Project2.ipynb

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      "source": [
        "Zach Makki\n",
        "\n",
        "Advanced Data Science\n",
        "\n",
        "Project #2\n",
        "\n",
        "02/07/2022"
      ],
      "metadata": {
        "id": "5_OW4SH3n9DM"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rating</th>\n",
              "      <th>calories</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>sodium</th>\n",
              "      <th>#cakeweek</th>\n",
              "      <th>#wasteless</th>\n",
              "      <th>22-minute meals</th>\n",
              "      <th>3-ingredient recipes</th>\n",
              "      <th>30 days of groceries</th>\n",
              "      <th>advance prep required</th>\n",
              "      <th>anthony bourdain</th>\n",
              "      <th>caviar</th>\n",
              "      <th>iced coffee</th>\n",
              "      <th>iced tea</th>\n",
              "      <th>pressure cooker</th>\n",
              "      <th>valentine's day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20052.000000</td>\n",
              "      <td>1.593500e+04</td>\n",
              "      <td>15890.000000</td>\n",
              "      <td>1.586900e+04</td>\n",
              "      <td>1.593300e+04</td>\n",
              "      <td>20052.000000</td>\n",
              "      <td>20052.000000</td>\n",
              "      <td>20052.000000</td>\n",
              "      <td>20052.000000</td>\n",
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              "      <td>20052.000000</td>\n",
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              "      <td>20052.000000</td>\n",
              "      <td>20052.000000</td>\n",
              "      <td>20052.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.714467</td>\n",
              "      <td>6.322958e+03</td>\n",
              "      <td>100.160793</td>\n",
              "      <td>3.468775e+02</td>\n",
              "      <td>6.225975e+03</td>\n",
              "      <td>0.000299</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.000848</td>\n",
              "      <td>0.001346</td>\n",
              "      <td>0.000349</td>\n",
              "      <td>0.005436</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.000898</td>\n",
              "      <td>0.000399</td>\n",
              "      <td>0.01162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.340829</td>\n",
              "      <td>3.590460e+05</td>\n",
              "      <td>3840.318527</td>\n",
              "      <td>2.045611e+04</td>\n",
              "      <td>3.333182e+05</td>\n",
              "      <td>0.017296</td>\n",
              "      <td>0.007062</td>\n",
              "      <td>0.029105</td>\n",
              "      <td>0.036671</td>\n",
              "      <td>0.018681</td>\n",
              "      <td>0.073530</td>\n",
              "      <td>0.009987</td>\n",
              "      <td>0.007062</td>\n",
              "      <td>0.009987</td>\n",
              "      <td>0.029948</td>\n",
              "      <td>0.019971</td>\n",
              "      <td>0.10717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.750000</td>\n",
              "      <td>1.980000e+02</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>8.000000e+01</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.375000</td>\n",
              "      <td>3.310000e+02</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.700000e+01</td>\n",
              "      <td>2.940000e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.375000</td>\n",
              "      <td>5.860000e+02</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>3.300000e+01</td>\n",
              "      <td>7.110000e+02</td>\n",
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              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.011122e+07</td>\n",
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              "      <td>1.722763e+06</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
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            "text/plain": [
              "             rating      calories  ...  pressure cooker  valentine's day\n",
              "count  20052.000000  1.593500e+04  ...     20052.000000      20052.00000\n",
              "mean       3.714467  6.322958e+03  ...         0.000399          0.01162\n",
              "std        1.340829  3.590460e+05  ...         0.019971          0.10717\n",
              "min        0.000000  0.000000e+00  ...         0.000000          0.00000\n",
              "25%        3.750000  1.980000e+02  ...         0.000000          0.00000\n",
              "50%        4.375000  3.310000e+02  ...         0.000000          0.00000\n",
              "75%        4.375000  5.860000e+02  ...         0.000000          0.00000\n",
              "max        5.000000  3.011122e+07  ...         1.000000          1.00000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "#Import the needed libs\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "dataFrame = pd.read_csv(\"https://raw.githubusercontent.com/zachmakki15/ADVDS/main/epi_r.csv\");\n",
        "dataFrame.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets look at the features/attributes of this dataset"
      ],
      "metadata": {
        "id": "VzT0PP3aie_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Display top of table\n",
        "dataFrame.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "lu-smPTNhuFX",
        "outputId": "ea65ae8c-54ce-4581-fc23-d65a67cbd14b"
      },
      "execution_count": 133,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "      <th>calories</th>\n",
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              "      <th>fat</th>\n",
              "      <th>sodium</th>\n",
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              "      <th>0</th>\n",
              "      <td>Lentil, Apple, and Turkey Wrap</td>\n",
              "      <td>2.500</td>\n",
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              "      <th>1</th>\n",
              "      <td>Boudin Blanc Terrine with Red Onion Confit</td>\n",
              "      <td>4.375</td>\n",
              "      <td>403.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1439.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Potato and Fennel Soup Hodge</td>\n",
              "      <td>3.750</td>\n",
              "      <td>165.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>165.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>Mahi-Mahi in Tomato Olive Sauce</td>\n",
              "      <td>5.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>Spinach Noodle Casserole</td>\n",
              "      <td>3.125</td>\n",
              "      <td>547.0</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2be4c67a-7c5c-478c-b120-f4168dc5fcc4')\"\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-2be4c67a-7c5c-478c-b120-f4168dc5fcc4 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-2be4c67a-7c5c-478c-b120-f4168dc5fcc4');\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",
              "  "
            ],
            "text/plain": [
              "                                         title  ...  valentine's day\n",
              "0              Lentil, Apple, and Turkey Wrap   ...                0\n",
              "1  Boudin Blanc Terrine with Red Onion Confit   ...                0\n",
              "2                Potato and Fennel Soup Hodge   ...                0\n",
              "3             Mahi-Mahi in Tomato Olive Sauce   ...                0\n",
              "4                    Spinach Noodle Casserole   ...                0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Show table info\n",
        "dataFrame.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHOpPTxdj6yF",
        "outputId": "3c5cce7a-e08e-4a2f-c6f2-71f67cf9846b"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20052 entries, 0 to 20051\n",
            "Data columns (total 18 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   title                  20052 non-null  object \n",
            " 1   rating                 20052 non-null  float64\n",
            " 2   calories               15935 non-null  float64\n",
            " 3   protein                15890 non-null  float64\n",
