Machine_Learning.ipynb

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      "source": [
        "#**Introduction to Machine Learning**"
      ],
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      "cell_type": "markdown",
      "source": [
        "###How To Load Machine Learning Data"
      ],
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      "source": [
        "#####Considerations When Loading CSV Data"
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        "There are a number of considerations when loading your machine learning data from CSV files.\n",
        "For reference, you can learn a lot about the expectations for CSV files by reviewing the [CSV\n",
        "request for comment titled Common Format and MIME Type for Comma-Separated Values\n",
        "(CSV) Files](https://tools.ietf.org/html/rfc4180)\n",
        "\n",
        "<br>\n",
        "\n",
        "**File Header**\n",
        "\n",
        "Does your data have a file header? If so this can help in automatically assigning names to each\n",
        "column of data. If not, you may need to name your attributes manually. Either way, you should\n",
        "explicitly specify whether or not your CSV file had a file header when loading your data.\n",
        "\n",
        "<br>\n",
        "\n",
        "**Comments**\n",
        "\n",
        "Does your data have comments? Comments in a CSV file are indicated by a hash (#) at the\n",
        "start of a line. If you have comments in your file, depending on the method used to load your\n",
        "data, you may need to indicate whether or not to expect comments and the character to expect\n",
        "to signify a comment line.\n",
        "\n",
        "<br>\n",
        "\n",
        "**Delimiter**\n",
        "\n",
        "The standard delimiter that separates values in fields is the comma (,) character. Your file could\n",
        "use a different delimiter like tab or white space in which case you must specify it explicitly.\n",
        "\n",
        "**Quotes**\n",
        "\n",
        "Sometimes field values can have spaces. In these CSV files the values are often quoted. The\n",
        "default quote character is the double quotation marks character. Other characters can be used,\n",
        "and you must specify the quote character used in your file."
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        "**Pima Indians Dataset**\n",
        "\n",
        "The Pima Indians dataset is used to demonstrate data loading in this lesson. It will also be used\n",
        "in many of the lessons to come. This dataset describes the medical records for Pima Indians\n",
        "and whether or not each patient will have an onset of diabetes within five years. As such it\n",
        "is a classification problem. It is a good dataset for demonstration because all of the input\n",
        "attributes are numeric and the output variable to be predicted is binary (0 or 1). The data is\n",
        "freely available from the [UCI Machine Learning Repository](https://drive.google.com/file/d/1nVmN0BfbJsHdvOhVJzzsjqJlIkqwQbj2/view?usp=sharing)"
      ],
      "metadata": {
        "id": "_fJm2mQ-fAET"
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    {
      "cell_type": "markdown",
      "source": [
        "**Load CSV Files with the Python PANDAS Standard Library**\n",
        "\n"
      ],
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        "id": "wGD08EMzfYyc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data=pd.read_csv('/content/pima-indians-diabetes.csv')"
      ],
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      },
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              "     Preg  Plas  Pres  skin  test  mass   pedi  age  class\n",
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        "data1 =pd.read_csv('/content/diabetes.csv', names=['header1','header2','header3','header4','header5','header6','header7','header8'])"
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              "    header1  header2  header3  header4  header5  header6  header7  header8\n",
              "6       148       72       35        0     33.6    0.627       50        1\n",
              "1        85       66       29        0     26.6    0.351       31        0\n",
              "8       183       64        0        0     23.3    0.672       32        1\n",
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              "0       137       40       35      168     43.1    2.288       33        1\n",
              "..      ...      ...      ...      ...      ...      ...      ...      ...\n",
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              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1343acf-693b-41a0-94ad-bdf1e84c6b61')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a1343acf-693b-41a0-94ad-bdf1e84c6b61 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a1343acf-693b-41a0-94ad-bdf1e84c6b61');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**2. Import File from URL**"
      ],
      "metadata": {
        "id": "aIn9hvvRnRJW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data3 = pd.read_csv('http://winterolympicsmedals.com/medals.csv')"
      ],
      "metadata": {
        "id": "inaiMH4znCDp"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data3"
      ],
      "metadata": {
        "id": "hOATa18GnYud",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "248aa15c-20d8-4c89-e776-771d56d20520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year      City       Sport      Discipline  NOC            Event  \\\n",
              "0     1924  Chamonix     Skating  Figure skating  AUT       individual   \n",
              "1     1924  Chamonix     Skating  Figure skating  AUT       individual   \n",
              "2     1924  Chamonix     Skating  Figure skating  AUT            pairs   \n",
              "3     1924  Chamonix   Bobsleigh       Bobsleigh  BEL         four-man   \n",
              "4     1924  Chamonix  Ice Hockey      Ice Hockey  CAN       ice hockey   \n",
              "...    ...       ...         ...             ...  ...              ...   \n",
              "2306  2006     Turin      Skiing       Snowboard  USA        Half-pipe   \n",
              "2307  2006     Turin      Skiing       Snowboard  USA        Half-pipe   \n",
              "2308  2006     Turin      Skiing       Snowboard  USA        Half-pipe   \n",
              "2309  2006     Turin      Skiing       Snowboard  USA  Snowboard Cross   \n",
              "2310  2006     Turin      Skiing       Snowboard  USA  Snowboard Cross   \n",
              "\n",
              "     Event gender   Medal  \n",
              "0               M  Silver  \n",
              "1               W    Gold  \n",
              "2               X    Gold  \n",
              "3               M  Bronze  \n",
              "4               M    Gold  \n",
              "...           ...     ...  \n",
              "2306            M  Silver  \n",
              "2307            W    Gold  \n",
              "2308            W  Silver  \n",
              "2309            M    Gold  \n",
              "2310            W  Silver  \n",
              "\n",
              "[2311 rows x 8 columns]"
            ],
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              "\n",
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              "      <th>0</th>\n",
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              "      <td>M</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
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              "      <td>Figure skating</td>\n",
              "      <td>AUT</td>\n",
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              "      <td>W</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
              "      <td>Skating</td>\n",
              "      <td>Figure skating</td>\n",
              "      <td>AUT</td>\n",
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              "      <td>X</td>\n",
              "      <td>Gold</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
              "      <td>Bobsleigh</td>\n",
              "      <td>Bobsleigh</td>\n",
              "      <td>BEL</td>\n",
              "      <td>four-man</td>\n",
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              "      <td>Bronze</td>\n",
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              "      <th>4</th>\n",
              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
              "      <td>Ice Hockey</td>\n",
              "      <td>Ice Hockey</td>\n",
              "      <td>CAN</td>\n",
              "      <td>ice hockey</td>\n",
              "      <td>M</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2306</th>\n",
              "      <td>2006</td>\n",
              "      <td>Turin</td>\n",
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              "      <td>Snowboard</td>\n",
              "      <td>USA</td>\n",
              "      <td>Half-pipe</td>\n",
              "      <td>M</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2307</th>\n",
              "      <td>2006</td>\n",
              "      <td>Turin</td>\n",
              "      <td>Skiing</td>\n",
              "      <td>Snowboard</td>\n",
              "      <td>USA</td>\n",
              "      <td>Half-pipe</td>\n",
              "      <td>W</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2308</th>\n",
              "      <td>2006</td>\n",
              "      <td>Turin</td>\n",
              "      <td>Skiing</td>\n",
              "      <td>Snowboard</td>\n",
              "      <td>USA</td>\n",
              "      <td>Half-pipe</td>\n",
              "      <td>W</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2309</th>\n",
              "      <td>2006</td>\n",
              "      <td>Turin</td>\n",
              "      <td>Skiing</td>\n",
              "      <td>Snowboard</td>\n",
              "      <td>USA</td>\n",
              "      <td>Snowboard Cross</td>\n",
              "      <td>M</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2310</th>\n",
              "      <td>2006</td>\n",
              "      <td>Turin</td>\n",
              "      <td>Skiing</td>\n",
              "      <td>Snowboard</td>\n",
              "      <td>USA</td>\n",
              "      <td>Snowboard Cross</td>\n",
              "      <td>W</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2311 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33f0a97e-abea-4d9d-9da5-778db75a8724')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-33f0a97e-abea-4d9d-9da5-778db75a8724 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-33f0a97e-abea-4d9d-9da5-778db75a8724');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**3. Read Text File**"
      ],
      "metadata": {
        "id": "qHT8lVovnjSq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use read_table() function to pull data from text file. We can also use read_csv() with sep= \"\\t\" to read data from tab-separated file."
      ],
      "metadata": {
        "id": "qOkrbDf2n1rk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data4 = pd.read_table('/content/requirements.txt',names=['libraries'])"
      ],
      "metadata": {
        "id": "Sc6ajRdXnZwA"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data4"
      ],
      "metadata": {
        "id": "8DjTYIN9n6pj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "outputId": "e3e6f604-f3da-4ecd-9111-cdec7558a126"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 libraries\n",
              "0           appdirs==1.4.3\n",
              "1       certifi==2020.6.20\n",
              "2             click==7.1.2\n",
              "3           distlib==0.3.0\n",
              "4             Flask==1.1.2\n",
              "5        Flask-Cors==3.0.8\n",
              "6         gunicorn==20.0.4\n",
              "7      itsdangerous==1.1.0\n",
              "8           Jinja2==2.11.2\n",
              "9           joblib==0.15.1\n",
              "10       MarkupSafe==1.1.1\n",
              "11           numpy==1.18.1\n",
              "12           pandas==1.0.1\n",
              "13  python-dateutil==2.8.1\n",
              "14            pytz==2019.3\n",
              "15    scikit-learn==0.22.1\n",
              "16            scipy==1.4.1\n",
              "17             six==1.14.0\n",
              "18      virtualenv==20.0.7\n",
              "19         Werkzeug==1.0.1\n",
              "20       wincertstore==0.2"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>libraries</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>appdirs==1.4.3</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>certifi==2020.6.20</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>click==7.1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>distlib==0.3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Flask==1.1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Flask-Cors==3.0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>gunicorn==20.0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>itsdangerous==1.1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Jinja2==2.11.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>joblib==0.15.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>MarkupSafe==1.1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>numpy==1.18.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>pandas==1.0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>python-dateutil==2.8.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>pytz==2019.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>scikit-learn==0.22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>scipy==1.4.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>six==1.14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>virtualenv==20.0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Werkzeug==1.0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>wincertstore==0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dede7816-c243-462a-8c24-02ce66ee2c25')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dede7816-c243-462a-8c24-02ce66ee2c25 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dede7816-c243-462a-8c24-02ce66ee2c25');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data5 = pd.read_csv('/content/requirements.txt',names=['libraries'])"
      ],
      "metadata": {
        "id": "9qoXyu0in7NW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data5"
      ],
      "metadata": {
        "id": "1LcZrdDIoISt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "outputId": "a82eeceb-a5ce-4e63-eb89-8f469014c4e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               libraries\n",
              "0     certifi==2020.6.20\n",
              "1         chardet==3.0.4\n",
              "2           click==7.1.2\n",
              "3           Flask==1.1.2\n",
              "4             idna==2.10\n",
              "5    itsdangerous==1.1.0\n",
              "6         Jinja2==2.11.2\n",
              "7         joblib==0.15.1\n",
              "8           jsonify==0.5\n",
              "9      MarkupSafe==1.1.1\n",
              "10         numpy==1.19.0\n",
              "11      requests==2.24.0\n",
              "12  scikit-learn==0.23.1\n",
              "13          scipy==1.5.0\n",
              "14          sklearn==0.0\n",
              "15  threadpoolctl==2.1.0\n",
              "16       urllib3==1.25.9\n",
              "17       Werkzeug==1.0.1\n",
              "18     wincertstore==0.2\n",
              "19              gunicorn"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>libraries</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>certifi==2020.6.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chardet==3.0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>click==7.1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Flask==1.1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>idna==2.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>itsdangerous==1.1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Jinja2==2.11.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>joblib==0.15.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>jsonify==0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MarkupSafe==1.1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>numpy==1.19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>requests==2.24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>scikit-learn==0.23.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>scipy==1.5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>sklearn==0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>threadpoolctl==2.1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>urllib3==1.25.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Werkzeug==1.0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>wincertstore==0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>gunicorn</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b4394d1-8221-4081-96a7-9e470f61dc3b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b4394d1-8221-4081-96a7-9e470f61dc3b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b4394d1-8221-4081-96a7-9e470f61dc3b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**4. Read Excel File**"
      ],
      "metadata": {
        "id": "Q-sMHGh6o9Uk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The read_excel() function can be used to import excel data into Python."
      ],
      "metadata": {
        "id": "uQq0Yq6YpE9i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data6 = pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/RBRTEd.xls')"
      ],
      "metadata": {
        "id": "dQuthBcEoI_W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pip install xlrd==1.2.0"
      ],
      "metadata": {
        "id": "_Xu8HUi_pex-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data6"
      ],
      "metadata": {
        "id": "C_3wtGebpMrN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "outputId": "ca340677-b870-48fd-e75b-783659387fd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0                                        Unnamed: 1  \\\n",
              "0          NaN                                 Workbook Contents   \n",
              "1          NaN  Europe Brent Spot Price FOB (Dollars per Barrel)   \n",
              "2          NaN                                               NaN   \n",
              "3          NaN    Click worksheet name or tab at bottom for data   \n",
              "4          NaN                                    Worksheet Name   \n",
              "5          NaN                                            Data 1   \n",
              "6          NaN                                               NaN   \n",
              "7          NaN                                     Release Date:   \n",
              "8          NaN                                Next Release Date:   \n",
              "9          NaN                                  Excel File Name:   \n",
              "10         NaN                          Available from Web Page:   \n",
              "11         NaN                                           Source:   \n",
              "12         NaN                                For Help, Contact:   \n",
              "13         NaN                                               NaN   \n",
              "\n",
              "                                           Unnamed: 2   Unnamed: 3 Unnamed: 4  \\\n",
              "0                                                 NaN          NaN        NaN   \n",
              "1                                                 NaN          NaN        NaN   \n",
              "2                                                 NaN          NaN        NaN   \n",
              "3                                                 NaN          NaN        NaN   \n",
              "4                                         Description  # Of Series  Frequency   \n",
              "5    Europe Brent Spot Price FOB (Dollars per Barrel)            1      Daily   \n",
              "6                                                 NaN          NaN        NaN   \n",
              "7                                           8/24/2022          NaN        NaN   \n",
              "8                                           8/31/2022          NaN        NaN   \n",
              "9                                          rbrted.xls          NaN        NaN   \n",
              "10  https://www.eia.gov/dnav/pet/hist/LeafHandler....          NaN        NaN   \n",
              "11                                                NaN          NaN        NaN   \n",
              "12                                infoctr@eia.doe.gov          NaN        NaN   \n",
              "13                                     (202) 586-8800          NaN        NaN   \n",
              "\n",
              "               Unnamed: 5  \n",
              "0                     NaN  \n",
              "1                     NaN  \n",
              "2                     NaN  \n",
              "3                     NaN  \n",
              "4         Latest Data for  \n",
              "5               8/22/2022  \n",
              "6                     NaN  \n",
              "7                     NaN  \n",
              "8                     NaN  \n",
              "9                     NaN  \n",
              "10                    NaN  \n",
              "11                    NaN  \n",
              "12                    NaN  \n",
              "13  8/24/2022 10:48:04 AM  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-24f6748f-d4a3-44e8-888d-49eb76de2119\">\n",
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              "      <div>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 1</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
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              "      <th>1</th>\n",
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          },
          "metadata": {},
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      ]
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    {
      "cell_type": "code",
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      "metadata": {
        "id": "bZx7_9NYpwK1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Skip rows while importing\n",
        "Suppose you want to skip first 5 rows and wants to read data from 6th row (6th row would be a header row) E.g skiprows = 5.\n",
        " \n",
        "You can also skip some last number of rows by using negaive indexing. e.g skiprows = -5"
      ],
      "metadata": {
        "id": "ljOdIQ_aqMB1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data6 = pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/RBRTEd.xls',skiprows= -5)"
      ],
      "metadata": {
        "id": "Fi8ADkTcpxre"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "data6"
      ],
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "<br>\n",
        "\n",
        "#**Understand Your Data With Descriptive Statistics**"
      ],
      "metadata": {
        "id": "x2qpRQ11rtWR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Peek at Your Data"
      ],
      "metadata": {
        "id": "c6nfbMlxr7dS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is no substitute for looking at the raw data. Looking at the raw data can reveal insights that you cannot get any other way. It can also plant seeds that may later grow into ideas on how to better pre-process and handle the data for machine learning tasks. You can review the first 10 or 20 rows of your data using the head() function on the Pandas DataFrame."
