Aerofit (1).ipynb

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      "source": [
        "**Introduction**"
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      "source": [
        " Aerofit is a leading brand in the field of fitness equipment. Aerofit provides a product\n",
        "range including machines such as treadmills, exercise bikes, gym equipment, and\n",
        "fitness accessories to cater to the needs of all categories of people."
      ],
      "metadata": {
        "id": "S0Rni8Dpi8cS"
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      "source": [
        "**Business Problem**"
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      "source": [
        "The market research team at AeroFit wants to identify the characteristics of the target\n",
        "audience for each type of treadmill offered by the company, to provide a better\n",
        "recommendation of the treadmills to new customers. The team decides to investigate\n",
        "whether there are differences across the product with respect to customer\n",
        "characteristics\n"
      ],
      "metadata": {
        "id": "A0otu8XgjJh8"
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    {
      "cell_type": "code",
      "source": [
        "#Importing libraries,loading the dataset and performing EDA"
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        "id": "hxjq61GojRUa"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from prettytable import prettytable"
      ],
      "metadata": {
        "id": "ZwkdqT1xLRH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('aerofit_treadmill.txt')\n",
        "\n"
      ],
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        "id": "erRm3s7GLbX-"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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              "  Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  Miles\n",
              "0   KP281   18    Male         14        Single      3        4   29562    112\n",
              "1   KP281   19    Male         15        Single      2        3   31836     75\n",
              "2   KP281   19  Female         14     Partnered      4        3   30699     66\n",
              "3   KP281   19    Male         12        Single      3        3   32973     85\n",
              "4   KP281   20    Male         13     Partnered      4        2   35247     47"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 180,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"KP281\",\n          \"KP481\",\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 18,\n        \"max\": 50,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          45,\n          33,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 12,\n        \"max\": 21,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Partnered\",\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16506,\n        \"min\": 29562,\n        \"max\": 104581,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          88396,\n          103336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 21,\n        \"max\": 360,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          95,\n          169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "(180, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 92
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        "There are 180 rows and 9 columns i dataset"
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      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing**"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product          0\n",
              "Age              0\n",
              "Gender           0\n",
              "Education        0\n",
              "MaritalStatus    0\n",
              "Usage            0\n",
              "Fitness          0\n",
              "Income           0\n",
              "Miles            0\n",
