Diabetes Classification.ipynb

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      "name": "Diabetes Classification.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/krishnakaushik25/Diabetes-Prediction-ML-model-Deployment/blob/main/Diabetes%20Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UrS-p85lx7GO"
      },
      "source": [
        "# **Diabetes Prediction**\n",
        "\n",
        "*Predict whether a person has diabetes or not.*\n",
        "\n",
        "*Dataset Link: https://www.kaggle.com/johndasilva/diabetes*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "37m1W1rLMjeq",
        "outputId": "5476059d-2d7a-4875-adaf-1b595e803ea2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xUDcfBU3JDc"
      },
      "source": [
        "# Importing essential libraries\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeLwzX1AMQLj"
      },
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/diabetes.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SjBB0JyK3JDk"
      },
      "source": [
        "# Exploring the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jge6AkPG3JDk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90453073-c881-40ce-830e-970f7b600d67"
      },
      "source": [
        "# Returns number of rows and columns of the dataset\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 9)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 6
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    {
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        "outputId": "a55a6298-4ca0-4253-ec91-fcedfe42a13f"
      },
      "source": [
        "# Returns an object with all of the column headers \n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "9ec89be2-47ad-4d2d-a5d0-0938d21b0254"
      },
      "source": [
        "# Returns different datatypes for each columns (float, int, string, bool, etc.)\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   int64\n",
              "Glucose                       int64\n",
              "BloodPressure                 int64\n",
              "SkinThickness                 int64\n",
              "Insulin                       int64\n",
              "BMI                         float64\n",
              "DiabetesPedigreeFunction    float64\n",
              "Age                           int64\n",
              "Outcome                       int64\n",
              "dtype: object"
            ]
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          "height": 203
        },
        "outputId": "51e05625-705c-4ff1-dbbd-ec3840c472a0"
      },
      "source": [
        "# Returns the first x number of rows when head(num). Without a number it returns 5\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
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              "      <th>DiabetesPedigreeFunction</th>\n",
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              "      <th>Outcome</th>\n",
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              "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
              "0            2      138             62  ...                     0.127   47        1\n",
              "1            0       84             82  ...                     0.233   23        0\n",
              "2            0      145              0  ...                     0.630   31        1\n",
              "3            0      135             68  ...                     0.365   24        1\n",
              "4            1      139             62  ...                     0.536   21        0\n",
              "\n",
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        "outputId": "f050fc52-a326-4b99-d212-1ca15087c667"
      },
      "source": [
        "# Returns basic information on all columns\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000 entries, 0 to 1999\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               2000 non-null   int64  \n",
            " 1   Glucose                   2000 non-null   int64  \n",
            " 2   BloodPressure             2000 non-null   int64  \n",
            " 3   SkinThickness             2000 non-null   int64  \n",
            " 4   Insulin                   2000 non-null   int64  \n",
            " 5   BMI                       2000 non-null   float64\n",
            " 6   DiabetesPedigreeFunction  2000 non-null   float64\n",
            " 7   Age                       2000 non-null   int64  \n",
            " 8   Outcome                   2000 non-null   int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 140.8 KB\n"
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      "source": [
        "# Returns basic statistics on numeric columns\n",
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              "      <th>Pregnancies</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>3.70350</td>\n",
              "      <td>3.306063</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>17.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Glucose</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>121.18250</td>\n",
              "      <td>32.068636</td>\n",
              "      <td>0.000</td>\n",
              "      <td>99.000</td>\n",
              "      <td>117.000</td>\n",
              "      <td>141.000</td>\n",
              "      <td>199.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BloodPressure</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>69.14550</td>\n",
              "      <td>19.188315</td>\n",
              "      <td>0.000</td>\n",
              "      <td>63.500</td>\n",
              "      <td>72.000</td>\n",
              "      <td>80.000</td>\n",
              "      <td>122.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SkinThickness</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>20.93500</td>\n",
              "      <td>16.103243</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>23.000</td>\n",
              "      <td>32.000</td>\n",
              "      <td>110.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Insulin</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>80.25400</td>\n",
              "      <td>111.180534</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>40.000</td>\n",
              "      <td>130.000</td>\n",
              "      <td>744.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMI</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>32.19300</td>\n",
              "      <td>8.149901</td>\n",
              "      <td>0.000</td>\n",
              "      <td>27.375</td>\n",
              "      <td>32.300</td>\n",
              "      <td>36.800</td>\n",
              "      <td>80.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.47093</td>\n",
              "      <td>0.323553</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.244</td>\n",
              "      <td>0.376</td>\n",
              "      <td>0.624</td>\n",
              "      <td>2.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>33.09050</td>\n",
              "      <td>11.786423</td>\n",
              "      <td>21.000</td>\n",
              "      <td>24.000</td>\n",
              "      <td>29.000</td>\n",
              "      <td>40.000</td>\n",
              "      <td>81.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outcome</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.34200</td>\n",
              "      <td>0.474498</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           count       mean  ...      75%     max\n",
              "Pregnancies               2000.0    3.70350  ...    6.000   17.00\n",
              "Glucose                   2000.0  121.18250  ...  141.000  199.00\n",
              "BloodPressure             2000.0   69.14550  ...   80.000  122.00\n",
              "SkinThickness             2000.0   20.93500  ...   32.000  110.00\n",
              "Insulin                   2000.0   80.25400  ...  130.000  744.00\n",
              "BMI                       2000.0   32.19300  ...   36.800   80.60\n",
              "DiabetesPedigreeFunction  2000.0    0.47093  ...    0.624    2.42\n",
              "Age                       2000.0   33.09050  ...   40.000   81.00\n",
              "Outcome                   2000.0    0.34200  ...    1.000    1.00\n",
              "\n",
              "[9 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "qUL3dLHF3JD4",
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        },
        "outputId": "389bb0e4-7c82-439a-e17b-56f28b5e3d8f"
      },
      "source": [
        "# Returns true for a column having null values, else false\n",
        "df.isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                 False\n",
              "Glucose                     False\n",