      ],
      "metadata": {
        "id": "QW9e-oH0s5yT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail(50)"
      ],
      "metadata": {
        "id": "JNb_QA9krxKX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "517303c1-a948-41f0-cd89-d8a6eeb0da90"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Preg  Plas  Pres  skin  test  mass   pedi  age  class\n",
              "718     1   108    60    46   178  35.5  0.415   24      0\n",
              "719     5    97    76    27     0  35.6  0.378   52      1\n",
              "720     4    83    86    19     0  29.3  0.317   34      0\n",
              "721     1   114    66    36   200  38.1  0.289   21      0\n",
              "722     1   149    68    29   127  29.3  0.349   42      1\n",
              "723     5   117    86    30   105  39.1  0.251   42      0\n",
              "724     1   111    94     0     0  32.8  0.265   45      0\n",
              "725     4   112    78    40     0  39.4  0.236   38      0\n",
              "726     1   116    78    29   180  36.1  0.496   25      0\n",
              "727     0   141    84    26     0  32.4  0.433   22      0\n",
              "728     2   175    88     0     0  22.9  0.326   22      0\n",
              "729     2    92    52     0     0  30.1  0.141   22      0\n",
              "730     3   130    78    23    79  28.4  0.323   34      1\n",
              "731     8   120    86     0     0  28.4  0.259   22      1\n",
              "732     2   174    88    37   120  44.5  0.646   24      1\n",
              "733     2   106    56    27   165  29.0  0.426   22      0\n",
              "734     2   105    75     0     0  23.3  0.560   53      0\n",
              "735     4    95    60    32     0  35.4  0.284   28      0\n",
              "736     0   126    86    27   120  27.4  0.515   21      0\n",
              "737     8    65    72    23     0  32.0  0.600   42      0\n",
              "738     2    99    60    17   160  36.6  0.453   21      0\n",
              "739     1   102    74     0     0  39.5  0.293   42      1\n",
              "740    11   120    80    37   150  42.3  0.785   48      1\n",
              "741     3   102    44    20    94  30.8  0.400   26      0\n",
              "742     1   109    58    18   116  28.5  0.219   22      0\n",
              "743     9   140    94     0     0  32.7  0.734   45      1\n",
              "744    13   153    88    37   140  40.6  1.174   39      0\n",
              "745    12   100    84    33   105  30.0  0.488   46      0\n",
              "746     1   147    94    41     0  49.3  0.358   27      1\n",
              "747     1    81    74    41    57  46.3  1.096   32      0\n",
              "748     3   187    70    22   200  36.4  0.408   36      1\n",
              "749     6   162    62     0     0  24.3  0.178   50      1\n",
              "750     4   136    70     0     0  31.2  1.182   22      1\n",
              "751     1   121    78    39    74  39.0  0.261   28      0\n",
              "752     3   108    62    24     0  26.0  0.223   25      0\n",
              "753     0   181    88    44   510  43.3  0.222   26      1\n",
              "754     8   154    78    32     0  32.4  0.443   45      1\n",
              "755     1   128    88    39   110  36.5  1.057   37      1\n",
              "756     7   137    90    41     0  32.0  0.391   39      0\n",
              "757     0   123    72     0     0  36.3  0.258   52      1\n",
              "758     1   106    76     0     0  37.5  0.197   26      0\n",
              "759     6   190    92     0     0  35.5  0.278   66      1\n",
              "760     2    88    58    26    16  28.4  0.766   22      0\n",
              "761     9   170    74    31     0  44.0  0.403   43      1\n",
              "762     9    89    62     0     0  22.5  0.142   33      0\n",
              "763    10   101    76    48   180  32.9  0.171   63      0\n",
              "764     2   122    70    27     0  36.8  0.340   27      0\n",
              "765     5   121    72    23   112  26.2  0.245   30      0\n",
              "766     1   126    60     0     0  30.1  0.349   47      1\n",
              "767     1    93    70    31     0  30.4  0.315   23      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c8558b7-123c-4d9a-9aac-01bc7ff1af33\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Preg</th>\n",
              "      <th>Plas</th>\n",
              "      <th>Pres</th>\n",
              "      <th>skin</th>\n",
              "      <th>test</th>\n",
              "      <th>mass</th>\n",
              "      <th>pedi</th>\n",
              "      <th>age</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>718</th>\n",
              "      <td>1</td>\n",
              "      <td>108</td>\n",
              "      <td>60</td>\n",
              "      <td>46</td>\n",
              "      <td>178</td>\n",
              "      <td>35.5</td>\n",
              "      <td>0.415</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>719</th>\n",
              "      <td>5</td>\n",
              "      <td>97</td>\n",
              "      <td>76</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>35.6</td>\n",
              "      <td>0.378</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>720</th>\n",
              "      <td>4</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>29.3</td>\n",
              "      <td>0.317</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>721</th>\n",
              "      <td>1</td>\n",
              "      <td>114</td>\n",
              "      <td>66</td>\n",
              "      <td>36</td>\n",
              "      <td>200</td>\n",
              "      <td>38.1</td>\n",
              "      <td>0.289</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>722</th>\n",
              "      <td>1</td>\n",
              "      <td>149</td>\n",
              "      <td>68</td>\n",
              "      <td>29</td>\n",
              "      <td>127</td>\n",
              "      <td>29.3</td>\n",
              "      <td>0.349</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>723</th>\n",
              "      <td>5</td>\n",
              "      <td>117</td>\n",
              "      <td>86</td>\n",
              "      <td>30</td>\n",
              "      <td>105</td>\n",
              "      <td>39.1</td>\n",
              "      <td>0.251</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>724</th>\n",
              "      <td>1</td>\n",
              "      <td>111</td>\n",
              "      <td>94</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32.8</td>\n",
              "      <td>0.265</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>725</th>\n",
              "      <td>4</td>\n",
              "      <td>112</td>\n",
              "      <td>78</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>39.4</td>\n",
              "      <td>0.236</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>1</td>\n",
              "      <td>116</td>\n",
              "      <td>78</td>\n",
              "      <td>29</td>\n",
              "      <td>180</td>\n",
              "      <td>36.1</td>\n",
              "      <td>0.496</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>727</th>\n",
              "      <td>0</td>\n",
              "      <td>141</td>\n",
              "      <td>84</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>32.4</td>\n",
              "      <td>0.433</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>728</th>\n",
              "      <td>2</td>\n",
              "      <td>175</td>\n",
              "      <td>88</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>0.326</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>2</td>\n",
              "      <td>92</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.141</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>730</th>\n",
              "      <td>3</td>\n",
              "      <td>130</td>\n",
              "      <td>78</td>\n",
              "      <td>23</td>\n",
              "      <td>79</td>\n",
              "      <td>28.4</td>\n",
              "      <td>0.323</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>731</th>\n",
              "      <td>8</td>\n",
              "      <td>120</td>\n",
              "      <td>86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28.4</td>\n",
              "      <td>0.259</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>732</th>\n",
              "      <td>2</td>\n",
              "      <td>174</td>\n",
              "      <td>88</td>\n",
              "      <td>37</td>\n",
              "      <td>120</td>\n",
              "      <td>44.5</td>\n",
              "      <td>0.646</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>733</th>\n",
              "      <td>2</td>\n",
              "      <td>106</td>\n",
              "      <td>56</td>\n",
              "      <td>27</td>\n",
              "      <td>165</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.426</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>734</th>\n",
              "      <td>2</td>\n",
              "      <td>105</td>\n",
              "      <td>75</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.560</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>735</th>\n",
              "      <td>4</td>\n",
              "      <td>95</td>\n",
              "      <td>60</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>35.4</td>\n",
              "      <td>0.284</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>736</th>\n",
              "      <td>0</td>\n",
              "      <td>126</td>\n",
              "      <td>86</td>\n",
              "      <td>27</td>\n",
              "      <td>120</td>\n",
              "      <td>27.4</td>\n",
              "      <td>0.515</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>737</th>\n",
              "      <td>8</td>\n",
              "      <td>65</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.600</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>738</th>\n",
              "      <td>2</td>\n",
              "      <td>99</td>\n",
              "      <td>60</td>\n",
              "      <td>17</td>\n",
              "      <td>160</td>\n",
              "      <td>36.6</td>\n",
              "      <td>0.453</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>739</th>\n",
              "      <td>1</td>\n",
              "      <td>102</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.5</td>\n",
              "      <td>0.293</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>11</td>\n",
              "      <td>120</td>\n",
              "      <td>80</td>\n",
              "      <td>37</td>\n",
              "      <td>150</td>\n",
              "      <td>42.3</td>\n",
              "      <td>0.785</td>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>3</td>\n",
              "      <td>102</td>\n",
              "      <td>44</td>\n",
              "      <td>20</td>\n",
              "      <td>94</td>\n",
              "      <td>30.8</td>\n",
              "      <td>0.400</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>1</td>\n",
              "      <td>109</td>\n",
              "      <td>58</td>\n",
              "      <td>18</td>\n",
              "      <td>116</td>\n",
              "      <td>28.5</td>\n",
              "      <td>0.219</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>743</th>\n",
              "      <td>9</td>\n",
              "      <td>140</td>\n",
              "      <td>94</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32.7</td>\n",
              "      <td>0.734</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>744</th>\n",
              "      <td>13</td>\n",
              "      <td>153</td>\n",
              "      <td>88</td>\n",
              "      <td>37</td>\n",
              "      <td>140</td>\n",
              "      <td>40.6</td>\n",
              "      <td>1.174</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>745</th>\n",
              "      <td>12</td>\n",
              "      <td>100</td>\n",
              "      <td>84</td>\n",
              "      <td>33</td>\n",
              "      <td>105</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.488</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746</th>\n",
              "      <td>1</td>\n",
              "      <td>147</td>\n",
              "      <td>94</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>49.3</td>\n",
              "      <td>0.358</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>747</th>\n",
              "      <td>1</td>\n",
              "      <td>81</td>\n",
              "      <td>74</td>\n",
              "      <td>41</td>\n",
              "      <td>57</td>\n",
              "      <td>46.3</td>\n",
              "      <td>1.096</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>3</td>\n",
              "      <td>187</td>\n",
              "      <td>70</td>\n",
              "      <td>22</td>\n",
              "      <td>200</td>\n",
              "      <td>36.4</td>\n",
              "      <td>0.408</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>6</td>\n",
              "      <td>162</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24.3</td>\n",
              "      <td>0.178</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>4</td>\n",
              "      <td>136</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>31.2</td>\n",
              "      <td>1.182</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>1</td>\n",
              "      <td>121</td>\n",
              "      <td>78</td>\n",
              "      <td>39</td>\n",
              "      <td>74</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0.261</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>3</td>\n",
              "      <td>108</td>\n",
              "      <td>62</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.223</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>0</td>\n",
              "      <td>181</td>\n",
              "      <td>88</td>\n",
              "      <td>44</td>\n",
              "      <td>510</td>\n",
              "      <td>43.3</td>\n",
              "      <td>0.222</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>754</th>\n",
              "      <td>8</td>\n",
              "      <td>154</td>\n",
              "      <td>78</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>32.4</td>\n",
              "      <td>0.443</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>755</th>\n",
              "      <td>1</td>\n",
              "      <td>128</td>\n",
              "      <td>88</td>\n",
              "      <td>39</td>\n",
              "      <td>110</td>\n",
              "      <td>36.5</td>\n",
              "      <td>1.057</td>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>756</th>\n",
              "      <td>7</td>\n",
              "      <td>137</td>\n",
              "      <td>90</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.391</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>757</th>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36.3</td>\n",
              "      <td>0.258</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>758</th>\n",
              "      <td>1</td>\n",
              "      <td>106</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.5</td>\n",
              "      <td>0.197</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>759</th>\n",
              "      <td>6</td>\n",
              "      <td>190</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.5</td>\n",
              "      <td>0.278</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>760</th>\n",
              "      <td>2</td>\n",
              "      <td>88</td>\n",
              "      <td>58</td>\n",
              "      <td>26</td>\n",
              "      <td>16</td>\n",
              "      <td>28.4</td>\n",
              "      <td>0.766</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>761</th>\n",
              "      <td>9</td>\n",
              "      <td>170</td>\n",
              "      <td>74</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.403</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>762</th>\n",
              "      <td>9</td>\n",
              "      <td>89</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0.142</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c8558b7-123c-4d9a-9aac-01bc7ff1af33')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dimensions of Your Data"
      ],
      "metadata": {
        "id": "4db0mFqwsfPm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You must have a very good handle on how much data you have, both in terms of rows and columns.\n",
        "1. Too many rows and algorithms may take too long to train. Too few and perhaps you do not have enough data to train the algorithms.\n",
        "2. Too many features and some algorithms can be distracted or suffer poor performance due to the curse of dimensionality.\n",
        "\n",
        "You can review the shape and size of your dataset by printing the shape property on the Pandas DataFrame."
      ],
      "metadata": {
        "id": "ruNQSyWctM0S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "id": "e87Zq8hprzhG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "073224f9-1c49-491d-dfa4-369b9d969194"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The results are listed in rows then columns. You can see that the dataset has **768 rows** and\n",
        "**9 columns**."