            " 4   fat                    15869 non-null  float64\n",
            " 5   sodium                 15933 non-null  float64\n",
            " 6   #cakeweek              20052 non-null  int64  \n",
            " 7   #wasteless             20052 non-null  int64  \n",
            " 8   22-minute meals        20052 non-null  int64  \n",
            " 9   3-ingredient recipes   20052 non-null  int64  \n",
            " 10  30 days of groceries   20052 non-null  int64  \n",
            " 11  advance prep required  20052 non-null  int64  \n",
            " 12  anthony bourdain       20052 non-null  int64  \n",
            " 13  caviar                 20052 non-null  int64  \n",
            " 14  iced coffee            20052 non-null  int64  \n",
            " 15  iced tea               20052 non-null  int64  \n",
            " 16  pressure cooker        20052 non-null  int64  \n",
            " 17  valentine's day        20052 non-null  int64  \n",
            "dtypes: float64(5), int64(12), object(1)\n",
            "memory usage: 2.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lets examine the one non-numeric column, title\n",
        "dataFrame['title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZuqjQAtuj9cJ",
        "outputId": "297268c5-af6e-440f-eb07-acd8b4a96c35"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pastry Dough                                   28\n",
              "Chicken Stock                                  26\n",
              "Turkey Giblet Stock                            20\n",
              "Turkey Stock                                   15\n",
              "Simple Syrup                                   13\n",
              "                                               ..\n",
              "Fruity Lemonade Ice Pops                        1\n",
              "Szechwan-Style Eggplant with Pita Wedges        1\n",
              "Poached Eggs with Tomato Cilantro Sauce         1\n",
              "Greens and Grains Scramble                      1\n",
              "Baked Ham with Marmalade-Horseradish Glaze      1\n",
              "Name: title, Length: 17736, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From examining the data it seems we only need to create histograms for the continuous data columns, calories, protient, fat and sodium"
      ],
      "metadata": {
        "id": "Z4zfM6DUmbG1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataFrame.hist(column=['calories','protein','fat','sodium'], bins=20, figsize=(20,15))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        },
        "id": "aJZeQzmTtvpa",
        "outputId": "97aad758-cb2f-4dd8-fa59-127e5b82bce3"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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qY2Nj/3dVfbKqPlpVf1JVR46te3FV7a6qT1XVWWPjZw9ju6vq4rHxk6rq5mH8rVV1+EY+QAAAvklvBwCsxWqOQHpTkrOXjN2Y5Cmtte9P8ldJXpwkVXVKknOTPHm4ze9W1WFVdViS1yU5J8kpSZ47zE2SVyR5VWvtiUnuT3LBuh4RAAA9b4reDgA4QBMDpNbae5Lct2TsHa21/cPVm5IcPyzvSnJVa+0rrbU7k+xOctpw2d1au6O19tUkVyXZVVWV5JlJrhluf0WSZ6/zMQEAsAK9HQCwFjs2YBs/m+Stw/JxGTUdi/YMY0ly95LxZyR5TJIHxhqW8fkAAEzfVHq7qrowyYVJMjc3l4WFhY3Y94eYOyJ54VP3T554gDZrf7eCffv2qc+Uqfn0qflsqPtsrStAqqqXJNmf5MqN2Z2J97fpjYYmYzK/tJOp0WRqtDrqNJkaTaZGrNY0e7vW2mVJLkuSnTt3tvn5+U25n9dceW1eeetG/M30W9113vyGb3OrWFhYyGb9PFmemk+fms+Gus/Wmt9Nq+r5SX4iyRmttTYM701ywti044exrDD+hSRHVtWO4S9V4/MfYhqNhiZjMr+0k6nRZGq0Ouo0mRpNpkasxix6OwDg0LGaD9F+iKo6O8mvJvnJ1tqXx1Zdl+Tcqnp4VZ2U5OQktyR5X5KTh2/lODyjD2O8bmhO3p3kOcPtz09y7doeCgAAa6G3AwAmmRggVdVbkrw3yZOqak9VXZDktUkeneTGqvpwVf1ekrTWbktydZKPJ/nzJBe11r42/AXqF5LckOQTSa4e5ibJi5L8u6randF582/c0EcIAMA36O0AgLWYeK5Wa+25ywyv2Ai01i5Ncuky49cnuX6Z8Tsy+iYPAAA2md4OAFiLNZ3CBgAAAMD2IUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACANhGquryqrq3qj42NnZ0Vd1YVbcP/x41jFdVvbqqdlfVR6vq1LHbnD/Mv72qzh8b/8GqunW4zaurqqb7CAGAzTAxQNJkAABsKW9KcvaSsYuTvLO1dnKSdw7Xk+ScJCcPlwuTvD4Z9YJJLknyjCSnJblksR8c5vzrsdstvS8A4BC0miOQ3hRNBgDAltBae0+S+5YM70pyxbB8RZJnj42/uY3clOTIqjo2yVlJbmyt3ddauz/JjUnOHtZ9R2vtptZaS/LmsW0BAIewHZMmtNbeU1UnLhnelWR+WL4iyUKSF2WsyUhyU1UtNhnzGZqMJKmqxSZjIUOTMYwvNhlvX8+DAgDggMy11u4Zlj+bZG5YPi7J3WPz9gxjvfE9y4w/RFVdmNEfHDM3N5eFhYX1PYIVzB2RvPCp+zd8u5u1v1vBvn371GfK1Hz61Hw21H22JgZIK5h6k5FMp9HQZEzml3YyNZpMjVZHnSZTo8nUiAPRWmtV1aZwP5cluSxJdu7c2ebn5zflfl5z5bV55a1rbXlXdtd58xu+za1iYWEhm/XzZHlqPn1qPhvqPlvrfjedVpMx3NemNxqajMn80k6mRpOp0eqo02RqNJkasQqfq6pjW2v3DEeP3zuM701ywti844exvfnm0eiL4wvD+PHLzAcADnFr/Ra2zw3NRQ6gyVhpXJMBADBb1yVZ/JKT85NcOzb+vOGLUk5P8uBwFPoNSc6sqqOGz7U8M8kNw7ovVtXpwxejPG9sWwDAIWytAZImAwDgEFRVb0ny3iRPqqo9VXVBkpcn+fGquj3Jjw3Xk+T6JHck2Z3k95P8fJIMn2v5siTvGy4vXfysy2HOG4bbfDo+2xIAtoSJ52oNTcZ8kmOqak9G36b28iRXDw3HZ5L89DD9+iTPyqhh+HKSFySjJqOqFpuM5KFNxpuSHJFRg6HJAADYJK21566w6oxl5rYkF62wncuTXL7M+PuTPGU9+wgAHHxW8y1smgwAAACAbWytp7ABAAAAsE0IkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHStK0Cqqv+rqm6rqo9V1Vuq6hFVdVJV3VxVu6vqrVV1+DD34cP13cP6E8e28+Jh/FNVddb6HhIAAGuhtwMAVrLmAKmqjkvyb5PsbK09JclhSc5N8ookr2qtPTHJ/UkuGG5yQZL7h/FXDfNSVacMt3tykrOT/G5VHbbW/QIA4MDp7QCAnvWewrYjyRFVtSPJtye5J8kzk1wzrL8iybOH5V3D9Qzrz6iqGsavaq19pbV2Z5LdSU5b534BAHDg9HYAwLJ2rPWGrbW9VfVfk/x1kv+Z5B1JPpDkgdba/mHaniTHDcvHJbl7uO3+qnowyWOG8ZvGNj1+m29RVRcmuTBJ5ubmsrCwsNbdX9HcEckLn7p/8sQDtBn7Oiv79u3bUo9nM6jRZGq0Ouo0mRpNpkasxrR7u2n0dYnebha85kyfmk+fms+Gus/WmgOkqjoqo78wnZTkgSR/lNFhypumtXZZksuSZOfOnW1+fn7D7+M1V16bV9665rKs6K7z5jd8m7OysLCQzaj9VqJGk6nR6qjTZGo0mRqxGtPu7abR1yV6u1nwmjN9aj59aj4b6j5b6zmF7ceS3Nla+3xr7R+SvC3JDyc5cjjsOUmOT7J3WN6b5IQkGdZ/Z5IvjI8vcxsAAKZDbwcArGg9AdJfJzm9qr59ON/9jCQfT/LuJM8Z5pyf5Nph+brheob172qttWH83OGbPE5KcnKSW9axXwAAHDi9HQCwovV8BtLNVXVNkg8m2Z/kQxkdhvxnSa6qqt8cxt443OSNSf6wqnYnuS+jb+dIa+22qro6owZlf5KLWmtfW+t+AQBw4PR2AEDPuk4Ib61dkuSSJcN3ZJlv2mit/X2Sn1phO5cmuXQ9+wIAwPro7QCAlaznFDYAAAAAtgEBEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAASZKqOrKqrqmqT1bVJ6rqH1fV0VV1Y1XdPvx71DC3qurVVbW7qj5aVaeObef8Yf7tVXX+7B4RALBR1hUgaTIAALaU30ny562170vytCSfSHJxkne21k5O8s7hepKck+Tk4XJhktcnSVUdneSSJM9IclqSSxb7QQDg0LXeI5A0GQAAW0BVfWeSH03yxiRprX21tfZAkl1JrhimXZHk2cPyriRvbiM3JTmyqo5NclaSG1tr97XW7k9yY5Kzp/hQAIBNsGOtNxxrMp6fjJqMJF+tql1J5odpVyRZSPKijDUZSW4ajl46dph7Y2vtvmG7i03GW9a6bwAAHLCTknw+yR9U1dOSfCDJLyWZa63dM8z5bJK5Yfm4JHeP3X7PMLbS+Leoqgsz+qNi5ubmsrCwsGEPZNzcEckLn7p/w7e7Wfu7Fezbt099pkzNp0/NZ0PdZ2vNAVKm3GQk02k0NBmT+aWdTI0mU6PVUafJ1GgyNWKVdiQ5NckvttZurqrfyTePJE+StNZaVbWNuLPW2mVJLkuSnTt3tvn5+Y3Y7EO85spr88pb19PyLu+u8+Y3fJtbxcLCQjbr58ny1Hz61Hw21H221vNuOtUmY9jepjcamozJ/NJOpkaTqdHqqNNkajSZGrFKe5Lsaa3dPFy/JqPe7nNVdWxr7Z7h6PF7h/V7k5wwdvvjh7G9+ebR6IvjC5u43wDAFKznM5CWazJOzdBkJMkBNBnLjQMAMCWttc8mubuqnjQMnZHk40muS7L4JSfnJ7l2WL4uyfOGL0o5PcmDw1HoNyQ5s6qOGj7X8sxhDAA4hK05QNJkAABsOb+Y5Mqq+miSpyf5T0lenuTHq+r2JD82XE+S65PckWR3kt9P8vNJMnyu5cuSvG+4vHTxsy4BgEPXes/VWmwyDs+ogXhBRqHU1VV1QZLPJPnpYe71SZ6VUZPx5WFuWmv3VdVik5FoMgAAZqK19uEkO5dZdcYyc1uSi1bYzuVJLt/YvQMAZmldAZImAwAAAGDrW89nIAEAAACwDQiQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdK07QKqqw6rqQ1X1p8P1k6rq5qraXVVvrarDh/GHD9d3D+tPHNvGi4fxT1XVWevdJwAA1kZvBwAsZyOOQPqlJJ8Yu/6KJK9qrT0xyf1JLhjGL0hy/zD+qmFequqUJOcmeXKSs5P8blUdtgH7BQDAgdPbAQAPsa4AqaqOT/LPk7xhuF5JnpnkmmHKFUmePSzvGq5nWH/GMH9Xkqtaa19prd2ZZHeS09azXwAAHDi9HQCwkh3rvP1vJ/nVJI8erj8myQOttf3D9T1JjhuWj0tyd5K01vZX1YPD/OOS3DS2zfHbfIuqujDJhUkyNzeXhYWFde7+Q80dkbzwqfsnTzxAm7Gvs7Jv374t9Xg2gxpNpkaro06TqdFkasQBmFpvN42+LtHbzYLXnOlT8+lT89lQ99lac4BUVT+R5N7W2geqan7jdmllrbXLklyWJDt37mzz8xt/t6+58tq88tb15moPddd58xu+zVlZWFjIZtR+K1GjydRoddRpMjWaTI1YjWn3dtPo6xK93Sx4zZk+NZ8+NZ8NdZ+t9byb/nCSn6yqZyV5RJLvSPI7SY6sqh3DX6qOT7J3mL83yQlJ9lTVjiTfmeQLY+OLxm8DAMB06O0AgBWt+TOQWmsvbq0d31o7MaMPSnxXa+28JO9O8pxh2vlJrh2WrxuuZ1j/rtZaG8bPHb7J46QkJye5Za37BQDAgdPbAQA9G388b/KiJFdV1W8m+VCSNw7jb0zyh1W1O8l9GTUmaa3dVlVXJ/l4kv1JLmqtfW0T9gsAgAOntwMANiZAaq0tJFkYlu/IMt+00Vr7+yQ/tcLtL01y6UbsCwAA66O3AwCWWvMpbAAAAABsDwIkAAAAALoESAAAAAB0CZAAAAAA6BIgAQAAANAlQAIAAACgS4AEAAAAQJcACQAAAIAuARIAAAAAXQIkAAAAALoESAAAAAB0CZAAAAAA6BIgAQAAANAlQAIAAACgS4AEAAAAQJcACQAAAIAuARIAAAAAXQIkAAAAALoESAAAAAB0CZAAAAAA6BIgAQAAANAlQAIAAACgS4AEAAAAQJcACQAAAA6ekC8AACAASURBVIAuARIAAAAAXQIkAAAAALoESAAAAAB0CZAAAAAA6BIgAQAAANAlQAIAAACgS4AEAAAAQJcACQAAAIAuARIAAAAAXWsOkKrqhKp6d1V9vKpuq6pfGsaPrqobq+r24d+jhvGqqldX1e6q+mhVnTq2rfOH+bdX1fnrf1gAABwIvR0A0LOeI5D2J3lha+2UJKcnuaiqTklycZJ3ttZOTvLO4XqSnJPk5OFyYZLXJ6OmJMklSZ6R5LQklyw2JgAATI3eDgBY0ZoDpNbaPa21Dw7Lf5fkE0mOS7IryRXDtCuSPHtY3pXkzW3kpiRHVtWxSc5KcmNr7b7W2v1Jbkxy9lr3CwCAA6e3AwB6dmzERqrqxCQ/kOTmJHOttXuGVZ9NMjcsH5fk7rGb7RnGVhoHAGAGptHbVdWFGR25lLm5uSwsLGzY/o+bOyJ54VP3b/h2N2t/t4J9+/apz5Sp+fSp+Wyo+2ytO0Cqqkcl+eMkv9xa+2JVfWNda61VVVvvfYzd16Y3GpqMyfzSTqZGk6nR6qjTZGo0mRpxIKbV27XWLktyWZLs3Lmzzc/Pb8RmH+I1V16bV966IX8z/RZ3nTe/4dvcKhYWFrJZP0+Wp+bTp+azoe6zta5306p6WEYNxpWttbcNw5+rqmNba/cMhzHfO4zvTXLC2M2PH8b2JplfMr6w3P1No9HQZEzml3YyNZpMjVZHnSZTo8nUiNWadm8HABw61vMtbJXkjUk+0Vr7rbFV1yVZ/LaN85NcOzb+vOEbO05P8uBwOPQNSc6sqqOGD1g8cxgDAGBK9HYAQM96DrX54ST/MsmtVfXhYew/JHl5kqur6oIkn0ny08O665M8K8nuJF9O8oIkaa3dV1UvS/K+Yd5LW2v3rWO/AAA4cHo7AGBFaw6QWmt/maRWWH3GMvNbkotW2NblSS5f674AALA+ejsAoGfNp7ABAAAAsD0IkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoEiABAAAA0CVAAgAAAKBLgAQAAABAlwAJAAAAgC4BEgAAAABdAiQAAAAAugRIAAAAAHQJkAAAAADoOmgCpKo6u6o+VVW7q+riWe8PAABrp7cDgK3loAiQquqwJK9Lck6SU5I8t6pOme1eAQCwFno7ANh6DooAKclpSXa31u5orX01yVVJds14nwAAWBu9HQBsMTtmvQOD45LcPXZ9T5JnLJ1UVRcmuXC4uq+qPrUJ+3JMkr/d6I3WKzZ6izO1KTXaYtRoMjVaHXWaTI0m2+waPW4Tt82haWJvN6W+LtHbzYLX5elT8+lT89lQ9+lYtrc7WAKkVWmtXZbkss28j6p6f2tt52bex6FOjSZTo8nUaHXUaTI1mkyNOBhNo69LPP9nQc2nT82nT81nQ91n62A5hW1vkhPGrh8/jAEAcOjR2wHAFnOwBEjvS3JyVZ1UVYcnOTfJdTPeJwAA1kZvBwBbzEFxCltrbX9V/UKSG5IcluTy1tptM9qdTT+UegtQo8nUaDI1Wh11mkyNJlMjpkpvt+2p+fSp+fSp+Wyo+wxVa23W+wAAAADAQexgOYUNAAAAgIOUAAkAAACArm0bIFXV2VX1qaraXVUXL7P+4VX11mH9zVV14vT3crZWUaPnV9Xnq+rDw+VfzWI/Z6WqLq+qe6vqYyusr6p69VC/j1bVqdPex4PBKuo0X1UPjj2Pfm3a+zhrVXVCVb27qj5eVbdV1S8tM2dbP59WWaNt/VyqqkdU1S1V9ZGhRr+xzJxt/97G9jGpj2Gyqrqrqm4dXlPfP4wdXVU3VtXtw79HDeMrvk9V1fnD/Nur6vyx8R8ctr97uG1N/1HO1nJ90jRqvNJ9bBcr1P3Xq2rvWB/xrLF1Lx5q+KmqOmtsfNnXmRp9gcDNw/hba/RlAtv6fXilXs7z/RDTWtt2l4w+zPHTSR6f5PAkH0lyypI5P5/k94blc5O8ddb7fRDW6PlJXjvrfZ1hjX40yalJPrbC+mcleXuSSnJ6kptnvc8HaZ3mk/zprPdzxjU6Nsmpw/Kjk/zVMr9v2/r5tMoabevn0vDceNSw/LAkNyc5fcmcbf3e5rJ9LqvpY1xWVce7khyzZOy/JLl4WL44ySuG5WXfp5IcneSO4d+jhuWjhnW3DHNruO05s37MM6jxQ/qkadR4pfvYLpcV6v7rSX5lmbmnDK8hD09y0vDacljvdSbJ1UnOHZZ/L8m/GZa37fvwSr2c5/uhddmuRyCdlmR3a+2O1tpXk1yVZNeSObuSXDEsX5PkjG32V5HV1Ghba629J8l9nSm7kry5jdyU5MiqOnY6e3fwWEWdtr3W2j2ttQ8Oy3+X5BNJjlsybVs/n1ZZo21teG7sG64+bLgs/aaM7f7exvahj9k8468jVyR59tj4cu9TZyW5sbV2X2vt/iQ3Jjl7WPcdrbWb2uh/dW8e29a2sUKfNI0ar3Qf28IB9qe7klzVWvtKa+3OJLszeo1Z9nVmeF99Zkbvs8lDf4bb8n2408t5vh9CtmuAdFySu8eu78lD/yPyjTmttf1JHkzymKns3cFhNTVKkv9jOKTwmqo6YTq7dshYbQ1J/nGNTrt5e1U9edY7M0vDocw/kNHRI+M8nwadGiXb/LlUVYdV1YeT3JtRc7Xi82ibvrexfXjN3BgtyTuq6gNVdeEwNtdau2dY/mySuWF5pZr3xvcsM850arzSfWx3vzD83+bysdOcDrTuj0nywPA+Oz7+Ldvazu/DS3o5z/dDyHYNkNgY/z3Jia21788o+b1iwnxYzgeTPK619rQkr0ny32a8PzNTVY9K8sdJfrm19sVZ78/BaEKNtv1zqbX2tdba05Mcn+S0qnrKrPcJOKT909baqUnOSXJRVf3o+Mrhr/xLj3RkA02jxn6O3/D6JE9I8vQk9yR55Wx3Z2vq9XKe7we/7Rog7U0yfrTM8cPYsnOqakeS70zyhans3cFhYo1aa19orX1luPqGJD84pX07VKzmebbttda+uHjaTWvt+iQPq6pjZrxbU1dVD8vozfTK1trblpmy7Z9Pk2rkufRNrbUHkrw7ydlLVm339za2j23/mrkRWmt7h3/vTfInGZ2y87nFU6iHf+8dpq9U89748cuMM50ar3Qf21Zr7XPDH2K+nuT3M3q+Jwde9y9kdLrVjiXj37Kt7fg+vEIv5/l+CNmuAdL7kpw8fDr+4Rl9gNl1S+Zcl2TxE92fk+RdQ1q5XUys0ZLPX/nJjM5j5ZuuS/K84RsETk/y4Nihkwyq6rvGviHhtIxel7bNG2ky+paJJG9M8onW2m+tMG1bP59WU6Pt/lyqqsdW1ZHD8hFJfjzJJ5dM2+7vbWwfq+n16KiqR1bVoxeXk5yZ5GP51teR85NcOyyv9D51Q5Izq+qo4ZSgM5PcMKz7YlWdPrx2P29sW9vdNGq80n1sW0v+b/O/Z/R8T0a1OrdG36B2UpKTM/qw5mVfZ4b31Xdn9D6bPPRnuC3fhzu9nOf7oaQdBJ/kPYtLRp/q/lcZfXL+S4axlyb5yWH5EUn+KKMPSbslyeNnvc8HYY3+c5LbMvrGgXcn+b5Z7/OU6/OWjA5v/YeMzrG9IMnPJfm5YX0led1Qv1uT7Jz1Ph+kdfqFsefRTUn+yaz3eQY1+qcZHUr70SQfHi7P8nw64Bpt6+dSku9P8qGhRh9L8mvDuPc2l215Wa6PcTmg+j1+eD39yPDautgLPibJO5PcnuR/JDl6GF/xfSrJzw6vO7uTvGBsfOfwevXpJK9NUrN+3DOo83J90qbXeKX72C6XFer+h0NdP5pR4HDs2PyXDDX8VMa+LXCl15nh9+eW4efxR0kePoxv2/fhrNzLeb4fQpfFggIAAADAsrbrKWwAAAAArJIACQAAAIAuARIAAAAAXQIkAAAAALoESACwTVXV5VV1b1V9bBVzX1VVHx4uf1VVD0xjHwEAODj4FjYA2Kaq6keT7Evy5tbaUw7gdr+Y5Adaaz+7aTsHAMBBxRFIALBNtdbek+S+8bGqekJV/XlVfaCq/qKqvm+Zmz43yVumspMAABwUdsx6BwCAg8plSX6utXZ7VT0jye8meebiyqp6XJKTkrxrRvsHAMAMCJAAgCRJVT0qyT9J8kdVtTj88CXTzk1yTWvta9PcNwAAZkuABAAs+rYkD7TWnt6Zc26Si6a0PwAAHCR8BhIAkCRprX0xyZ1V9VNJUiNPW1w/fB7SUUneO6NdBABgRgRIALBNVdVbMgqDnlRVe6rqgiTnJbmgqj6S5LYku8Zucm6Sq5qvcAUA2HZKDwgAAABAjyOQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEgAAAAAdAmQAAAAAOgSIAEAAADQJUACAAAAoEuABAAAAECXAAkAAACALgESAAAAAF0CJAAAAAC6BEj/f3v3H2X7WdeH/v25ORBBkARSj5ikJtaIN0BRPA2xeu0ANgnYEu66yAqmEjTX3GpALaiAtsYirAW3IgoKNjVpgjcmxFRNrg3EXGBWqiXhN4QQgWMI5qSRIPmBBwR64HP/2N8Jm8nMd+bMnh9nzrxea82avZ/vj3k+z95z5jnvZ3/3BgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiRgQ1XV46rqg1X1d1X1M1vdHwAA1k9VdVV9x3D7d6vq3211n4CNUd291X0ADmNVdVGSz3X3v1lhvxOSfDLJQ7r7wCZ0DQCAGVVVJzmpu/dudV+AjeUVSMBG+7Ykt2x1JwAAAFg7ARKwYarqHUmemuS3q2p/Vf1sVX2gqj5XVXdU1a9O7X7D8P2+Yd/v2/QOAwDsIFX10qq6c3irgY9V1dOr6siq+s2q+h/D129W1ZFTx/xCVd01bPuJRee7pKpeOdx+QVX9+aLt05e7XVJVb6yqtw5zv7+oqm8Zft69VfWXVfU9mzEOwOoIkIAN091PS/Lfkrywux+R5ENJnp/kqCQ/nOSnqurZw+4/OHw/qrsf0d3v2vQOAwDsEFX1uCQvTPJPuvuRSU5PcnuSX05yapLvTvKkJKck+bfDMWck+fkk/zzJSUl+aMZuPHc49zFJvpTkXUneP9y/KslvzHh+YB0JkIBN093z3X1zd3+1uz+c5PIk/2yr+wUAsAN9JcmRSU6uqod09+3d/VdJzk7yiu6+u7s/k+TfJ/mx4ZjnJvnP3f2R7v58kl+dsQ9/3N3v6+4vJvnjJF/s7jd391eSvCWJVyDBIUSABGyaqnpKVb2zqj5TVfcn+deZrDABALCJhje9/rlMQqC7q+qKqvrWJN+a5FNTu35qaMvw/Y5F22bx6anbf7/E/UfMeH5gHQmQgM30B0muSXJ8dz8qye8mqWGbj4QEANhE3f0H3f0DmXzoSSd5TZL/Mdxf8A+HtiS5K8nxi7Yt5/NJHr5wp6q+ZT36DGwdARKwmR6Z5J7u/mJVnZLkR6e2fSbJV5N8+5b0DABgB6mqx1XV04Y3yP5iJq/4+WombzHwb6vqH1TVMUl+Jcn/Mxx2ZZIXVNXJVfXwJBeM/IgPJXl8VX13VX1DZr/cDdhiAiRgM/10kldU1d9lMhm5cmFDd38hyauS/EVV3VdVp25RHwEAdoIjk7w6yd8m+Zsk35zk5UlemeS9ST6c5OZM3tT6lUnS3W9N8ptJ3pFk7/B9Sd398SSvSPL/JflEkj9fbl9ge6huV40AAAAAsDyvQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGLVrqzuwVsccc0yfcMIJ637ez3/+8/nGb/zGdT/vdqF+9atf/TuV+tde//ve976/7e5/sM5dYgfZqHld4nd7FsZuNsZvNsZv7YzdbIzf8nO7bRsgnXDCCXnve9+77uedn5/P3Nzcup93u1C/+tU/t9Xd2DLqV/9a66+qT61vb9hpNmpel/jdnoWxm43xm43xWztjNxvjt/zcziVsAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMWjFAqqqLq+ruqvrIovYXVdVfVtUtVfV/T7W/vKr2VtXHqur0qfYzhra9VfWyqfYTq+qmof0tVfXQ9SoOAAAAgNmt5hVIlyQ5Y7qhqp6a5MwkT+ruxyf59aH95CRnJXn8cMwbq+qIqjoiye8keUaSk5M8b9g3SV6T5HXd/R1J7k1y7qxFAQCwNIuDAMBarBggdfcNSe5Z1PxTSV7d3V8a9rl7aD8zyRXd/aXu/mSSvUlOGb72dvdt3f3lJFckObOqKsnTklw1HH9pkmfPWBMAAMu7JBYHAYCDtGuNx31nkv+tql6V5ItJfr6735Pk2CQ3Tu23b2hLkjsWtT8lyWOS3NfdB5bY/0Gq6rwk5yXJ7t27Mz8/v8buL2///v0bct7tQv3qV//8Vndjy6hf/Tu5/p2ku2+oqhMWNa+4OJjkk1W1sDiYDIuDSVJVC4uDt2ayOPijwz6XJvnVJG/amGoAgM2y1gBpV5JHJzk1yT9JcmVVffu69WoZ3X1hkguTZM+ePT03N7fuP2N+fj4bcd7tQv3qV//cVndjy6hf/Tu5frZmcRAA2D7WGiDtS/JH3d1J3l1VX01yTJI7kxw/td9xQ1uWaf9skqOqatcw0ZjeHwCAzbHpi4Ob8cryxKvrZmHsZmP8ZmP81s7Yzcb4LW+tAdKfJHlqkndW1XcmeWiSv01yTZI/qKrfSPKtSU5K8u4kleSkqjoxk4DorCQ/2t1dVe9M8pxM3hfpnCRXz1DPzG6+8/684GX/dd3Pe/urf3jdzwkAsE42fXFwM15ZniRvuOzqvPbPP7/u590JczuvTJyN8ZuN8Vs7Yzcb47e8Fd9Eu6ouT/KuJI+rqn1VdW6Si5N8+/DpHVckOacnbklyZZKPJnlbkvO7+yvDBOKFSa5LcmuSK4d9k+SlSV48XFP/mCQXrW+JAACsYGFxMEssDp5VVUcOC4ELi4PvybA4OHzK2llJrhkCqIXFweQQWBwEANbHiq9A6u7nLbPpXy2z/6uSvGqJ9muTXLtE+2352psxAgCwgYbFwbkkx1TVviQXZLI4ePGwOPjlDIuDSW6pqoXFwQMZFgeH8ywsDh6R5OJFi4NXVNUrk3wgFgcB4LCw1kvYAADYhiwOAgBrseIlbAAAAADsbAIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFErBkhVdXFV3V1VH1li20uqqqvqmOF+VdXrq2pvVX24qp48te85VfWJ4eucqfbvraqbh2NeX1W1XsUBAAAAMLvVvALpkiRnLG6squOTnJbkr6ean5HkpOHrvCRvGvZ9dJILkjwlySlJLqiqo4dj3pTkJ6eOe9DPAgBgfVgcBADWYsUAqbtvSHLPEptel+QXk/RU25lJ3twTNyY5qqoem+T0JNd39z3dfW+S65OcMWz7pu6+sbs7yZuTPHu2kgAAGHFJLA4CAAdp11oOqqozk9zZ3R9atKh0bJI7pu7vG9rG2vct0b7czz0vk8lLdu/enfn5+bV0f9TuhyUveeKBdT/vRvR1I+zfv3/b9HUjqF/96p/f6m5sGfXv7Pp3ku6+oapOWGLTwuLg1VNtDywOJrmxqhYWB+cyLA4mSVUtLA7OZ1gcHNoXFgffujHVAACb5aADpKp6eJJfymSFalN194VJLkySPXv29Nzc3Lr/jDdcdnVee/OacrVRt589t+7n3Ajz8/PZiHHdLtSvfvXPbXU3toz6d3b9O91WLA5uxsJgYnFwFoLl2Ri/2Ri/tTN2szF+y1tLUvKPkpyYZGGCcVyS91fVKUnuTHL81L7HDW13ZrJSNd0+P7Qft8T+AABsgq1aHNyMhcHE4uAsBMuzMX6zMX5rZ+xmY/yWt5o30f463X1zd39zd5/Q3SdksrL05O7+myTXJHn+8IaLpya5v7vvSnJdktOq6ujh+vjTklw3bPtcVZ06vMHi8/P1L5sGAGBjTS8O3p6vLQ5+S8YXB5drtzgIAIehFQOkqro8ybuSPK6q9lXVuSO7X5vktiR7k/ynJD+dJMP18b+W5D3D1ysWrpkf9vm94Zi/imvkAQA2jcVBAGA1Vnw9b3c/b4XtJ0zd7iTnL7PfxUkuXqL9vUmesFI/AACY3bA4OJfkmKral+SC7r5omd2vTfLMTBb6vpDkx5PJ4mBVLSwOJg9eHLwkycMyWRi0OAgAh4H1vyAcAIBDlsVBAGAtDvo9kAAAAADYWQRIAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAqBUDpKq6uKrurqqPTLX9h6r6y6r6cFX9cVUdNbXt5VW1t6o+VlWnT7WfMbTtraqXTbWfWFU3De1vqaqHrmeBAAAAAMxmNa9AuiTJGYvark/yhO7+x0k+nuTlSVJVJyc5K8njh2PeWFVHVNURSX4nyTOSnJzkecO+SfKaJK/r7u9Icm+Sc2eqCACAZVkcBADWYsUAqbtvSHLPorY/6+4Dw90bkxw33D4zyRXd/aXu/mSSvUlOGb72dvdt3f3lJFckObOqKsnTklw1HH9pkmfPWBMAAMu7JBYHAYCDtGsdzvETSd4y3D42k0Bpwb6hLUnuWNT+lCSPSXLfVBg1vf+DVNV5Sc5Lkt27d2d+fn7Wvj/I7oclL3nigZV3PEgb0deNsH///m3T142gfvWrf36ru7Fl1L+z699JuvuGqjphUdufTd29MclzhtsPLA4m+WRVLSwOJsPiYJJU1cLi4K2ZLA7+6LDPpUl+Ncmb1r8SAGAzzRQgVdUvJzmQ5LL16c647r4wyYVJsmfPnp6bm1v3n/GGy67Oa29ej1zt691+9ty6n3MjzM/PZyPGdbtQv/rVP7fV3dgy6t/Z9fN1NmVxcDMWBhOLg7MQLM/G+M3G+K2dsZuN8VvempOSqnpBkn+R5Ond3UPznUmOn9rtuKEty7R/NslRVbVrmGhM7w8AwCbazMXBzVgYTCwOzkKwPBvjNxvjt3bGbjbGb3mreRPtB6mqM5L8YpJndfcXpjZdk+Ssqjqyqk5MclKSdyd5T5KThjdVfGgm19JfMwRP78zXXiZ9TpKr11YKAABrNbU4ePYqFgeXa39gcXBROwCwza0YIFXV5UneleRxVbWvqs5N8ttJHpnk+qr6YFX9bpJ09y1Jrkzy0SRvS3J+d39leHXRC5Ncl+TWJFcO+ybJS5O8eLim/jFJLlrXCgEAGGVxEABYyYqv5+3u5y3RvGzI092vSvKqJdqvTXLtEu235WtvxggAwAYaFgfnkhxTVfuSXJDJp64dmcniYJLc2N3/urtvqaqFxcEDGRYHh/MsLA4ekeTiRYuDV1TVK5N8IBYHAeCwsP4XhAMAcMiyOAgArMWa3gMJAAAAgJ1DgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwasUAqaourqq7q+ojU22Prqrrq+oTw/ejh/aqqtdX1d6q+nBVPXnqmHOG/T9RVedMtX9vVd08HPP6qqr1LhIAAACAtVvNK5AuSXLGoraXJXl72fu+jwAAHJZJREFUd5+U5O3D/SR5RpKThq/zkrwpmQROSS5I8pQkpyS5YCF0Gvb5yanjFv8sAADWicVBAGAtVgyQuvuGJPcsaj4zyaXD7UuTPHuq/c09cWOSo6rqsUlOT3J9d9/T3fcmuT7JGcO2b+ruG7u7k7x56lwAAKy/S2JxEAA4SLvWeNzu7r5ruP03SXYPt49NcsfUfvuGtrH2fUu0L6mqzstk8pLdu3dnfn5+jd1f3u6HJS954oF1P+9G9HUj7N+/f9v0dSOoX/3qn9/qbmwZ9e/s+neS7r6hqk5Y1Hxmkrnh9qVJ5pO8NFOLg0lurKqFxcG5DIuDSVJVC4uD8xkWB4f2hcXBt25cRQDAZlhrgPSA7u6q6vXozCp+1oVJLkySPXv29Nzc3Lr/jDdcdnVee/PMw/Igt589t+7n3Ajz8/PZiHHdLtSvfvXPbXU3toz6d3b9bP7i4GYsDCYWB2chWJ6N8ZuN8Vs7Yzcb47e8tSYln66qx3b3XcMq1N1D+51Jjp/a77ih7c58bVVroX1+aD9uif0BANgCm7U4uBkLg4nFwVkIlmdj/GZj/NbO2M3G+C1vNW+ivZRrkiy8WeI5Sa6ean/+8IaLpya5f1jNui7JaVV19HB9/GlJrhu2fa6qTh3eYPH5U+cCAGBzfHpYFMxBLA4u125xEAAOQysGSFV1eZJ3JXlcVe2rqnOTvDrJP6+qTyT5oeF+klyb5LYke5P8pyQ/nSTD9fG/luQ9w9crFq6ZH/b5veGYv4pr5AEANpvFQQBg1Iqv5+3u5y2z6elL7NtJzl/mPBcnuXiJ9vcmecJK/QAAYHbD4uBckmOqal8mn6b26iRXDguFn0ry3GH3a5M8M5OFvi8k+fFksjhYVQuLg8mDFwcvSfKwTBYGLQ4CwGFg/S8IBwDgkGVxEABYi7W+BxIAAAAAO4QACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGDVTgFRV/6aqbqmqj1TV5VX1DVV1YlXdVFV7q+otVfXQYd8jh/t7h+0nTJ3n5UP7x6rq9NlKAgAAAGA9rTlAqqpjk/xMkj3d/YQkRyQ5K8lrkryuu78jyb1Jzh0OOTfJvUP764b9UlUnD8c9PskZSd5YVUestV8AAKyNxUEAYDmzXsK2K8nDqmpXkocnuSvJ05JcNWy/NMmzh9tnDvczbH96VdXQfkV3f6m7P5lkb5JTZuwXAAAHweIgADBmzQFSd9+Z5NeT/HUmwdH9Sd6X5L7uPjDsti/JscPtY5PcMRx7YNj/MdPtSxwDAMDmsTgIACxp11oPrKqjM5kgnJjkviR/mMkq04apqvOSnJcku3fvzvz8/Lr/jN0PS17yxAMr73iQNqKvG2H//v3bpq8bQf3qV//8Vndjy6h/Z9fPZHGwqhYWB/8+yZ/lIBYHq2p6cfDGqVMvuTi4GfO6xNxuFv5dmI3xm43xWztjNxvjt7w1B0hJfijJJ7v7M0lSVX+U5PuTHFVVu4aJxnFJ7hz2vzPJ8Un2Dataj0ry2an2BdPHfJ3uvjDJhUmyZ8+enpubm6H7S3vDZVfntTfPMixLu/3suXU/50aYn5/PRozrdqF+9at/bqu7sWXUv7PrZ/MXBzdjXpeY283CvwuzMX6zMX5rZ+xmY/yWN8t7IP11klOr6uHDy5WfnuSjSd6Z5DnDPuckuXq4fc1wP8P2d3R3D+1nDW/EeGKSk5K8e4Z+AQBw8B5YHOzu/5nk6xYHh32WWhzMWhcHAYDtY5b3QLopk+vd35/k5uFcFyZ5aZIXV9XeTF7GfNFwyEVJHjO0vzjJy4bz3JLkykzCp7clOb+7v7LWfgEAsCYWBwGAZc30et7uviDJBYuab8sSb5TY3V9M8iPLnOdVSV41S18AAFi77r6pqhYWBw8k+UAmi4P/NckVVfXKoW16cfD3h8XBezL55LV09y1VtbA4eCAWBwHgsLD+F4QDALAtWRwEAJYzy3sgAQAAALADCJAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARs0UIFXVUVV1VVX9ZVXdWlXfV1WPrqrrq+oTw/ejh32rql5fVXur6sNV9eSp85wz7P+Jqjpn1qIAADh45nYAwHJmfQXSbyV5W3d/V5InJbk1ycuSvL27T0ry9uF+kjwjyUnD13lJ3pQkVfXoJBckeUqSU5JcsDAxAQBgU5nbAQBLWnOAVFWPSvKDSS5Kku7+cnffl+TMJJcOu12a5NnD7TOTvLknbkxyVFU9NsnpSa7v7nu6+94k1yc5Y639AgDg4JnbAQBjds1w7IlJPpPkP1fVk5K8L8nPJtnd3XcN+/xNkt3D7WOT3DF1/L6hbbn2B6mq8zJZ4cru3bszPz8/Q/eXtvthyUueeGDdz7sRfd0I+/fv3zZ93QjqV7/657e6G1tG/Tu7fpJswdwOANg+ZgmQdiV5cpIXdfdNVfVb+dpLmpMk3d1V1bN0cNH5LkxyYZLs2bOn5+bm1uvUD3jDZVfntTfPMixLu/3suXU/50aYn5/PRozrdqF+9at/bqu7sWXUv7PrJ8kmz+02Y2EwsTg4C8HybIzfbIzf2hm72Ri/5c2SlOxLsq+7bxruX5XJJOPTVfXY7r5reBnz3cP2O5McP3X8cUPbnUnmFrXPz9AvAAAO3qbO7TZjYTCxODgLwfJsjN9sjN/aGbvZGL/lrfk9kLr7b5LcUVWPG5qenuSjSa5JsvBpG+ckuXq4fU2S5w+f2HFqkvuHl0Nfl+S0qjp6eIPF04Y2AAA2ibkdADBm1uWYFyW5rKoemuS2JD+eSSh1ZVWdm+RTSZ477Httkmcm2ZvkC8O+6e57qurXkrxn2O8V3X3PjP0CAODgmdsBAEuaKUDq7g8m2bPEpqcvsW8nOX+Z81yc5OJZ+gIAwGzM7QCA5az5EjYAAAAAdgYBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMAoARIAAAAAowRIAAAAAIwSIAEAAAAwSoAEAAAAwCgBEgAAAACjBEgAAAAAjBIgAQAAADBKgAQAAADAKAESAAAAAKMESAAAAACMEiABAAAAMEqABAAAAMComQOkqjqiqj5QVX863D+xqm6qqr1V9ZaqeujQfuRwf++w/YSpc7x8aP9YVZ0+a58AAAAAWD/r8Qqkn01y69T91yR5XXd/R5J7k5w7tJ+b5N6h/XXDfqmqk5OcleTxSc5I8saqOmId+gUAwEGyOAgALGWmAKmqjkvyw0l+b7hfSZ6W5Kphl0uTPHu4feZwP8P2pw/7n5nkiu7+Und/MsneJKfM0i8AANbM4iAA8CCzvgLpN5P8YpKvDvcfk+S+7j4w3N+X5Njh9rFJ7kiSYfv9w/4PtC9xDAAAm8TiIACwnF1rPbCq/kWSu7v7fVU1t35dGv2Z5yU5L0l2796d+fn5df8Zux+WvOSJB1be8SBtRF83wv79+7dNXzeC+tWv/vmt7saWUf/Orp8HLCwOPnK4v+rFwaqaXhy8ceqcSy4Obsa8LjG3m4V/F2Zj/GZj/NbO2M3G+C1vzQFSku9P8qyqemaSb0jyTUl+K8lRVbVrmGgcl+TOYf87kxyfZF9V7UryqCSfnWpfMH3M1+nuC5NcmCR79uzpubm5Gbq/tDdcdnVee/Msw7K028+eW/dzboT5+flsxLhuF+pXv/rntrobW0b9O7t+Nn9xcDPmdYm53Sz8uzAb4zcb47d2xm42xm95a76Erbtf3t3HdfcJmVzn/o7uPjvJO5M8Z9jtnCRXD7evGe5n2P6O7u6h/azhjRhPTHJSknevtV8AAKzJwuLg7UmuyOTStQcWB4d9lloczFoXBwGA7WM9PoVtsZcmeXFV7c3kZcwXDe0XJXnM0P7iJC9Lku6+JcmVST6a5G1Jzu/ur2xAvwAAWIbFQQBgzLq8nre755PMD7dvyxJvlNjdX0zyI8sc/6okr1qPvgAAsK5emuSKqnplkg/k6xcHf39YHLwnk9Ap3X1LVS0sDh6IxUEAOCys/wXhAABsaxYHAYDFNuISNgAAAAAOIwIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFFrDpCq6viqemdVfbSqbqmqnx3aH11V11fVJ4bvRw/tVVWvr6q9VfXhqnry1LnOGfb/RFWdM3tZAAAcDHM7AGDMLK9AOpDkJd19cpJTk5xfVScneVmSt3f3SUnePtxPkmckOWn4Oi/Jm5LJpCTJBUmekuSUJBcsTEwAANg05nYAwLLWHCB1913d/f7h9t8luTXJsUnOTHLpsNulSZ493D4zyZt74sYkR1XVY5OcnuT67r6nu+9Ncn2SM9baLwAADp65HQAwprp79pNUnZDkhiRPSPLX3X3U0F5J7u3uo6rqT5O8urv/fNj29iQvTTKX5Bu6+5VD+79L8vfd/etL/JzzMlnhyu7du7/3iiuumLnvi919z/359N+v+2nzxGMftf4n3QD79+/PIx7xiK3uxpZRv/rVr/6dapb6n/rUp76vu/esc5fYQpsxt9uMeV1ibjeLnf7v4qyM32yM39oZu9kYv+XndrtmPXFVPSLJf0nyc939ucm8YqK7u6pmT6i+dr4Lk1yYJHv27Om5ubn1OvUD3nDZ1XntzTMPy4Pcfvbcup9zI8zPz2cjxnW7UL/61T+31d3YMurf2fXzNZs1t9uMeV1ibjcL/y7MxvjNxvitnbGbjfFb3kyfwlZVD8lkgnFZd//R0Pzp4eXLGb7fPbTfmeT4qcOPG9qWawcAYBOZ2wEAy5nlU9gqyUVJbu3u35jadE2ShU/bOCfJ1VPtzx8+sePUJPd3911JrktyWlUdPbzB4mlDGwAAm8TcDgAYM8vreb8/yY8lubmqPji0/VKSVye5sqrOTfKpJM8dtl2b5JlJ9ib5QpIfT5Luvqeqfi3Je4b9XtHd98zQLwAADp65HQCwrDUHSMMbJtYym5++xP6d5PxlznVxkovX2hcAAGZjbgcAjJnpPZAAAAAAOPwJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABglAAJAAAAgFECJAAAAABGCZAAAAAAGHXIBEhVdUZVfayq9lbVy7a6PwAArJ25HQAcXg6JAKmqjkjyO0mekeTkJM+rqpO3tlcAAKyFuR0AHH4OiQApySlJ9nb3bd395SRXJDlzi/sEAMDamNsBwGFm11Z3YHBskjum7u9L8pTFO1XVeUnOG+7ur6qPbUBfjknyt+t90nrNep9xw2xI/duI+tWv/p1L/Wuv/9vWsyMcFlac223SvC4xt5vFTv93cVbGbzbGb+2M3WyM3zJzu0MlQFqV7r4wyYUb+TOq6r3dvWcjf8ahTP3qV7/6t7ofW0X9O7t+Nt9mzOsSz+1ZGLvZGL/ZGL+1M3azMX7LO1QuYbszyfFT948b2gAA2H7M7QDgMHOoBEjvSXJSVZ1YVQ9NclaSa7a4TwAArI25HQAcZg6JS9i6+0BVvTDJdUmOSHJxd9+yRd3Z8JdSH+LUv7Opf2dT/8620+tnHZnbHTaM3WyM32yM39oZu9kYv2VUd291HwAAAAA4hB0ql7ABAAAAcIgSIAEAAAAwascESFV1RlV9rKr2VtXLlth+ZFW9Zdh+U1WdMLXt5UP7x6rq9M3s93pZRf0vrqqPVtWHq+rtVfVtU9u+UlUfHL625RtgrqL+F1TVZ6bq/D+ntp1TVZ8Yvs7Z3J6vj1XU/7qp2j9eVfdNbTscHv+Lq+ruqvrIMturql4/jM+Hq+rJU9sOh8d/pfrPHuq+uar+e1U9aWrb7UP7B6vqvZvX6/Wzivrnqur+qef5r0xtG/3d2Q5WUf8vTNX+keF3/tHDtm3/+LMzzDLPY7Z50k43yxyD2f5G73RVdXxVvXP4P9wtVfWzS+zj+beMVY6f599i3X3Yf2Xy5o1/leTbkzw0yYeSnLxon59O8rvD7bOSvGW4ffKw/5FJThzOc8RW17QB9T81ycOH2z+1UP9wf/9W17AJ9b8gyW8vceyjk9w2fD96uH30Vte03vUv2v9FmbzZ6WHx+A81/GCSJyf5yDLbn5nkrUkqyalJbjpcHv9V1v9PF+pK8oyF+of7tyc5Zqtr2OD655L86RLtB/W7c6h+rVT/on3/ZZJ3HE6Pv6/D/2uWeZ6v2eZJvtY+x/C16vFb8m+0r06SxyZ58nD7kUk+vsTvruffbOPn+bfoa6e8AumUJHu7+7bu/nKSK5KcuWifM5NcOty+KsnTq6qG9iu6+0vd/ckke4fzbScr1t/d7+zuLwx3b0xy3Cb3cSOt5vFfzulJru/ue7r73iTXJzljg/q5UQ62/ucluXxTerZJuvuGJPeM7HJmkjf3xI1Jjqqqx+bwePxXrL+7//tQX3L4/f6v5vFfziz/dhwyDrL+w+73nx1hlnkeh8m/dVtlhjkGmelv9I7X3Xd19/uH23+X5NYkxy7azfNvGascPxbZKQHSsUnumLq/Lw9+cjywT3cfSHJ/kses8thD3cHWcG4mSfWCb6iq91bVjVX17I3o4AZbbf3/x/DSzquq6viDPPZQtuoaanLp4olJ3jHVvN0f/9VYbowOh8f/YC3+/e8kf1ZV76uq87aoT5vh+6rqQ1X11qp6/NC2ox7/qnp4JgHpf5lq3imPP9vbLPM8ZpsnsbId9bdkgyz1N5opw2W535PkpkWbPP9WYWT8Es+/r7NrqzvAoaWq/lWSPUn+2VTzt3X3nVX17UneUVU3d/dfbU0PN8z/m+Ty7v5SVf1fmaxSPm2L+7QVzkpyVXd/ZaptJzz+JKmqp2YSIP3AVPMPDI//Nye5vqr+clgtPJy8P5Pn+f6qemaSP0ly0hb3aSv8yyR/0d3TK8E74fEHVmaexFbxN3oFVfWITBZ/fq67P7fV/dluVhg/z79FdsorkO5MMr1SctzQtuQ+VbUryaOSfHaVxx7qVlVDVf1Qkl9O8qzu/tJCe3ffOXy/Lcl8JunsdrJi/d392amafy/J96722G3gYGo4K4suXzkMHv/VWG6MDofHf1Wq6h9n8tw/s7s/u9A+9fjfneSPs/0u4V1Rd3+uu/cPt69N8pCqOiY76PEfjP3+H7aPP4eFWeZ5zDZPYmU77W/Juhr5G02SqnpIJuHHZd39R0vs4vk3YqXx8/x7sJ0SIL0nyUlVdWJVPTSTSfLiT5O6JsnCJyw9J5M3Ee2h/azh0ztOzCRxfPcm9Xu9rFh/VX1Pkv+YSXh091T70VV15HD7mCTfn+Sjm9bz9bGa+qevBX5WJtfAJsl1SU4bxuHoJKcNbdvJap7/qarvyuSNot811XY4PP6rcU2S5w+fVHFqkvu7+64cHo//iqrqHyb5oyQ/1t0fn2r/xqp65MLtTOpf8lNStrOq+paF90KpqlMy+dv42azyd+dwUFWPyuSVp1dPte2Ix5/DwizzPGabJ7Gy5eYYrMLI3+gdbxiXi5Lc2t2/scxunn/LWM34ef492I64hK27D1TVCzP5j98RmXzC1C1V9Yok7+3uazJ58vx+Ve3N5I3czhqOvaWqrszkP80Hkpy/6PKeQ94q6/8PSR6R5A+H35G/7u5nJflfk/zHqvpqJr8wr+7ubRUgrLL+n6mqZ2XyGN+TyaeNpLvvqapfy2RylSSvWHR5xyFvlfUnk+f8FYsm1Nv+8U+Sqro8k09ROKaq9iW5IMlDkqS7fzfJtZl8SsXeJF9I8uPDtm3/+Cerqv9XMnkvkDcOv/8HuntPkt1J/nho25XkD7r7bZtewIxWUf9zkvxUVR1I8vdJzhp+D5b83dmCEmayivqT5H9P8mfd/fmpQw+Lx5/D3yzzPGabJ7H2OQYTM/yNZrKw+2NJbq6qDw5tv5TkHyaef6uwmvHz/Fukdnj9AAAAAKxgp1zCBgAAAMAaCZAAAAAAGCVAAgAAAGCUAAkAAACAUQIkANihquriqrq7qj6yyv2fW1UfrapbquoPNrp/AACs3sHM7arqdVX1weHr41V134rH+BQ2ANiZquoHk+xP8ubufsIK+56U5MokT+vue6vqm7v77s3oJwAAKzuYud2i416U5Hu6+yfG9vMKJADYobr7hiT3TLdV1T+qqrdV1fuq6r9V1XcNm34yye90973DscIjAIBDyEHO7aY9L8nlK51fgAQATLswyYu6+3uT/HySNw7t35nkO6vqL6rqxqo6Y8t6CADAai03t0uSVNW3JTkxyTtWOtGuDekeALDtVNUjkvzTJH9YVQvNRw7fdyU5KclckuOS3FBVT+zuFa+XBwBg860wt1twVpKruvsrK51PgAQALPhfktzX3d+9xLZ9SW7q7v+Z5JNV9fFMAqX3bGYHAQBYtbG53YKzkpy/2pMBAKS7P5dJOPQjSVITTxo2/0kmrz5KVR2TySVtt21FPwEAWNkKc7sM74d0dJJ3reZ8AiQA2KGq6vJMJgyPq6p9VXVukrOTnFtVH0pyS5Izh92vS/LZqvpokncm+YXu/uxW9BsAgAc7yLldMnn10RXd3as6/yr3AwAAAGCH8gokAAAAAEYJkAAAAAAYJUACAAAAYJQACQAAAIBRAiQAAAAARgmQAAAAABglQAIAAABg1P8P2OEhAp6Q6HUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The histograms are showing weird results for calories, protein, fat, and sodium\n",