              "BloodPressure               False\n",
              "SkinThickness               False\n",
              "Insulin                     False\n",
              "BMI                         False\n",
              "DiabetesPedigreeFunction    False\n",
              "Age                         False\n",
              "Outcome                     False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "PN4nWFrV3JD6",
        "colab": {
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          "height": 203
        },
        "outputId": "11c3b945-bc35-4c68-940d-708c8a3a0dc0"
      },
      "source": [
        "df = df.rename(columns={'DiabetesPedigreeFunction':'DPF'})\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.365</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>139</td>\n",
              "      <td>62</td>\n",
              "      <td>41</td>\n",
              "      <td>480</td>\n",
              "      <td>40.7</td>\n",
              "      <td>0.536</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  ...    DPF  Age  Outcome\n",
              "0            2      138             62  ...  0.127   47        1\n",
              "1            0       84             82  ...  0.233   23        0\n",
              "2            0      145              0  ...  0.630   31        1\n",
              "3            0      135             68  ...  0.365   24        1\n",
              "4            1      139             62  ...  0.536   21        0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FlGgOBhauyBf"
      },
      "source": [
        "# Importing essential libraries for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyrQmKVR3JD8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "34f52d41-85af-4c86-f9f5-216390f16eb9"
      },
      "source": [
        "# Plotting the Outcomes based on the number of dataset entries\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.countplot(x='Outcome', data=df)\n",
        "\n",
        "# Removing the unwanted spines\n",
        "plt.gca().spines['top'].set_visible(False)\n",
        "plt.gca().spines['right'].set_visible(False)\n",
        "\n",
        "# Headings\n",
        "plt.xlabel('Has Diabetes')\n",
        "plt.ylabel('Count')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XrbvKzGH3JD_"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "XwBCwiaM3JD_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aafb9444-b0d1-4b2f-89a7-fdb086b35039"
      },
      "source": [
        "# Replacing the 0 values from ['Glucose','BloodPressure','SkinThickness','Insulin','BMI'] by NaN\n",
        "df_copy = df.copy(deep=True)\n",
        "df_copy[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']] = df_copy[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']].replace(0,np.NaN)\n",
        "df_copy.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies        0\n",
              "Glucose           13\n",
              "BloodPressure     90\n",
              "SkinThickness    573\n",
              "Insulin          956\n",
              "BMI               28\n",
              "DPF                0\n",
              "Age                0\n",
              "Outcome            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "HKCVpoMu3JEB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "outputId": "028eb3ea-1c9d-4aa5-d7da-df9a93fdb92c"
      },
      "source": [
        "# To fill these Nan values the data distribution needs to be understood\n",
        "# Plotting histogram of dataset before replacing NaN values\n",
        "p = df_copy.hist(figsize = (15,15))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xyzzfBX5lQQOTJEmaI8+0SZIkSVKPmbRJkiRJUo/ZPFKSJElahHbmht1rVm7htFmW94bd/eSZNkmSJEnqMZM2SZIkSeoxkzZJkiRJ6jGTNkmSJEnqMZM2SZIkSeoxkzZJkiRJ6rE5JW1JViS5NMkXk9yc5NlJ9ktyZZJb2vO+bd4keXuSdUluSHLUaDdBkiRJkpauuZ5pOxf4SFU9EXgqcDNwJnBVVR0BXNXGAY4FjmiPM4B3DTViSZIkSVpGtpu0JdkH+FngfICq+l5V3QusBta22dYCJ7bh1cCF1bkGWJHkwKFHLkmSJEnLwK5zmOcw4KvAnyd5KnAd8Gpgoqo2tXnuACba8EHA7QPLb2hlmwbKSHIG3Zk4JiYmmJqamlPAE3t2d3Afprm+9jht3rx5UcQ5bG63JEmSlru5JG27AkcBv1FV1yY5lx81hQSgqipJzeeFq+o84DyAVatW1eTk5JyWe8dFl3H2jXMJe+7WnzK31x6nqakp5rqPlhK3W5IkScvdXK5p2wBsqKpr2/ildEncndPNHtvzXW36RuCQgeUPbmWSJEmSpHnabtJWVXcAtyd5Qis6BvgCcDlwais7FbisDV8OvKz1Ivks4L6BZpSSJEmSpHmYazvD3wAuSvJw4Fbg5XQJ3yVJTgduA05q834YOA5YB3y7zStJkiRJ2gFzStqq6npg1SyTjpll3gJeuZNxSdIOS7IH8I/A7nT13KVV9cYkFwA/B9zXZj2tqq5PErpbmxxHd7DptKr6zMJHLkmS9FDD7dFDkvrhfuDoqtqcZDfgE0n+rk37/6rq0hnzD95f8pl095d85oJFK0mStA1zvbm2JC0a7T6Rm9vobu2xrR5uvb+kJEnqLZM2SUtSkl2SXE/Xs+2VAz3gviXJDUnOSbJ7K9va/SUlSZLGzuaRkpakqnoAODLJCuCDSZ4CvB64A3g43X0iXwf89lzXmeQM4AyAiYmJod0AvS83Ux9FHGtWbpn3MhN77thyO2vmti/l92VH9SWWvsQhSQvFpE3SklZV9ya5GnhBVf1hK74/yZ8D/7WNz+n+klV1Hl2yx6pVq2pYN0Dvy83URxHHaWdeMe9l1qzcwtk3LvzP0/pTJh80vpTflx3Vl1j6EockLRSbR0pacpI8up1hI8mewPOAL05fp9Z6izwR+HxbxPtLSuqFJOuT3Jjk+iSfbmX7JbkyyS3ted9WniRvT7KuNfs+arzRSxoVz7RJWooOBNYm2YV2T8mq+lCSjyd5NBDgeuAVbX7vLympT36+qr42MH4mcFVVnZXkzDb+Ouz5Vlo2TNokLTlVdQPwtFnKj97K/N5fUlKfrQYm2/BaYIouafthz7fANUlWJDnQlgLS0mPSJkmS1B8FfDRJAX/arqWdGEjE7gAm2vDWer59UNI2WydKi6kzl6US6zg6WNqWrXX61Md9vVQ+AzvDpE2SJKk/nltVG5P8GHBlki8OTqyqagndnM3WidJi6sxlqcS6Ix0zjdLWOn2a2SlTHyyVz8DOsCMSSZKknqiqje35LuCDwDOAOwc6UjqQ7v6TMMeebyUtfiZtkiRJPZBkryR7Tw8Dz6fr5fZy4NQ226nAZW3Ynm+lZcLmkZIkSf0wAXywuysJuwJ/WVUfSfIp4JIkpwO3ASe1+e35VlomTNokSZJ6oKpuBZ46S/ndwDGzlNvzrbRM2DxSkiRJknrMpE2SJEmSeszmkZIkAYfO6I57zcotO91F9/qzjt+p5SVJAs+0SZIkSVKvmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPzSlpS7I+yY1Jrk/y6Va2X5Irk9zSnvdt5Uny9iTrktyQ5KhRboAkSZIkLWXzOdP281V1ZFWtauNnAldV1RHAVW0c4FjgiPY4A3jXsIKVJEmSpOVmZ5pHrgbWtuG1wIkD5RdW5xpgRZIDd+J1JGlekuyR5J+TfC7JTUne3MoPS3JtawnwgSQPb+W7t/F1bfqh44xfkiRp0K5znK+AjyYp4E+r6jxgoqo2tel3ABNt+CDg9oFlN7SyTQNlJDmD7kwcExMTTE1NzSmQiT1hzcotcwx7bub62uO0efPmRRHnsLnd2kH3A0dX1eYkuwGfSPJ3wGuBc6rq4iTvBk6naw1wOnBPVR2e5GTgrcCLxxW8JEnSoLkmbc+tqo1Jfgy4MskXBydWVbWEbs5a4ncewKpVq2pycnJOy73joss4+8a5hj0360+Z22uP09TUFHPdR0uJ260dUVUFbG6ju7VHAUcDv9rK1wJvokvaVrdhgEuBP06Sth5JkqSxmlPzyKra2J7vAj4IPAO4c7rZY3u+q82+EThkYPGDW5kkLZgkuyS5nq5uuhL4MnBvVU2fqp9uBQADLQTa9PuA/Rc2YkmSpNlt95RVkr2Ah1XVN9vw84HfBi4HTgXOas+XtUUuB16V5GLgmcB9A80oJWlBVNUDwJFJVtAdbHrizq5zR5t1b09fmsOOIo4dac4+imbwO2IYcQxjf/bl8wH9iaUvcUjSQplLO8MJ4INJpuf/y6r6SJJPAZckOR24DTipzf9h4DhgHfBt4OVDj1qS5qiq7k1yNfBsuo6Rdm1n0wZbAUy3ENiQZFdgH+DuWda1Q826t6cvzWFHEcdpZ14x72XWrNwy9GbwO2IYcQyj+X1fPh/Qn1j6EockLZTt/hpV1a3AU2cpvxs4ZpbyAl45lOgkaQckeTTw/Zaw7Qk8j65zkauBFwEX89AWAqcCn2zTP+71bJIkqS/GfyhTkobvQGBtkl3ort29pKo+lOQLwMVJfhf4LHB+m/984H1J1gFfB04eR9CSJEmzMWkDDt2B5jvbs/6s44e+TklzU1U3AE+bpfxWuo6UZpZ/F/iVBQhNkiRp3nbm5tqSJEmSpBEzaZMkSZKkHjNpkyRJkqQeM2mTJEmSpB4zaZMkSZKkHjNpkyRJkqQeM2mTJEmSpB4zaZMkSeqJJLsk+WySD7Xxw5Jcm2Rdkg8keXgr372Nr2vTDx1n3JJGy6RNkiSpP14N3Dww/lbgnKo6HLgHOL2Vnw7c08rPafNJWqJM2iRJknogycHA8cCftfEARwOXtlnWAie24dVtnDb9mDa/pCVo13EHIEmSJAD+CPhNYO82vj9wb1VtaeMbgIPa8EHA7QBVtSXJfW3+r81caZIzgDMAJiYmmJqaYvPmzUxNTY1qO4ZqqcS6ZuWWWcvHZWLP2WPq475eKp+BnWHSJkmSNGZJTgDuqqrrkkwOc91VdR5wHsCqVatqcnKSqakpJieH+jIjs1RiPe3MKxY2mO1Ys3ILZ9/40FRg/SmTCx/MdiyVz8DOMGmTJEkav+cAL0xyHLAH8CjgXGBFkl3b2baDgY1t/o3AIcCGJLsC+wB3L3zYkhaC17RJkiSNWVW9vqoOrqpDgZOBj1fVKcDVwIvabKcCl7Xhy9s4bfrHq6oWMGRJC8ikTZIkqb9eB7w2yTq6a9bOb+XnA/u38tcCZ44pPkkLwOaRkiRJPVJVU8BUG74VeMYs83wX+JUFDUzS2HimTZIkSZJ6zKRNkiRJknrMpE3SkpPkkCRXJ/lCkpuSvLqVvynJxiTXt8dxA8u8Psm6JF9K8ovji16SJOnBvKZN0lK0BVhTVZ9JsjdwXZIr27RzquoPB2dO8iS63tqeDDwW+FiSn6yqBxY0akmSpFl4pk3SklNVm6rqM234m8DNwEHbWGQ1cHFV3V9VXwHWMcuF/5IkSeMw5zNtSXYBPg1srKoTkhwGXEzX/ex1wEur6ntJdgcuBJ5Od5PHF1fV+qFHLklzkORQ4GnAtXQ3r31VkpfR1WdrquoeuoTumoHFNjBLkpfkDOAMgImJCaampoYS4+bNm4e2rr7FsWbllnkvM7Hnji03bMOIYxj7sy+fD+hPLH2JQ5IWynyaR76a7mj1o9r4W+maGV2c5N3A6cC72vM9VXV4kpPbfC8eYsySNCdJHgn8FfCaqvpGkncBvwNUez4b+LW5rq+qzgPOA1i1alVNTk4OJc6pqSmGta6+xXHamVfMe5k1K7dw9o3jb70/jDjWnzK503H05fMB/YmlL3FI0kKZU/PIJAcDxwN/1sYDHA1c2mZZC5zYhle3cdr0Y9r8krRgkuxGl7BdVFV/DVBVd1bVA1X1A+A9/KgJ5EbgkIHFD25lkiRJYzfXa9r+CPhN4AdtfH/g3qqabjcy2JToIOB2gDb9vja/JC2IdqDofODmqnrbQPmBA7P9MvD5Nnw5cHKS3VvT7yOAf16oeCVJkrZlu+0+kpwA3FVV1yWZHNYL7+i1IX251mF7ht3Wfrm233e7tYOeA7wUuDHJ9a3sDcBLkhxJ1zxyPfAfAarqpiSXAF+g63nylfYcKUmS+mIujfWfA7yw3c9oD7pr2s4FViTZtZ1NG2xKNN3MaEOSXYF96DokeZAdvTbkHRdd1otrHbZnGNcxDFqu7ffdbu2IqvoEMFuz7A9vY5m3AG8ZWVCSJEk7aLvNI6vq9VV1cFUdSncfo49X1SnA1cCL2mynApe14cvbOG36x6uqhhq1JEmSJC0TO3OfttcBr02yju6atfNb+fnA/q38tcCZOxeiJEmSJC1f82pnWFVTwFQbvpVZbj5bVd8FfmUIsUmSJEnSsrczZ9okSZIkSSNm0iZJkiRJPWbSJkmSJEk91v++8yVJC+LGjfdx2plXjDsMSZI0g2faJEmSJKnHPNMmSdKIHDqEM5drVm550BnQ9Wcdv9PrlCQtLp5pkyRJkqQeM2mTJEmSpB4zaZMkSZKkHjNpkyRJkqQeM2mTJEmSpB4zaZMkSZKkHjNpkyRJkqQe8z5tkiRJPZBkD+Afgd3p/qNdWlVvTHIYcDGwP3Ad8NKq+l6S3YELgacDdwMvrqr1Ywl+idnReyzOvK+iNCyeaZMkSeqH+4Gjq+qpwJHAC5I8C3grcE5VHQ7cA5ze5j8duKeVn9Pmk7QEmbRJWnKSHJLk6iRfSHJTkle38v2SXJnklva8bytPkrcnWZfkhiRHjXcLJC1H1dncRndrjwKOBi5t5WuBE9vw6jZOm35MkixQuJIWkM0jJS1FW4A1VfWZJHsD1yW5EjgNuKqqzkpyJnAm8DrgWOCI9ngm8K72LEkLKskudE0gDwfeCXwZuLeqtrRZNgAHteGDgNsBqmpLkvvomlB+bcY6zwDOAJiYmGBqaorNmzczNTU14q0ZjnHEumbllu3PNIuJPXd82YW2tVj7+Lnw82rSNjI72hZ6a9as3MLkUNcoLV1VtQnY1Ia/meRmuj83q+GHX6W1wBRd0rYauLCqCrgmyYokB7b1SNKCqaoHgCOTrAA+CDxxCOs8DzgPYNWqVTU5OcnU1BSTk5M7u+oFMY5Yd/S6tDUrt3D2jYvj7/XWYl1/yuTCB7Mdfl5N2iQtcUkOBZ4GXAtMDCRidwATbfiHR6ub6SPZD0raZjtaPQx9OYLYlyPExrHtOMb5WenLZ7UvcYxSVd2b5Grg2cCKJLu2s20HAxvbbBuBQ4ANSXYF9qHrkETSEmPSJmnJSvJI4K+A11TVNwYv9aiqSlLzWd9sR6uHoS9HEN9x0WW9OELclyPVfY1jnEfB+/JZ7Uscw5bk0cD3W8K2J/A8us5FrgZeRNeD5KnAZW2Ry9v4J9v0j7cWA5KWmPH/GknSCCTZjS5hu6iq/roV3znd7DHJgcBdrXz6aPW0wSPZkrRQDgTWtuvaHgZcUlUfSvIF4OIkvwt8Fji/zX8+8L4k64CvAyePI2hJo2fSJmnJab2nnQ/cXFVvG5g0fVT6LB56tPpVSS6m64DkPq9nk7TQquoGuubcM8tvBZ4xS/l3gV9ZgNAkjZlJm6Sl6DnAS4Ebk1zfyt5Al6xdkuR04DbgpDbtw8BxwDrg28DLFzZcSZKkrTNpk7TkVNUngK3dq+iYWeYv4JUjDUqSJGkHbffm2kn2SPLPST7XblL75lZ+WJJr281oP5Dk4a189za+rk0/dLSbIEmSJElL13aTNuB+4OiqeipwJPCCJM+i683onKo6HLgHOL3NfzpwTys/p80nSZIkSdoB203aqrO5je7WHgUcDVzaytcCJ7bh1W2cNv2YDPazLUmSJEmaszld09a6nrclmKcAACAASURBVL0OOBx4J/Bl4N52k0f40Y1oYeAmtVW1Jcl9wP7A12asc4duUtuXm50utIk9x3tD1XFZDjdQnc1y3W5JkiQ91JyStqp6ADgyyQrgg8ATd/aFd/QmtX25+etCW7NyCyctwRuJbs9SvYHq9izX7ZYkSdJDzeWath+qqnuBq4FnAyuSTGdPgzei/eFNatv0fYC7hxKtJEmSJC0z2z1lleTRwPer6t4kewLPo+tc5GrgRcDFPPQmtacCn2zTP96605YkSZLUY4eeecXQ17n+rOOHvs7lZi7tDA8E1rbr2h4GXFJVH0ryBeDiJL8LfBY4v81/PvC+JOuArwMnjyBuSZIkSVoWtpu0VdUNwNNmKb8VeMYs5d8FfmUo0UmSJEnSMjeva9okSZIkSQvLpE2SJEmSesykTZIkSZJ6zKRNkiRJknrMpE2SJEmSesykTZIkSZJ6zKRNkiRJknrMpE2SJEmSesykTZIkSZJ6zKRN0pKT5L1J7kry+YGyNyXZmOT69jhuYNrrk6xL8qUkvzieqCVJkmZn0iZpKboAeMEs5edU1ZHt8WGAJE8CTgae3Jb5kyS7LFikkiRJ22HSJmnJqap/BL4+x9lXAxdX1f1V9RVgHfCMkQUnSZI0T7uOOwBJWkCvSvIy4NPAmqq6BzgIuGZgng2t7CGSnAGcATAxMcHU1NRQgtq8efPQ1rUzJvaENSu3jDsM49hOHO+46LKhv8bKg/aZ03x9+az2JQ5JWigmbZKWi3cBvwNUez4b+LX5rKCqzgPOA1i1alVNTk4OJbCpqSmGta6d8Y6LLuPsG8f/s7Bm5RbjWOA41p8yOaf5+vJZ7UsckrRQbB4paVmoqjur6oGq+gHwHn7UBHIjcMjArAe3MkmSpF4waZO0LCQ5cGD0l4HpniUvB05OsnuSw4AjgH9e6PgkSZK2ZvztPiRpyJK8H5gEDkiyAXgjMJnkSLrmkeuB/whQVTcluQT4ArAFeGVVPTCOuCVJkmZj0iZpyamql8xSfP425n8L8JbRRSRJ25bkEOBCYILu4NJ5VXVukv2ADwCH0h1wOqmq7kkS4FzgOODbwGlV9ZlxxC5p9GweKUmSNH5b6Hq1fRLwLOCV7T6SZwJXVdURwFVtHOBYuubcR9D1avuuhQ9Z0kIxaZMkSRqzqto0faasqr4J3Ex3+5HVwNo221rgxDa8GriwOtcAK2ZcuytpCbF5pCRJUo8kORR4GnAtMFFVm9qkO+iaT0KX0N0+sNj0PSY3McNs95hcTPe6G0esO3qPxr7c33EuFjLWnX3//LyatEmSJPVGkkcCfwW8pqq+0V261qmqSlLzXeds95hcTPe6G0esp515xQ4t15f7O87FQsY613tBbo2fV5tHSpIk9UKS3egStouq6q9b8Z3TzR7b812t3HtMSsuISZskSdKYtd4gzwdurqq3DUy6HDi1DZ8KXDZQ/rJ0ngXcN9CMUtISs92kLckhSa5O8oUkNyV5dSvfL8mVSW5pz/u28iR5e5J1SW5IctSoN0KSJGmRew7wUuDoJNe3x3HAWcDzktwC/EIbB/gwcCuwDngP8OtjiFnSAplLQ9bpLmg/k2Rv4LokVwKn0XVBe1aSM+m6oH0dD+6C9pl0XdA+cxTBS5IkLQVV9QkgW5l8zCzzF/DKkQYlqTe2e6bNLmglSZIkaXzm1WXMMLugna372blYTF2pDtPEnjvfXepitJi6eB2m5brdksbj0Dn2lLdm5ZY596q3/qzjdyYkSdKAOSdtw+6CdrbuZ+fiHRddtmi6Uh2mNSu3cNIi6ep0mBZTF6/DtFy3W5IkSQ81p+xnW13QVtUmu6BdvOZ6dHU+PLoqSZIkDc9ceo+0C1pJkiRJGpO5nGmb7oL2xiTXt7I30HU5e0mS04HbgJPatA8Dx9F1Qftt4OVDjViSJEmSlpHtJm12QStJkiRJ47Pd5pGSJEmSpPExaZMkSZKkHjNpkyRJkqQeM2mTtCQleW+Su5J8fqBsvyRXJrmlPe/bypPk7UnWJbkhyVHji1ySJOnBTNokLVUXAC+YUXYmcFVVHQFc1cYBjgWOaI8zgHctUIySJEnbZdImaUmqqn8Evj6jeDWwtg2vBU4cKL+wOtcAK5IcuDCRSpIkbdtc7tMmSUvFRFVtasN3ABNt+CDg9oH5NrSyTQNlJDmD7kwcExMTTE1NDSWozZs3D21dO2NiT1izcsu4wzCOnsYB84tllJ/pvnxnJGmhmLRJWpaqqpLUPJc5DzgPYNWqVTU5OTmUWKamphjWunbGOy66jLNvHP/PwpqVW4yjh3HA/GJZf8rkyOLoy3dGkhaKzSMlLSd3Tjd7bM93tfKNwCED8x3cyiRJksbOpE3ScnI5cGobPhW4bKD8Za0XyWcB9w00o5QkSRqrfrS3kKQhS/J+YBI4IMkG4I3AWcAlSU4HbgNOarN/GDgOWAd8G3j5ggcsSZK0FSZtkpakqnrJViYdM8u8BbxytBFJkiTtGJtHSpIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPmbRJkiRJUo/Ze+QicuiZV4w7BEmS5mQUv1nrzzp+6OuUpMXAM22SJEmS1GOeadPQDfPo6pqVWzjtzCs8uipJkqRlyzNtkiRJPZDkvUnuSvL5gbL9klyZ5Jb2vG8rT5K3J1mX5IYkR40vckmjZtImSZLUDxcAL5hRdiZwVVUdAVzVxgGOBY5ojzOAdy1QjJLGwKRNkiSpB6rqH4GvzyheDaxtw2uBEwfKL6zONcCKJAcuTKSSFprXtEmSJPXXRFVtasN3ABNt+CDg9oH5NrSyTcyQ5Ay6s3FMTEwwNTXF5s2bmZqaGlnQwzSOWNes3LJDy03suePLLrSFjHVn3z8/r3NI2pK8FzgBuKuqntLK9gM+ABwKrAdOqqp7kgQ4FzgO+DZwWlV9ZuhRS5IkLTNVVUlqB5Y7DzgPYNWqVTU5OcnU1BSTk5PDDnEkxhHraTvYqdqalVs4+8bFcU5kIWNdf8rkTi3v53VuzSMvwPbVkiRJ43DndLPH9nxXK98IHDIw38GtTNIStN2kzfbVkiRJY3M5cGobPhW4bKD8Za0XyWcB9w00o5S0xOzoOdGRtK+e0wsvorbCw7Tct3uxtGMelsXUdluSNBxJ3g9MAgck2QC8ETgLuCTJ6cBtwElt9g/TXY6yju6SlJcveMA9MMx7w0p9ttMNWYfZvnou3nHRZYumrfAwLaY20sM0vd072xZ6sVlMbbclScNRVS/ZyqRjZpm3gFeONiJJfbGjXf7bvlqSJEmSFsCOJm22r5a0aCVZn+TGJNcn+XQr2y/JlUluac/7jjtOSZIkmEPS1tpXfxJ4QpINrU31WcDzktwC/EIbh6599a107avfA/z6SKKWpJ3381V1ZFWtauNb6xVXkiRprLZ7kZTtqyUtE6vpOgCArlfcKeB14wpGkiRp2vLr2UKSoICPtk6U/rR1jLS1XnF/aEd7vd2evvQW2pdeao2jn3HA+GOZ/p705TsjSQvFpE3ScvTcqtqY5MeAK5N8cXDi1nrF3dFeb7enL72F9qV33r70lmscDzXuWKZ7Eu7Ld0aSFko/fgUkaQFV1cb2fFeSDwLPoPWKW1WbZvSK20ujuDfRmpVDX6UkSRqCHe09UpIWpSR7Jdl7ehh4PvB5tt4rriRJ0lh5pk3ScjMBfDAJdHXgX1bVR5J8Crik9ZB7G3DSGGOUJEn6IZM2SctKVd0KPHWW8ruZpVdcSZKkcbN5pCRJkiT1mEmbJEmSJPWYSZskSZIk9ZjXtEmSJEkamZ29Tc2alVs4bcY61p91/E6tc7HxTJskSZIk9ZhJmyRJkiT1mM0jtazt7On62Sy30/WSJEkaLc+0SZIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKP2RGJJEmSpEVluXUm55k2SZIkSeoxz7RpURjF0RRJkiRpMfBMmyRJkiT1mEmbJEmSJPWYzSMlacS217x3zcotnGYTYEmStBWeaZMkSZKkHhvJmbYkLwDOBXYB/qyqzhrF60h9NIxOU2aeeelzF7RLiXWXpMXGektaHoaetCXZBXgn8DxgA/CpJJdX1ReG/VqSNCzWXZIWm1HWW33qtdkm5FooozjwDsM5+D6KM23PANZV1a0ASS4GVgP+8ZF20HK7geSYWHdJWmyst6RlIlU13BUmLwJeUFX/vo2/FHhmVb1qxnxnAGe00ScAX5rjSxwAfG1I4S4mbvfy0rftflxVPXrcQYzSXOqunai3tqcv77dxPJhxPFRfYplLHNZbP5pvtrqrL+/lXBjraBjraOxMrFutt8bWe2RVnQecN9/lkny6qlaNIKRec7uXl+W63X23o/XW9vTl/TYO49ievsTSlzgWi9nqrsW0D411NIx1NEYV6yh6j9wIHDIwfnArk6Q+s+6StNhYb0nLxCiStk8BRyQ5LMnDgZOBy0fwOpI0TNZdkhYb6y1pmRh688iq2pLkVcDf03U/+96qummILzH0pkmLhNu9vCzX7R6bBai7tqUv77dxPJhxPFRfYulLHGO1k/XWYtqHxjoaxjoaI4l16B2RSJIkSZKGZxTNIyVJkiRJQ2LSJkmSJEk9tmiStiQvSPKlJOuSnDnueBZKkvVJbkxyfZJPjzueUUry3iR3Jfn8QNl+Sa5Mckt73necMY7CVrb7TUk2tvf9+iTHjTNGDdds3+uF/qwnecLA5+v6JN9I8pqF+OzN57uezttb3X9DkqNGHMcfJPlie60PJlnRyg9N8p2B/fLuEcex1fchyevb/vhSkl8ccRwfGIhhfZLrW/ko98chSa5O8oUkNyV5dStf8M/IUjDf/dkHSXZJ8tkkH2rjhyW5tr3HH0jX6crYJVmR5NJWZ9yc5Nl93a9J/kt7/z+f5P1J9ujTfu3L78JOxDrrb0ebNpQ6e1EkbUl2Ad4JHAs8CXhJkieNN6oF9fNVdeRiuT/FTrgAeMGMsjOBq6rqCOCqNr7UXMBDtxvgnPa+H1lVH17gmDR6M7/XC/pZr6ovTX++gKcD3wY+2CaP+rN3AXP/rh8LHNEeZwDvGnEcVwJPqaqfAv4FeP3AtC8P7JdXjDgOmOV9aL99JwNPbsv8SfuNHEkcVfXigc/JXwF/PTB5VPtjC7Cmqp4EPAt4ZdvucXxGloL57s8+eDVw88D4W+m+D4cD9wCnjyWqhzoX+EhVPRF4Kl3MvduvSQ4C/jOwqqqeQtdpzcn0a79eQD9+F+biAub42zHMOntRJG3AM4B1VXVrVX0PuBhYPeaYNGRV9Y/A12cUrwbWtuG1wIkLGtQC2Mp2a/kZ52f9GLo/4LctxIvN87u+GriwOtcAK5IcOKo4quqjVbWljV5Dd9+rkZpnHbAauLiq7q+qrwDr6H4jRxpHkgAnAe8fxmttJ45NVfWZNvxNuj/CBzGGz8hSsAP7c6ySHAwcD/xZGw9wNHBpm6UXsSbZB/hZ4HyAqvpeVd1LT/crXY/xeybZFXgEsIke7de+/C7MxTx/O4ZWZy+WpO0g4PaB8Q2tbDko4KNJrktyxriDGYOJqtrUhu8AJsYZzAJ7VTvN/t6+NK/Q0Mz2vR7nZ/1kHvxnfByfva1t/zjr/18D/m5g/LDWZOsfkvzbBXj92d6Hce2PfwvcWVW3DJSNfH8kORR4GnAt/fyMLCpz3J/j9kfAbwI/aOP7A/cO/CHuy/t7GPBV4M/b9+DPkuxFD/drVW0E/hD4v3TJ2n3AdfRzvw5arN/5wd+OocW6WJK25ey5VXUU3angVyb52XEHNC7V3Z9iudyj4l3ATwBH0lWwZ483HA3ZNr/XC/lZb9cwvBD4X61o7J+9PnzXk/wWXbOyi1rRJuDHq+ppwGuBv0zyqBGGMPb3YYaX8ODEfuT7I8kj6ZpkvqaqvjE4rQ+fkcVmMezPJCcAd1XVdeOOZQ52BY4C3tW+B99iRlPIHu3XfenO+BwGPBbYi9mbZPdWX/bl9szy2zE0iyVp2wgcMjB+cCtb8trREarqLrrrTYbSDGYRuXP6lHd7vmvM8SyIqrqzqh6oqh8A72H5ve9L2la+1+P6rB8LfKaq7mwxjeuzt7XtX/D6P8lpwAnAKe2PAq1py91t+Drgy8BPjiqGbbwP49gfuwL/DvjAQHwj3R9JdqNLMC6qqunr6HrzGVls5rk/x+k5wAuTrKe7FOZouuvGVrTPIfTn/d0AbKiqa9v4pXRJXB/36y8AX6mqr1bV9+muTX0O/dyvgxbVd3623w6GGOtiSdo+BRzRerl5OF1TnsvHHNPIJdkryd7Tw8Dzgc9ve6kl53Lg1DZ8KnDZGGNZMDPaZv8yy+99X7K28b0e12f9QWdQxvjZ29r2Xw68rPUW9izgvoHmMkOX5AV0TbNeWFXfHih/9PTF40keT3cB/K0jjGNr78PlwMlJdk9yWIvjn0cVR/MLwBerasNAfCPbH+0apvOBm6vqbQOTevEZWWx2YH+OTVW9vqoOrqpD6f7rfbyqTgGuBl7UZutLrHcAtyd5Qis6BvgCPdyvdM0in5XkEe3zMB1r7/brDIvmO7+13w6GWWdX1aJ4AMfR9cbyZeC3xh3PAm3z44HPtcdNS3276f44bgK+T3cE63S6tuxXAbcAHwP2G3ecC7Td7wNuBG5oX/gDxx2nj6G937N+r8fxWadrInM3sM9A2cg/e/P5rgOh6z34yy2uVSOOYx3d9QfXt8e727z/T3u/rgc+A/zSiOPY6vsA/FbbH18Cjh1lHK38AuAVM+Yd5f54Ll0zqBsG3ofjxvEZWQqP+e7PvjyASeBDbfjxdH9019E15d593PG1uI4EPt327d8A+/Z1vwJvBr5IdwDofcDufdqvffld2IlYZ/3taPMPpc5OW5kkSZIkqYcWS/NISZIkSVqWTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zalpAkpyX5xFamnZLko0N6nUpy+M68TpI3JfmLYcQjSaOSZCrJv2/DQ6tHJUmaD5O2RSjJc5P8U5L7knw9yf9J8tPbWqaqLqqq589h3W9Isrk9vpvkgYHxm7a3/FxfR5KGIcn6JL+wEK9l/SZpobS67Tvt/9c9Sa5IckibdkE7gL56xjLntPLT2vhWD+Zr8TFpW2SSPAr4EPAOYD/gIODNwP3DWH9V/c+qemRVPRJ4BfDJ6fGqevIwXkOSJEnb9Uvt/9iBwJ10//2m/QvwsumRJLsCJwFfXtAItWBM2hafnwSoqvdX1QNV9Z2q+mhV3TBzxiR/kOQTSfaZebSlHYl5RZJbktyb5J1JMo84fmG2ZWd5nScnubKdEbwzyRtmiXO3JO9P8ldJHt6aTl6S5MIk30xyU5JVA/M/ts371SRfSfKfB6Y9I8mnk3yjvd7bWvkeSf4iyd0t5k8lmZjH9krqsem6J8kftqPSX0ly7Izpt7Y65StJTmnlD2qqneTQVj/uurXXGBjf2XpUkrarqr4LXAo8aaD4b4HnJtm3jb8AuAG4Y4HD0wIxaVt8/gV4IMnaJMcOfFl/KMnDkrwH+Cng+VV131bWdQLw022+k4BfnEcc2102yd7Ax4CPAI8FDgeumjHPnsDf0J0pPKmqvtcmvRC4GFgBXA788fS20VVUn6M7y3gM8Jok069/LnBuVT0K+AngklZ+KrAPcAiwP91ZxO/MY3sl9d8zgS8BBwC/D5yfzl7A24Fjq2pv4GeA64f0mjtTj0rSdiV5BPBi4JqB4u8ClwEnt/GXARcucGhaQCZti0xVfQN4LlDAe4CvJrl84KzRbsD76ZpO/lJVfXsbqzurqu6tqv8LXA0cOY9Q5rLsCcAdVXV2VX23qr5ZVdcOTH8UXUL3ZeDlVfXAwLRPVNWHW9n7gKe28p8GHl1Vv11V36uqW9t+mK60vg8cnuSAqtpcVdcMlO8PHN7OUF7X9qWkpeO2qnpPqzfW0jUpmq4bfwA8JcmeVbWpqrZ7je4c7Uw9Kknb8jdJ7gXuA54H/MGM6RcCL0uyAvg5uoPgWqJM2hahqrq5qk6rqoOBp9CdxfqjNvlwYDXw5oGzVlszeAr928Aj5xHGXJY9hG23rX4W3dHps6qqtrP+PVpzpccBj21Nke5tldkb+NEfs9PpmpB+sTWBPKGVvw/4e+DiJP+a5PeT7Lb9zZS0iPyw3hg4YPXIqvoW3VHqVwCb2gX9Txz2azL/elSStuXEqloB7AG8CviHJI+ZnlhVnwAeDfwW8KGqsgXREmbStshV1ReBC+iSN4CbgZcDf5fkCeOKq7kdePw2pn8U+D3gqnlcX3Y78JWqWjHw2LuqjgOoqluq6iXAjwFvBS5NsldVfb+q3lxVT6JrGnUCAxfwSlraqurvq+p5dGffvkh3hh7gW8AjBmZ9zMxlJWmcWguhvwYeoGttNegvgDXYNHLJM2lbZJI8McmaJAe38UOAlzDQzrmq3k939uljSX5iPJECXS+XByb5/9m7/3i76vrO9693QRHRGn7Y0zQwDX3I1LGmImYAR2/vqdQK6BjmXktxqIJlHmnvAztaM1OCM49a2zqDvSqjHYfbTLGGjoqM1QsVxpoiZ7zOvaBCkZ86Rg2STCBFfmhkio393D/WN7A5nCTnnOyz9zrnvJ6Px37stb7rx/6svU++WZ+1vt/vemuSw5I8O8kpgytU1R8AH6VL3I6ZxT6/CHwvyUVJDk9ySJIXpj3yIMmvJHluVf0d8HDb5u+S/HySNUkOAb5L11zy74Z1oJL6K8lEknWtb9tjwG6e+Pd/K/BzSf5ekucAF48rTkmaSeubuw44ku7i/KAP0DWd/PzIA9NImbQtPt+j62x/U5Lv0yVrd9BdZXlcVW0Gfhf4XJLVI45xbwzfo6tI/jFdE6KvAz8/w3q/R9cO+y+THHWAff6Q7i7ZicC3gAeAP6YbZAS60ZPuTLKbblCSc1pzgR+nG3npu3QV3n+lazIpaen7EeBtwP8AHqTr+/F/AFTVFuDjdKOu3Ux3sUmS+uDP2/nMd4F3AedN749bVQ9W1fUzdDPREhN/Y0mSJEnqL++0SZIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPHTruAACOOeaYWr169bjDeJLvf//7HHHEEeMOY7+McTiM8cBuvvnmB6rquWMLoIf2V2+N+/caFY9zaVlqx7kY6632fNWPDxT9FPDbdM/g+jiwGtgGnF1VDyUJ3UjJZ9I93P38qrplf59xsOdcffk7MQ7j6Hsc84lhv/VWVY399ZKXvKT65oYbbhh3CAdkjMNhjAcGfLl6UFf06bW/emvcv9eoeJxLy1I7zsVebwGH0D0u5yeBPwA2tvKNwLvb9JnAfwECnArcdKD9Huw5V1/+TozjyYzjyfoQx3xi2F+9ZfNISZKk/jkN+EZV3QOsAza38s3AWW16HXBFO9+7EViRZOXoQ5W00HrRPFKSJElPcg7wsTY9UVU72/R9wESbXgXcO7DN9la2c6CMJOuB9QATExNMTU3NO6jdu3cf1PbDYhzG0fc4hh3DrJK2JNuA7wE/BPZU1dokRzGk9tWSJEnqJHk68Frg4unLqqqS1Fz2V1WbgE0Aa9eurcnJyXnHNjU1xcFsPyzGYRx9j2PYMcyleeTPV9WJVbW2zW8Erq+qE4Dr2zzAGcAJ7bUeuGxYwUqSJC0DZwC3VNX9bf7+vc0e2/uuVr4DOG5gu2NbmaQl5mD6tNm+WpIkafhezxNNIwGuAc5r0+cBVw+UvzGdU4FHBppRSlpCZtunrYDPttvxf9Rus/emffVC6ENb2AMxxuEwRklSXyQ5Angl8GsDxZcAVyW5ALgHOLuVX0fXHWUrXZeUN40wVEkjNNuk7eVVtSPJjwFbknx1cOG421cvhD60hT0QYxwOY5Qk9UVVfR84elrZd+hGk5y+bgEXjig0SWM0q+aRVbWjve8CPgWcjO2rJUmSJGnBHTBpS3JEkmfvnQZ+EbgD21dLkiRJ0oKbTfPICeBT3Uj+HAp8tKo+k+RL2L56n1ZvvHbo+9x2yauHvk9Ji5N1jCQtTF0I1ofqnwMmbVX1TeBFM5TbvlqSJEmSFtjBDPkvSb2U5BlJvpjkK0nuTPLOVv7hJN9Kcmt7ndjKk+QDSbYmuS3JSeM9AkmSpCfMdvRISVpMHgNeUVW7kzwN+EKS/9KW/cuq+sS09c8ATmivU4DL2rskSdLYeadN0pJTnd1t9mnttb/HkqwDrmjb3Qis2Ds6riRJ0riZtElakpIckuRWuseRbKmqm9qid7UmkJcmOayVrQLuHdh8eyuTJEkaO5tHSlqSquqHwIlJVtCNgPtC4GLgPuDpwCbgIuB3Z7vPJOuB9QATExNMTU3NuN7u3bv3uWxYNqzZM/R9zjXmURxnH3ickqRxM2mTtKRV1cNJbgBOr6r3tOLHkvwJ8C/a/A7guIHNjm1l0/e1iS7ZY+3atTU5OTnjZ05NTbGvZcNy/kIM+X/u5JzWH8Vx9oHHKUkaN5tHSlpykjy33WEjyeHAK4Gv7u2nlu7Bk2cBd7RNrgHe2EaRPBV4pKp2jiF0SZKkp/BOm6SlaCWwOckhdBenrqqqTyf5XJLnAgFuBX69rX8dcCawFXgUeNMYYpYkSZqRSZukJaeqbgNePEP5pw4K9AAAIABJREFUK/axfgEXLnRckiRJ82HzSEmSJEnqMZM2SZIkSeoxkzZJkiRJ6jGTNkmSJEnqMZM2SZIkSeoxkzZJkiRJ6jGTNkmSJEnqMZM2SZIkSeoxkzZJkiRJ6jGTNkmSJEnqMZM2SZKkHkiyIsknknw1yd1JXprkqCRbkny9vR/Z1k2SDyTZmuS2JCeNO35JC8ekTZIkqR/eD3ymqp4PvAi4G9gIXF9VJwDXt3mAM4AT2ms9cNnow5U0KiZtkiRJY5bkOcDPAZcDVNUPquphYB2wua22GTirTa8DrqjOjcCKJCtHHLakETl03AFIkiSJ44G/Bv4kyYuAm4G3ABNVtbOtcx8w0aZXAfcObL+9le1kmiTr6e7GMTExwdTU1LyD3L1790FtPyx749iwZs+C7H+2x9i372PcjGPhYjBpkyRJGr9DgZOA36iqm5K8nyeaQgJQVZWk5rrjqtoEbAJYu3ZtTU5OzjvIqakpDmb7Ydkbx/kbr12Q/W87d3JOcYybcfQvjmHHYNIGrJ7hH/yGNXsWrCKQJEmaZjuwvapuavOfoEva7k+ysqp2tuaPu9ryHcBxA9sf28okLUH2aZMkSRqzqroPuDfJT7ei04C7gGuA81rZecDVbfoa4I1tFMlTgUcGmlFKWmK80yZJktQPvwF8JMnTgW8Cb6K7wH5VkguAe4Cz27rXAWcCW4FH27q9NlPLpvmyRZSWG5M2SZKkHqiqW4G1Myw6bYZ1C7hwwYOS1As2j5QkSZKkHvNOmyQtsGE2CZIkScuPd9okLTlJnpHki0m+kuTOJO9s5ccnuSnJ1iQfb/1GSHJYm9/alq8eZ/ySJEmDTNokLUWPAa+oqhcBJwKnt9HV3g1cWlXPAx4CLmjrXwA81MovbetJkiT1gkmbpCWnOrvb7NPaq4BX0D37CGAzcFabXtfmactPS5IRhStJkrRfJm2SlqQkhyS5le5BtFuAbwAPV9Wetsp2YFWbXgXcC9CWPwIcPdqIJUmSZuZAJJKWpKr6IXBikhXAp4DnH+w+k6wH1gNMTEwwNTU143q7d+9+0rINa/bMuF7f7Ot49mX6cS5VHqckadxmnbQlOQT4MrCjql6T5HjgSrqr0TcDb6iqHyQ5DLgCeAnwHeCXq2rb0COXpFmoqoeT3AC8FFiR5NB2N+1YYEdbbQdwHLA9yaHAc+jqr+n72gRsAli7dm1NTk7O+JlTU1MMLlssD4Dddu7knNaffpxLlccpSRq3uTSPfAtw98C8Hfol9VKS57Y7bCQ5HHglXf11A/C6ttp5wNVt+po2T1v+ufbgWkmSpLGbVdKW5Fjg1cAft/lgh35J/bUSuCHJbcCXgC1V9WngIuBtSbbStRK4vK1/OXB0K38bsHEMMUuSJM1ots0j/x3wW8Cz2/zRzLJDf5K9HfofGNzhbPuGjMJM/U0mDu9fP5Tp39Fi6H9gjMOxGGLsk6q6DXjxDOXfBE6eofxvgF8aQWiSJElzdsCkLclrgF1VdXOSyWF98Gz7hozCTP1NNqzZw3tv79c4LdP7myyG/gfGOByLIUZJkiQtjNlkJS8DXpvkTOAZwI8C7+cgO/RLkiRJkg7sgH3aquriqjq2qlYD59B10D8XO/RLkiRJ0oI7mIdr26FfkiRJkhbYnDptVdUUMNWm7dAvSZIkSQvsYO60SZIkSZIWmEmbJEmSJPWYSZskSZIk9ZhJmyRJkiT1mEmbJEmSJPWYSZskSZIk9ZhJmyRJkiT1mEmbJElSTyTZluT2JLcm+XIrOyrJliRfb+9HtvIk+UCSrUluS3LSeKOXtFBM2iRJkvrl56vqxKpa2+Y3AtdX1QnA9W0e4AzghPZaD1w28kgljYRJmyRJUr+tAza36c3AWQPlV1TnRmBFkpXjCFDSwjp03AFIkiTpcQV8NkkBf1RVm4CJqtrZlt8HTLTpVcC9A9tub2U7B8pIsp7uThwTExNMTU3NO7jdu3fPe/sNa/bM+3Onmzh8uPubbrbHeDDfxzAZR//iGHYMJm2SJEn98fKq2pHkx4AtSb46uLCqqiV0s9YSv00Aa9eurcnJyXkHNzU1xXy3P3/jtfP+3Ok2rNnDe29fuNPYbedOzmq9g/k+hsk4+hfHsGOweaQkSVJPVNWO9r4L+BRwMnD/3maP7X1XW30HcNzA5se2MklLjEmbJElSDyQ5Ismz904DvwjcAVwDnNdWOw+4uk1fA7yxjSJ5KvDIQDNKSUuIzSMlSZL6YQL4VBLoztE+WlWfSfIl4KokFwD3AGe39a8DzgS2Ao8Cbxp9yJJGwaRNkiSpB6rqm8CLZij/DnDaDOUFXDiC0CSNmc0jJS05SY5LckOSu5LcmeQtrfx3kuxoD629NcmZA9tc3B5Q+7Ukrxpf9JIkSU/mnTZJS9EeYENV3dL6h9ycZEtbdmlVvWdw5SQvAM4Bfgb4CeAvk/z9qvrhSKOWJEmagXfaJC05VbWzqm5p098D7qZ7dtG+rAOurKrHqupbdP1DTl74SCVJkg7MpE3SkpZkNfBi4KZW9OYktyX5UJIjW9m+HlArSZI0djaPlLRkJXkW8GfAW6vqu0kuA34PqPb+XuBX57C/9cB6gImJCaampmZcb/fu3U9atmHNnvkdwIjt63j2ZfpxLlUepyRp3EzaJC1JSZ5Gl7B9pKo+CVBV9w8s/4/Ap9vsrB5QW1WbgE0Aa9eurcnJyRk/e2pqisFl52+8dv4HMkLbzp2c0/rTj3Op8jglSeNm80hJS066hxxdDtxdVe8bKF85sNo/oXtoLXQPqD0nyWFJjgdOAL44qnglSZL2xzttkpailwFvAG5Pcmsrezvw+iQn0jWP3Ab8GkBV3ZnkKuAuupEnL3TkSEmS1BcmbZKWnKr6ApAZFl23n23eBbxrwYKSJEmaJ5tHSpIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPHTBpS/KMJF9M8pUkdyZ5Zys/PslNSbYm+XiSp7fyw9r81rZ89cIegiRJkiQtXbO50/YY8IqqehFwInB6klOBdwOXVtXzgIeAC9r6FwAPtfJL23qSJEmSpHk4YNJWnd1t9mntVcArgE+08s3AWW16XZunLT8tSYYWsSRJkiQtI4fOZqUkhwA3A88DPgh8A3i4qva0VbYDq9r0KuBegKrak+QR4GjggWn7XA+sB5iYmGBqauqgDuRgbFiz5yllE4fPXD5O07+j3bt3j/V7mw1jHI7FEKMkSZIWxqyStqr6IXBikhXAp4DnH+wHV9UmYBPA2rVra3Jy8mB3OW/nb7z2KWUb1uzhvbfP6usZmW3nTj5pfmpqinF+b7NhjMOxGGLU4rd6hrpwfzas2TNj/Tlo2yWvPpiQpGWnXSj/MrCjql6T5HjgSroL4DcDb6iqHyQ5DLgCeAnwHeCXq2rbmMKWtMDmNHpkVT0M3AC8FFiRZG9Wcyywo03vAI4DaMufQ1eZSJIkaf/eAtw9MO8YApJmNXrkc9sdNpIcDrySrjK5AXhdW+084Oo2fU2bpy3/XFXVMIOWJElaapIcC7wa+OM2HxxDQBKzax65Etjcbtf/CHBVVX06yV3AlUl+H/gr4PK2/uXAnybZCjwInLMAcUuSJC01/w74LeDZbf5oDnIMARjuOAIH08d6mGMFLPTYA7M9xr70OTeO/sUx7BgOmLRV1W3Ai2co/yZw8gzlfwP80lCikyRJWgaSvAbYVVU3J5kc5r6HOY7AwfSxPlAf2LlY6LEHpo8jsC996XNuHP2LY9gx9GukDUmSpOXpZcBrk5wJPAP4UeD9tDEE2t22mcYQ2O4YAtLSN6eBSCRJkjR8VXVxVR1bVavpupZ8rqrOxTEEJGHSJmkJSnJckhuS3JXkziRvaeVHJdmS5Ovt/chWniQfSLI1yW1JThrvEUjS4y4C3tbGCjiaJ48hcHQrfxuwcUzxSRoBm0dKWor2ABuq6pYkzwZuTrIFOB+4vqouSbKR7iTnIuAM4IT2OgW4rL1L0shV1RQw1aYdQ0CSd9okLT1VtbOqbmnT36N7TMkqnjxE9vShs6+ozo10fUhWjjhsSZKkGZm0SVrSkqymGwH3JmCiqna2RfcBE2368aGzm8FhtSVJksbK5pGSlqwkzwL+DHhrVX138LmzVVVJ5tRpf7bPOpr+bJaFfJbQOM3mOUnjfk7OMPTheT+jsFyOU5IWI5M2SUtSkqfRJWwfqapPtuL7k6ysqp2t+eOuVr536Oy9BofVftxsn3U0/dksw3w2UZ/M5jlJs33WUZ/14Xk/o7BcjlOSFiOTtkVk9bQTvw1r9hz0yeC2S159UNtLfZTultrlwN1V9b6BRXuHyL6Epw6d/eYkV9INQPLIQDNKSZKksTJpk7QUvQx4A3B7kltb2dvpkrWrklwA3AOc3ZZdB5wJbAUeBd402nAlSZL2zaRN0pJTVV8Aso/Fp82wfgEXLmhQkiRJ8+TokZIkSZLUYyZtkiRJktRjJm2SJEmS1GMmbZIkSZLUYyZtkiRJktRjJm2SJEmS1GMmbZIkSZLUYyZtkiRJktRjJm2SJEmS1GMmbZIkSZLUYyZtkiRJktRjh447AEmSJKlPVm+8dlbrbVizh/Nnue62S159MCFpmfNOmyRJkiT1mEmbJEmSJPWYSZskSZIk9ZhJmyRJkiT1mEmbJElSDyR5RpIvJvlKkjuTvLOVH5/kpiRbk3w8ydNb+WFtfmtbvnqc8UtaOCZtkiRJ/fAY8IqqehFwInB6klOBdwOXVtXzgIeAC9r6FwAPtfJL23qSliCTNkmSpB6ozu42+7T2KuAVwCda+WbgrDa9rs3Tlp+WJCMKV9II+Zw2SZKknkhyCHAz8Dzgg8A3gIerak9bZTuwqk2vAu4FqKo9SR4BjgYemLbP9cB6gImJCaampuYd3+7du+e9/YY1ew680ixNHD7c/Y0ijoP53g/kYH4X41gcMZi0SZIk9URV/RA4MckK4FPA84ewz03AJoC1a9fW5OTkvPc1NTXFfLef7UOoZ2PDmj289/bxn8bOJY5t504uWBwH87sYx+KIweaRkpacJB9KsivJHQNlv5NkR5Jb2+vMgWUXt478X0vyqvFELUlPqKqHgRuAlwIrkuzNDI4FdrTpHcBxAG35c4DvjDhUSSNg0iZpKfowcPoM5ZdW1YntdR1AkhcA5wA/07b5D615kiSNVJLntjtsJDkceCVwN13y9rq22nnA1W36mjZPW/65qqrRRSxpVA6YtCU5LskNSe5qw8++pZUflWRLkq+39yNbeZJ8oF21vi3JSQt9EJI0qKo+Dzw4y9XXAVdW1WNV9S1gK3DyggUnSfu2ErghyW3Al4AtVfVp4CLgbUm20vVZu7ytfzlwdCt/G7BxDDFLGoHZNMLdA2yoqluSPBu4OckW4Hzg+qq6JMlGuoriIuAM4IT2OgW4rL1L0ri9OckbgS/T1WsP0XXkv3FgncFO/k8y28780zsf96Gz/EKYTQf8cXcEH4Y+dGgfheVynH1WVbcBL56h/JvMcDGpqv4G+KURhCZpzA6YtFXVTmBnm/5ekrvpTmjWAZNttc3AFF3Stg64ot2evzHJiiQr234kaVwuA36Pbvjs3wPeC/zqXHYw28780zsfD7PzfZ/MpgP+Qna8H5U+dGgfheVynJK0GM2pT1uS1XRXgG4CJgYSsfuAiTb9+PCzzT6vWkvSqFTV/VX1w6r6O+A/8sRV68c78jeDnfwlSZLGbtZjpSZ5FvBnwFur6ruDz26sqkoyp46vw3xmyMGaqXlPX57/sT/DiHGhv/fF0NzGGJeHaXf8/wmwd2TJa4CPJnkf8BN0Tbu/OIYQJUmSZjSrpC3J0+gSto9U1Sdb8f17T4KSrAR2tfJZXbUe5jNDDtZMTZf68vyP/RlGjAvddGkxNLcxxqUnycfomm8fk2Q78A5gMsmJdM0jtwG/BlBVdya5CriLrg/vhe05SZIkSb1wwDP+dLfULgfurqr3DSzaO8zsJTx1+Nk3J7mSbgCSR+zPJmmUqur1MxRfPkPZ3