python04_seaborn.ipynb

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        "<a href=\"https://colab.research.google.com/github/abdurrahman720/python_ml/blob/main/python04_seaborn.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",
      "source": [
        "# Seaborn\n",
        "*  **Data Visualization Library**\n"
      ],
      "metadata": {
        "id": "H9hbHW9q3o4g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "importing library"
      ],
      "metadata": {
        "id": "3ufv4nsB4RvI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "7eKCxJHA4VBg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   **Note: Seaborn has some built-in data sets**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "gBdn0IpK4n_r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#total bill vs tips dataset\n",
        "tips = sns.load_dataset('tips')\n",
        "tips.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "MTUrYiod4k4z",
        "outputId": "20bf6269-f757-43a7-ebac-6b38262ab152"
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      "execution_count": 2,
      "outputs": [
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            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ],
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              "      <th>total_bill</th>\n",
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              "      <td>16.99</td>\n",
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              "      <td>3.61</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tips",
              "summary": "{\n  \"name\": \"tips\",\n  \"rows\": 244,\n  \"fields\": [\n    {\n      \"column\": \"total_bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.902411954856856,\n        \"min\": 3.07,\n        \"max\": 50.81,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          22.12,\n          20.23,\n          14.78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3836381890011822,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          3.35,\n          1.5,\n          6.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smoker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Sat\",\n          \"Fri\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Lunch\",\n          \"Dinner\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
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      "cell_type": "code",
      "source": [
        "sns.set_theme()"
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      "metadata": {
        "id": "LinlH20o6wFF"
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      "execution_count": 16,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "**Visualize Data**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "*   Replot\n",
        "\n",
        "**We are going to export a figure that shows the ratio of tips and total bill for different times.It also shows whether the customer is male or female. There is size indicator also**"
      ],
      "metadata": {
        "id": "OmbEAGXG7V6c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize the data\n",
        "sns.relplot(data=tips, x='total_bill', y='tip', col='time', hue='sex', style='sex', size='size')"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 15,
      "outputs": [
        {
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              "<seaborn.axisgrid.FacetGrid at 0x7afaf6182bf0>"
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          "execution_count": 15
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          "data": {
            "text/plain": [
              "<Figure size 1110.38x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   **Scatter Plot**\n"
      ],
      "metadata": {
        "id": "useqxoZXCpv9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iris = sns.load_dataset('iris')"
      ],
      "metadata": {
        "id": "eJ1Cz_4k8wiu"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MBD6nZ98Dgjw",
        "outputId": "f5a36a9c-d3a3-4b10-8e5d-de9d561d4669"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b5dd6bb4-5920-4896-bede-ecbb6ef04ca2 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-b5dd6bb4-5920-4896-bede-ecbb6ef04ca2');\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",
              "\n",
              "\n",
              "<div id=\"df-36941734-3cfd-472b-8104-8623485aaf2c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-36941734-3cfd-472b-8104-8623485aaf2c')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-36941734-3cfd-472b-8104-8623485aaf2c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "iris",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"setosa\",\n          \"versicolor\",\n          \"virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We are gonna find out the species from a iris dataset based on sepal length and petal length**"
      ],
      "metadata": {
        "id": "q7Fzv4MlDpeC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='sepal_length', y='petal_length', hue='species', data=iris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "jRLHW8urDik2",
        "outputId": "da78572b-581f-43ef-d319-c65947c6407a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sepal_length', ylabel='petal_length'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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5I0aM4oQTJhEKBXnssYfJzc3j0UefIhKJ8M03X/HnP/+Byy+/ikGDhgDQvXsPNE3jiisuYsuWTVxxxVTy8wt59dUXmTdvLg8//CQ9e/bC76/i1FNPoH//gZxyyq9wOJysXbsay7I5/fSzAHjttZewLJsOHTqiqgrffvs1L7/8Avfe+zCDBw9Jqu3JSOXrIBV0XSU/30dpqb9B2p8uMrXdkLltz9R2Q9Ntu6oqrA6s4l9fPxK3vH1uW64fcRVqHUvK97ftBQU+NG3/Z8ZID02KBKNm3GAG4MefdhCMmnj1+puyVFZWxpYtm7jmmt8xevQYgFpBwiOPPEDPnr34xz/+hfJzruzOnbty3nln8M03XzJy5Gi6d+8BQHFxe/r27Vdz3y+//IylSxdz9933M3z4SACGDx/JGWeczLPPPsltt/2LDRvWU1VVxeWXT6VLl64ADBkyrFYdTz31jJr/tyyLQYOGsGbNat5++424AY0QQoj0omkqS3asSFi+oXwzJgYqDZ8jRwKaFAnsY0v2QMjAm1V/v/5zc3Np3boNjz76AJWVFRx66FBatSoCIBQKsXDhfK666hpM06y5T/v2HWjVqoilS5cwcuTohOeeP38ePp+vJpgB0HWdsWOP4OOPPwKgbdtifD4fd911O7/61ZkMHjyE/Pz8WufZvn0b06Y9xOzZ37Nr186aDdV69OiVssdBCCGSparVP/pkW4FYtm3TKqtFwvIspw9VaZz1RhLQpIjXXfdDua/yZCmKwj33PMC0aQ9xzz13EgwG6dGjF1dffR3t2hVjmib33XcP9913T8x9t2/fVue5KysryM8viDmen19IRUU5ADk5Ofz73w/yxBPTuPXWmzFNk/79B3LdddfTpUtXLMvihht+S1VVFZdccjnt2rXH4/Hw+OOPsG3b1tQ8CEIIkQzdIqKGWF+2EcMy6ZTfAaftbrRJrunIMCz6t+qFpmqYlhlTfnz38Tgtd6PkypGAJkU8Do1BPVry40/x59B4HBrU83SlDh06cuutd2IYBgsXzmfatAf54x+v47XX3kNRFKZMuZAxY8bF3C83N6/O8+bk5FBaWhJzvLR0Fzk5uTV/9+7dl7vvvo9wOMTcubN58MF7ufHG3/Hyy2+xceMGli//idtvv4vDD/+lDuFw+GCbK4QQKWPrJvN3LeTJuS9i2tXzOhQUTupxFEcdMg4lKl+Xu7ksD9cf9mvu+uZRoma05viQtgM4vP3wRstiLM9Qiii2zZWT+/PQ6wtqBTW7Vzk15NJtXdcZNOhQzjnnAm644beUlOyib99+rFu3hp49r0x4P4ejeswzEonUOt6//0Cef/5Zvv/+W4YNGwGAYRh8/vks+vcfEHMel8vNyJGj2bRpI/feezfhcLgmcNH1X8ZVt27dwsKF82nfvkPSbRZCiGRUmOU8Nuf5WsdsbN76aTo9WnShk6ezDEH9zDYV2ns6cNdRN7GhYgtVkSo65XfEp3pRwo23v5QENCmk2TZTJ/dvlDw0K1eu4IEH/s348RNp166Yqqoqnn32Kdq0aUu7dsVceeU1XHPNr7n55hsZP34i2dnZ7NixnR9++I7jjjuRwYOHUFBQSFZWNjNmfESbNm1xOp106dKNkSNH06tXH/7+979wxRVTKSgo5NVXX2LXrp1MmXIRUL0k+91332LMmHEUFbWmpGQXr776Mv36DcDlctGx4yG0alXEI488gGVZBIMBnnjiUVq2bFXvj40QQtRFcyhM/+mzhOVvLPuQa4ZcimLJV+Zutgm66aGLtwt4wTJtiB2BalDy7KSYYtt4dfWXCcAN1DNTWFhIYWEhzz77FDt37sDny2LAgIHcfPP/oWka/foN4KGHHueJJx7l9tv/RjQapWXLIoYMGUpxcXsAVFXlT3+6hWnTHuTaa68kEonU5KG56657efDBe3noofsIhYJ0796Te+6pXjkFUFxcjKoqTJv2EGVlpeTk5DJs2Aguv/wqAJxOJ7fd9k/uuedO/vKXG2jVqojzz7+IuXNns2zZkgZ5jIQQIh4Lix2BXQnLS4PlmLaJLl+ZMdKp10ry0KRR/pF4GioXSzqSPDRNKz9FsjK13ZC5bU+Xduu6woxNn/HK4nfjlo9sfyhTep+OHU1dav90aXtjqK88NLKXkxBCiIxmGDaj2g/Bo7tjyjRF5ZSex4DRuPsUiX2TgEYIIUTGc5lebhn3W7oUdKw51ja7iJvGXEMWOQ01e0AkQQYEhRBCZDzLhFw1n+uGXkbYDmPZFm7VjcNyNcq+ROLASUAjhBBCUD3BVbEcuHen7TdplARx4uDIkJMQQgghmjzpoRFCCNEsaZpKWA1iYqKh4rY9GEZqe1x0XSWsBDF+vobL9mCm+BoHQ3XYhAlhY+PAgWY602qJdX2QgEYIIUSzYzuizNm5mFcXv0dpqJxsVxYn9ZjIyLaHokRSk83WdhgsKF3Oi4veYlegFJ/Dy/HdxzOmw4hGy5irKApRR5DXlr7HVxtmY1omXQo6cv6A0yjUW4DZfAdmmm/LhBBCZCRVhy82fstjc56nNFS9gW5luIrnFrzO2ys+QnEkn/dF0xXmbJvHg9//l12BUgD80QAvL36HFxa/CQ4j6WscDMMR4h9f3s/n676r2TxyVck6/jrrHsqtUhSl+S4/l4BGCCFEsxJWg7yx7MO4ZR+v/oIQwZRc46XF78Qt+2r9D4TsUNLXOFCqqrC2fAPbqmI3SbZsixcWvYWtR+Pcs3mQgEYIIUSz4o8EiJjxv7ht26bs516bZASMIIFo4sBoa9V2GrozRNc1Zm+en7B8yfblmEojb7hUj9IyoHnjjTc4+eST6devH8OHD+eSSy4hFGr4aFccmKlTL+P6669N+Xlvu+2vTJlyesrPK4RonpzaL/NXFBTcuguFX6ILt+5K/hpq3XNkfE5vgyfjs22bAk9ewvJsVxbNeRV62k0Kfvjhh3nssce44oorGDhwIKWlpXzzzTeYZvONKpuL3/3uhgPad0OIdKboFmElxM5gCW7NSY4rF6fpwpKPorTnVj10L+zEkHYDaJ/blspwFVlOH9v9O/l09Tdk6VmQ5MiLS3HTs0VXlu1cGVOW7coi350HkeSucaCiUZNRHYby5rKP4pYf2/UInLa72ebWSauAZvXq1TzwwAM89NBDjB07tub40Ucf3Yi1OjAeNYpm+LEjARSXF1PzEbQaZ7Z7qoTDIVyu2D1O9tapU+cGqM3B2992CGE7o3yw+lPeWzGT3fv3Zjt9/PawyylytMaWoCat6aaTq4ZdwEM/PM3zC96sOd4xrx3XjLwEl+XGSPJLXTUcXH7oudz6xb01k4Khuvfn+lG/xmm6aYwtJ71kcemhZ/P4nBew92hjv1Y9GdV+GGa4eQYzkGYBzeuvv05xcXGtYKYpydaClH74CME1v4xhejoNJP+Yy6k0PfV67ffff4c777yVN954n4KCwprjFRXlnHTS0Vx77R84+eRTWbRoAdOmPcSSJYvQNI2RI0dzzTW/Iz+/AIAtWzZz2mkn8ac/3cLChfP57LNPadGiBc888xILFszj0UcfZOXK5ViWTZs2bTjrrCkce+wJQPWQk9fr5Z///E/N9deuXcO0aQ/x449ziETCFBd34Nxzz+eoo44BIBwOM23ag8yYMZ3Kygo6dDiECy+8lLFjj6izvatWreTBB//DggXz0DSNoUOHM3Xqb2ndunXNbUaPHsLll0+lsrKCDz98j2AwyMcff56qh1w0U7qu8uPOZby7fEat45URP//44n7+ddSfcZjeRqqd2B+WZvDUjy/z087VtY6vK9vEfd8+we+G/xqV5H5o2raN2/Rxy5jfsqlyC6tK19EmqxVdCg7BZXqxGmu7BENlYGF/7j66Gwu2LcUfDdC/qBd5zrxGW0reUNIqoJk/fz7du3fnoYce4tlnn6WyspK+ffty4403MmDAgKTOreuxQyGWlboZWx41GhPMAATXzIMPHyX76KsOuKdm94QyRWGfY7FjxhzBXXfdzqefzuDUU8+oOT5r1icAHHnkBBYtWsDVV1/OiBGj+NvfbicUCvLYYw9zww2/49FHn6p1vkcffYCRI0fz17/ehmVZ+P1VXH/9tfTvP5C//vU2HA4na9euprKyMmGdNmxYzxVXXEirVkVce+3vKSgoZM2aVWzbtrXmNn//+0189903XHbZlXTocAgffvgeN910Pbfffhfjxh0Rd1Ldtm1bueqqS2nXrpi//OX/iETCTJv2MFdffRlPP/0CXq+v5ravvvoCvXv344Yb/oJp1r2MUtOUuK+ThrZ72C7Thu/Spd0RNcTrSz+IWxY1o8zbupjRbUZimqn7/Z0ubW9o9dXuKqqYt3Vx3LK1ZRsJWgFy9fyUXMtleeiS1YVuOV2xbbs6eZ0Cql7390v9Pucqup3N6DYjAKpfqxZp841fX21Pk+ZV27FjB4sWLWL58uXccssteDweHnnkES666CKmT59OYWHhvk8Sh6oq5Of7Yo6HQho7d6op+SLTo/6YYGa34Jp55Jt+dEfBQZ17f570vLwcRo4cxYwZ0znjjLNqjs+c+RHDh4+goCCfP//5D/Ts2Zt//vPumlwE3bt35+yzT+P777/msMNG11yrW7ce3HTTLTXnWbp0CVVVVVx55dV07doNgBEjRtSqg6IoKMovweNTTz2Gw+HgsceewufLAmDkyJE1t1+xYjmfffYpf/zjnzjllF8BMHr0aLZt28JTTz3GuHFHoGnqz+f95Tl65ZUXME2D++57iNzcXAB69erFWWf9ig8/fI/TTz+z5ho5Obm12huPZSmoqkpurhe3O32GpHJy6rdXL101drt3BSLs8O9KWL6+YjM5Peqnjo3d9saS6naXlOysszxgBDikVXFKr3mwMvU5h9S3Pa0CGtu2CQQC3HvvvfTs2ROAAQMGcOSRR/K///2Pa6655qDOa1k2FRWBmOORSBjLsjBNG8NI7teWFY49/97lhpJ3QOdUlOpgxjSt/ZotP3780dxyy41s3LiZ1q1bs3PnTn78cS433fQ3qqoCLFgwn6uuuoZw+JfZcG3aFNOqVRGLFi1i2LDDan51jhw5qtZjUlTUFp/Px513/oNf/epMBg8eQn5+7V84tm1j29Tcb/bs7xk7djwulzfu4zt37lwAxo4dX6v8iCOO4v777yEYDOJ0un4+7y/P0bx5PzJ48BB8vuyaY8XFHenatRvz5v3I5Mm/rIgaPvywn3fKTfwAmqaNZVmUlwcIBht/coSmqeTkeKioCKa0FyDdpUu7Tc2mOLcNG8o3xy3vUdCZ8vJAStPIp0vb96TrKihgW9RbnQ603bs/E1HANBJ/Lro1N4qi1Mx/2luWI4vSUn8yVU9aOj7nDWV/256T4zmgXpy0CmhycnLIy8urCWYA8vLy6N27NytXxs4kPxDxvlBTuSW84qx7TH1f5fHsfi/u79K/UaNG43Z7mDnzI84553w++eRjnE4nhx8+jsrKCkzT5L777uG+++6Jue/27dtq/Z2fX7s3LCcnh3//+0GeeGIat956M6Zp0r//QK677nq6dOkatz7l5WW0aNEiYX0rKyvQdZ2cnNxaxwsKCrBtm8rKSgoKYpdXVlZW0K1b95jj+fmFVFTUzi9RULD/vWKpCGxTyTSttKpPQ2nsduu2kzP7nsS/vnokpszn9NKzRTcikfoJfBu77QCKBiHVz9drZ7O+fBPdC7swpG3/ep0Xsj/tthxRtge3M2vdN1i2zdiOI2jjLUKNOmNu63K4ObzDMD5f911MWf+iXrgVD0Y0Pd5b6fCcN5ZUtz2tApquXbuyfv36uGXhcLiBa3NgTN2Hp9PA6jkze/F0Goip+6jvKe8ul5sxY8Yyc+Z0zjnnfGbOnM6oUYfj8VR36ymKwpQpFzJmzLiY++bm5tX6O94ITe/efbn77vsIh0PMnTubBx+8lxtv/B0vv/xW3Prk5uaxc2firt+cnFwMw6CiooKcnJya4yUlJSiKQnZ2doL75VBaWhJzvLR0F+3bd9zraPNN8y3qh2nadPR14NLBZ/Pcwjdqkqe1z23L1cMuxGl4sJrpsldVgw2hDdz51UM1afO/3zSPV5e8y81jryVfbdEoGxxazghPL3ilVtK4r9b/wIDWvblk4NmokdpBjR1VOa33iaiKyufrvsOyLRQUhrYbwJT+v0IJp9VXn0iRtHpWjzjiCF5//XWWLl1Kr169ACgtLWXx4sVccMEFjVu5fQhaDvKPuRw+fLRWUPPLKqeGmV0+YcLR/OEP1/Ldd9+wePFCzj33/Op6eDz07duPdevW0LPnlUldw+VyM3LkaDZt2si9995NOBzG5YrtSRkyZBizZs3kyiuvrjVRd7f+/QcC8OmnM5g0aXLN8U8/nUG3bj3weDxxo/f+/Qfy9ttv1AqE1q9fy6pVKzn++JOSapsQAIqhc2iLQfQd3xN/NICuanhUL3oz37E4ooa499snaoKZ3UJGmPu//y9/OuxqVCu2R6Q+qarC6vL1cTPgzt+6hOWlq+mT0ztm6EINOzm9xyQm9ZhI0Ajj1ly4FDeEtYaqumhgaRXQTJgwgX79+vGb3/yG6667DpfLxbRp03A6nZx99tmNXb19qjQ9ZB99FXm789A4vZi6r8GCGYChQ0eQm5vL7bf/naysbEaMGFVTduWV13DNNb/m5ptvZPz4iWRnZ7Njx3Z++OE7jjvuRAYPHpLwvF9//SXvvvsWY8aMo6ioNSUlu3j11Zfp129A3GAG4MILL+Xrr7/g17++hHPOOY/CwhasXbuaUCjEOeecT9eu3Rg79ggeeODfhMNhOnToyPTpH7Bo0QJuv/3uhHU5/fSzee+9d/jtb6dy3nkXEYmEeeyxhykqas1xx5148A+eEHswDRsdN7m4wQRMmm3PzG5lkQr80fjzAbdUbiNgBsmiYQMaWzP5cNWshOUfrvyUHkO6oZixgYodVXHiw4kPGmevSNGA0iqgUVWVadOmcfvtt3PzzTcTjUYZMmQIzz33HC1btmzs6u2XoOUANQ/cedUHGnhoVNd1xo0bz1tvvc4JJ0zC4fglmOrXbwAPPfQ4TzzxKLff/jei0SgtWxYxZMhQiovb13ne4uJiVFVh2rSHKCsrJScnl2HDRnD55VclvE/79h14+OEnefTRB7j77jswTZP27Ttw7rkX1Nzm5pv/j0cffZDnnvsvFRXVeWhuvfVORo8ek/C8RUWteeCBaTz44H/4+99vQlU1hg4dxtVX/zZuT5AQYv8YCfY/2s1shIyClm0RNhKn3A0bEWzbQkF6XjKdYieaBt6MmKZFSUnsjPZoNMKuXVsoLGyDw9Gwvzr2l66rGTthrKHanm6vA11Xyc/3UVrqz6jnPhPbrSiAbmGqURy6hmo4iYZT33bFYWEoUWwbnLYLK0FvRcQR4PfT/45px9Yhy+njtiP/iB5JXWqD/XnOdV3ly63f8OyC1+KWn9HnRI5sNwbDiP0q0zSFqBrBViywFZyWO21WFGXi6323/W17QYGv6a5yEkKITKGqUKVW8MrCd5i7dTEuzckRnQ5jYpex6BFXSjY2VDUIqFW8tuQ9ftg0H13VGXvICI7rdiR6xBOzrNlle5jc6zheWfJuzLnOH3gaLtPT4PsAGYbF0LYDeH/lJ7W2GADId+cyqsNQjDjp/G3dYJV/Ay8tepuNFVso8rXgV72Pp0d+V5Ro886Ym6mkhyaNfpnHIz000kOTKTKt3UG9kj/PvJOwWXs4pTi7DdePuhItkvyO0BFngD/NvINgNFTreCtfC246/Jq417AdBmsq1vLq0vfYXrWL9rltOaPviRS5WqMYqR3W2d/nXFUVInqQ6as/44t132PbFqM6DK3ebNHwxkzUVnWYvWMuT/z4Ysy5Jvc6lgntx2IbjZuVOdNe73uSHhohhGgmFIfNa0vejwlmADZWbmFt+Qa6+boltaJKdcB7y2fGBDMA2/07WbJzOQPzB8QMwShRnW5Z3fj98CuxsNDQ0EwHVpwhnYZiWTZ6xM2JhxzNxE7jgJ+HziLxJ2pH1BDPL3wz7rneWvYRYzoMx4Hsx9XcZNbGIUIIkQaihBPuNQTw9YbZSW/HEiHM3C0LE5Z/s3E2thr/17Fp2qhRB3rUhRLV02apummAHnWhRxPPAwLwR/0EjdhADsC0LUpC5XHLRNMmAY0QQjQwBQWPnnhyrc/pJdmkkIpd9zW8Di9qM008qSl1D405NZlD0xzJkJMQQjQwp+1mYpexvLgofpbtIw45jEgkucQpLtwc0+0ItlZtZ8whwwhFw6iKiqZqvLn0IyZ2HoOZomEkVYOIFsIf9QMKPt2L0/LU21YJ++LVvLTJasWWqu0xZTmubHKc2ZB4JbhooiSgEUKIBmYaNqPaD2X25vmsLFlbq2xSz4nkOfKg7pQw+2QYFgNb92HxDo2/ffqfmi0c8j25XDnsPFr7WmHFH5U5MLrFisrVTJvzHFWR6sUXue4crhx6Hu097bGNhu8F0k0XvxlxMX//7N+15hA5NAe/HXkpDtPV0CnCRAOQVU5ptLolHlnlJKucMkWmtVtRwHRE2OLfxjcb5+BzeBjVYRjZajaKkfxvTVVV2WJs5O+z/hNTpqs6d068EXck/n5p+0tRoIwS/jzzTuy9JueqisqdR/0Jr5GT4N71+5yrGoS1IIu2/8SKXavpmFfM4DZ9f95kM6WXOiiZ9nrfk6xyEkKIZsS2QY04ae/qQNfencnKcqf0y81yhHltwQdxywzL4JPVX3NK1+MIh5L4dtdt3lr0YUwwA9UZfj9a+Rm/6noiZiNsO2CZ4DA9DCkczPBWQ7AsCyNiSc9MMyYBjRBCNCLLsolGU99lELUNNldsTVi+vnwTBgbJTD427CjrK7YkLF9XvhFTMaERtyUwTSttsgOL+iWrnDLIbbf9lSlTTk/Z+Z544lGOOurwRq+HyEyKouBwaEkvb66Lqip4PA5crv3/7edy6Xg8jnqt1/5wKDqtsxPvgdcuuzW6ktxvWl3RaZvdKmF5cU4bNPndLBqIvNIyyAUXXEIwGEzZ+U488WQOO2x0o9dDZJY9554s2LCEXFc2g9v0x217IM6OywdDVcFwhthUtYO5axeS5fIxvN1APKoXJRJ/ya/qgKBSxbebF7HDv4teLbtxSF573KavUeZIaIaLyb2O4bYdD8SWKSrju4wmHEyyZ8hQObnnMczZHJvvRlEUjuk6DjPa7KdpijQhAU2K2Q6TkB0gGA3idXhxKR6UaHrsAtuuXfE+bxMOh3C59m/zuVatimjVqqhe6iFEIqYzzF1fP8q68o01x55f+Ba/HjqFPnm9wUy+Z8RwhvnPt0+wqmRdzbFXF7/HlAGnMqLNYNgrqFGdsLpqNfd8/RjmzzNOP1z5GS29Bfx57G9wWV6sBo5pTNOidVYRUwZM5qVF7xD5eSftbKePS4ecjU/zYie5ksq2oUAv4Mqh5/Pkjy8SMsIA+Bxerhh6LllK4gnBQqSaBDQpZLrCPD73eRZsW1pzrH9RLy4ZfDZaOPl9Wery/vvvcOedt/LGG+9TUFBYc7yiopyTTjqaa6/9A4sXL2TZsiU8++zLNff5xz/+xiOPPMnjjz/CokULOO64E/ntb//I6tWruPvuO1i6dDGtWhVx0UWX8emnM6isrOSBB6YB1UNOL774Pz7++AsA5s6dzW9+cwX//vcDvPfeO3z11Rfk5OQwefJpnHPO+TV1uu22v9aqB8COHdt55JEH+P77b/H7/bRu3ZpTTz2NU089E4APPniXt99+g7Vr12DbNl27duPKK39D79596/VxFelFc8DbK2bWCmYAbGwe/uFZ7j76ZlymL6lrOF0q76z6olYws9uz81+jb1EPssirdTyk+Ln3mydqgpnddgRK+O+Pr3Dp4HMg1LAft5qmsmD7TyzZsYLfjLgIy7ZQFZWIGeXtZR8zpf+ptNSKkt8E09Dol9+HO8f/mYpIJYqikO3Mwmm668zmK0SqSUCTIrbDjAlmABZsW8rjc5/nsoHn1WtPzZgxR3DXXbfz6aczOPXUM2qOz5r1CQBHHjmBxYvjp0H/299u4qSTTuG88y7C5XITDof47W+nkpWVzV/+8ncAnnrqMaqqqmjbtt0+6/Kvf93O0Ucfxz/+8S+++GIWDz98P126dGPEiMPi3r68vIzLL78QgMsuu5K2bduxYcN6tmzZVHObrVu3cMwxx9OuXTHRaJQZMz5i6tTL+O9/X6BDh4778xCJZiCshPh0zVdxy2xs5m1dzKiiEUkN8QTwM3PNlwnLv14/m0ldjiUYrO7e0DSVdWUba3pA9jZ/6xJCVgg3WQddp4MRUUO8v+ITNpRvZs7mhSgotVYjTV/9Oef1Ph0jkvyQkGWAjpsC5efe3Qiymkg0OAloUiRkB2KCmd0WbFtKyA7gIbmcD3XJyspixIhRzJjxUa2AZsaMjxg2bAQ5ObkJ7ztp0mTOPfeCmr9ff/0VSktLePjhJ2jTpi0APXv25swzT9mvgGbcuCO5+OLLARgyZBjffPMVs2bNTBjQvPjic5SVlfLcc6/WXO/QQ4fWykNz4YWX1tzesiyGDh3O0qWL+eCDd7n88qv2WSfRPNi2nTBwACgPVfcQJGt3Erp4KsJV7HkJRYHKcGyeq91sbIw66lx/bPyRQK167KkyXIVlWyS7xYIQ6UJWOaVIsI4PQCDhRmmpNGHC0SxatJCtW6uXau7cuZN58+YyYcLRdd5v74m9y5YtoXPnLjXBBUCbNm3p0qXbftVj6NARNf+vKAodOx7C9u2xKch3mzPnBwYPHlLrentbu3YNN974e048cSJjxgxj3LgRrF+/jg0bYocFRPOl46BLQeIeuQGteyU9AVfHQe+WiV/rh7btRyTyy9CSYVh0LTwEgDZZrTitz/FcOPh0JnYdg8fhptCTj7uOPZU0R3VvSlmwAk1LXXDhwEn/ol4Jy4e1G4Rqx+81VlUF22FgOMLgMFMSJApR36SHJkU8Dk/d5bob6nk8edSo0bjdHmbO/IhzzjmfTz75GKfTyeGHj6vzfvn5hbX+3rlzJ3l5+XFuV0A4vO/ALDu7dk+Uw+GgsrIy4e0rKsrp3LlLwvJAwM9vfzuVvLw8rr76OoqK2uByObnjjluJRGRDlkyimQ7OG/Ar/jrrHvZOct6loCMtPS2wkxxCUaMOzu5/MjfPvAvTrh0cFee0oUNOO4xw7ePZuo9rRlyMPxpg5uqvKAuW07mgI9eMuAhd1fGRTXivDwBVVQjrft5eOYsfNs3DpTmZ2HUsQ9oMQA0nn7Haiiqc2OMovtkwh7BZ+31S4Mmjf6ueGJHY4M92GGwIbOHVJe+x3b+TDrntOK338RQ4WoAhv4FF+pJXZ4q4FW/CX0P9i3rhVrz1XgeXy82YMWOZOXM6ADNnTmfUqMPxeOoOtvb+9dWiRQvKykpjbldaWpK6yu4hJyeXnTt3JCxftGgh27dv48Ybb2HixGMZMGAgPXv2xu+vqpf6iPRlWTYt9Jb8bdzv6F7YCQC37uKE7hO4dvilaNEUBAKWTYFWwF+P+C09W3QFwKU5OarL4Vw/+tc4onF6W0yN5btW8/icF1hTup7SUDlzNi/gn18+gqaoGEbs8uiQ7ufPn/yTj1bOoiRYxpaq7Tw97xXu+/4JLGdqAnWvmcWtR17PoDZ9UVDQVZ2xh4zg5rHX4TBiPxcU3ebbLXP4xxf3s3zXaspCFSzYtpS/fHoXKypXounSUyPSl/TQpIgS1bhk8NlxVzldOvhslHDDLN2eMOFo/vCHa/nuu29YvHgh5557/r7vtJeePXvz4YfvsXnzppo5M1u2bGbVqhX06zcg1VVmyJBhvPji/9i6dSutW7eOKd/dK+Rw/LJUduHC+WzZsplOnTqnvD4izZkqLbUifjPkUkwMFEXBabsxw/ES8B8cK6rSQm3N1KEXYhBFQcGNByMUf7JrmBAfrPg09jy2xeNzXuSGw6aimr8EW6rD5u2fptea47Lbil1r2FS1hY7uQ7Cs5FpkWeBTcrik/zkYA6KAgtN2YkUUrDiPVkQNJdwB/Im5L3LrEX9Ep35XbApxsCSgSSEt7OKygedV56ExQnh0N27F22DBDFTPX8nNzeX22/9OVlY2I0aMOuBzHH/8iTzzzJNcf/11XHzxZQA8+eQ0CgoKUdXUd+qdccbZfPjhe0ydeikXXHAxbdsWs3nzRjZu3MAVV1xNnz798Hi83HPPnZx77gXs2LGdJ554lJYtE2coFc2bZdkolo7+80eYmbJQZs9rWChhBw6qA+lEI8aqqrCqdG3C82yq3ErICuPll4AmQoTvN85LeJ/P1n3LhX06Y0WS3xLBtoGohv7z9gN1zTDaFSzFSLDWujJcRSDqJ0cCGpGmZMgpxZSohsfIpoCWeIzsBk+qp+s648aNZ+fOHYwbd2StXo395XK5ueeeB8jJyeHvf/8LDz10P2eeeS7Fxe3x+VK/9DQ3N4+HH36C/v0H8tBD9/P731/DCy/8ryZpX0FBIf/3f3dQWlrCDTf8jpdffoE//OFPkqBPpA2HWvf7TI0zqVbXEv+edGmORll7pCl1f16pinxliPSl2HvPrGuGTNOipCR2WWU0GmHXri0UFrbB4Uh+7L0+7Ll0uTFVVJRz+umTOP30s7noossa5JoN1fZ0ex3oukp+vi+lOy83BfXZbkUBWzcI2SGiloFHc+OyPZhGaj7+Is4Av//o7zGTiAF6FHbh6iEXo0R/CWB0XeGD9TN4c9lHcc/3t3G/S03SuwNkOEPcMOMfcVdlFvla8OfR18adp6RpKhEtRNAIoijgUT3opmufQ2aZ+loHafv+tL2gwIem7X8QLUNOIq7//e+/FBQU0rp1G3bt2skLL/wP07Q4/viTGrtqQhwQRVGIOgI88eOLNfPbvA4Pp/U5gaFFA1GiB96LuTen5ebiwWcxbc5ztY77HF4uHnwmquGolQfGMGzGdxrN95vmsblyW637jO04kgJXQdLbEhwMh+lm6rALuOubR2utInNoDq4adgFOy42516CVottsCm3g0TnPsd2/E4C22UX8euh5tNBaYqV+I3Eh4pIemjT6ZR5PY/XQPP/8M7z11uvs2LEDTdPo3bsvV1xxFb169WmwOkgPTWb9cquvdlvOCLd9eS9bq2JX0l126DkcWjgoNdfTTSqtCj5e/QU7/LvoV9SToW0H4jK8cXsqFEXBcIRYXrKKL9f/gFt3MbHLGFq6W6KmIMg6WIpmEVD8zFzzFZsqttCtoBOjOwzFbfniBid+rZwbZ9we0zvl0BzcOeFPuKKJt6LI1Nc6SNulh0Y0mLPPPo+zzz6vsashRFIUBbYFdsQNZgBeXvwOfcf2REvFRFdDI5t8Tu92MhYmqq1hRKy4q4mgOuuxFnHRN7cv/Qf3IcvnpqoiTDTauF9utqniIZuTOx/3SzuiVtzJxKrD5p2lH8cdaouaUWau+ZITOx0jO26LBiEzvIQQzZamqawt25CwvCxUQTTFYztG1MKKKvv9q9s0LWyjOkdMOvWX7087okRZWZo4W/fyXasx6zujqBA/k4AGYjKOiswiz3/zZVk2rXwtEpa7dBf6Plb2HChdV3E6deohw0Ha0dBp6Y3NKr5bK18LNBp2pafIXBnwlktM06rfaJFIuJFrIhrT7udfq2MZrWiaLMumY24xHkf8vZQmdhmDy647k/b+0nSIugIsrljCp5s/Z4uxGdMdataBjWJoTOp5TMLy47odSYK0NkKkXEZ/gquqhseTRVVVdZp/p9OVdpuwWZaCaWZmD0J9t922bSKRMFVVpXg8WfWSNFA0Pqfp5qYx13DHlw9SGf5lu4whbftzdOexmCn4PaPqsDW6hTs+eYiQ8csJO+S24/ejLkcLJd6csimzbZsiVyvO7T+Z5xe++fPu3aCrOhcNOpNcLQ9bAhrRQDI6oAHIySkAqAlq0o2qqlhWZs2A362h2u7xZNW8DkTzY5lQoBZy6xF/pCRUQmXYT5usVnhUL0o4NR+BYS3AnZ8+XCuYAVhfvonnFrzBBf1Ox2rAjOENSTF0RrYexqFt+7O5chsqKm2yW+E0XdiymaVoQBkf0CiKQm5uIdnZ+Zhmev2U0DSF3Fwv5eWBjOulaai2a5ouPTMZwLJAj7hopbahyKtgmzakKD+KqqpsrNhCMBp/J/ofNs3njL4n4iL1WbbThqHixEsnd2fAxo5QD5tRCFG3jA9odlNVFVVt/Bwke9J1FbfbTTBoZmSegkxtu6hfqZ4ErqpQGixLWG7ZFlEzmhE7IMkEe9GYJKARQogkGIZFp/wOCctzXdm4dHfCHiFNVzDUCP6IgqrWzxw+XVeJEgEFdMuJacqPBNH8SEAjhBBJynXm0KNFF37auSqm7LS+J+DFR3Sv1HSqqhDVg3y5aS7fbpyDU3NybNcj6JLXCTWSmkzBqqoQdYT4dvN8vlz/PbqqcXSXsfQo6IoaSa8eaSGSJQGNEEIkSQ27mDrsAt5c9iGfr/ueqBkl35PL6X1OpF/LnkTDsT0iET3I3z67h5I9hqt+2rmKQW36clH/M1HjbAJ5oKJ6iFs/v7dmjyWAFbvW0KtlN6489HwJakSzIgGNEEKkgBpycVqPSZzQfQJRy8ClOfHaWUTCsWNNqg4frvy0VjCz249bFrG92w7aOIqTmpOi6Qqfrv+mVjCz29IdK1hfuZHOni773BFbiKZClncIIUSKmGFwRnz4jFz0sIdIJP7EmYgS5sv13yc8z6drv0Z3JPfxHFFCfLbuu4TlM1d/iSI/aUUzIgGNEEKkofRK8SlE+pOARgiRkKqC4jYIO6vw6+WY7iAuV90/63VdxXSECeqVRB1BFH3fQxouzcJjVhDZtRm3HUDTGv+jSdNUTEeEoF5JxBFAcVikKpG403YxusOwhOXjDjks6V23nbabsR2HJywf32l0yrL4qqqC7TAI6X7Cuh8cRtplXRfNn3Q4CiHicjg0Amol/5v3OnO2LMS2bdpkF3HewFPpkNUOwrErcWzdYGnFKp5d8Dq7AqVoqsbo9kM4tfcJ6BFXzG7SiqKQpfgp/+x5di77BiwTPa+I/AkXoLfoSthKzWqfA6ZbrA9u4Kl5L7OlchuKojC0TX/O7j8Zl+FNet6JZcAx3cbx3aYfY+bRDG7Tl9beVtiR5K5hGjZjOozki3Xfs22veTS9W3anQ04xVpLXAFA0m1KrhCd+eIFVJdU7b/ds0ZWLBp1BNrlYKUpgKMS+KHYGZEIyTYuSEn9jV+OA6bpKfr6P0lJ/xiWXk7Y3ftsNd5BbZ93LjkBJreMKCn8Zdw3FrvZEo798W+m6yrLKZfz7m8djznVIXnt+N+LymFU1Pi3Erpf/j+iuTTH3aXXGTUQKujV4lmxVVdhubuWvn96DvVe+2xbeAm4ecx1aJLk0eYqiUKWWURIq5aedq5m3dQlu3clhHYbg1px0L+iKHkl+/ydFUTAcQeZsXcgX677DoelM7DyW7gVdUrbCKeSo4oYZtxM1o7WOexxubh9/I45I4s0/0+W13hik7ftue0GB74B6axu/X1cIkXacTo1VJWtjghkAG5vnF7xJRAvUOh5WgvxvwRtxz7e2bAO7QiW1hmwURcEq2RQ3mAEonfk0Ljv+dgL1ydSiPLfgjZhgBmBnoIQ15euTToBn6wbPLXyDO754iGU7VzGgdS+6FHTkvZ9m8uD3z7B054qUDLvZto0WcTOy5XB+O+wKfnPopfTJ7ZOyYEZ1wAcrPo0JZgCC0RCfr/sWTZehJ9EwJKARQsRwuXQWbvspYfmqknWYdu1fVlHbYId/V8L7rChZW+tLWtdVwusXJbx9dOcGVCv2i7K+WYrJil1rEpYv2LoEXU/uo9PAYPnO1UD1Euo3l37Euz/NZGvVDgB+3LoINYV7WZqmhRLVIaqlNEtwlAhLdixPWL5w+zJMJb32yBPNV1oFNK+//jo9evSI+e+uu+5q7KoJ0azsq4fBNG0KvfkJy3NcWTHLcDRFxaElnvNS4MmrNffEsmy0nJY1fytOD5ovDxS15m+7kTYOzXFnV9cBhVx3Dh7HL8M/LXyFJLsJvIJSc414WngLUFK8zklRlJRNat5NQyO3jnbku3NRaZ67jIv0k5aTgh9//HGys395kxQVFTVibYRoPmxHFL/lZ0vldnLd2RS6C3AY7phJroFAhBHFg3ltyftxk7sd3W0sWUoWoT02KHLZHsZ1HMnHqz+Pub1Dc9Alv2OtSaimaeE5pB96226oo05ml2rjjwZo6y1EX7sEj2UTUX3QwInfnJabE7qPpyxUQb+inuzw78Ktu3DrLt5dPoNhbQdiGMnNdHXZbk7qfhSPzX0+bvnYjiMS5rA5UE7VwGkFMHZsRNF0tIK2hFUvUTP5YFE1dU7qcTRLd6yMW35ct/EpW0klxL6kZUDTp08fCgoKGrsaQjQrlivCtDn/Y+H2ZTXHct053DD6KvLUgpigxqN5mDrsAh76/ulaw0sDW/dhTMfhhIK1v3AtA07qMZF15RtZvmt1zXGn5uD6UVfiND0xs1LCehZVx5zPP795jEA0WHP8sHaDmDJ4MtFQw69ZMA2bkcWH8srid7jjiwdrjrt1F78ZcSEe1Rtnds2BMQyLAa36MKr9EL7aMLvmuKqoXHro2fiUrCSvUM2jRggvnM6ur1+Hn59DRXNQePyVONv2I2Il9xVgWTbts9pxfPfxvLd8Zs1xBYXT+55IC1chdsOPGooMlVarnF5//XVuvPFGvvnmm5QGNLLKqemRtqe27arD5pXlb/PJmq9iyrJdWdx6xPXxV9U4DSKE+WnXKqoiAXq17EqOMws1lHgFjuWMUhYpY1XJOvLcORyS1x6X6Ym7fDfqDHL9x7cSiTOp9My+JzGu7eGYRsN+RGmayg+7ZvPE3Bdjy1SNu466CWfUl5Jr2Q4Dv+Xnp50r8To9dM0/BIflBiMFvSeqgmvXMra/cnucUoU2F99FlVaY9HWgepJziCDLdq5EVVR6tuiK03ajGHUPN8n7XNqeylVOadlDc8IJJ1BaWkrbtm05/fTTueSSS9C05MZhk53E1xh2P5HpkGSsoUnbU9v2oOLn8wRp8CvDVWzz7+AQb8eYPDFYTnScDG15KKpK9TCIQd2fHJaLVnoRrVu3xrbBtmxQqvcv2pOqKizcuTJuMAPw7vKZHFY8FJeeeNlvfYiqId5c9lHcMtMymbNlIePajU7N5FrbSZ7iZHTbEWRluamoCGJipeST2UmYsq9eTXRhqn6cjnv0uRgpCRidOHAyvNUQgF96+/bRDnmfS9tTKa0CmpYtW3L11VczYMAAFEXhk08+4T//+Q/btm3j5ptvPujzqqpCfn5qflE1hpychv1ATyfS9tTwl1dgWIknM+wKlTKwbe99nseX4rfRtnU7EpZVRfyoGuTnNux7d2cgTEmgLGH55qpt9fa6TOV5jcowRkXsxpQ15aVb8Lk1VEdyOXVSQd7nmSnVbU+rgObwww/n8MMPr/l79OjRuFwunn76aa644gpatWp1UOe1LJuKisC+b5hmNE0lJ8dT/asthUstmwJp+/61XVFA0cC2LRRbS5jBVlcd+Jxe/JH474N2Wa0pLW3YYVlFUehW0ClheVFWS2xTSVgvTVMxMVEVFSuFw1K2ptAxrx1ryzbGLe/dohvl5YGU7lJdH693p6LhbNOF4IrZcctd7XtTFTQxq1LzvKuqgq5Y2IBhqfu1U7i8z6XtdbU9J8fT9Iec9nTsscfy5JNPsnTp0oMOaIAmPUZpmlaTrn8ypO2J2245omwPbmf66s8JGWFGtR9Cz8Ju6FF3zJeJU3czudexPDv/tZjzdMwtJt+VhxFp+Me5OLsthd58dgVKY8rO7jcJh+nG2OsDT1UVInqQ+VuXMHvzfHJd2RzVZQwFzoLqXCtJUi0HZ/c7mX988UBMWa4rm24FnVO2AmlvqXy9m4pK3qjTCK6cWzMheDfF6cbbazSV4dS0w6eGMLeuomLBTBRFJWvgRNTCDvit/ct2LO9zaXsqpH1AI4SIZTmivLrsXT5b903Nsflbl9Amu4gbR09FC9ceRjANm+FtBmNaJm8s/ZCgEUJBYVCbvlww8DS0iDPplTsHw2G4uenwa3hs7vM1CdqynT7O6ncyXbI7Y8b5sAvrfv4269+Uhsprjn2x/nsm9zqGI9uPQTGSX7nTNrslFw8+k1cXv0d5uBKAboWdOG/AZDyKRlNYuGPbEHG3oNXpf6Lkg0cxKqqH95ytOlJ43FUElGxiJ00duCwtSMnb/ya86ZcEe4EVs3F3Gkje0Vfsd1AjRLLSPqB5//330TSN3r33Pb4vRCZQFCgJl9QKZnbbUrmNmWu+5NgOE2JWBykRB4e3GcWwtoMImWGcmgMXbpSIlorvtYNiWTYu28tVgy8gTAgTC5fiwmm5465uUhw2ryx+r1Yws9vrSz9kRPEQfOQkVSeny+aVpdNZW7GFcwdMxqU70RSVtWWbuOuradww6gpyaLnvE6WBiKWhFXSnxdl/h0gARVGxHF4CxOYeOhi6rhJeM79WMLNbaM08zO2rUFv1TenwnBCJpFVAc/HFFzN8+HB69OgBwMyZM3n55Zc577zzaNmyaXyACFHfdF1j1qqvE5bPWvM14zuORiN2sqdl2Djw4MADP3d+NPZXjW0DUR2fnrPPpZxRJcy3G+ckPNfcLQs4ou2YpLqxI3aQzzbMJmyEa+XT2e2r9T8wucckgsGm0E9T3a3vxwO7V4tZkKpn3WGFKPsx/oowgMq5H5J1bE/Cki1YNIC0Cmg6derEa6+9xtatW7Esi0MOOYQ//elPTJkypbGrJkTaUBQSLnUGiNaxmqmps207Zg+pPUXMKEoK8vvXtSIsbEZTvoVAk2Xb2Gbix8o2oiiNHjKLTJFWAc1NN93U2FUQIu0ZhsXhHYfXyjC7p+HFg3DgpLGmGaqqgosQmm1gKwoR1Ud0H6uQNB3CWpAd/hCKpqJajrjDFA6c9C/qxYJtS+OeZ3CbfkSj8Se6arpCRA1h2RY6OprpjH8NxcXQNv34dtO8uOc5rP1gQqGm0TtzsHRdxWX5UWwLS1EJK764q1Gimhtfr9GU7Xgh7nmy+o3DUJzQaK9GkUnSKqARQuybZdm0y2pDjxZd+GnnqlplPqeXk3pMxIo0TheCUzXQKzZSOvNpItvWoLi85Bx6DFkDJ1JlxE4OVRQwnWE+WDWLGau/JGSE6VLQkfMG/IoWjpYxGXMVQ+fc/pO56ZN/xvRSDWs7gDxHLvFm7NquCB+t/ZIPV84iGA3RMa+YKf1PpY27NeyVzdaMapze5wTmb1tG0AjVKuvXsjtF3pZYkYN8gJoAnxYmvOJ7dnzzGmZVGXpeEXljzsLZtjdBy1nrtoZhk937cCrnfYy5V84bR0FbnB0HUJWhK3hEw0urrQ/qi2x90PRI2/fddssVYe6WBXy06jPCRoSh7QZwTNdxuAxfo0zC1DQVZ8lytr90a0yZq0Nv8o6/hoBZe16P5Yxw7/ePs7Jkba3jCgq3jLuOIr1NTFtUDQJqFe/89DELti3F5/RyQvcJ9CnsgRKJ3e3bckZ5ZM7TLN4RO3H1xsOn0tF9SEzvg+6AIJW8+9MM5m5bisfh5rjOhzO4TT+scOpX7aTL692lmYS/f5XKOR/ElOVPuBCl+1iie40wqaqCDz/+BTPxL/4cFJWs/kfi7TOGKstbZz6adGl3Y5C2Z8jWB0KIfVPDToa3HMagov7Y2LhwY0ZsrEaas+CyA5TMeCpuWXj9EhR/CYqnTc2KKkWBHcFdMcEMgI3N0/Nf5XfDLkexagcplgluM4szepzMKT2OQ0XFYbkwE+TRKY+Uxw1mAP477xX+dNjVqOZePQ9RcJDNGb1O5pQeR6Og4lSzCIeb7/wkAIcZYOfc+JN8yz5/gdZdhhClduZmy7KpxItj4IkU9psAikJE9VBp2DT+lHORSTJvEwkhmhHTtNCiTvSoCzPauF8eqhkhumtTwvLQxqW1fm3pusbCBHNhANaUridK4gDCiiroURdq1JEw26iuqzHDcnvaUrmNiJ14/CgSViDqxY66m30wA2BW7IhJwrebHQlhh6sS3jdqQMD2ELDcKdofSogDIwGNECI1VDV2B8o9aJ6cWsMPtm2T485OeHun5kBNcjmRbdtkuxLvBaUpKpoiS4p3U511762jaLFDekKki6SHnL744gteffVVNmzYQEVFRcx4qaIozJgxI9nLCCHSXFT14ut9GP5Fn8cWqhqu4h5Umr98PhiGxYCi3igo2HGGJo7oNAqX7SbemiWfI4oeDWD4S1EdLhR3NkEth2i0du+Cadp0K+yMpqhxl3sf1n4ILtxx1+C4NBOHFcTyl6HoDnBnE6Rx5iclw6HZuKwAdqCsOuh05xJSvZjxHlhvLpovF9Mfm7jQ2aojpsOXkgVLigJuNYIe8RPcsB6P04eh+wjZzkZL8iiavqQCmscff5y7776bwsJC+vfvX5MQTwiRecKmSu7oM4lsXkm0ZPMvBYpKy5N/S0jxxUypcFkerhx2Pg99/3StoKZjbjEndJuAGY69To4epPK7t6mY81HN8IiW04Kiyb8HX2uie61ycpkerhlxCf/59nGsPYKaNtlFnNbnBKxwbC+QR4sQXjiDnV+/Dj/npNGy8ml5yh8IZbWNHwykIbcaxVz9PVtnPo1tVA+tqZ4sWpx0DUZ+F6JW7U76IF5anvpHtr3wd+zoLyu8VG8OhSddSwA3yc6LURTIUgPsevd+wht+GXJ0d+hDwfFTqbI8EtSIg5LUKqcxY8bQpUsXpk2bhsORvl2Rssqp6ZG2N822K4qCVwlglW4itHYBWnYh7s6DCGtZRM0EI9yaRVgN8uPWRZSHKuhf1Isibyu0aOyvdZdLw1o6k5IZ/429tstLuwvupNyOM4yl2UTUIPO3LWZnsJR+LXvSJqs1etQV06usaSr6ptnsfPu+2Gs4XLS56G4q7az9fUj2S30856qq4K5Yw7bn/xpbqKi0ueQeqpS8mCJNVfDYlUQ2LiGyYwOutt1wtO5CUMnCNJOPNDxahIr3/kNo/ZKYMvch/ck59mqCZvp+n6RKU36fJystVzlVVFRw9NFHp3UwI4RoOLZt47c9qPnd0Fv0wLbt6jwkdfVomCou08fYtqPIyfFQVuYnGom/VssVLWfrt2/Gv3Y4QGjTMhwdh8cMPWEqOE0vI1sNR1EUTNPCithxh7pcdoBdX7wc/xrRMKE189C7Jre9QkNwKhHKE7QD26Jq/kwcQ04julc7TMumiiy0DiNwHDKSqGkRtlK3YkmL+uMGMwChtQvIj/pBzUvJtURmSWpScL9+/VizZk2q6iKEaCYsyyYaNQ/oS3/3SqU6+4xtE7OqLGFxZPs6dD3xJF/DsIhGzTrnwahYGKXbEl9j2xpUNf33PlAtg2jploTlxo51qHWsIjPNfT9WB8MOB+suj9RdLkQiSQU0f/3rX/n444955513UlUfIYRITNHQcloAoOe2wttjGJ5O/WtWV7lad0649cH+slBxFLZNWO5q2y0lQy+7qaqCw3FgK610XcPp1OvsjrdUB44WxQnLna27YDVCKjLF7a273FV3uRCJHNCr+cQTT4w5ZhgG119/PX/9619p3bo1qrpXqnJF4e23306ulkIIAYQdeeSPORNV07HCAUIbl6FmF5Iz9HiCaxbgbNuNUJJDQWHFS96Ys9jxxl0xZYrLi7NDX6oS5L05EIoCXiWEVbqRwNKvCLm8+PqOxXDlEdpri4HdHJqF26wisPhbQjs34u7YF0+HPgSV7JhcPGHLQd7o09m6dmHsiTQdX78jqGyEYTNT9+HpPIjg6h9jyjzdhmDo3rqHKIVI4IACmry8vLjHOnbsmKr6CCFEQuGwQW77nmx76TaiJb8Mp1TM/oDCoy4iqiW/rNg0LZxF3cmfcAFln72AHa1eaqXnt6blyb8lqGal5AvXp4YoeetuwptX1Byr+OE9ckacjHvgcTFBja6CvmsVm1+9ozpdMuBf/DmqJ5uic/5OQC+oNTxk2zZGVhtanPgbSj5+AitUvTBCyy6gxUnXENSyGyVwCJoO8o++DGY8QXDF7g1WFbzdh5I7/iKqMmBCsKgfspdTGpNZ8NL2TGr7/rTbqVmEv3meqnnxc1u1ufReqpTclNSnJn9LqBJF07GcWYTwpGROia4r2Is/ouyz5+OWtz7/Dvyu1rWOZal+tj7xu1rLqXdzte1K7qTrCZqxPTu6Bm47gB2srM5D48oiiLfR8+m4NQOH6YdoCBweopqXkJk5u/Fk6vsc6m+VU1JzaN588002btyYsHzTpk28+eabyVxCCCFqOO0Q/oWfJSwPrpyDrqcmAXrUVKiyffhdranSWxCw3CkLApxWgMq5HyYsr5o/E6fzly93RQGzbFvcYAYgvHklWjQQt8wwocry4ncV4Xe0xG+lJihLVsjUCer5uNt2JajnZVQwI+pHUu/8G2+8kR9/jB0H3W3+/PnceOONyVxCCCF+YdvYZjRxcbgKJcntEhqCQvXeSInYoSpqL5NW6rw9gG01/72mhKhLUgHNvkarAoEAmib7pGQyTVOwHFFMZxjbYTSJ5a6p4tAVvEoQnxrEpdfPL2KHruBVg/iUAC5t393WiqLg1qL4lABeNZSy3oxkuVwaXquSaMlWPGoYNUG1TMWBq13PhOdxdx6MYcSfGKJpCl41hE8J4NYM6iPu0XV1j2tEEwZXhuLE02VwwvN4e4+u1RVv2zZ6YbuEt9d8eeCU1UEisx1wH9+yZctYtmxZzd+zZ8/GjJMHvKKighdffJFOnTolV0PRZNkOgxUV63h16btsrdpJcU5rzuhzEm09bVCM5tu9rCgKWWoA/4/TKV/wCbYRxdtzBDnDT8av5qaku786fXwI/4JPKJ/3MVY0jLfroeSO+hUBNZd4i3CcqokjsJ2yz5+rHqLw5ZM78mR8HQfiN11J1+lg5eoB/Iu+Ytuc6ZihStzte5M/9iwi7kLCRu3IJoyL/Anns/XZm2omxu7mat8bslrG3Szap4WJrPqBXd+/gxkox13ci7yxZxF2tYhJ/3+wfFqY6Nr5lHz7BkZVKa62Xckbcw5RbysiVu0fdmFTI2fUrwis+KFm0vFujhbt0Vt1JrTX6ySq+cgafDRVcz+KuXb+URdWby2RouR3QjRFBzwp+IEHHuCBBx6ovrOi1NlLk5OTw5133skRRxyRXC2TJJOCG56i23y++WteXPRWTNllh57DoMIB1NVD3pTbnqUG2PnyrbX3MwJUt4/W599BZbzU/HvYn7b71CAlr99JZPvaWscVp5s2F9xJJbUnxmqairN0BdtfvJW9v/R8/Y7AO+qshEuF61OOFmTX+w8RWrfX0mJVp+2U/yPgbRezHNmhWrjCOymb9TzBdYvQPFlkH3osnj5jqTLdMdfwqBGqPn2SwE/f1i5QVIrO+RuhrA5JB5luNULg6xfxL/hkrxKFVmfcRKSgW0w7NBU8RinlX75EcMUcFIeLrIETyBp0dML9jLxaBGPdj5R//TpGxU5cRYeQN+5czNxiwlbTWx3UlN/nyZK2p8HWB6effjrjxo3Dtm1OO+00fvOb3zBmzJhat1EUBY/HQ4cOHdD15vtLXCQWUUO8uuS9uGXPLniNPuN7oBP75dPUaZpKZOOymGAGwAr5qZz9Ps7hpxMxDn68Q1UVzJ1rY4IZqJ6XUf71a3gOv4DwHnsnuewAuz56nHi/4P0LPyVn+EmgNmxAo6pgVe6IDWYALINdM5+m8KTrqKR271HUUjGcRWQdPZVcIoBCWPVSZcQPSrRweWwwA2BblH78BHmTbyRIcj1UuuGPE8wA2JRMf4wWZ/wV/16vd9MCv5aP98hLyT3ifBwOjaDtpjKc+AM+YDrRDxlJYYcBKFhY6IRwpcUkXyEa2wFHG61ataJVq1YAPPPMM3Tp0oXCwsKUV0w0bWWhcowEXTDBaIiqqJ+8ZhjQ6IqJf8kXCcsDy7/DO+QkIngO/hq6SmBxHddYMZvsUWcS5pc5FYoRxKgjDX5k8wrUDsMb9IvR4XAQWDk3YXl44zIUMwxxgg3btgnZOjUfYQnqrWkqoVWLE14jsm0tmhEG5eADGlVViGxalbDcKN0K0SBosa9324awoVUnm8v2YZTuuyfZMCyMWu8dCWaEgCQ3pxw2bFiq6iGaGV2t+6WlKVrz/BxWFFRH4kBNdbixFSXJtit1podXHS5savcAKWrdk/MVZ2MElzZqXdfV9OrJQkm+ThRnHcGjolb/l6R9PX77evyFEMlLKqA577zz6ixXFAWXy0Xr1q0ZPnw4Rx99tAxBZYgcZzbZriwqw1UxZUW+Fng1b9IZXdNRxFDIGnwM/mXfxC3POvQYIoqXeI13aRYO048dCBCxvDhVD6aix8yliEYNsvqPT5hcLmvQRKKaF/YYgjE0L+6OfQmtWxR7B1XHWdT55x2VG044bJDTfRiln78Yt9zX6zBM3QeRg7+GaVp42/euDlrizBb2dhtCVPPEzZjr0MBl+bHDVaDp2I4sQsTmorEsG2erQ6r3k4rTK+nu2BdDj3+NA6Uo4FKj6EYAOxpCcXqJar5aw4t70zUFl+2HUBWoKrbTR1jxxczpEaKpSyq6sG2bbdu2sX79enJzc2nXrnpZ4aZNmygvL6djx45kZWUxf/58Xn75ZaZNm8ZTTz1FQUFBSiov0pfDdHHt8Iu5/csHaw09uXQXVw+/CKflwmyGXTS2bWPntMbX/8iYORWudt1xdx0Wdx8gnxbG/8Ob7Jz7cc2XortTfwqOvpwqfLWCGtsGy1tIztATqPjh3VrncRYdgrfvuJj5JGHLQcHES9n63F+wAhV7lCi0OOEqQoq3UXrMTIeP/HHnUDrruVrH9dxW5I8+jfJI8r0nYdVL4TGXseuDR2od17ILyDtiClVWbO+JW41irv6BrbP+hx2uTljnaNGeFiddQ8DZMiaoCSleWpw4lZ1v3cueD6TqzSF/4iX4LSfJPsCKAlmKn9IPHyW4ZsHPF9DJHnQUvmEnx12p5lQN1K3L2P7RtJrnXc8rosWJVxP2FZNhc1FFM5fU1gezZ8/mqquu4sYbb+TEE0+syTljmiZvv/02d955J4888ggDBgzgjTfe4C9/+QunnHIKt956a8oasD9klVPjUHQIKX6+2ziXteUb6VbQiSFt++MyfVj72K24qbfdo4ZR/DvxL5iJHQ3j6zsOtaAYvxU7/OHUbaJz3qDiu9hNXJ1Fnck/5XoCVvzVO2pwF1XzP8EO+/H2GYPWoiMBO/4KGVVV8FJFZN0iQqt/RM9vjbfvWCJ6Tsyy4obk08KooTIq53+KFSjD220IruKeVJGTsl4Ep2rgjJbjXzgLs2IH7i5DcLbvRYCsmOBE0xQcO5aw49U7Y86jun0UnX8nVXZWnGuYOI0KAos+wyjdirvzIJwd+xJUsva5O/f+vN69apjSN+8ksnV1TFn2sBNxDplca7K5oih4g5vY+syfYm6v6E7aXHQXleTUWa/61tTf58mQtqfBKqc9/fOf/2Ty5MmcfPLJtY5rmsYpp5zC8uXLuf3223nppZeYPHky8+bN45NP4q0EEM2RbYALH+OLx6G0r+6aNyIWVjPsmdlb0HKheNvhOvwiwCZsWAkn3DpNPyVz4qfBj2xbjRIqhzhzNIKWE8XdBtfo8wGbSB3XgOrHvwof2iEjcXc5DNuGqqjZ6EN/ftMFjiKyxp2Py6Xh90coDxmksmIRSyeiFeIcdjoOwDTtn3vKYh8vlx2kJMEeS1bIT2TDErSOI2KCrYilEVHzcRx6KrpS9zUOhhIqixvMAFTN/YjWg44mwi+BlkuNUv7Fy3FvbxsR/Is+wzFoEtFoZn2ZiuYrqf7cn376ieLi4oTlxcXFtZLw9enTh/Ly8mQuKZqgaNQkEjEz7leIbUMkYhCJmHUGGnY0hG0knihilG1PmGF5f6+xJ9O0iERMotFG2Gq5DqFQFFXV6vV1EolUvxbr6vlRsYjuSLxHXXjTT2ha4mX3u1/vqZyjoigKRvmOhOW2EcGOBGsdU60okR3rEt4nsmUFqi3bJYjmI6mApmXLlnz44YdYVuwb17IsPvjgA1q0aFFzrKysjNzc1OyEK0RzoeguqGMVjJ5dKHlGUkTTVHRdq3O/JwsVPa9VwnJHq451Ph/7c40DZds2enYdcw9VDWWv1XWWqqPnFSW8i17YHkuR1Vei+UgqoLnwwgv54YcfOOuss3j11Vf5/vvv+f7773nllVc488wzmTNnDhdddFHN7T/88EP69++fdKWFaE6imhdfn8Pjlul5ReDLb+AaNT8OzSJLqURd8zXW/LdwV6zGpwbj7ucUUb3kjjot7nkU3Ymn86C4vUi6ZpOtVKGt/7b6GmUrf75GigIbbz56fuu4Rb7eo6tXtu0hYjvJHR2/HSgqWQMnEI1KoCyaj6QmBQM8//zz3HfffZSVldW8cW3bJi8vj6uvvppzzjkHgEgkwrx582jXrl3NaqiGIpOCm55Ma3uWFqJs+mMEV82pOeYoaEvLX/0Rv5qXET009fWcO1Qbfddytr/2z1r7PzladqDlr26k0oydqO3VIgTnvkfF9+/ULPdWPdm0nPwHIjkd2Hv/S121cVasY/tLt9XaDVzPb0OrM26iyvbFnahdc7v9aLuqKviscna8difRXZtqjnu6DCZv4mVxt31wqxGMFV9TOut/YFYPLykuLy1OvBqzRXeijTgZHDLvfb4naXvqJwUnHdAARKNRFi1axObN1ene27ZtS9++fXE40mNvEQlomp5MbLtHi6JFq7CqStF9OVjObAJ2bN6T5qq+nvNspYrNj19b84W+J1+/cXgOPy9mE0wAl2biMAOYlTtRHS7w5hFSvMTZi5csxc/WJ38bs9EkgLf7MLzjLyds1jGsuJ9tV1UFN0HUcAVmsAotKx/TkUXQTPxZ61QtnHYAq3IXqBpqVj4hfGmxZDsT3+e7SdvTbJXTbg6Hg0GDBjFo0KBUnE6IjBQ0HaDmoxcU4vv5zW5J8rOkqKpCeNPyuMEMgH/xl+QcdhphfDFlYVMjTDZk/7yZaIKnQlEUjF0b4gYzUL0VRe64KdXnSpJl2QRwg8MNjp/n+exjbnfEUqtXP2X9vAJKXlKimUpJQLNy5Uo2bNiQcAXT3su6hRCiIaiqgumvY2WlZVT/l8Q0F0Vhr2SFe7Gt6mEomX8rRL1KKqBZv349f/jDH1iwYAGJRq4URZGARoh6ousqDisIto2heYgm2HF6Tz6niWqEsFWdEJ566e52ahaaFcFWNCI4G23YzDQtvMU9Epbr+W2wNFdSvRaWZeMs6pSwXMsuAIdHekaEqGdJBTQ333wzy5cv509/+hNDhgwhJ6dxs04KkUmytBCRNfMpm/M+VjSCt8dwsgdMIKBmx81M69JtXJESyj55g/DmFWhZeeQOn4SnqDOVRmo2p9Q1G7dRTuXX7xBatwjNm0POiJNRi7oSiJOav77ZNtjeQlzFPQlvXBZTnj/hAsIJ9tY6EKYrF0+XQQRX/Rh7jSPOI6T4kr6GEKJuSU0K7t+/P5dffjlXXXVVKuuUcjIpuOmRtu8rDX6I8g8eiNlsUvXm0HrKbVTatedr6LqKu2ItW57/e8wGijlDj8c79CT8RnIBh6oqeMPb2PrsTTGJArMGHoVnxOmErMSTV+vzOc/SQlTNeZeqH2dgR0M4WhSTP/58zPxORKzUbJibpYUIzJtOxZwPsCNBHAVtyDtiCnbLboTraDdk7us9U9sN0va0mxScn59PdnbyE92EEPtPVRXs0k1xd862AhVUfPcOrsPOJrJH3OKx/ez46LG4u0FX/PAe2QMngJJcQONSwpRMfyJu1uOqeR+TfeixoDXOxrRVphvnkF9RNOhYFNvCUp2EFU9Ks/lWmW4cgyZRNGACimViqQ7Cild2tRaigSSVWO/MM8/k7bffxoy3jlEIUS90XcW/6NOE5YFlX+EwA7WOKdEg0R0bEt4ntHEZup7cztaaGSK86afE11i38IB+baVaxAC/7aOKbAKWq14Cjahh47e8P1/DLcGMEA0oqR6aQw45BMuymDRpEqeeeiqtW7eu2XF7TxMnTkzmMkKIvShqHW9dVYvdDnEf2WqVOrZe2H/Kz/8lWCAQr15CCJEiSQU01113Xc3/33nnnXFvoygKS5cuTeYyQog9RKMmvgHjqVo4K255Vr8jqtPg7zG6ZOseXG27obq8ZPUfh6LpKKpGcM18Khd8hqtdDyrijGUrSvWuzboRrF567PT+nFwuNjQxNA+eTv0Jrpkft17ujv2olB4LIUQ9SSqgeeaZZ1JVDyHEfrJtULKL8PUZjX/xl7XK9LwisgdPpGKvqTIBvLQ4cSqBZd+y66MnsEJVoKh4uw+l7Xn/R9Thg2jt++xOtV/ywTRCa6vn62jZBRQcdTF6q+4xE13Dlk7ehAsJP3tT9fn3kDfmTCKaVxb6CCHqTVIBzbBhw1JVDyHEAVDsKO72vfF2GkjVsq+xI2E8nQfibNEOM1CF4s6qlRtKwSa4ZgGln73wy0lsi8BP32FU7CT/5OvZ++PAi59tz9+CWVVac8ysLGHH6/+i6KxbUHM718ovY9sQ0PJpff4dBJZ9TWj1PLSsPLKHHI/pa1nnCichhEhWStYrRiIRFi9ezK5duxg8eDAFBY2zkkGITKDrKuHVCyj5cBpadgHeLoNRdAf+Zd9QunU17o59yD72WkL2LwGEy/Kz/YuX454vsmUVir8E3G1qjmmaQnTzilrBzJ5KP32GvJNvIEjtlVGWZVNJFnqfo8nqPR4UjYBZs7+jEELUm6SXHDzzzDOMHj2as88+m6uvvpqffqpe5VBSUsLw4cN59dVXk66kEOIXqqoQXls9T8WsLKFy3gwqZn9AZOtqAMJbVqHZe40fGWGsYGXCc0Z2rEPTfpk4rGkqoXULE99+6xq0OjYRMgy7ei8kgzp3mRZCiFRJKqB57bXX+Mc//sHhhx/ObbfdVquLu6CggBEjRvD+++8f9Pn9fj9jxoyhR48eLFyY+MNViOZkX8unbbs6ZX/C+2cVYCu1z6HoDqhjZZSeXYi1Ry+KZdno+W1r/tayC6qv+fM5tOwCrGQ2QIpD2cdKrHg0TUFVU1sPIUTTlNSQ01NPPcX48eO5++67KS2N7Zru06cPzz777EGf/6GHHpIcNyIjqCpkKQGsyp1E1q3Dn9sKX2E7/HpOTCbNaNQkq88Yyr99K+5YTs7IyTHp/KOaF1+fw/EvjM1fo7qz0PLb1vpBYhgW2d2GEF67gJwhx2BU7sIOB3G0KK7uCXJnEVF91IqCDpKu2rjtAMa2DVSsrcBd1BnTlUPQdCa8j0eNoIXLCW9Yg+bNQm/RkZDixbAkuBEiUyUV0Kxbt44pU6YkLM/Ly6OsrOygzr1q1Sqef/55/vjHP3LLLbccZA2FSH+qCtlKFdtf+xeR7et+Oe7JpvUZfybobRsT1ET0HFqefB07376vejn1z7IGHYWjQz8iey2PDhsquaNPxyjbSnjDL2kUVE82RWfcREDxxaSPMXQv2YMmsP31u2tdw9t9GPkTLoy7zPtA6aqFs2wNW1+9s1aGYU+XQeRNvJwqM3aPqSwtTNn0RwmumltzTNGdtDz1esjvIkGNEBkqqYAmJycnbs/MbitXrqRly5YHde5bb72VM888k06dEu9iK0Rz4FUj7Jr+VK1gBsAKVrLtlTtoPeX/qKD2FiMRS8PRug+tL/k3xs4NWNEQzqJOGJoPvxl/NVGl6SHvhGtRQuVEd2xAy8pHy2tNAG/cjhbdCLD5jXtieoECy7/H1b4Xeo8jMfZjd++6eOwAm1+5Hcza68yDq37EuWAGjgEnEN2jyKFDYP6MWsEMgG1E2P7KHbS99N9UItuxCJGJkgpoxowZw8svv8zZZ58dU7ZixQpeeeUVTj311AM+74cffsjy5