Task_1.ipynb

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        "<a href=\"https://colab.research.google.com/github/sanzidaanee/artificial-inteligence/blob/main/Task_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "V5xl-085UuiK"
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      "source": [
        "### [Task 1: Exploratory Data Analysis](https://)"
      ],
      "metadata": {
        "id": "ERJvx1QVU0hv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import all libraries\n",
        "!pip install seaborn\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
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      "execution_count": 51,
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        {
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          "text": [
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.10/dist-packages (from seaborn) (1.25.2)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (2.0.3)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
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            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#upload dataset from google drive\n",
        "from google.colab import drive\n",
        "drive.mount ('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import dataset\n",
        "path ='/content/drive/MyDrive/Data_Sci_Intern/Forage/Cognizant/CSV/sample_sales_data.csv'\n",
        "df = pd.read_csv(path)\n"
      ],
      "metadata": {
        "id": "MCC-J4cc1_Vp"
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      "execution_count": 4,
      "outputs": []
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      "source": [
        "#drop columns\n",
        "df.drop(columns=[\"Unnamed: 0\"], inplace=True, errors='ignore')"
      ],
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        "id": "5EzAVA5CIhZT"
      },
      "execution_count": 5,
      "outputs": []
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      "source": [
        "df.head()"
      ],
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          "height": 380
        },
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        "outputId": "fd2b2569-c2e4-40ca-f777-2e67302ca848"
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      "execution_count": 6,
      "outputs": [
        {
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              "                         transaction_id            timestamp  \\\n",
              "0  a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 09:51:38   \n",
              "1  931ad550-09e8-4da6-beaa-8c9d17be9c60  2022-03-06 10:33:59   \n",
              "2  ae133534-6f61-4cd6-b6b8-d1c1d8d90aea  2022-03-04 17:20:21   \n",
              "3  157cebd9-aaf0-475d-8a11-7c8e0f5b76e4  2022-03-02 17:23:58   \n",
              "4  a81a6cd3-5e0c-44a2-826c-aea43e46c514  2022-03-05 14:32:43   \n",
              "\n",
              "                             product_id category customer_type  unit_price  \\\n",
              "0  3bc6c1ea-0198-46de-9ffd-514ae3338713    fruit          gold        3.99   \n",
              "1  ad81b46c-bf38-41cf-9b54-5fe7f5eba93e    fruit      standard        3.99   \n",
              "2  7c55cbd4-f306-4c04-a030-628cbe7867c1    fruit       premium        0.19   \n",
              "3  80da8348-1707-403f-8be7-9e6deeccc883    fruit          gold        0.19   \n",
              "4  7f5e86e6-f06f-45f6-bf44-27b095c9ad1d    fruit         basic        4.49   \n",
              "\n",
              "   quantity  total payment_type  \n",
              "0         2   7.98     e-wallet  \n",
              "1         1   3.99     e-wallet  \n",
              "2         2   0.38     e-wallet  \n",
              "3         4   0.76     e-wallet  \n",
              "4         2   8.98   debit card  "
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              "      <th>transaction_id</th>\n",
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              "      <td>2022-03-02 09:51:38</td>\n",
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              "      <th>1</th>\n",
              "      <td>931ad550-09e8-4da6-beaa-8c9d17be9c60</td>\n",
              "      <td>2022-03-06 10:33:59</td>\n",
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              "      <td>0.38</td>\n",
              "      <td>e-wallet</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2022-03-02 17:23:58</td>\n",
              "      <td>80da8348-1707-403f-8be7-9e6deeccc883</td>\n",
              "      <td>fruit</td>\n",
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              "      <td>0.76</td>\n",
              "      <td>e-wallet</td>\n",
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              "      <th>4</th>\n",
              "      <td>a81a6cd3-5e0c-44a2-826c-aea43e46c514</td>\n",
              "      <td>2022-03-05 14:32:43</td>\n",
              "      <td>7f5e86e6-f06f-45f6-bf44-27b095c9ad1d</td>\n",
              "      <td>fruit</td>\n",
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              "      <td>4.49</td>\n",
              "      <td>2</td>\n",
              "      <td>8.98</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7829,\n  \"fields\": [\n    {\n      \"column\": \"transaction_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7829,\n        \"samples\": [\n          \"12cfc21c-c2ef-49d9-a36b-96744669dda0\",\n          \"638011d7-98c3-4128-8a25-c94466c9a2a9\",\n          \"550c9a72-3263-4697-b7b9-10a464dbad79\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 7738,\n        \"samples\": [\n          \"2022-03-03 12:10:18\",\n          \"2022-03-06 12:21:38\",\n          \"2022-03-03 18:08:37\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 300,\n        \"samples\": [\n          \"74808ebf-57da-45d2-ab84-cb2cf2a2a797\",\n          \"fbeb39cc-8cd0-4143-bdfb-77658a02dec9\",\n          \"3bc41fec-bf85-4354-b832-d56751d2f283\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"fruit\",\n          \"baked goods\",\n          \"beverages\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"standard\",\n          \"non-member\",\n          \"premium\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.388088406528456,\n        \"min\": 0.19,\n        \"max\": 23.99,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          19.19,\n          23.99,\n          3.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.446679898774388,\n        \"min\": 0.19,\n        \"max\": 95.96,\n        \"num_unique_values\": 256,\n        \"samples\": [\n          43.98,\n          5.96,\n          18.57\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"payment_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"debit card\",\n          \"credit card\",\n          \"e-wallet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "pVlEQpH8O6ja",
        "outputId": "4858882c-acab-47bb-88c2-589e875e1168"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            transaction_id            timestamp  \\\n",
              "7824  6c19b9fc-f86d-4526-9dfe-d8027a4d13ee  2022-03-03 18:22:09   \n",
              "7825  1c69824b-e399-4b79-a5e7-04a3a7db0681  2022-03-04 19:14:46   \n",
              "7826  79aee7d6-1405-4345-9a15-92541e9e1e74  2022-03-03 14:00:09   \n",
              "7827  e5cc4f88-e5b7-4ad5-bc1b-12a828a14f55  2022-03-04 15:11:38   \n",
              "7828  afd70b4f-ee21-402d-8d8f-0d9e13c2bea6  2022-03-06 13:50:36   \n",
              "\n",
              "                                product_id           category customer_type  \\\n",
              "7824  bc6187a9-d508-482b-9ca6-590d1cc7524f  cleaning products         basic   \n",
              "7825  707e4237-191c-4cc9-85af-383a6c1cb2ab  cleaning products      standard   \n",
              "7826  a9325c1a-2715-41df-b7f4-3078fa5ecd97  cleaning products         basic   \n",
              "7827  707e4237-191c-4cc9-85af-383a6c1cb2ab  cleaning products         basic   \n",
              "7828  d6ccd088-11be-4c25-aa1f-ea87c01a04db  cleaning products    non-member   \n",
              "\n",
              "      unit_price  quantity  total payment_type  \n",
              "7824       14.19         2  28.38     e-wallet  \n",
              "7825       16.99         1  16.99  credit card  \n",
              "7826       14.19         2  28.38  credit card  \n",
              "7827       16.99         4  67.96         cash  \n",
              "7828       14.99         4  59.96   debit card  "
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>transaction_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>product_id</th>\n",
              "      <th>category</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>unit_price</th>\n",
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              "      <th>7824</th>\n",
              "      <td>6c19b9fc-f86d-4526-9dfe-d8027a4d13ee</td>\n",
              "      <td>2022-03-03 18:22:09</td>\n",
              "      <td>bc6187a9-d508-482b-9ca6-590d1cc7524f</td>\n",
              "      <td>cleaning products</td>\n",
              "      <td>basic</td>\n",
              "      <td>14.19</td>\n",
              "      <td>2</td>\n",
              "      <td>28.38</td>\n",
              "      <td>e-wallet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7825</th>\n",
              "      <td>1c69824b-e399-4b79-a5e7-04a3a7db0681</td>\n",
              "      <td>2022-03-04 19:14:46</td>\n",
              "      <td>707e4237-191c-4cc9-85af-383a6c1cb2ab</td>\n",
              "      <td>cleaning products</td>\n",
              "      <td>standard</td>\n",
              "      <td>16.99</td>\n",
              "      <td>1</td>\n",
              "      <td>16.99</td>\n",
              "      <td>credit card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7826</th>\n",
              "      <td>79aee7d6-1405-4345-9a15-92541e9e1e74</td>\n",
              "      <td>2022-03-03 14:00:09</td>\n",
              "      <td>a9325c1a-2715-41df-b7f4-3078fa5ecd97</td>\n",
              "      <td>cleaning products</td>\n",
              "      <td>basic</td>\n",
              "      <td>14.19</td>\n",
              "      <td>2</td>\n",
              "      <td>28.38</td>\n",
              "      <td>credit card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7827</th>\n",
              "      <td>e5cc4f88-e5b7-4ad5-bc1b-12a828a14f55</td>\n",
              "      <td>2022-03-04 15:11:38</td>\n",
              "      <td>707e4237-191c-4cc9-85af-383a6c1cb2ab</td>\n",
              "      <td>cleaning products</td>\n",
              "      <td>basic</td>\n",
              "      <td>16.99</td>\n",
              "      <td>4</td>\n",
              "      <td>67.96</td>\n",
              "      <td>cash</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7828</th>\n",
              "      <td>afd70b4f-ee21-402d-8d8f-0d9e13c2bea6</td>\n",
              "      <td>2022-03-06 13:50:36</td>\n",
              "      <td>d6ccd088-11be-4c25-aa1f-ea87c01a04db</td>\n",
              "      <td>cleaning products</td>\n",
              "      <td>non-member</td>\n",
              "      <td>14.99</td>\n",
              "      <td>4</td>\n",
              "      <td>59.96</td>\n",
              "      <td>debit card</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        document.querySelector('#df-4380b6ec-6ed3-4c10-9a8d-686c57a1ff92 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    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",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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-40afcb92-dd99-4e7f-a29c-d179cdd0425b 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",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Descriptive Statistics**"
      ],
      "metadata": {
        "id": "svLkL7s3iMUz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPFIcLvRiZ6W",
        "outputId": "8a0fced5-26ec-4306-c0dc-c1a667462e32"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['transaction_id', 'timestamp', 'product_id', 'category',\n",
              "       'customer_type', 'unit_price', 'quantity', 'total', 'payment_type'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYwdM3zKO-AU",
        "outputId": "05e18212-7e27-464d-d3c1-5b5a5b1ed49e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transaction_id    7829\n",
              "timestamp         7829\n",