vXfBbxr4SKSJEmav9ncpnkZ8Abg9iS3trK30yVrVyW5ALgHOLstuw44k27Y7EeBNw01YkmSJElaRmYzeuQXgOxj8WkzrF/AhQcZ1z6tXqKjsEmSJEnSTOY0eqQkSZIkabRM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpxw74cG1JkiQtL6s3Xjtj+YY1ezh/H8skLRzvtEmSJElSj5m0SZIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPmbRJkiRJUo+ZtEmSJI1ZkuOS3JDkriR3JnlLKz8qyZYkX2/vR7byJPlAkq1Jbkty0niPQNJCMmmTJEkavz3Ahqp6AXAqcGGSFwAbgeur6gTg+jYPcAZwQnutBy4bfciSRsWkTZIkacyqamdV3dKmvwfcDawC1gGb22qbgbPa9DrgiurcCKxIsnLEYUsaEZM2SZKkHkmyGngxcBMwUVU726L7gIk2vQq4d2Cz7a1M0hJ06LgDkKSFkORDwGuAXVX1wlZ2FPBxYDWwDTi7qh5KEuD9wJnAo8D5e694S9IoJXkW8GfAW6vqu1311KmqSlLz2Od6uiaUTExMMDU1dcBtNqzZM2P5xOH7XjZKizGO2Xzv87V79+4F3b9xjD8GkzZJS9WHgX8PXDFQtrdvyCVJNrb5i3hy35BT6PqGnDLSaCUte0meRpewfaSqPtmK70+ysqp2tuaPu1r5DuC4gc2PbWVPUVWbgE0Aa9eurcnJyQPGcv7Ga2cs37BmD++9ffynj4sxjm3nTi5YHFNTU8zmd11oxrFwMdg8UtKSVFWfBx6cVmzfEEm91O74Xw7cXVXvG1h0DXBemz4PuHqg/I1tFMlTgUcGmlFKWmLGf4lCkkZnrn1DnnQCNNsmRtObRPShCc9CmE2zoHE3TxmGPjSzGYXlcpw99jLgDcDtSW5tZW8HLgGuSnIBcA9wdlt2HV2T7q10zbrfNNpwJY2SSZukZWk+fUNm28RoepOIfTUzWuxm0yxoIZsDjUofmtmMwnI5zr6qqi8A2cfi02ZYv4ALFzQoSb1h80hJy8n9e5s9zrdviCRJ0qiZtElaTuwbIkmSFh2bR0pakpJ8DJgEjkmyHXgH9g2RJEmLkEmbpCWpql6/j0X2DZEkSYuKzSMlSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqccOmLQl+VCSXUnuGCg7KsmWJF9v70e28iT5QJKtSW5LctJCBi9JkiRJS91s7rR9GDh9WtlG4PqqOgG4vs0DnAGc0F7rgcuGE6YkSZIkLU8HTNqq6vPAg9OK1wGb2/Rm4KyB8iuqcyOwIsnKYQUrSZIkScvNfPu0TVTVzjZ9HzDRplcB9w6st72VSZIkSZLm4dCD3UFVVZKa63ZJ1tM1oWRiYoKpqalZbbdhzZ65ftS8TBw+us+ar2HEONvvfb5279694J9xsIxRkiRJfTbfpO3+JCuramdr/rirle8AjhtY79hW9hRVtQnYBLB27dqanJyc1Qefv/HaeYY8NxvW7OG9tx90TrughhHjtnMnhxPMPkxNTTHb33ZcjFGSJEl9Nt8z/muA84BL2vvVA+VvTnIlcArwyEAzSknSMrN6AS60bbvk1UPfpyRJfXbApC3Jx4BJ4Jgk24F30CVrVyW5ALgHOLutfh1wJrAVeBR40wLELEmSJEnLxgGTtqp6/T4WnTbDugVceLBBSZIkSZI68x09UpIkSZI0AiZtkiRJktRj/R4eUZIkSVoCHJhJB8M7bZIkST2Q5ENJdiW5Y6DsqCRbkny9vR/ZypPkA0m2JrktyUnji1zSQjNpkyRJ6ocPA6dPK9sIXF9VJwDXt3mAM4AT2ms9cNmIYpQ0BiZtkpadJNuS3J7k1iRfbmUzXs2WpFGpqs8DD04rXgdsbtObgbMGyq+ozo3AiiQrRxOppFGzT5uk5ernq+qBgfm9V7MvSbKxzV80ntAk6XETVbWzTd8HTLTpVcC9A+ttb2U7mSbJerq7cUxMTDA1NXXAD92wZs/MwRy+72WjZBydvb/l7t27Z/W7LjTjWLgYTNokqbMOmGzTm4EpTNok9UhVVZKax3abgE0Aa9eurcnJyQNuc/4+Bs3YsGYP7719/KePxtHZdu4k0CVvs/ldF5pxLFwM4/9rl6TRK+Cz7eTnj9oJzb6uZj9utlerp19d68PV4IUwrivMo7562ocrtqOwXI5zEbo/ycqq2tmaP+5q5TuA4wbWO7aVSVqCTNokLUcvr6odSX4M2JLkq4ML93U1e7ZXq6dfXdvXFevFblxXmPdeWR6VPlyxHYXlcpyL0DXAecAl7f3qgfI3J7kSOAV4ZODCk6QlxqRN0rJTVTva+64knwJOZt9XsyVpJJJ8jK6Z9jFJtgPvoEvWrkpyAXAPcHZb/TrgTGAr8CjwppEHLGlkTNokLStJjgB+pKq+16Z/Efhd9n01W5JGoqpev49Fp82wbgEXLmxEkvrCpE3ScjMBfCoJdHXgR6vqM0m+xMxXsyVJksbKpE3SslJV3wReNEP5d5jharYkSdK4+XBtSZIkSeoxkzZJkiRJ6jGTNkmSJEnqMfu0SZIWldUL9Ny7bZe8ekH2K0nSwfJOmyRJkiT1mEmbJEmSJPWYSZskSZIk9ZhJmyRJkiT1mAORSJK0QBZi0BQHTJGk5cc7bZIkSZLUY95pW+a8CixJkiT1m3faJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcccPVKSJPY9mu6GNXs4fwFG2pWkg7W33hpmPeUo4P3knTZJkiRJ6jGTNkmSJEnqMZtHSpK0iOyrGefBsDmUJPWbd9okSZIkqccW5E5bktOB9wOHAH9cVZcsxOeonwavAg+rY6xXgTUK1l2SFhvrLWl5GHrSluQQ4IPAK4HtwJeSXFNVdw37syRpWKy7pOGyGefCs97SQjiYf7v7uljvv92DtxB32k4GtlbVNwGSXAmsA6xAtCwsxInKQgw5bgX6FNZdWrZWb7zWRxssTtZbWhS8iHPwUlXD3WHyOuD0qvpnbf4NwClV9eZp660H1rfZnwa+NtRADt4xwAPjDuIAjHE4jPHAfrKqnjvGz19ws6m75lBvjfv3GhWPc2lZasdpvfXEesM85+rL34lxPJlxPFkf4phPDPust8Y2emRVbQI2jevzDyTJl6tq7bjj2B9jHA5j1GzNtt5aLr+Xx7m0LJfjXI6Gec7Vl78T4zCOvscx7BgWYvTIHcBxA/PHtjJJ6jPrLkmLjfWWtEwsRNL2JeCEJMcneTpwDnDNAnyOJA2TdZekxcZ6S1omht48sqr2JHkz8Bd0w89+qKruHPbnjEBvm24OMMbhMEYNu+5aLr+Xx7m0LJfjXDLGdM7Vl78T43gy43iyPsQx1BiGPhCJJEmSJGl4FqJ5pCRJkiRpSEzaJEmSJKnHTNqAJMcluSHJXUnuTPKWVn5Uki1Jvt7ej+xBrIck+askn27zxye5KcnWJB9vHZHHGd+KJJ9I8tUkdyd5ad++xyS/2X7nO5J8LMkzxv09JvlQkl1J7hgom/F7S+cDLdbbkpw0yli1b0lOT/K19ttsHHc8B2Ou9eJi/7ucbd2a5LA2v7UtXz3OuOdiLvXzYv89NRx9OT9q/09/MclXWhzvbOUj/7+7D+dhSbYluT3JrUm+3MpGfq41lzplAWP46fY97H19N8lbx/R9LOj5pUlbZw+woapeAJwKXJjkBcBG4PqqOgG4vs2P21uAuwfm3w1cWlXPAx4CLhhLVE94P/CZqno+8CK6WHvzPSZZBfxzYG1VvZCu4/Y5jP97/DBw+rSyfX1vZwAntNd64LIRxaj9SHII8EG63+cFwOtbPbJYzbVeXOx/l7OtWy8AHmrll7b1Fou51M+L/ffUcPTl/Ogx4BVV9SLgROD0JKcynv+7+3Ie9vNVdeLAc8DGca419nO+qvpa+x5OBF4CPAp8atRxjOT8sqp8TXsBVwOvBL4GrGxlK4GvjTmuY+n+8F4BfBoI3ZPWD23LXwr8xRjjew7wLdoANwPlvfkegVXAvcBRdKOnfhp4VR++R2A1cMeBvjfgj4DXz7Ser/G9pv/dABcDF487riEe337rxcX8dzmXupVulL6XtulD23oZR9xzPMY51c+L+ff0tXCvPpwfAc8EbgFOGfX/3X05DwO2AcdMKxvpbzLXOmVEfxu/CPy3MX0fC35+6Z22aVpTlxcDNwETVbWzLboPmBhTWHv9O+C3gL9r80cDD1fVnja/ne6PZlyOB/4a+JPWdOCPkxxBj77HqtoBvAf4NrATeAS4mX59j3vt63vbWzHs1Zd4l7sl+7vMsl5czMc/l7r18eNsyx9p6/fdXOvnxfx7agGM+/yoNUu8FdgFbAG+wej/7+7LeVgBn01yc5L1rWzUv0kfz/nOAT7WpkcaxyjOL03aBiR5FvBnwFur6ruDy6pLkcf2fIQkrwF2VdXN44phFg4FTgIuq6oXA99n2u3oHnyPRwLr6CqbnwCO4KnNEntn3N+blq8+14vDsEjq1mHoff2s/upDPVBVP6yuCdyxwMnA8xf6Mwf1rK54eVWdRNeM+cIkPze4cES/Sa/qlNZX7LXAf56+bBRxjOL80qStSfI0ugrpI1X1yVZ8f5KVbflKuqs74/Iy4LVJtgFX0t2afz+wIsneh6QfC+wYT3hAdwVhe1Xd1OY/QfcPuk/f4y8A36qqv66qvwU+Sffd9ul73Gtf39sO4LiB9foS73K35H6XOdaLi/X451q3Pn6cbflzgO+MMuB5mmv9vFh/Tw1Z384gEMRpAAAgAElEQVSPquph4Aa6pmaj/L+7N+dh7a4OVbWLrv/WyYz+N+nbOd8ZwC1VdX+bH3UcC35+adJGN0oWcDlwd1W9b2DRNcB5bfo8urbcY1FVF1fVsVW1mu727+eq6ly6iut1bbVxx3gfcG+Sn25FpwF30aPvke629alJntl+970x9uZ7HLCv7+0a4I3pnAo8MtAEQOPzJeCENlLU0+n+nV4z5pjmbR714qL8u5xH3Tp4/K9r6/f+7tQ86udF+XtquPpyfpTkuUlWtOnD6frV3c0I/+/uy3lYkiOSPHvvNF0/rjsY8W/Sw3O+1/NE00jGEMfCn18uZKe8xfICXk532/Q24Nb2OpOurfL1wNeBvwSOGnesLd5J4NNt+qeALwJb6W4JHzbm2E4Evty+y/8bOLJv3yPwTuCrdJXcnwKHjft7pKtodgJ/S3f16oJ9fW90HZ8/SNee/3a6kYrG/nfpq2j1xn9vv82/Gnc8B3ksc6oXl8Lf5WzqVuAZbX5rW/5T4457Dsc36/p5KfyevobyN9OL8yPgZ4G/anHcAfx2Kx/L/92zqSsW8LN/CvhKe9259/+aUf8m7TN7cc5H1xTxO8BzBsrGEceCnl+mfYgkSZIkqYdsHilJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSJkmSJEk9ZtImSZIkST1m0iZJkiRJPWbSpllLsi3J/0zyvSQPJ/l/k/x6kh9pyz+c5AdJdid5MMmWJM9vy34nyd+2ZXtfvzXeI5K0XCSZSvJQksPGHYuk5SHJ+UluT/JokvuSXJZkxSy33ZbkFxY6Ri0eJm2aq39cVc8GfhK4BLgIuHxg+R9U1bOAY4FdwIcHln28qp418PqDUQUtaflKshr4X4ACXjvWYCQtC0k2AO8G/iXwHOBUunOnLUmePs7YtDiZtGlequqRqroG+GXgvCQvnLb8UeCjwAtn2l6SRuiNwI10F5HO21uY5Ogkf57ku0m+lOT3k3xhYPnzW4uBB5N8LcnZow9d0mKT5EeBdwK/UVWfqaq/raptwNnAauBXWuuk3x/YZjLJ9jb9p8DfA/58sGVSkpe3Vk4PJ7k3yfmt/DlJrkjy10nuSfKvB1pBnZ/kvyW5tG33zST/qJXfm2RXksF68bAk70ny7ST3J/m/khw+iu9N+2fSpoNSVV8EttNdxX5ckmcB5wJ/NY64JGnAG4GPtNerkky08g8C3wd+nC6ZGzxxOQLYQnfx6ceAc4D/kOQFI4xb0uL0j4BnAJ8cLKyq3cB1wCv3t3FVvQH4Nl3rpmdV1R8k+UngvwB/CDwXOBG4tW3yh3R3834K+F/p6rw3DezyFOA24Gi6Ou1K4B8CzwN+Bfj37bwNulZUf7/t/3nAKuC353b4WggmbRqG/wEc1ab/RZKHga3As4DzB9Y7u13l2fv6iRHHKWmZSfJyuiZJV1XVzcA3gH+a5BDgfwfeUVWPVtVdwOaBTV8DbKuqP6mqPVX1V8CfAb804kOQtPgcAzxQVXtmWLazLZ+rfwr8ZVV9rN25+05V3drqsnOAi6vqe+2O3nuBNwxs+61Wl/0Q+DhwHPC7VfVYVX0W+AHwvCQB1gO/WVUPVtX3gH/T9q8xO3TcAWhJWAU82KbfU1X/eh/rXVVVvzKimCQJurtnn62qB9r8R1vZx+j+D7x3YN3B6Z8ETmkXofY6FPjTBYxV0tLwAHBMkkNnSNxWtuVzdRzdRafpjgGeBtwzUHYP3bnZXvcPTP9PgKqaXvYsujt4zwRu7vI3AAIcMo94NWQmbTooSf4hXcXwBbrb75LUC60fxtnAIUnua8WHASuACWAP3aBJ/70tO25g83uB/1pV+23GJEkz+P+Ax4D/Dbhqb2FrgngG8HbgxXQJ0l4/Pm0fNW3+XuDkGT7rAeBv6S403dXK/h6wYx5xP0CXwP1MVc1ney0gm0dqXpL8aJLX0LWL/k9Vdfu4Y5Kkac4Cfgi8gK5/xonAPwD+H7o+H58EfifJM9vjSd44sO2ngb+f5A1JntZe/zDJPxjtIUhabKrqEbqBSP4wyemt/lhNl8Btp7tjfytwZpKjkvw48NZpu7mfro/aXh8BfiHJ2UkObQMpndiaPF4FvCvJs1vft7cB/2kecf8d8B+BS5P8GECSVUleNdd9afhM2jRXf57ke3RXfP4V8D6e3NlVkvriPOBPqurbVXXf3hfw7+kGSnozXef9++hOoj5Gd3Wc1pfjF+n6cvyPts676e7USdJ+tccavR14D/Bd4Ca6c6fTquoxujrnK8A24LN0fc0G/VvgX7cxAP5FVX0bOBPYQNcl5VbgRW3d36AbVOmbdC2fPgp8aJ6hX0Q3LsGNSb4L/CXw0/Pcl4YoVdPvvkqStPwkeTfw41V13gFXliRphLzTJklaltpz2H42nZOBC4BPjTsuSZKmcyASSdJy9Wy6JpE/Qdd/5L3A1WONSJKkGdg8UpIkSZJ6zOaRkiRJktRjvWgeecwxx9Tq1asfn//+97/PEUccMb6A5sGYR8OYR2N6zDfffPMDVfXcMYbUO9PrrUF9+837FI+xzKxPsUC/4plvLNZbM9tf3TWoT38D+7NY4oTFE6txDt9sY91vvVVVY3+95CUvqUE33HBDLTbGPBrGPBrTYwa+XD2oK/r0ml5v7e/7G7c+xWMsM+tTLFX9ime+sVhvzb3uGtSnv4H9WSxxVi2eWI1z+GYb6/7qLZtHSpIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SZIkSVKPmbRJkiRJUo+ZtEmSJElSj5m0SVqSkvxmkjuT3JHkY0mekeT4JDcl2Zrk40me3tY9rM1vbctXjzd6SZKkJ5i0SVpykqwC/jmwtqpeCBwCnAO8G7i0qp4HPARc0Da5AHiolV/a1pMkSeoFkzZJS9WhwOFJDgWeCewEXgF8oi3fDJzVpte1edry05JkhLFKkiTt06HjDmCuVm+8duj73HbJq4e+T0njU1U7krwH+DbwP4HPAjcDD1fVnrbadmBVm14F3Nu23ZPkEeBo4IHB/SZZD6wHmJiYYGpqasbP371795OW3b7jkWEc1pOsWfWcWa87PZ5xMpaZ9SkW6Fc8fYplObl9xyOcP+RzLs+3pPmbVdKW5DeBfwYUcDvwJmAlcCXdic3NwBuq6gdJDgOuAF4CfAf45araNvzQJWlmSY6ku3t2PPAw8J+B0w92v1W1CdgEsHbt2pqcnJxxvampKQaXDfvEB2DbuTN/9mziGSdjmVmfYoF+xdOnWCRpXA7YPNK+IZIWoV8AvlVVf11Vfwt8EngZsKI1lwQ4FtjRpncAxwG05c+hu+gkSZI0drPt02bfEEmLybeBU5M8s9U/pwF3ATcAr2vrnAdc3aavafO05Z+rqhphvJIkSft0wOaR4+gbsr/26xvW7Jmx/GAMo638Ymxzb8yjYcyjV1U3JfkEcAuwB/grumaN1wJXJvn9VnZ52+Ry4E+TbAUepGtNIEmS1AsHTNrG0Tdkf+3Xx903ZF8WY5t7Yx4NYx6PqnoH8I5pxd8ETp5h3b8BfmkUcUmSJM3VbJpH2jdEkiRJksZkNkmbfUMkSZIkaUwOmLRV1U10A4rcQjfc/4/QNWu8CHhb6wNyNE/uG3J0K38bsHEB4pYkSZKkZWFWz2mzb4gkSZIkjcdsh/yXJEmSJI2BSZskSZIk9ZhJmyRJkiT1mEmbJEmSJPWYSZskSZIk9ZhJmyRJkiT1mEmbJEnSiCT5UJJdSe4YKPs/k3w1yW1JPpVkxcCyi5NsTfK1JK8aKD+9lW1N4jNxpSXOpE2SJGl0PgycPq1sC/DCqvpZ4L8DFwMkeQFwDvAzbZv/kOSQJIcAHwTOAF4AvL6tK2mJMmmTJEkakar6PPDgtLLPVtWeNnsjcGybXgdcWVWPVdW3gK3Aye21taq+WVU/AK5s60paog4ddwCSJEl63