        "\n",
        "Lets remove all duplicate values and replace all missing values with the mean value for their respective column"
      ],
      "metadata": {
        "id": "h3mJ8McWvEoa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop all duplicate titles\n",
        "dataFrame.drop_duplicates(subset=\"title\",keep=False,inplace=True)"
      ],
      "metadata": {
        "id": "ut_i43Z4zUqw"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Show updated datset info\n",
        "dataFrame.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_k0de9RGzXWU",
        "outputId": "6596b730-1733-4c48-e63f-0c22fd431853"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 15833 entries, 0 to 20051\n",
            "Data columns (total 18 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   title                  15833 non-null  object \n",
            " 1   rating                 15833 non-null  float64\n",
            " 2   calories               12550 non-null  float64\n",
            " 3   protein                12534 non-null  float64\n",
            " 4   fat                    12517 non-null  float64\n",
            " 5   sodium                 12550 non-null  float64\n",
            " 6   #cakeweek              15833 non-null  int64  \n",
            " 7   #wasteless             15833 non-null  int64  \n",
            " 8   22-minute meals        15833 non-null  int64  \n",
            " 9   3-ingredient recipes   15833 non-null  int64  \n",
            " 10  30 days of groceries   15833 non-null  int64  \n",
            " 11  advance prep required  15833 non-null  int64  \n",
            " 12  anthony bourdain       15833 non-null  int64  \n",
            " 13  caviar                 15833 non-null  int64  \n",
            " 14  iced coffee            15833 non-null  int64  \n",
            " 15  iced tea               15833 non-null  int64  \n",
            " 16  pressure cooker        15833 non-null  int64  \n",
            " 17  valentine's day        15833 non-null  int64  \n",
            "dtypes: float64(5), int64(12), object(1)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Show updated value counts\n",
        "dataFrame['title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XgDH_ZnzbzL",
        "outputId": "cbd7929f-14a9-4165-ff3e-d394c27ed0fd"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lentil, Apple, and Turkey Wrap                                1\n",
              "Olive Oil-Baked Beet Chips With Sea Salt And Black Pepper     1\n",
              "Campanelle with White Beans, Lemon, and Burrata               1\n",
              "Green-Curry Chicken with Peas and Basil                       1\n",
              "Paad Thai                                                     1\n",
              "                                                             ..\n",
              "Peppery Fig and Cider Compote                                 1\n",
              "Bacon and Cabbage Soup                                        1\n",
              "Lamb with Peas and Tomatoes                                   1\n",
              "Cranberry Crunch Salad                                        1\n",
              "Baked Ham with Marmalade-Horseradish Glaze                    1\n",
              "Name: title, Length: 15833, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now all duplicate titles have been dropped and there is only one recipe per title in the dataset\n",
        "\n",
        "Now lets replace all the missing values using the sklearn simple imputer"
      ],
      "metadata": {
        "id": "lT23WeGrz62p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import sklearn simple imputer and pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "imputer = SimpleImputer(strategy=\"mean\")\n",
        "\n",
        "dataFrame_cont = dataFrame[['calories', 'protein', 'fat', 'sodium']]\n",
        "imputer.fit(dataFrame_cont)\n",
        "dataFrame_cont_arr = imputer.transform(dataFrame_cont)\n",
        "dataFrame_cont_tr = pd.DataFrame(dataFrame_cont_arr, columns=dataFrame_cont.columns, index=dataFrame_cont.index)\n",
        "dataFrame_cont_tr = np.log10(dataFrame_cont_tr + 1)\n",
        "dataFrame_cont_tr.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7PlxNCXoz0lC",
        "outputId": "5a428028-ec07-4561-ce7b-5ee3e4204796"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9c333618-9d15-474e-a03d-5b42902b7ebb\">\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>calories</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>sodium</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.630428</td>\n",
              "      <td>1.491362</td>\n",
              "      <td>0.903090</td>\n",
              "      <td>2.748188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.606381</td>\n",
              "      <td>1.278754</td>\n",
              "      <td>1.380211</td>\n",
              "      <td>3.158362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.220108</td>\n",
              "      <td>0.845098</td>\n",
              "      <td>0.903090</td>\n",
              "      <td>2.220108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.860978</td>\n",
              "      <td>1.936145</td>\n",
              "      <td>2.601506</td>\n",
              "      <td>3.861289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.738781</td>\n",
              "      <td>1.322219</td>\n",
              "      <td>1.518514</td>\n",
              "      <td>2.656098</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c333618-9d15-474e-a03d-5b42902b7ebb')\"\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",
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              "\n",
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              "          const element = document.querySelector('#df-9c333618-9d15-474e-a03d-5b42902b7ebb');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   calories   protein       fat    sodium\n",
              "0  2.630428  1.491362  0.903090  2.748188\n",
              "1  2.606381  1.278754  1.380211  3.158362\n",
              "2  2.220108  0.845098  0.903090  2.220108\n",
              "3  3.860978  1.936145  2.601506  3.861289\n",
              "4  2.738781  1.322219  1.518514  2.656098"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All continuous columns have been normalized and had their missing values replaced with the respective column mean, now lets look at the histograms again "
      ],
      "metadata": {
        "id": "FmFCWI1cmCfK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataFrame_cont_tr.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "PF8YgUFemUb1",
        "outputId": "322f5143-4721-48f8-eae3-8e5d99f6419b"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>calories</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>sodium</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15833.000000</td>\n",
              "      <td>15833.000000</td>\n",
              "      <td>15833.000000</td>\n",
              "      <td>15833.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.830175</td>\n",
              "      <td>1.254465</td>\n",
              "      <td>1.524821</td>\n",
              "      <td>2.692338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.632391</td>\n",
              "      <td>0.591290</td>\n",
              "      <td>0.723165</td>\n",