      ],
      "metadata": {
        "id": "R6_CxTRPtgBU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "##Data Type For Each Attribute"
      ],
      "metadata": {
        "id": "35k7x6kBtp3l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The type of each attribute is important. Strings may need to be converted to floating point values or integers to represent categorical or ordinal values. You can get an idea of the types of attributes by peeking at the raw data, as above. You can also list the data types used by the DataFrame to characterize each attribute using the dtypes property."
      ],
      "metadata": {
        "id": "MLVRX6S1t8rZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes"
      ],
      "metadata": {
        "id": "E5B3guattXI0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afff44a2-0d6c-4475-e447-4e54fa1fe878"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Preg       int64\n",
              "Plas       int64\n",
              "Pres       int64\n",
              "skin       int64\n",
              "test       int64\n",
              "mass     float64\n",
              "pedi     float64\n",
              "age        int64\n",
              "class      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can see that most of the attributes are integers and that **mass** and **pedi** are *floating* point types."
      ],
      "metadata": {
        "id": "3o7gQqAnuH5j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Descriptive Statistics\n",
        "\n",
        "Descriptive statistics can give you great insight into the shape of each attribute. Often you can create more summaries than you have time to review. The describe() function on the Pandas DataFrame lists 8 statistical properties of each attribute. They are:\n",
        "1. Count.\n",
        "2. Mean.\n",
        "3. Standard Deviation.\n",
        "4. Minimum Value.\n",
        "5. 25th Percentile.\n",
        "6. 50th Percentile (Median). \n",
        "7. 75th Percentile.\n",
        "8. Maximum Value."
      ],
      "metadata": {
        "id": "wiPUUSZEuXt8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "id": "YyLeN5sHuE1J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "65b0e01f-084c-4a78-b8f2-1a16106348c4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Preg        Plas        Pres        skin        test        mass  \\\n",
              "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
              "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
              "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
              "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
              "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
              "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
              "\n",
              "             pedi         age       class  \n",
              "count  768.000000  768.000000  768.000000  \n",
              "mean     0.471876   33.240885    0.348958  \n",
              "std      0.331329   11.760232    0.476951  \n",
              "min      0.078000   21.000000    0.000000  \n",
              "25%      0.243750   24.000000    0.000000  \n",
              "50%      0.372500   29.000000    0.000000  \n",
              "75%      0.626250   41.000000    1.000000  \n",
              "max      2.420000   81.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0f569d1-8f9e-4ee0-a416-34001bc007bf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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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>Preg</th>\n",
              "      <th>Plas</th>\n",
              "      <th>Pres</th>\n",
              "      <th>skin</th>\n",
              "      <th>test</th>\n",
              "      <th>mass</th>\n",
              "      <th>pedi</th>\n",
              "      <th>age</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0f569d1-8f9e-4ee0-a416-34001bc007bf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0f569d1-8f9e-4ee0-a416-34001bc007bf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a0f569d1-8f9e-4ee0-a416-34001bc007bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can transpose the results"
      ],
      "metadata": {
        "id": "9fqj3eqqurzD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe().T"
      ],
      "metadata": {
        "id": "AYetw25dupJH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "bb3d69cd-bd26-45da-b428-e53bdb2606fa"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       count        mean         std     min       25%       50%        75%  \\\n",
              "Preg   768.0    3.845052    3.369578   0.000   1.00000    3.0000    6.00000   \n",
              "Plas   768.0  120.894531   31.972618   0.000  99.00000  117.0000  140.25000   \n",
              "Pres   768.0   69.105469   19.355807   0.000  62.00000   72.0000   80.00000   \n",
              "skin   768.0   20.536458   15.952218   0.000   0.00000   23.0000   32.00000   \n",
              "test   768.0   79.799479  115.244002   0.000   0.00000   30.5000  127.25000   \n",
              "mass   768.0   31.992578    7.884160   0.000  27.30000   32.0000   36.60000   \n",
              "pedi   768.0    0.471876    0.331329   0.078   0.24375    0.3725    0.62625   \n",
              "age    768.0   33.240885   11.760232  21.000  24.00000   29.0000   41.00000   \n",
              "class  768.0    0.348958    0.476951   0.000   0.00000    0.0000    1.00000   \n",
              "\n",
              "          max  \n",
              "Preg    17.00  \n",
              "Plas   199.00  \n",
              "Pres   122.00  \n",
              "skin    99.00  \n",
              "test   846.00  \n",
              "mass    67.10  \n",
              "pedi     2.42  \n",
              "age     81.00  \n",
              "class    1.00  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Preg</th>\n",
              "      <td>768.0</td>\n",
              "      <td>3.845052</td>\n",
              "      <td>3.369578</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>6.00000</td>\n",
              "      <td>17.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Plas</th>\n",
              "      <td>768.0</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>0.000</td>\n",
              "      <td>99.00000</td>\n",
              "      <td>117.0000</td>\n",
              "      <td>140.25000</td>\n",
              "      <td>199.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pres</th>\n",
              "      <td>768.0</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>0.000</td>\n",
              "      <td>62.00000</td>\n",
              "      <td>72.0000</td>\n",
              "      <td>80.00000</td>\n",
              "      <td>122.00</td>\n",
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              "    <tr>\n",
              "      <th>skin</th>\n",
              "      <td>768.0</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>23.0000</td>\n",
              "      <td>32.00000</td>\n",
              "      <td>99.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test</th>\n",
              "      <td>768.0</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>30.5000</td>\n",
              "      <td>127.25000</td>\n",
              "      <td>846.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mass</th>\n",
              "      <td>768.0</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.000</td>\n",
              "      <td>27.30000</td>\n",
              "      <td>32.0000</td>\n",
              "      <td>36.60000</td>\n",
              "      <td>67.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pedi</th>\n",
              "      <td>768.0</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.24375</td>\n",
              "      <td>0.3725</td>\n",
              "      <td>0.62625</td>\n",
              "      <td>2.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>768.0</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>21.000</td>\n",
              "      <td>24.00000</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>41.00000</td>\n",
              "      <td>81.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>768.0</td>\n",
              "      <td>0.348958</td>\n",
              "      <td>0.476951</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eada18b2-4b65-4664-96a8-6533f40b146d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eada18b2-4b65-4664-96a8-6533f40b146d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eada18b2-4b65-4664-96a8-6533f40b146d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can see that we have the five number summary of each of the attributes. This gives you a statiscal description of the dataset you are dealing with.\n",
        "\n",
        "You can see that you do get a lot of data. You will note some calls to pandas.set option() in the recipe to change the precision of the numbers and the preferred width of the output. This is to make it more readable for this example. When describing your data this way, it is worth taking some time and reviewing observations from the results. This might include the presence of NA values for missing data or surprising distributions for attributes."
      ],
      "metadata": {
        "id": "eUiDjursu1Np"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Class Distribution (Classification Only)"
      ],
      "metadata": {
        "id": "_hQvyCmrvU2x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "On classification problems you need to know how balanced the class values are. Highly imbalanced problems (a lot more observations for one class than another) are common and may need special handling in the data preparation stage of your project. You can quickly get an idea of the distribution of the class attribute in Pandas."
      ],
      "metadata": {
        "id": "IJ6eKuDCvgFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "66XoFhRZeoCT",
        "outputId": "09d5be23-d28f-46b3-b70c-0aa8a3fe62a3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Preg  Plas  Pres  skin  test  mass   pedi  age  class\n",
              "0      6   148    72    35     0  33.6  0.627   50      1\n",
              "1      1    85    66    29     0  26.6  0.351   31      0\n",
              "2      8   183    64     0     0  23.3  0.672   32      1\n",
              "3      1    89    66    23    94  28.1  0.167   21      0\n",
              "4      0   137    40    35   168  43.1  2.288   33      1\n",
              "5      5   116    74     0     0  25.6  0.201   30      0\n",
              "6      3    78    50    32    88  31.0  0.248   26      1\n",
              "7     10   115     0     0     0  35.3  0.134   29      0\n",
              "8      2   197    70    45   543  30.5  0.158   53      1\n",
              "9      8   125    96     0     0   0.0  0.232   54      1\n",
              "10     4   110    92     0     0  37.6  0.191   30      0\n",
              "11    10   168    74     0     0  38.0  0.537   34      1\n",
              "12    10   139    80     0     0  27.1  1.441   57      0\n",
              "13     1   189    60    23   846  30.1  0.398   59      1\n",
              "14     5   166    72    19   175  25.8  0.587   51      1\n",
              "15     7   100     0     0     0  30.0  0.484   32      1\n",
              "16     0   118    84    47   230  45.8  0.551   31      1\n",
              "17     7   107    74     0     0  29.6  0.254   31      1\n",
              "18     1   103    30    38    83  43.3  0.183   33      0\n",
              "19     1   115    70    30    96  34.6  0.529   32      1\n",
              "20     3   126    88    41   235  39.3  0.704   27      0\n",
              "21     8    99    84     0     0  35.4  0.388   50      0\n",
              "22     7   196    90     0     0  39.8  0.451   41      1\n",
              "23     9   119    80    35     0  29.0  0.263   29      1\n",
              "24    11   143    94    33   146  36.6  0.254   51      1\n",
              "25    10   125    70    26   115  31.1  0.205   41      1\n",
              "26     7   147    76     0     0  39.4  0.257   43      1\n",
              "27     1    97    66    15   140  23.2  0.487   22      0\n",
              "28    13   145    82    19   110  22.2  0.245   57      0\n",
              "29     5   117    92     0     0  34.1  0.337   38      0\n",
              "30     5   109    75    26     0  36.0  0.546   60      0\n",
              "31     3   158    76    36   245  31.6  0.851   28      1\n",
              "32     3    88    58    11    54  24.8  0.267   22      0\n",
              "33     6    92    92     0     0  19.9  0.188   28      0\n",
              "34    10   122    78    31     0  27.6  0.512   45      0\n",
              "35     4   103    60    33   192  24.0  0.966   33      0\n",
              "36    11   138    76     0     0  33.2  0.420   35      0\n",
              "37     9   102    76    37     0  32.9  0.665   46      1\n",
              "38     2    90    68    42     0  38.2  0.503   27      1\n",
              "39     4   111    72    47   207  37.1  1.390   56      1\n",
              "40     3   180    64    25    70  34.0  0.271   26      0\n",
              "41     7   133    84     0     0  40.2  0.696   37      0\n",
              "42     7   106    92    18     0  22.7  0.235   48      0\n",
              "43     9   171   110    24   240  45.4  0.721   54      1\n",
              "44     7   159    64     0     0  27.4  0.294   40      0\n",
              "45     0   180    66    39     0  42.0  1.893   25      1\n",
              "46     1   146    56     0     0  29.7  0.564   29      0\n",
              "47     2    71    70    27     0  28.0  0.586   22      0\n",
              "48     7   103    66    32     0  39.1  0.344   31      1\n",
              "49     7   105     0     0     0   0.0  0.305   24      0"
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            "text/html": [
              "\n",
              "  <div id=\"df-86344171-53ec-47b1-9603-a93e064328a7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Preg</th>\n",
              "      <th>Plas</th>\n",
              "      <th>Pres</th>\n",
              "      <th>skin</th>\n",
              "      <th>test</th>\n",
              "      <th>mass</th>\n",
              "      <th>pedi</th>\n",
              "      <th>age</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>116</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.6</td>\n",
              "      <td>0.201</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>78</td>\n",
              "      <td>50</td>\n",
              "      <td>32</td>\n",
              "      <td>88</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.248</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.3</td>\n",
              "      <td>0.134</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>197</td>\n",
              "      <td>70</td>\n",
              "      <td>45</td>\n",
              "      <td>543</td>\n",
              "      <td>30.5</td>\n",
              "      <td>0.158</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>125</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.232</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.6</td>\n",
              "      <td>0.191</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>168</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.537</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>10</td>\n",
              "      <td>139</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27.1</td>\n",
              "      <td>1.441</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>189</td>\n",
              "      <td>60</td>\n",
              "      <td>23</td>\n",
              "      <td>846</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.398</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5</td>\n",
              "      <td>166</td>\n",
              "      <td>72</td>\n",
              "      <td>19</td>\n",
              "      <td>175</td>\n",
              "      <td>25.8</td>\n",
              "      <td>0.587</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>7</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.484</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>118</td>\n",
              "      <td>84</td>\n",
              "      <td>47</td>\n",
              "      <td>230</td>\n",
              "      <td>45.8</td>\n",
              "      <td>0.551</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7</td>\n",
              "      <td>107</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29.6</td>\n",
              "      <td>0.254</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>103</td>\n",
              "      <td>30</td>\n",
              "      <td>38</td>\n",
              "      <td>83</td>\n",
              "      <td>43.3</td>\n",
              "      <td>0.183</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>70</td>\n",
              "      <td>30</td>\n",
              "      <td>96</td>\n",
              "      <td>34.6</td>\n",
              "      <td>0.529</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>3</td>\n",
              "      <td>126</td>\n",
              "      <td>88</td>\n",
              "      <td>41</td>\n",
              "      <td>235</td>\n",
              "      <td>39.3</td>\n",
              "      <td>0.704</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>8</td>\n",
              "      <td>99</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.4</td>\n",
              "      <td>0.388</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>7</td>\n",
              "      <td>196</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.8</td>\n",
              "      <td>0.451</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>9</td>\n",
              "      <td>119</td>\n",
              "      <td>80</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.263</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>11</td>\n",
              "      <td>143</td>\n",
              "      <td>94</td>\n",
              "      <td>33</td>\n",
              "      <td>146</td>\n",
              "      <td>36.6</td>\n",
              "      <td>0.254</td>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>10</td>\n",
              "      <td>125</td>\n",
              "      <td>70</td>\n",
              "      <td>26</td>\n",
              "      <td>115</td>\n",
              "      <td>31.1</td>\n",
              "      <td>0.205</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>7</td>\n",
              "      <td>147</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.4</td>\n",
              "      <td>0.257</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>97</td>\n",
              "      <td>66</td>\n",
              "      <td>15</td>\n",
              "      <td>140</td>\n",
              "      <td>23.2</td>\n",
              "      <td>0.487</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>13</td>\n",
              "      <td>145</td>\n",
              "      <td>82</td>\n",
              "      <td>19</td>\n",
              "      <td>110</td>\n",
              "      <td>22.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>5</td>\n",
              "      <td>117</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34.1</td>\n",
              "      <td>0.337</td>\n",
              "      <td>38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>5</td>\n",
              "      <td>109</td>\n",
              "      <td>75</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0.546</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>3</td>\n",
              "      <td>158</td>\n",
              "      <td>76</td>\n",
              "      <td>36</td>\n",
              "      <td>245</td>\n",
              "      <td>31.6</td>\n",
              "      <td>0.851</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3</td>\n",
              "      <td>88</td>\n",
              "      <td>58</td>\n",
              "      <td>11</td>\n",
              "      <td>54</td>\n",
              "      <td>24.8</td>\n",
              "      <td>0.267</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>6</td>\n",
              "      <td>92</td>\n",
              "      <td>92</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>19.9</td>\n",
              "      <td>0.188</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>10</td>\n",
              "      <td>122</td>\n",
              "      <td>78</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>27.6</td>\n",
              "      <td>0.512</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>4</td>\n",
              "      <td>103</td>\n",
              "      <td>60</td>\n",
              "      <td>33</td>\n",
              "      <td>192</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.966</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>11</td>\n",
              "      <td>138</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33.2</td>\n",
              "      <td>0.420</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>9</td>\n",
              "      <td>102</td>\n",
              "      <td>76</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.665</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2</td>\n",
              "      <td>90</td>\n",
              "      <td>68</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>38.2</td>\n",
              "      <td>0.503</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>4</td>\n",
              "      <td>111</td>\n",
              "      <td>72</td>\n",
              "      <td>47</td>\n",
              "      <td>207</td>\n",
              "      <td>37.1</td>\n",
              "      <td>1.390</td>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>3</td>\n",
              "      <td>180</td>\n",
              "      <td>64</td>\n",
              "      <td>25</td>\n",
              "      <td>70</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.271</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>7</td>\n",
              "      <td>133</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40.2</td>\n",
              "      <td>0.696</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>7</td>\n",
              "      <td>106</td>\n",
              "      <td>92</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>22.7</td>\n",
              "      <td>0.235</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>9</td>\n",
              "      <td>171</td>\n",
              "      <td>110</td>\n",
              "      <td>24</td>\n",
              "      <td>240</td>\n",
              "      <td>45.4</td>\n",
              "      <td>0.721</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>7</td>\n",
              "      <td>159</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>27.4</td>\n",
              "      <td>0.294</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0</td>\n",
              "      <td>180</td>\n",
              "      <td>66</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1.893</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1</td>\n",
              "      <td>146</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29.7</td>\n",
              "      <td>0.564</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2</td>\n",
              "      <td>71</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.586</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>7</td>\n",
              "      <td>103</td>\n",
              "      <td>66</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>39.1</td>\n",
              "      <td>0.344</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>7</td>\n",
              "      <td>105</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.305</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86344171-53ec-47b1-9603-a93e064328a7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
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              "\n",
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              "          document.querySelector('#df-86344171-53ec-47b1-9603-a93e064328a7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86344171-53ec-47b1-9603-a93e064328a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['class'].value_counts()"
      ],
      "metadata": {
        "id": "LKAYyOpQuwIz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47593689-dea0-4bc4-aab3-159fe72b8aef"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can observe that there are more zeros(0) that ones(1). Ideally, we don't want one class to be way too smaller than the other as it will result in *data imbalance*, a situation we will deal with later on in this course."