              "product_id        7829\n",
              "category          7829\n",
              "customer_type     7829\n",
              "unit_price        7829\n",
              "quantity          7829\n",
              "total             7829\n",
              "payment_type      7829\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LiewVuPsPIc-",
        "outputId": "fb8634cf-8908-4099-d80d-383ba8df31a2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7829, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Check Missing Data**"
      ],
      "metadata": {
        "id": "G1A53xK1PglQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6hmXkxuPZW9",
        "outputId": "d9c58a27-4497-4247-9e25-94e00d90193a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transaction_id    0\n",
              "timestamp         0\n",
              "product_id        0\n",
              "category          0\n",
              "customer_type     0\n",
              "unit_price        0\n",
              "quantity          0\n",
              "total             0\n",
              "payment_type      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Describe the Data"
      ],
      "metadata": {
        "id": "ZE74xD_pRbTz"
      }
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    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "hTxQEDEvReiR",
        "outputId": "885c6905-546c-4e7b-86a8-35c51686136e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7829 entries, 0 to 7828\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   transaction_id  7829 non-null   object \n",
            " 1   timestamp       7829 non-null   object \n",
            " 2   product_id      7829 non-null   object \n",
            " 3   category        7829 non-null   object \n",
            " 4   customer_type   7829 non-null   object \n",
            " 5   unit_price      7829 non-null   float64\n",
            " 6   quantity        7829 non-null   int64  \n",
            " 7   total           7829 non-null   float64\n",
            " 8   payment_type    7829 non-null   object \n",
            "dtypes: float64(2), int64(1), object(6)\n",
            "memory usage: 550.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAzIsyc6SbPM",
        "outputId": "30704df6-9190-4143-9fa6-588b5f075ea4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "transaction_id     object\n",
              "timestamp          object\n",
              "product_id         object\n",
              "category           object\n",
              "customer_type      object\n",
              "unit_price        float64\n",
              "quantity            int64\n",
              "total             float64\n",
              "payment_type       object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()\n"
      ],
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        "colab": {
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          "height": 300
        },
        "id": "BY3g9o1-KIo_",
        "outputId": "cc6b04bd-1a08-4c94-c666-361f688bf762"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        unit_price     quantity        total\n",
              "count  7829.000000  7829.000000  7829.000000\n",
              "mean      7.819480     2.501597    19.709905\n",
              "std       5.388088     1.122722    17.446680\n",
              "min       0.190000     1.000000     0.190000\n",
              "25%       3.990000     1.000000     6.570000\n",
              "50%       7.190000     3.000000    14.970000\n",
              "75%      11.190000     4.000000    28.470000\n",
              "max      23.990000     4.000000    95.960000"
            ],
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              "      <th>mean</th>\n",
              "      <td>7.819480</td>\n",
              "      <td>2.501597</td>\n",
              "      <td>19.709905</td>\n",
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              "      <th>std</th>\n",
              "      <td>5.388088</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2764.9602830357985,\n        \"min\": 0.19,\n        \"max\": 7829.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.819480137948653,\n          7.19,\n          7829.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2767.1301249782196,\n        \"min\": 1.0,\n        \"max\": 7829.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          7829.0,\n          2.501596627921829,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2758.870573735675,\n        \"min\": 0.19,\n        \"max\": 7829.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          19.70990547962703,\n          14.97,\n          7829.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='all')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "QFQgUVS0nAdc",
        "outputId": "c3156bb1-eef9-429b-e870-f0732b393c2b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              transaction_id            timestamp  \\\n",
              "count                                   7829                 7829   \n",
              "unique                                  7829                 7738   \n",
              "top     a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 19:32:20   \n",
              "freq                                       1                    2   \n",
              "mean                                     NaN                  NaN   \n",
              "std                                      NaN                  NaN   \n",
              "min                                      NaN                  NaN   \n",
              "25%                                      NaN                  NaN   \n",
              "50%                                      NaN                  NaN   \n",
              "75%                                      NaN                  NaN   \n",
              "max                                      NaN                  NaN   \n",
              "\n",
              "                                  product_id category customer_type  \\\n",
              "count                                   7829     7829          7829   \n",
              "unique                                   300       22             5   \n",
              "top     ecac012c-1dec-41d4-9ebd-56fb7166f6d9    fruit    non-member   \n",
              "freq                                     114      998          1601   \n",
              "mean                                     NaN      NaN           NaN   \n",
              "std                                      NaN      NaN           NaN   \n",
              "min                                      NaN      NaN           NaN   \n",
              "25%                                      NaN      NaN           NaN   \n",
              "50%                                      NaN      NaN           NaN   \n",
              "75%                                      NaN      NaN           NaN   \n",
              "max                                      NaN      NaN           NaN   \n",
              "\n",
              "         unit_price     quantity        total payment_type  \n",
              "count   7829.000000  7829.000000  7829.000000         7829  \n",
              "unique          NaN          NaN          NaN            4  \n",
              "top             NaN          NaN          NaN         cash  \n",
              "freq            NaN          NaN          NaN         2027  \n",
              "mean       7.819480     2.501597    19.709905          NaN  \n",
              "std        5.388088     1.122722    17.446680          NaN  \n",
              "min        0.190000     1.000000     0.190000          NaN  \n",
              "25%        3.990000     1.000000     6.570000          NaN  \n",
              "50%        7.190000     3.000000    14.970000          NaN  \n",
              "75%       11.190000     4.000000    28.470000          NaN  \n",
              "max       23.990000     4.000000    95.960000          NaN  "
            ],
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              "      <th>unique</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>a1c82654-c52c-45b3-8ce8-4c2a1efe63ed</td>\n",
              "      <td>2022-03-02 19:32:20</td>\n",
              "      <td>ecac012c-1dec-41d4-9ebd-56fb7166f6d9</td>\n",
              "      <td>fruit</td>\n",
              "      <td>non-member</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>cash</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>114</td>\n",
              "      <td>998</td>\n",
              "      <td>1601</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.819480</td>\n",
              "      <td>2.501597</td>\n",
              "      <td>19.709905</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.388088</td>\n",
              "      <td>1.122722</td>\n",
              "      <td>17.446680</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.570000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.190000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.190000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>28.470000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.990000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>95.960000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e125269d-d6fa-4d87-9b9c-4b98e2d227a1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "        document.querySelector('#df-e125269d-d6fa-4d87-9b9c-4b98e2d227a1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    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",
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              "    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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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",
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              "    (() => {\n",
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              "      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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"transaction_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"7829\",\n          \"a1c82654-c52c-45b3-8ce8-4c2a1efe63ed\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"timestamp\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000002\",\n        \"max\": \"2022-03-02 19:32:20\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7738,\n          \"2\",\n          \"7829\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          300,\n          \"114\",\n          \"7829\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          22,\n          \"998\",\n          \"7829\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          \"1601\",\n          \"7829\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2764.9602830357985,\n        \"min\": 0.19,\n        \"max\": 7829.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.819480137948653,\n          7.19,\n          7829.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2767.1301249782196,\n        \"min\": 1.0,\n        \"max\": 7829.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          7829.0,\n          2.501596627921829,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2758.870573735675,\n        \"min\": 0.19,\n        \"max\": 7829.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          19.70990547962703,\n          14.97,\n          7829.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"payment_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          \"2027\",\n          \"7829\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_outliers_iqr(data):\n",
        "    outliers = {}\n",
        "    numeric_cols = data.select_dtypes(include=[np.number]).columns\n",
        "    for col in numeric_cols:\n",
        "        Q1 = data[col].quantile(0.25)\n",
        "        Q3 = data[col].quantile(0.75)\n",
        "        IQR = Q3 - Q1\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "        outliers[col] = data[(data[col] < lower_bound) | (data[col] > upper_bound)]\n",
        "    return outliers\n",
        "\n",
        "outliers = detect_outliers_iqr(df)"
      ],
      "metadata": {
        "id": "A-lEaifp2Z90"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outliers**"
      ],
      "metadata": {