              "Age_group        0\n",
              "dtype: int64"
            ]
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          "metadata": {},
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        }
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      "source": [
        "df['Age_group'] = pd.cut(df['Age'], 3, labels=['young-adult','middleaged-adult','seniorCitizen'])"
      ],
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        "id": "X4DqDcSBd4ZY"
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            "text/plain": [
              "  Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "0   KP281   18    Male         14        Single      3        4   29562   \n",
              "1   KP281   19    Male         15        Single      2        3   31836   \n",
              "2   KP281   19  Female         14     Partnered      4        3   30699   \n",
              "3   KP281   19    Male         12        Single      3        3   32973   \n",
              "4   KP281   20    Male         13     Partnered      4        2   35247   \n",
              "\n",
              "   Miles Age_group  \n",
              "0    112     young  \n",
              "1     75     young  \n",
              "2     66     young  \n",
              "3     85     young  \n",
              "4     47     young  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 180,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"KP281\",\n          \"KP481\",\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 18,\n        \"max\": 50,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          45,\n          33,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 12,\n        \"max\": 21,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Partnered\",\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16506,\n        \"min\": 29562,\n        \"max\": 104581,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          88396,\n          103336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 21,\n        \"max\": 360,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          95,\n          169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"young\",\n          \"adult\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note**\n",
        "\n",
        "*   We do not have any NAN values in the data set\n",
        "*   We have created an extra column for better Analysis\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Izl6dr-8k-y0"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U4yhxWHA69i1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Statistical Description of Categorical variable**"
      ],
      "metadata": {
        "id": "nW5HzP_OlcdA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "OCnAWb_q7HqC",
        "outputId": "cbad333d-cb67-45cc-c5e2-33fbe0cccba9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Product Gender MaritalStatus\n",
              "count      180    180           180\n",
              "unique       3      2             2\n",
              "top      KP281   Male     Partnered\n",
              "freq        80    104           107"
            ],
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-47ab8c7b-13d3-4b63-aa23-eaf9cd62e562\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-47ab8c7b-13d3-4b63-aa23-eaf9cd62e562')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47ab8c7b-13d3-4b63-aa23-eaf9cd62e562 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"80\",\n          \"180\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"104\",\n          \"180\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"107\",\n          \"180\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Statistical Description of Numerical variables**"
      ],
      "metadata": {
        "id": "FmATPMrAlsKw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "kzWcs-_a7h4d",
        "outputId": "ece7c11b-f0c1-447d-8b7e-4d62292b5082"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age   Education       Usage     Fitness         Income  \\\n",
              "count  180.000000  180.000000  180.000000  180.000000     180.000000   \n",
              "mean    28.788889   15.572222    3.455556    3.311111   53719.577778   \n",
              "std      6.943498    1.617055    1.084797    0.958869   16506.684226   \n",
              "min     18.000000   12.000000    2.000000    1.000000   29562.000000   \n",
              "25%     24.000000   14.000000    3.000000    3.000000   44058.750000   \n",
              "50%     26.000000   16.000000    3.000000    3.000000   50596.500000   \n",
              "75%     33.000000   16.000000    4.000000    4.000000   58668.000000   \n",
              "max     50.000000   21.000000    7.000000    5.000000  104581.000000   \n",
              "\n",
              "            Miles  \n",
              "count  180.000000  \n",
              "mean   103.194444  \n",
              "std     51.863605  \n",
              "min     21.000000  \n",
              "25%     66.000000  \n",
              "50%     94.000000  \n",
              "75%    114.750000  \n",
              "max    360.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4fa1190a-4c01-411b-802b-65e835d455eb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Usage</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Income</th>\n",
              "      <th>Miles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>28.788889</td>\n",
              "      <td>15.572222</td>\n",
              "      <td>3.455556</td>\n",
              "      <td>3.311111</td>\n",
              "      <td>53719.577778</td>\n",
              "      <td>103.194444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.943498</td>\n",
              "      <td>1.617055</td>\n",
              "      <td>1.084797</td>\n",
              "      <td>0.958869</td>\n",
              "      <td>16506.684226</td>\n",
              "      <td>51.863605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29562.000000</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>44058.750000</td>\n",
              "      <td>66.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>50596.500000</td>\n",
              "      <td>94.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>58668.000000</td>\n",
              "      <td>114.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>104581.000000</td>\n",
              "      <td>360.000000</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-4fa1190a-4c01-411b-802b-65e835d455eb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4fa1190a-4c01-411b-802b-65e835d455eb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4fa1190a-4c01-411b-802b-65e835d455eb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-477180f7-e7c1-4f75-bc4c-40351b7b7724\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-477180f7-e7c1-4f75-bc4c-40351b7b7724')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-477180f7-e7c1-4f75-bc4c-40351b7b7724 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.58832332198464,\n        \"min\": 6.943498135399795,\n        \"max\": 180.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.788888888888888,\n          26.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.04362112875324,\n        \"min\": 1.6170548978065569,\n        \"max\": 180.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          180.0,\n          15.572222222222223,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.474604277313155,\n        \"min\": 1.0847970343962436,\n        \"max\": 180.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          180.0,\n          3.4555555555555557,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.63086276036247,\n        \"min\": 0.958868565619312,\n        \"max\": 180.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          180.0,\n          3.311111111111111,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31403.855763201762,\n        \"min\": 180.0,\n        \"max\": 104581.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          53719.57777777778,\n          50596.5,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106.52090041797727,\n        \"min\": 21.0,\n        \"max\": 360.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          103.19444444444444,\n          94.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**#UNIVARIENT ANALYSIS**"