      ],
      "metadata": {
        "id": "qaM5niOHv2WH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Correlations Between Attributes"
      ],
      "metadata": {
        "id": "q4C7wTQiwQ4f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Correlation refers to the relationship between two variables and how they may or may not change together. The most common method for calculating correlation is Pearson’s Correlation Coefficient, that assumes a normal distribution of the attributes involved. A correlation of -1 or 1 shows a full negative or positive correlation respectively. Whereas a value of 0 shows no correlation at all. Some machine learning algorithms like linear and logistic regression can suffer poor performance if there are highly correlated attributes in your dataset. As such, it is a good idea to review all of the pairwise correlations of the attributes in your dataset. You can use the corr() function on the Pandas DataFrame to calculate a correlation matrix."
      ],
      "metadata": {
        "id": "Cdt1sy_Fwc0N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr()"
      ],
      "metadata": {
        "id": "rmU953Ibvq58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "e1dd2021-e236-41d1-cc5b-cffa839abb87"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Preg      Plas      Pres      skin      test      mass      pedi  \\\n",
              "Preg   1.000000  0.129459  0.141282 -0.081672 -0.073535  0.017683 -0.033523   \n",
              "Plas   0.129459  1.000000  0.152590  0.057328  0.331357  0.221071  0.137337   \n",
              "Pres   0.141282  0.152590  1.000000  0.207371  0.088933  0.281805  0.041265   \n",
              "skin  -0.081672  0.057328  0.207371  1.000000  0.436783  0.392573  0.183928   \n",
              "test  -0.073535  0.331357  0.088933  0.436783  1.000000  0.197859  0.185071   \n",
              "mass   0.017683  0.221071  0.281805  0.392573  0.197859  1.000000  0.140647   \n",
              "pedi  -0.033523  0.137337  0.041265  0.183928  0.185071  0.140647  1.000000   \n",
              "age    0.544341  0.263514  0.239528 -0.113970 -0.042163  0.036242  0.033561   \n",
              "class  0.221898  0.466581  0.065068  0.074752  0.130548  0.292695  0.173844   \n",
              "\n",
              "            age     class  \n",
              "Preg   0.544341  0.221898  \n",
              "Plas   0.263514  0.466581  \n",
              "Pres   0.239528  0.065068  \n",
              "skin  -0.113970  0.074752  \n",
              "test  -0.042163  0.130548  \n",
              "mass   0.036242  0.292695  \n",
              "pedi   0.033561  0.173844  \n",
              "age    1.000000  0.238356  \n",
              "class  0.238356  1.000000  "
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Preg</th>\n",
              "      <th>Plas</th>\n",
              "      <th>Pres</th>\n",
              "      <th>skin</th>\n",
              "      <th>test</th>\n",
              "      <th>mass</th>\n",
              "      <th>pedi</th>\n",
              "      <th>age</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Preg</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.129459</td>\n",
              "      <td>0.141282</td>\n",
              "      <td>-0.081672</td>\n",
              "      <td>-0.073535</td>\n",
              "      <td>0.017683</td>\n",
              "      <td>-0.033523</td>\n",
              "      <td>0.544341</td>\n",
              "      <td>0.221898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Plas</th>\n",
              "      <td>0.129459</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.152590</td>\n",
              "      <td>0.057328</td>\n",
              "      <td>0.331357</td>\n",
              "      <td>0.221071</td>\n",
              "      <td>0.137337</td>\n",
              "      <td>0.263514</td>\n",
              "      <td>0.466581</td>\n",
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              "    <tr>\n",
              "      <th>Pres</th>\n",
              "      <td>0.141282</td>\n",
              "      <td>0.152590</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>0.088933</td>\n",
              "      <td>0.281805</td>\n",
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              "      <td>0.239528</td>\n",
              "      <td>0.065068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skin</th>\n",
              "      <td>-0.081672</td>\n",
              "      <td>0.057328</td>\n",
              "      <td>0.207371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.436783</td>\n",
              "      <td>0.392573</td>\n",
              "      <td>0.183928</td>\n",
              "      <td>-0.113970</td>\n",
              "      <td>0.074752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test</th>\n",
              "      <td>-0.073535</td>\n",
              "      <td>0.331357</td>\n",
              "      <td>0.088933</td>\n",
              "      <td>0.436783</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.197859</td>\n",
              "      <td>0.185071</td>\n",
              "      <td>-0.042163</td>\n",
              "      <td>0.130548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mass</th>\n",
              "      <td>0.017683</td>\n",
              "      <td>0.221071</td>\n",
              "      <td>0.281805</td>\n",
              "      <td>0.392573</td>\n",
              "      <td>0.197859</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.140647</td>\n",
              "      <td>0.036242</td>\n",
              "      <td>0.292695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pedi</th>\n",
              "      <td>-0.033523</td>\n",
              "      <td>0.137337</td>\n",
              "      <td>0.041265</td>\n",
              "      <td>0.183928</td>\n",
              "      <td>0.185071</td>\n",
              "      <td>0.140647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.033561</td>\n",
              "      <td>0.173844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.544341</td>\n",
              "      <td>0.263514</td>\n",
              "      <td>0.239528</td>\n",
              "      <td>-0.113970</td>\n",
              "      <td>-0.042163</td>\n",
              "      <td>0.036242</td>\n",
              "      <td>0.033561</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.238356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>0.221898</td>\n",
              "      <td>0.466581</td>\n",
              "      <td>0.065068</td>\n",
              "      <td>0.074752</td>\n",
              "      <td>0.130548</td>\n",
              "      <td>0.292695</td>\n",
              "      <td>0.173844</td>\n",
              "      <td>0.238356</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-508a3d0e-8825-43fc-a0ee-e81c00d2b5e1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-508a3d0e-8825-43fc-a0ee-e81c00d2b5e1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-508a3d0e-8825-43fc-a0ee-e81c00d2b5e1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Skew of Univariate Distributions"
      ],
      "metadata": {
        "id": "hHNRB4AAyFUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.skew()"
      ],
      "metadata": {
        "id": "w1N89hEjxI6o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7735c37e-891f-4bbe-f298-3ea9b73b74b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Preg     0.901674\n",
              "Plas     0.173754\n",
              "Pres    -1.843608\n",
              "skin     0.109372\n",
              "test     2.272251\n",
              "mass    -0.428982\n",
              "pedi     1.919911\n",
              "age      1.129597\n",
              "class    0.635017\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Missing Values In Dataset"
      ],
      "metadata": {
        "id": "k8BxAPu-gvG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxAbnsRAg3lU",
        "outputId": "6afc518b-1c0f-467e-a58a-6a89e0dfba0e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Preg     0\n",
              "Plas     0\n",
              "Pres     0\n",
              "skin     0\n",
              "test     0\n",
              "mass     0\n",
              "pedi     0\n",
              "age      0\n",
              "class    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The skew result show a positive (right) or negative (left) skew. Values closer to zero show less skew."
      ],
      "metadata": {
        "id": "wK8ZiByNyTKA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "<br>\n",
        "\n",
        "#**Understand Your Data With Visualization**"
      ],
      "metadata": {
        "id": "G2Wy950K3QjC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You must understand your data in order to get the best results from machine learning algorithms. The fastest way to learn more about your data is to use data visualization. In this session, you will discover exactly how you can visualize your machine learning data in Python using Pandas."
      ],
      "metadata": {
        "id": "Mu_fDDwp3Ygx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Univariate Plots"
      ],
      "metadata": {
        "id": "nhqqBM4d3job"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section we will look at three techniques that you can use to understand each attribute of your dataset independently.\n",
        "1. Histograms.\n",
        "2. Density Plots.\n",
        "3. Box and Whisker Plots."
      ],
      "metadata": {
        "id": "QjoySxue3mVE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Histograms\n",
        "\n",
        "A fast way to get an idea of the distribution of each attribute is to look at histograms. Histograms group data into bins and provide you a count of the number of observations in each bin. From the shape of the bins you can quickly get a feeling for whether an attribute is Gaussian, skewed or even has an exponential distribution. It can also help you see possible outliers."