        "id": "_wq9dYcAxjPx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to detect outliers using IQR\n",
        "def detect_outliers_iqr(df):\n",
        "    outliers = {}\n",
        "    for col in df.columns:\n",
        "        Q1 = df[col].quantile(0.25)\n",
        "        Q3 = df[col].quantile(0.75)\n",
        "        IQR = Q3 - Q1\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "        outliers[col] = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
        "    return outliers\n"
      ],
      "metadata": {
        "id": "uVNj5fuvz2z_"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display outliers\n",
        "for col, outlier_data in outliers.items():\n",
        "    print(f\"\\nOutliers in {col}:\")\n",
        "    print(outlier_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEgH59Ty1afv",
        "outputId": "9b67fd97-5524-4656-bb0a-e3c41156a2b1"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Outliers in unit_price:\n",
            "                            transaction_id            timestamp  \\\n",
            "6919  db0bbcd9-bd3d-4fe1-a25c-38b2e42593e6  2022-03-07 16:01:01   \n",
            "6920  7b2c8f54-40bd-4a84-9b8a-95b70dfe6189  2022-03-05 09:54:33   \n",
            "6924  f94c36a9-0afe-411a-aa46-f1c1e3b58122  2022-03-02 11:23:44   \n",
            "6928  f4339aea-dd5f-49de-9691-8f90b52898a8  2022-03-06 09:49:58   \n",
            "6933  6566dbad-9ed4-40c0-8e7d-b389a19ffb2d  2022-03-07 18:47:02   \n",
            "6947  49e0b66a-46fc-495e-8828-19a49347c6b5  2022-03-02 11:17:24   \n",
            "6952  72d3db8c-ee4a-4a78-8d36-1abda9781c1b  2022-03-02 10:51:43   \n",
            "6963  2a1f2139-3c30-4ead-893f-c477ebe08a70  2022-03-04 14:25:19   \n",
            "6966  a7da42c8-a187-405a-864c-7be2938ca684  2022-03-06 17:07:55   \n",
            "6969  49df7d20-57a3-4662-babb-bcfdcac0b7f0  2022-03-02 18:50:41   \n",
            "6970  64d7b0fd-d6bb-423b-8d58-11ea9916c370  2022-03-05 13:14:09   \n",
            "6975  b820aac2-df64-4909-966f-0cfe0eb7b5cc  2022-03-04 12:15:29   \n",
            "6985  22169b71-3548-410f-aa37-694d8bb9a960  2022-03-06 16:14:20   \n",
            "6986  c2eacbc6-51ac-45d7-9ede-1fc49db80af8  2022-03-05 15:59:22   \n",
            "6998  b972d340-b7c2-4120-bd47-d8083dfbd90f  2022-03-01 19:42:56   \n",
            "7017  45d1448a-d08b-42d5-bd2b-fda2918a0192  2022-03-06 16:33:22   \n",
            "7019  6b1ae519-51ea-453a-a6d9-be98980cb58a  2022-03-04 18:33:31   \n",
            "7020  03c9372d-ab26-43af-b680-a551a6de8d77  2022-03-01 16:00:23   \n",
            "7021  802433d3-b716-4da6-9aa9-711a7265b3a1  2022-03-07 14:36:49   \n",
            "7022  e5a38584-7a3b-482d-ac2c-7df68fc198a3  2022-03-04 15:10:32   \n",
            "7041  ccd4a21f-976e-418a-ae5c-fbec4cf6c252  2022-03-05 10:52:41   \n",
            "7042  753c42a8-151f-4b72-9e8a-04a3cd77955c  2022-03-07 19:25:52   \n",
            "7047  8c80709b-0c98-4267-8aef-90f35fdee2fb  2022-03-07 15:37:59   \n",
            "7069  bd71336f-37da-4ad7-bf05-9775d7ed5311  2022-03-03 17:15:31   \n",
            "7102  bb715749-a314-44a3-96d0-24951aa0d338  2022-03-07 11:17:42   \n",
            "7103  9c58d9f3-63bb-4b8b-b1da-f01add6795fa  2022-03-01 19:27:41   \n",
            "7106  ce68dd24-f4d4-4b35-930c-ba5e7561d08f  2022-03-01 12:57:57   \n",
            "7115  6dd84e50-6100-46a6-b6b1-720072662310  2022-03-03 14:15:26   \n",
            "7117  76dacb22-6851-4eb7-90ca-b396c6655cc8  2022-03-02 18:58:13   \n",
            "7122  f5d92d91-3800-490b-a7c9-d30a7f6ccce4  2022-03-04 12:16:34   \n",
            "7133  8ac36bc9-f7e7-41e3-a748-bcb5d8dbfb6d  2022-03-01 13:44:44   \n",
            "\n",
            "                                product_id  category customer_type  \\\n",
            "6919  9a506969-5baa-4057-b55f-760c37506479  medicine      standard   \n",
            "6920  9a506969-5baa-4057-b55f-760c37506479  medicine    non-member   \n",
            "6924  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine       premium   \n",
            "6928  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine    non-member   \n",
            "6933  9a506969-5baa-4057-b55f-760c37506479  medicine    non-member   \n",
            "6947  9a506969-5baa-4057-b55f-760c37506479  medicine         basic   \n",
            "6952  9a506969-5baa-4057-b55f-760c37506479  medicine          gold   \n",
            "6963  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine          gold   \n",
            "6966  9a506969-5baa-4057-b55f-760c37506479  medicine      standard   \n",
            "6969  9a506969-5baa-4057-b55f-760c37506479  medicine          gold   \n",
            "6970  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine       premium   \n",
            "6975  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine    non-member   \n",
            "6985  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine       premium   \n",
            "6986  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine       premium   \n",
            "6998  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine    non-member   \n",
            "7017  9a506969-5baa-4057-b55f-760c37506479  medicine         basic   \n",
            "7019  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine    non-member   \n",
            "7020  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine         basic   \n",
            "7021  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine      standard   \n",
            "7022  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine       premium   \n",
            "7041  9a506969-5baa-4057-b55f-760c37506479  medicine       premium   \n",
            "7042  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine         basic   \n",
            "7047  9a506969-5baa-4057-b55f-760c37506479  medicine         basic   \n",
            "7069  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine         basic   \n",
            "7102  9a506969-5baa-4057-b55f-760c37506479  medicine       premium   \n",
            "7103  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine       premium   \n",
            "7106  9a506969-5baa-4057-b55f-760c37506479  medicine      standard   \n",
            "7115  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine    non-member   \n",
            "7117  9a506969-5baa-4057-b55f-760c37506479  medicine    non-member   \n",
            "7122  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine         basic   \n",
            "7133  99d5e147-5746-43d1-81e3-4a0faccc43bc  medicine          gold   \n",
            "\n",
            "      unit_price  quantity  total payment_type  \n",
            "6919       23.99         3  71.97     e-wallet  \n",
            "6920       23.99         4  95.96     e-wallet  \n",
            "6924       22.19         3  66.57         cash  \n",
            "6928       22.19         2  44.38     e-wallet  \n",
            "6933       23.99         2  47.98     e-wallet  \n",
            "6947       23.99         3  71.97  credit card  \n",
            "6952       23.99         3  71.97  credit card  \n",
            "6963       22.19         2  44.38     e-wallet  \n",
            "6966       23.99         1  23.99  credit card  \n",
            "6969       23.99         1  23.99         cash  \n",
            "6970       22.19         4  88.76         cash  \n",
            "6975       22.19         1  22.19     e-wallet  \n",
            "6985       22.19         3  66.57         cash  \n",
            "6986       22.19         3  66.57         cash  \n",
            "6998       22.19         4  88.76         cash  \n",
            "7017       23.99         1  23.99   debit card  \n",
            "7019       22.19         4  88.76   debit card  \n",
            "7020       22.19         3  66.57  credit card  \n",
            "7021       22.19         3  66.57  credit card  \n",
            "7022       22.19         1  22.19     e-wallet  \n",
            "7041       23.99         3  71.97     e-wallet  \n",
            "7042       22.19         4  88.76         cash  \n",
            "7047       23.99         3  71.97     e-wallet  \n",
            "7069       22.19         1  22.19  credit card  \n",
            "7102       23.99         4  95.96  credit card  \n",
            "7103       22.19         4  88.76   debit card  \n",
            "7106       23.99         2  47.98  credit card  \n",
            "7115       22.19         2  44.38         cash  \n",
            "7117       23.99         4  95.96     e-wallet  \n",
            "7122       22.19         4  88.76   debit card  \n",
            "7133       22.19         2  44.38  credit card  \n",
            "\n",
            "Outliers in quantity:\n",
            "Empty DataFrame\n",
            "Columns: [transaction_id, timestamp, product_id, category, customer_type, unit_price, quantity, total, payment_type]\n",
            "Index: []\n",
            "\n",
            "Outliers in total:\n",
            "                            transaction_id            timestamp  \\\n",
            "3793  abc70421-eb57-4914-beda-2f0d0d7a1991  2022-03-01 10:11:37   \n",
            "3808  63f868b0-d816-4a7e-902c-8d76a6c339c2  2022-03-06 09:44:27   \n",
            "3823  da3c8c70-0677-4b42-aadd-bf6c5df9380c  2022-03-04 13:55:47   \n",
            "3861  decfd610-8d7c-483f-b936-8ad15a3f3eb9  2022-03-05 11:13:48   \n",
            "3879  4b833593-1271-4aee-985e-068e38768de6  2022-03-06 11:07:35   \n",
            "...                                    ...                  ...   \n",
            "7792  08a56967-d20b-4920-be53-d47eef50dd81  2022-03-05 10:11:07   \n",
            "7795  019704ba-ec9b-43b3-a9db-d879605f4a9e  2022-03-01 11:22:48   \n",
            "7813  b538dbd6-cdaa-4de3-8179-7fe9e7346d9a  2022-03-02 16:48:19   \n",
            "7821  a8109d22-e192-41d4-911d-84c772a68013  2022-03-02 10:42:44   \n",
            "7827  e5cc4f88-e5b7-4ad5-bc1b-12a828a14f55  2022-03-04 15:11:38   \n",
            "\n",
            "                                product_id           category customer_type  \\\n",
            "3793  2c50b273-ed34-4917-a78b-67e98dc6b7b2          beverages       premium   \n",
            "3808  2c50b273-ed34-4917-a78b-67e98dc6b7b2          beverages       premium   \n",
            "3823  1c459fbc-e9b9-43a3-91ef-e4f2d733ea4b          beverages    non-member   \n",
            "3861  8eb4b799-e606-4ef6-bccf-d7c6e0581087          beverages         basic   \n",
            "3879  b35e3b8c-1fed-4bf1-9b12-6ebdad2c6b94          beverages       premium   \n",
            "...                                    ...                ...           ...   \n",
            "7792  0e4c10f4-77bc-4c67-86b2-b4da5ded19bf  cleaning products         basic   \n",
            "7795  1af1cbf1-e439-4cb4-937f-be983d8ffd29  cleaning products         basic   \n",
            "7813  0e4c10f4-77bc-4c67-86b2-b4da5ded19bf  cleaning products      standard   \n",
            "7821  6c8d0a2a-576a-432f-a090-c123dee91aaa  cleaning products          gold   \n",
            "7827  707e4237-191c-4cc9-85af-383a6c1cb2ab  cleaning products         basic   \n",
            "\n",
            "      unit_price  quantity  total payment_type  \n",
            "3793       16.49         4  65.96     e-wallet  \n",
            "3808       16.49         4  65.96   debit card  \n",
            "3823       16.19         4  64.76  credit card  \n",
            "3861       19.99         4  79.96         cash  \n",
            "3879       15.99         4  63.96   debit card  \n",
            "...          ...       ...    ...          ...  \n",
            "7792       16.99         4  67.96         cash  \n",
            "7795       16.49         4  65.96         cash  \n",
            "7813       16.99         4  67.96   debit card  \n",
            "7821       15.49         4  61.96  credit card  \n",
            "7827       16.99         4  67.96         cash  \n",
            "\n",
            "[300 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Box plot to visualize outliers in numeric columns\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.boxplot(data=df.select_dtypes(include=[np.number]))\n",
        "plt.title('Box Plot to Visualize Outliers')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "60xacsx93E6w",
        "outputId": "bc957e25-d33a-45ab-8904-1c286915ebbe"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "_pDBqJSkzqer",