              "      <td>0.882826</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.406540</td>\n",
              "      <td>0.778151</td>\n",
              "      <td>1.113943</td>\n",
              "      <td>2.176091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.687529</td>\n",
              "      <td>1.278754</td>\n",
              "      <td>1.431364</td>\n",
              "      <td>2.705008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.110926</td>\n",
              "      <td>1.886491</td>\n",
              "      <td>1.944483</td>\n",
              "      <td>3.301247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.478728</td>\n",
              "      <td>5.303129</td>\n",
              "      <td>6.236226</td>\n",
              "      <td>7.442089</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c77bfdc3-2ae0-40da-8210-915ff153f04f');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           calories       protein           fat        sodium\n",
              "count  15833.000000  15833.000000  15833.000000  15833.000000\n",
              "mean       2.830175      1.254465      1.524821      2.692338\n",
              "std        0.632391      0.591290      0.723165      0.882826\n",
              "min        0.000000      0.000000      0.000000      0.000000\n",
              "25%        2.406540      0.778151      1.113943      2.176091\n",
              "50%        2.687529      1.278754      1.431364      2.705008\n",
              "75%        3.110926      1.886491      1.944483      3.301247\n",
              "max        7.478728      5.303129      6.236226      7.442089"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataFrame_cont_tr.hist(bins=50, figsize=(20,15))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "fqPunqVkqQvW",
        "outputId": "0e347631-f103-4334-d869-a3174d832e33"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9171890f90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f91715eb9d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9171618fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f91715d9610>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "These histograms now look much cleaner then they did before the columns were normalized using log(x+1)"
      ],
      "metadata": {
        "id": "nfT2OYVJ0wqz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets add the rating column to our numeric dataFrame and calculate Pearsons R correlation between the numeric columns (calories, protein, fat, sodium)"
      ],
      "metadata": {
        "id": "GCuBPaVxApkW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataFrame_cont_tr['rating'] = dataFrame['rating']\n",
        "corr_matrix = dataFrame_cont_tr.corr()\n",
        " \n",
        "corr_matrix[\"rating\"].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8TBds1L4M9R",
        "outputId": "101f6e6e-db83-4c49-a082-7852e951da48"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating      1.000000\n",
              "protein     0.093521\n",
              "sodium      0.075710\n",
              "fat         0.058590\n",
              "calories    0.015062\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The correlation for each numeric column to rating is relativly low. Lets now put all our data back into a final dataFrame and show scatter plots for the relationships between calories and rating, protein and rating, sodium and rating, and fat and rating"
      ],
      "metadata": {
        "id": "4jt05jDd3ByX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataFrame['protein'] = dataFrame_cont_tr['protein']\n",
        "dataFrame['calories'] = dataFrame_cont_tr['calories']\n",
        "dataFrame['fat'] = dataFrame_cont_tr['fat']\n",
        "dataFrame['sodium'] = dataFrame_cont_tr['sodium']\n",
        "sns.pairplot(dataFrame, x_vars=['calories', 'protein', 'fat', 'sodium'], y_vars=['rating'], height=4, aspect=1, kind='scatter')\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "zmzLMAs93eYH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "606d0c01-1592-4d77-8892-d51546427b19"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since our Pearson correlation is low and the scatter plots are not showing us any linear relationship lets do PCA on the dimensions we have in our dataset"
      ],
      "metadata": {
        "id": "ZSmnjQZVBc7I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA()\n",
        "dataFrame_pca = dataFrame.drop(columns=['title'])\n",
        "pca.fit(dataFrame_pca)\n",
        "cumlativeSum = np.cumsum(pca.explained_variance_ratio_)\n",
        "d = np.argmax(cumlativeSum >= 0.95) + 1\n",
        "pca = PCA(n_components = d)\n",
        "pca_tr = pca.fit_transform(dataFrame_pca)"
      ],
      "metadata": {
        "id": "7L6iqL4ECDmU"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets examine the results from PCA"
      ],
      "metadata": {
        "id": "b__DhOq7SABK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print ('Scaled data shape: ', dataFrame_pca)\n",
        "print ('Transformed data shape: ', pca_tr.shape)\n",
        "print ('Explained variance by n components: ', pca.explained_variance_ratio_)\n",
        "print ('#Components explaining 95% of variance: ', d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cqiktO_SDlf",
        "outputId": "deac2427-2ec6-4865-88cb-11865578000b"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scaled data shape:         rating  calories   protein  ...  iced tea  pressure cooker  valentine's day\n",
            "0       2.500  2.630428  1.491362  ...         0                0                0\n",
            "1       4.375  2.606381  1.278754  ...         0                0                0\n",
            "2       3.750  2.220108  0.845098  ...         0                0                0\n",
            "3       5.000  3.860978  1.936145  ...         0                0                0\n",
            "4       3.125  2.738781  1.322219  ...         0                0                0\n",
            "...       ...       ...       ...  ...       ...              ...              ...\n",
            "20046   3.750  2.999565  1.748188  ...         0                0                0\n",
            "20048   4.375  2.827369  1.361728  ...         0                0                0\n",
            "20049   4.375  2.751279  1.505150  ...         0                0                0\n",
            "20050   4.375  2.800717  1.662758  ...         0                0                0\n",
            "20051   4.375  2.748963  1.869232  ...         0                0                0\n",
            "\n",
            "[15833 rows x 17 columns]\n",
            "Transformed data shape:  (15833, 3)\n",
            "Explained variance by n components:  [0.49466627 0.43002097 0.04208692]\n",
            "#Components explaining 95% of variance:  3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets plot the elbow chart"
      ],
      "metadata": {
        "id": "F4aPoP-DTUHL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.xlabel('No. of Components')\n",
        "plt.ylabel('Explained Variance')\n",
        "plt.title('Elbow Plot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "u3XNFlahTW4S",
        "outputId": "951df37d-850f-416d-8e52-3ce45a2add88"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have scaled the data and performed PCA, it is now time to feed the scaled data into a logisitc regression model\n",
        "\n",
        "Logistic models will be created for multiple recipe types, the first will be #cakeweek\n",
        "\n",
        "#Cakeweek Test"
      ],
      "metadata": {
        "id": "d7MgjsssUOcQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "#First test is #cakeweek\n",
        "x = dataFrame_cont_arr\n",
        "y = dataFrame['#cakeweek']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 100)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "isThisCake = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=isThisCake)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=isThisCake, columns=isThisCake)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "2NOUyCxENeT9",
        "outputId": "2d0bb292-2c4c-4f9c-b931-56c97406b381"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 4)\n",
            "#Training and testing cases (11083, 4) (4750, 4)\n",
            "Confusion matrix:\n"
          ]
        },
        {