      ],
      "metadata": {
        "id": "MPqM72JN3sXQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data.hist(figsize=(10,10))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "7q6TgWc2zp8j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "b0c6cc71-6f8c-41e3-e2cf-d5b1bd755374"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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ZQ/R1NhQzMzOrOZ/RMjMzM8uJEy2zDpB0iaQtku5pKJsm6XpJ96e/+6RySbpA0npJd0s6pLzIzcwsT060zDpjOXD0iLLFwI0RMQu4Md0HOIZsAMZZZHO0XVhQjGZmVjAnWmYdEBE3A4+NKJ4HrEjLK4DjGsovjcytwNQRI2SbmVmX8MXwZvnpiYhNafkRoCct7ws83LDdhlS2iREkLSQ760VPT8+os9VXaSb7PGLpnz3U9mN7dp/Y48djrP3u9vfJzF7MiZZZASIiJEUbj1sGLAOYM2dOjDZbfZVmss8jlgVt9gaGLMlauqaYf3WDJ/btcH23v09m9mJuOjTLz+bhJsH0d0sq3wjMbNhuv1RmZmZdxme0zPKzEjgZWJL+XttQfpqkK4DDgK0NTYxWY2ONw9c/e6jp2bnBJcfmFZKZlcyJllkHSLoc6AOmS9oAfJIswbpK0qnAQ8DxafNVwFxgPfAr4JTCAzYzs0I40TLrgIg4YZRVRzbZNoBF+UZkZmZV4Gu0zMzMzHIyZqLlEa/NzMzM2tPKGa3leMRrMzMzs3Eb8xqtiLhZUu+I4nlkF/5CNuL1AHAGDSNeA7dKmipphntUmZmZ5at38XWj9my18rR7MXxhI14XOarzRPTsPvao0GWry0jQdYnTzFon6RLgXcCWiHhDKpsGXAn0AoPA8RHxuCQB55P1zv0VsCAi7iwjbrOJmnCvw7xHvP7cZdcWNqrzRPTPHuL4io+yXJeRoOsSp5mNy3Lg88ClDWXDl6EskbQ43T+DF16GchjZZSiHFRqtWYe02+vQI16bmVnLPPG6TVbtniryiNdmZjZRk3Li9bz0zx6qxeU2nXgf6vR+jploecRrMzPL22SaeD0vC9LF8FW/3GasyddbUaf3s5Vehx7x2szM8rB5uGe6L0OxbuWR4c3MrCzDl6HAiy9DOSkNgn04vgzFaqza5xfNzKwr+DIUm6ycaJmZWe58GYpNVm46NDMzM8uJz2hVQG+b0yUMLjm2w5GYmZnla7J95/mMlpmZmVlOnGiZmZmZ5cSJlpmZmVlOnGiZmZmZ5cSJlpmZmVlOnGiZmZmZ5cSJlpmZmVlOnGiZmZmZ5cSJlpmZmVlOPDK8mVnJ2h0pG+o7WrbZZOFEyyxnkgaBp4DtwFBEzJE0DbgS6AUGgeMj4vGyYjQzs3y46dCsGG+NiIMiYk66vxi4MSJmATem+2Zm1mWcaJmVYx6wIi2vAI4rMRYzM8uJmw7N8hfA9yQF8KWIWAb0RMSmtP4RoKfZAyUtBBYC9PT0MDAw0PQFtm3bNuq6ouURS//sobYf27P7xB7fSXnE0u6xrtJnxqybOdEyy98REbFR0iuA6yX9pHFlRERKwl4kJWXLAObMmRN9fX1NX2BgYIDR1hUtj1gWTOBi8f7ZQyxdU41/dXnEMnhiX1uPq9JnxqybTajpUNKgpDWS7pJ0RyqbJul6Sfenv/t0JlSzeoqIjenvFuAa4FBgs6QZAOnvlvIiNDOzvHTip9VbI+LRhvvDF/kukbQ43T+jA69jVjuSpgAviYin0vI7gf8DrAROBpakv9eWF6XVWbtDQyw/ekqHIzGzZvI4nz4P6EvLK4ABnGjlYrz/YPtnD7Fg8XUed6dYPcA1kiCrb1+NiO9I+iFwlaRTgYeA40uM0axUHgLFutlEE63cL/Kt0oWsO9KzO3zusvZOSvTP7nAwoxg+llW/ALabLtKNiAeANzYp/yVwZPERmVWWW0esK0000cr9It/PXXZtZS5k3ZEqXXA7muEY2714tii+SNfMcOuIdYkJZQaNF/lKesFFvhGxyRf5mplZCybVECh56Z89VJtWoHY0vn91ej/bTrR8ka+ZmXXIpBoCJS8LFl9Xi9aVdjW2xtTp/ZzIu+GLfM3MbMLcOmLdrO1Eyxf5mpnZRLl1xLpdd55fNDOzunDriHU1J1pmVph2B9e07uXWEet2E5qCx8zMzMxG50TLzMzMLCduOjQzs663ZuNWFrTRdO0py2yifEbLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCceR8ta1u70KR6HxszMJiuf0TIzMzPLiRMtMzMzs5y46dDMzMwqr/Hylf7ZQy1PqVT25StOtMzMzCqk3ethrZqcaE1CrsRmZmbF8DVaZmZmZjlxomVmZmaWk9yaDiUdDZwP7AT8c0Qsyeu1zOqok3VkzcatLV8Y2qjdi0R31Pw8notUzcbi7xKbqLLHgMwl0ZK0E/BPwDuADcAPJa2MiHV5vJ5V23g/5MNf1GX3FMmT64jZ2FxPrBvkdUbrUGB9RDwAIOkKYB7gymGWcR0xG1tt64k7HdkwRUTnn1R6L3B0RHwg3X8fcFhEnNawzUJgYbp7AHDfKE83HXi040F2Xh3irEOMkG+cr4qIl+f03C1rpY6k8jrWkyrFAtWKpy6x1Kae1LSO5Mn7WYyW60hpwztExDJg2VjbSbojIuYUENKE1CHOOsQI9YmzCHWsJ1WKBaoVj2PpvDrWkTx5P6snr16HG4GZDff3S2VmlnEdMRub64nVXl6J1g+BWZL2l7QrMB9YmdNrmdWR64jZ2FxPrPZyaTqMiCFJpwHfJeuSe0lErG3z6cY8JVwRdYizDjFCfeJsW4frCFTrmFUpFqhWPI5lHCbpd8lEeT8rJpeL4c3MzMzMI8ObmZmZ5caJlpmZmVlOKp1oSTpa0n2S1ktaXHY8zUgalLRG0l2S7ig7nmGSLpG0RdI9DWXTJF0v6f70d58KxniWpI3peN4laW6ZMVZdFepIszpQ1GdtPJ9zZS5Ix+puSYcUFM+on2lJZ6Z47pN0VIdjmSnpJknrJK2V9NFUXtrxKUsV6kkeyqx7eapavZ6oyiZaen7qhWOAA4ETJB1YblSjemtEHFSxMT2WA0ePKFsM3BgRs4Ab0/0yLefFMQKcl47nQRGxquCYaqNidWRkHSjqs7ac1j/nxwCz0m0hcGFB8UCTz3R6r+YDr0+P+UJ6TztlCOiPiAOBw4FF6TXLPD6Fq1g9yUNZdS9Py6lWvZ6QyiZaNEy9EBH/CQxPvWAtiIibgcdGFM8DVqTlFcBxhQY1wigxWuuqXEcK+ayN83M+D7g0MrcCUyXNKCCe0cwDroiIZyLiQWA92XvaqVg2RcSdafkp4F5gX0o8PiWpcj3JQ6X+z7ejavV6oqqcaO0LPNxwf0Mqq5oAvidptbKpIKqsJyI2peVHgJ4yg9mB09Ip4EvqeNq7QFWpI83qQJmftdFeu8zj1ewzXVg8knqBg4HbqObxyVO37hdUr+7lqbaf2yonWnVxREQcQnb6cpGkPyg7oFZENq5HFcf2uBB4DXAQsAlYWm441oId1oEyP2sV+ZyX+pmWtAfwDeD0iHiycV1Fjo+1r7J1L091268qJ1q1mHohIjamv1uAa+jgqf8cbB4+pZr+bik5nheJiM0RsT0ingUuotrHs2yVqCOj1IEyP2ujvXYpx2sHn+nc45G0C1mSdVlEXJ2KK3V8CtCt+1XFupen2n5uq5xoVX7qBUlTJO05vAy8E7hnx48q1Urg5LR8MnBtibE0NaJt/T1U+3iWrfQ6soM6UOZnbbTXXgmclHopHQ5sbWiKyM0OPtMrgfmSdpO0P9nFvLd38HUFXAzcGxHnNqyq1PEpQOn1JA8VrXt5qu/nNiIqewPmAv8B/BT4RNnxNInv1cCP021tlWIELidrpvgtWZv1qcDLyHpr3A/cAEyrYIxfBtYAd5NVoBllH8sq38quI6PVgaI+a+P5nAMi63320/QZm1NQPKN+poFPpHjuA47pcCxHkDWv3A3clW5zyzw+Zd3Kric57VOpdS/nfatUvZ7ozVPwmJmZmeWkyk2HZmZmZrXmRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRKsGJJ0l6SujrPu4pH8uOiYzMzMbmxOtmouIv4uID5Qdh1mRJA1KevsEn2OBpFs6FZOZWTNOtMzMzMxy4kSrYiSdIWmjpKck3SfpyBHrd5F0uaRvSNq1sVlRUq+kkHSypJ9JelTSJ8rZE7N8SPoy8LvA/ydpm6S/lnS4pH+X9ISkH0vqa9h+gaQHUp16UNKJkv4b8EXgzek5nihpd8w6Ip3l/StJd0t6WtLFknokfTt99m+QtE/a9muSHpG0VdLNkl7f8DxzJa1Lj9ko6S9T+XRJ30p17DFJ/ybJOUQLfJAqRNIBwGnAf4+IPYGjgMGG9bsD3wSeAY6PiP8c5amOAA4AjgT+Nn2pmHWFiHgf8DPg3RGxB3AZcB3waWAa8JfANyS9XNIU4ALgmFSn/gdwV0TcC3wY+EFE7BERU8vYF7MO+xPgHcDrgHcD3wY+Dryc7Pv+I2m7bwOzgFcAd5LVoWEXAx9K9eUNwPdTeT+wIT1XT3reyHFfuoYTrWrZDuwGHChpl4gYjIifpnV7Ad8BfgqcEhHbd/A8n4qIX0fEj4EfA2/MNWqzcv05sCoiVkXEsxFxPXAHMDetfxZ4g6TdI2JTRKwtLVKzfH0uIjZHxEbg34DbIuJHEfEb4BrgYICIuCQinoqIZ4CzgDdK2js9x2/JvoP2iojHI+LOhvIZwKsi4rcR8W8R4USrBU60KiQi1gOnk33wt0i6QtIr0+rDgd8DlrTw4X6kYflXwB6djtWsQl4F/Glq0ngiNQMeAcyIiKeBPyM7e7VJ0nWS/muZwZrlaHPD8q+b3N9D0k6Slkj6qaQneb7VZHr6+ydkP1IekvSvkt6cyv9fYD3wvdQUvzi3vegyTrQqJiK+GhFHkH15BPCZtOp7wN8DN0rqKSs+s4po/LHxMPDliJjacJsSEUsAIuK7EfEOsl/jPwEuavIcZpPF/wTmAW8H9gZ6U7kAIuKHETGPrFnxm8BVqfypiOiPiFcDfwR8bOQ1xNacE60KkXSApLdJ2g34DdkvkGeH10fEZ4GvkiVb00d5GrPJYDPw6rT8FeDdko5Kv9ZfKqlP0n7pYuB56VqtZ4BtPF+nNgP7Sdq1+PDNSrMnWV34JfA7wN8Nr0gdrE6UtHdE/BZ4klRfJL1L0mslCdhKdqnLsy96dnsRJ1rVshuwBHiUrPnvFcCZjRtExNlkvzJukDSt8AjNquHvgb9JzYR/RvYL/ePAL8jOcP0V2f+3lwAfA34OPAb8IfC/0nN8H1gLPCLp0UKjNyvPpcBDwEZgHXDriPXvAwZTs+KHgRNT+SzgBrIfKz8AvhARNxUScc3J17KZmZmZ5cNntMzMzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMxqQtJ3yo7BJgd/1sw6Z+eyAwCYPn169Pb2Nl339NNPM2XKlGID2oEqxVOlWKAe8axevfrRiHh5Hq8naRB4imx8maGImJOG4LiSbFDAQbI5Kh9PY9GcTzYC86+ABQ1TXTS11157HTVnzpym3YSrcOwdQ7XimGAMT3YylqLU6btkNI6zs/KKc1zfJRFR+u1Nb3pTjOamm24adV0ZqhRPlWKJqEc8wB2R0+eYLJGaPqLss8DitLwY+Exanks2savIple6baznr3o9cQzPq0IcE4khz3qS563qdaQVjrOz8opzPHXETYdm+ZoHrEjLK4DjGsovTXX2VmCqpBllBGhmZvmpRNOhWZcIsglXA/hSRCwDeiJiU1r/CDA8T+W+ZCOYD9uQyjY1lCFpIbAQoKenh4GBgaYvvG3btlHXFcUxVCuOKsRgZk60zDrpiIjYKOkVwPWSftK4MiIiJWEtS8naMoA5c+ZEX19f0+0GBgYYbV1RHEO14qhCDGbmXodmHRMRG9PfLcA1wKHA5uEmwfR3S9p8IzCz4eH7pTIzM+siTrTMOkDSFEl7Di8D7wTuAVYCJ6fNTgauTcsrgZOUORzY2tDEaGZmXaLyTYdrNm5lweLrxv24wSXH5hCN2ah6gGuyURvYGfhqRHxH0g+BqySdCjwEHJ+2X0XW83A92fAOp0zkxV1PzKy3jf8B4P8Deat8omVWBxHxAPDGJuW/BI5sUh7AogJCM9TJjg0AACAASURBVDOzErnp0MzMzCwnTrTMzMzMcuJEy8zMzCwnTrTMzMzMcuJEy8zMcidppqSbJK2TtFbSR1P5WZI2Sror3eY2POZMSesl3SfpqPKiN2ufex2amVkRhoD+iLgzjTm3WtL1ad15EfEPjRtLOhCYD7weeCVwg6TXRcT2QqM2myCf0TIzs9xFxKaIuDMtPwXcSza/52jmAVdExDMR8SDZmHOH5h+pWWf5jJaZmRVKUi9wMHAb8BbgNEknAXeQnfV6nCwJu7XhYcMTr498rtpMvN6KicTZP3uorce183qT4Xh2ihMtMzMrjKQ9gG8Ap0fEk5IuBM4GIv1dCry/1eer08TrrZhInO3MDgEweOL4X28yHM9OcdOhmZkVQtIuZEnWZRFxNUBEbI6I7RHxLHARzzcPeuJ16wpOtMzMLHfKJgK9GLg3Is5tKJ/RsNl7yCZjh2zi9fmSdpO0PzALuL2oeM06xU2HZmZWhLcA7wPWSLorlX0cOEHSQWRNh4PAhwAiYq2kq4B1ZD0WF7nHodWREy0zM8tdRNwCqMmqVTt4zDnAObkFZVYANx2amZmZ5WTMRGsHo/lOk3S9pPvT331SuSRdkEbzvVvSIXnvhJmZmVkVtXJGa3g03wOBw4FFacTexcCNETELuDHdBziG7KLFWWRjm1zY8ajNzMzMamDMRGsHo/nOA1akzVYAx6XlecClkbkVmDqiV4mZmZnZpDCui+FHjObbExGb0qpHgJ60vC/wcMPDhkfz3dRQ1vJovj27tzfabV4jwVZhlNlhVYoFHI+ZmdlILSdaTUbzfW5dRISkGM8Ltzqa7+cuu5ala8bfObKdkW5bUYVRZodVKRZwPGZmZiO11Ouw2Wi+wObhJsH0d0sq92i+ZmZmZrTW67DpaL5ko/aenJZPBq5tKD8p9T48HNja0MRo1rUk7STpR5K+le7vL+m21AP3Skm7pvLd0v31aX1vmXGbmVl+WmmTG2003yXAVZJOBR4Cjk/rVgFzgfXAr4BTOhqxWXV9lKyzyF7p/meA8yLiCklfBE4l64V7KvB4RLxW0vy03Z+VEbCZWW8bk1H3zx6ir/OhdKUxE60djOYLcGST7QNYNMG4zGpF0n7AsWSjWH8snQl+G/A/0yYrgLPIEq15aRng68DnJSnVHTMz6yKegsesM/4R+Gtgz3T/ZcATETHcZXa49y009MyNiCFJW9P2j4580jr1zq1CL88qxFCVOKoQg5k50TKbMEnvArZExGpJfZ187jr1zq1CL88qxFCVOKoQg5k50TLrhLcAfyRpLvBSsmu0zicbrHfndFarsfftcM/cDZJ2BvYGfll82GZmljdPKm02QRFxZkTsFxG9wHzg+xFxInAT8N602cieucM9dt+btvf1WWZmXciJlll+ziC7MH492TVYF6fyi4GXpfKP8fw8oWZm1mXcdGjWQRExAAyk5QeAQ5ts8xvgTwsNzMzMSuEzWmZmZmY5caJlZmZmlhMnWmZmljtJMyXdJGmdpLWSPprKp0m6XtL96e8+qVySLkhTVd0t6ZBy98CsPU60zMysCENAf0QcCBwOLJJ0IFlnkBsjYhZwI893DjkGmJVuC8lmVTCrHV8M30HtzBcFMLjk2A5HYmZWLRGxCdiUlp+SdC/ZLAnz4Llp81aQdSY5I5VfmoY+uVXSVEkz0vOY1YYTLTMzK5SkXuBg4DagpyF5egToScvPTVWVDE9j9YJEq9VpquoyJdFE4mxnGq529eze2Sm88lKF992JlpmZFUbSHsA3gNMj4sls/vVMRISkcQ3e2+o0VXWZkmgicS5os1WlHf2zhzi+y49np/gaLTMzK4SkXciSrMsi4upUvFnSjLR+BrAllQ9PVTWscRors9pwomVmZrlTdurqYuDeiDi3YVXjlFQjp6o6KfU+PBzY6uuzrI7cdGhmZkV4C/A+YI2ku1LZx4ElwFWSTgUeAo5P61YBc4H1wK+AU4oN16wznGiNMFbPwf7ZQ4W2g5uZdYOIuAXQKKuPbLJ9AItyDcqsAG46NDMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8vJmImWpEskbZF0T0PZWZI2Sror3eY2rDszzbZ+n6Sj8grczMzMrOpaOaO1HDi6Sfl5EXFQuq0CSDOxzwdenx7zBUk7dSpYMzMzszoZM9GKiJuBx1p8vnnAFRHxTEQ8SDbQ3KETiM/MzMystiYyYOlpkk4C7gD6I+JxspnVb23YZni29Rdpdcb1nt3bm5E8r9nP241nR9qNtQqzkjdyPGZmZi/UbqJ1IXA2EOnvUuD943mCVmdc/9xl17J0zfjDHDyx+fONZaxR3/tnD7UVz460G2sVZiVv5HjMzMxeqK1ehxGxOSK2R8SzwEU83zzo2dZtUpL0Ukm3S/qxpLWSPpXK95d0W+ogcqWkXVP5bun++rS+t8z4zcwsH20lWpJmNNx9DzDcI3ElMD99iewPzAJun1iIZrXwDPC2iHgjcBBwtKTDgc+QdRx5LfA4cGra/lTg8VR+XtrOzMy6TCvDO1wO/AA4QNKGNMP6ZyWtkXQ38FbgfwNExFrgKmAd8B1gUURszy16s4qIzLZ0d5d0C+BtwNdT+QrguLQ8L90nrT9S0mgT7pqZWU2NebFRRJzQpPjiHWx/DnDORIIyq6M0lMlq4LXAPwE/BZ6IiOHeE42dQ/YFHgaIiCFJW4GXAY+OeM5KdhpppgqdD6oQQ1XiqEIMZjaxXodm1iCdvT1I0lTgGuC/duA5K9lppJkqdD6oQgxViaMKMZiZp+Ax67iIeAK4CXgzMFXScAbU2DnkuY4jaf3ewC8LDtXMzHLmRMusAyS9PJ3JQtLuwDuAe8kSrvemzU4Grk3LK9N90vrvR0QUF7GZmRXBTYdmnTEDWJGu03oJcFVEfEvSOuAKSZ8GfsTz1zdeDHxZ0nqymRfmlxG0mZnly4mWWQdExN3AwU3KH6DJNFQR8RvgTwsIzawSJF0CvAvYEhFvSGVnAR8EfpE2+3jD3Llnkg2Dsh34SER8t/CgS9I7xsDZVi9uOjQzsyIsB45uUn5eRByUbsNJ1oFkZ3lfnx7zhXS22Kx2nGiZmVnuIuJmsmbyVswDroiIZyLiQWA9Tc4Mm9WBmw7NzKxMp0k6CbgD6I+Ix8nGmbu1YZvGMeheoNWx5uoyrti2bdvon139cb57du/sOHx5qcL77kTLzMzKciFwNtksCmcDS4H3j+cJWh1rri7jig0MDLD0lqfLDmNM/bOHOL4mx7Ps991Nh2ZmVoqI2BwR2yPiWeAinm8efG6cuaRxDDqzWnGiZWZmpZA0o+Hue4B70vJKYL6k3STtD8wCbi86PrNOcNOhmZnlTtLlQB8wXdIG4JNAn6SDyJoOB4EPAUTEWklXAeuAIWBRmuLKrHacaJmZWe4i4oQmxRc3KRve/hzgnPwiMiuGmw7NzMzMcuJEy8zMzCwnTrTMzMzMcuJEy8zMzCwnTrTMzMzMcuJEy8zMzCwnTrTMzMzMcuJEy8zMzCwnXTtgae/i68oOwczMzCa5Mc9oSbpE0hZJ9zSUTZN0vaT70999UrkkXSBpvaS7JR2SZ/BmZmZmVdZK0+Fy4OgRZYuBGyNiFnBjug9wDNnkn7OAhcCFnQnTzMzMrH7GTLQi4mbgsRHF84AVaXkFcFxD+aWRuRWYOmJ2djMzM7NJo91rtHoiYlNafgToScv7Ag83bLchlW1iBEkLyc560dPTw8DAQPMX2h36Zw+1GWbn5RHPaPs+lm3btrX92DxM5ngkzQQuJasLASyLiPMlTQOuBHqBQeD4iHhckoDzgbnAr4AFEXFnIcGamVlhJnwxfESEpGjjccuAZQBz5syJvr6+ptt97rJrWbqmOtfs988e6ng8gyf2tfW4gYEBRjtuZZjk8QwB/RFxp6Q9gdWSrgcWkDWzL5G0mKyZ/Qxe2Mx+GFkz+2FFBWtmZsVod3iHzcNNgunvllS+EZjZsN1+qcysq0XEpuEzUhHxFHAv2dlcN7ObmU1i7Z6aWQmcDCxJf69tKD9N0hVkv863NjQxmk0KknqBg4HbmGAze95N7J1sWq1C03EVYqhKHFWIwcxaSLQkXQ70AdMlbQA+SZZgXSXpVOAh4Pi0+Sqya07Wk113ckoOMZtVlqQ9gG8Ap0fEk9mlWJl2mtnzbmJvt9m6mSo0HVchhqrEUYUYzKyFRCsiThhl1ZFNtg1g0USDMqsjSbuQJVmXRcTVqXizpBkRscnN7GZmk4+n4DHrgNSL8GLg3og4t2HVcDM7vLiZ/aQ0yO/huJndupwHv7bJyomWWWe8BXgf8DZJd6XbXLJm9ndIuh94e7oPWTP7A2TN7BcBf1FCzGZFWo4Hv7ZJqDrjJpjVWETcAmiU1W5mt0kvIm5OHUUazSO7BhiyXrkDZMOfPNcrF7hV0tThJvhiojXrHCdaZmZWlsIGv65LL8xt27bRP3t72WGMqWf3zvZazksV3ncnWmZmVrq8B7+uSy/MgYEBlt7ydNlhjKl/9hDH1+R4lv2++xotMzMriwe/tq7nRMvMzMriXrnW9dx0aGZmufPg1zZZOdGqgN7F17X1uOVHT+lwJGZm+fDg1zZZuenQzMzMLCdOtMzMzMxy4qZDs0ms3WbrwSXHdjgSM7Pu5DNaZmZmZjlxomVmZmaWEydaZmZmZjnxNVpmZmY2br7GszU+o2VmZmaWEydaZmZmZjlxomVmZmaWEydaZmZmZjlxomVmZmaWEydaZmZmZjmZ0PAOkgaBp4DtwFBEzJE0DbgS6AUGgeMj4vGJhWlmZmZWP504o/XWiDgoIuak+4uBGyNiFnBjum9mZmY26eTRdDgPWJGWVwDH5fAaZpUi6RJJWyTd01A2TdL1ku5Pf/dJ5ZJ0gaT1ku6WdEh5kZuZWZ4mOjJ8AN+TFMCXImIZ0BMRm9L6R4CeZg+UtBBYCNDT08PAwEDTF+jZHfpnD00wzM6pUjzbtm0b9biVYZLHsxz4PHBpQ9nw2d0lkhan+2cAxwCz0u0w4ML018zMusxEE60jImKjpFcA10v6SePKiIiUhL1ISsqWAcyZMyf6+vqavsDnLruWpWuqM1NQ/+yhysSz/OgpjHbcyjAwMDBp44mImyX1jiieBwwHsAIYIEu05gGXRkQAt0qaKmlGww8UMzPrEhPKGCJiY/q7RdI1wKHA5uEvDUkzgC0diNOsjkY7u7sv8HDDdhtS2YsSraqe+W0WRxXOaFYhhqrEUYUYWuWOVdbN2k60JE0BXhIRT6XldwL/B1gJnAwsSX+v7USgZnW2o7O7Yzyukmd+B098cRxVOKNZhRiqEkcVYhint0bEow33R2t6N6uVifxn7gGukTT8PF+NiO9I+iFwlaRTgYeA4yceplktjXZ2dyMws2G7/VKZmT1vtKZ3s1ppO9GKiAeANzYp/yVw5ESCMusSo53dXQmcJukKsovgt/r6LJvkcu9YVZem1G3bttE/e3vZYYxpIpcrFPk+VOF9r8ZV3WY1J+lysl/f0yVtAD5JlmA1O7u7CpgLrAd+BZxSeMBm1ZJ7x6q6NKUODAyw9Janyw5jTBPpGNbs0oO8VOF9d6Jl1gERccIoq150djf1NlyUb0Rm9eGOVdbNPNehmZmVRtIUSXsOL5N1rLqH55vewR2rrMZ8RsvMzMrkjlXW1ZxomZlZadyxyrqdEy0zG7fexde9qKx/9hALmpQ3GlxybF4hmZlVkq/RMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJ5zo0s8I0myOxFZ4j0eqm3c96/+wh/NXcXXxGy8zMzCwnTpvNzMysMJPtzLYTrRpbs3ErC9r4wNb1w2pm1i7/v7SyuOnQzMzMLCe5JVqSjpZ0n6T1khbn9TpmdeU6YjY21xOru1yaDiXtBPwT8A5gA/BDSSsjYl0er2dWN64j49PqNR39s4faah4ayc1F1eB6Yt0gr2u0DgXWR8QDAJKuAOYBrhwVMNkuRKwo15EKcx2pDNcTe0479bJ/9hB9nQ9lXPJKtPYFHm64vwE4LKfXsi7V5V92riNdaPgz26kzaxMxVgyuJzZZlP1dUlqvQ0kLgYXp7jZJ942y6XTg0WKiGttHKhRP0bHoM2NuUolj0xBns3heVWgwE1SnelKFulGFGKoSx1gxjFGfa1NP8q4jLfzf66gqfHZaMRni7FQdySvR2gjMbLi/Xyp7TkQsA5aN9USS7oiIOZ0Nr31ViqdKsYDjGacx6wjUq544hmrFUYUYOqBrv0tG4zg7qwpx5tXr8IfALEn7S9oVmA+szOm1zOrIdcRsbK4nVnu5nNGKiCFJpwHfBXYCLomItXm8llkduY6Yjc31xLpBbtdoRcQqYFUHnmrMU8IFq1I8VYoFHM+4dLCOQDX21TE8rwpxVCGGCevi75LROM7OKj1ORUTZMZiZmZl1JU/BY2ZmZpaTSidaZU69IGmmpJskrZO0VtJHU/lZkjZKuivd5hYY06CkNel170hl0yRdL+n+9HefAuI4oGH/75L0pKTTizw2ki6RtEXSPQ1lTY+FMhekz9Hdkg7JK66iFVVHdlAfCj/mknaS9CNJ30r395d0W3qtK9NF00jaLd1fn9b3djCGqZK+Luknku6V9Oaij4Wk/53ei3skXS7ppWUcizoo87tkR8Zbr0qOtaV6V7bx1M3CREQlb2QXPv4UeDWwK/Bj4MACX38GcEha3hP4D+BA4CzgL0s6JoPA9BFlnwUWp+XFwGdKeJ8eIRtTpLBjA/wBcAhwz1jHApgLfBsQcDhwWxnvX07HvpA6soP6UPgxBz4GfBX4Vrp/FTA/LX8R+F9p+S+AL6bl+cCVHYxhBfCBtLwrMLXIY0E2kOeDwO4Nx2BBGcei6reyv0vGiG1c9arkWFuqd2XfxlM3C4up7IOyg4P1ZuC7DffPBM4sMZ5ryebbOotqJVr3ATPS8gzgvoJjeifwf9NyoccG6OWFiVbTYwF8CTih2XZ1vpVZRxrqQ6HHnGwcpRuBtwHfIkteHgV2HnlMyHqqvTkt75y2Uwdi2JssydGI8sKOBc+PmD4t7du3gKOKPhZ1uFXtu2SMWHdYr0qMq+V6V3Kc46qbRd2q3HTYbOqFfcsIJJ1mPxi4LRWdlpoALin4FGQA35O0WtloyAA9EbEpLT8C9BQYD2S/ji9vuF/WsYHRj0VlPksdVsp+jagPRR/zfwT+Gng23X8Z8EREDDV5nediSOu3pu0nan/gF8C/pKaUf5Y0hQKPRURsBP4B+BmwiWzfVlP8saiDWtT/FutVWcZT78o03rpZiConWpUgaQ/gG8DpEfEkcCHwGuAgsn9wSwsM54iIOAQ4Blgk6Q8aV0aWrhfWjTS1yf8R8LVUVOaxeYGij8Vk0aQ+PCfvYy7pXcCWiFid12u0aGeyZusLI+Jg4Gmy5ojnFHAs9iGbXHl/4JXAFODovF7P8lVmvRpLhepdK0qvm81UOdFqaYqSPEnahezDf1lEXA0QEZsjYntEPAtcRDa7fCHSr1giYgtwTXrtzZJmpHhnAFuKiocs4bszIjanuEo7Nslox6L0z1JOCt2vZvWBYo/5W4A/kjQIXEHWjHE+MFXS8JiAja/zXAxp/d7ALycYA2S/3jdExPAZ7q+T/XMv8li8HXgwIn4REb8FriY7PkUfizqodP0fZ70qw3jrXZnGWzcLUeVEq9SpFyQJuBi4NyLObSif0bDZe4B7Rj42p3imSNpzeJns2qh7yI7JyWmzk8na+ItyAg3NhmUdmwajHYuVwEmp99fhwNaG08h1VlgdGa0+UOAxj4gzI2K/iOgl29fvR8SJwE3Ae0eJYTi296btJ/xLNiIeAR6WdEAqOhJYR7Gfv58Bh0v6nfTeDMdQ6LGoicpO49NGvSpcG/WuNG3UzcICq+yNrLfOf5D1GPlEwa99BNnpxbuBu9JtLvBlYE0qX0lBF1WT9Zj5cbqtHT4eZG3lNwL3AzcA0wqKZwrZL+K9G8oKOzZkCd4m4Ldkv2JOHe1YkF24+U/pc7QGmFPkZynn96GQOrKD+lDKMQf6eL7306uB24H1ZM3Yu6Xyl6b769P6V3fw9Q8C7kjH45vAPkUfC+BTwE/IftB8GditjGNRh1tR9aSNuMZVr8q+tVLvyr6Np24WdfPI8GZmZmY5qXLToZmZmVmtOdEyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4kTrS4kabmkT6fl35d0X9kxmZlZ8SQtkHRL2XFMZjuXHYDlKyL+DTig7DjMzMwmI5/RMjMzM8uJE60KkTQo6UxJ6yQ9LulfJL00rXuXpLskPSHp3yX9XsPjDpZ0p6SnJF0JvLRhXZ+kDSXsjlmuJC2W9NP0uV8n6T2pfCdJSyU9KulBSadJCkk7p/V7S7pY0iZJGyV9WtJO5e6N2cRJminpakm/kPRLSZ9vss35kh6W9KSk1ZJ+v2HdoZLuSOs2Szo3lb9U0lfScz4h6YeSeorctzpzolU9JwJHAa8BXgf8jaSDgUuADwEvA74ErJS0m6RdgW8CXwamAV8D/qSMwM0K9lPg94G9gU8BX5E0A/ggcAxwEHAIcNyIxy0HhoDXAgcD7wQ+UEzIZvlIPxa+BTwE9AL7Alc02fSHZHVjGvBV4GvDP+iB84HzI2Ivsu+gq1L5yWT1bCbZd9CHgV/nsiNdyIlW9Xw+Ih6OiMeAc4ATgIXAlyLitojYHhErgGeAw9NtF+AfI+K3EfF1sopk1tUi4msR8fOIeDYirgTuBw4Fjif7stgQEY8DS4Yfk36FzwVOj4inI2ILcB4wv4RdMOukQ4FXAn+VPtu/iYgXXQQfEV+JiF9GxFBELAV24/nreH8LvFbS9IjYFhG3NpS/DHht+g5aHRFPFrBPXcGJVvU83LD8EFnFeRXQn07ZPiHpCbJfFq9Mt40RESMeZ9bVJJ3U0Jz+BPAGYDpZnWisR43LryL7YbKp4XFfAl5RVNxmOZkJPBQRQzvaSNJfSrpX0tb0+d+brN4AnErWkvKT1Dz4rlT+ZeC7wBWSfi7ps5J2yWk/uo4TreqZ2bD8u8DPyb4ozomIqQ2334mIy4FNwL6SNOJxZl1L0quAi4DTgJdFxFTgHkBkdWK/hs0b69TDZGeDpzfUpb0i4vUFhW6Wl4eB3x2+FrGZdD3WX5Od9d0n1ZutZPWGiLg/Ik4g++HxGeDrkqak1pJPRcSBwP8A3gWclO/udA