        "outputId": "bc790033-f7c9-4d32-ac51-1bd9d06853fe"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove outliers using IQR method for numeric columns only\n",
        "def remove_outliers_iqr(data):\n",
        "    numeric_cols = data.select_dtypes(include=[np.number]).columns\n",
        "    for col in numeric_cols:\n",
        "        Q1 = data[col].quantile(0.25)\n",
        "        Q3 = data[col].quantile(0.75)\n",
        "        IQR = Q3 - Q1\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "        data = data[(data[col] >= lower_bound) & (data[col] <= upper_bound)]\n",
        "    return data\n",
        "\n",
        "df_no_outliers = remove_outliers_iqr(df)"
      ],
      "metadata": {
        "id": "LeTFsIvb3ZBV"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the DataFrame without outliers\n",
        "print(\"\\nDataFrame after Removing Outliers:\")\n",
        "print(df_no_outliers.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZirIxAJV3kzr",
        "outputId": "cd947d8b-0d46-46bd-fa07-dd49b196c1bc"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "DataFrame after Removing Outliers:\n",
            "                         transaction_id            timestamp  \\\n",
            "0  a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 09:51:38   \n",
            "1  931ad550-09e8-4da6-beaa-8c9d17be9c60  2022-03-06 10:33:59   \n",
            "2  ae133534-6f61-4cd6-b6b8-d1c1d8d90aea  2022-03-04 17:20:21   \n",
            "3  157cebd9-aaf0-475d-8a11-7c8e0f5b76e4  2022-03-02 17:23:58   \n",
            "4  a81a6cd3-5e0c-44a2-826c-aea43e46c514  2022-03-05 14:32:43   \n",
            "\n",
            "                             product_id category customer_type  unit_price  \\\n",
            "0  3bc6c1ea-0198-46de-9ffd-514ae3338713    fruit          gold        3.99   \n",
            "1  ad81b46c-bf38-41cf-9b54-5fe7f5eba93e    fruit      standard        3.99   \n",
            "2  7c55cbd4-f306-4c04-a030-628cbe7867c1    fruit       premium        0.19   \n",
            "3  80da8348-1707-403f-8be7-9e6deeccc883    fruit          gold        0.19   \n",
            "4  7f5e86e6-f06f-45f6-bf44-27b095c9ad1d    fruit         basic        4.49   \n",
            "\n",
            "   quantity  total payment_type  \n",
            "0         2   7.98     e-wallet  \n",
            "1         1   3.99     e-wallet  \n",
            "2         2   0.38     e-wallet  \n",
            "3         4   0.76     e-wallet  \n",
            "4         2   8.98   debit card  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Visualization**"
      ],
      "metadata": {
        "id": "XjY8rIiyOhFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_continuous_distribution(df = None, column: str = None, height: int = 8):\n",
        "  _ = sns.displot(df, x=column, kde=True, height=height, aspect=height/5).set(title=f'Distribution of {column}');\n"
      ],
      "metadata": {
        "id": "FWbERNXkUAux"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_continuous_distribution(df, 'unit_price')\n",
        "plot_continuous_distribution(df, 'quantity')\n",
        "plot_continuous_distribution(df, 'total')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WsZqoh4Hne8P",
        "outputId": "bcb27879-d9ad-4865-b5d1-eef941309054"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_unique_values(df, column):\n",
        "  num_unique_values = len(df[column].unique())\n",
        "  value_counts = df[column].value_counts()\n",
        "  print(f\"Column: {column} has {num_unique_values} unique values\\n\")\n",
        "  print(value_counts)"
      ],
      "metadata": {
        "id": "nBRM4L-FUAeb"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_unique_values(df, 'category')\n",
        "get_unique_values(df, 'customer_type')\n",
        "get_unique_values(df, 'payment_type')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVsxzVB1sPC-",
        "outputId": "a93184f7-69d1-4de5-8c69-5ff4bc9cbd0f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: category has 22 unique values\n",
            "\n",
            "category\n",
            "fruit                    998\n",
            "vegetables               846\n",
            "packaged foods           507\n",
            "baked goods              443\n",
            "canned foods             431\n",
            "refrigerated items       425\n",
            "kitchen                  382\n",
            "meat                     382\n",
            "dairy                    375\n",
            "beverages                301\n",
            "cheese                   293\n",
            "cleaning products        292\n",
            "baking                   264\n",
            "snacks                   263\n",
            "frozen                   263\n",
            "seafood                  253\n",
            "medicine                 243\n",
            "baby products            224\n",
            "condiments and sauces    181\n",
            "personal care            177\n",
            "pets                     161\n",
            "spices and herbs         125\n",
            "Name: count, dtype: int64\n",
            "Column: customer_type has 5 unique values\n",
            "\n",
            "customer_type\n",
            "non-member    1601\n",
            "standard      1595\n",
            "premium       1590\n",
            "basic         1526\n",
            "gold          1517\n",
            "Name: count, dtype: int64\n",
            "Column: payment_type has 4 unique values\n",
            "\n",
            "payment_type\n",
            "cash           2027\n",
            "credit card    1949\n",
            "e-wallet       1935\n",
            "debit card     1918\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_categorical_distribution(data: pd.DataFrame = None, column: str = None, height: int = 8, aspect: int = 2):\n",
        "  _ = sns.catplot(data=data, x=column, kind='count', height=height, aspect=aspect).set(title=f'Distribution of {column}');\n"
      ],
      "metadata": {
        "id": "1h0L2_lrUw1x"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_categorical_distribution(df, 'category')\n",
        "plot_categorical_distribution(df, 'customer_type')\n",
        "plot_categorical_distribution(df, 'payment_type')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "s77lucAxsiib",
        "outputId": "aa4649bd-75f9-47f5-f4ee-1f087dee73b4"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1611.11x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1611.11x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1611.11x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df, y='category').set(title=f'Distribution of Category')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "wExgRM-4uMRs",
        "outputId": "52f9e753-3e6b-4c70-9c02-6ba5b4fb6e64"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Distribution of Category')]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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QoEGZQObatWtzPX5CvEuqzMxCeOJACCGEEO+1evXqYWJiwv79+99J/yNGjODHH3/k7t27lClT5p1cQ4i8kJpiIYQQogRLTU0lLS1NbV9wcDAXLlzI9jXfBeH58+f88ssvfPrpp5IQi/eG1BQLIYQQJdiff/6Ju7s7n3/+Oebm5ly9epXly5djamrKwIEDC/Ra8fHxHDhwgE2bNvHgwQOGDRtWoP0L8W9IUiyEEEKUYGXLlqVBgwasXLmS+/fvo6enR/v27QkICMjyYN+/deXKFXr06EHFihVZtGhRrtZsFuK/IjXFQgghhBCixJOaYiGEEEIIUeJJUiyEEEIIIUo8qSkWIhcyMjL466+/MDAwKPBX5gohhBDi3cjMzOTx48eYm5urvbY9O5IUC5ELf/31FxYWFoUdhhBCCCHyITY2lipVqryxjSTFQuTCq9e6xsbGYmhoWMjRCCGEECI3kpKSsLCwyNXr2SUpFiIXXpVMdJi9E01t3UKORgghhCg+zs7p9c6vkZvSR3nQTryXMjMzGTBgAOXKlUOlUhEeHp6vfoKDg1GpVCQkJBRofEIIIYQoXiQpFu+lPXv2EBgYyO+//05cXBy1a9fOVz9NmzYlLi4OIyMjAAIDAzE2Ni7ASIUQQghRHEj5hHgvRUVFYWZmRtOmTbM9/uLFC7S0tN7aj5aWFqampgUdnhBCCCGKGZkpFu8db29vhgwZQkxMDCqVCmtra9zc3PD19WX48OFUqFABDw8PoqOjs5RWJCQkoFKpCA4OBtTLJ4KDg+nTpw+JiYmoVCpUKhVTpkwplHsUQgghxPtFkmLx3lm4cCHffPMNVapUIS4ujtDQUABWr16NlpYWISEhLF++PM/9Nm3alAULFmBoaEhcXBxxcXGMHj26oMMXQgghRBEk5RPivWNkZISBgQGamppqpQ82NjbMnj1b+RwdHZ2nfrW0tDAyMkKlUr21pCIlJYWUlBTlc1JSUp6uJYQQQoiiRWaKRZHRoEGD/+xaM2fOxMjISNnkxR1CCCFE8SZJsSgy9PT01D6/el1jZmamsi81NbVAruXn50diYqKyxcbGFki/QgghhHg/SVIsiiwTExMA4uLilH1vW89YS0uL9PT0t/atra2NoaGh2iaEEEKI4ktqikWRpaurS+PGjQkICKBq1arEx8czYcKEN55jbW1NcnIyBw8epG7dupQpU4YyZcr8RxELIYQQ4n0lM8WiSPvpp59IS0ujQYMGDB8+HH9//ze2b9q0KQMHDqRbt26YmJioPbgnhBBCiJJLlfl6QaYQIltJSUkYGRlRd8hyNLV1CzscIYQQotg4O6fXO+v71e/vxMTEt5ZCSvmEEHlw1N9L6ouFEEKIYkjKJ4QQQgghRIknSbEQQgghhCjxJCkWQgghhBAlntQUC5EHLSb8Kg/aCSGEyJN3+SCZKDgyUyzeO1OmTKFevXpvbOPt7U2nTp3+k3iEEEIIUfxJUizeGUlchRBCCFFUSFIshBBCCCFKPEmKi6EffvgBc3NzMjIy1PZ//PHH9O3bF4Dt27dTv359dHR0qFatGlOnTiUtLU1pe/XqVZo1a4aOjg4ODg4cOHAAlUrFtm3blDaxsbF4enpibGxMuXLl+Pjjj4mOjgZelkCsXr2a7du3o1KpUKlUBAcHAzBu3DhsbW0pU6YM1apVY+LEiaSmpma5j++//x4LCwvKlCmDp6cniYmJOd5zRkYGM2fOpGrVqujq6lK3bl02bdqkHH/06BE9evTAxMQEXV1dbGxsWLVqVV6HVgghhBDFlDxoVwx17dqVIUOGcPjwYVq3bg3Aw4cP2bNnD7t27eLYsWP06tWLRYsW0bx5c6KiohgwYAAAkydPJj09nU6dOmFpacnp06d5/Pgxo0aNUrtGamoqHh4eNGnShGPHjlGqVCn8/f1p27YtFy9eZPTo0URERJCUlKQkn+XKlQPAwMCAwMBAzM3NuXTpEv3798fAwICxY8cq/d+4cYMNGzbw22+/kZSUhI+PD4MGDSIoKCjbe545cya//PILy5cvx8bGhqNHj/L5559jYmKCq6srEydO5MqVK+zevZsKFSpw48YNnj17luMYpqSkkJKSonxOSkrKx3dCCCGEEEWFJMXFUNmyZWnXrh1r165VkuJNmzZRoUIFWrZsyYcffshXX31F7969AahWrRrTpk1j7NixTJ48mf379xMVFUVwcDCmpqYATJ8+nTZt2ijXWL9+PRkZGaxcuRKVSgXAqlWrMDY2Jjg4mA8//BBdXV1SUlKUPl6ZMGGC8rW1tTWjR49m3bp1aknx8+fP+fnnn6lcuTIAixcvpn379sybNy9LfykpKcyYMYMDBw7QpEkT5Z6OHz/O999/j6urKzExMTg5OdGwYUPlum8yc+ZMpk6dmrsBF0IIIUSRJ0lxMdWjRw/69+/P0qVL0dbWJigoiO7du6OhocGFCxcICQlh+vTpSvv09HSeP3/O06dPiYyMxMLCQi35bNSokVr/Fy5c4MaNGxgYGKjtf/78OVFRUW+Mbf369SxatIioqCiSk5NJS0vL8upkS0tLJSEGaNKkCRkZGURGRmZJim/cuMHTp0/VknaAFy9e4OTkBMCXX37Jp59+yrlz5/jwww/p1KkTTZs2zTFGPz8/Ro4cqXxOSkrCwsLijfclhBBCiKJLkuJiqmPHjmRmZrJz506cnZ05duwY8+fPByA5OZmpU6fSuXPnLOfp6Ojkqv/k5GQaNGiQbTmDiYlJjuedPHmSHj16MHXqVDw8PDAyMmLdunXMmzcvl3eWfSwAO3fuVEukAbS1tQFo164dt2/fZteuXezfv5/WrVszePBg5s6dm22f2trayrlCCCGEKP4kKS6mdHR06Ny5M0FBQdy4cQM7Ozvq168PQP369YmMjKRGjRrZnmtnZ0dsbCz37t2jUqVKAISGhqq1qV+/PuvXr6dixYpZZnlf0dLSIj09XW3fiRMnsLKyYvz48cq+27dvZzk3JiaGv/76C3NzcwBOnTqFhoYGdnZ2Wdo6ODigra1NTEwMrq6uOQ0JJiYm9O7dm969e9O8eXPGjBmTY1IshBBCiJJFkuJirEePHnTo0IHLly/z+eefK/snTZpEhw4dsLS0pEuXLkpJxR9//IG/vz9t2rShevXq9O7dm9mzZ/P48WOlDvhV/XCPHj2YM2cOH3/8Md988w1VqlTh9u3bbNmyhbFjx1KlShWsra3Zu3cvkZGRlC9fHiMjI2xsbIiJiWHdunU4Ozuzc+dOtm7dmiV2HR0devfuzdy5c0lKSmLo0KF4enpmKZ2Alw/ujR49mhEjRpCRkUGzZs1ITEwkJCQEQ0NDevfuzaRJk2jQoAG1atUiJSWF33//HXt7+3c08kIIIYQoamRJtmKsVatWlCtXjsjISD777DNlv4eHB7///jv79u3D2dmZxo0bM3/+fKysrADQ1NRk27ZtJCcn4+zsTL9+/ZSZ3VflFWXKlOHo0aNYWlrSuXNn7O3t8fHx4fnz58rMcf/+/bGzs6Nhw4aYmJgQEhLC//73P0aMGIGvry/16tXjxIkTTJw4MUvsNWrUoHPnznz00Ud8+OGH1KlTh6VLl+Z4r9OmTWPixInMnDkTe3t72rZty86dO6latSrwctbaz8+POnXq0KJFCzQ1NVm3bl3BDLQQQgghijxVZmZmZmEHId5/ISEhNGvWjBs3blC9evXCDuc/l5SUhJGREXWHLEdTW7ewwxFCCFGEnJ3Tq7BDKLFe/f5OTEzMsdzzFUmKRba2bt2Kvr4+NjY23Lhxg2HDhlG2bFmOHz9e2KEVirz8UAkhhBDi/ZCX399SUyyy9fjxY8aNG0dMTAwVKlTA3d39X60QIYQQQgjxPpOZYiFyQWaKhRBCiKJHZoqFeEdaTPhVaoqFyIHUTQohijJZfUIIIYQQQpR4khQXkMzMTAYMGEC5cuVQqVSEh4fn2FalUrFt27b/LLb/SmBgIMbGxjkej46OfuvYCCGEEEIUBkmKC8iePXsIDAzk999/Jy4ujtq1a+fYNi4ujnbt2v2H0eXsbYlsQbKwsFAbm+DgYFQqFQkJCf/J9YUQQgghciI1xW/x4sULtLS03touKioKMzMzmjZt+ta+snsrW0HLbdz/JU1Nzf/k3oUQQggh8kpmiv/Bzc0NX19fhg8fToUKFfDw8ADgjz/+oF27dujr61OpUiV69uzJ33//DYC3tzdDhgwhJiYGlUqFtbX1G/v6Z/nEiRMnqFevHjo6OjRs2JBt27ZlKTN40/XfdK1vv/0WR0dH9PT0sLCwYNCgQSQnJwMvZ2r79OlDYmIiKpUKlUrFlClTAEhJSWH06NFUrlwZPT09PvjgA4KDg9XGKjAwEEtLS8qUKcMnn3zCgwcP3ji2r5dPREdH07JlSwDKli2LSqXC29sbgIyMDGbOnEnVqlXR1dWlbt26bNq0Senn1Qzz3r17cXJyQldXl1atWhEfH8/u3buxt7fH0NCQzz77jKdPnyrnbdq0CUdHR3R1dSlfvjzu7u48efLkjTELIYQQomSQpDgbq1evRktLi5CQEJYvX05CQgKtWrXCycmJsLAw9uzZw7179/D09ARg4cKFfPPNN1SpUoW4uDhCQ0Nz7OufkpKS6NixI46Ojpw7d45p06Yxbtw4tTZvu/6brqWhocGiRYu4fPkyq1ev5tChQ4wdOxaApk2bsmDBAgwNDYmLiyMuLo7Ro0cD4Ovry8mTJ1m3bh0XL16ka9eutG3bluvXrwNw+vRpfHx88PX1JTw8nJYtW+Lv75/rMbawsGDz5s0AREZGEhcXx8KFCwGYOXMmP//8M8uXL+fy5cuMGDGCzz//nCNHjqj1MWXKFJYsWcKJEyeIjY3F09OTBQsWsHbtWnbu3Mm+fftYvHgx8LJkxcvLi759+xIREUFwcDCdO3cmpxUJU1JSSEpKUtuEEEIIUXxJ+UQ2bGxsmD17tvLZ398fJycnZsyYoez76aefsLCw4Nq1a9ja2mJgYJBtecA/+/qntWvXolKpWLFiBTo6Ojg4OPDnn3/Sv39/pc2SJUveev2crjV8+HDla2tra/z9/Rk4cCBLly5FS0sLIyMjVCqVWtwxMTGsWrWKmJgYzM3NARg9ejR79uxh1apVzJgxg4ULF9K2bVslwba1teXEiRPs2bPnreMLL0spypUrB0DFihWVuuaUlBRmzJjBgQMHaNKkCQDVqlXj+PHjfP/997i6uip9+Pv74+LiAoCPjw9+fn5ERUVRrVo1ALp06cLhw4cZN24ccXFxpKWl0blzZ6ysrABwdHTMMb6ZM2cyderUXN2LEEIIIYo+SYqz0aBBA7XPFy5c4PDhw+jr62dpGxUVpSSluenrnyIjI6lTpw46OjrKvkaNGuXr