      ],
      "metadata": {
        "id": "zmUnlFdO85yx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Product vise sales\n",
        "plt.figure(figsize=(2,3))\n",
        "sns.countplot(x=df['Product'])\n",
        "plt.title('Product_sales')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "XayXuTNJAQzy",
        "outputId": "79ea2a55-f3e1-4fbe-da4b-33661b2f3c72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Product_sales')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "\n",
        "1.   KP281 has more sales compared to other varients this might be due to low price\n",
        "2.   Lets understand this further detail in upcoming\n",
        "\n"
      ],
      "metadata": {
        "id": "KOq-JE679WWA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Distribution of Product over Discrete variables**"
      ],
      "metadata": {
        "id": "SjpEWGmZ9yK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure,axes=plt.subplots(nrows=1,ncols=3,figsize=(16,3))\n",
        "sns.countplot(x=df['Education'],ax=axes[0])\n",
        "sns.countplot(x=df['Usage'],ax=axes[1])\n",
        "sns.countplot(x=df['Age_group'],ax=axes[2])\n",
        "axes[0].set_title('Education wise distribution')\n",
        "axes[1].set_title('Usage wise Distribution')\n",
        "axes[2].set_title('Age wise Distribution')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8g-Rzh-X7hD7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "outputId": "35d21685-0187-4bb2-80d1-333b2d0c1d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**\n",
        "\n",
        "\n",
        "*   Young adults are more likely to purchase the product followed by midaged-adults and senior citizens\n",
        "*   Mostly customers are fond of using these threadmills 3 times a week\n",
        "\n"
      ],
      "metadata": {
        "id": "XWl0UXWk_m7I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Distribution for Continous Variables**"
      ],
      "metadata": {
        "id": "XrH-TqDEAR8_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig,axes=plt.subplots(nrows=1,ncols=2,figsize=(12,2))\n",
        "\n",
        "sns.boxplot(x=df['Age'],ax=axes[0])\n",
        "sns.boxplot(x=df['Income'],ax=axes[1])\n",
        "axes[0].set_title('Distribution of AGE')\n",
        "axes[1].set_title('Distribution of Income')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "hEoCq_WqFY2-",
        "outputId": "9a9bf769-5888-48b5-dda2-378e686844da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "*   Majority of the customers are in the age group(23-33)\n",
        "*   Majority of customers have an income level between (45,000-60,000)\n",
        "\n"
      ],
      "metadata": {
        "id": "IUmWpMqxB2c4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gender_counts=df['Gender'].value_counts()\n",
        "Single_Married=df['MaritalStatus'].value_counts()"
      ],
      "metadata": {
        "id": "lQwvbScNGInz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Gender Wise distribution\n",
        "\n",
        "gender_counts=df['Gender'].value_counts()\n",
        "Single_Married=df['MaritalStatus'].value_counts()\n",
        "\n",
        "\n",
        "plt.figure(figsize=(3, 3))\n",
        "plt.pie(gender_counts,labels=gender_counts.index,autopct='%1.1f%%', startangle=60)\n",
