDjMrLD/A+EiFPjQiqu9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lrSXpW232n0qaEmH9vPxH4PBljtDMaqeTxLFrRCxpTRMR/we0m61+dERcALyc938R8Ahg\nZsNIJ4ljRPPjV0mrAO0exy7Oo35F3n8bYOGgojSzWumkHcfZwLWSzsjLBwFntdn/SOAy0tyzvwfG\nAHt3EoykucBTpCuUFz0Nglk9ddKt/nuSZgPvz6u+GRFXt9l/lqT3ApuSnsTcFxEvFIhpp4h4osD+\nZtZlHc3kFhFXAVcBSNpB0n9ExD+12jdPHP0hYFw+/y6SiIgTW+1vZr2no8SRG4DtB3yM1Ffll212\n/zXwN+BOcgVpAQFck3u+/jQipvSJw1NAmtVAu5ajbyYli/2AJ4DzSd3wdxrgnGMjouXAxR3YISLm\nS3od8BtJ90bE7xobcyKZkuMblt3qzXpBu6cq9wI7A7tFxA4RcQqdPVa9UtIuyxJMRMzPPx8HLgEq\nHfPDzFprlzj2Ah4Fpkn6maT30Vmfk5uASyQ9K2mRpKckLRroIEmrSVqj8R7YBZjTQXlm1mXtmpxf\nClyav8R7klp/vk7SqcAlEXFNP4eeCGwL3BnFhhdbl5RwGnGdkytlzaxmOnkcu5g06/w5ktYC9gG+\nSpqdvpVHgDkFkwYR8SCweZFjzKwaHT1VacjNzZdUUPbjQWC6pCuB55qO9eNYs2GiUOLo0EP5tVJ+\nmdkwU3riiIjjyj6nmdVL6YlD0hjgK8DbSPOwAC0ncDKzHtVJ79iizia1AdkIOA6YC9za7gAz6y1D\nkTjWyaN9vRAR10XEZ0gNycxsmBiKytFGT9hHJX0Y+DOwdpv9zazHDEXi+JakkcBRwCmkUdGPGIJy\nhqVuTyf48MMPd62sww/v3giSI0d2b7LBbk/bOW7cuK6VNXfu3Jbrh+KpyuX57UJgoA5xZtaDSksc\nkv6lzeaIiG+WVZaZVavMK47FLdatRppndh3AicNsmCgtcUTECY33uZfrYaTxSc8jTehkZsNEqXUc\nktYmDVb8SeBMYMvcv8XMhpEy6ziOJ43hMQV4R0Q8Xda5zaxeymwAdhRp9rZvAH/Og/h0PJCPmfWO\nMus4hqIVqpnVkL/sZlaYE4eZFVarxCFplKSLJN0r6R5J21Ydk5m92lD0VRmMk4GrImJvSSsBq1Yd\nkJm9Wm0SR+4YtyNwIEBEPA88X2VMZtZanW5VNgL+Apwh6TZJp+WpGZaQdLCkGZJmVBOimUG9EscK\nwJbAqRGxBanvyzHNO0TElIiYEBETqgjQzJI6JY55wLyIuDkvX0RKJGZWM7VJHBHxGPCIpE3zqvcB\nd1cYkpn1ozaVo9khwNn5icqDpN61ZlYztUocEXE74PoLs5qrza2KmfUOJw4zK8yJw8wKc+Iws8Kc\nOMysMCcOMyvMicPMCnPiMLPCatUAzGDSpEldLa+b87lOmzata2XNnj27a2WdfPLJXSsLYMGCBV0t\nrxVfcZhZYU4cZlaYE4eZFebEYWaFOXGYWWFOHGZWmBOHmRXmxGFmhTlxmFlhtUkckjaVdHvTa5Gk\n7jVrNLOO1abJeUTcB4wHkDQCmA9cUmlQZtZSba44+ngf8EBEPFx1IGb2anVNHPsC51YdhJm1VrvE\nkedU2QO4sMU2zx1rVgO1qeNosiswKyL+X98NETEFmAIgKbodmJkltbviAPbDtylmtVarxCFpNeDv\ngV9WHYuZ9a9WtyoRsRhYp+o4zKy9Wl1xmFlvcOIws8KcOMysMCcOMyvMicPMCnPiMLPCnDjMrDAn\nDjMrTBG92eVD0l+AZel2Pxp4ouRw6sCfq/f0wmd7Q0SM6buyZxPHspI0IyImVB1H2fy5ek8vfzbf\nqphZYU4cZlbY8pg4plQdwBDx5+o9PfvZlrs