+tld68CBA8ycOZOrV6+SlJREWloaz58/5+nTp5QpUybbmC5dukR6enqW+0pJSaF8+fIARERE8Mknn6gdb9KkSa6T4pzcuHGDp0+f0qZNG7X9L168wMnJSW1fnTp1lK8rVapEmTJllIT41b4zZ84AULduXVq3bo2joyMeHh58+OGHdOnShbJly2Ybh5+fHyNHjlQ+JyUlYWFh8a/uTQghhBDvL0mKs6Gnp6f2OTk5mY4dOzJr1qwsbc3MzPLUV37k9vr/vFZ0dDQdOnTgyy+/ZPr06ZQrV47jx4/j4+PDixcvckyKk5OT0dTU5OzZs2hqaqodyy4xL0iv6p137txJ5cqV1Y5pa2urfS5durTytUqlUvv8al9GRgbwcmZ6//79nDhxQimrGD9+PKdPn6Zq1apZ4tDW1s5yPSGEEEIUX5IU50L9+vXZvHkz1tbWlCpVsENmZ2fHL7/8QkpKipKEvV6T/G+uf/bsWTIyMpg3bx4aGi/Lxzds2KDWRktLi/T0dLV9Tk5OpKenEx8fT/PmzbPt297entOnT6vtO3XqVK5je3VtQO36Dg4OaGtrExMTo1YqURBUKhUuLi64uLgwadIkrKys2Lp1q9qMsBBCCCFKJnnQLhcGDx7Mw4cP8fLyIjQ0lKioKPbu3UufPn2yJJR59dlnn5GRkcGAAQOIiIhg7969zJ07F3iZxP2b69eoUYPU1FQWL17MzZs3WbNmTZaH/aytrUlOTubgwYP8/fffPH36FFtbW3r06EGvXr3YsmULt27d4syZM8ycOZOdO3cCMHToUPbs2cPcuXO5fv06S5YsyXPphJWVFSqVit9//5379++TnJyMgYEBo0ePZsSIEaxevZqoqCjOnTvH4sWLWb16dZ76f93p06eZMWMGYWFhxMTEsGXLFu7fv4+9vX2++xRCCCFE8SFJcS6Ym5sTEhJCeno6H374IY6OjgwfPhxjY2NlBja/DA0N+e233wgPD6devXqMHz+eSZMmASh1xvm9ft26dfn222+ZNWsWtWvXJigoiJkzZ6q1adq0KQMHDqRbt26YmJgoD+qtWrWKXr16MWrUKOzs7OjUqROhoaFYWloC0LhxY1asWMHChQupW7cu+/btY8KECXm698qVKzN16lS++uorKlWqhK+vLwDTpk1j4sSJzJw5E3t7e9q2bcvOnTuzLXPILUNDQ44ePcpHH32Era0tEyZMYN68ee/NS1SEEEIIUbhUmTmtSSUKTVBQkLJ+sK6ubmGHI3j5oJ2RkRF1hyxHU1u+J0Jk5+ycXoUdghBCqHn1+zsxMRFDQ8M3tpWa4vfAzz//TLVq1ahcuTIXLlxg3LhxeHp6SkL8Hjrq7/XWHyohhBBCFD2SFL8H7t69y6RJk7h79y5mZmZ07dqV6dOnF3ZYQgghhBAlhpRPCJELefnzixBCCCHeD1I+IcQ70mLCr1JTXIJIjawQQpQcsvqEEEIIIYQo8SQpFu9MZmYmAwYMoFy5cqhUKsLDwws7JCGEEEKIbEn5hHhn9uzZQ2BgIMHBwVSrVo0KFSoUdkhCCCGEENmSpFi8M1FRUZiZmdG0adNsj7948UJ51bMQQgghRGGS8gnxTnh7ezNkyBBiYmJQqVRYW1vj5uaGr68vw4cPp0KFCnh4eABw5MgRGjVqhLa2NmZmZnz11VekpaUBEB0djUqlyrK5ubkp1zp+/DjNmzdHV1cXCwsLhg4dypMnT5Tj1tbWzJgxg759+2JgYIClpSU//PDDfzoeQgghhHi/SVIs3omFCxfyzTffUKVKFeLi4ggNDQVg9erVaGlpERISwvLly/nzzz/56KOPcHZ25sKFCyxbtowff/wRf39/ACwsLIiLi1O28+fPU758eVq0aAG8nI1u27Ytn376KRcvXmT9+vUcP35ceWX0K/PmzaNhw4acP3+eQYMG8eWXXxIZGZlj/CkpKSQlJaltQgghhCi+ZJ1i8c4sWLCABQsWEB0dDYCbmxtJSUmcO3dOaTN+/Hg2b95MREQEKpUKgKVLlzJu3DgSExPR0Pi//297/vw5bm5umJiYsH37djQ0NOjXrx+ampp8//33Srvjx4/j6urKkydP0NHRwdramubNm7NmzRrg5QOApqamTJ06lYEDB2Yb+5QpU5g6dWqW/fKa55JFlmQTQoiiLS/rFMtMsfhPNWjQQO1zREQETZo0URJiABcXF5KTk7lz545a2759+/L48WPWrl2rJMsXLlwgMDAQfX19ZfPw8CAjI4Nbt24p59apU0f5WqVSYWpqSnx8fI5x+vn5kZiYqGyxsbH/6r6FEEII8X6TB+3Ef0pPTy9f5/n7+7N3717OnDmDgYGBsj85OZkvvviCoUOHZjnH0tJS+bp06dJqx1QqFRkZGTleT1tbG21t7XzFKoQQQoiiR5JiUajs7e3ZvHkzmZmZymxxSEgIBgYGVKlSBYDNmzfzzTffsHv3bqpXr652fv369bly5Qo1atT4z2MXQgghRPEh5ROiUA0aNIjY2FiGDBnC1atX2b59O5MnT2bkyJFoaGjwxx9/0KtXL8aNG0etWrW4e/cud+/e5eHDhwCMGzeOEydO4OvrS3h4ONevX2f79u1ZHrQTQgghhHgTSYpFoapcuTK7du3izJkz1K1bl4EDB+Lj48OECRMACAsL4+nTp/j7+2NmZqZsnTt3Bl7WCh85coRr167RvHlznJycmDRpEubm5oV5W0IIIYQoYmT1CSFy4dXTq7L6RMkiq08IIUTRlpfVJ6SmWIg8OOrv9dYfKiGEEEIUPVI+IYQQQgghSjxJioUQQgghRIkn5RNC5EGLCb+WiJpiqaUVQghR0shMcSFwc3Nj+PDhhR1GgbO2tmbBggU5Hvf29qZTp07v5Nrvsm8hhBBCFH8yU1wItmzZkuUNa0IIIYQQovBIUlwIypUrV9ghFBvp6enKm/CEEEIIIfJLyifyYdOmTTg6OqKrq0v58uVxd3fnyZMnwP/9GX/q1KmYmJhgaGjIwIEDefHihXL+P8snUlJSGDduHBYWFmhra1OjRg1+/PFH5fgff/xBu3bt0NfXp1KlSvTs2ZO///47V/H8U3p6Oj4+PlStWhVdXV3s7OxYuHChWptX9zB37lzMzMwoX748gwcPJjU1VWkTHx9Px44d0dXVpWrVqgQFBeV6/N7Ub0pKCqNHj6Zy5cro6enxwQcfEBwcrBwPDAzE2NiYHTt24ODggLa2NjExMcrxN417XsZJCCGEECWLzBTnUVxcHF5eXsyePZtPPvmEx48fc+zYMV5/B8rBgwfR0dEhODiY6Oho+vTpQ/ny5Zk+fXq2ffbq1YuTJ0+yaNEi6taty61bt5SkNyEhgVatWtGvXz/mz5/Ps2fPGDduHJ6enhw6dChX8bwuIyODKlWqsHHjRsqXL8+JEycYMGAAZmZmeHp6Ku0OHz6MmZkZhw8f5saNG3Tr1o169erRv39/4GXi/Ndff3H48GFKly7N0KFDiY+Pf+v4va1fX19frly5wrp16zA3N2fr1q20bduWS5cuYWNjA8DTp0+ZNWsWK1eupHz58lSsWPGt457XcRJCCCFEySJvtMujc+fO0aBBA6Kjo7Gysspy3Nvbm99++43Y2FjKlCkDwPLlyxkzZgyJiYloaGjg5uZGvXr1WLBgAdeuXcPOzo79+/fj7u6epT9/f3+OHTvG3r17lX137tzBwsKCyMhIkpOT3xhPbvj6+nL37l02bdqk3ENwcDBRUVFoamoC4OnpiYaGBuvWrVNiPnPmDM7OzgBcvXoVe3t75s+fn+NDhG/rNyYmhmrVqhETE6P2mmZ3d3caNWrEjBkzCAwMpE+fPoSHh1O3bt1cj3t4eHiexiklJYWUlBTlc1JSEhYWFiXmjXay+oQQQojiIC9vtJPyiTyqW7curVu3xtHRka5du7JixQoePXqUpc2rxAygSZMmJCcnExsbm6W/8PBwNDU1cXV1zfZ6Fy5c4PDhw+jr6ytbzZo1AYiKispVPP/03Xff0aBBA0xMTNDX1+eHH35QK0EAqFWrlpK4ApiZmSkzwREREZQqVYoGDRoox2vWrImxsfEbr/u2fi9dukR6ejq2trZq93vkyBGioqKUc7S0tKhTp06Wvt807nkdp5kzZ2JkZKRsFhYWb703IYQQQhRdkhTnkaamJvv372f37t04ODiwePFi7OzsuHXrVr7609V986xjcnIyHTt2JDw8XG27fv06LVq0yHM869atY/To0fj4+LBv3z7Cw8Pp06ePWu0tkGV1DJVKRUZGRr7uMbf9Jicno6mpydmzZ9XuNSIiQq3uWVdXN88P1+V1nPz8/EhMTFS27P6HRgghhBDFhyTF+aBSqXBxcWHq1KmcP38eLS0ttm7dqhy/cOECz549Uz6fOnUKfX39bGcbHR0dycjI4MiRI9leq379+ly+fBlra2tq1Kihtunp6eUqnteFhITQtGlTBg0ahJOTEzVq1FCbhc2NmjVrkpaWxtmzZ5V9kZGRJCQk5Kmff3JyciI9PZ34+Pgs92pqavrW89827nkZJ21tbQwNDdU2IYQQQhRfkhTn0enTp5kxYwZhYWHExMSwZcsW7t+/j729vdLmxYsX+Pj4cOXKFXbt2sXkyZPx9fVFQyPrcFtbW9O7d2/69u3Ltm3buHXrFsHBwWzYsAGAwYMH8/DhQ7y8vAgNDSUqKoq9e/fSp08f0tPTcxXP62xsbAgLC2Pv3r1cu3aNiRMnEhoamqcxsLOzo23btnzxxRecPn2as2fP0q9fv7fOer+Nra0tPXr0oFevXmzZsoVbt25x5swZZs6cyc6dO996/pvGPa/jJIQQQoiSRVafyCNDQ0OOHj3KggULSEpKwsrKinnz5tGuXTulTevWrbGxsaFFixakpKTg5eXFlClTcuxz2bJlfP311wwaNIgHDx5gaWnJ119/DYC5uTkhISGMGzeODz/8kJSUFKysrGjbti0aGhq5iud1X3zxBefPn6dbt26oVCq8vLwYNGgQu3fvztM4rFq1in79+uHq6kqlSpXw9/dn4sSJeeojp379/f0ZNWoUf/75JxUqVKBx48Z06NDhree+adzzOk5CCCGEKFlk9YkC5u3tTUJCAtu2bSvsUEQBevX0qqw+IYQQQhQdeVl9QmaKhciDo/5eUl8shBBCFENSUyyEEEIIIUo8mSkuYIGBgYUdghBCCCGEyCOZKRZCCCGEECWezBQLkQctJvxaIh60E+Lfkoc1hRBFjcwUiwI1ZcoU6tWr98Y2ISEhODo6Urp0aTp16vTOYgkODkalUv3rl4oIIYQQoviTmWLxnxs5ciT16tVj9+7d6OvrF3Y4QgghhBAyUyz+e1FRUbRq1YoqVapgbGxc2OEIIYQQQkhS/D7KyMhg9uzZ1KhRA21tbSwtLZk+fbpyfNy4cdja2lKmTBmqVavGxIkTSU1NVY6/KmFYs2YN1tbWGBkZ0b17dx4/fqy0cXNzY+jQoYwdO5Zy5cphamqa5a17CQkJ9OvXDxMTEwwNDWnVqhUXLlxQaxMQEEClSpUwMDDAx8eH58+f53hf0dHRqFQqHjx4QN++fVGpVMpqHUeOHKFRo0Zoa2tjZmbGV199RVpamnJuSkoKQ4cOpWLFiujo6NCsWbMsr6fetWsXtra26Orq0rJlS6Kjo9WO3759m44dO1K2bFn09PSoVasWu3bteuP3QgghhBAlgyTF7yE/Pz8CAgKYOHEiV65cYe3atVSqVEk5bmBgQGBgIFeuXGHhwoWsWLGC+fPnq/URFRXFtm3b+P333/n99985cuQIAQEBam1Wr16Nnp4ep0+fZvbs2XzzzTfs379fOd61a1fi4+PZvXs3Z8+epX79+rRu3ZqHDx8CsGHDBqZMmcKMGTMICwvDzMyMpUuX5nhfFhYWxMXFYWhoyIIFC4iLi6Nbt278+eeffPTRRzg7O3PhwgWWLVvGjz/+iL+/v3Lu2LFj2bx5M6tXr+bcuXPUqFEDDw8PJZbY2Fg6d+5Mx44dCQ8Pp1+/fnz11Vdq1x88eDApKSkcPXqUS5cuMWvWLCnfEEIIIQQgr3l+7zx+/BgTExOWLFlCv379cnXO3LlzWbduHWFhYcDLmeI5c+Zw9+5dDAwMgJdJ5dGjRzl16hTwcqY4PT2dY8eOKf00atSIVq1aERAQwPHjx2nfvj3x8fFoa2srbWrUqMHYsWMZMGAATZs2xcnJie+++0453rhxY54/f054eHiO8RobG7NgwQK8vb0BGD9+PJs3byYiIgKVSgXA0qVLGTduHImJiTx79oyyZcsSGBjIZ599BkBqairW1tYMHz6cMWPG8PXXX7N9+3YuX76sXOerr75i1qxZPHr0CGNjY+rUqcOnn37K5MmT3zqmKSkppKSkKJ+TkpKwsLAoMa95FuLfktUnhBDvg7y85llmit8zERERpKSk0Lp16xzbrF+/HhcXF0xNTdHX12fChAnExMSotbG2tlYSYgAzMzPi4+PV2tSpU0ft8+ttLly4QHJyMuXLl0dfX1/Zbt26RVRUlBLrBx98oNZHkyZN8nXPTZo0URJiABcXF5KTk7lz5w5RUVGkpqbi4uKiHC9dujSNGjUiIiIi17EMHToUf39/XFxcmDx5MhcvXswxppkzZ2JkZKRsFhYWeb4vIYQQQhQdkhS/Z3R13zwLefLkSXr06MFHH33E77//zvnz5xk/fjwvXrxQa1e6dGm1zyqVioyMjFy3SU5OxszMjPDwcLUtMjKSMWPG5Pf2ClW/fv24efMmPXv25NKlSzRs2JDFixdn29bPz4/ExERli42N/Y+jFUIIIcR/SZLi94yNjQ26urocPHgw2+MnTpzAysqK8ePH07BhQ2xsbLh9+3aBx1G/fn3u3r1LqVKlqFGjhtpWoUIFAOzt7Tl9+rTaea/KM/LC3t6ekydP8nolT0hICAYGBlSpUoXq1aujpaVFSEiIcjw1NZXQ0FAcHByUPs6cOfPWWCwsLBg4cCBbtmxh1KhRrFixItuYtLW1MTQ0VNuEEEIIUXxJUvye0dHRYdy4cYwdO5aff/6ZqKgoTp06xY8//gi8TJpjYmJYt24dUVFRLFq0iK1btxZ4HO7u7jRp0oROnTqxb98+oqOjOXHiBOPHj1dql4cNG8ZPP/3EqlWruHbtGpMnT1ar6c2tQYMGERsby5AhQ7h69Srbt29n8uTJjBw5Eg0NDfT09Pjyyy8ZM2YMe/bs4cqVK/Tv35+nT5/i4+MDwMCBA7l+/TpjxowhMjKStWvXKitbvDJ8+HD27t3LrVu3OHfuHIcPH8be3v5fj5UQQgghij55ecd7aOLEiZQqVYpJkybx119/YWZmxsCBAwH43//+x4gRI/D19SUlJYX27dszceLELMup/VsqlYpdu3Yxfvx4+vTpw/379zE1NaVFixbKShjdunUjKiqKsWPH8vz5cz799FO+/PJL9u7dm6drVa5cmV27djFmzBjq1q1LuXLl8PHxYcKECUqbgIAAMjIy6NmzJ48fP6Zhw4bs3buXsmXLAmBpacnmzZsZMWIEixcvplGjRsyYMYO+ffsqfaSnpzN48GDu3LmDoaEhbdu2zbJqhxBCCCFKJll9QohcePX0qqw+IUTuyOoTQoj3QV5Wn5CZYiHy4Ki/l9QXCyGEEMWQ1BQLIYQQQogST5JiIYQQQghR4klSLIQQQgghSjypKRYiD1pM+FUetBOiCJIH/4QQbyMzxUWQt7c3nTp1KuwwsuXm5sbw4cPf2OaHH37AwsICDQ0NFixY8M5imTJlCvXq1Xtn/QshhBCi+JCZYvGfSkpKwtfXl2+//ZZPP/0UIyOjwg5JCCGEEEKSYvHfiomJITU1lfbt22NmZlbY4QghhBBCAFI+UeDc3Nzw9fXF19cXIyMjKlSowMSJE3n9HSlr1qyhYcOGGBgYYGpqymeffUZ8fLxaP5cvX6ZDhw4YGhpiYGBA8+bNiYqKyvaaoaGhmJiYMGvWLAD27NlDs2bNMDY2pnz58nTo0CHLuSdOnKBevXro6OjQsGFDtm3bhkqlIjw8XGnzxx9/0K5dO/T19alUqRI9e/bk77//Vo4/efKEXr16oa+vj5mZGfPmzXvj2AQGBuLo6AhAtWrVUKlUREdHA7Bs2TKqV6+OlpYWdnZ2rFmzRu3cmJgYPv74Y/T19TE0NMTT05N79+6ptQkICKBSpUoYGBjg4+PD8+fP1Y4HBwfTqFEj9PT0MDY2xsXFhdu3b78xZiGEEEKUDJIUvwOrV6+mVKlSnDlzhoULF/Ltt9+ycuVK5XhqairTpk3jwoULbNu2jejoaLy9vZXjf/75Jy1atEBbW5tDhw5x9uxZ+vbtS1paWpZrHTp0iDZt2jB9+nTGjRsHvExWR44cSVhYGAcPHkRDQ4NPPvmEjIwM4GUJQ8eOHXF0dOTcuXNMmzZNOfeVhIQEWrVqhZOTE2FhYezZs4d79+7h6emptBkzZgxHjhxh+/bt7Nu3j+DgYM6dO5fjuHTr1o0DBw4AcObMGeLi4rCwsGDr1q0MGzaMUaNG8ccff/DFF1/Qp08fDh8+DEBGRgYff/wxDx8+5MiRI+zfv5+bN2/SrVs3pe8NGzYwZcoUZsyYQVhYGGZmZixdulQ5npaWRqdOnXB1deXixYucPHmSAQMGoFKpso01JSWFpKQktU0IIYQQxZe85rmAubm5ER8fz+XLl5WE66uvvmLHjh1cuXIl23PCwsJwdnbm8ePH6Ovr8/XXX7Nu3ToiIyMpXbp0lvbe3t4kJCTQu3dvevXqxcqVK9USxH/6+++/MTEx4dKlS9SuXZvly5czYcIE7ty5g46ODgArV66kf//+nD9/nnr16uHv78+xY8fYu3ev0s+dO3ewsLAgMjISc3Nzypcvzy+//ELXrl0BePjwIVWqVGHAgAE5PkAXHh6Ok5MTt27dwtraGgAXFxdq1arFDz/8oLTz9PTkyZMn7Ny5k/3799OuXTtu3bqFhYUFAFeuXKFWrVqcOXMGZ2dnmjZtipOTE999953SR+PGjXn+/Dnh4eE8fPiQ8uXLExwcjKura45j9cqUKVOYOnVqlv3ymmchiiZZfUKIkikvr3mWmeJ3oHHjxmozkE2aNOH69eukp6cDcPbsWTp27IilpSUGBgZKkhYTEwO8TBybN2+ebUL8yunTp+natStr1qzJkhBfv34dLy8vqlWrhqGhoZJ8vuo/MjKSOnXqKAkxQKNGjdT6uHDhAocPH0ZfX1/ZatasCUBUVBRRUVG8ePGCDz74QDmnXLly2NnZ5WmsACIiInBxcVHb5+LiQkREhHLcwsJCSYgBHBwcMDY2Vmvzeizwctxfj83b2xsPDw86duzIwoULiYuLyzEmPz8/EhMTlS02NjbP9yWEEEKIokOS4v/YkydP8PDwwNDQkKCgIEJDQ9m6dSsAL168AEBX9+0zkdWrV6dmzZr89NNPpKamqh3r2LEjDx8+ZMWKFZw+fZrTp0+r9Z8bycnJdOzYkfDwcLXt+vXrtGjRItf9vE9WrVrFyZMnadq0KevXr8fW1pZTp05l21ZbWxtDQ0O1TQghhBDFlyTF78CrJPSVU6dOYWNjg6amJlevXuXBgwcEBATQvHlzatasmeUhuzp16nDs2LEsye7rKlSowKFDh7hx4waenp5K2wcPHhAZGcmECRNo3bo19vb2PHr0SO1cOzs7Ll26REpKirIvNDRUrU39+vW5fPky1tbW1KhRQ23T09OjevXqlC5dWu1eHz16xLVr1/I2WIC9vT0hISFq+0JCQnBwcFCOx8bGqs3WXrlyhYSEBLU22Y37Pzk5OeHn58eJEyeoXbs2a9euzXO8QgghhCh+JCl+B2JiYhg5ciSRkZH8+uuvLF68mGHDhgFgaWmJlpYWixcv5ubNm+zYsYNp06apne/r60tSUhLdu3cnLCyM69evs2bNGiIjI9XaVaxYkUOHDnH16lW8vLxIS0ujbNmylC9fnh9++IEbN25w6NAhRo4cqXbeZ599RkZGBgMGDCAiIoK9e/cyd+5cAKXsY/DgwTx8+BAvLy9CQ0OJiopi79699OnTh/T0dPT19fHx8WHMmDEcOnSIP/74A29vbzQ08v5PasyYMQQGBrJs2TKuX7/Ot99+y5YtWxg9ejQA7u7uODo60qNHD86dO8eZM2fo1asXrq6uNGzYEIBhw4bx008/sWrVKq5du8bkyZO5fPmyco1bt27h5+fHyZMnuX37Nvv27eP69evY29vnOV4hhBBCFD+SFL8DvXr14tmzZzRq1IjBgwczbNgwBgwYAICJiQmBgYFs3LgRBwcHAgIClIT0lfLly3Po0CGSk5NxdXWlQYMGrFixItsaY1NTUw4dOsSlS5fo0aMHmZmZrFu3jrNnz1K7dm1GjBjBnDlz1M4xNDTkt99+Izw8nHr16jF+/HgmTZoEoNQZm5ubExISQnp6Oh9++CGOjo4MHz4cY2NjJfGdM2cOzZs3p2PHjri7u9OsWTMaNGiQ5/Hq1KkTCxcuZO7cudSqVYvvv/+eVatW4ebmBrxM1Ldv307ZsmVp0aIF7u7uVKtWjfXr1yt9dOvWjYkTJzJ27FgaNGjA7du3+fLLL5XjZcqU4erVq3z66afY2toyYMAABg8ezBdffJHneIUQQghR/MjqEwXMzc2NevXqvdPXF78LQUFB9OnTh8TExFzVNJc0r55eldUnhCiaZPUJIUqmvKw+IW+0K6F+/vlnqlWrRuXKlblw4QLjxo3D09NTEuK3OOrvJQ/dCSGEEMWQJMUl1N27d5k0aRJ3797FzMyMrl27Mn369MIOSwghhBCiUEj5hBC5kJc/vwghhBDi/SDlE0K8Iy0m/Co1xQVMaj2FEEK8D2T1CSGEEEIIUeKVyKTY2tpabXUIlUrFtm3bCi2eokLGSQghhBDFVYlMiv8pLi6Odu3aFXYYBAYGYmxsXNhhCCGEEEKUOFJTzMsXYAghhBBCiJKrUGeKMzIymD17NjVq1EBbWxtLS0u1ZcEuXbpEq1at0NXVpXz58gwYMIDk5GTluLe3N506dWLu3LmYmZlRvnx5Bg8eTGpqqtImPj6ejh07oqurS9WqVQkKCsoSx+tlAdHR0ahUKjZs2EDz5s3R1dXF2dmZa9euERoaSsOGDdHX16ddu3bcv39frZ+VK1dib2+Pjo4ONWvWZOnSpcqxV/1u2bKFli1bUqZMGerWrcvJkycBCA4OVl6eoVKpUKlUTJkyBYClS5diY2ODjo4OlSpVokuXLjmO6YMHD/Dy8qJy5cqUKVMGR0dHfv31V7U2bm5uDB06lLFjx1KuXDlMTU2Va71y/fp1WrRogY6ODg4ODuzfvz/Ha76yadMmHB0dle+Xu7s7T548ASA0NJQ2bdpQoUIFjIyMcHV15dy5c1nGJzw8XNmXkJCASqUiODhY2Xf58mU6dOiAoaEhBgYGNG/enKioqFx9D168eIGvry9mZmbo6OhgZWXFzJkz33pfQgghhCj+CnWm2M/PjxUrVjB//nyaNWtGXFwcV69eBeDJkyd4eHjQpEkTQkNDiY+Pp1+/fvj6+hIYGKj0cfjwYczMzDh8+DA3btygW7du1KtXj/79+wMvE+e//vqLw4cPU7p0aYYOHUp8fPxbY5s8eTILFizA0tKSvn378tlnn2FgYMDChQspU6YMnp6eTJo0iWXLlgEv3wg3adIklixZgpOTE+fPn6d///7o6enRu3dvpd/x48czd+5cbGxsGD9+PF5eXty4cYOmTZuyYMECJk2aRGRkJAD6+vqEhYUxdOhQ1qxZQ9OmTXn48CHHjh3LMe7nz5/ToEEDxo0bh6GhITt37qRnz55Ur16dRo0aKe1Wr17NyJEjOX36NCdPnsTb2xsXFxfatGlDRkYGnTt3plKlSpw+fZrExESGDx/+xvGKi4vDy8uL2bNn88knn/D48WOOHTvGqxX/Hj9+TO/evVm8eDGZmZnMmzePjz76iOvXr2NgYPDW7wfAn3/+SYsWLXBzc+PQoUMYGhoSEhJCWlparr4HixYtYseOHWzYsAFLS0tiY2OJjY3N9lopKSmkpKQon5OSknIVoxBCCCGKpkJLih8/fszChQtZsmSJkjRWr16dZs2aAbB27VqeP3/Ozz//jJ6eHgBLliyhY8eOzJo1i0qVKgFQtmxZlixZgqamJjVr1qR9+/YcPHiQ/v37c+3aNXbv3s2ZM2dwdnYG4Mcff8Te3v6t8Y0ePRoPDw8Ahg0bhpeXFwcPHsTFxQUAHx8fteR88uTJzJs3j86dOwNQtWpVrly5wvfff6+WFI8ePZr27dsDMHXqVGrVqsWNGzeoWbMmRkZGqFQqtXKOmJgY9PT06NChAwYGBlhZWeHk5JRj3JUrV2b06NHK5yFDhrB37142bNiglhTXqVOHyZMnA2BjY8OSJUs4ePAgbdq04cCBA1y9epW9e/dibm4OwIwZM95Ydx0XF0daWhqdO3fGysoKAEdHR+V4q1at1Nr/8MMPGBsbc+TIETp06JBjv6/77rvvMDIyYt