        "plt.axis('equal')\n",
        "plt.title('Gender Distribution')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#Martialstatus wise Customers\n",
        "plt.figure(figsize=(3, 3))\n",
        "plt.pie(Single_Married,labels=Single_Married.index,autopct='%1.1f%%', startangle=60)\n",
        "plt.axis('equal')\n",
        "plt.title('Marital Status')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "XxRTn96eaU3U",
        "outputId": "97c9e5cc-0a9e-454d-8fc4-b16205006060"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\\\n",
        "\n",
        "\n",
        "*   Majority of the customers are male\n",
        "*   The probability that a randomly choosen cuatomer will be Maried is almost 60%\n",
        "\n"
      ],
      "metadata": {
        "id": "4e3nNzDpCsnl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**'Bi-varient Analysis'**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "brHPiMgndUE6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ae9bx9ttFq30"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig,axes=plt.subplots(nrows=1,ncols=3,figsize=(16,3))\n",
        "sns.countplot(x=df['Product'],hue=df['Gender'],ax=axes[0])\n",
        "sns.countplot(x=df['Product'],hue=df['MaritalStatus'],ax=axes[1])\n",
        "sns.countplot(x=df['Product'],hue=df['Age_group'],ax=axes[2])\n",
        "axes[0].set_title('Product vs Gender')\n",
        "axes[1].set_title('Product vs MaritalStatus')\n",
        "axes[2].set_title('Product vs Age_group')\n",
        "axes[0].legend(prop={'size': 8})\n",
        "axes[1].legend(prop={'size': 8})\n",
        "axes[2].legend(prop={'size': 8})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "id": "TON-yNesLL7t",
        "outputId": "37f40298-8945-4e84-f712-468cdfdd63f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x300 with 3 Axes>"
            ],
            "image/png": 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ZbNOmjdq2bWv6+ULpQ6ERpVJ0dLS2bNmirVu36tChQ/rf//6Xo2Bkb2+fYxr3n3/+KV9fX1WuXNmsvUmTJqbt2f+1tbWVv7+/2X6WXFp77Ngx2dra5iiOWcLe3l5hYWFav369UlNTJUlr1qxRenq6WdKbNGmS3Nzc1KZNG919990aNWqUduzYccf+K1eurKtXr+ZoHzlypLZs2aItW7bkmI5+5MgRbdq0yawQWb16dYWGhkq6cRnWzfz8/MyeZyer7DUVs8fk7rvvzhHHreNx5MgRJSUlqUaNGjmOf/Xq1RzHrlev3h0/AwAg59xQ3DnnVo899phWrVqlo0eP6qeffrrtZWe//fab+vfvLw8PD7m7u6t69eqmZT9uLbzdddddFt/45eTJkxo2bJiqVq0qNzc3Va9eXV26dMn1eLdycnLSP//5T/3+++86e/asPv30U7Vr106fffaZRo8ena/j79ixQ6GhoXJ1dZWnp6eqV69uWmuyKAuNXbp0UVhYmKZNm6Zq1arpoYce0qJFi0zjD6B8I//dUBL5b+nSpapXr56cnJx09OhRHT16VP7+/qpUqZKWLVtm2i/7s2vQoIHZ66tWrZqj6FfQ87K85HVMe3v7XNfNlHKeZ2X3kdvYNm7c2LS9MHI7T2zYsKHZesMoXVijEaVSmzZtTHdAy4uTk1OJ3Nkqr2/GSupOzIMGDdKHH36or7/+Wv369dNnn32mxo0bm92ps0mTJjp8+LA2btyoTZs2afXq1frggw80efJkTZs2Lc++GzdurLi4OJ05c8bsW6KGDRuqYcOGkmT2jZR0Y3Hi+++/XxMnTsy1z+zXZbOzs8t1P+OWhaLzIysrSzVq1DBLxjfLXtsqG3eYBpAf5Jz/V5w551aDBw9WZGSknn76aXl5eal79+657peYmKguXbrI3d1d06dPl7+/v5ydnbVv3z5NmjQpx4L3lv7tz8zM1P33369Lly5p0qRJaty4sVxdXXXmzBkNGzasQAvs16xZU4MGDVJYWJiaNWumzz77TIsXL85z7Ubpxkn0fffdp8aNG+vtt99W7dq15ejoqK+++kpz5szJ1/Hz+3NkY2Ojzz//XLt27dKGDRu0efNmPfnkk3rrrbe0a9euXK96AFB+kP/+X3Hmv+TkZG3YsEEpKSm5Fs2WL1+umTNnFnhGZkHPy4qSJbnWxsYm13PBkhprFD8KjShX6tSpo2+//VZXrlwx+4Yt+7Kq7EXa69Spo6ysLB07dszsW5fDhw/n6LNKlSq5Xup067cy/v7+ysrK0qFDhxQYGJhnjAVNIJ07d1bNmjW1cuVKdezYUd99951pUeObubq6auDAgRo4cKDS0tI0YMAAzZw5U5GRkTmKhdn69OmjFStWaNmyZXkmqFv5+/vr6tWrpm/KLJU9JkeOHMmx7dbx8Pf317fffqsOHTpQRARgdeScguWcW/n5+alDhw7atm2bnn/++TyLb9u2bdPFixe1Zs0ade7c2dSefVfUwsrrszlw4ID++9//6uOPP9bQoUNN7Vu2bCn0sRwcHBQQEKAjR47or7/+ko+PT57H37Bhg1JTU/XFF1+YXRVw6/Igt3sP2bNeEhMTzRbfz2tGSbt27dSuXTvNnDlTy5cv15AhQ7RixQo99dRT+X2LACoQ8l/B8t+aNWuUkpKiefPmmW7+le3w4cN65ZVXtGPHDnXs2NH02R09etRs1uDFixdNV4RlK6rzspuP2a1bN1N7RkaGTpw4oYCAgHz3cfjwYdPdy7MdPnzYtF26MdbZy2jdLK8cldt54n//+988Z1vC+rh0GuXKAw88oMzMTL3//vtm7XPmzJGNjY169eolSab/vvvuu2b75XZnLn9/fyUlJenXX381tZ07d05r1641269fv36ytbXV9OnTc8w2uPkbG1dX13yv0SRJtra2evjhh7VhwwZ98sknysjIMJvCL91IPDdzdHRU06ZNZRjGbddcevTRR9W0aVO99tpr2rVrV6773Ppt06OPPqqdO3dq8