cu5//77Wbx4cTJVrJFsWvfGsDtNfGOmi28smdR21QgQXDk3bpnpL8Os2ImelxtTZqMS0nJQWvcFIPTzkFFdL/UIHvB4UA9pg2HbRO3qiXTqXvfRNJXgou8TLk+q+O4tWnUfSUj37ruBCaiqQmjVophgZrfK2e9T1H88tu6rOeay/Wyb/V78E1oGofWLcHUdHTfxXzrLpNf7njK13SBt3/PfVEkqoLn22ms5/fTTOeGEEzjiiCNQFIU333yT1157jenTp9OyZUuuvPLKAzpnMBjkjjvu4LrrriMrKyuZ6tVQVYX8fN++b5imcnI8jV2FRpMJbQ9t2UKi7QIAzKpS8js1/Ot3R9m2hGVmVRm6ppCfm1y9Ssq3JyyzwgF01SY/75drRMv8WOFAwvuYZdvJyzn4IKuxZcLrPZ5MbTdI21MpqYCmqKiI119/nXvuuYcPPvgA27Z566238Pl8HH/88fz+978/4Jw0Dz/8MIWFhQfVs5OIZdlUVCT+EExXmqaSk+OhoiKYcZvc1XfbVVVBt6OAjak6G/UXvdfpQXF6sCPBuOWOFsWUlvrjlimKggMDbBNDddVKdJcMVVVwdexH5Y8fxy13tulC1FKoSlCv/aEoCs7iXgnLHYXtiFparWu4bLX6+K5Nce/jat+LsrJArQnOTUGmvtcztd0gbd+ftufkeA6oFyfpxHqFhYXcdttt3HbbbZSUlGBZFgUFBah792Hvh02bNvHkk0/y4IMPUllZnTMjEAjU/Ov3+/H5Du4X4d6TKpsS07SadP2TUR9t96khjE0/Uf7jdLBtfP2PwN2+L37b0yg5U6KuHHJHTKLs8xdjytwd+4I7O+5j4FXDWDvXUjHnPaxoGG+vUbi7DCGALyWBTVbrrmjZhZiVu2LK8o88j6DlwkpylZOroBg9vzVG6daYsrwjzyOEB3OPttuah/wjz2P7K7fH3F7PK0IraE8w2nRXRmbqez1T2w3S9lS2XbHT6KfMd999x3nnnZewfMCAAbz8cvxsp3UxTYuSkoP/JdlYdF0lP99Haak/417w9dV2nxqi9N17CW+svWGqo2VHWvzqBqrMxun+zdaDhJZ+Rdk3b1bvg6TpZPU5nLzRp1FuxA6heNUwlZ8+ReCnb2sd13Na0ursv1JpJT9E5dWjqP4dlH3+EsE1CwAbPbcV+YefjrO4FxUpuIaiKGQpVZR98l8CK+aAbaFlF5A//gJo3StmvygAl2qgbFtKyYz/Vgdbioqn62Dyx19IlZ3V5HpnIHPf65nabpC270/bCwp89ddD88ADDxzIzYHqD6yrrrpqv27bq1evmA0vly5dyu23387f/vY3+vXrd8DXF2I3TVMwtvwUE8wARHesI7RyNlrXsY3S/VtpeHD3nUib7sOxjRCqw4XpyKY8HLsEWVEUqNwaE8wAGBU7qJr7IY5DTyWZjgpFAcW/k20v3kr2wPHkHHo0tmVhBSsp/+FdnBuW4D5sCmEzuSXStm1ThQ/v+CvIOyKAioWpOgkSf0dvqN4EU2vdn5bn3ArRIIrmIKp5qbK0JhnMCCFSI60CmpycHIYPHx63rE+fPvTp0+eAry/EbjoGVQnmhAD4588gr8uwmP2JGkoobBEiG92VW/PrJd721A6Hin/BJwnPU7XoM1oNOo4oB9/bpOsa/iVfYYUDlH/3DuXfvVOrPLpzE1kjf0WY5Cfg2jaETQ1T/6Xd5j5+sZqmjR8PaD+3semOMgkhUuSAApply5bVVz2EaAB2nbsk2rZV12KjtGLXNXfFskhJQ+p8rJrIAyWEyBgNugA+EAjwwAMPsHHjxv2+z/Dhw/npp59kuEkkzcCJr//4hOW+vkcQ1WIz0zYUTVPwKUHcRhnR8p3oWvygIRq18PUbl/A83t6jiGrJzQUyDBNvr1EJy309R2CojfdYCSHE3ho8oHnwwQfZsGFDQ15WCKB6crizuDfOotjs03p+a7w9RySd+fZgedQI2obZ7Hj+JrZM+w0bp11LZM6bZGnhmNvato2S1w53pwExZZovj5yhJxIxkp3bAvha4O0ROwSserLIHX06YSPzEoIJIdJX0su2D5R0VYvG5Lc9FE7+I+HVc6ia9zG2ZZHVbxyeHiOpsrw0xpiTrisYK7+nZPrjNcfsSJDyr98gunMzWUdeTNCqvVFjwHKTd8yviW5YTNWc96uXbfcYia/fOPxkkYr15wHLRc6RF+HtdRiV37+LFQ7g6T6MrP7j8SvZkKKcN0IIkQoNHtAI0ZhsG6pMN3qXw8nrNASAqOqm0rBprAk0LrOK7XFy0AAEln9H7uFngBaboNJvutHaDyW3uB/YFobmSXk7/KYLrc1Ack7qjWJbGNrPj5X8MBFCpBkJaERGMgwLY/dqpsbuaYgGq3PPJCou2YxaVBg3WZ5pWgT5ufemnobLTNPGxFGv1xBCiGTJILgQjUzR9kgep6ioLi+oWs0hzZMtQ7VCCLEP0kMjRCMzdC+eroNxt+uBq3UnjKpSNE82ZqCC8tnvo+a0rGsFtRBCCCSgEaLRhS0HhUddzM73H6b0sxdqjut5RRT96nqCqq/xh8WEECLNyZCTEI3MqVqUfv4ioXWLah03yrax7ZU7cFpNb6d4IYRoaA3aQ1NQUMDMmTNp2bJlQ15WiLTmMP3sXPJV3DKjfAe2vwQ87Rq4VkII0bQ06F5OqqrSrp18MAuxJ9sI17nNgFlZguorjrvKSQghRLW02pxSiEykONwouhPbiMQt1/NaEU4QzCgKOHQNsDFMW4IeIUTGks0phWhkEc1HztDjKf/mjZgyZ5uu2O68eJtu41EjKFXb8c//GCscxNvncBxFXfHbHsl7J4TIOLLKSYhGFjEUfAOPwbZMKmd/gG1GAQVP10HkH3UJVZYr5j4eNULg+9eomvtRzbHA8u9xtOxAy1/dSKWZ3OaUQgjR1EhAI0Qa8JsunIeeQusBEyEaQHN7iaoeqgwtprdFUUAN7KwVzOwW3bEe/4JPcAw4gahk9RVCZJCkA5ply5bxv//9jyVLllBZWYll1e4bVxSFGTNmJHsZIZq9iKEQIQvdk0N+vg9/qR87zmRhh0On6tvE76mq+R/Tou+RRJFeGiFE5kgqD813333HaaedxqxZs2jVqhUbNmygffv2tGrVis2bN+P1ehk6dGiq6iqEAMDGjoYTllrRCEojbbQphBCNJamA5r777qN9+/Z8+OGH/OMf/wDg8ssv54UXXuDFF19k27ZtHHPMMSmpqMgcLs3CY/sxKnbiUM3Grk7aMQwLb+8xCcu93YdhaO64Zaqq4FHDZKkBfGoQXVfqq5pCCNGgkhpyWrJkCVdffTVZWVmUl5cD1Aw5DRgwgDPOOIN7772XsWPHJl9T0expmoLXqqD885fZuewbFBS8vUeRe9ip+JUcWZL8M8uy0Vp0wNmmC5Etq2qVKS4vuSNPodKI/a3iVA30io2UfvoskS2r0Hx55Aw/CV+PUfjN2InHQgjRlCQV0Giahs/nAyAnJwdd19m1a1dNefv27Vm1alWiuwtRi9eqZOszf8IKVgJgA/6Fswit/pGiKf+gEl+j1i+dBGwPhSf/nuDSL6n6cTpWNIy32xByhp+MX82JWeataSrqjpVse/WOmmOmv4zST57Bu3kFWUdcSNB0NnArhBAidZIacurQoQNr164Fqif/du7cudYE4FmzZtGiRYukKigyg0OvDl52BzN7Mv3lBJZ+ha7J8Mhutg1Vpgel90QKz/w7LafcifuwKVSSgxUnZ43L9lP68RNxzxVY9g1aJPZxF0KIpiSpgGbs2LG89957GIYBwIUXXsj06dOZOHEiEydO5JNPPuGMM85ISUVF86ZZYYIrf0hYHlz+HbqdeCJspjIMm4DtIWB7CJuJAz4lGsIo35GwPLx5FZoEjEKIJiypIacrr7yS8847D03TADjllFNQVZXp06ejaRpXXHEFkydPTklFRTOnaCjOxMuMFZcXW9EasELNi6LpgAIJVj9pHh+mTFESQjRhSQU0DoeD/Pz8WscmTZrEpEmTkqqUyDwR20HOsBPZsTH+9hrZQ08gYusk+kIWdTM0D57OAwmu/jGmTNEc6C07EpJJ10KIJiypIafx48czc+bMhOWffvop48ePT+YSIkNYlo1W1BVvz8Niynx9xqC2OERWOSUhbOnkT7gILbugdoGi0uKU3xJWZMK1EKJpS6qHZtOmTQQCgYTlgUCAzZs3J3MJkUH8pousceeTM+wEAku/QlEUPL1GYXkKCMiy4qTYNvjVHFqdcyvRrasIrZ2Pnt8GT9chhLVsopbMnxFCNG1Jb32gKIk/CBcuXEhOTk6ylxAZJGi5UDxtcY08m+xsN6WlfgwjzrIdccAsy6YKL2rr/riKB1b/bVhxd/IWQoim5oADmqeffppnnnkGqA5m/vGPf/Dvf/875nZVVVVUVFRwwgknJF9LkVFsGwxDMgTXF8uyiUTk8RVCNC8HHNAUFhbSrVs3oHrIqaioiKKiopjbeb1e+vTpw9lnn518LUWTpmkqmqZgmjamue/uAEUBXU9qepcQQogMc8ABzQknnFDT6zJlyhSuvPJKRo4cmfKKiaZPU8FrVxFas5DI9rU423TDU9yLgJKVcIKvR42gBnYRmPcNEUXB23MkpjufoCVzaIQQQiSW1ByaZ599NlX1EM2Mqiq4A5vY8sLfsaOhn49+hOr2UXT23wk4W8QENV4tTOVnzxJY8mXNsfJv38LXbxxZo84iIEGNEEKIBJLu16+qqmLatGlcfPHFnHzyySxYsACAsrIynnrqKdatW5d0JUXT4yHAjtf/uUcwU80K+dn51j24CdY6rqoK1rZVtYKZ3fwLZ2HvWoeqykocIYQQ8SUV0GzdupWTTz6Z++67j61bt/LTTz/h9/sByMvL48UXX5RenEwVrMCsKotbFN21CSXir3XMSZTKH95JeLrKH97FoRiprKEQQohmJKkhp3/+85/4/X7efPNNCgoKOOyw2knRJkyYwKxZs5K5hGiibGMf+y6Z0b1efSZWOHFOIyvsR7FNQLY/EEIIESupHpqvvvqKKVOm0LVr17j5aNq3b8+WLVuSuYRoorSsfFDjBx+Kw43izq51zFRdeLoemvB8nm7DMFSZQyOEECK+pAKaUChEQUFBwvLdw08i80RUL7nDT4pbljfmTMKqt9axqAG+fkeierJibq96c/D2Ho1hyNYHQggh4ksqoOnSpQs//PBDwvIZM2bQu3fvZC4hmqiwqeEeeCyFx1+JntMSAEdBG1qc/Dsc3Q4jasb26PmVbFpP+Qe+3qOqe3dUHV/fMbSechsBJTbQEUIIIXZLag7N+eefzw033ECPHj049thjAbBtm3Xr1vHAAw8wb9487r///pRUVDQ9AdOJ3nEEhe37o2JhoRJWvAmT61mWTSU5eMZeRO7Yc9B0jbDtpDKigCm9M0IIIRJLKqCZNGkSmzdv5t577+U///kPAJdccgm2baOqKtdddx0TJkxIRT1FE2UYFgbuPY7sO1Nw2NQw9Szyc3xUlfr36z5CCCEyW9KbU/76179m0qRJTJ8+nXXr1mFZFh06dGDixIm0b98+FXUUQgghhKhTSjbMqaqqIhqNYts2iqJgWRbBYHDfdxRCCCGESIGkemgikQg333wzb731Vs0wE4BlWdxzzz2ceOKJ3HrrrTidzpRUVgghhBAinqQCmn/961+8+eabnH322Zx77rl06NABRVFYt24dzz77LC+88AK5ubn8+c9/TlV9hRBCCCFiJDXk9PbbbzNp0iRuvvlmOnfujK7raJpG586dueWWWzjxxBN5++23U1VXIYQQQoi4kgpoDMNgwIABCcsHDRqEaZrJXEIIIYQQYp+SCmhGjx7Nl1/G7o682xdffMGoUaP2+3yfffYZ5557LiNGjKBv376MHz+e22+/ncrKymSqKTKAqirouio7cgshRIZKag7NNddcw7XXXsvUqVM555xz6NChAwDr1q3jueeeY/Pmzfz73/+mrKys1v3y8vLinq+srIz+/fszZcoU8vLyWLFiBffffz8rVqzgySefTKaqoplSVQUvfsxdG4nsWI+zZXv0wvYEFR+mJOMTQoiMkVRAc9xxxwGwfPlyZs6cWavMtqu/TI4//viY+y1dujTu+SZNmlTr7+HDh+N0OvnLX/7Ctm3bKCoqSqa6oplRVQWvWcr2F/6KWVVWc1zz5dLqzFsIOAqxLAlqhBAiEyQV0Fx11VVxd9lOpd29OdFotF6vI5oeNyF2vnl3rWAGwPSXs/ONf1Fw+i0EamUpFkII0VwlFdBcffXVqapHLaZpYhgGK1eu5MEHH+TII4+kuLg4qXPqekpyCDYoTVNr/ZtJ9qftariK6I4NccuiJVtQI1Xobm/c8nSWqc97prYbMrftmdpukLbv+W+qJL31QX044ogj2LZtGwCHH344d999d1LnU1WF/HxfKqrWKHJyPI1dhUZTV9tDmzfXeV/Fisrz3gRlarshc9ueqe0GaXsqpWVAM23aNILBICtXruThhx/miiuu4KmnnkLTtIM6n2XZVFQEUlzL+qdpKjk5Hioqggl3qG6u9qftHmcWqDpYRmyhooIri9JSfz3XNPUy9XnP1HZD5rY9U9sN0vb9aXtOjueAenHSMqDp2bMnUJ3Hpl+/fkyaNImPP/6YY4455qDPaRhN9wVjmlaTrn8y6mp7RPOSM/Q4Kr6LTd6YPfhoIqq3ST9umfq8Z2q7IXPbnqntBml7KtuelgHNnnr06IHD4WD9+vWNXRWRZsKmim/wCWi+PMq/eR0rWIXqziJnxEm4e43Fbx5cj54QQoimJ+0Dmvnz5xONRpOeFCyaJ7/pRO85nlbdRqBYUWzVQUTz4TdkubYQQmSStApopk6dSt++fenRowdut5tly5bxxBNP0KNHDyZMmNDY1RNpyjBsDLygADYgwYwQQmSctApo+vfvz/vvv8+0adOwbZt27dpx2mmncfHFF+N0Ohu7ekIIIYRIU2kV0Fx22WVcdtlljV0NIYQQQjQxmZfRRwghhBDNjgQ0QgghhGjyJKARQgghRJMnAY0QQgghmjwJaIQQQgjR5ElAI4QQQogmTwIaIYQQQjR5EtAIIYQQosmTgEYIIYQQTZ4ENEIIIYRo8iSgEUIIIUSTJwGNEEIIIZo8CWiEEEII0eRJQCOEEEKIJk8CGiGEEEI0eRLQCCGEEKLJk4BGCCGEEE2eBDRCCCGEaPIkoBFCCCFEkycBjRBCCCGaPAlohBBCCNHkSUAjhBBCiCZPAhohhBBCNHkS0AghhBCiyZOARgghhBBNngQ0QgghhGjyJKARQgghRJMnAY0QQgghmjwJaIQQQgjR5ElAI4QQQogmTwIaIYQQQjR5EtAIIYQQosmTgEYIIYQQTZ4ENEIIIYRo8iSgEUIIIUSTJwGNEEIIIZo8CWiEEEII0eRJQCOEEEKIJk8CGiGEEEI0eRLQCCGEEKLJk4BGCCGEEE2eBDRCCCGEaPIkoBFCCCFEkycBjRBCCCGaPL2xK7CnDz74gLfffpvFixdTUVFBx44dmTJlCqeeeiqKojR29YQQQgiRptIqoPnvf/9Lu3btuOGGG8jPz+frr7/mL3/5C1u3bmXq1KmNXT0hhBBCpKm0CmgefvhhCgoKav4eOXIkZWVlPPXUU1x55ZWoqoyQCSGEECJWWkUIewYzu/Xq1YuqqioCgUAj1EgIIYQQTUFa9dDEM2fOHIqKisjKykrqPLqeVrHbftE0tda/mUTannltz9R2Q+a2PVPbDdL2Pf9NlbQOaGbPns3777/PH//4x6TOo6oK+fm+FNWq4eXkeBq7Co1G2p55MrXdkLltz9R2g7Q9lRTbtu2UnjFFtm7dymmnnUaXLl148sknk5o/Y5oWFRXBFNauYWiaSk6Oh4qKIKZpNXZ1gOrgMBi1UAC3U8Uy6+flcyBtVxQwbYiaNrqqoKuQnq/q/ZOOz3tDyNR2Q+a2PVPbDdL2/Wl7To7ngHpx0rKHpqKigksvvZS8vDzuv//+lEwGNoym+4IxTavR66+qCmELZvywji/mbULXVI4a1oHD+rVBx663AGJfbbcVhR0VYV76eDkbtlfQrmUWZ0zoQVGeG6UpRzWkx/PeGDK13ZC5bc/UdoO0PZVtT7uAJhQKcfnll1NZWclLL71EdnZ2Y1dJAGEL/vTwV+wqD9Uce+rdJXw6ewN/umAYOg0fPKiawvzVJfz7hR9rju0sCzF/xU6uPm0Ah3ZvUW89SEIIIdJLWs1GMgyDa6+9ltWrV/P4449TVFTU2FUSVHcPzpy9oVYws9varZUsW1faKBPbwobNo28sjFv22FuLCBsSzAghRKZIq4Dmb3/7G59++ilXXHEFVVVVzJs3r+a/SCTS2NXLWGHT4ot5mxKWfzJnA43RYVoRiBIIGXHLQhGTcr+8ZoQQIlOk1ZDTV199BcAdd9wRUzZz5kyKi4sbukoCUFDQ6+iB0VQFBRp80ElT694OQ91HuRBCiOYjrQKaTz75pLGrIOJwagpHD+/A428vjlt+3GGHNMqyoiyPTl62i7LKcExZjs9JjtdJw4dZQgghGkNaDTmJ9GSaFsP7tKZzu5yYsiG9iuhQlI1lNXzg4NQUfnfWYHStdk+Mpipcd9ZgnNJDI4QQGSOtemhE+tKxuWHKUJZvKGPmD+vRNZVjRx5C+6IstEZaHm2ZNsUtvPznunHM+H4dqzdX0LF1NkcP74jPpWFZmbkUUgghMpEENGK/2HZ1UNPvkHz6HpJfc7AxemZq1cuy8eoKk8d0xrRsVFXBNCzsRq6XEEKIhiUBjTgg6ZrRcndyJlMCGSGEyEgyh0Y0C4pSnS9HSbNpM7qu4nBojV2NWnY/VrIKTAjRnEgPjWjSVFUhYsG2XQE27qiiXcssWhd4cao07nCYqhCM2ixdtYOIYdGnUyE+t4baiHVSFDBQKK0Ms3pTOYW5bjoUZePSlEYfOhRCiGRJQCOaLFVVCEQtbnn8W3aU/rL5aGGum79dOpIsp9ooX9S2qvDt4m088fYi9rz8UUM7cMZR3VEbabKygcIdz85m1cbymmMel85fLx1BUY5LghohRJMmQ06iyTJs+Ndzc2oFMwC7ykPc+exsoo003ae0KsJjb9UOZgA+/mE9S9bswuFo+Ledoqr878NltYIZgGDY4G+Pf0tY9rwSQjRxEtCIJssfNlizuSJu2YZtlfjD8bdFqE9Ol84H36xLWP7W56sJNcIeUyHD4sv5m+OWBUIGm3b4UdJtApIQQhwACWhEkxWOmEmV1wfTsimpCCYsL60MYzVC3p6oYda5AqykIoQqnwZCiCZMPsIymKKAqqlYioK6n7tlu1w6lqpia/W3esftdhC1YWdZEKcz8TWyvU40VcHl0Bg7uJhfHdmNIw4txuXUUBXI9jnqpX510VXo26VFwvLuHfJw6Q3/tnM5NPKyXAnLD2mTgynDTkKIJkwmBWcoW1EoqYrw5uer2LLLT/f2eRw/qhNZTi3u5FBVVQhb8PW8zXzx4yY0TeGoYR3p07kAPUU9DpoGIUvh+wVbmDV3AwoKRwwpZmC3lrhUG3OvDheXrnL5yf1oke/hs7kb+XH5dtq2yOL6c4ewoyyAW1eJmchSz8Jhk1H92vDmrJVUBqK1yjRV4YyjekAjTAp2O1TOOaYnD746P6asZ8cC8rKcjbIflxBCpIpi283/U8w0LUpK/I1djQOm6yr5+T5KS/01ieNSQlX4ccVO7n+l9pebrin8/bLDaFfgiUmgZygKf3v8OzbtqKp1vHenAq49c1BKghpDUfjHf39g7Zba82K6FOfyxylDYq6haSort1Ry21Pf1YpbFAVumDKUHsU5jbJyR9dVyoIG095YyKLVu4DqHpBLT+5HmwIP7CM5YX0975aiMH/lTp79cBlllWF0TWXc4GLOmNCt0bav2FO9vd6bgExte6a2G6Tt+9P2ggIf2n6OHoD00GSkiGnzyBsLY44bps19L//I3y8dWeuF4XTpzPhmXUwwA7BkTQkrNpTR75D8pN6UHo+DmXM3xQQzAKs2ljN/xU5G9S0iFPplom/ItLj3pR9jOmFsG+57ZR73/H97dx4eVXU3cPx772zZSQgJEJKwRBKWAGFfZA0IgliF1yoiaEFRWhGrUgX7KqWPtda3tqAWKah1w7WgqAUKCogiIMYCspQlEAhbQJKQPZOZe94/QgKTmQkBEpLJ/D7P49POPXc5v9zczI97tpmDsNRDP1eHwyDMZuLXE1IoLTNwGooAq4kAk1avf7h0peiVFEVyu0jsDgOLSSfAomM00NmfhRDickgfGj+jaXDqbBFlXr5YT50torjK6KDCUgcbtx/3es4vt2WirnKETH5JGRvSjnktX/d9JsV21zoXFJWRV2j3uH9hcZlbk8+1ZBgK3VAEmjRCLDpmpRrEv8KcDgOLBsEWvXzyQUlmhBCNhCQ0fkhRffOCW6mqftZdhfJw0OXR0Kqtl/JQeqlLXipOIYQQjYckNI2ErmsYmkaxU1FqALrmcV0jpaBlZDBmL+2S0RGBBNlcWyKDbWYGd2/l9dqpveLQvCQPFqsJu4JCh4FdgdXquZUz2GZiSPdYr9cY2iOOoCojnkIDLYQGeR7JFBRgJizI6rFM1zWUrlPsVBQ5FYauX7Kd1mTWKQOKHYoyxSVHhek6YDJR4oQip8Kp6ZjrYXRTVWazjkOVx2GvQRxCCOErpA9NY6BrZJ0rZfEnP5J+/ByaBr06NGfKzZ0JMruv02M1adx3S2cWLXftR2PSNR66PQWbSXMZwmu3OxjWM44NPxzj1Nkil2Pax4WTFO+5/4yh63y2KYN/bTpMYYmDsGAr44Zex8BuLTFVqVNJiYN+yS1Y+91RMrPyXcratAyje1IUJSWuTUg2s8aDP0/hT29tcxug8+Bt3bCZNYwqQ5ErOuwu+WQXP6b/BEBS6wim3ZpMsxCbxyYYQ9PYsusUH3yxn3MFdgJtZm4e2JaRfeM9rs1kseicK3by2qfb2X7gDErBdbHhTLs1mebhNox6mFgPyuPYfiibt1bu4afcEqxmnRF9WjNuSAImJU1PQgjfJqOcGrCa9ATXNDhX4uSxBRvdJk4LD7Xxpwevx9M7DKVpnMkrYfn6g5zKLqJ9bDi3DEkg1GZCeRy2rVNqKDbtPME3O05g0jVu6BNPSmKU5xFOJp2l/97Hl99nuhWNH3odPxvYBqNKTE4NnEpj2+5TbPjPsfJh2z1j6dmxObqmMFe5jG4xkfbf04SHBvDltqMcO1NATLMQhveOIye/hF4doqHKNUoVzFqwkcIS135CNouJFx4eTJDZ9bWWbtJZ830m7/57n1scQ3vEMnlUElqV+O0KHn/5G7f+PWaTxp9nDibMVv38PXUx+sFk0vnh4FkWfPAft7IuCZE8fHsKej3/KZBRH/4Xu7/GDRJ7XYxykvfNPk5pOu+v3edxFtjc/FJ2HPjJY1OHphTNw2z8clwXnvpFHyaPSiLEontMZgAMw8CCYlTvWObc3YsnJvdiQOfmXodrF9udrEtzT2YAPvvmEEVVOviazDqffXOY6c99SXGpg1/9Tzd+Ob4L+UV2pj/3JWu2HMVUJY6iUievLN/Jn5emERRo4fquMYQFW/jLuz+waPmPFJe6Tlxjs5lZ932mWzIDUFrmZMXGdExVJgsscRgsW3fQYxwbfjhGSZWH0WYzs2X3KY+dlR1OxUdf7kcz182EhNUpdRq8uXKPx7If089yrshz52ohhPAV0uTk48qcBrvPz3Xiyff/PU2/TtEey5QCnAYmcGua8aa01Inp/MH2ar4Dz54r8TpPW5nDIL/ITkDIhT4udofBjgPlTUDvrtnHu2tc34hsP/ATN/Zr7ZKBnyssxeFUOJwOvvjuqNt1zhXaCQy7MDtucZmzspnJkz2HsyktM1weisKSMkrLvC+h8FNuMXGRQZWfHYZix4EzXvffm5GD3eH0+NasLpXYneTml3otP3w8j2ZJzWS2YCGEz5I3NNdQXaz9p2saTS6a0r550yAiQi98btYkAI3avbCuc8l1f4ICLqQFZpNGWHD5MgUVbFXehJh0zWVq/uAAM8EXnSM81PV4gAAvHYwvlLtew2LSCQ/2Pv1/kxArZpPrNayXeJsSHOCamphNOuEhAdVeQ7/EL0Jd/J5YzCZ03fuJw0Nt9TGBsRBC1Bp5Q3MtaBolDoPjZwoJtJmICg/EatK8Nu9cDqtJY9zQBFDQPj6CY6cLsFlMREUE8sV3R0ntGUtZNW8YLotJp6TM4NipfEy6TkxUMAFmHeWhI21okJXE+HCG944nPMRGbkEpTcMCOJ1dxJZdJ8uTlYte4ejAuKHXERxkYUTveHILyt8mNAm2sfa7I4y9vp3bMgbBAWbiW4Ry9JRrJ2KAVlEh55ONC8cop5Oxg9qxZfcpj+H9bFACZg0ubpAKsproEB/Bf4/muO3fLDyAsCrrRZWWlDF6QBvWe2lu+9mgBALMusd7YmgahSUOsk7m0/RcKU2CrVh0907dVyLAojOgS0uPK24H2szERYfgB93phBCNmCQ0dczQNFZ8fYjPNx2u/P4OtJl5YnIvWkcHX3VS43QadL0uipXfZvC3f+6o/M63WU08fEd3QoOtl5xqvyYMTWfdtkw++GJ/ZX8dq1nnl//TjW4JkW7rEwVbdR6+ozt/fHMbx05fmGE4IbYJv5nUC5tJw3HRaB/DUMRGh5AUH8Gzb3yH43zTh9mkM2VsJ1pEBqGqXCPIovObSb2Yu3gz2XklldvDQ2w8PrkXITYTpRdNEmgY0KJpELcPb8+HXx5wOdeofq1JiG3i1kFNBx6e0J25SzZzOufCKtqhQRZ++4s+biPCAJqGWrnnpo68tXKvS7Pb4B6tSG7X1GMy49Q0Fny4nV3pF5oPoyMCeWpqX0JtntfXuhzKaXD36I5kZuVz5KIE0GY18fS9fbGZaidxEkKI+iKjnOqQ2azz/f6fePHD7e5lJo0XHx1KgMl7M0BNeoJbLDo7D+fwf++kuZXpGvz1kaGEWq+uZdFsLl8z6fevbXUr0zR44eHBhAe45sbe1mUC6JLQ7Pyomgsx6Toczynht69867EOf5oxkOZhVpe8ydA01n5/jCHdW3H8TAFHTuYR3yKUuOhQvtp+jOE949zWKMotdrDmu6P07dyCg8dyMQxF+7hwfkz/iZ5J0bRqGuT2pkLXNewGnDxbyOGTebRqFkx889BqkwClaxSXGew4cAZ7mUFKYhShgWaPw7zRNf6x8r987WE25qiIQP7wQP9a+ZeHpoFDaZw5V8z+zFyiwwNp16oJtlp6W3i1ZNSH/8Xur3GDxC5rOfmYEofioypvAio4nIrNu05yQ884HI4rbxIqdiiWrfc8CsdQsG7bUe4Yfp3LGkiXy27g9RpKwcpNGdwzugNl9gvXKCx2eExmAH5M/4kiu4MQy0W/qLrOxxvSvdZhxVfp3H9LMhgXflaFpU4+WLuPD9buo3PbpnRqF8mnXx+qfMvRr3NLlyHSJpPOp98cYkPaMdZsySC2eSi6pvHh+bdOGSfyeOi2rm5TEBuGwgzENwumbfMQDENV/ueNZiiCTBqDu7RE0zTsdofXlb9LHAabdro3BQGcySkmO7+U6FDvfX9qSikwoWgZHkBsZExlDA0hmRFCiKslCU0dO5Nb5LUsM6ug2o6aNWEYijM53q9x4myht+/RGnM6jWqvcSq7kLIqzS75lxgGXFzqIMRy8SgnxelqrpGVU0SZ4/wIq8pzXJhob/fhbHYfznY5pqjU4ZLQOAzFyZ/K39QZCre+N1nZRTjOJy+eKKVcmslqoib9l+xlzmqTo+xzJbRoElBrTUJK4Xf/IhRCNH4yyqkO6Rq0adkEgN6dmvPgbd2Ydmsy0RGBAHRuF4mzmv4tJpOOvcxZ7Ss3i1mvvIYnSfER1Y5yMltMmMzVT8tvMeu0jfF+jfax4ViqZAER1bxR0HWNkEDXzrQ2i05CNddIaNXEbWRUaJAVb/mgpuG29IHZpJEUH+H1GtfFhWOpxaUANE3DbDZhvsQIowCrGdv5EVmd2jZlRJ94+nRuUbk8RYvIYOnfIoQQlyBvaOqQWYNpt3YmwGrhQGYOW3adItBm4qHbU1AKmjcN9JzQaBr5pQ7+ufEQWdlFdEloRr/kFgR46LNhUjBxVBJzFrrPrxJoM3N9txhKSz2sOq1rFJY6Wbspg+NnCujYpikDu8V4vIauDG4fkci2Pafc3vbYLCZG9ImnrMokdoE2M706RvP93tNulx7WI5Zgm8llpmBnmZNbhyawcftxt0kCzSadmwe2w1HlbUeg1cTAbq08rgTet3MLAm0mlw7RTofBqH6tWbU5w221cV3X+J+h13kcsXUlnJrGqewiVm/JwF5mkNozjutim2BGuc3PE2DWuWdMR2KahbDr0FkOnzhHs/BAfjulD3syzpavV9X4u7oJIcRVkU7BdaxM05i3ZAsnfnK9/qh+rfl56nXunUR1jR8PZzP//f+4fIcFB5h59lfX0yTA7P6vdU1n37FclqzYVTnap3WLUGbe0Z2oMCuOMqPK7hr7j+fxp7e2uSQoAVYTf5g+gMgQq/s1TDqHT+azaPlOzuSWj/aJjQ5hxs9TaBEe4HENJKeu886qvXy940R5PxSTzvDecdyW2h6Th0lPDJOJQ8dzee3T3WRllzc/tYwM5t6fdaZtTCiah0nfnLrOh1/sZ31aJg6nwqRrDO4Ry8SRSR6voZ9f92rBB/+pvCfREYHMuC2F2Kggr/1cLodT03hrlXsn34RWTZh9dy+3jsq6rpFT5ODJVzZRfNGoLE2DWXf1pFPrcJQfTHgnnST9L3Z/jRsk9rroFCwJTR0yW0x8uO4gn2867LH8+YcG0izYtVnErmDGnzfg8JAgJMVH8Ju7enhcc8dq1Sm0KwqKyzDpGkEBZgJMmsdfljIFD//1K0rs7v074puH8tTUPm5fulA+rX9BqYPCEge6Vv01KuhmncJSJ8WlDoICLATbTDg99CsxmXT+k36W5esPMnZg28rJAnPzS/nsm0NMHJVE5/hwjzPZ6hadwhKD4tIygmxmgmxmjGo6WptMGnYnFNkdKAVBNvP54ddX/0dF0zQyzxbx9OLNHsunjO3E0JQYnBf9zAxN4w9vbuPwCfdO1FazzouPDcVaB5PtNTTyB97/YvfXuEFil1FOPqa4zMn6H455LV+3LZNfjOlAUVF5k5CmaRw5mecxmQHYdzSHkjInQR76u9jtBhYgIvD8La2mA+vpnGKPyQzA0az88g67VvcZcktLHVjgwhDtGnSSNRwGgSaNwKDyPjOekhko77C78tsMjmbls3DZTrfylZsySIpL8dgbyCgzCDRReY3qkhkAp1NhAkIrY1S1NuW/btJYtTnDa/nqLUfon9zC5cErtjs9JjNQviTEyZ8KaRMdLK1OQghRDekUXKeqf3tRvkbQha9oTYOyS3wZX23nUE0rX/+pOp4WuqxriupjtzsMt+HUDZW9mpFNnmK81D21V/k9EUII4U4SmjpkNev06uh5YUiAQSmtXOaHMQxV7WiiFpFBBF5i/aJLUaq8X4q3UTdNwwLc1ie6FiwmjUEprbyWD+neCvNVDnG/JpRiWM84r8UDusRgq/KGrWI5DE90DeKah9brsgQms44DjTLKJwzU6mKxKSGEuEqS0NQh5XBy58gOBNrck5AObSKIjQrBqNJxNcCsc8vgdm776xr8cnxXrNXMLFxTNrPGhBGJbts1DaaP64Kt+vUY64TTYTCwawxREe5f7M2bBtG7U/Na6eNS15xORWJcOG1ahLqVNQmxMmZAG5f+M1B+z6eP7+pxUcrxw9q7JUDXiqaVd3BevvEQD//1Kx54bh1//WA72UVleB0vL4QQ9UQ6Bdcxk0WnoMTJ8vUHSdt3mkCbmVH9WnN9lxhMyvMXtKFpHDh2jn+uO8DZcyW0jwvnzpFJhNfi8F1D08g4lc8HX+zndE4R7WKacOfIJCJDbWj19Cvh1DROni3iuz2n2LLrJBoa/bu0pGfH5sREBnv9eTU0mgYONL7efpx/bz1KmcNgYLeWjBnQlkCz+9pPFQflFNp5d80+Dh7LJSo8iNtHtCehZVi93o9n39jG4SozPpt0jf97aBARQZZafXMknST9L3Z/jRskdhnldIXqM6GpoFtMlNidaBoEW03YvXTKrWAyaTiUhsmsg2GgnEatdwrVdQ2HKu8zYzZp6Ip6a9owmXU+//YIy9YfoGeH5qQkRqGA/+w7zQ/7TjNxZBKjesf51INvMumUOhVKKWxm3ePQ9otpGihNw2lAUJAV5XBc8vekrui6xuGsAuZ5WL8LoFv7KGbe1rVWky35A+9/sftr3CCxyygnH2aUOSuH3tbkS8rpVFjMGhFNAstveh3kGYah0DnfemCoeu1za3cYbNubhaFg294stu3NcinfujuLYT1ifaqN1Ok0yh8wjUsmM3D+5dv55Ccs2EpOjocJEa8Rs1ln6+5TXst3HjyDQ8G1720lhBCe+dL3g2jEKua18SYk0IIunVGvGaWonAvIkyCbWcZdCSEaFEloLpOuaziAvFInZ/LtlBhKOkjWApMGtwxO8Fr+s0Ht0H1l3HYjUFbm5PquMV7Lxwxoi80sv/dCiIZDmpwug65rFNgNnntrG8dOF5Rv02Bk39bcltoe3cNU+6JmDEOREBPG4O6t2Pgf1yUDhveOI755iCzQeI0F20zcf2syiz/Z5bL9utgmjOzb2m20lhBC1CdJaC6D3YCnF2+uXC8Jypf+Wb3lCOFhAYzuEyd/5K+CrhSTb+zAzQPb8e2PJ9E1GNC1JU2CrB6XexB1S1OKvh2b0yWhGVt2nSSv0E7vTi1oGRmEGUPelwkhGhRJaGpI0zROnCl0SWYu9unGdIZ1b4VF3sJfFZNSNAux8POh7QgNDfTLEQANiaYUwRadUb3jy4ejOwyUqt8O5EII4YkkNDWk6xrHzhR4LS8qcWB3GFgs0i3paimFJDENjOMSS3IIIUR9a3AJzZEjR3jttdfYsWMHBw4coF27dnz++ef1XS0MQxEXHeK1PDjAjLWeZnStoGkaFouOUuWdOoUQQgh/0eASmgMHDvDVV1/RrVs3DMOo1zVsLqaUomVkMM3CA/gp173Z6dah1xFg0eutD40TjazsIjbtPEmA1cSglFaEBVmk74kQQgi/0OASmtTUVEaMGAHA7Nmz2bVr1yWOuHasOsyb1p//eyeNjPPTwZt0jTED2jCsR6v6S2Y0jfkfbGf3obOV2z7+Kp3xQxMY07+NJDVCCCEavQaX0Oh6w+2DYhjlHSR/e09vikod2MsMQoIs5fNx1NOQYrNZ59udJ12SmQrLN6TTL7klUaHWWl82QQghhGhIGlxCU1fMtdi/xaaDzVxl0vc6mFyvYg2L6taysDsVn2867LV8zdajTL2pg891sq1J7I2Vv8bur3GD/8bur3GDxH7x/9YWv0hodF0jIiK4vqtxxcLCAr2W/XSumKISh9fy/CI7gUE2zD760FQXe2Pnr7H7a9zgv7H7a9wgsdcmv0hoDEORl1dU39W4bCaTTlhYIHl5xTi9LG6oaxo9OkSzIe2Yx/KB3WIoLCjxuVl2axJ7Y+Wvsftr3OC/sftr3CCx1yT2sLBAWW3bE19rcrmY02lUW/+fp7Zn848nKa2yinfLZsEkxUfUaHXvhupSsTdm/hq7v8YN/hu7v8YNEnttxu6b7RDCRZBF588PDaJfcgtMukagzczNA9syb1o/mblYCCGEX/CbNzSNmTIUIVad+3+WzJSxndDQsJo0DKestyOEEMI/NLiEpri4mK+++gqA48ePU1BQwOrVqwHo06cPTZs2rc/qNVhKAcqgfOyVwnBKKiOEEMJ/NLiE5uzZszz88MMu2yo+v/XWW/Tt27c+qiWEEEKIBqzBJTSxsbHs27evvqshhBBCCB8inYKFEEII4fMkoRFCCCGEz5OERgghhBA+TxIaIYQQQvg8SWiEEEII4fMkoRFCCCGEz5OERgghhBA+TxIaIYQQQvg8TSnV6OfIV0phGL4Zpsmk+93S8hUkdv+L3V/jBv+N3V/jBon9UrHruoam1XyFZb9IaIQQQgjRuEmTkxBCCCF8niQ0QgghhPB5ktAIIYQQwudJQiOEEEIInycJjRBCCCF8niQ0QgghhPB5ktAIIYQQwudJQiOEEEIInycJjRBCCCF8niQ0QgghhPB5ktAIIYQQwudJQiOEEEIInycJjRBCCCF8nrm+KyCgsLCQ0aNHk5WVxT//+U+6dOnidd/U1FSOHz/utn3nzp3YbLa6rGatWL58OXPmzHHbPm3aNGbNmuX1OKUUS5Ys4d133yU7O5uOHTsyZ84cUlJS6rC2tetKY/f1e17h448/5s033yQ9PZ2goCC6dOnCyy+/TEBAgNdjPvroI1599VVOnDhB27ZteeSRRxg2bNg1rHXtuNzYJ0+ezHfffee2feXKlSQkJNR1dWuFtxgA/vKXv3DTTTd5LPP1Z/1K424sz/mXX37JokWLOHjwIMHBwfTs2ZNZs2YRFxdX7XG1cd8loWkAFi5ciNPprPH+o0aNYurUqS7brFZrbVerTr366quEhoZWfm7evHm1+y9ZsoQXX3yRWbNmkZSUxNKlS5k6dSorVqy45IPS0Fxu7OD79/yVV15hyZIlTJ8+nZSUFHJycti8eXO1v/f/+te/eOqpp5g+fTr9+vVj5cqVzJgxg6VLl/rMlxtcWewAPXr04IknnnDZFhsbW5dVrVVz586loKDAZdubb77JmjVr6N+/v9fjfP1Zv9K4wfef861btzJjxgxuvfVWHnnkEXJzc1mwYAFTp07ls88+q/YfL7Vy35WoVwcPHlQpKSnqvffeU4mJiWrnzp3V7j9s2DA1b968a1S72rds2TKVmJiozp49W+NjSkpKVI8ePdQLL7xQua20tFQNGzZMzZ07tw5qWTeuJHalfP+ep6enq06dOqkNGzZc1nEjR45Ujz76qMu2O+64Q9133321Wb06daWxT5o0Sd1///11VKv6k5qaqqZNm+a1vLE861VdKm6lfP85V0qpp556SqWmpirDMCq3bd68WSUmJqpt27Z5Pa627rv0oalnzzzzDBMmTKBt27b1XZUG64cffqCgoIDRo0dXbrNardxwww1s3LixHmsmamL58uXExsYyZMiQGh+TmZlJRkaGyz0HGDNmDJs3b8Zut9d2NevElcTeWP3www8cO3aMm2++udp9GtuzXpO4GwuHw0FwcDCaplVuq3gbrZTyelxt3XdJaOrR6tWr2b9/Pw8++OBlHffZZ5+RnJxM9+7dmTZtGvv27aujGtadsWPH0rFjR4YPH87f//73al+/Hzp0CIB27dq5bE9ISODEiROUlJTUaV1r2+XEXsGX7/mOHTtITExk4cKF9O/fn+TkZCZMmMCOHTu8HlNxz6sm+gkJCZSVlZGZmVmnda4tVxJ7he+++46UlBS6dOnCpEmT2LZt2zWocd35/PPPCQoKYvjw4V73aWzPOtQs7gq+/JwDjB8/nvT0dJYuXUp+fj6ZmZn85S9/oVOnTvTo0cPrcbV136UPTT0pLi7mueee45FHHiEkJKTGx6WmptK1a1diYmLIzMxk0aJFTJw4kU8++cQn2pejoqJ46KGH6NatG5qmsW7dOubPn09WVhZPP/20x2Py8vKwWq1uHePCwsJQSnHu3Llq22YbiiuJHXz/np85c4Zdu3axf/9+5s6dS2BgIIsWLWLq1KmsWbOGyMhIt2POnTsHlN/ji1V8rihv6K4kdoDevXtzyy230KZNG06fPs1rr73GlClTePvtt+nevfs1juLqORwOVq1aRWpqKkFBQV73ayzPeoWaxg2+/5wD9OrVi5dffpnHHnuM3//+9wB07NiRV199FZPJ5PW4Wrvvl91IJmrFCy+8oMaPH1/Z1rhly5Ya9aGpKisrS/Xo0cOn25efe+451bFjR5WVleWxfOHChSo5Odlt+6pVq1RiYqI6depUXVexzlwqdk987Z6PHDlSJSYmqr1791Zuy8nJUd27d1fz58/3eMyKFStUYmKiOn36tMv2nTt3qsTERJWWllanda4tVxK7J4WFhWrYsGE+1X/oYhs2bFCJiYlq3bp11e7X2J71msbtia8950oplZaWpnr16qX++Mc/qs2bN6tVq1apm2++WY0bN04VFxd7Pa627rs0OdWD48eP8/rrrzNz5kzy8/PJy8ujqKgIgKKiIgoLC2t8rujoaHr27Mnu3bvrqrp1bvTo0TidTvbu3euxPCwsDLvdTmlpqcv2vLw8NE2jSZMm16KadeJSsXvia/c8LCyM8PBwOnToULktPDycTp06cfDgQY/HVNzT/Px8l+15eXku5Q3dlcTuSVBQEEOGDPGZe17V559/Tnh4OAMHDqx2v8b2rNc0bk987TmH8j6h/fr1Y/bs2fTr148bb7yRxYsXs2fPHlasWOH1uNq675LQ1INjx45RVlbG/fffT+/evenduzfTp08H4O6772bKlCn1XMOGpaJd9fDhwy7bDx06RExMjE+9gvZH1113ndeyqn/AKlTc84q29QqHDh3CYrH4zCv4K4m9sSkpKeGLL77gxhtvxGKxVLtvY3rWLyfuxiI9Pd0leQdo0aIFERERHD161OtxtXXfJaGpBx07duStt95y+a9iwrV58+Yxd+7cGp8rKyuLtLS0aifja+hWrlyJyWSiU6dOHst79OhBSEgIq1atqtxWVlbGmjVrGDx48LWqZp24VOye+No9HzZsGLm5uS5voXJycti9ezedO3f2eExcXBxt2rRh9erVLttXrlxJ//79fWZujiuJ3ZOioiI2bNjgM/f8YuvWraOoqKhGo3wa07N+OXF74mvPOUBMTAx79uxx2Xb8+HFycnJo1aqV1+Nq675Lp+B6EBYWRt++fT2Wde7cufIP3T333MOJEydYu3YtUP76cv369QwZMoTo6GgyMzNZvHgxJpPJZ97q3HvvvfTt25ekpCSgfFbJDz/8kLvvvpuoqCjAPW6bzcYDDzzASy+9RNOmTUlMTOS9994jNzeXe++9t95iuVxXEntjuOcjRoygS5cuzJw5k0ceeQSbzcbixYuxWq1MnDgRgCeffJJPPvnE5Y/hQw89xKxZs4iPj6dv376sXLmSnTt38s4779RXKJftSmL//vvvefXVV7nhhhto1aoVp0+f5h//+AdnzpxhwYIF9RnOFfnss8+IiYmhZ8+ebmWN9VmHy4u7MTznABMmTODZZ5/lmWeeITU1ldzcXF555RUiIyNdhmTX1X2XhKYBMwzDZUhvbGwsp0+f5tlnnyU/P5/Q0FD69evHzJkzfeYVfNu2bVm2bBmnTp3CMAzatGnDk08+yeTJkyv3qRo3lC8PoJTi9ddfr5wW+7XXXvOZuOHKYm8M91zXdRYvXswf//hHnn76acrKyujVqxdLly6tTOQ83fOxY8dSXFzMkiVLWLx4MW3btuXll1/2qVE+VxJ7VFQUZWVl/PWvfyU3N5fAwEC6d+/OvHnz6Nq1a32FckXOnTvH119/zT333OMyN0mFxvqsX27cjeE5h/IuE1arlffee49ly5YRHBxMSkoK8+fPJyIionK/urrvmlLVzHYjhBBCCOEDpA+NEEIIIXyeJDRCCCGE8HmS0AghhBDC50lCI4QQQgifJwmNEEIIIXyeJDRCCCGE8HmS0AghhBDC50lCI4QQQgifJwmNEKJBmTx5ssvsyXV1TH146aWXSEpKIjs7u76rIkSjIwmNEELUskWLFvHFF1/UdzWE8CuS0AghRC37+9//LgmNENeYJDRCCCGE8HmS0AjhhwoKCvjDH/5AamoqycnJ9O/fnylTprB79+7KfXbs2MG9995Lz5496datG5MmTSItLc3lPBV9QtLT03n44Yfp0aMHffv25ZlnnqG0tNRl32XLlnH33XfTv39/kpOTGTNmDO+++26dxWi323nxxRe54YYbSE5OZsiQITz//PPY7XaX/ZKSkvj973/PF198wdixY0lOTuamm25i48aNbufcunUr48ePp0uXLowYMYL333+/8mdw8fmKior4+OOPSUpKIikpidmzZ7ucJz8/n9mzZ9OrVy969uzJnDlzKC4urpsfhBB+wlzfFRBCXHtz587l3//+N5MmTSIhIYHc3FzS0tJIT0+nc+fObN68mWnTppGcnMyMGTPQNI3ly5dzzz338O6779K1a1eX8/3617+mVatWPPbYY2zfvp23336bvLw8nn/++cp93nvvPdq3b09qaipms5n169czb948lFLcddddtRqfYRj88pe/JC0tjdtvv52EhAT279/Pm2++SUZGBgsXLnTZPy0tjTVr1jBx4kSCg4N5++23mTlzJuvXryciIgKAPXv2cN999xEVFcVDDz2EYRj87W9/o2nTpi7nev755/nf//1funbtyu233w5AfHy8288rNjaWRx99lD179vDRRx/RtGlTfvOb39Tqz0EIv6KEEH6nZ8+eat68eR7LDMNQI0eOVFOnTlWGYVRuLy4uVqmpqWrKlCmV21588UWVmJiopk+f7nKO3/3udyoxMVHt3bvX5fiqpk6dqoYPH+6ybdKkSWrSpEmXFU/VYz755BPVoUMHtW3bNpf93nvvPZWYmKjS0tIqtyUmJqrOnTurI0eOVG7bu3evSkxMVG+//XbltgceeEB169ZNnTp1qnJbRkaG6tSpk0pMTHS5TkpKinriiSfc6lnx85ozZ47L9gcffFD16dPnsmIWQriSJich/FBYWBg7duwgKyvLrWzv3r1kZGRw8803k5OTQ3Z2NtnZ2RQVFdG/f3+2bduGYRgux1R9wzJp0iQAl2abgICAyv+fn59PdnY2ffr0ITMzk/z8/NoMj9WrV5OQkEC7du0q65+dnU2/fv2A8qajiw0YMMDlLUqHDh0ICQkhMzMTAKfTyebNmxk+fDjNmzev3K9169YMGjTosus3YcIEl8+9evUiNzeXgoKCyz6XEKKcNDkJ4YdmzZrF7NmzGTp0KJ07d2bIkCHceuutxMXFkZGRAcATTzzh9fj8/HyaNGlS+bl169Yu5fHx8ei6zrFjxyq3paWl8dJLL7F9+3a3/iL5+fmEhobWQmTljhw5Qnp6Ov379/dYfvbsWZfPLVu2dNunSZMm5OXlVe5fUlLiFie4x14TMTExLp/DwsIAOHfuHCEhIZd9PiGEJDRC+KUxY8bQq1cv1q5dy6ZNm3jttddYsmQJL730EkopAB5//HE6duzo8figoKBqz69pmsvno0eP8otf/IJ27doxe/ZsWrZsicVi4auvvuKNN95we+NztQzDIDExkTlz5ngsb9Gihctnk8nkcb+Kn0Vt03XPL8fr6npC+ANJaITwU9HR0dx1113cddddnD17lnHjxrFo0aLKJCAkJIQBAwbU6FxHjhwhLi7O5bNhGMTGxgKwbt067HY7r7zyisvbiapNP7UlPj6e//73v/Tv398tuboSkZGR2Gw2jhw54lbmaZsQ4tqTPjRC+Bmn0+nWZyUyMpLo6GjsdjvJycnEx8fz+uuvU1hY6Ha8p2n7ly5d6vL5nXfeAWDw4MHAhTcgF7+ByM/PZ9myZVcXjBejR48mKyuLDz/80K2spKSEoqKiyzqfyWRiwIABfPnlly79jo4cOcLXX3/ttn9QUFBlc5UQ4tqQNzRC+JnCwkKGDBnCqFGj6NChA0FBQXz77bf8+OOPzJ49G13XeeaZZ5g2bRpjx45l/PjxNG/enKysLLZu3UpISAiLFi1yOeexY8eYPn06gwYNYvv27Xz66aeMHTuWDh06AHD99ddjsViYPn06EyZMoLCwkI8++ojIyEjOnDlT6zHecsstrFq1irlz57J161Z69OiB0+nk0KFDrF69mldffZUuXbpc1jlnzJjBN998w5133smdd96JYRi88847tG/fnr1797rsWzH0/R//+AfR0dHExsbSrVu32gxRCFGFJDRC+JmAgADuvPNONm3axJo1a1BKER8fz9y5c5k4cSIAffv25YMPPmDhwoW88847FBUVERUVRdeuXbnjjjvczjl//nwWLFjACy+8gNlsZtKkSTz++OOV5e3atePFF19k/vz5/OlPf6JZs2bceeedNG3alCeffLLWY9R1nb/97W+88cYbrFixgrVr1xIYGEhsbCyTJ0+mbdu2l33O5ORklixZwvPPP8+CBQto2bIlM2fO5NChQxw6dMhl39mzZ/P0008zf/58SkpKGDdunCQ0QtQxTUkvNCHEFXrppZd4+eWX2bx5s9sEc/7iV7/6FQcPHmTNmjX1XRUh/Jr0oRFCiBoqKSlx+ZyRkcHGjRvp06dPPdVICFFBmpyEEA1WdnY2TqfTa7nFYiE8PPya1WfEiBGMGzeOuLg4jh8/zvvvv4/FYuG+++67ZnUQQngmCY0QosG67bbbOH78uNfyPn368Pbbb1+z+gwaNIh//etfnDlzBqvVSkpKCo8++iht2rS5ZnUQQngmfWiEEA1WWlqa26rdFwsLCyM5Ofka1kgI0VBJQiOEEEIInyedgoUQQgjh8yShEUIIIYTPk4RGCCGEED5PEhohhBBC+DxJaIQQQgjh8yShEUIIIYTPk4RGCCGEED7v/wFZivoJI1qkFwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#try with sepal length and petal width\n",
        "sns.scatterplot(x='sepal_length', y='petal_width', hue='species', data=iris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "KnNvX_k5D606",
        "outputId": "d6d1e23f-ff87-4384-8e68-43954787b383"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sepal_length', ylabel='petal_width'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*  **Count Plot**\n",
        "\n",
        "*Titanic datasets*"
      ],
      "metadata": {
        "id": "EpLQ2od0EXYC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = sns.load_dataset('titanic')\n",
        "titanic.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RX7Q9tVvEeDH",
        "outputId": "0aaa74b8-da93-4458-89c7-eb81ea9558ed"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
            ],
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ca77f43d-edf0-401f-a44c-45aceca4e39f');\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",
              "\n",
              "\n",
              "<div id=\"df-b42ab255-41c1-4054-a0aa-eeaa019d5b2b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b42ab255-41c1-4054-a0aa-eeaa019d5b2b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b42ab255-41c1-4054-a0aa-eeaa019d5b2b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count total people according to class**\n"
      ],
      "metadata": {
        "id": "VUzTqSBdFExm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='class',data=titanic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "MI522-70EsbI",
        "outputId": "85a909d4-a423-4a51-b01c-3772e9e06bf8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='class', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predict whether a person survived or not**"
      ],
      "metadata": {