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "markdown",
      "source": [
        "In the test cases it appears none of the recipes in the randomized test data were cake week recipes, which makes sense as only 5 total recipes in the dataset are cake week recipes\n",
        "\n",
        "#Wasteless Test"
      ],
      "metadata": {
        "id": "vAQ1p8lMJHF_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Second test is #wasteless\n",
        "x = dataFrame_cont_arr\n",
        "y = dataFrame['#wasteless']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 100)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "labels = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=labels)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=labels, columns=labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "s9F4CVIsJF7W",
        "outputId": "e1cbd045-7da6-4521-8227-7953be329967"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 4)\n",
            "#Training and testing cases (11083, 4) (4750, 4)\n",
            "Confusion matrix:\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "\n",
              "    [theme=dark] .colab-df-convert {\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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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b6d69b94-b292-4c88-80ca-41f7292f6e3b 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-b6d69b94-b292-4c88-80ca-41f7292f6e3b');\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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              "          docLink.innerHTML = docLinkHtml;\n",
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    {
      "cell_type": "markdown",
      "source": [
        "This also seems as though there were 0 recipes in the test data that are wasteless recipes, which also makes sense as there is only 1 wasteless recipe in the entire dataset\n",
        "\n",
        "#22-minute meals Test"
      ],
      "metadata": {
        "id": "EWCtkyxBPWPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataFrame_cont_arr\n",
        "y = dataFrame['22-minute meals']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 10)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "labels = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=labels)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=labels, columns=labels)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "2jNmBJQ4PrNn",
        "outputId": "720e3b3f-3943-415f-c33e-194e1f73fd7a"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 4)\n",
            "#Training and testing cases (11083, 4) (4750, 4)\n",
            "Confusion matrix:\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      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-4e4b1ac9-05d9-4421-bcfb-228dbdfad7fa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4e4b1ac9-05d9-4421-bcfb-228dbdfad7fa');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  "
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here I had to lower the random state parameter in the train test split to prevent the model from failing to converge\n",
        "\n",
        "#3-ingredient recipes Test"
      ],
      "metadata": {
        "id": "ZUEnvrJ-QifI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataFrame_cont_arr\n",
        "y = dataFrame['3-ingredient recipes']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 100)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "isThisCake = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=isThisCake)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=isThisCake, columns=isThisCake)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "4_wxRPZ1RBct",
        "outputId": "9a2d9c63-b9e3-4bfc-eb92-7615b8ab3dd6"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 4)\n",
            "#Training and testing cases (11083, 4) (4750, 4)\n",
            "Confusion matrix:\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The models were never able to pick up on any of the recipes as all of the data is very sprase. We will now add another column to the dataset called healthy. "
      ],
      "metadata": {
        "id": "72gLk-nGRcxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Add new column healthy\n",
        "healthyList = []\n",
        "for row in dataFrame.index:\n",
        "  highCal = 0\n",
        "  highSod = 0;\n",
        "  highFat = 0;\n",
        "  if (dataFrame['calories'][row] > dataFrame['calories'].mean()):\n",
        "    highCal = 1\n",
        "  if (dataFrame['sodium'][row] > dataFrame['sodium'].mean()):\n",
        "    highSod = 1;\n",
        "  if (dataFrame['fat'][row] > dataFrame['fat'].mean()):\n",
        "    highFat = 1;\n",
        "  isHealthy = highCal + highSod + highFat\n",
        "  if (isHealthy >= 2):\n",
        "    healthyList.append(0)\n",
        "  else:\n",
        "    healthyList.append(1)\n",
        "\n",
        "len(healthyList)\n",
        "dataFrame.insert(5, \"Healthy\", healthyList, True)\n",
        "dataFrame.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "7CKe91GjTRtF",
        "outputId": "cedb19c5-aa48-4345-f01d-0f51035476a9"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>rating</th>\n",
              "      <th>calories</th>\n",
              "      <th>protein</th>\n",
              "      <th>fat</th>\n",
              "      <th>Healthy</th>\n",
              "      <th>sodium</th>\n",
              "      <th>#cakeweek</th>\n",
              "      <th>#wasteless</th>\n",
              "      <th>22-minute meals</th>\n",
              "      <th>3-ingredient recipes</th>\n",
              "      <th>30 days of groceries</th>\n",
              "      <th>advance prep required</th>\n",
              "      <th>anthony bourdain</th>\n",
              "      <th>caviar</th>\n",
              "      <th>iced coffee</th>\n",
              "      <th>iced tea</th>\n",
              "      <th>pressure cooker</th>\n",
              "      <th>valentine's day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15833.000000</td>\n",
              "      <td>15833.000000</td>\n",
              "      <td>15833.000000</td>\n",
              "      <td>15833.000000</td>\n",
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              "      <td>15833.000000</td>\n",
              "      <td>15833.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.723828</td>\n",
              "      <td>2.830175</td>\n",
              "      <td>1.254465</td>\n",
              "      <td>1.524821</td>\n",
              "      <td>0.594202</td>\n",
              "      <td>2.692338</td>\n",
              "      <td>0.000379</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.001074</td>\n",
              "      <td>0.001705</td>\n",
              "      <td>0.000442</td>\n",
              "      <td>0.00600</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>0.001011</td>\n",
              "      <td>0.000379</td>\n",
              "      <td>0.012884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.309980</td>\n",
              "      <td>0.632391</td>\n",
              "      <td>0.591290</td>\n",
              "      <td>0.723165</td>\n",
              "      <td>0.491061</td>\n",
              "      <td>0.882826</td>\n",
              "      <td>0.019464</td>\n",
              "      <td>0.007947</td>\n",
              "      <td>0.032751</td>\n",
              "      <td>0.041261</td>\n",
              "      <td>0.021023</td>\n",
              "      <td>0.07723</td>\n",
              "      <td>0.011239</td>\n",
              "      <td>0.007947</td>\n",
              "      <td>0.011239</td>\n",
              "      <td>0.031774</td>\n",
              "      <td>0.019464</td>\n",
              "      <td>0.112780</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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.750000</td>\n",
              "      <td>2.406540</td>\n",
              "      <td>0.778151</td>\n",
              "      <td>1.113943</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.176091</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>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.375000</td>\n",
              "      <td>2.687529</td>\n",
              "      <td>1.278754</td>\n",
              "      <td>1.431364</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.705008</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.375000</td>\n",