K8CH2/Tq+iSuL22tzKAe6eVn7KvHSZZD6wHmJiYYGpq6oBBTBwOG9bsOeB6czGbz52r3bt3L8h+F8JiidU4h28YsZq0SZIk9UCSfwXsAT4yzP1W1SZgE8DatWtrcnLygNv84Ueu5r23D/c0cdu5B/7cuZqammI2x9MHiyVW4xy+YcRq0iZJkjRmSc4HXgOcVlXVincAxw2sdmwrYz/lkpYg+7RJkiSNUZLTgd8CXltVjw4sugY4J8lhSY4HTgC+CHwJOCHJ8UmeTjdYyTWjjlvS6HinTZIkaUSSfAyYBI5Jsh14B91okYcBW5IA3FhVv15Vdya5CriLrtnkhVX1w7afNwN/ARwCfKiq7hz5wUgaGZM2SUtOkp/miY78AD8F/DZwRStfDWwDzq6qh9KdJb0fOBN4FDi/qm4ZZcySloeqev0MxZfvZ/13Ae+aofw64Lohhiapx2weKWnJqaqvVdWJVXUi8BK6ROxTwEbg+qo6Abi+zUP3rKMT2ms9cNnoo5YkSZqZSZukpe404BtVdQ/dc4w2t/LNwFlteh1wRXVuBFYkWTn6UCVJkp7K5pGSlrpzgI+16Ymq2tmm7wMm2vQqnvrMo1XAzoGyWT/raPrzWIb9rCOY2/OO+vQsG2OZWZ9igX7F06dYJGlcDpi02TdE0mLVRlV7LV0n/yepqkpST91q32b7rKPpz2M5f+O1c/mYWZnL84769CwbY5lZn2KBfsXTp1gkaVwO2DzSviGSFrEzgFuq6v42f//eZo/tfVcr39+zkCRJksZqrn3a7BsiaTF5PU80jYTuOUbntenzgKsHyt+YzqnAIwPNKCVJksZqrn3aRtI3ZH/t18fdN2RfFmObe2MeDWMejyRHAK8Efm2g+BLgqiQXAPcAZ7fy6+iadG+la03wphGGKkmStF+zTtpG2Tdkf+3Xx903ZF8WY5t7Yx4NYx6Pqvo+cPS0su/QtRiYvm4BF44oNEmSpDmZS/NI+4ZIkiRJ0ojNJWmzb4gkSZIkjdismkfaN0SSJEmSxmNWSZt9QyRJkiRpPOY65L8kSZIkaYRM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZIkqcdM2iRJkiSpx0zaJEmSJKnHTNokSZJGKMmHkuxKcsdA2VFJtiT5ens/spUnyQeSbE1yW5KTBrY5r63/9STnjeNYJI2GSZskSdJofRg4fVrZRuD6qjoBuL7NA5wBnNBe64HLoEvygHcApwAnA+/Ym+hJWnpM2iQtSUlWJPlEkq8muTvJS+dzJVuShq2qPg88OK14HbC5TW8Gzhoov6I6NwIrkqwEXgVsqaoHq+ohYAtPTQQlLRGHjjsASVog7wc+U1WvS/J04JnA2+muZF+SZCPdleyLePKV7FPormSfMp6wJS1TE1W1s03fB0y06VXAvQPrbW9l+yp/iiTr6e7SMTExwdTU1IGDORw2rNkzh/APbDafO1e7d+9ekP0uhMUSq3EO3zBiNWmTtOQkeQ7wc8D5AFX1A+AHSdYBk221zcAUXdL2+JVs4MZ2l27lwAmUJI1MVVWSGuL+NgGbANauXVuTk5MH3OYPP3I17719uKeJ28498OfO1dTUFLM5nj5YLLEa5/ANI9ZZ/WtMsgL4Y+CFQAG/CnwN+DiwGtgGnF1VDyUJ3RXuM4FHgfOr6paDilKS5uZ44K+BP0nyIuBm4C3M/Ur2k5K22V6tnn5FbdhXq2FuV6z7dDXSWGbWp1igX/H0KZYFdv/ei0Wt+eOuVr4DOG5gvWNb2Q6euAi1t3xqBHFKGoPZXkKxmZGkxeRQ4CTgN6rqpiTv54lO/cD8rmTP9mr19Ctq52+8di4fMytzuWLdp6uRxjKzPsUC/YqnT7EssGuA84BL2vvVA+VvTnIl3fnUIy2x+wvg3wwMPvKLwMUjjlnSiBxwIJKBZkb/f3v3H2RXWed5/P0dAgioBNDpZRPWYIm47GYUJoW4WlZLwOKHRagaRBxGgYqbmhFdHLIlcaZq/DFuLe4OIv4onEgcgoUixh9kgZmVAbqsmTKo/BAEdGhjlGQDKEI06zjas9/94zxNbprb3bfpe885t/N+VXX1+ZV7P3365unnec5znrMBqmFGmfk0c79hVpLqsh3Ynpl3lfVNVI24xyfLox57siWp7yLiC8A3gWMjYntErKZqrJ0aEY8Ap5R1gFuBrcA48BngnQCZ+XPgL4Fvl68PlW2SFqBerrTVPsxopqEQTQ8zms4wDt8wcz3MXL/MfCwiHo2IYzPzB8BK4KHy1XNPdgPRJe0DMvOt0+xa2eXYBC6e5nU+C3y2j9EktVQvjbbahxnNNBSi6WFG0xnG4RtmroeZG/Nu4PoypHsrcBHV6IIbS6/2j4Fzy7G3Ut2HO051L+5F9ceVJEnqrpdGW7dhRuuY+w2zklSbzLwPWNFl15x6siVJkpo26z1tmfkY8GhEHFs2TQ4zmrxhFp49zOjt5WG1J+EwI0mSJEl6znqdPdJhRpIkSZLUgJ4abQ4zkiRJkqRmzDo8UpIkSZLUHBttkiRJktRiNtokSZIkqcVstEmSJElSi9lokyRJkqQWs9EmSZIkSS1mo02SJEmSWsxGmyRJkiS1WE8P117olq27Zd6vsXb5BBd2vM62y8+c92tKkiRJklfaJEmSJKnFbLRJkiRJUovZaJMkSZKkFrPRJkmSJEktZqNNkiRJklrMRpskSZIktZiNNkkLUkRsi4gHIuK+iPhO2XZ4RNwWEY+U74eV7RERH4+I8Yi4PyJOaDa9JEnSHjbaJC1kb8jMV2XmirK+Drg9M48Bbi/rAKcDx5SvNcDVtSeVJEmaRk+NNnusJS0Qq4CNZXkjcHbH9uuysgVYHBFHNhFQkiRpqkVzOPYNmfmzjvXJHuvLI2JdWb+MvXusX03VY/3qPuWVpF4l8PWISOCvM3M9MJKZO8v+x4CRsrwEeLTj324v23Z2bCMi1lBdiWNkZISxsbGub7x79+699q1dPjHPH+XZpnvvXvI0ySzdtSkLtCtPm7IMWkT8KfAOqvLrAeAi4EjgBuAI4G7gbZn5m4g4ELgO+H3gSeAtmbmtidySBm8ujbapVgGjZXkjMEbVaHumxxrYEhGLI+LIjoqSJNXhdZm5IyJ+F7gtIr7fuTMzszToelYafusBVqxYkaOjo12PGxsbo3PfhetumVvyHmw7v/t795KnSWbprk1ZoF152pRlkCJiCfBfgOMy858j4kbgPOAM4MrMvCEiPg2spuoQXw08lZkvi4jzgI8Ab2kovqQB6/Wetske67tLTzPMvcdakmqTmTvK9yeArwInAo9PDnss358oh+8Ajur450vLNkmq0yLgoIhYBBxMdbX/ZGBT2T91WPfkcO9NwMqIiBqzSqpRr1fa+t5jPdMwo5mGQgximFE/jBy0d7ZhGMoxjENOzFyPYczcKSIOAX4nM39Zlt8IfAjYDFwAXF6+31T+yWbgXRFxA9Vw7l2ODpBUp1LP+ivgJ8A/A1+nGg75dGZOVjA6O8Kf6STPzImI2EU1hPJnSFpwemq0dfZYR8RePdaZufO59FjPNMxopqEQgxhm1A9rl09wxQN7Tudchi41ZRiHnJi5HsOYeYoR4Kul03kR8PnM/LuI+DZwY0SsBn4MnFuOv5VqCNI48Cuq+0gkqTZlQrdVwNHA08CXgNP69No93Y/baWpndD8MojNwmDoZhyWrOfuvH1lnbbTZYy1p2GTmVuCVXbY/Cazssj2Bi2uIJknTOQX4UWb+FCAivgK8lmo220XlaltnR/hkJ/n2MpzyUKoJSZ6l1/txO33i+pv26ozuh0F0aA9TJ+OwZDVn//Ujay//G+2xliRJGqyfACdFxMFUwyNXAt8B7gTOoZpBcmon+QXAN8v+O0oHlKQFaNZGmz3WkiRJg5WZd0XEJuAeYAK4l+rq2C3ADRHx4bJtQ/knG4DPRcQ48HOqmSYlLVD9ve4tSZKk5yQz3w+8f8rmrVRzCUw99tfAm+vIJal5vU75L0mSJElqgI02SZIkSWoxG22SJEmS1GI22iRJkiSpxWy0SZIkSVKL2WiTJEmSpBaz0SZJkiRJLWajTZIkSZJazEabJEmSJLWYjTZJkiRJajEbbZIkSZLUYjbaJEmSJKnFbLRJkiRJUovZaJMkSZKkFlvUdABJkiRJatqydbcM5HWvPe2Qeb+GV9okLVgRsV9E3BsRN5f1oyPirogYj4gvRsQBZfuBZX287F/WZG5JkqRONtokLWSXAA93rH8EuDIzXwY8Bawu21cDT5XtV5bjJEmSWqHnRps91pKGSUQsBc4ErinrAZwMbCqHbATOLsuryjpl/8pyvCRJUuPmck/bZI/1C8v6ZI/1DRHxaaqe6qvp6LGOiPPKcW/pY2ZJ6sXHgPcCLyjrRwBPZ+ZEWd8OLCnLS4BHATJzIiJ2leN/1vmCEbEGWAMwMjLC2NhY1zfevXv3XvvWLp/oetx8TPfeveRpklm6a1MWaFeeNmWRpKb01Gjr6LH+b8ClHT3Wf1gO2Qh8gKrRtqosQ9Vj/cmIiMzM/sWWpOlFxJuAJzLz7ogY7dfrZuZ6YD3AihUrcnS0+0uPjY3Rue/CAdzYvO387u/dS54mmaW7NmWBduVpUxZJakqvV9pq7bGeqVdtED3W/TBy0N7ZhqFXcBh7L81cj2HMPMVrgbMi4gzgeVQjBK4CFkfEolJ2LQV2lON3AEcB2yNiEXAo8GT9sXs3lxmu1i6f6KnhuO3yM+cTSZIkDcisjbYmeqxn6lUbRI91P6xdPsEVD+w5nXPpBW/KMPZemrkew5i5U2a+D3gfQCm3/mtmnh8RXwLOAW4ALgBuKv9kc1n/Ztl/h6MDJElSW/RypW3B91hL2mdcBtwQER8G7gU2lO0bgM9FxDjwc+C8hvJJkiQ9y6yNNnusJQ2zzBwDxsryVuDELsf8GnhzrcEkSZJ6NJ/ntF1GNSnJONU9a5091keU7ZcC6+YXUZIkaeGLiMURsSkivh8RD0fEayLi8Ii4LSIeKd8PK8dGRHy8PGLp/og4oen8kgZnLlP+22MtSZI0OFcBf5eZ55Tn3x4M/Blwe2ZeHhHrqDrDLwNOB44pX6+mmsH71c3EljRo87nSJkmSpD6IiEOB11NGLmXmbzLzaapHKW0sh20Ezi7Lq4DrsrKFaq6BI2uOLakmc7rSJkmSpIE4Gvgp8DcR8UrgbuASYCQzd5ZjHgNGyvIzj1gqJh+/tJMpZnrM0nSmPsqoHwbxKJlhekTNsGTdl3MO6tFi/chqo02SJKl5i4ATgHdn5l0RcRVT5gXIzIyIOU/uNtNjlqbzietv2utRRv0wiMchDdMjaoYl676cc1CPFrv2tEPmndXhkZIkSc3bDmzPzLvK+iaqRtzjk8Mey/cnyv7JRyxN6nz8kqQFxkabJElSwzLzMeDRiDi2bFoJPMSeRynBsx+x9PYyi+RJwK6OYZSSFhiHR0qSJLXDu4Hry8yRW4GLqDrYb4yI1cCPgXPLsbcCZwDjwK/KsZIWKBttkiRJLZCZ9wEruuxa2eXYBC4eeChJrWCjbUCWDeBGxm2Xn9n315QkSZLUbt7TJkmSJEktZqNNkiRJklrMRpskSZIktZiNNkmSJElqMRttkiRJktRiNtokSZIkqcVstEmSJElSi9lokyRJkqQWs9EmacGJiOdFxLci4rsR8WBEfLBsPzoi7oqI8Yj4YkQcULYfWNbHy/5lTeaXJEnqNGujzcqPpCH0L8DJmflK4FXAaRFxEvAR4MrMfBnwFLC6HL8aeKpsv7IcJ0mS1Aq9XGmz8iNpqGRld1ndv3wlcDKwqWzfCJxdlleVdcr+lRERNcWVJEma0aLZDsjMBKar/Pxh2b4R+ABwNVXl5wNl+ybgkxER5XUkqRYRsR9wN/Ay4FPAD4GnM3OiHLIdWFKWlwCPAmTmRETsAo4AfjblNdcAawBGRkYYGxvr+t67d+/ea9/a5RNdj6vLyEG9ZZju5+mnqeemSWaZXpvytCmLJDVl1kYb1F/5mamAbrryM51eK0Xz0e8/WsP4h9DM9RjGzFNl5r8Cr4qIxcBXgVf04TXXA+sBVqxYkaOjo12PGxsbo3Pfhetume9bz8va5RNc8cDsxf2280cHnmXquWmSWabXpjxtyiJJTemp0VZ35WemArrpys90eq0UzUe/K1TD+IfQzPUYxszTycynI+JO4DXA4ohYVDqclgI7ymE7gKOA7RGxCDgUeLKRwJIkSVPMafbIzHwa2KvyU3Z1q/xg5UdSEyLixaWTiYg4CDgVeJiq/DqnHHYBcFNZ3lzWKfvvcEi3JElqi15mj7TyI2nYHAncGRH3A98GbsvMm4HLgEsjYpxq2PaGcvwG4Iiy/VJgXQOZJUmSuuplPN+RwMZyX9vvADdm5s0R8RBwQ0R8GLiXvSs/nyuVn58D5w0gtyRNKzPvB47vsn0rcGKX7b8G3lxDNEmSpDnrZfZIKz+SJEmS1JA53dMmSZIkSaqXjTZJkiRJajEbbZIkSZLUYjbaJEmSJKnFbLRJkiRJUovZaJMkSWqJiNgvIu6NiJvL+tERcVdEjEfEFyPigLL9wLI+XvYvazK3pMGy0SZJktQelwAPd6x/BLgyM18GPAWsLttXA0+V7VeW4zNaSQcAABIOSURBVCQtUDbaJEmSWiAilgJnAteU9QBOBjaVQzYCZ5flVWWdsn9lOV7SAjTrw7UlSfuGZetu6ftrbrv8zL6/prSAfQx4L/CCsn4E8HRmTpT17cCSsrwEeBQgMyciYlc5/mdTXzQi1gBrAEZGRhgbG5s1yMhBsHb5xKzHzUUv7ztXu3fvHsjrDsKwZN2Xc/b7Mz+pH1lttEmSJDUsIt4EPJGZd0fEaD9fOzPXA+sBVqxYkaOjs7/8J66/iSse6G81cdv5s7/vXI2NjdHLz9MGw5J1X8554QA6LwGuPe2QeWe10SZJktS81wJnRcQZwPOAFwJXAYsjYlG52rYU2FGO3wEcBWyPiEXAocCT9ceWVAfvaZMkSWpYZr4vM5dm5jLgPOCOzDwfuBM4pxx2AXBTWd5c1in778jMrDGypBrZaJMkSWqvy4BLI2Kc6p61DWX7BuCIsv1SYF1D+STVwOGRkqSBmTq5ydrlE/O+Z8DJTbTQZeYYMFaWtwIndjnm18Cbaw0mqTFeaZMkSZKkFrPRJkmSJEktZqNNkiRJklrMRpukBScijoqIOyPioYh4MCIuKdsPj4jbIuKR8v2wsj0i4uMRMR4R90fECc3+BJIkSXvM2miz8iNpCE0AazPzOOAk4OKIOI5qdrXbM/MY4Hb2zLZ2OnBM+VoDXF1/ZEmSpO56udJm5UfSUMnMnZl5T1n+JfAwsARYBWwsh20Ezi7Lq4DrsrKF6mG2R9YcW5IkqatZp/zPzJ3AzrL8y4jorPyMlsM2Uk1NexkdlR9gS0Qsjogjy+tIUq0iYhlwPHAXMNJRFj0GjJTlJcCjHf9se9m2V7kVEWuoOqMYGRlhbGys63vu3r17r31rl0/M62eYr5GDms8wqR9ZpjvvczX199SkNmWBduVpUxZJasqcntNWV+VnpgK6LRWPqeqoFPX7j9Yw/iE0cz2GMXM3EfF84MvAezLzFxHxzL7MzIjIubxeZq4H1gOsWLEiR0dHux43NjZG5775PpdsvtYun+CKB9rxWM5+ZNl2/mhfskz9PTWpTVmgXXnalEWSmtLzX846Kz8zFdBNV36mU0elqF8VlUnD+IfQzPUYxsxTRcT+VGXW9Zn5lbL58ckr/2X44xNl+w7gqI5/vrRskyRJalxPs0fOVPkp+638SGqNqHqVNgAPZ+ZHO3ZtBi4oyxcAN3Vsf3uZSOkkYJdDuiVJUlv0MnuklR9Jw+a1wNuAkyPivvJ1BnA5cGpEPAKcUtYBbgW2AuPAZ4B3NpBZkiSpq17G801Wfh6IiPvKtj+jquzcGBGrgR8D55Z9twJnUFV+fgVc1NfEkjSLzPwHIKbZvbLL8QlcPNBQkiRJz1Evs0da+ZEkSZKkhvR0T5skSZIkqRk22iRJkiSpxWy0SZIkSVKL2WiTJEmSpBaz0SZJkiRJLWajTZIkSZJazEabJEmSJLVYLw/XVkssW3dLX19v7fIJRvv6ipIkSZL6zUabJGmo9KsDa+3yCS7seK1tl5/Zl9eVJKnfHB4pSZIkSS1mo02SJEmSWsxGmyRJUsMi4qiIuDMiHoqIByPikrL98Ii4LSIeKd8PK9sjIj4eEeMRcX9EnNDsTyBpkGy0SZIkNW8CWJuZxwEnARdHxHHAOuD2zDwGuL2sA5wOHFO+1gBX1x9ZUl1stEmSJDUsM3dm5j1l+ZfAw8ASYBWwsRy2ETi7LK8CrsvKFmBxRBxZc2xJNXH2SEmSpBaJiGXA8cBdwEhm7iy7HgNGyvIS4NGOf7a9bNvJFBGxhupqHCMjI4yNjc2aYeSgaobVfurlfedq9+7dA3ndQRiWrPtyzn5/5if1I6uNNkmSpJaIiOcDXwbek5m/iIhn9mVmRkTO9TUzcz2wHmDFihU5Ojo667/5xPU3ccUD/a0mbjt/9vedq7GxMXr5edpgWLLuyzkv7PMzkSdde9oh887q8EhJkqQWiIj9qRps12fmV8rmxyeHPZbvT5TtO4CjOv750rJN0gI0a6MtIj4bEU9ExPc6tjmTkaRWs+ySNEyiuqS2AXg4Mz/asWszcEFZvgC4qWP720v5dRKwq2MYpaQFppcrbdcCp03Z5kxGktruWiy7JA2P1wJvA06OiPvK1xnA5cCpEfEIcEpZB7gV2AqMA58B3tlAZkk1mXWwcmZ+o9wQ22kVMFqWNwJjwGV0zGQEbImIxRFxpD0/kupm2SVpmGTmPwAxze6VXY5P4OKBhpLUGs/1DtOBzmQ00wwrg5rVZb4GMcvSoI0cNJiZnAZpWGY06mTmVplX2dXrDGxTz1/TZUObyqc2Z2nyM9+2/3NtytOmLJLUlHlPCzSImYxmmg1mULO6zNfa5RN9n2Vp0NYun+DcIZgdqNOwzGjUyczt9FzKrl5nYJt6/pout9pUPrU5yyBmtutV2/7PtSlPm7JIUlOe6+yRzmQkaRhZdkmSpKHzXBttzmQkaRhZdkmSpKEz6xiViPgC1Y37L4qI7cD7qWYuujEiVgM/Bs4th98KnEE1k9GvgIsGkFmSZmXZJUmSFopeZo986zS7nMlIUmtZdkmSpIXiuQ6PlCRJkiTVoB1TeKkxywYwq922y8/s+2tK0qBZHkqS2sorbZIkSZLUYjbaJEmSJKnFbLRJkiRJUovZaJMkSZKkFnMiEkmSBqTXyU3WLp/gwh6PdXITSdr3eKVNkiRJklrMK22SJA0RH00gSfser7RJkiRJUovZaJMkSZKkFnN4pPrOoTuSJElS/3ilTZIkSZJazEabJEmSJLWYjTZJkiRJajEbbZIkSZLUYjbaJEmSJKnFbLRJkiRJUosNZMr/iDgNuArYD7gmMy8fxPtIUj9ZdmlfNfVRLWuXT3DhPB/f4qNa6mG5Je0b+t5oi4j9gE8BpwLbgW9HxObMfKjf76V9x2SFoh8ViUEbREXFZ98NnmWXpGFjuSXtOwZxpe1EYDwztwJExA3AKsACRPuEQfRYqxaWXVIf9auzqbMMtbPpWSy3pH1EZGZ/XzDiHOC0zHxHWX8b8OrMfNeU49YAa8rqscAPOna/CPhZX4MNnpnrYeZ6TM38ksx8cVNh6tBL2TVLudWpbb/zNuUxS3dtygLtyvNcs1hu7Tmu17KrU5s+AzMZlpwwPFnN2X+9Zp223BrIPW29yMz1wPpu+yLiO5m5ouZI82Lmepi5HsOYuQ4zlVud2nb+2pTHLN21KQu0K0+bsgyrXsuuTsNy3oclJwxPVnP2Xz+yDmL2yB3AUR3rS8s2SWozyy5Jw8ZyS9pHDKLR9m3gmIg4OiIOAM4DNg/gfSSpnyy7JA0byy1pH9H34ZGZORER7wL+N9X0s5/NzAfn+DJzuoTfEmauh5nrMYyZ56VPZdektp2/NuUxS3dtygLtytOmLK3S53JrqmE578OSE4Ynqzn7b95Z+z4RiSRJkiSpfwYxPFKSJEmS1Cc22iRJkiSpxRprtEXEaRHxg4gYj4h1XfYfGBFfLPvviohl9ad8VqbZMl8YET+NiPvK1zuayDkl02cj4omI+N40+yMiPl5+pvsj4oS6M3bJNFvm0YjY1XGe/6LujF0yHRURd0bEQxHxYERc0uWYVp3rHjO37ly3zXTnMSIOj4jbIuKR8v2wGrI8LyK+FRHfLVk+WLYfXcrR8VKuHjDoLB2Z9ouIeyPi5hZk2RYRD5TP8nfKttp/T+V9F0fEpoj4fkQ8HBGvaegzc2zH/+/7IuIXEfGeBs/Ln5bP7vci4gvlM93YZ2Zf0EPdphX1sR5yXlrK4fsj4vaIeEkbc3Yc9wcRkRHR2JT1vWSNiHM7/r59vu6MJcNsv/t/V/4O31t+/2c0lHOwde7MrP2L6mbZHwIvBQ4AvgscN+WYdwKfLsvnAV9sIuscM18IfLLJnF1yvx44AfjeNPvPAP4WCOAk4K4hyDwK3Nx0zimZjgROKMsvAP6py+ejVee6x8ytO9dt+5ruPAL/A1hXtq8DPlJDlgCeX5b3B+4qn7UbgfPK9k8Df1Lj+bkU+Pzk56jhLNuAF03ZVvvvqbzXRuAdZfkAYHFTWToy7Qc8Brykoc/vEuBHwEEdn5ULm/zMLPQvhqQ+1mPONwAHl+U/aWvOctwLgG8AW4AVLf7dHwPcCxxW1n+3pTnXT5YLVH9/tzV0Tgda527qStuJwHhmbs3M3wA3AKumHLOK6o8awCZgZUREjRmn6iVz62TmN4Cfz3DIKuC6rGwBFkfEkfWk666HzK2TmTsz856y/EvgYaoKSKdWneseM2sWM5zHzjJsI3B2DVkyM3eX1f3LVwInU5WjtWUBiIilwJnANWU9msoyg9p/TxFxKNUf9w0AmfmbzHy6iSxTrAR+mJk/bjDLIuCgiFgEHAzspH2fmYVkWOpjs+bMzDsz81dldQvVM+vq1mtd8S+BjwC/rjPcFL1k/c/ApzLzKYDMfKLmjNBbzgReWJYPBf5Pjfn2hBhwnbupRtsS4NGO9e08u7L4zDGZOQHsAo6oJV13vWQG+INyyXNTRBzVZX/b9Ppztc1ryhCwv42I/9B0mE5l6MjxVFc5OrX2XM+QGVp8rttmynkcycydZddjwEhNGfaLiPuAJ4DbqHoony7lKNT7ufsY8F7g/5X1IxrMAtUf9q9HxN0RsaZsa+L3dDTwU+BvynCeayLikIaydDoP+EJZrj1LZu4A/gr4CVVjbRdwN81+Zha6YamPzfXv52qqKxp1mzVnGRJ3VGbeUmewLno5py8HXh4R/xgRWyLitNrS7dFLzg8AfxQR24FbgXfXE23O5lUPdCKS/vpfwLLM/D2qytLGWY7Xc3MP8JLMfCXwCeBrDed5RkQ8H/gy8J7M/EXTeXoxS+bWnuu2mek8ZjUuopbnq2Tmv2bmq6h6mU8EXlHH+04VEW8CnsjMu5t4/2m8LjNPAE4HLo6I13furPH3tIhqCM3VmXk88H+phiA2kQWAcp/YWcCXpu6rK0u5b24VVaP23wKHAE1UEjXEIuKPgBXA/2w6y1QR8TvAR4G1TWfp0SKqIZKjwFuBz0TE4kYTdfdW4NrMXEo1BPFz5VwvKE39QDuAzqtQS8u2rseUYRKHAk/Wkq67WTNn5pOZ+S9l9Rrg92vKNh+9/C5aJTN/MTkELDNvBfaPiBc1HIuI2J+q0n59Zn6lyyGtO9ezZW7ruW6bac7j45PDHsr3WoeVlOF2dwKvoRqCsajsqutz91rgrIjYRjWc5WTgqoayAM9cyZkc4vNVqkZtE7+n7cD2zJy8sr2JqhHX5GfmdOCezHy8rDeR5RTgR5n508z8LfAVqs9RY5+ZfcCw1Md6+vsZEacAfw6c1VEfq9NsOV8A/EdgrJSNJwGbG5qMpJdzuh3YnJm/zcwfUd2zfUxN+Sb1knM11b2vZOY3gecBbayrzKse2FSj7dvAMWVGqAOohmRsnnLMZuCCsnwOcEfp7WvKrJmnjEs9i+relrbbDLy9zGhzErCrY0hMK0XEv5kcTx8RJ1J9jpts0E/eq7MBeDgzPzrNYa06171kbuO5bpsZzmNnGXYBcFMNWV482QsaEQcBp1KVQ3dSlaO1ZcnM92Xm0sxcRlVe3pGZ5zeRBSAiDomIF0wuA28EvkcDv6fMfAx4NCKOLZtWAg81kaXDW9kzNJKGsvwEOCkiDi7/rybPSyOfmX3EsNTHeqmDHQ/8NVWDrYl7r2CWnJm5KzNflJnLStm4hSrvd9qWtfga1VU2Softy4GtdYakt5w/oSoviIh/T9Vo+2mtKXszv3pgNjC7Su6ZQeWfqO63+POy7UNUH16oTviXgHHgW8BLm8o6h8z/HXiQamabO4FXtCDzF6juDfgtVY/JauCPgT8u+wP4VPmZHqChWYzmmPldHed5C/CfWpD5dVTDh+4H7itfZ7T5XPeYuXXnum1fM5zHI4DbgUeAvwcOryHL71HN9HU/VYPkL8r2l5ZydLyUqwfWfI5G2TN7ZCNZyvt+t3w92FGG1/57Ku/7KuA75Xf1NeCwBrMcQtUZc2jHtqayfBD4fvn8fg44sOnP70L/YkjqYz3k/Hvg8Y5yeHMbc045dowG6wI9nNOgGs75EFW95byW5jwO+MdSvt8HvLGhnAOtc0d5EUmSJElSCy24m/QkSZIkaSGx0SZJkiRJLWajTZIkSZJazEabJEmSJLWYjTZJkiRJajEbbZIkSZLUYjbaJEmSJKnF/j8slIp2qmL6tAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HK9AaWn-3JEE"
      },
      "source": [
        "# Replacing NaN value by mean, median depending upon distribution\n",
        "df_copy['Glucose'].fillna(df_copy['Glucose'].mean(), inplace=True)\n",
        "df_copy['BloodPressure'].fillna(df_copy['BloodPressure'].mean(), inplace=True)\n",
        "df_copy['SkinThickness'].fillna(df_copy['SkinThickness'].median(), inplace=True)\n",
        "df_copy['Insulin'].fillna(df_copy['Insulin'].median(), inplace=True)\n",
        "df_copy['BMI'].fillna(df_copy['BMI'].median(), inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHzZCpss3JEG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "outputId": "989d1b06-3d33-4e0d-a2ba-7cd1c33d7bf9"
      },
      "source": [
        "# Plotting histogram of dataset after replacing NaN values\n",
        "p = df_copy.hist(figsize=(15,15))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "DfDSEvG93JEI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f74c6190-1803-4676-abf3-f3877a67839a"
      },
      "source": [
        "df_copy.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies      0\n",
              "Glucose          0\n",
              "BloodPressure    0\n",
              "SkinThickness    0\n",
              "Insulin          0\n",
              "BMI              0\n",
              "DPF              0\n",
              "Age              0\n",
              "Outcome          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--ARcFzq3JEK"
      },
      "source": [
        "# Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC6gk8wvzRaN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcf0d76a-0983-4358-c8aa-81c231755a75"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.drop(columns='Outcome')\n",
        "y = df['Outcome']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)\n",
        "print('X_train size: {}, X_test size: {}'.format(X_train.shape, X_test.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train size: (1500, 8), X_test size: (500, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEU3-uwu3JEK"
      },
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJdY_Qof3JEQ"
      },
      "source": [
        "# Using GridSearchCV to find the best algorithm for this problem\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZH88KB8V3JES",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "eacd493a-2a50-407e-d592-6a79cfde106b"
      },
      "source": [
        "# Creating a function to calculate best model for this problem\n",
        "def find_best_model(X, y):\n",
        "    models = {\n",
        "        'logistic_regression': {\n",
        "            'model': LogisticRegression(solver='lbfgs', multi_class='auto'),\n",
        "            'parameters': {\n",
        "                'C': [1,5,10]\n",
        "               }\n",
        "        },\n",
        "        \n",
        "        'decision_tree': {\n",
        "            'model': DecisionTreeClassifier(splitter='best'),\n",
        "            'parameters': {\n",
        "                'criterion': ['gini', 'entropy'],\n",
        "                'max_depth': [5,10]\n",
        "            }\n",
        "        },\n",
        "        \n",
        "        'random_forest': {\n",
        "            'model': RandomForestClassifier(criterion='gini'),\n",
        "            'parameters': {\n",
        "                'n_estimators': [10,15,20,50,100,200]\n",
        "            }\n",
        "        },\n",
        "        \n",
        "        'svm': {\n",
        "            'model': SVC(gamma='auto'),\n",
        "            'parameters': {\n",
        "                'C': [1,10,20],\n",
        "                'kernel': ['rbf','linear']\n",
        "            }\n",
        "        }\n",
        "\n",
        "    }\n",
        "    \n",
        "    scores = [] \n",
        "    cv_shuffle = ShuffleSplit(n_splits=5, test_size=0.20, random_state=0)\n",
        "        \n",
        "    for model_name, model_params in models.items():\n",
        "        gs = GridSearchCV(model_params['model'], model_params['parameters'], cv = cv_shuffle, return_train_score=False)\n",
        "        gs.fit(X, y)\n",
        "        scores.append({\n",
        "            'model': model_name,\n",
        "            'best_parameters': gs.best_params_,\n",
        "            'score': gs.best_score_\n",
        "        })\n",
        "        \n",
        "    return pd.DataFrame(scores, columns=['model','best_parameters','score'])\n",
        "\n",
        "find_best_model(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>best_parameters</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>logistic_regression</td>\n",
              "      <td>{'C': 1}</td>\n",