8nWtWzSNJ+kqYBnwCuJPtC+bCkw5SZIulYSXsCPyC73uQjknaR9Mdkp5DNutkUIIBfAEg6heyMFmTXlXxU0r6SpgJnDD8oIjYB3wOWStpL0kskvUbSHxYbvlnH3U72I2NJ+o54qaS3jNhmT7Lvi18AO0v6W2Cv4ZWS/lzSyyPiWeCJVPyspLdKmp2uA3uSrCnx2bx3qFs40aqer5J9ETxAdrHvpyPiDrILfD8PPA6sBxYARMR/An+c7j8G/BlwddFBmxUpItYBS8l+aGwGZgP/N62+iKwO3Q38CFhF9uWyPa0/CdgVWEdWn74OzCgqdrM8RMR24N1knTx+Bmwg+z5o9F3gO8B/kF1i8hte2LR+NLBW0jayC+PnR8Svgf9CVk+eBO4F/pWsOdFaoBde2mNlkjQIfCAibig7FrNuIekY4IsR8aqyYzGzycdntMysq0jaXdJcSTtL2hf4JHBN2XGZ2eTkRMvMuo3IxtV6nKzp8F7gb0uNyMwmLTcdmpmZmeXEZ7TMzMzMcjLqeBtFmj59evT29gLw9NNPM2XKlHIDKpD3t1irV69+NCJeXloAE9BYT6D8YzkRjr0crcZe13oyso40qtL7VqVYoFrx1CWWcdWRiCj99qY3vSmG3XTTTTGZeH+LBdwRFfjMt3NrrCcR5R/LiXDs5Wg19rrWk5F1pJ19L0KVYomoVjx1iWU8dcRNh2ZmZmY5caJlZmZmlhMnWmYdkKa7uF3SjyWtlfSpVL6/pNskrZd0paRdU/n/397dB8lR33cef3/Cg8CYQwKSLUWoLPmsnItEZyBbBh+uqwWMLYTLIhXMiePMg5VTHuCCY13Fwv7DziWuE7kANsQhlg2HcMkIIuOTDjgnstAW5SoLDBhLQpiwBvmQakEGhIyOMs7i7/3Rv1law+7OTM/0PH5eVVvT/eue2W/3bm9/t39Ps9L6WNq+oJPxm7WDpD2SdqbJwB9NZSdK2iLpmfQ6J5VL0s3pGtkh6YzORm9WjBMts9Z4Azg3It4HnAYskXQW2cSsN0XEe8jGdVqR9l8BHEjlN6X9zAbBORFxWkQMp/XVwNaIWARsTesAFwCL0tdK4Na2R2rWAk60zFogtY88lFaPSl8BnEs2RxjAOuCitLwsrZO2nydJbQrXrJvkr4Xqa+TOdG1tB2ZL8pyU1nO6YniHmSxYfX+h9+1Zc2GLIzGbWZrZ/jGySV2/QjYp+KsRMZF22QvMS8vzSJO5RsSEpIPAScBLVZ+5kuy/eYaGhhgdHZ3cdujQocn1nfsOFop58bwTCr2vWfnYe41jb0oA/yQpgK9GxFpgKCLG0/YXgKG0PHmNJJXrZzxXNuM1krf/lYPcsn5TwwGXcY10wc/hMN0UTz/G0vWJllmviIg3gdMkzSabW++9LfjMtcBagOHh4RgZGZncNjo6SmX9yqL/kFw2UnOfMuRj7zWOvSkfjIh9kn4D2CLpx/mNEREpCavbTNdI3i3rN3HDzsZveWVcI13wczhMN8XTj7G46tCsxSLiVWAb8AGy6o7KX/dTgH1peR8wHyBtPwF4uc2hmrVVROxLt8J0mQAAGVBJREFUr/vJ/hl5P/BipUowve5Pu09eI0n++jHrGU60zFpA0q+nJ1lIOhY4n2wy423AxWm3K4BK3cXmtE7a/mAaBM+sL0k6TtLxlWXgw8AuDr8Wqq+Ry1Pvw7OAg7kqRrOe4apDs9aYC6xL7bR+DbgnIu6TtBvYIOmvgB8Ct6X9bwO+IWkMeAVY3omgzdpoCPh26vNxJPDNiPiOpB8A90haAfwUuCTt/wCwFBgDXgeuan/IZs2rmWhJOgZ4CJiV9t8YEZ+XtBDYQNaA9zHgExHxS0mzgDuB3yWrCvkPEbGnpPjNukJE7ABOn6L8WbLqkeryXwAfb0NoZl0hXQvvm6L8ZeC8KcoDuLoNoZmVqp6qQ48PZGZmZlZAzUTL4wOZmZmZFVNXG612jg9UPW7FqsUTFNEt43DU0k1jhrTDoB2vmZkNtroSrXaOD1Q9bkWvjQ/UqG4aM6QdBu14zcxssDU0vIPHBzIzMzOrX81Ey+MDmZmZmRVTT9WhxwcyMzMzK6BmouXxgczMzMyK8RQ8ZmZmZiVxomVmZmZWEidaZmZmZiVxomVmZmZWEidaZmZmZiVxomVmZmZWEidaZmZmZiVxomXWJEnzJW2TtFvSk5KuTeVfkLRP0hPpa2nuPddJGpP0tKSPdC56MzMrU12TSpvZjCaAVRHxuKTjgcckbUnbboqIv8nvLOlUshkTfhv4TeC7kn4rTd5uZmZ9xE+0zJoUEeMR8Xhafo1sLtB5M7xlGbAhIt6IiOeAMaaYZcHMzHqfn2iZtZCkBWRTVj0MnA1cI+ly4FGyp14HyJKw7bm37WWaxEzSSmAlwNDQEKOjo5PbDh06NLm+avFEoXjzn9dO+dh7jWM3s0Y40TJrEUnvBL4FfCoifi7pVuAvgUivNwCfbOQzI2ItsBZgeHg4RkZGJreNjo5SWb9y9f2FYt5z2UjNfcqQj73XOHYza4SrDs1aQNJRZEnW+oi4FyAiXoyINyPiV8DXeKt6cB8wP/f2U1KZWd+TdISkH0q6L60vlPRw6hxyt6SjU/mstD6Wti/oZNxmRTnRMmuSJAG3AU9FxI258rm53X4P2JWWNwPL041kIbAIeKRd8Zp12LVk7RgrrifrNPIe4ACwIpWvAA6k8pvSfmY9p2ai5a7rZjWdDXwCOLfqevhrSTsl7QDOAf4MICKeBO4BdgPfAa52j0MbBJJOAS4Evp7WBZwLbEy7rAMuSsvL0jpp+3lpf7OeUk8bLXddN5tBRHwPmOoG8MAM7/ki8MXSgjLrTl8C/hw4Pq2fBLwaEZXeHPmOIfOA5wEiYkLSwbT/S/kPnKnDSN7QscU6jZTReaDbOiV0Uzz9GEvNRCsixoHxtPyapLq7rgPPSap0Xf9+09GamVlPkvRRYH9EPCZppFWfO1OHkbxb1m/ihp2N9/8qo8NIt3VK6KZ4+jGWhn7rWtl1fbr/QqozyF7rtt6obsre22HQjtfMJp0NfCxVqx8D/Cvgy8BsSUemp1r5jiGVTiN7JR0JnAC83P6wzZpTd6LV6q7r0/0XUp1B9lq39UZ1U/beDoN2vGaWiYjrgOsA0hOt/xoRl0n6B+BiYANwBbApvWVzWv9+2v5gRES74zZrVl29Dt113czMSvIZ4NOpmclJZD14Sa8npfJPA6s7FJ9ZU2o+0Zqp63pqvwVv77r+TUk3kjWGd9d1MzObFBGjwGhafpYppqCKiF8AH29rYGYlqKfqsNJ1faekJ1LZZ4FLJZ1GVnW4B/hDyLquS6p0XZ/AXdfNzMxsQNXT69Bd183MzMwK8MjwZmZmZiVxomVmZmZWEidaZmZmZiVxomVmZmZWEidaZmZmZiVxomVmZmZWEidaZmZmZiVxomVmZmZWEidaZi0gab6kbZJ2S3pS0rWp/ERJWyQ9k17npHJJulnSmKQdks7o7BGYmVkZnGiZtcYEsCoiTgXOAq6WdCrZRLhbI2IRsJW3Jsa9gGwe0EXASuDW9odsZmZlc6Jl1gIRMR4Rj6fl14CngHnAMmBd2m0dcFFaXgbcGZntwGxJc9sctpmZlayeSaXNrAGSFgCnAw8DQxExnja9AAyl5XnA87m37U1l47kyJK0ke+LF0NAQo6Ojk9sOHTo0ub5q8UShWPOf10752HuNYzezRjjRMmshSe8EvgV8KiJ+Lr01H3tEhKRo5PMiYi2wFmB4eDhGRkYmt42OjlJZv3L1/YXi3XPZSM19ypCPvdc4djNrRM2qQzfyNauPpKPIkqz1EXFvKn6xUiWYXven8n3A/NzbT0llZmbWR+p5olVp5Pu4pOOBxyRtAa4ka+S7RtJqska+n+HwRr5nkjXyPbOM4GeyoOh/+GsubHEkNgiUPbq6DXgqIm7MbdoMXAGsSa+bcuXXSNpAdn0czFUxmplZn6iZaKU//uNp+TVJ+Ua+I2m3dcAoWaI12cgX2C5ptqS5volYnzsb+ASwU9ITqeyzZAnWPZJWAD8FLknbHgCWAmPA68BV7Q3XzMzaoaE2Wu1o5FvdWLNoI9+i2t1QdNAap/br8UbE9wBNs/m8KfYP4OpSgzIzs46rO9FqVyPf6saaRRv5FtXuxsGD1jh10I7XzMwGW13jaLmRr5mZNUPSMZIekfSj1LHqL1L5QkkPpw5Ud0s6OpXPSutjafuCTsZvVlQ9vQ5rNfKFtzfyvTz1PjwLN/I1MzN4Azg3It4HnAYsSfeI64GbIuI9wAFgRdp/BXAgld+U9jPrOfU80ao08j1X0hPpaylZI9/zJT0DfCitQ9bI91myRr5fA/6k9WGbmVkvSbMgHEqrR6WvAM4FNqby6tkTKrMqbATOU77NilmPqKfXoRv5mplZ0yQdATwGvAf4CvAT4NWIqPR6qnSeglzHqoiYkHQQOAl4qeozp509IW/o2GKdq8rovNNtnYK6KZ5+jMUjw5uZWVtExJvAaZJmA98G3tuCz5x29oS8W9Zv4oadjd/yyugg1W2dgropnn6MxZNKm5lZW0XEq8A24ANkE6pXMqB856nJjlVp+wnAy20O1axpTrTMzKx0kn49PclC0rHA+cBTZAnXxWm36o5VlQ5XFwMPpqYpZj3FVYdmZtYOc4F1qZ3WrwH3RMR9knYDGyT9FfBDsl7upNdvSBoDXgGWdyJos2Y50TIzs9JFxA6ymUWqy58F3j9F+S+Aj7chNLNSuerQzMzMrCROtMzMzMxK4kTLzMzMrCRuo2VmZmZ9a8Hq+wu9744lx7Xk+/uJlpmZmVlJ/ETLrAUk3Q58FNgfEb+Tyr4A/GfgZ2m3z0bEA2nbdWST5r4J/GlE/GPbg6b4f3p71lzY4kjMzPqTn2iZtcYdwJIpym+KiNPSVyXJOpVsTKDfTu/5uzS2kJmZ9RknWmYtEBEPkQ2qWI9lwIaIeCMingPGmGIcITMz632uOjQr1zWSLgceBVZFxAFgHrA9t8/eVPY2klYCKwGGhoYOm0k+P7P8qsUTJYQ+vWZntM/H3mscu5k1omai1attT8y6wK3AXwKRXm8APtnIB0TEWmAtwPDwcORnks/PLH9lwbZWRe25bKTmPjPJx95rHLuZNaKeqsM7cNsTs4ZFxIsR8WZE/Ar4Gm9VD+4D5ud2PSWVmZlZn6mZaLntiVkxkubmVn8P2JWWNwPLJc2StBBYBDzS7vjMzKx8zbTRKqXtSXUbgl5re9KoQWsz0a/HK+kuYAQ4WdJe4PPAiKTTyKoO9wB/CBART0q6B9gNTABXR8SbnYjbzMzKVTTRKq3tSXUbgl5re9KoQWsz0a/HGxGXTlF82wz7fxH4YnkRmZlZNyg0vIPbnpiZmZnVVijRctsTMzMzs9rqGd7BbU/MzMzMCqiZaLntiZmZmVkxHhm+StFJdsET7ZqZmdnhPNehmZmVTtJ8Sdsk7Zb0pKRrU/mJkrZIeia9zknlknSzpDFJOySd0dkjMCvGiZaZmbXDBNmYi6cCZwFXp9lEVgNbI2IRsDWtA1xA1qFqEdmYi7e2P2Sz5jnRMjOz0kXEeEQ8npZfA54iG9B6GbAu7bYOuCgtLwPujMx2YHZVj3eznuA2WmZm1laSFgCnAw8DQxExnja9AAyl5XnA87m3VWYaGc+VTTvLSLWhY4vNNFLGTBbdNkNGN8VTRixFZ5hpVSxOtMzMrG0kvRP4FvCpiPi5pMltERGSopHPm26WkWq3rN/EDTsbv+WVMVtIt82Q0U3xlBFL0Rlm7lhyXEticdWhmZm1haSjyJKs9RFxbyp+sVIlmF73p3LPNGJ9wYmWmZmVTtmjq9uApyLixtymzcAVafkKYFOu/PLU+/As4GCuitGsZ7jq0MzM2uFs4BPATklPpLLPAmuAeyStAH4KXJK2PQAsBcaA14Gr2huuWWs40TIzs9JFxPcATbP5vCn2D+DqUoMyawNXHZqZmZmVxImWWQtIul3Sfkm7cmUe8drMbMA50TJrjTuAJVVlHvHazGzAOdEya4GIeAh4parYI16bmQ24mo3hJd0OfBTYHxG/k8pOBO4GFgB7gEsi4kDqvvtlsp4irwNXVqZcMBtATY14DTOPep0ftbjoyMdFNTtacjeNRN0ox25mjain1+EdwN8Cd+bKKlUiayStTuuf4fAqkTPJqkTObGXAZr2oyIjX6X3TjnqdH0G56MjHRTU7WnY3jUTdKMduZo2oWXXoKhGzwjzitZnZgCs6jlZpVSLVj7bbXSXSjCKP5AftUf6AHW9lxOs1vH3E62skbSB74usRr83M+lTTA5a2ukqk+tF2u6tEmlGkOmXQHuX36/FKugsYAU6WtBf4PH084vWCgtflnjUXtjgSM7PuVjTRelHS3IgYd5WIGUTEpdNs8ojXZmYDrOjwDp4E1MzMzKyGeoZ3GKgqETMrT6XKcdXiiYaaBbjK0cx6Vc1Ey1UiZmZmZsV4ZHgzMzOzkjjRMjMzMyuJEy0zMzOzkjjRMjMzMyuJEy0zMzOzkjjRMjMzMyuJEy0zMzOzkjjRMjOz0km6XdJ+SbtyZSdK2iLpmfQ6J5VL0s2SxiTtkHRG5yI3a44TLTMza4c7gCVVZauBrRGxCNia1gEuABalr5XArW2K0azlnGiZmVnpIuIh4JWq4mXAurS8DrgoV35nZLYDsyXNbU+kZq1VcwoeMzOzkgxFxHhafgEYSsvzgOdz++1NZeNUkbSS7KkXQ0NDjI6OTv2Njs3m2GzUdJ/XjEOHDpXyuUV1UzxlxFLk597KWJxomZlZx0VESIoC71sLrAUYHh6OkZGRKfe7Zf0mbtjZ+C1vz2VTf14zRkdHmS7OTuimeMqIpZEJ7PPuWHJcS2Jx1aFZySTtkbRT0hOSHk1lUzYCNhswL1aqBNPr/lS+D5if2++UVGbWc5p6oiVpD/Aa8CYwERHDkk4E7gYWAHuASyLiQHNh9oYFBbLmVYsnGGl9KNZ9zomIl3LrlUbAayStTuuf6UxoZh2zGbgCWJNeN+XKr5G0ATgTOJirYjTrKa2oOvQNxKxxy2Ayx14HjOLrZFpF/okB2LPmwhZHYkVJuovsd/5kSXuBz5MlWPdIWgH8FLgk7f4AsBQYA14Hrmp7wGYtUkYbLd9AzA4XwD+l9idfTW1KpmsEfJiZGvrmG2oWbezZKUUbJjdqEBoyN6KTsUfEpdNsOm+KfQO4utyIzNqj2USr5TeQ6j8EvXYDadTQseXcDLpVL9+kmvDBiNgn6TeALZJ+nN84UyPgmRr65huNFm3s2SmrFk8UapjcqEFoyNyIXo7drFc1+5eu5TeQ6j8EvXYDadSqxRNcMkB/+AbxD31E7Euv+yV9G3g/qRFwRIxXNQI2M7M+0lSvw/wNBDjsBgJv60ViNnAkHSfp+Moy8GFgF281AobDGwGbmVkfKZxo+QZiVpch4HuSfgQ8AtwfEd8hawR8vqRngA+ldTMz6zPNVB0OAd+WVPmcb0bEdyT9gKl7kZgNnIh4FnjfFOUvM0UjYDMz6y+FEy3fQMzMzMxm5pHhzczMzEriuQ7NrG95oFMz6zQ/0TIzMzMriRMtMzMzs5K46tDMrMpMVY6rFk9MO5CyqxzNrJoTrS7gdiRmZmb9yVWHZmZmZiVxomVmZmZWEidaZmZmZiVxGy0zsxZxe0szq+ZEq4f5j7qZmVl3c9WhmZmZWUmcaJmZmZmVxImWmZmZWUlKS7QkLZH0tKQxSavL+j5mvcrXiFltvk6s15XSGF7SEcBXgPOBvcAPJG2OiN1lfD9rTNFG9EUVbXzfz439fY1YXjPXZC/8vhfl68T6QVm9Dt8PjEXEswCSNgDLAF8cAyh/E5lpnrgB42vEWqKRJC1//