        "id": "bVIevxNRF1Yg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='alive', data=titanic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "heejxirfF2Uo",
        "outputId": "db1e5f89-ba41-45cc-aaaf-739fb1f5d1c9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='alive', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   **Bar chart**\n",
        "\n",
        "**Getting survival statistic based one sex and class**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "5fgx2_PAHE4h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='sex', y='survived', hue='class' , data=titanic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "oFSkDBlFHLZv",
        "outputId": "b3b60a6e-763f-49a8-81b6-71cb6d09d67b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sex', ylabel='survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***House Price***"
      ],
      "metadata": {
        "id": "IMwVQ4jBIRLj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "housing  = fetch_california_housing()\n",
        "print(housing) #dtype is np array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xCAAXQvdIcIm",
        "outputId": "6df78609-edb5-4cb9-e0ce-9e0749952d57"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
            "          37.88      , -122.23      ],\n",
            "       [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
            "          37.86      , -122.22      ],\n",
            "       [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
            "          37.85      , -122.24      ],\n",
            "       ...,\n",
            "       [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
            "          39.43      , -121.22      ],\n",
            "       [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
            "          39.43      , -121.32      ],\n",
            "       [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
            "          39.37      , -121.24      ]]), 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]), 'frame': None, 'target_names': ['MedHouseVal'], 'feature_names': ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], 'DESCR': '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 20640\\n\\n    :Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n    :Attribute Information:\\n        - MedInc        median income in block group\\n        - HouseAge      median house age in block group\\n        - AveRooms      average number of rooms per household\\n        - AveBedrms     average number of bedrooms per household\\n        - Population    block group population\\n        - AveOccup      average number of household members\\n        - Latitude      block group latitude\\n        - Longitude     block group longitude\\n\\n    :Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nA household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surprisingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house = pd.DataFrame(housing.data, columns=housing.feature_names) #converting into pandas df\n",
        "house.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xYB0a5HzHbTl",
        "outputId": "a00c488d-92dc-4ccb-bbd2-0f95562d45b6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  \n",
              "0    -122.23  \n",
              "1    -122.22  \n",
              "2    -122.24  \n",
              "3    -122.25  \n",
              "4    -122.25  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b952b4a-4514-44f8-9b6e-76a33a817edd\" 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>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.3252</td>\n",
              "      <td>41.0</td>\n",
              "      <td>6.984127</td>\n",
              "      <td>1.023810</td>\n",
              "      <td>322.0</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>37.88</td>\n",
              "      <td>-122.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.3014</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6.238137</td>\n",
              "      <td>0.971880</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>2.109842</td>\n",
              "      <td>37.86</td>\n",
              "      <td>-122.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.2574</td>\n",
              "      <td>52.0</td>\n",
              "      <td>8.288136</td>\n",
              "      <td>1.073446</td>\n",
              "      <td>496.0</td>\n",
              "      <td>2.802260</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.6431</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5.817352</td>\n",
              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
              "      <td>52.0</td>\n",
              "      <td>6.281853</td>\n",
              "      <td>1.081081</td>\n",
              "      <td>565.0</td>\n",
              "      <td>2.181467</td>\n",
              "      <td>37.85</td>\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "house",
              "summary": "{\n  \"name\": \"house\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452688,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.58555761211165,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243187,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.473910856795466,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.462121765341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213618,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571153,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025882,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house['Price'] = housing.target #adding data\n",
        "house.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MLT9KlrPJGWa",
        "outputId": "ccb21170-23a8-4222-9ef8-2cc0359ce724"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
              "\n",
              "   Longitude  Price  \n",
              "0    -122.23  4.526  \n",
              "1    -122.22  3.585  \n",
              "2    -122.24  3.521  \n",
              "3    -122.25  3.413  \n",
              "4    -122.25  3.422  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
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              "      <td>322.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "      <td>1.073059</td>\n",
              "      <td>558.0</td>\n",
              "      <td>2.547945</td>\n",
              "      <td>37.85</td>\n",
              "      <td>-122.25</td>\n",
              "      <td>3.413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.8462</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11fc79ef-cdc3-47da-9d0b-b3fcf4c3c5c7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "house",
              "summary": "{\n  \"name\": \"house\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452688,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.58555761211165,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243187,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.473910856795466,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.462121765341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213618,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571153,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025882,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441387,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Distribution Plot\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3Tv3CYxwKtyb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(house['Price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "_DEOADQxKxnd",
        "outputId": "699308b8-1109-46ff-c3a0-6e5520af1a2d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7afaf529feb0>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation**\n",
        "\n",
        "\n",
        "*   Positive Correlation\n",
        "*   Negative Correlation\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Heat Map**\n",
        "\n",
        "If crime rate as CRIM increases then price will decrease. This is Negatively Correlative.\n",
        "\n",
        "If RM as room increases then price will increase. This is Positively Correlative.\n",
        "\n",
        "\n",
        " The first value as relative is opposite than the absolute value.\n",
        "\n"
      ],
      "metadata": {
        "id": "C11RbJ-NLf_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = house.corr() #refer from pd df.\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(correlation, cbar=True, square=True, fmt='.1f',annot=True, annot_kws={'size':8}, cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "id": "OyZ_y3HXL40B",
        "outputId": "c2d4df61-f73a-4026-bc6a-222415921f39"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1u8iJiypLzBs"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}