              "      <td>3.110926</td>\n",
              "      <td>1.886491</td>\n",
              "      <td>1.944483</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.301247</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>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.478728</td>\n",
              "      <td>5.303129</td>\n",
              "      <td>6.236226</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.442089</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "             rating      calories  ...  pressure cooker  valentine's day\n",
              "count  15833.000000  15833.000000  ...     15833.000000     15833.000000\n",
              "mean       3.723828      2.830175  ...         0.000379         0.012884\n",
              "std        1.309980      0.632391  ...         0.019464         0.112780\n",
              "min        0.000000      0.000000  ...         0.000000         0.000000\n",
              "25%        3.750000      2.406540  ...         0.000000         0.000000\n",
              "50%        4.375000      2.687529  ...         0.000000         0.000000\n",
              "75%        4.375000      3.110926  ...         0.000000         0.000000\n",
              "max        5.000000      7.478728  ...         1.000000         1.000000\n",
              "\n",
              "[8 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The new column healthy has now been added to the dataset, lets see if this new column is at all correlated to rating"
      ],
      "metadata": {
        "id": "VFtAqUL1ZV2q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = dataFrame.corr()\n",
        "corr_matrix[\"rating\"].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJeku3QkZckT",
        "outputId": "14c602a0-4443-4cee-c329-b4696c45840d"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rating                   1.000000\n",
              "protein                  0.093521\n",
              "sodium                   0.075710\n",
              "fat                      0.058590\n",
              "valentine's day          0.024995\n",
              "calories                 0.015062\n",
              "22-minute meals          0.012617\n",
              "#wasteless               0.007743\n",
              "30 days of groceries     0.007588\n",
              "pressure cooker          0.005034\n",
              "#cakeweek                0.003486\n",
              "iced coffee              0.002906\n",
              "anthony bourdain        -0.021227\n",
              "iced tea                -0.022127\n",
              "caviar                  -0.022593\n",
              "Healthy                 -0.029459\n",
              "advance prep required   -0.031225\n",
              "3-ingredient recipes    -0.055412\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The new column healthy is not correlated to rating, lets run the regressions again and see if the new column made any difference there\n",
        "\n",
        "#Cakeweek Test W/Healthy"
      ],
      "metadata": {
        "id": "370GkDNkZ9JQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_col = np.reshape(healthyList, (len(healthyList), -1))\n",
        "dataFrame_cont_healthy = np.append(dataFrame_cont_arr, new_col, 1)\n",
        "#First test is #cakeweek\n",
        "x = dataFrame_cont_healthy\n",
        "y = dataFrame['#cakeweek']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 40)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "isThisCake = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=isThisCake)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=isThisCake, columns=isThisCake)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "_cVII8E3aLI1",
        "outputId": "8ba0ae82-77d0-4243-d788-cae2a8f1ad11"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 5)\n",
            "#Training and testing cases (11083, 5) (4750, 5)\n",
            "Confusion matrix:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
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          "output_type": "execute_result",
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    {
      "cell_type": "markdown",
      "source": [
        "#Wasteless Test W/Healthy"
      ],
      "metadata": {
        "id": "R1NbklimdeRc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataFrame_cont_healthy\n",
        "y = dataFrame['#wasteless']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 100)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "isThisCake = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=isThisCake)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=isThisCake, columns=isThisCake)"
      ],
      "metadata": {
        "id": "f80dCawdaG8i",
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          "height": 164
        },
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      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 5)\n",
            "#Training and testing cases (11083, 5) (4750, 5)\n",
            "Confusion matrix:\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "markdown",
      "source": [
        "#22-minute meal Test W/Healthy"
      ],
      "metadata": {
        "id": "i96kCvXLdpsp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataFrame_cont_healthy\n",
        "y = dataFrame['22-minute meals']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 10)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "isThisCake = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=isThisCake)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=isThisCake, columns=isThisCake)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "V21LNjA0dowz",
        "outputId": "2fbb0803-6284-4fd5-d2bb-3b5d2ea263bd"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 5)\n",
            "#Training and testing cases (11083, 5) (4750, 5)\n",
            "Confusion matrix:\n"
          ]
        },
        {
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    {
      "cell_type": "markdown",
      "source": [
        "#3-ingredient recipe Test W/Healthy"
      ],
      "metadata": {
        "id": "tSw5T62KeUIW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataFrame_cont_healthy\n",
        "y = dataFrame['3-ingredient recipes']\n",
        "\n",
        "#Split the data into 70/30 train test split\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.30, random_state = 10)\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(xTrain, yTrain)\n",
        "yPred = classifier.predict(xTest)\n",
        "\n",
        "#Create labels and confusion matrix\n",
        "isThisCake = [0,1]\n",
        "confusionMatrix = confusion_matrix(yTest, yPred, labels=isThisCake)\n",
        "\n",
        "#Print results\n",
        "print ('#Total cases', x.shape)\n",
        "print ('#Training and testing cases', xTrain.shape, xTest.shape)\n",
        "print ('Confusion matrix:')\n",
        "pd.DataFrame(confusionMatrix, index=isThisCake, columns=isThisCake)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
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        "id": "6ssHdH-3eJEn",
        "outputId": "bab3d0ac-1a9b-49fa-8516-3a6e04ca35ca"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Total cases (15833, 5)\n",
            "#Training and testing cases (11083, 5) (4750, 5)\n",
            "Confusion matrix:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
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      "cell_type": "markdown",
      "source": [
        "Overall adding the new feature healthy did not improve the overall performance of the logistic regressions for #cakeweek, #wasteless, 22-minute meals, or 3-ingredient recipes. In fact it seemed to worsen performance as multiple tests failed to converge regardless of what the random state was set to.\n",
        "\n",
        "In both the tests with and without healthy the model was able to accurately predict when a recipe was not the catoegory we were looking for, but when it was it was unable to do so. I believe this is because the data is so sparse, if the dataset had a higher number of total recipes which fell under each category I believe that the results of the logistic regression would then work better."
      ],
      "metadata": {
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