6DjMbvOXxisPMBsmJw8wKW24Sh6QPSrpP0v2Sjqk6\nnrJI2lDSNEl3S7pL0mFVx1QmSSMk3Sbp8qpjKYukUZIuknSvpHskbVt1TEUtF3UceYKnP5KGJZwH\n3ArsFxF3VxpYCSStB6wXEbMkrQHMBCYNh88GIOlIYAKwZkTsVnU8ZZB0JnB9RJyWR/VfNSIWVB1X\nEcvLFcfWwP0R8WBEPA+cB+xZcUyliIhHI2JWfv8UcA+wQbVRlUPSWODDwGlVx1IWSSOBHYGfA0TE\n872WNGD5SRwbAI80Lc9jmHy5mkkaB2wB3FxtJKX5IfAV4OWqAynRRsBfgDPyLdhpeZDunrK8JI5h\nT9LqwMXA4RGxqOp4BkvSbsDjETGz6lhKtgKwJXBqRGwBLAZ6rs5teUkc84ENm5bH5nXDgqQVSUnj\n7IgYLlNLbA/sIWku6dZyZ0m/qDakUswD5kVE46rwIlIi6SnLS+K4FdhE0ka5Mmpf4LKKYyqFJJHu\nl++JiBOrjqcsEfG1iBgbEeNI/16/jYj9Kw5r0CLiMeARSZvmVe8Deq4iu1bzqgyViHhR0peAq4ER\nwOkRcVfFYZVle+BTwJ2Sbs/rvh4RV1QYk7V3CHB2/iP2IHBQxfEUtlw8jjWzci0vtypmViInDjMr\nzInDzApz4jCzwpw4zKwwJ44ak/SSpNslzZF0oaRVB3GuiY0eppL2aNdDOPfe/MdlKGOypKP7WT8/\nf5Y/SfqlpM2atp/WvNzPub8g6YD8/kBJ63cQjyT9KPeIvkPSoBpaSTok92i9S9L3B3OuXufEUW/P\nRsT4iHg78DzwheaN+YtR+N8wIi6LiO+22WUUUDhxDOCk/Fk2Ac4HfitpTI7ncwP15o2In0TEWXnx\nQGDAxAHsCmySXwcDpy5r8JJ2InWM3Dwi3gb8YFnPNRw4cfSO64E3SRqXxxU5C5gDbChpF0k3SpqV\nr0xWhyVjkNwraRawV+NE+S/2j/P7dSVdIml2fm0HfBfYOF8hHJ/3+7KkW/Nf7uOaznWspD9KugHY\nlA5ExPnANcAn8jmmS5qQ3382n+8WST9rinOypKMl7U3qZn92jm+VNkXtCZwVyU3AqDwMwbL4IvDd\niHguf4bHl/E8w4ITRw+QtALpr+ededUmwH/mv3yLgW8A74+ILYEZwJGSVgZ+BuwObAW8vp/T/wi4\nLiI2J/WZuIvU6eqBfIXwZUm75DK3BsYDW0naUdJWpObg44EPAe8q8LFmAW/p8znXB/4Z2IbUIvYt\nfQ+KiIvyZ/xkju9ZSf8maY8WZZTZK/rNwHsk3SzpOklFPuuws1w0Oe9hqzQ1I7+e1CdlfeDh/BcU\n0pdsM+D3qdsKKwE3kr50D0XEnwByB7GDW5SxM3AAQES8BCyUtFaffXbJr9vy8uqkRLIGcElEPJPL\nKNL/Ry3WbU1KYn/N57uQ9IVtKyL+pUC5y2oFYG3S7/tdwAWS3hjLadNrJ456ezYixjevyMlhcfMq\n4DcRsV+f/V5x3CAJ+E5E/LRPGYcP4pxbkK4chlKZvaLnAb/MieIWSS+TpnD8y+BC7E2+Vel9NwHb\nS3oTgKTVJL0ZuBcYJ2njvN9+/Rx/Len+vTG+50jgKdLVRMPVwGea6k42kPQ64HfAJEmrKA1buHsn\nAUv6KOkK5tw+m24F3itprXx79tF+TtE3vv5cBhyQK5G3ARZGxKOdxNjCpcBOAPn3uxL1n/d1yPiK\no8dFxF8kHQicK+m1efU3IuKPkg4G/lvSM6RbnVZftsOAKZI+C7wEfDEibpT0e0lzgCtzPcdbgRvz\nFc/TwP55nNPzgdnA46Qvfn+OkLQ/sBqpUnfniHjFX+uImC/p28AtwF9JyW9hi3NNBX4i6VlgW+Br\nwIyI6HurdAWp7uV+4BkG1wv1dOD0/Dt5Hvj08nqbAu4dazUjafWIeDpfcVxCGgLhkqrjslfyrYrV\nzeRcITwHeIh0i2A14ysOMyvMVxwVkhSSTmhaPlrS5Ipi+WRuUNV4vdx4MpMbaN3XtO11ef2RShNB\n3SHpWklvKFhmc0O0SRqg2XnZJP2hg30OV1NTf0krSZqSG6ndmyt6m/f/aP53nTAUMdeFE0e1ngP2\nkjS66kAi4uzcoGo8aSjChyLi9qZdGg2uxje1mrwNmBAR7yQNujuY/huTSO1RuiYitutgt8OB5j5C\nx5JGX38zKd7rGhvyk6XDGD7TU/TLiaNaLwJTgCP6bpC0e26leJuk/5G0bl4/WdKZkq6X9LCkvSR9\nX9Kdkq5SGvEcSVvlFo4zJV1dsKn1fqSRxduKiGmNxl+kx8JjBzpG0kH5r/UtpNahKDVz3wM4Pl/R\nbKzUTL5xzCaNZUlzmz7vLU2PocdIulipWfytkrbvIJan88+J+aqqMS3j2fkR7qGkBnfTJE3Lh30G\n+E7+/C9HRPMj2W8C3wP+NlDZPS8i/KroRXqsuSYwFxgJHA1MztvWYmkd1OeAE/L7ycANwIrA5qTH\njLvmbZeQ/nKvCPwBGJPXf5z0dAJSR7kvDBDXA8Dbm5ank5q7305qEq4Wx/yY9Bi43XnXA/4XGENq\nB/F74Md521Rg76Z9pwHj8/tvA4fk93OBY/P7A4DL8/tzgB3y+78jjfoOqV/Laf39/vPPiaTHvmNJ\nf0xvbDrXXGB0fj+K1IT9RFKT+QuBdfO2LYGLm35fE6r+/zWUL7fjqFhELFLqsHYo8GzTprHA+flK\nYSXSE4aGKyPiBUl3kkZtvyqvvxMYR+ps9nbgN7ndxQjg0VzeT9rFI+ndwDMRMadp9ScjtbFYgzR/\ny6eAs5qO2Z/0BX3vAB/33cD0yO03chuQ/pqUnwYcpDR37MdJzdEbzm36eVJ+/35gs/x5AdbMj3Zn\nkBLvQG6JiHk5rttJv8cb+uyzAunf5Q8RcWSO7QeSPk1KJgd2UM6w4FuVevgh8FlS46iGU0h/jd8B\nfB5YuWlbo4fmy8ALkf/MkaZKXIHURPyuWFon8Y6I2KXDWPalT4vOiJiffz5F+su+5Ess6f2k+/49\nIvccLcnFpI59uwEzI+LJ5pBavH8NsE3TZ94gIp4uUF5z7C/RunHkk6QrvMakVxeSrjTWICXq6UoT\nSG0DXDacK0idOGogUqeuC0jJo2EkS/tVfLrgKe8DxkjaFtJMb5LeNtBBSmN7fIym+g1JKzQqb3P9\nyW6kNhZI2gL4KSlpPN7nXPe2KOJmUpPydfK59mna9opm5BHxN1JT91OBM/qc5+NNP2/M768hzVfS\nKL+svjpL4soJ+tekWxvIkylFxMKIGB0R4yJNIHUT6Xcy1H1xKuPEUR8nkDpNNUwGLpQ0k4J9IiLi\neWBv4HuSZpPqJraDJSNpfaGfQ3cEHomIB5vWvRa4WtId+TzzSd31AY4n9ZS9MFdqXpbLGE2L3q+R\n+olMJn3Zfw/c07T5PODLuTK40b/mbNJV1DV9TrVWjucwllYsHwpMyI+G7yYPeiRpgqTBzHY/Bbiq\nqXL0q6RGaneQbtmOGsS5e5YbgFnplCaMfmNE/GiQ5zkaGBkR/9y0bi6p4nG57WBWB64ctdJFxOWD\nPYekS4CNSeOFWM34isPMCnMdh5kV5sRhZoU5cZhZYU4cZlaYE4eZFebEYWaF/X/CcS6k2YFEBgAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OOZ2vXyUtTxlXdCz3SnoKtX+LpBcCd04qyoholX7acRwJnC7pkLr8NsooYGPZHzieMvfs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\nwuPEnwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}