26dZQuXRoAW1tb5fjbvgcxMTHY2NjQrFkzVCqVEmd2Zs6cydSpU3MVlxBCCCGKvkIrn4iIiCAlJYXWrVvneLxu3bpKQgzg4uJCRkaGMpMKUKtWLTQ1NZXPZmZmykxwREQEpUqVokGDBsrxmjVr5uphtjp16ihfv0rAX0/yKlWqpFznyZMnREVF4ePjg76+vrL5+/ur/Wn/n/2amZkBvHHmuk2bNlhZWVGtWjV69uxJUFAQT58+zbF9eno606ZNw9HRkXLlyqGvr8/evXuJiYnJMY5Xsbw+bhYWFkpCDNCkSZMcrwlQt25dWrdujaOjI127dmXFihU8evRIOX7v3j369++PjY0NRkZGGBoakpycnCWuNwkPD6d58+ZKQvy63HwPvL29CQ8Px87OjqFDh7Jv374cr+Xn50diYqKy5TSjLIQQQojiodBminV1C+YFCP9MkFQqFRkZGQXar0qlynbfq+u8qnNesWIFH3zwgVo/ryfsOfX7pngNDAw4d+4cwcHB7Nu3j0mTJjFlyhRCQ0OzTe7nzJnDwoULWbBgAY6Ojujp6TF8+HBevHiRYxz/vJ/80NTUZP/+/Zw4cYJ9+/axePFixo8fz+nTp6latSq9e/fmwYMHLFy4ECsrK7S1tWnSpIkSl4bGy/8/e/0Fi6/XhsOb/83k5ntQv359bt26xe7duzlw4ACenp64u7uzadOmLP1pa2ujra2dj5EQQgghRFFUaDPFNjY26OrqcvDgwWyP29vbc+HCBeVBLYCQkBA0NDSws7PL1TVq1qxJWloaZ8+eVfZFRkaSkJDwr2L/p0qVKmFubs7NmzepUaOG2la1atVc96OlpUV6enqW/aVKlcLd3Z3Zs2dz8eJFoqOjOXToULZ9hISE8PHHH/P5559Tt25dqlWrxrVr1/J0P/b29sTGxhIXF6fsO3Xq1FvPU6lUuLi4MHXqVM6fP4+WlhZbt25V4ho6dCgfffQRtWrVQltbm7///ls518TEBEDtmq8/dAcvZ7ePHTuWJVmG3H8PDA0N6datGytWrGD9+vVs3ryZhw8f5m5ghBBCCFFsFdpMsY6ODuPGjWPs2LFoaWnh4uLC/fv3uXz5Mj4+PvTo0YPJkyfTu3dvpkyZwv379xkyZAg9e/ZUyhnexs7OjrZt2/LFF1+wbNkySpUqxfDhwwtslvp1U6dOZejQoRgZGdG2bVtSUlIICwvj0aNHjBw5Mld9WFtbk5yczMGDB6lbty5lypTh0KFD3Lx5kxYtWlC2bFl27dpFRkZGjv9jYGNjw6ZNmzhx4gRly5bl22+/5d69ezg4OOT6Xtzd3bG1taV3797MmTOHpKQkxo8f/8ZzTp8+zcGDB/nwww+pWLEip0+f5v79+0r9to2NDWvWrKFhw4YkJSUxZswYte+Drq4ujRs3JiAggKpVqxIfH8+ECRPUruHr68vixYvp3r07fn5+GBkZcerUKRo1aoSdnd1bvwfffvstZmZmODk5oaGhwcaNGzE1NZW1oYUQQghRuEuyTZw4kVGjRjFp0iTs7e3p1q2bUtdapkwZ9u7dy8OHD3F2dqZLly60bt2aJUuW5Okaq1atwtzcHFdXVzp37syAAQOoWLFigd9Lv379WLlyJatWrcLR0RFXV1cCAwPzNFPctGlTBg4cSLdu3TAxMWH27NkYGxuzZcsWWrVqhb29PcuXL+fXX3+lVq1a2fYxYcIE6tevj4eHB25ubpiamtKpU6c83YuGhgZbt27l2bNnNGrUiH79+qktlZcdQ0NDjh49ykcffYStrS0TJkxg3rx5ysN5P/74I48ePaJ+/fr07NmToUOHZvk+/PTTT6SlpdGgQQOGDx+Ov7+/2vHy5ctz6NAhkpOTcXV1pUGDBqxYsUIpBXnb98DAwIDZs2fTsGFDnJ2diY6OZteuXUrphhBCCCFKLlXm60WcQohsJSUlYWRkRN0hy9HULvi/NJRkZ+f0KuwQhBBCFFOvfn8nJiZiaGj4xrbyRjsh8uCov9dbf6iEEEIIUfTI342FEEIIIUSJJ0mxEEIIIYQo8aR8Qog8aDHhV6kpFnkiNdNCCFE0yEyxEEIIIYQo8SQpLiHc3NwYPnx4YYchhBBCCPFekqRYCCGEEEKUeJIUi0Lz4sWLwg5BCCGEEAKQpLhESUtLw9fXFyMjIypUqMDEiRN59e6WlJQURo8eTeXKldHT0+ODDz4gODgYeLnwta6uLrt371brb+vWrRgYGPD06VMAYmNj8fT0xNjYmHLlyvHxxx8THR2ttPf29qZTp05Mnz4dc3Nz5VXVr17/bGBggKmpKZ999pnyZsNXduzYgY2NDTo6OrRs2ZLVq1ejUqlISEhQ2hw/fpzmzZujq6uLhYUFQ4cO5cmTJ8rxpUuXKn1UqlSJLl26FNTQCiGEEKKIk6S4BFm9ejWlSpXizJkzLFy4kG+//ZaVK1cC4Ovry8mTJ1m3bh0XL16ka9eutG3bluvXr2NoaEiHDh1Yu3atWn9BQUF06tSJMmXKkJqaioeHBwYGBhw7doyQkBD09fVp27at2ozwwYMHiYyMZP/+/fz+++8ApKamMm3aNC5cuMC2bduIjo7G29tbOefWrVt06dKFTp06ceHCBb744gvGjx+vFktUVBRt27bl008/5eLFi6xfv57jx4/j6+sLQFhYGEOHDuWbb74hMjKSPXv20KJFixzHKiUlhaSkJLVNCCGEEMWXvOa5hHBzcyM+Pp7Lly+jUqkA+Oqrr9ixYwd79uyhWrVqxMTEYG5urpzj7u5Oo0aNmDFjBtu2baNnz57cu3ePMmXKkJSURKVKldi6dStt27bll19+wd/fn4iICKX/Fy9eYGxszLZt2/jwww/x9vZmz549xMTEoKWllWOsYWFhODs78/jxY/T19fnqq6/YuXMnly5dUtpMmDCB6dOn8+jRI4yNjenXrx+ampp8//33Spvjx4/j6urKkydP2LVrF3369OHOnTsYGBi8dbymTJnC1KlTs+yX1zyLvJIl2YQQovDk5TXPMlNcgjRu3FhJWAGaNGnC9evXuXTpEunp6dja2qKvr69sR44cISoqCoCPPvqI0qVLs2PHDgA2b96MoaEh7u7uAFy4cIEbN25gYGCgnF+uXDmeP3+u9AHg6OiYJSE+e/YsHTt2xNLSEgMDA1xdXQGIiYkBIDIyEmdnZ7VzGjVqpPb5woULBAYGqsXv4eFBRkYGt27dok2bNlhZWVGtWjV69uxJUFCQUvaRHT8/PxITE5UtNjY2T2MthBBCiKJFXt4hSE5ORlNTk7Nnz6Kpqal2TF9fHwAtLS26dOnC2rVr6d69O2vXrqVbt26UKlVK6aNBgwYEBQVl6d/ExET5Wk9PT+3YkydP8PDwwMPDg6CgIExMTIiJicHDwyNPD+IlJyfzxRdfMHTo0CzHLC0t0dLS4ty5cwQHB7Nv3z4mTZrElClTCA0NxdjYOMs52traaGtr5/r6QgghhCjaJCkuQU6fPq32+dSpU9jY2ODk5ER6ejrx8fE0b948x/N79OhBmzZtuHz5MocOHcLf3185Vr9+fdavX0/FihXf+ueJ1129epUHDx4QEBCAhYUF8LJ84nV2dnbs2rVLbV9oaKja5/r163PlyhVq1KiR47VKlSqFu7s77u7uTJ48GWNjYw4dOkTnzp1zHa8QQgghiicpnyhBYmJiGDlyJJGRkfz6668sXryYYcOGYWtrS48ePejVqxdbtmzh1q1bnDlzhpkzZ7Jz507l/BYtWmBqakqPHj2oWrUqH3zwgXKsR48eVKhQgY8//phjx45x69YtgoODGTp0KHfu3MkxplezuIsXL+bmzZvs2LGDadOmqbX54osvuHr1KuPGjePatWts2LCBwMBAAKUcZNy4cZw4cQJfX1/Cw8O5fv0627dvVx60+/3331m0aBHh4eHcvn2bn3/+mYyMDGUFDCGEEEKUbJIUlyC9evXi2bNnNGrUiMGDBzNs2DAGDBgAwKpVq+jVqxejRo3Czs6OTp06ERoaiqWlpXK+SqXCy8uLCxcu0KNHD7W+y5Qpw9GjR7G0tKRz587Y29vj4+PD8+fP3zhzbGJiQmBgIBs3bsTBwYGAgADmzp2r1qZq1aps2rSJLVu2UKdOHZYtW6asPvGqxKFOnTocOXKEa9eu0bx5c5ycnJg0aZLy4KCxsTFbtmyhVatW2Nvbs3z5cn799Vdq1ar17wdWCCGEEEWerD4hiqTp06ezfPny/+wBuFdPr8rqEyKvZPUJIYQoPHlZfUJqikWRsHTpUpydnSlfvjwhISHMmTNHKY34Lx3198pTzbQQQgghigZJikWRcP36dfz9/Xn48CGWlpaMGjUKPz+/wg5LCCGEEMWElE8IkQt5+fOLEEIIId4PUj4hxDvSYsKvUlMsijSpcRZCiOzJ6hPivePm5sbw4cNz1TY4OBiVSkVCQsI7jUkIIYQQxZskxaJIa9q0KXFxcRgZGRV2KEIIIYQowvKVFD958qSg4xAiX7S0tDA1NVVe4vFP6enpZGRk/MdRCSGEEKKoyVdSXKlSJfr27cvx48cLOh5Rwjx58oRevXqhr6+PmZkZ8+bNUzu+Zs0aGjZsiIGBAaampnz22WfEx8crx/9ZPhEYGIixsTE7duzAwcEBbW1tjh8/TunSpbl7965a38OHD3/ja62FEEIIUXLkKyn+5ZdfePjwIa1atcLW1paAgAD++uuvgo5NlABjxozhyJEjbN++nX379hEcHMy5c+eU46mpqUybNo0LFy6wbds2oqOj8fb2fmOfT58+ZdasWaxcuZLLly/TsGFDqlWrxpo1a9T6DQoKom/fvu/q1oQQQghRhOQrKe7UqRPbtm3jzz//ZODAgaxduxYrKys6dOjAli1bSEtLK+g4RTGUnJzMjz/+yNy5c2ndujWOjo6sXr1a7d9P3759adeuHdWqVaNx48YsWrSI3bt3k5ycnGO/qampLF26lKZNm2JnZ0eZMmXw8fFh1apVSpvffvuN58+f4+npmW0fKSkpJCUlqW1CCCGEKL7+1YN2JiYmjBw5kosXL/Ltt99y4MABunTpgrm5OZMmTeLp06cFFacohqKionjx4gUffPCBsq9cuXLY2dkpn8+ePUvHjh2xtLTEwMAAV1dXAGJiYnLsV0tLizp16qjt8/b25saNG5w6dQp4WWbh6emJnp5etn3MnDkTIyMjZbOwsMj3fQohhBDi/fevkuJ79+4xe/ZsHBwc+Oqrr+jSpQsHDx5k3rx5bNmyhU6dOhVQmKIkevLkCR4eHhgaGhIUFERoaChbt24F4MWLFzmep6urm+XBu4oVK9KxY0dWrVrFvXv32L179xtLJ/z8/EhMTFS22NjYgrkpIYQQQryX8vXyji1btrBq1Sr27t2Lg4MDgwYN4vPPP8fY2Fhp07RpU+zt7QsqTlEMVa9endKlS3P69GksLS0BePToEdeuXcPV1ZWrV6/y4MEDAgIClJnasLCwfF+vX79+eHl5UaVKFapXr46Li0uObbW1tdHW1s73tYQQQghRtOQrKe7Tpw9eXl6EhITg7OycbRtzc3PGjx//r4ITxZu+vj4+Pj6MGTOG8uXLU7FiRcaPH4+Gxss/YFhaWqKlpcXixYsZOHAgf/zxB9OmTcv39V7NOvv7+/PNN98U1G0IIYQQohjIc/lEWloaM2fOZOrUqTkmxPDyT9iTJ0/+V8GJ4m/OnDk0b96cjh074u7uTrNmzWjQoAHwsmY9MDCQjRs34uDgQEBAAHPnzs33tTQ0NPD29iY9PZ1eveRVt0IIIYT4P6rMzMzMvJ5UpkwZIiIisLKyehcxCfHO+Pj4cP/+fXbs2JGn85KSkjAyMqLukOVoauu+o+iEePfOzpH/IRRClByvfn8nJiZiaGj4xrb5Kp9o1KgR58+fl6RYFBmJiYlcunSJtWvX5jkhft1Rf6+3/lAJIYQQoujJV1I8aNAgRo0axZ07d2jQoEGWZa3+uRyWEIXt448/5syZMwwcOJA2bdoUdjhCCCGEeM/kq3zi1YNQah2pVGRmZqJSqUhPTy+Q4IR4X+Tlzy9CCCGEeD+88/KJW7du5SswIYQQQggh3kf5SoqllliUVC0m/CoP2om3kofZhBCi6Mn3G+2ioqIYMmQI7u7uuLu7M3ToUKKiogoyNlEMREdHo1KpCA8PL+xQhBBCCCFylK+k+NWb7M6cOUOdOnWoU6cOp0+fplatWuzfv7+gYxRCCCGEEOKdylf5xFdffcWIESMICAjIsn/cuHHydL8QQgghhChS8jVTHBERgY+PT5b9ffv25cqVK/86KFH0ZGRkMHv2bGrUqIG2tjaWlpZMnz5dOX7z5k1atmxJmTJlqFu3LidPnlQ7//jx4zRv3hxdXV0sLCwYOnQoT548UY6npKQwevRoKleujJ6eHh988AHBwcHK8du3b9OxY0fKli2Lnp4etWrVYteuXcrxP/74g3bt2qGvr0+lSpXo2bMnf//997sbECGEEEIUKflKik1MTLKtEQ0PD6dixYr/NiZRBPn5+REQEMDEiRO5cuUKa9eupVKlSsrx8ePHM3r0aMLDw7G1tcXLy4u0tDTgZX1627Zt+fTTT7l48SLr16/n+PHj+Pr6Kuf7+vpy8uRJ1q1bx8WLF+natStt27bl+vXrAAwePJiUlBSOHj3KpUuXmDVrFvr6+gAkJCTQqlUrnJycCAsLY8+ePdy7dw9PT8//cISEEEII8T7L1zrF33zzDfPnz+err76iadOmAISEhDBr1ixGjhzJxIkTCzxQ8f56/PgxJiYmLFmyhH79+qkdi46OpmrVqqxcuVL568KVK1eoVasWERER1KxZk379+qGpqcn333+vnHf8+HFcXV158uQJ8fHxVKtWjZiYGMzNzZU27u7uNGrUiBkzZlCnTh0+/fRTJk+enCU+f39/jh07xt69e5V9d+7cwcLCgsjISGxtbbOck5KSQkpKivI5KSkJCwsLec2zyBVZfUIIId4P73yd4okTJ2JgYMC8efPw8/MDwNzcnClTpjB06ND8dCmKsIiICFJSUmjdunWObV5/y6GZmRkA8fHx1KxZkwsXLnDx4kWCgoKUNpmZmWRkZHDr1i1u3rxJenp6luQ1JSWF8uXLAzB06FC+/PJL9u3bh7u7O59++qlyzQsXLnD48GFl5vh1UVFR2SbFM2fOZOrUqXkYBSGEEEIUZflKilUqFSNGjGDEiBE8fvwYAAMDgwINTBQdurpvnzktXbq08rVKpQJe1iEDJCcn88UXX2T7P1SWlpZcvHgRTU1Nzp49i6amptrxV4luv3798PDwYOfOnezbt4+ZM2cyb948hgwZQnJyMh07dmTWrFlZ+n+VoP+Tn58fI0eOVD6/mikWQgghRPGUr6T4dZIMCxsbG3R1dTl48GCW8oncqF+/PleuXKFGjRrZHndyciI9PZ34+HiaN2+eYz8WFhYMHDiQgQMH4ufnx4oVKxgyZAj169dn8+bNWFtbU6pU7v7Ja2tro62tned7EUIIIUTRlK+k2MnJSZnte51KpUJHR4caNWrg7e1Ny5Yt/3WA4v2no6PDuHHjGDt2LFpaWri4uHD//n0uX778xpKKV8aNG0fjxo3x9fWlX79+6OnpceXKFfbv38+SJUuwtbWlR48e9OrVi3nz5uHk5MT9+/c5ePAgderUoX379gwfPpx27dpha2vLo0ePOHz4MPb29sDLh/BWrFiBl5cXY8eOpVy5cty4cYN169axcuXKLLPPQgghhCh58rX6RNu2bbl58yZ6enq0bNmSli1boq+vT1RUFM7OzsTFxeHu7s727dsLOl7xnpo4cSKjRo1i0qRJ2Nvb061bN+Lj43N1bp06dThy5AjXrl2jefPmODk5MWnSJLWH6latWkWvXr0YNWoUdnZ2dOrUidDQUCwtLQFIT09n8ODB2Nvb07ZtW2xtbVm6dCnwst49JCSE9PR0PvzwQxwdHRk+fDjGxsZoaOT7pY5CCCGEKEbytfpE//79sbS0zLLKhL+/P7dv32bFihVMnjyZnTt3EhYWVmDBClFYXj29KqtPiNyQ1SeEEOL9kJfVJ/KVFBsZGXH27NksNaA3btygQYMGJCYmcvXqVZydnZUH8YQoyvLyQyWEEEKI90Nefn/n62/HOjo6nDhxIsv+EydOoKOjA7xcWeDV10IIIYQQQrzP8vWg3ZAhQxg4cCBnz57F2dkZgNDQUFauXMnXX38NwN69e6lXr16BBSqEEEIIIcS7kq/yCYCgoCCWLFlCZGQkAHZ2dgwZMoTPPvsMgGfPnimrUQhR1En5hBBCCFH0vPOaYiFKGnnQThRH8kCgEKK4e+c1xQAJCQlKucTDhw8BOHfuHH/++Wd+uxRCCCGEEKJQ5CspvnjxIra2tsyaNYs5c+aQkJAAwJYtW/Dz8yvI+IQoMNHR0ahUKsLDwws7FCGEEEK8Z/KVFI8cORJvb2+uX7+uVjP80UcfcfTo0QILTgghhBBCiP9CvpLi0NBQvvjiiyz7K1euzN27d/91UKL4c3NzY8iQIQwfPpyyZctSqVIlVqxYwZMnT+jTpw8GBgbUqFGD3bt3K+f88ccftGvXDn19fSpVqkTPnj35+++/leN79uyhWbNmGBsbU758eTp06EBUVJRyvGrVqsD/vabczc3tP7tfIYQQQrzf8pUUa2trk5SUlGX/tWvXMDEx+ddBiZJh9erVVKhQgTNnzjBkyBC+/PJLunbtStOmTTl37hwffvghPXv25OnTpyQkJNCqVSucnJwICwtjz5493Lt3D09PT6W/J0+eMHLkSMLCwjh48CAaGhp88sknZGRkAHDmzBkADhw4QFxcHFu2bMkxtpSUFJKSktQ2IYQQQhRf+Vp9ol+/fjx48IANGzZQrlw5Ll68iKamJp06daJFixYsWLDgHYQqihM3NzfS09M5duwYAOnp6RgZGdG5c2d+/vlnAO7evYuZmRknT57kwIEDHDt2jL179yp93LlzBwsLCyIjI7G1tc1yjb///hsTExMuXbpE7dq1iY6OpmrVqpw/f/6ta2hPmTKFqVOnZtkvq0+I4kRWnxBCFHfvfPWJefPmkZycTMWKFXn27Bmurq7UqFEDAwMDpk+fnq+gRclTp04d5WtNTU3Kly+Po6Ojsq9SpUoAxMfHc+HCBQ4fPoy+vr6y1axZE0Apkbh+/TpeXl5Uq1YNQ0NDrK2tAYiJiclzbH5+fiQmJipbbGxsfm9TCCGEEEVAvt5oZ2RkxP79+wkJCeHChQskJydTv3593N3dCzo+UYyVLl1a7bNKpVLbp1KpgJevDE9OTqZjx47MmjUrSz9mZmYAdOzYESsrK1asWIG5uTkZGRnUrl2bFy9e5Dk2bW1ttLW183yeEEIIIYqmfCXFP//8M926dcPFxQUXFxdl/4sXL1i3bh29esmf5ETBql+/Pps3b8ba2ppSpbL+s33w4AGRkZGsWLGC5s2bA3D8+HG1NlpaWsDLUg0hhBBCiNflq3yiT58+JCYmZtn/+PFj+vTp86+DEuKfBg8ezMOHD/Hy8iI0NJSoqCj27t1Lnz59SE9Pp2zZspQvX54ffviBGzducOjQIUaOHKnWR8WKFdHV1VUe0svu37AQQgghSqZ8JcWZmZnKn7Zfd+fOHYyMjP51UEL8k7m5OSEhIaSnp/Phhx/i6OjI8OHDMTY2RkNDAw0NDdatW8fZs2epXbs2I0aMYM6cOWp9lCpVikWLFvH9999jbm7Oxx9/XEh3I4QQQoj3TZ5Wn3i1vuuFCxeoVauW2p+x09PTuXXrFm3btmXDhg3vJFghCsurp1dl9QlRnMjqE0KI4i4vq0/kqaa4U6dOAISHh+Ph4YG+vr5yTEtLC2traz799NO8RyxEEXHU3+utP1RCCCGEKHrylBRPnjwZAGtra7p166b2imchhBBCCCGKqnytPtG7d++CjkMIIYQQQohCk6+kOD09nfnz57NhwwZiYmKyrAP78OHDAglOiPdNiwm/Sk1xMSX1tUIIUbLla/WJqVOn8u2339KtWzcSExMZOXIknTt3RkNDgylTphRwiEIIIYQQQrxb+UqKg4KCWLFiBaNGjaJUqVJ4eXmxcuVKJk2axKlTpwo6RlGC/PDDD1hYWKChocGCBQve2XWmTJlCvXr13ln/QgghhCha8pUU3717F0dHRwD09fWVlyB06NCBnTt3Flx0okRJSkrC19eXcePG8eeffzJgwIDCDkkIIYQQJUS+kuIqVaoQFxcHQPXq1dm3bx8AoaGhaGtrF1x0okSJiYkhNTWV9u3bY2ZmRpkyZQo7JCGEEEKUEPlKij/55BMOHjwIwJAhQ5g4cSI2Njb06tWLvn37FmiA4v2yadMmHB0d0dXVpXz58ri7u/PkyRMAVq5cib29PTo6OtSsWZOlS5eqnTtu3DhsbW0pU6YM1apVY+LEiaSmpgIQGBio/PWhWrVqqFQqoqOjAVi2bBnVq1dHS0sLOzs71qxZo9ZvTEwMH3/8Mfr6+hgaGuLp6cm9e/fU2gQEBFCpUiUMDAzw8fHh+fPn72J4hBBCCFFE5Wv1iYCAAOXrbt26YWVlxYkTJ7CxsaFjx44FFpx4v8TFxeHl5cXs2bP55JNPePz4MceOHSMzM5OgoCAmTZrEkiVLcHJy4vz58/Tv3x89PT1lCT8DAwMCAwMxNzfn0qVL9O/fHwMDA8aOHUu3bt2wsLDA3d2dM2fOYGFhgYmJCVu3bmXYsGEsWLAAd3d3fv/9d/r06UOVKlVo2bIlGRkZSkJ85MgR0tLSGDx4MN26dSM4OBiADRs2MGXKFL777juaNWvGmjVrWLRoEdWqVcvxXlNSUkhJSVE+JyUlvdOxFUIIIUThytNrnl+ZOXMmlSpVyjIr/NNPP3H//n3GjRtXYAGK98e5c+do0KAB0dHRWFlZqR2rUaMG06ZNw8vLS9nn7+/Prl27OHHiRLb9zZ07l3Xr1hEWFga8fFOik5MTt27dwtraGgAXFxdq1arFDz/8oJzn6enJkydP2LlzJ/v376ddu3bcunULCwsLAK5cuUKtWrU4c+YMzs7ONG3aFCcnJ7777julj8aNG/P8+XPCw8OzjW3KlClMnTo1y355zXPxJUuyCSFE8ZOX1zznq3zi+++/p2bNmln216pVi+XLl+enS1EE1K1bl9atW+Po6EjXrl1ZsWIFjx494smTJ0RFReHj44O+vr6y+fv7ExUVpZy/fv16XFxcMDU1RV9fnwkTJhATE/PGa0ZERODi4qK2z8XFhYiICOW4hYWFkhADODg4YGxsrNbmgw8+UOujSZMmb7yun58fiYmJyhYbG/v2ARJCCCFEkZWv8om7d+9iZmaWZb+JiYnyAJ4ofjQ1Ndm/fz8nTpxg3759LF68mPHjx/Pbb78BsGLFiizJp6amJgAnT56kR48eTJ06FQ8PD4yMjFi3bh3z5s37z+8jN7S1teWhUSGEEKIEyddMsYWFBSEhIVn2h4SEYG5u/q+DEu8vlUqFi4sLU6dO5fz582hpaSnf95s3b1KjRg21rWrVqgCcOHECKysrxo8fT8OGDbGxseH27dtvvZ69vX2Wf2shISE4ODgox2NjY9Vmcq9cuUJCQoJam9OnT6v1IetpCyGEEOJ1+Zop7t+/P8OHDyc1NZVWrVoBcPDgQcaOHcuoUaMKNEDx/jh9+jQHDx7kww8/pGLFipw+fZr79+9jb2/P1KlTGTp0KEZGRrRt25aUlBTCwsJ49OgRI0eOxMbGhpiYGNatW4ezszM7d+5k69atb73mmDFj8PT0xMnJCXd3d3777Te2bNnCgQMHAHB3d8fR0ZEePXqwYMEC0tLSGDRoEK6urjRs2BCAYcOG4e3tTcOGDXFxcSEoKIjLly+/8UE7IYQQQpQs+UqKx4wZw4MHDxg0aBAvXrwAQEdHh3HjxuHn51egAYr3h6GhIUePHmXBggUkJSVhZWXFvHnzaNeuHQBlypRhzpw5jBkzBj09PRwdHRk+fDgA//vf/xgxYgS+vr6kpKTQvn17Jk6c+NbXgnfq1ImFCxcyd+5chg0bRtWqVVm1ahVubm7Ay5nr7du3M2TIEFq0aIGGhgZt27Zl8eLFSh/dunUjKiqKsWPH8vz5cz799FO+/PJL9u7d+y6GSQghhBBFUL5Wn3glOTmZiIgIdHV1sbGxkRpMUWy9enpVVp8ovmT1CSGEKH7ysvrEv0qKhSgp8vJDJYQQQoj3wztfkk0IIYQQQojiRJJiIYQQQghR4uXrQTshSqoWE36VmmIh/gWp3RZCvK9kplgIIYQQQpR4khQXEW5ubsryZvkVHByMSqUiISGhQGICiI6ORqVSER4eXmB9FiSVSsW2bdsKOwwhhBBCvOckKRZCCCGEECWeJMVCCCGEEKLEk6S4CElLS8PX1xcjIyMqVKjAxIkTeX2Z6TVr1tCwYUMMDAwwNTXls88+Iz4+Psf+nj59Srt27XBxcVFKKlauXIm9vT06OjrUrFmTpUuXqp1z5swZnJyc0NHRoWHDhpw/f/6