              "      <td>0.782667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>decision_tree</td>\n",
              "      <td>{'criterion': 'gini', 'max_depth': 10}</td>\n",
              "      <td>0.902000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>random_forest</td>\n",
              "      <td>{'n_estimators': 10}</td>\n",
              "      <td>0.940000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>svm</td>\n",
              "      <td>{'C': 20, 'kernel': 'rbf'}</td>\n",
              "      <td>0.860667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 model                         best_parameters     score\n",
              "0  logistic_regression                                {'C': 1}  0.782667\n",
              "1        decision_tree  {'criterion': 'gini', 'max_depth': 10}  0.902000\n",
              "2        random_forest                    {'n_estimators': 10}  0.940000\n",
              "3                  svm              {'C': 20, 'kernel': 'rbf'}  0.860667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsFM9vUt3JEU"
      },
      "source": [
        "*Note: Since the Random Forest algorithm has the highest accuracy, we futher fine tune the model using hyperparameter optimization.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8uyaELV3JEU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50936e6f-80fa-4f12-92da-88d9ed05e171"
      },
      "source": [
        "# Using cross_val_score for gaining average accuracy\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(RandomForestClassifier(n_estimators=200, random_state=0), X_train, y_train, cv=5)\n",
        "print('Average Accuracy : {}%'.format(round(sum(scores)*100/len(scores)), 3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Accuracy : 94%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGpQhKFq3JEb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8304765d-4c24-4456-b61d-b9f6258b193a"
      },
      "source": [
        "# Creating Random Forest Model\n",
        "classifier = RandomForestClassifier(n_estimators=200, random_state=0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DdlS0qSO1nBN"
      },
      "source": [
        "# Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pluF-HMb3JEj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2aa93b4b-d596-4afd-dc48-da2a9b009fbb"
      },
      "source": [
        "# Creating a confusion matrix\n",
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[330,   4],\n",
              "       [  2, 164]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X13MSU9P3JEl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "bd67be96-289e-480a-e998-62436532837c"
      },
      "source": [
        "# Plotting the confusion matrix\n",
        "plt.figure(figsize=(10,7))\n",
        "p = sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt='g')\n",
        "plt.title('Confusion matrix for Random Forest Classifier Model - Test Set')\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.ylabel('Actual Values')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DB-PXfo2epo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb18c242-dc4c-4d30-9f50-6dc1ac15318c"
      },
      "source": [
        "# Accuracy Score\n",
        "score = round(accuracy_score(y_test, y_pred),4)*100\n",
        "print(\"Accuracy on test set: {}%\".format(score))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on test set: 98.8%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1sscutg4T7G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f29be699-0efb-407d-bbdc-f02f15c6f67e"
      },
      "source": [
        "# Classification Report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       334\n",
            "           1       0.98      0.99      0.98       166\n",
            "\n",
            "    accuracy                           0.99       500\n",
            "   macro avg       0.99      0.99      0.99       500\n",
            "weighted avg       0.99      0.99      0.99       500\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxuVQ8WzZ4cY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6cc90633-7fd1-4147-e5e2-0557d0d49cf0"
      },
      "source": [
        "# Creating a confusion matrix for training set\n",
        "y_train_pred = classifier.predict(X_train)\n",
        "cm = confusion_matrix(y_train, y_train_pred)\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[982,   0],\n",
              "       [  0, 518]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pufLHBFCaSll",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "9f1ba5ea-ac3d-412c-a8cd-e520079e0010"
      },
      "source": [
        "# Plotting the confusion matrix\n",
        "plt.figure(figsize=(10,7))\n",
        "p = sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt='g')\n",
        "plt.title('Confusion matrix for Random Forest Classifier Model - Train Set')\n",
        "plt.xlabel('Predicted Values')\n",
        "plt.ylabel('Actual Values')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiYrU_y0afzM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ea11c939-b751-4301-8bd0-c42c91da1bf3"
      },
      "source": [
        "# Accuracy Score\n",
        "score = round(accuracy_score(y_train, y_train_pred),4)*100\n",
        "print(\"Accuracy on trainning set: {}%\".format(score))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on trainning set: 99.94%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2j5fvnhsakyg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "7f684298-20e7-4e78-f144-55d1b3ebc90c"
      },
      "source": [
        "# Classification Report\n",
        "print(classification_report(y_train, y_train_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1044\n",
            "           1       1.00      1.00      1.00       556\n",
            "\n",
            "    accuracy                           1.00      1600\n",
            "   macro avg       1.00      1.00      1.00      1600\n",
            "weighted avg       1.00      1.00      1.00      1600\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HuKxvMZT1kJO"
      },
      "source": [
        "# Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKJeJuzj3JEd"
      },
      "source": [
        "# Creating a function for prediction\n",
        "def predict_diabetes(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DPF, Age):\n",
        "    preg = int(Pregnancies)\n",
        "    glucose = float(Glucose)\n",
        "    bp = float(BloodPressure)\n",
        "    st = float(SkinThickness)\n",
        "    insulin = float(Insulin)\n",
        "    bmi = float(BMI)\n",
        "    dpf = float(DPF)\n",
        "    age = int(Age)\n",
        "\n",
        "    x = [[preg, glucose, bp, st, insulin, bmi, dpf, age]]\n",
        "    x = sc.transform(x)\n",
        "\n",
        "    return classifier.predict(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Nl2AajS3JEf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "139b7a34-3f83-4777-d56f-12ea6e5ee7a9"
      },
      "source": [
        "# Prediction 1\n",
        "# Input sequence: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DPF, Age\n",
        "prediction = predict_diabetes(2, 81, 72, 15, 76, 30.1, 0.547, 25)[0]\n",
        "if prediction:\n",
        "  print('Oops! You have diabetes.')\n",
        "else:\n",
        "  print(\"Great! You don't have diabetes.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Great! You don't have diabetes.\n",
            "[0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tA0LvQX95hyu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8068fd8-fbfe-45c4-b67b-da3bf2f80151"
      },
      "source": [
        "# Prediction 2\n",
        "# Input sequence: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DPF, Age\n",
        "prediction = predict_diabetes(1, 117, 88, 24, 145, 34.5, 0.403, 40)[0]\n",
        "if prediction:\n",
        "  print('Oops! You have diabetes.')\n",
        "else:\n",
        "  print(\"Great! You don't have diabetes.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Oops! You have diabetes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4zezK0W3JEh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa1ea6af-ff09-4431-8db1-6075ec11b093"
      },
      "source": [
        "# Prediction 2\n",
        "# Input sequence: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DPF, Age\n",
        "prediction = predict_diabetes(5, 120, 92, 10, 81, 26.1, 0.551, 67)[0]\n",
        "if prediction:\n",
        "  print('Oops! You have diabetes.')\n",
        "else:\n",
        "  print(\"Great! You don't have diabetes.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Great! You don't have diabetes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dyFcLKbPsjE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}