fVIgubrxHpeWYnWPOD53Ppe4Mz8DpJWAivT6iFJT6flk4GXSoqr6/ypj7fldP2Mm99V5vduQM1rBGa8TqCHf3d6+fe+X2Lvl+ukxjWSV+jnVuM8FdVtv0PdFE/XxHLO9TPGUvc10rFxtCJiLbC2ulzSoxEx3IGQOsLHazOZ7jqB3j6Xjr0zejn26cx0jeR107F3UyzQXfH0YyxlNYbfB8zPrZ+Sysws42vErDZfJ9bzykq0fgAskrRQ0tHAcmBzSd/LrBf5GjGrzdeJ9bxSqg4jYkLSNcA/AkcAt0fEk3W+veYj4D7j4x1ATV4jFb18Lh17Z/RU7C26Tiq66di7KRbornj6LhZFRCs+x8zMzMyqeGR4MzMzs5I40TIzMzMrSdckWoM2zYKk2yXtl7Sr07GUTdJ8Sdsk7Zb0pKRrOx1Tr5nuHEo6UdIWSc+k1zmdjnU6ko6Q9ENJ96X1hZIeTtf83amxc9eRNFvSRkk/lvSUpA/0ynmX9Gfp92WXpLskHdMr570Rte4fkmalYx1Lx74gt+26VP60pI+0IZZPp+t4h6Stkt6V2/ampCfSV9ON/uuI5UpJP8t9zz/Ibbsi/X4/I+mKNsRyUy6Of5b0am5bq8/LjPdfZW5Ose6QdEZuW+PnJSI6/kXWyPEnwLuBo4EfAad2Oq6Sj/nfA2cAuzodSxuOdS5wRlo+Hvjnfv/5tuscAn8NrE7lq4HrOx3rDMfwaeCbwH1p/R5geVr+e+CPOx3jNHGvA/4gLR8NzO6F80422OdzwLG5831lr5z3Bo6z5v0D+BPg79PycuDutHxq2n8WsDB9zhElx3IO8I60/MeVWNL6oTaflyuBv53ivScCz6bXOWl5TpmxVO3/X8g6PrT8vKTPm/H+CywF/g8g4Czg4WbOS7c80ZqcZiEifglUplnoWxHxEPBKp+Noh4gYj4jH0/JrwFNkNwGr0wzncBlZIkB6vagzEc5M0inAhcDX07qAc4GNaZeujF3SCWR/lG8DiIhfRsSr9Mh5J+tZfqykI4F3AOP0wHlvUD33j/zPayNwXvodXAZsiIg3IuI5YCx9XmmxRMS2iHg9rW4nGxusDM3cVz8CbImIVyLiALAFWNLGWC4F7mri+82ojvvvMuDOyGwHZkuaS8Hz0i2J1lTTLPhG3IfSI/vTgYc7G0nvqjqHQxExnja9AAx1KKxavgT8OfCrtH4S8GpETKT1br3mFwI/A/5nqvb8uqTj6IHzHhH7gL8B/i9ZgnUQeIzeOO+NqOf+MblPOvaDZL+Drb73NPp5K8ienFQcI+lRSdslNZsA1xvL76fqsY2SKoPDduy8pKrUhcCDueJWnpd6TBdvofPSLYmWDQBJ7wS+BXwqIn7e6Xh60UznMLJn2103XoukjwL7I+KxTsdSwJFkVQy3RsTpwP8jqyqc1MXnfQ7Zf+YLgd8EjqO5pxLWQpL+EzAM/I9c8bsim/LlPwJfkvSvSw7jfwMLIuLfkj2dWVdj/3ZYDmyMiDdzZe0+Ly3VLYmWp1noc5KOIksQ1kfEvZ2OpxdNcw5fTI+0Sa/7OxXfDM4GPiZpD1mVwbnAl8kex1cGTe7Wa34vsDciKk9gN5IlXr1w3j8EPBcRP4uIfwHuJftZ9MJ5b0Q994/JfdKxnwC8XOd7Wx0Lkj4EfA74WES8USlPTyGJiGeBUbIn16XFEhEv577/14HfbeQ4WhlLznKqqg1bfF7qMV28hc5LtyRanmahj6W2ELcBT0XEjZ2OpxfNcA43A5WeL1cAm9odWy0RcV1EnBIRC8iu7Qcj4jJgG3Bx2q1bY38BeF7Sv0lF5wG76YHzTlZleJakd6Tfn0rsXX/eG1TP/SP/87qY7HcwUvny1CtxIbAIeKTMWCSdDnyVLMnanyufI2lWWj6ZLCneXXIsc3OrHyNr+wnZSPwfTjHNAT6cykqLJcXzXrJG5t/PlbX6vNRjM3B56n14FnAwNRUodl5a2ZK/yV4AS8l6Uv0E+Fyn42nD8d5F1m7iX8j+a17R6ZhKPNYPklWt7ACeSF9LOx1XL31Ndw7J2plsBZ4Bvguc2OlYaxzHCG/1Onw32U1tDPgHYFan45sm5tOAR9O5/19kN4KeOO/AXwA/BnYB3yDrXdcT573B43zb/QP4b2TJDMAx6VjH0rG/O/fez6X3PQ1c0IZYvgu8mLuON6fyfwfsJOuRt7MV94Q6YvnvwJPpe24D3pt77yfT+RoDrio7lrT+BWBN1fvKOC9vu/8CfwT8Udou4Csp1p3AcDPnxVPwmJmZmZWkW6oOzczMzPqOEy0zMzOzkjjRMjMzMyuJEy0zMzOzkjjRMjMzMyuJEy0zMzOzkjjRMjMzMyvJ/weMpYN2Rz5Q0AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that perhaps the attributes age, pedi and test may have an exponential or skewed distribution. We can also see that perhaps the mass and pres and plas attributes may have a Gaussian or nearly Gaussian distribution. This is interesting because many machine learning techniques assume a Gaussian univariate distribution on the input variables."
      ],
      "metadata": {
        "id": "YsB4BWe2329H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Density Plots\n",
        "Density plots are another way of getting a quick idea of the distribution of each attribute. The plots look like an abstracted histogram with a smooth curve drawn through the top of each bin, much like your eye tried to do with the histograms."
      ],
      "metadata": {
        "id": "aJzZAz654v4M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Tuple\n",
        "data.plot(kind='density',subplots=True,layout=(3,3),sharex=False,figsize=(15,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "YiDqyKZJ3zh7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "f1ffcc01-4efe-4476-8942-2dadfa8266eb"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see the distribution for each attribute is clearer than the histograms."
      ],
      "metadata": {
        "id": "rDTf5J9D5_6Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Box and Whisker Plots\n",
        "Another useful way to review the distribution of each attribute is to use Box and Whisker Plots or boxplots for short. Boxplots summarize the distribution of each attribute, drawing a line for the median (middle value) and a box around the 25th and 75th percentiles (the middle 50% of the data). The whiskers give an idea of the spread of the data and dots outside of the whiskers show candidate outlier values (values that are 1.5 times greater than the size of spread of the middle 50% of the data)."
      ],
      "metadata": {
        "id": "1vfaw1Zf6UGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot(kind='box', subplots =True, layout=(3,3),sharex=False,sharey=False, figsize=(15,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ROFLIjPl5qEa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "8afb68db-8f2f-48a3-a5fa-c80966b099e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the spread of attributes is quite different. Some like age, test and skin\n",
        "appear quite skewed towards smaller values."
      ],
      "metadata": {
        "id": "_k-H7cVD6q7u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Multivariate Plots\n",
        "This section provides examples of two plots that show the interactions between multiple variables in your dataset.\n",
        "1. Correlation Matrix Plot. \n",
        "2. Scatter Plot Matrix."
      ],
      "metadata": {
        "id": "1vQKU5RV6u3e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Correlation Matrix Plot\n",
        "Correlation gives an indication of how related the changes are between two variables. If two variables change in the same direction they are positively correlated. If they change in opposite directions together (one goes up, one goes down), then they are negatively correlated. You can calculate the correlation between each pair of attributes. This is called a correlation matrix. You can then plot the correlation matrix and get an idea of which variables have a high correlation with each other. This is useful to know, because some machine learning algorithms like linear and logistic regression can have poor performance if there are highly correlated input variables in your data."
      ],
      "metadata": {
        "id": "n5PzLMPq624E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(data.corr(),annot=True)"
      ],
      "metadata": {
        "id": "W-aVzs1Xwg7W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "08ee3829-1bb9-47ec-f326-ebf50f1d69fa"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2143bc0410>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Scatter Plot Matrix\n",
        "A scatter plot shows the relationship between two variables as dots in two dimensions, one axis for each attribute. You can create a scatter plot for each pair of attributes in your data. Drawing all these scatter plots together is called a scatter plot matrix. Scatter plots are useful for spotting structured relationships between variables, like whether you could summarize the relationship between two variables with a line. Attributes with structured relationships may also be correlated and good candidates for removal from your dataset."
      ],
      "metadata": {
        "id": "CkEGqPSW7Vb5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.plotting.scatter_matrix(data,figsize=(15,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KFB12_gf6dve",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "outputId": "0407b74f-85ee-46e4-ea99-338f272f7385"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 81 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Like the Correlation Matrix Plot above, the scatter plot matrix is symmetrical. This is useful to look at the pairwise relationships from different perspectives. Because there is little point of drawing a scatter plot of each variable with itself, the diagonal shows histograms of each attribute."
      ],
      "metadata": {
        "id": "bC91D28f9H_f"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dzcxD99K7y9W"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}