tccfFxdG+fXt0dXWpWrUqa9euxdramgULFihtYmJi+Pjjj9HX18fQ0BBPT0/u3bun1s+yZcuoXr06Wlpa2NnZsWbNGrXj169fp0WLFujo6ODg4MD+/fvVjr948QJfX1/MzMzQ0dHBysqKmTNnvjV+IYQQQhR/svpEEbJ69Wp8fHw4c+YMYWFhDBgwAEtLS/r37w9Aamoq06ZNw87Ojvj4eEaOHIm3tze7du3K0ldCQgLt27dHX1+f/fv3U6ZMGYKCgpg0aRJLlizBycmJ8+fP079/f/T09OjduzfJycl06NCBNm3a8Msvv3Dr1i2GDRv21rh79erF33//TXBwMKVLl2bkyJFqyXpGRoaSEB85coS0tDQGDx5Mt27dCA4OBmDr1q0MGzaMBQsW4O7uzu+//06fPn2oUqUKLVu2JCMjg86dO1OpUiVOnz5NYmJilhrsRYsWsWPHDjZs2IClpSWxsbHExsZmG3NKSgopKSnK56SkpLfepxBCCCGKLkmKixALCwvmz5+PSqXCzs6OS5cuMX/+fCUp7tu3r9K2WrVqLFq0CGdnZ5KTk9HX11eO3b17l27dumFjY8PatWvR0tICYPLkycybN4/OnTsDULVqVa5cucL3339P7969Wbt2LRkZGfz444/o6OhQq1Yt7ty5w5dffpljzFevXuXAgQOEhobSsGFD4OVstI2NjdLm4MGDXLp0iVu3bmFhYQHAzz//TK1atQgNDcXZ2Zm5c+fi7e3NoEGDABg5ciSnTp1i7ty5tGzZkgMHDnD16lX27t2Lubk5ADNmzKBdu3bKdWJiYrCxsaFZs2aoVCqsrKxyjHvmzJlMnTo1F98VIYQQQhQHUj5RhDRu3BiVSqV8btKkCdevXyc9PR2As2fP0rFjRywtLTEwMMDV1RV4mQy+rk2bNtSoUYP169crCfGTJ0+IiorCx8cHfX19ZfP39ycqKgqAiIgI6tSpg46OjloMbxIZGUmpUqWoX7++sq9GjRqULVtW+RwREYGFhYWSEAM4ODhgbGxMRESE0sbFxUWtbxcXF7XjFhYWSkKcXWze3t6Eh4djZ2fH0KFD2bdvX45x+/n5kZiYqGw5zSgLIYQQoniQmeJi4smTJ3h4eODh4UFQUBAmJibExMTg4eHBixcv1Nq2b9+ezZs3c+XKFRwdHQFITk4GYMWKFXzwwQdq7TU1Nf+bm3jH6tevz61bt9i9ezcHDhzA09MTd3d3Nm3alKWttrY22trahRClEEIIIQqDzBQXIadPn1b7fOrUKWxsbNDU1OTq1as8ePCAgIAAmjdvTs2aNXN8yC4gIIDevXvTunVrrly5AkClSpUwNzfn5s2b1KhRQ22rWrUqAPb29ly8eJHnz5+rxfAmdnZ2pKWlqT2Qd+PGDR49eqR8tre3z1Lfe+XKFRISEnBwcFDahISEqPUdEhKidjw2Npa4uLg3xmZoaEi3bt1YsWIF69evZ/PmzTx8+PCN9yCEEEKI4k9miouQmJgYRo4cyRdffMG5c+dYvHgx8+bNA8DS0hItLS0WL17MwIED+eOPP5g2bVqOfc2dO5f09HRatWpFcHAwNWvWZOrUqQwdOhQjIyPatm1LSkoKYWFhPHr0iJEjR/LZZ58xfvx4+vfvj5+fH9HR0cydO/eNMdesWRN3d3cGDBjAsmXLKF26NKNGjUJXV1cpBXF3d8fR0ZEePXqwYMEC0tLSGDRoEK6urkod8pgxY/D09MTJyQl3d3d+++03tmzZwoEDB5Q+bG1t6d27N3PmzCEpKYnx48erxfLtt99iZmaGk5MTGhoabNy4EVNTU4yNjfP7LRFCCCFEMSEzxUVIr169ePbsGY0aNWLw4MEMGzaMAQMGAGBiYkJgYCAbN27EwcGBgICAtyas8+fPx9PTk1atWnHt2jX69evHypUrWbVqFY6Ojri6uhIYGKjMFOvr6/Pbb79x6dIlnJycGD9+PLNmzXpr3D///DOVKlWiRYsWfPLJJ/Tv3x8DAwOlNlmlUrF9+3bKli1LixYtcHd3p1q1aqxfv17po1OnTixcuJC5c+dSq1Ytvv/+e1atWoWbmxsAGhoabN26VRmffv36MX36dLU4DAwMmD17Ng0bNsTZ2Zno6Gh27dqFhob8GAghhBAlnSrz9YVuhfgP3LlzBwsLCw4cOEDr1q0LO5xcSUpKwsjIiLpDlqOprVvY4QhRZJ2d06uwQxBClCCvfn8nJiZiaGj4xraSFIt37tChQyQnJ+Po6EhcXBxjx47lzz//5Nq1a5QuXbqww8uVvPxQCSGEEOL9kJff31JTLN651NRUvv76a27evImBgQFNmzYlKCioyCTEQgghhCj+ZKZYiFyQmWIhhBCi6JGZYiHekRYTfpWa4hJE6l+FEKLkkMfuRYFxc3Nj+PDh+T4/ODgYlUpFQkJCtsejo6NRqVSEh4fn+xpCCCGEENmRmWJRZFhYWBAXF0eFChUKOxQhhBBCFDOSFIsiQ1NTE1NT08IOQwghhBDFkJRPiAKVlpaGr68vRkZGVKhQgYkTJ/LqWc41a9bQsGFDDAwMMDU15bPPPsvxVdQAT58+pV27dri4uJCQkJClfOJVucXBgwdp2LAhZcqUoWnTpkRGRqr14+/vT8WKFTEwMKBfv3589dVX1KtX710NgRBCCCGKIEmKRYFavXo1pUqV4syZMyxcuJBvv/2WlStXAi+XZps2bRoXLlxg27ZtREdH4+3tnW0/CQkJtGnThoyMDPbv3//GVzGPHz+eefPmERYWRqlSpejbt69yLCgoiOnTpzNr1izOnj2LpaUly5YtK8hbFkIIIUQxIOUTokBZWFgwf/58VCoVdnZ2XLp0ifnz59O/f3+1ZLVatWosWrQIZ2dnkpOT0dfXV47dvXuXbt26YWNjw9q1a9HS0nrjNadPn46rqysAX331Fe3bt+f58+fo6OiwePFifHx86NOnDwCTJk1i3759JCcnv7HPlJQUUlJSlM9JSUl5HgshhBBCFB0yUywKVOPGjVGpVMrnJk2acP36ddLT0zl79iwdO3bE0tISAwMDJZGNiYlR66NNmzbUqFGD9evXvzUhBqhTp47ytZmZGYBSlhEZGUmjRo3U2v/zc3ZmzpyJkZGRsllYWLz1HCGEEEIUXZIUi//E8+fP8fDwwNDQkKCgIEJDQ9m6dSsAL168UGvbvn17jh49ypUrV3LV9+tvxnuVkGdkZPyreP38/EhMTFS22NjYf9WfEEIIId5vUj4hCtTp06fVPp86dQobGxuuXr3KgwcPCAgIUGZdw8LCsu0jICAAfX19WrduTXBwMA4ODvmOx87OjtDQUHr1+r+XMISGhr71PG1tbbS1tfN9XSGEEEIULTJTLApUTEwMI0eOJDIykl9//ZXFixczbNgwLC0t0dLSYvHixdy8eZMdO3Ywbdq0HPuZO3cuPXr0oFWrVly9ejXf8QwZMoQff/yR1atXc/36dfz9/bl48aJaiYcQQgghhMwUiwLVq1cvnj17RqNGjdDU1GTYsGEMGDAAlUpFYGAgX3/9NYsWLaJ+/frMnTuX//3vfzn2NX/+fNLT02nVqhXBwcG5qi/+px49enDz5k1Gjx7N8+fP8fT0xNvbmzNnzvyb2xRCCCFEMaPKfLWIrBAlRJs2bTA1NWXNmjW5PicpKQkjIyPqDlmOprbuO4xOvE/Ozun19kZCCCHeW69+fycmJmJoaPjGtjJTLIq1p0+fsnz5cjw8PNDU1OTXX3/lwIED7N+/P1/9HfX3eusPlRBCCCGKHkmKRbGmUqnYtWsX06dP5/nz59jZ2bF582bc3d0LOzQhhBBCvEckKRbFmq6uLgcOHCjsMIQQQgjxnpPVJ4QQQgghRIknM8VC5EGLCb/Kg3YliDxoJ4QQJYfMFItiw9vbm06dOhV2GEIIIYQogiQpFkIIIYQQJZ4kxUIIIYQQosSTpFgUqE2bNuHo6Iiuri7ly5fH3d2dJ0+eKKUNc+fOxczMjPLlyzN48GBSU1OVc9esWUPDhg0xMDDA1NSUzz77jPj4eLX+L1++TIcOHTA0NMTAwIDmzZsTFRWVbSyhoaGYmJgwa9YsAC5cuEDLli0xMDDA0NCQBg0aEBYW9u4GQwghhBBFhjxoJwpMXFwcXl5ezJ49m08++YTHjx9z7NgxXr008fDhw5iZmXH48GFu3LhBt27dqFevHv379wcgNTWVadOmYWdnR3x8PCNHjsTb25tdu3YB8Oeff9KiRQvc3Nw4dOgQhoaGhISEkJaWliWWQ4cO0blzZ2bPns2AAQOAl698dnJyYtmyZWhqahIeHk7p0qX/o9ERQgghxPtMkmJRYOLi4khLS6Nz585YWVkB4OjoqBwvW7YsS5YsQVNTk5o1a9K+fXsOHjyoJMV9+/ZV2larVo1Fixbh7OxMcnIy+vr6fPfddxgZGbFu3TolmbW1tc0Sx9atW+nVqxcrV66kW7duyv6YmBjGjBlDzZo1AbCxscnxXlJSUkhJSVE+JyUl5WdIhBBCCFFESPmEKDB169aldevWODo60rVrV1asWMGjR4+U47Vq1UJTU1P5bGZmplYecfbsWTp27IilpSUGBga4uroCL5NZgPDwcJo3b/7G2d3Tp0/TtWtX1qxZo5YQA4wcOZJ+/frh7u5OQEBAjmUXADNnzsTIyEjZLCws8jYYQgghhChSJCkWBUZTU5P9+/eze/duHBwcWLx4MXZ2dty6dQsgSzKrUqnIyMgA4MmTJ3h4eGBoaEhQUBChoaFs3boVgBcvXgAv3073NtWrV6dmzZr89NNPavXKAFOmTOHy5cu0b9+eQ4cO4eDgoFzjn/z8/EhMTFS22NjYvA2GEEIIIYoUSYpFgVKpVLi4uDB16lTOnz+PlpZWjonn665evcqDBw8ICAigefPm1KxZM8tDdnXq1OHYsWNZkt3XVahQgUOHDnHjxg08PT2ztLW1tWXEiBHs27ePzp07s2rVqmz70dbWxtDQUG0TQgghRPElSbEoMKdPn2bGjBmEhYURExPDli1buH//Pvb29m8919LSEi0tLRYvXszNmzfZsWMH06ZNU2vj6+tLUlIS3bt3JywsjOvXr7NmzRoiIyPV2lWsWJFDhw5x9epVvLy8SEtL49mzZ/j6+hIcHMzt27cJCQkhNDQ0V7EJIYQQoviTpFgUGENDQ44ePcpHH32Era0tEyZMYN68ebRr1+6t55qYmBAYGMjGjRtxcHAgICCAuXPnqrUpX748hw4dIjk5GVdXVxo0aMCKFSuyrTE2NTXl0KFDXLp0iR49eqChocGDBw/o1asXtra2eHp60q5dO6ZOnVpg9y+EEEKIokuV+Wq9LCFEjpKSkjAyMqLukOVoar+9tlkUD2fn9CrsEIQQQvwLr35/JyYmvrUUUpZkEyIPjvp7SX2xEEIIUQxJ+YQQQgghhCjxJCkWQgghhBAlniTFQgghhBCixJOaYiHyoMWEX+VBu/ecPBwnhBAiP2SmuBC5ubkxfPjwf9VHcHAwKpWKhISEAompsFhbW7NgwYLCDkMIIYQQJZQkxaJY8vb2plOnToUdhhBCCCGKCEmKRYF58eJFYYcghBBCCJEvkhQXsrS0NHx9fTEyMqJChQpMnDiR19+nsmbNGho2bIiBgQGmpqZ89tlnxMfHZ+knJCSEOnXqoKOjQ+PGjfnjjz8AePLkCYaGhmzatEmt/bZt29DT0+Px48fZxuXm5oavr+8bY7O2tmbatGn06tULQ0NDBgwYAMDmzZupVasW2traWFtbM2/ePLW+4+Pj6dixI7q6ulStWpWgoCC149HR0ahUKsLDw5V9CQkJqFQqgoODlX2XL1+mQ4cOGBoaYmBgQPPmzYmKimLKlCmsXr2a7du3o1KplPNevHiBr68vZmZm6OjoYGVlxcyZM9/w3RFCCCFESSFJcSFbvXo1pUqV4syZMyxcuJBvv/2WlStXKsdTU1OZNm0aFy5cYNu2bURHR+Pt7Z2lnzFjxjBv3jxCQ0MxMTGhY8eOpKamoqenR/fu3Vm1apVa+1WrVtGlSxcMDAzyHRvA3LlzqVu3LufPn2fixImcPXsWT09PunfvzqVLl5gyZQoTJ04kMDBQOcfb25vY2FgOHz7Mpk2bWLp0abaJ/pv8+eeftGjRAm1tbQ4dOsTZs2fp27cvaWlpjB49Gk9PT9q2bUtcXBxxcXE0bdqURYsWsWPHDjZs2EBkZCRBQUFYW1tn239KSgpJSUlqmxBCCCGKL1l9opBZWFgwf/58VCoVdnZ2XLp0ifnz59O/f38A+vbtq7StVq0aixYtwtnZmeTkZPT19ZVjkydPpk2bNsDLZLZKlSps3boVT09P+vXrR9OmTYmLi8PMzIz4+Hh27drFgQMH/lVsAK1atWLUqFHK5x49etC6dWsmTpwIgK2tLVeuXGHOnDl4e3tz7do1du/ezZkzZ3B2dgbgxx9/xN7ePk/j9t1332FkZMS6desoXbq0cq1XdHV1SUlJwdTUVNkXExODjY0NzZo1Q6VSYWVllWP/M2fOZOrUqXmKSQghhBBFl8wUF7LGjRujUqmUz02aNOH69eukp6cDcPbsWTp27IilpSUGBga4uroCLxO81zVp0kT5uly5ctjZ2REREQFAo0aNqFWrFqtXrwbgl19+wcrKihYtWvyr2AAaNmyodk5ERAQuLi5q+1xcXJTzIiIiKFWqFA0aNFCO16xZE2Nj4zfG8k/h4eE0b95cSYhzw9vbm/DwcOzs7Bg6dCj79u3Lsa2fnx+JiYnKFhsbm6f4hBBCCFG0SFL8Hnvy5AkeHh4YGhoSFBREaGgoW7duBfL+UFu/fv2UEoZVq1bRp08ftYQ3v/T09P51H/+kofHyn+Xr9cupqalqbXR1875WcP369bl16xbTpk3j2bNneHp60qVLl2zbamtrY2hoqLYJIYQQoviSpLiQnT59Wu3zqVOnsLGxQVNTk6tXr/LgwQMCAgJo3rw5NWvWzLH29tSpU8rXjx494tq1a2olCZ9//jm3b99m0aJFXLlyhd69e/+r2HJib29PSEiI2r6QkBBsbW3R1NSkZs2apKWlcfbsWeV4ZGSk2jrLJiYmAMTFxSn7Xn/oDqBOnTocO3YsS7L8ipaWltqM9iuGhoZ069aNFStWsH79ejZv3szDhw9zvB8hhBBClAySFBeymJgYRo4cSWRkJL/++iuLFy9m2LBhAFhaWqKlpcXixYu5efMmO3bsYNq0adn2880333Dw4EH++OMPvL29qVChgto6vWXLlqVz586MGTOGDz/8kCpVqvyr2HIyatQoDh48yLRp07h27RqrV69myZIljB49GgA7Ozvatm3LF198wenTpzl79iz9+vVTm/nV1dWlcePGBAQEEBERwZEjR5gwYYLadXx9fUlKSqJ79+6EhYVx/fp11qxZQ2RkJPByZYyLFy8SGRnJ33//TWpqKt9++y2//vorV69e5dq1a2zcuBFTU9M8l24IIYQQoviRpLiQ9erVi2fPntGoUSMGDx7MsGHDlKXNTExMCAwMZOPGjTg4OBAQEMDcuXOz7ScgIIBhw4bRoEED7t69y2+//YaWlpZaGx8fH168eKH28F5+Y8tJ/fr12bBhA+vWraN27dpMmjSJb775Rm3FjFWrVmFubo6rqyudO3dmwIABVKxYUa2fn376ibS0NBo0aMDw4cPx9/dXO16+fHkOHTpEcnIyrq6uNGjQgBUrVig1xv3798fOzo6GDRtiYmJCSEgIBgYGzJ49m4YNG+Ls7Ex0dDS7du1SyjWEEEIIUXKpMl8v3BTF2po1axgxYgR//fVXloT5n9zc3KhXr568evn/S0pKwsjIiLpDlqOpnfd6ZvHfOTunV2GHIIQQ4j3x6vd3YmLiW58PkiXZSoCnT58SFxdHQEAAX3zxxVsTYpGzo/5e8tCdEEIIUQzJ341LgNmzZ1OzZk1MTU3x8/Mr7HCEEEIIId47Uj4hRC7k5c8vQgghhHg/SPmEEO9Iiwm/Sk1xLkltrxBCiKJEyieEEEIIIUSJJ0mxEEIIIYQo8SQpFsVOYGCgvJBDCCGEEHkiSbEQQgghhCjxJCkW7x03Nzd8fX3x9fXFyMiIChUqMHHiRF4tlJKSksLo0aOpXLkyenp6fPDBBwQHBwMQHBxMnz59SExMRKVSoVKpmDJlCgBLly7FxsYGHR0dKlWqRJcuXQrpDoUQQgjxvpHVJ8R7afXq1fj4+HDmzBnCwsIYMGAAlpaW9O/fH19fX65cucK6/9fe3cfVfP//A3+cTjqdrk6pdEEpVCJx5jJX9aG59hnbsL59KuY7jMgqF20YG2qsTWObYQpDn9nwsUxGxejrogtRJAk7Pr7Rh3TJknPevz/8vL/OVIpS6XG/3c7tds779Xq/3s/X08pzL6/zfsfGwtbWFnv27MGIESOQmZmJ/v37Y82aNViyZAlycnIAAEZGRkhNTcWcOXOwbds29O/fH4WFhTh27Fi116+oqEBFRYX4uaSkpMHnTERERI2HRTE1SXZ2dvjyyy8hkUjg4uKCzMxMfPnllxg+fDiio6OhUqlga2sLAAgNDUV8fDyio6OxcuVKKBQKSCQSWFtbi+OpVCoYGhpizJgxMDY2Rvv27aFUKqu9fnh4OJYtW9bg8yQiIqKmgdsnqEnq168fJBKJ+NnDwwO5ubnIzMyEWq2Gs7MzjIyMxNfRo0eRl5dX7Xivv/462rdvjw4dOsDPzw/bt2/HvXv3qu0fFhaG4uJi8XX9+vV6nR8RERE1LVwppmalrKwMUqkUaWlpkEqlWm1GRkbVnmdsbIz09HQcOXIEv/32G5YsWYKlS5ciJSWlyjtVyGQyyGSy+g6fiIiImigWxdQknTp1SuvzyZMn4eTkBKVSCbVajYKCAgwaNKjKc/X09KBWq586rqurC29vb3h7e+Pjjz+GqakpEhMT8eabbzbIHIiIiKj5YFFMTZJKpUJwcDCmT5+O9PR0rF27FpGRkXB2doavry/8/f0RGRkJpVKJ//znP0hISIC7uztGjx4NBwcHlJWVISEhAd27d4eBgQESExNx5coVDB48GGZmZvj111+h0Wjg4uLS2FMlIiKiJoBFMTVJ/v7+uH//Pvr06QOpVIqgoCBMmzYNABAdHY3ly5cjJCQEN27cgIWFBfr164cxY8YAAPr3748ZM2Zg0qRJuHPnDj7++GN4e3tj9+7dWLp0Kf788084OTlh586d6Nq1a2NOk4iIiJoIifD45q9ETYSXlxd69OiBNWvWNHYoopKSEigUCnSfvR5Smbyxw2kW0lb7N3YIRETUwj3++7u4uBgmJiY19uVKMVEd/L7c55k/VERERNT88JZsRERERNTicaWYmpzHj2wmIiIiellYFBPVweBFO7mnuJ5wzzERETUl3D5BRERERC0ei2J6qSZPnoxx48Y1dhhEREREWlgUExEREVGLx6K4BVCr1dBoNI0dRqOprKxs7BCIiIioiWNR3MR4eXkhMDAQgYGBUCgUsLCwwOLFi/HkM1YqKioQGhqKtm3bwtDQEH379tW6Y0NMTAxMTU2xb98+dOnSBTKZDCqVCkeOHEGfPn1gaGgIU1NTDBgwAH/88Yd43rfffouOHTtCT08PLi4u2LZtm1ZsEokEmzZtwvjx42FgYAAnJyfs27dPbFer1Zg6dSocHR0hl8vh4uKCqKioOucgOTkZXl5eMDAwgJmZGYYPH467d+8CAOLj4zFw4ECYmprC3NwcY8aMQV5ennjutWvXIJFI8M9//hOenp7Q19fH9u3bAQCbNm2Cq6sr9PX10blzZ3zzzTd1jo2IiIheTSyKm6AtW7ZAV1cXp0+fRlRUFL744gts2rRJbA8MDMSJEycQGxuLc+fOYcKECRgxYgRyc3PFPvfu3cNnn32GTZs24fz582jdujXGjRsHT09PnDt3DidOnMC0adMgkUgAAHv27EFQUBBCQkKQlZWF6dOnY8qUKUhKStKKbdmyZZg4cSLOnTuHUaNGwdfXF4WFhQAAjUaDdu3aYdeuXbhw4QKWLFmCDz/8ED/++GOt556RkYGhQ4eiS5cuOHHiBI4fP46xY8dCrVYDAMrLyxEcHIzU1FQkJCRAR0cH48ePf2olfOHChQgKCkJ2djaGDx+O7du3Y8mSJVixYgWys7OxcuVKLF68GFu2bKkyjoqKCpSUlGi9iIiI6NXFxzw3MV5eXigoKMD58+fFgnXhwoXYt28fLly4AJVKhQ4dOkClUsHW1lY8z9vbG3369MHKlSsRExODKVOmICMjA927dwcAFBYWwtzcHEeOHIGnp+dT1x0wYAC6du2KDRs2iMcmTpyI8vJy7N+/H8CjleJFixbh008/BfCoQDUyMsKBAwcwYsSIKucTGBiImzdv4qeffgLw6It2RUVF2Lt3b5X9/+u//gsqlQrHjx+vVb5u374NS0tLZGZmws3NDdeuXYOjoyPWrFmDoKAgsV+nTp3w6aefwsfHRzy2fPly/Prrr/if//mfp8ZdunQpli1b9tRxPua5/vCWbERE1NDq8phnrhQ3Qf369RMLYgDw8PBAbm4u1Go1MjMzoVar4ezsDCMjI/F19OhRrW0Eenp6cHd3Fz+3bt0akydPxvDhwzF27FhERUUhPz9fbM/OzsaAAQO04hgwYACys7O1jj05pqGhIUxMTFBQUCAe+/rrr9GzZ09YWlrCyMgIGzZsgEqlqvXcH68UVyc3Nxc+Pj7o0KEDTExM4ODgAABPXaNXr17i+/LycuTl5WHq1KlaOVu+fLlWzp4UFhaG4uJi8XX9+vVaz4GIiIiaHz68o5kpKyuDVCpFWloapFKpVpuRkZH4Xi6XaxXWABAdHY05c+YgPj4e//znP7Fo0SIcOnQI/fr1q/X1W7VqpfVZIpGIWxdiY2MRGhqKyMhIeHh4wNjYGKtXr8apU6dqPb5cXvMq7NixY9G+fXts3LgRtra20Gg0cHNzw4MHD7T6GRoaiu/LysoAABs3bkTfvn21+v01h4/JZDLIZLJax01ERETNG4viJuivReTJkyfh5OQEqVQKpVIJtVqNgoICDBo0qM5jK5VKKJVKhIWFwcPDAzt27EC/fv3g6uqK5ORkBAQEiH2Tk5PRpUuXWo+dnJyM/v37Y+bMmeKx6lZiq+Pu7o6EhIQqty7cuXMHOTk52Lhxozj32myzsLKygq2tLa5cuQJfX986xUNEREQtA4viJkilUiE4OBjTp09Heno61q5di8jISACAs7MzfH194e/vj8jISCiVSvznP/9BQkIC3N3dMXr06CrHvHr1KjZs2IC///3vsLW1RU5ODnJzc+Hv/2hf57x58zBx4kQolUp4e3vjl19+we7du3H48OFax+3k5IStW7fi4MGDcHR0xLZt25CSkgJHR8dajxEWFoZu3bph5syZmDFjBvT09JCUlIQJEyagdevWMDc3x4YNG2BjYwOVSoWFCxfWatxly5Zhzpw5UCgUGDFiBCoqKpCamoq7d+8iODi41vERERHRq4lFcRPk7++P+/fvo0+fPpBKpQgKCsK0adPE9ujoaCxfvhwhISG4ceMGLCws0K9fP4wZM6baMQ0MDHDx4kVs2bIFd+7cgY2NDWbNmoXp06cDAMaNG4eoqCh8/vnnCAoKgqOjI6Kjo+Hl5VXruKdPn44zZ85g0qRJkEgk8PHxwcyZM3HgwIFaj+Hs7IzffvsNH374Ifr06QO5XI6+ffvCx8cHOjo6iI2NxZw5c+Dm5gYXFxd89dVXtYrxv//7v2FgYIDVq1dj3rx5MDQ0RLdu3TB37txax0ZERESvLt59oonx8vJCjx49sGbNmsYOhZ7w+NurvPtE/eHdJ4iIqKHV5e4TXCkmqoPfl/s884eKiIiImh/eko2IiIiIWjyuFDcxTz6umYiIiIheDhbFRHUweNFO7il+BXA/MxER/RW3T9BL5+XlpXXXBwcHh1p/sTAmJgampqYNEhcRERG1XFwppkaXkpKi9QS6mkyaNAmjRo1q4IiIiIiopWFRTI3O0tKy1n3lcvkzHwVNREREVFfcPkEiLy8vzJ49G3PnzoWZmRmsrKywceNGlJeXY8qUKTA2NkanTp20HsaRlZWFkSNHwsjICFZWVvDz88Pt27fF9vLycvj7+8PIyAg2Njbik/me9NftE0VFRZg+fTqsrKygr68PNzc3xMXFAXh6+8TSpUvRo0cPbNu2DQ4ODlAoFHjnnXdQWloq9tFoNAgPD4ejoyPkcjm6d++On376qR4zR0RERM0di2LSsmXLFlhYWOD06dOYPXs23n//fUyYMAH9+/dHeno6hg0bBj8/P9y7dw9FRUUYMmQIlEolUlNTER8fj1u3bmHixIniePPmzcPRo0fxr3/9C7/99huOHDmC9PT0aq+v0WgwcuRIJCcn44cffsCFCxcQEREBqVRa7Tl5eXnYu3cv4uLiEBcXh6NHjyIiIkJsDw8Px9atW7F+/XqcP38eH3zwAf7xj3/g6NGj9ZM0IiIiava4fYK0dO/eHYsWLQIAhIWFISIiAhYWFnjvvfcAAEuWLMG3336Lc+fO4fDhw1AqlVi5cqV4/ubNm2FnZ4dLly7B1tYW33//PX744QcMHToUwKOiu127dtVe//Dhwzh9+jSys7Ph7OwMAOjQoUONMWs0GsTExMDY2BgA4Ofnh4SEBKxYsQIVFRVYuXIlDh8+DA8PD3G848eP47vvvoOnp2eVY1ZUVKCiokL8XFJSUmMMRERE1LyxKCYt7u7u4nupVApzc3N069ZNPGZlZQUAKCgowNmzZ5GUlAQjI6OnxsnLy8P9+/fx4MED9O3bVzzeunVruLi4VHv9jIwMtGvXTiyIa8PBwUEsiAHAxsYGBQUFAIDLly/j3r17eP3117XOefDgAZRKZbVjhoeHY9myZbWOgYiIiJo3FsWkpVWrVlqfJRKJ1jGJRALg0epsWVkZxo4di88+++ypcWxsbHD58uU6X/95vkRXVcwajQYAUFZWBgDYv38/2rZtq9VPJpNVO2ZYWBiCg4PFzyUlJbCzs6tzbERERNQ8sCim5/baa6/h559/hoODA3R1n/5PqWPHjmjVqhVOnToFe3t7AMDdu3dx6dKlarctuLu749///jcuXbpUp9Xi6nTp0gUymQwqlaraa1ZFJpPVWDQTERHRq4VftKPnNmvWLBQWFsLHxwcpKSnIy8vDwYMHMWXKFKjVahgZGWHq1KmYN28eEhMTkZWVhcmTJ0NHp/r/7Dw9PTF48GC89dZbOHToEK5evYoDBw4gPj7+uWI0NjZGaGgoPvjgA2zZsgV5eXlIT0/H2rVrsWXLluedOhEREb1iuFJMz83W1hbJyclYsGABhg0bhoqKCrRv3x4jRowQC9/Vq1eL2yyMjY0REhKC4uLiGsf9+eefERoaCh8fH5SXl6NTp05ad5Ooq08//RSWlpYIDw/HlStXYGpqitdeew0ffvjhc49JRERErxaJIAhCYwdB1NSVlJRAoVCg++z1kMr48JDmLm21f2OHQEREL8Hjv7+Li4thYmJSY1+uFBPVwe/LfZ75Q0VERETND/cUExEREVGLx6KYiIiIiFo8FsVERERE1OJxTzFRHQxetJNftKNXBr9wSET0f7hSTM/Ny8sLc+fOrbJt8uTJGDdu3AuNXx9jEBEREdUGV4qpQURFReHJu/15eXmhR48eWLNmTeMFRURERFQNFsXUIBQKRWOHQERERFRr3D5B9Wb//v1QKBTYvn271taHyZMn4+jRo4iKioJEIoFEIsG1a9cAAOfPn8eYMWNgYmICY2NjDBo0CHl5eVrjfv7557CxsYG5uTlmzZqFyspKsa2iogKhoaFo27YtDA0N0bdvXxw5ckRsj4mJgampKQ4ePAhXV1cYGRlhxIgRyM/Pb+h0EBERUTPCopjqxY4dO+Dj44Pt27fD19dXqy0qKgoeHh547733kJ+fj/z8fNjZ2eHGjRsYPHgwZDIZEhMTkZaWhnfffRcPHz4Uz01KSkJeXh6SkpKwZcsWxMTEICYmRmwPDAzEiRMnEBsbi3PnzmHChAkYMWIEcnNzxT737t3D559/jm3btuH333+HSqVCaGhog+eEiIiImg9un6AX9vXXX+Ojjz7CL7/8Ak9Pz6faFQoF9PT0YGBgAGtra63zFAoFYmNj0apVKwCAs7Oz1rlmZmZYt24dpFIpOnfujNGjRyMhIQHvvfceVCoVoqOjoVKpYGtrCwAIDQ1FfHw8oqOjsXLlSgBAZWUl1q9fj44dOwJ4VEh/8sknNc6poqICFRUV4ueSkpLnyAwRERE1FyyK6YX89NNPKCgoQHJyMnr37l2nczMyMjBo0CCxIK5K165dIZVKxc82NjbIzMwEAGRmZkKtVj9VSFdUVMDc3Fz8bGBgIBbEj8coKCioMbbw8HAsW7asTvMhIiKi5otFMb0QpVKJ9PR0bN68Gb169YJEIqn1uXL5s+/3+9eCWSKRQKPRAADKysoglUqRlpamVTgDgJGRUY1jPHlnjKqEhYUhODhY/FxSUgI7O7tnxktERETNE4tieiEdO3ZEZGQkvLy8IJVKsW7duir76enpQa1Wax1zd3fHli1bUFlZWeNqcXWUSiXUajUKCgowaNCg54q/OjKZDDKZrF7HJCIioqaLX7SjF+bs7IykpCT8/PPP1T7Mw8HBAadOncK1a9dw+/ZtaDQaBAYGoqSkBO+88w5SU1ORm5uLbdu2IScnp9bX9fX1hb+/P3bv3o2rV6/i9OnTCA8Px/79++txhkRERPSqY1FM9cLFxQWJiYnYuXMnQkJCnmoPDQ2FVCpFly5dYGlpCZVKBXNzcyQmJqKsrAyenp7o2bMnNm7cWKdV4+joaPj7+yMkJAQuLi4YN24cUlJSYG9vX5/TIyIiolecRHjW5koiQklJCRQKBbrPXg+p7Nl7oYmag7TV/o0dAhFRg3r893dxcTFMTExq7Ms9xUR18Ptyn2f+UBEREVHzw+0TRERERNTisSgmIiIiohaPRTERERERtXjcU0xUB4MX7eQX7ahG/PIaEVHzxJXi53Dt2jVIJBJkZGS81OvGxMTA1NT0pV7zZZg8eTLGjRvX2GEQERFRC8aiuBmZNGkSLl261NhhNHmv6v88EBERUcPh9olmRC6XQy5vmv90/+DBA+jp6TV2GERERETPhSvF1dBoNFi1ahU6deoEmUwGe3t7rFixotr+WVlZGDlyJIyMjGBlZQU/Pz/cvn1bbI+Pj8fAgQNhamoKc3NzjBkzBnl5eWL74y0Zu3fvxt/+9jcYGBige/fuOHHihNjnryugS5cuRY8ePbBt2zY4ODhAoVDgnXfeQWlpqdintLQUvr6+MDQ0hI2NDb788kt4eXlV+zjmJ8f97rvvYGdnBwMDA0ycOBHFxcVin8dbHlasWAFbW1u4uLgAADIzMzFkyBDI5XKYm5tj2rRpKCsrE89Tq9UIDg4W8zB//nz89fkxDg4OWLNmjdaxHj16YOnSpeLnoqIiTJ8+HVZWVtDX14ebmxvi4uJw5MgRTJkyBcXFxZBIJJBIJOJ533zzDZycnKCvrw8rKyu8/fbb1eaAiIiIWhYWxdUICwtDREQEFi9ejAsXLmDHjh2wsrKqsm9RURGGDBkCpVKJ1NRUxMfH49atW5g4caLYp7y8HMHBwUhNTUVCQgJ0dHQwfvx4aDQarbE++ugjhIaGIiMjA87OzvDx8cHDhw+rjTMvLw979+5FXFwc4uLicPToUURERIjtwcHBSE5Oxr59+3Do0CEcO3YM6enpz5z/5cuX8eOPP+KXX35BfHw8zpw5g5kzZ2r1SUhIQE5ODg4dOoS4uDiUl5dj+PDhMDMzQ0pKCnbt2oXDhw8jMDBQPCcyMhIxMTHYvHkzjh8/jsLCQuzZs+eZ8TxJo9Fg5MiRSE5Oxg8//IALFy4gIiICUqkU/fv3x5o1a2BiYoL8/Hzk5+cjNDQUqampmDNnDj755BPk5OQgPj4egwcPrvYaFRUVKCkp0XoRERHRq4vbJ6pQWlqKqKgorFu3DgEBAQCAjh07YuDAgVX2X7duHZRKJVauXCke27x5M+zs7HDp0iU4Ozvjrbfe0jpn8+bNsLS0xIULF+Dm5iYeDw0NxejRowEAy5YtQ9euXXH58mV07ty5ymtrNBrExMTA2NgYAODn54eEhASsWLECpaWl2LJlC3bs2IGhQ4cCAKKjo2Fra/vMHPz555/YunUr2rZtCwBYu3YtRo8ejcjISFhbWwMADA0NsWnTJnHbxMaNG8XzDA0NxdyMHTsWn332GaysrLBmzRqEhYXhzTffBACsX78eBw8efGY8Tzp8+DBOnz6N7OxsODs7AwA6dOggtisUCkgkEjFOAFCpVDA0NMSYMWNgbGyM9u3bQ6lUVnuN8PBwLFu2rE5xERERUfPFleIqZGdno6KiQiwkn+Xs2bNISkqCkZGR+HpcxD7eIpGbmwsfHx906NABJiYmcHBwAPCoWHuSu7u7+N7GxgYAUFBQUO21HRwcxIL48TmP+1+5cgWVlZXo06eP2K5QKMStDjWxt7cXC2IA8PDwgEajQU5OjnisW7duWvuIs7Oz0b17d7EgBoABAwaI5xUXFyM/Px99+/YV23V1ddGrV69nxvOkjIwMtGvXTiyIa+P1119H+/bt0aFDB/j5+WH79u24d+9etf3DwsJQXFwsvq5fv16nGImIiKh54UpxFer6ZbaysjJxNfSvHhe2Y8eORfv27bFx40bY2tpCo9HAzc0NDx480OrfqlUr8b1EIgGAp7ZYVNf/8Tk19a9PTxa/9UlHR+epfcaVlZXi++f5sqGxsTHS09Nx5MgR/Pbbb1iyZAmWLl2KlJSUKu9UIZPJIJPJ6nwdIiIiap64UlwFJycnyOVyJCQk1Kr/a6+9hvPnz8PBwQGdOnXSehkaGuLOnTvIycnBokWLMHToULi6uuLu3bsNPItHWwpatWqFlJQU8VhxcXGtbuumUqnwv//7v+LnkydPQkdHp8ZVZldXV5w9exbl5eXiseTkZPE8hUIBGxsbnDp1Smx/+PAh0tLStMaxtLREfn6++LmkpARXr14VP7u7u+Pf//53tfPQ09ODWq1+6riuri68vb2xatUqnDt3DteuXUNiYmINWSAiIqKWgkVxFfT19bFgwQLMnz8fW7duRV5eHk6ePInvv/++yv6zZs1CYWEhfHx8kJKSgry8PBw8eBBTpkyBWq2GmZkZzM3NsWHDBly+fBmJiYkIDg5u8HkYGxsjICAA8+bNQ1JSEs6fP4+pU6dCR0dHXIWujr6+PgICAnD27FkcO3YMc+bMwcSJE7X26f6Vr6+veF5WVhaSkpIwe/Zs+Pn5iV9SDAoKQkREBPbu3YuLFy9i5syZKCoq0hpnyJAh2LZtG44dO4bMzEwEBARAKpWK7Z6enhg8eDDeeustHDp0CFevXsWBAwcQHx8P4NGWkrKyMiQkJOD27du4d+8e4uLi8NVXXyEjIwN//PEHtm7dCo1GU6utJERERPTqY1FcjcWLFyMkJARLliyBq6srJk2aVO3eXltbWyQnJ0OtVmPYsGHo1q0b5s6dC1NTU+jo6EBHRwexsbFIS0uDm5sbPvjgA6xevfqlzOOLL76Ah4cHxowZA29vbwwYMACurq7Q19ev8bxOnTrhzTffxKhRozBs2DC4u7vjm2++qfEcAwMDHDx4EIWFhejduzfefvttDB06FOvWrRP7hISEwM/PDwEBAfDw8ICxsTHGjx+vNU5YWBg8PT0xZswYjB49GuPGjUPHjh21+vz888/o3bs3fHx80KVLF8yfP19cHe7fvz9mzJiBSZMmwdLSEqtWrYKpqSl2796NIUOGwNXVFevXr8fOnTvRtWvXuqSTiIiIXlES4a+bN+mVVl5ejrZt2yIyMhJTp06tss/SpUuxd+/el/4Y66aspKQECoUC3Wevh1TWNB+gQk1D2mr/xg6BiIj+v8d/fxcXF8PExKTGvvyi3SvuzJkzuHjxIvr06YPi4mJ88sknAIA33nijkSNrnn5f7vPMHyoiIiJqflgUtwCff/45cnJyoKenh549e+LYsWOwsLBo7LCalcf/oMKHeBARETUfj//ers3GCG6fIKqFK1euPLWvmYiIiJqH69evo127djX24UoxUS20bt0awKNb1SkUikaOpmUpKSmBnZ0drl+/zq0rLxHz3niY+8bD3DeOhsy7IAgoLS2t1dN8WRQT1YKOzqMbtSgUCv6ibCQmJibMfSNg3hsPc994mPvG0VB5r+1iFm/JRkREREQtHotiIiIiImrxWBQT1YJMJsPHH38MmUzW2KG0OMx942DeGw9z33iY+8bRVPLOu08QERERUYvHlWIiIiIiavFYFBMRERFRi8eimIiIiIhaPBbFRERERNTisSgmqoWvv/4aDg4O0NfXR9++fXH69OnGDqlZCw8PR+/evWFsbIw2bdpg3LhxyMnJ0erz559/YtasWTA3N4eRkRHeeust3Lp1S6uPSqXC6NGjYWBggDZt2mDevHl4+PDhy5xKsxYREQGJRIK5c+eKx5j3hnPjxg384x//gLm5OeRyObp164bU1FSxXRAELFmyBDY2NpDL5fD29kZubq7WGIWFhfD19YWJiQlMTU0xdepUlJWVveypNCtqtRqLFy+Go6Mj5HI5OnbsiE8//RRP3meAuX9xv//+O8aOHQtbW1tIJBLs3btXq72+cnzu3DkMGjQI+vr6sLOzw6pVq+pvEgIR1Sg2NlbQ09MTNm/eLJw/f1547733BFNTU+HWrVuNHVqzNXz4cCE6OlrIysoSMjIyhFGjRgn29vZCWVmZ2GfGjBmCnZ2dkJCQIKSmpgr9+vUT+vfvL7Y/fPhQcHNzE7y9vYUzZ84Iv/76q2BhYSGEhYU1xpSandOnTwsODg6Cu7u7EBQUJB5n3htGYWGh0L59e2Hy5MnCqVOnhCtXrggHDx4ULl++LPaJiIgQFAqFsHfvXuHs2bPC3//+d8HR0VG4f/++2GfEiBFC9+7dhZMnTwrHjh0TOnXqJPj4+DTGlJqNFStWCObm5kJcXJxw9epVYdeuXYKRkZEQFRUl9mHuX9yvv/4qfPTRR8Lu3bsFAMKePXu02usjx8XFxYKVlZXg6+srZGVlCTt37hTkcrnw3Xff1cscWBQTPUOfPn2EWbNmiZ/VarVga2srhIeHN2JUr5aCggIBgHD06FFBEAShqKhIaNWqlbBr1y6xT3Z2tgBAOHHihCAIj34B6+joCDdv3hT7fPvtt4KJiYlQUVHxcifQzJSWlgpOTk7CoUOHBE9PT7EoZt4bzoIFC4SBAwdW267RaARra2th9erV4rGioiJBJpMJO3fuFARBEC5cuCAAEFJSUsQ+Bw4cECQSiXDjxo2GC76ZGz16tPDuu+9qHXvzzTcFX19fQRCY+4bw16K4vnL8zTffCGZmZlq/axYsWCC4uLjUS9zcPkFUgwcPHiAtLQ3e3t7iMR0dHXh7e+PEiRONGNmrpbi4GADQunVrAEBaWhoqKyu18t65c2fY29uLeT9x4gS6desGKysrsc/w4cNRUlKC8+fPv8Tom59Zs2Zh9OjRWvkFmPeGtG/fPvTq1QsTJkxAmzZtoFQqsXHjRrH96tWruHnzplbuFQoF+vbtq5V7U1NT9OrVS+zj7e0NHR0dnDp16uVNppnp378/EhIScOnSJQDA2bNncfz4cYwcORIAc/8y1FeOT5w4gcGDB0NPT0/sM3z4cOTk5ODu3bsvHKfuC49A9Aq7ffs21Gq1VgEAAFZWVrh48WIjRfVq0Wg0mDt3LgYMGAA3NzcAwM2bN6GnpwdTU1OtvlZWVrh586bYp6o/l8dtVLXY2Fikp6cjJSXlqTbmveFcuXIF3377LYKDg/Hhhx8iJSUFc+bMgZ6eHgICAsTcVZXbJ3Pfpk0brXZdXV20bt2aua/BwoULUVJSgs6dO0MqlUKtVmPFihXw9fUFAOb+JaivHN+8eROOjo5PjfG4zczM7IXiZFFMRI1q1qxZyMrKwvHjxxs7lFfe9evXERQUhEOHDkFfX7+xw2lRNBoNevXqhZUrVwIAlEolsrKysH79egQEBDRydK+2H3/8Edu3b8eOHTvQtWtXZGRkYO7cubC1tWXuSQu3TxDVwMLCAlKp9Klv39+6dQvW1taNFNWrIzAwEHFxcUhKSkK7du3E49bW1njw4AGKioq0+j+Zd2tr6yr/XB630dPS0tJQUFCA1157Dbq6utDV1cXRo0fx1VdfQVdXF1ZWVsx7A7GxsUGXLl20jrm6ukKlUgH4v9zV9LvG2toaBQUFWu0PHz5EYWEhc1+DefPmYeHChXjnnXfQrVs3+Pn54YMPPkB4eDgA5v5lqK8cN/TvHxbFRDXQ09NDz549kZCQIB7TaDRISEiAh4dHI0bWvAmCgMDAQOzZsweJiYlP/XNYz5490apVK6285+TkQKVSiXn38PBAZmam1i/RQ4cOwcTE5Knigx4ZOnQoMjMzkZGRIb569eoFX19f8T3z3jAGDBjw1G0HL126hPbt2wMAHB0dYW1trZX7kpISnDp1Siv3RUVFSEtLE/skJiZCo9Ggb9++L2EWzdO9e/ego6Nd7kilUmg0GgDM/ctQXzn28PDA77//jsrKSrHPoUOH4OLi8sJbJwDwlmxEzxIbGyvIZDIhJiZGuHDhgjBt2jTB1NRU69v3VDfvv/++oFAohCNHjgj5+fni6969e2KfGTNmCPb29kJiYqKQmpoqeHh4CB4eHmL741uDDRs2TMjIyBDi4+MFS0tL3hqsjp68+4QgMO8N5fTp04Kurq6wYsUKITc3V9i+fbtgYGAg/PDDD2KfiIgIwdTUVPjXv/4lnDt3TnjjjTeqvGWVUqkUTp06JRw/flxwcnLibcGeISAgQGjbtq14S7bdu3cLFhYWwvz588U+zP2LKy0tFc6cOSOcOXNGACB88cUXwpkzZ4Q//vhDEIT6yXFRUZFgZWUl+Pn5CVlZWUJsbKxgYGDAW7IRvUxr164V7O3tBT09PaFPnz7CyZMnGzukZg1Ala/o6Gixz/3794WZM2cKZmZmgoGBgTB+/HghPz9fa5xr164JI0eOFORyuWBhYSGEhIQIlZWVL3k2zdtfi2LmveH88ssvgpubmyCTyYTOnTsLGzZs0GrXaDTC4sWLBSsrK0EmkwlDhw4VcnJytPrcuXNH8PHxEYyMjAQTExNhypQpQmlp6cucRrNTUlIiBAUFCfb29oK+vr7QoUMH4aOPPtK6rRdz/+KSkpKq/L0eEBAgCEL95fjs2bPCwIEDBZlMJrRt21aIiIiotzlIBOGJR7oQEREREbVA3FNMRERERC0ei2IiIiIiavFYFBMRERFRi8eimIiIiIhaPBbFRERERNTisSgmIiIiohaPRTERERERtXgsiomIiIioxWNRTERE9JyuXbsGiUSCjIyMxg6FiF4Qi2IiIiIiavFYFBMRUbOl0WiwatUqdOrUCTKZDPb29lixYgUAIDMzE0OGDIFcLoe5uTmmTZuGsrIy8VwvLy/MnTtXa7xx48Zh8uTJ4mcHBwesXLkS7777LoyNjWFvb48NGzaI7Y6OjgAApVIJiUQCLy+vBpsrETUsFsVERNRshYWFISIiAosXL8aFCxewY8cOWFlZoby8HMOHD4eZmRlSUlKwa9cuHD58GIGBgXW+RmRkJHr16oUzZ85g5syZeP/995GTkwMAOH36NADg8OHDyM/Px+7du+t1fkT08ug2dgBERETPo7S0FFFRUVi3bh0CAgIAAB07dsTAgQOxceNG/Pnnn9i6dSsMDQ0BAOvWrcPYsWPx2WefwcrKqtbXGTVqFGbOnAkAWLBgAb788kskJSXBxcUFlpaWAABzc3NYW1vX8wyJ6GXiSjERETVL2dnZqKiowNChQ6ts6969u1gQA8CAAQOg0WjEVd7acnd3F99LJBJYW1ujoKDg+QMnoiaJRTERETVLcrn8hc7X0dGBIAhaxyorK5/q16pVK63PEokEGo3mha5NRE0Pi2IiImqWnJycIJfLkZCQ8FSbq6srzp49i/LycvFYcnIydHR04OLiAgCwtLREfn6+2K5Wq5GVlVWnGPT09MRziah5Y1FMRETNkr6+PhYsWID58+dj69atyMvLw8mTJ/H999/D19cX+vr6CAgIQFZWFpKSkjB79mz4+fmJ+4mHDBmC/fv3Y//+/bh48SLef/99FBUV1SmGNm3aQC6XIz4+Hrdu3UJxcXEDzJSIXgYWxURE1GwtXrwYISEhWLJkCVxdXTFp0iQUFBTAwMAABw8eRGFhIXr37o23334bQ4cOxbp168Rz3333XQQEBMDf3x+enp7o0KED/va3v9Xp+rq6uvjqq6/w3XffwdbWFm+88UZ9T5GIXhKJ8NcNVURERERELQxXiomIiIioxWNRTEREREQtHotiIiIiImrxWBQTERERUYvHopiIiIiIWjwWxURERETU4rEoJiIiIqIWj0UxEREREbV4LIqJiIiIqMVjUUxERERELR6LYiIiIiJq8VgUExEREVGL9/8A2uqJkyGb7ZkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select only numeric columns\n",
        "numeric_df = df.select_dtypes(include=[float, int])"
      ],
      "metadata": {
        "id": "Fp0hvOU6vC-n"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the numeric DataFrame\n",
        "print(\"\\nNumeric DataFrame:\")\n",
        "print(numeric_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGlxJr81vEs5",
        "outputId": "81c4dd3c-c928-447d-f130-9edec87e3494"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Numeric DataFrame:\n",
            "      unit_price  quantity  total\n",
            "0           3.99         2   7.98\n",
            "1           3.99         1   3.99\n",
            "2           0.19         2   0.38\n",
            "3           0.19         4   0.76\n",
            "4           4.49         2   8.98\n",
            "...          ...       ...    ...\n",
            "7824       14.19         2  28.38\n",
            "7825       16.99         1  16.99\n",
            "7826       14.19         2  28.38\n",
            "7827       16.99         4  67.96\n",
            "7828       14.99         4  59.96\n",
            "\n",
            "[7829 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the correlation matrix\n",
        "corr_matrix = numeric_df.corr()"
      ],
      "metadata": {
        "id": "6YtCifUgvXux"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the correlation matrix\n",
        "print(\"\\nCorrelation Matrix:\")\n",
        "print(corr_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0u7WqmWHvayT",
        "outputId": "ec3b3b32-57a4-4f63-8501-41eba34874ba"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Correlation Matrix:\n",
            "            unit_price  quantity     total\n",
            "unit_price    1.000000  0.024588  0.792018\n",
            "quantity      0.024588  1.000000  0.521926\n",
            "total         0.792018  0.521926  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def correlation_plot(data: pd.DataFrame = None):\n",
        "  corr = df.corr()\n",
        "  corr.style.background_gradient(cmap='coolwarm')"
      ],
      "metadata": {
        "id": "WpKI7-hkU3fn"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(numeric_df.corr())"
      ],
      "metadata": {
        "id": "750W1EgAU80Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "12900fe6-e21f-4183-e97d-09e515f2e582"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}