+bNOfZNTExURkZGft+apBuzPQIDA/Xxxx+bXQ62ZcsWHTp0KMexMzMz9dprr+XoJyMjo0CfKwBYipxzQ35zTm5mzJihKVOm5Lr2VLbsmfE3v6+0tDR98MEHBTrWrVxdXSUpx+eT2/EMw9A777xzxz6PHDmikydP5mhPTEzUzp07VaVKFdPs+4IcPykpSYsWLcr1PeQ2vtmXKd68Pte1a9f08ccfm+13+fLlHHk++8Sdy6cB5IX8d0N+89/SpUtVv359Pffcc3r44YfNHuPHj5ebm5vpiq377rtP9vb2mjdvnlkft37WUtGdl7Vq1UpeXl766KOPzF6zbNmyHMXN2/VRo0YNzZ8/3yx/fP311/r999/N7jTu7++vP/74QxcuXDC17d+/P8/L0NetW6czZ86Ynv/888/avXu36ecLpQ8zGlGu9O3bV926ddM///lPnThxQi1bttQ333yj9evX68UXXzT9wzswMFCDBw/WBx98oKSkJLVv314xMTE6evRojj4HDRqkSZMmqX///ho7dqyuX7+uefPmqWHDhtq3b59pvwYNGuif//ynXnvtNXXq1EkDBgyQk5OT9uzZI19fX0VFRUmSgoKCNG/ePM2YMUMNGjRQjRo1cnzrc6uBAwfqvffe05QpU9SiRQvT+ifZunfvLh8fH3Xo0EHe3t76/fff9f7776t379451k65mYODg9auXasePXqoY8eOGjBggDp16mS6ROyLL77QyZMnzRLDhAkT9MUXX6hPnz4aNmyYgoKCdO3aNR04cECff/65Tpw4keObujuJiopS79691bFjRz355JO6dOmS3nvvPTVr1sxsDckuXbro2WefVVRUlOLi4tS9e3c5ODjoyJEjWrVqld555x09/PDDBTo2ABQWOadgOSc3Xbp0Ma19mJf27durSpUqCg8P19ixY2VjY6NPPvmkUEtw3Mzf31+enp6aP3++KleuLFdXV7Vt21aNGzeWv7+/xo8frzNnzsjd3V2rV6/O18nW/v379dhjj6lXr17q1KmTqlatqjNnzujjjz/W2bNnNXfuXFMhMSgoSNKNG7ANGjRIDg4O6tu3r7p37y5HR0f17dtXzz77rK5evaqPPvpINWrU0Llz58yOl9f4du/eXX5+fhoxYoQmTJggOzs7LVy4UNWrVzcrhH788cf64IMP1L9/f/n7++vKlSv66KOP5O7urgceeMCizxdA+UX+y3/+O3v2rLZu3ZrjhjjZnJyc1KNHD61atUrvvvuuvL299cILL+itt97Sgw8+qJ49e2r//v36+uuvVa1aNbOZmkV1Xubo6KipU6dqzJgxCgkJ0aOPPqoTJ05o8eLF8vf3z9fsUAcHB73++usaPny4unTposGDByshIUHvvPOO6tatq3Hjxpn2ffLJJ/X222+rR48eGjFihM6fP6/58+erWbNmSk5OztF3gwYN1LFjRz3//PNKTU3V3Llz5eXlle8r8mAFJXyXa+C2Fi1aZEgy9uzZc9v9wsPDDVdX11y3XblyxRg3bpzh6+trODg4GHfffbcxe/ZsIysry2y/v//+2xg7dqzh5eVluLq6Gn379jVOnTplSDKmTJlitu8333xjNG/e3HB0dDQaNWpkLF261JgyZYqR26/QwoULjXvuucdwcnIyqlSpYnTp0sXYsmWLaXt8fLzRu3dvo3LlyoYko0uXLnf8XLKysozatWsbkowZM2bk2P7hhx8anTt3Nry8vAwnJyfD39/fmDBhgpGUlHTHvg3DMBITE43p06cb99xzj+Hm5mY4OjoatWvXNh5++GFjw4YNOfa/cuWKERkZaTRo0MBwdHQ0qlWrZrRv39548803jbS0NMMwDOP48eOGJGP27Nk5Xp/bZ7x69WqjSZMmhpOTk9G0aVNjzZo1Rnh4uFGnTp0cr//3v/9tBAUFGS4uLkblypWNFi1aGBMnTjTOnj1r2qdOnTpG79698/X+AVRM5JzcFVfOuV1euFlun/eOHTuMdu3aGS4uLoavr68xceJEY/PmzYYkY+vWrab9unTpYjRr1izXfrt06ZLj/a9fv95o2rSpYW9vb0gyFi1aZBiGYRw6dMgIDQ013NzcjGrVqhlPP/20sX//frN9DMPIMS4JCQnGrFmzjC5duhg1a9Y07O3tjSpVqhghISHG559/niOm1157zbjrrrsMW1tbQ5Jx/PhxwzAM44svvjACAgIMZ2dno27dusbrr79uLFy40Gwfw7j9+MbGxhpt27Y1HB0dDT8/P+Ptt982/cxn97Fv3z5j8ODBhp+fn+Hk5GTUqFHD6NOnj7F3797cBwdAuUD+y11x5L+33nrLkGTExMTkuc/ixYsNScb69esNwzCMjIwM49VXXzV8fHwMFxcXIyQkxPj9998NLy8v47nnnjN7bX7Oy/Lr3XffNerUqWM4OTkZbdq0MXbs2GEEBQUZPXv2NO2zdetWQ5KxatWqXPtYuXKlaVyqVq1qDBkyxDh9+nSO/ZYuXWrUr1/fcHR0NAIDA43NmzfnOPe7+d8Nb731llG7dm3DycnJ6NSpk7F///4CvTeULBvDsPDrYAAAAAAAABSLxMREValSRTNmzMh17cjikJWVperVq2vAgAH66KOPSuSYNztx4oTq1aun2bNna/z48SV+fBQeazQCAAAAAACUAn///XeOtux1Lbt27Vosx0xJScmxJMmSJUt06dKlYjsmyi/WaAQAAAAAACgFVq5cqcWLF+uBBx6Qm5ubfvzxR3366afq3r27OnToUKC+Ll26pLS0tDy329nZqXr16tq1a5fGjRunRx55RF5eXtq3b58WLFig5s2b65FHHrH0LaGCodAIAAAAAABQCgQEBMje3l5vvPGGkpOTTTeImTFjRoH7GjBggLZv357n9jp16ujEiROqW7euateurXfffVeXLl1S1apVNXToUM2aNUuOjo6WvB1UQKzRCAAAAAAAUM7Exsbq8uXLeW53cXEp8CxJ4E4oNAIAAAAAAACwGDeDAQCgCEydOlU2NjZmj8aNG5u2p6SkaNSoUfLy8pKbm5vCwsKUkJBgxYgBAAAAoGiV+zUas7KydPbsWVWuXFk2NjbWDgcAcBuGYejKlSvy9fWVrW3Z+y6sWbNm+vbbb03P7e3/P82OGzdOX375pVatWiUPDw+NHj1aAwYM0I4dOwp0DPIaAJQNZT2nlQRyGgCUHfnNa+W+0Hj27FnVrl3b2mEAAArg1KlTqlWrlrXDKDB7e3v5+PjkaE9KStKCBQu0fPlyhYSESJIWLVqkJk2aaNeuXWrXrl2+j0FeA4CypazmtJJATgOAsudOea3cFxorV64s6cYH4e7ubuVoAAC3k5ycrNq1a5v+dpc1R44cka+vr5ydnRUcHKyoqCj5+fkpNjZW6enpCg0NNe3buHFj+fn5aefOnbctNKampio1NdX0PHtpZfIaAJRuZT2nlQTO1QCg7MhvXiv3hcbsKfju7u4kLwAoI8ri5VNt27bV4sWL1ahRI507d07Tpk1Tp06ddPDgQcXHx8vR0VGenp5mr/H29lZ8fPxt+42KitK0adNytJPXAKBsKIs5raRwrgYAZc+d8lq5LzQCAFASevXqZfr/gIAAtW3bVnXq1NFnn30mFxeXQvcbGRmpiIgI0/PsbxIBAAAAoLSh0AgAFsjKylJaWpq1wyhzHB0dy/3C+J6enmrYsKGOHj2q+++/X2lpaUpMTDSb1ZiQkJDrmo43c3JykpOTUzFHC6A8ITeVLAcHB9nZ2Vk7DADIt8zMTKWnp1s7DJRSlp6rUWgEgEJKS0vT8ePHlZWVZe1QyhxbW1vVq1dPjo6O1g6l2Fy9elXHjh3TE088oaCgIDk4OCgmJkZhYWGSpMOHD+vkyZMKDg62cqQAyhNyk3V4enrKx8eHy6QBlGqGYSg+Pl6JiYnWDgWlmKXnahQaAaAQDMPQuXPnZGdnp9q1a5f72XlFKSsrS2fPntW5c+fk5+dXbk7Kxo8fr759+6pOnTo6e/aspkyZIjs7Ow0ePFgeHh4aMWKEIiIiVLVqVbm7u2vMmDEKDg4u0B2nAeB2yE0lzzAMXb9+XefPn5ck1axZ08oRAUDesouMNWrUUKVKlcrNv8NRdIriXK3UFBpnzZqlyMhIvfDCC5o7d64kKSUlRS+99JJWrFih1NRU9ejRQx988IG8vb2tGyyACi8jI0PXr1+Xr6+vKlWqZO1wypzq1avr7NmzysjIkIODg7XDKRKnT5/W4MGDdfHiRVWvXl0dO3bUrl27VL16dUnSnDlzZGtrq7CwMLOcBgBFhdxkHdnr8J4/f141atQoN5dRT506NcfNyBo1aqQ//vhDEudqQFmTmZlpKjJ6eXlZOxyUYpaeq5WKQuOePXv04YcfKiAgwKx93Lhx+vLLL7Vq1Sp5eHho9OjRGjBggHbs2GGlSAHghszMTEkq15f+Fqfszy0zM7PcFBpXrFhx2+3Ozs6Kjo5WdHR0CUUEoKIhN1lPdmE3PT293BQaJalZs2b69ttvTc/t7f//9JFzNaBsyV6TkS+icCeWnqtZvdB49epVDRkyRB999JFmzJhhak9KStKCBQu0fPlyhYSESJIWLVqkJk2aaNeuXVxqBqBU4HKDwuFzA4Diw9/YkldeP3N7e/tcb1rGuRpQdpXXv1coOpb+jFi90Dhq1Cj17t1boaGhZoXG2NhYpaenKzQ01NTWuHFj+fn5aefOnXkmr9TUVKWmppqeJycnF1/wAHCToAlLiqXf2NlD87Vf3bp1df36dZ05c8b0zdPWrVsVEhJitixFbqZOnarExMTb7gMAKJusnZ8kac2aNZo5c6YyMzOVkpIiX19fffvtt+rTp4/mzJmjRo0aFToOcljxOXLkiHx9feXs7Kzg4GBFRUXJz8+PczWgHCkNOaIs27hxo958801t27bttvudOHFCgYGBphvxTJ06VS+//LKcnZ2LP8gSZtVC44oVK7Rv3z7t2bMnx7b4+Hg5OjrK09PTrN3b21vx8fF59hkVFZVjLZGCKq5ftJK0tvJsa4dgEb/JB6wdAlAm+fn56YsvvjDd2XjBggVq1aqVlaMCLHNyegtrh2Ax8hoqsnPnzumZZ55RbGys6tSpI0nat2+fbGxs9NVXX1k5OuSlbdu2Wrx4sRo1aqRz585p2rRp6tSpkw4ePFjhz9UqSgEFQPGZNm2aXnzxxXJZaLTarehOnTqlF154QcuWLSvSDzYyMlJJSUmmx6lTp4qsbwAo7YYPH66FCxdKunFZ065du9SzZ09J0oEDB9SxY0fde++9atq0qdks8lu9+eabatOmje6991717NlTf/75Z4nEDwAofxISEmRnZ6eqVaua2u69917Z2Niobt26iouLkyR17dpV48ePV6dOneTv76/nnnvOtP+5c+fUvXt3NW3aVN27d9egQYM0derUXI9HDisavXr10iOPPKKAgAD16NFDX331lRITE/XZZ58Vuk/O1QDc7M0339Qzzzxjep6YmKhq1arp4sWLmjBhgpo3b67mzZtrzJgxSktLkyQNGzbMbAb7+PHjTflg6tSpGjhwoPr27aumTZsqJCREly5dknRjjcqRI0eqYcOGateunV566SV17do117ji4+PVrVs3BQUFqVmzZho9erSysrLM+rn77rvVpk0bbd261fS6bdu2KTAw0PT84MGDqlu3bo7+s/Nbp06dFBgYqPPnzxf0oyvVrFZojI2N1fnz53XvvffK3t5e9vb22r59u959913Z29vL29tbaWlppmml2RISEnJdJySbk5OT3N3dzR4AUFF06NBBJ06c0NmzZ/Xpp5/qkUceMS1KX7duXcXExGjfvn2KjY3V6tWrtWvXrhx9LF++XIcPH9bOnTu1b98+DRkyRCNHjizptwIAKCcCAgLUsWNH1alTR/3799fs2bN15syZXPc9duyYtm7dqoMHD2rz5s3auXOnJGns2LEKDg7WoUOHtGTJkjwvUSOHFR9PT081bNhQR48elY+PD+dqACz21FNPad26daa/JYsWLdJDDz2kzz77THv27FFsbKzi4uJ07NgxzZkzJ1997t69W4sXL9ahQ4dUo0YNffjhh5Kkf//73zpy5Ih+++03/fDDD/r111/z7MPT01MbNmxQbGysfv31V504ccL0Jcu///1vHT58WL/99pt+/PFH7du3r8Dve/78+ZKkH374QXFxcapRo0aB+yjNrFZovO+++3TgwAHFxcWZHq1atdKQIUNM/+/g4KCYmBjTaw4fPqyTJ08qODjYWmEDQKn3xBNPaPHixVq4cKGefPJJU/vff/+tp556Si1atFC7du30559/mmaR3GzdunX69ttvFRQUpMDAQL3xxhs6efJkCb4DAEB5Ymtrq9WrV+unn35Sz549tWPHDjVr1kxHjx7Nse/AgQNlb28vFxcXBQYG6tixY5KkmJgYU07z8fFRnz59cj0WOaz4XL16VceOHVPNmjUVFBTEuRoAi3l6eurhhx/WwoULZRiG5s2bp9GjR+vbb7/VsGHD5OTkJHt7ez399NPasmVLvvrs2bOnvLy8JEnBwcFmeeTxxx+Xg4ODHBwcFB4enmcfWVlZmjRpklq2bKl77rlHe/fuNZ03xcTEaOjQoXJ0dJSjo6PZ+RZusNoajZUrV1bz5s3N2lxdXeXl5WVqHzFihCIiIlS1alW5u7trzJgxCg4O5i5mAHAbQ4cO1b333quGDRvq7rvvNrX/4x//ULVq1fTLL7/I3t5eAwYMUEpKSo7XG4ahyMhIs8sYAACwVOPGjdW4cWM9++yz6tmzp7744osc+9y8pJKdnZ0yMjJy7SuvO2KSw4rO+PHj1bdvX9WpU0dnz57VlClTZGdnp8GDB8vDw4NzNQBFYuzYsXrwwQfVpEkTVa9eXffcc0+OfW7+m29vb6/MzEzT85SUFLm5uZmeFyaPjB07Vt9//70k6ZNPPtH69et1/vx57d69W87OzoqIiMj1vCk/sVVEVpvRmB9z5sxRnz59FBYWps6dO8vHx0dr1qyxdlgAUKr5+voqKipKr7/+uln75cuXVatWLdnb2+vw4cN5fivYr18/zZ8/32w9k19++aXY4wYAlE9nzpzRjh07TM8vX76s48ePy9/fP999hISEaPHixZJuXJ67cePGXPcjhxWd06dPa/DgwWrUqJEeffRReXl5adeuXapevbokztUAFI3GjRurfv36euaZZzR69GhJUmhoqJYsWaK0tDRlZGToP//5j7p37y5JatCggX7++WdJ0sWLF/N9U7GQkBAtX75c6enpSk9P15Il/39jqXfffdd0pW2LFi10+fJl+fj4yNnZWfHx8Vq1apVp39DQUC1dulTp6elKS0vTokWLTNvq16+vP//8UxcuXJB0o2iZl8qVKyspKSmfn1LZYtW7Tt/q1rVWnJ2dFR0drejoaOsEBABl1PDhw3O0vfLKK3riiSf08ccfy9/fXyEhIbm+dsiQIbp48aK6desmScrIyNCTTz6Z67eLAADcSUZGhqZPn67jx4+rUqVKysjIUHh4uB566CG98MIL+erjnXfeUXh4uJo2bSpfX1+1bds2xx2PJXJYUVqxYsVtt3OuBqCoPP300xo9erQefvhhSdIzzzyjY8eO6d5775V042ZhL774omnbww8/rCZNmqh+/fr5nkX97LPP6sCBA2ratKmqVKmiVq1a6ezZs7nu+8ILL+jhhx9Ws2bN5Ovrq9DQULNYDx48aOqnU6dOio2NlXRjwsfEiRPVpk0beXt7q1evXnnG89JLL+n+++9XpUqV9M0335SrdRptDMMwrB1EcUpOTpaHh4eSkpLyvdhw0IQld96plFtbeba1Q7CI3+QD1g4BuK2UlBQdP35c9erVM5uej/zJ6/MrzN/siqYifkYnp7ewdggWI6+hJJTn3PT333/LwcFB9vb2unjxotq1a6elS5eqbdu21g5NUu6ffUX8e11QZfVcLXb2UGuHABRYac4Ro0ePlre3t1599dViPc6VK1dUuXJlpaena8iQIQoKCtKkSZOK9ZhlkaXnaqVqRiMAAAAA3OrIkSMaOnSoDMNQWlqaRo4cWWqKjACAwjl79qxCQkJUtWpVbd68udiPFxoaqtTUVKWkpKhjx44aO3ZssR+zIqLQCAAAAKBUCwgIMN3xEwBQPvj6+uqPP/4osePt3r27xI5VkZXqm8EAAAAAAAAAKBsoNAIAAAAAAACwGIVGAAAAAAAAABaj0AgAAAAAAADAYtwMBgCKyMnpLYqlX7/JB4qlXwBAxUB+AgDkhRyBosaMRgAoJ+rWratGjRopMDBQgYGBeuqpp4r9mMOGDdPcuXOL/TgAgLLr5vzUtGlTRUdHF+j1J06c0Pz584spuvwbP368pk6dau0wAKBCOnv2rDp16pTn9mrVqunEiRO5bmvVqpW2bdsmSeratavWrVtX9AEWws1xlUQ/N5+7bdu2TZs2bbL42LlhRiMAlCMrV65UYGCgtcMAAMBMdn76888/FRAQoE6dOikgIOCOr8vIyDAVGp977rkijSkjI0P29pwOAUBZ4Ovrqx9++MHaYZQb27ZtU2Jionr27FnkfTOjEQDKsU8++URt27bVvffeq86dO2v//v2SpMWLFys0NFSDBw9W06ZN1b59ex06dEj9+/dXkyZN1L17d129elWSFBMTo+DgYN1zzz1q1qyZFixYkOux0tPT9fLLL6tNmzYKDAzUo48+qsuXL5fYewUAlH516tRRo0aNtGnTJrVu3VqBgYFq3bq1du7cadqnbt26mjRpktq0aaPw8HA999xzOnz4sAIDA/Xggw+a9pk8ebKCg4NVr149zZgxw/T6+Ph4Pfroo2rTpo1atGihV155Jc++b5e7zp07px49eqhp06YKDQ3V6dOnS+hTAoCKw8bGRjNnzlTbtm1Vt25drVu3TlFRUWrVqpXuvvtu00y9EydOyNPT0/S6L774Qk2aNFFAQIAmTpxo1udPP/2kwMBANW/eXMOHD1dGRkaux75y5YqefvpptWnTRgEBAXrmmWeUlpYmSXr77bfzzFPZ/bdo0UJPPvmkWrZsaYrzdjkov3Hl9/i59XPrjM2HH35YixcvNus7Li5O8+fP17JlyxQYGKjp06fnGUdhUGgEgHJk4MCBpkunZ86cqU8//VTff/+99u3bp5kzZ+qxxx4z7btnzx69/vrrOnTokPz9/dW3b1/Nnz9fv//+uxwdHfXxxx9Lku699179+OOP+uWXX/TDDz9o+vTpuZ5szZ49W66urvr5558VFxeXI7ECAHDgwAH98ccf6tatm/bs2aO4uDi99957Gj58uNl+Fy9e1O7du7Vs2TLNnz9fjRo1UlxcnL744gvTPomJidq5c6f27Nmj2bNn68yZM5Kk8PBwjRo1Sj///LN++eUX7d27V6tWrcq179vlrrFjx6pNmzY6dOiQPv74Y8XExJTAJwQAFY+bm5t2796tBQsW6PHHH1fNmjW1d+9e/etf/9KECRNy7H/+/HkNHz5cq1ev1q+//qoGDRro4sWLkqS0tDQNHDhQb775pg4ePKjBgwebJlvc6qWXXlKnTp30888/a//+/crKytI777wjSXriiSdyzVPZ/c+ZM0cHDhzQE088oV9//dXUZ145qCBx5ef4+e0nN4GBgXruuec0ZMgQxcXFafLkyQV6/Z1wrQAAlCM3Xzo9ceJE7d+/X23btjVtv3Tpkv7++29JUnBwsPz8/CTdWNcjPT1d3t7ekqTWrVvryJEjkm6ckI0YMUL//e9/ZW9vr4sXL+rgwYOqVauW2bHXrVunpKQkrV69WtKNJFi3bt3ifLsAgDJi4MCBcnFxUaVKlbRw4UJdvnxZXbp00cWLF2Vvb6/Dhw/r77//louLi6Qb60jZ2Njcts/sL8+qVaum+vXr6/jx4/L09FRMTIwSEhJM+129elWHDx82Pb+579vlrpiYGL355puSpLvuuss0mxIAULQGDhwo6cY5ybVr1zRo0CBJUps2bUznJDfbtWuXAgIC1LRpU0nSiBEjNGbMGEnSH3/8IXt7e4WGhkqSunfvrvr16+d63HXr1mnnzp16++23JUl///237OzsJEm//PKLZs6cmSNPHTlyRPb29urWrZskqVu3bvL395ckXbt2Lc8cVJC48nP8/PZjDRQaAaCcMgxD4eHh+te//pXrdmdnZ9P/29nZ5XiePQX/ueee0wMPPKDVq1fLxsZG9957r1JSUnI93nvvvafu3bsX8TsBAJR1N38RlpaWpqpVq2rr1q1q3bq1kpOT5eHhodTUVFOh0c3N7Y595pa3DMOQdOMk9ObtN7u574LkrjsVPgEAhZP99zq7yHfz89tdXpztTn+f89puGIZWr16thg0bmrWnpaVpwIABueap2/V/uxx086zHW1936NAh05dnHTp00Jw5cwp1fEmyt7dXZmam6Xlu523FjUunAaCcevDBB7V06VKdPHlSkpSVlaW9e/cWuJ/Lly+rTp06srGx0ffff5/n1Px+/fppzpw5un79uiTp+vXr+u233wr/BgAA5VJKSorS0tJMs+rfe++92+7v7u6upKSkfPXt5uambt26adasWaa2s2fP5rm+4u1yV2hoqBYuXCjpxnqNN1+2DQCwnuDgYP3666/6448/JEkLFy40ra3YuHFjZWRkaOvWrZKkb7/9VseOHcu1n379+un11183FTMvX76so0eP3jZPNWrUSOnp6dq+fbskafv27Tp69Kik2+eg28XVtGlTxcXFKS4uTtHR0bc9/p3eX4MGDbR7925J0vHjx/Xjjz/m+t4LklsLihmNAFBE/CYfsHYIZjp16qQ33nhD/fv3V0ZGhtLS0tS7d2+1atWqQP3MmjVLI0eO1GuvvabAwECzS7FvNmnSJKWmpqpt27amb9UmTZqkZs2aWfxeAACFV9ryk7u7u2bMmKE2bdqoWrVqpkvk8hIQEKBmzZqpefPmql+//h0LfsuWLVNERISaN28uGxsbubq66sMPP8yx5Id0+9z1zjvvaNiwYWratKnuuusuhYSEFP5NA0ApVdpyRH5Ur15dCxcuVP/+/eXo6KiePXvKy8tLkuTo6KiVK1dq5MiRyszMVOvWrdWyZctc+5kzZ45efvllBQYGytbWVvb29nrjjTfUoEGDPPOUk5OTVqxYoVGjRikrK0tBQUFq1KiR6UY1t8tB+Y3rdnnyTu9v4sSJGjhwoFq0aKFmzZrlee7Wv39/ffLJJwoMDNSAAQOKdJ1GGyN7bmc5lT3FNCkpSe7u7vl6TdCEJcUcVfFbW3m2tUOwSFn8Y4eKJSUlRcePH1e9evXyvDQLecvr8yvM3+yKpiJ+Rient7B2CBYjr6EkkJusJ7fPviL+vS6osnquFjt7qLVDAAqMHFF0rly5osqVK0u6cYPNBx98UMeOHVOlSpWsHFnRsPRcjRmNAAAAAAAAQD6sXr1ac+bMkWEYsre31yeffFJuioxFgUIjAAAAAAAAkA/Dhg3TsGHDrB1GqcXNYAAAAAAAAABYjEIjAFignC9zW2z43ACg+PA3tuRlZWVZOwQAAEoFLp0GgEJwcHCQjY2NLly4oOrVq5vuVIk7MwxDFy5ckI2NjRwcHKwdDgCUG+SmkmcYhtLS0nThwgXZ2trK0dHR2iEBAGBVFBoBoBDs7OxUq1YtnT59WidOnLB2OGWOjY2NatWqJTs7O2uHAgDlBrnJeipVqiQ/Pz/Z2nLBGACgYqPQCACF5Obmprvvvlvp6enWDqXMcXBwoMgIAMWA3FTy7OzsZG9vzwxSAGVSh/c6FEu/O8bsKJZ+b2fv3r2aPXu2Vq5cWajXr1y5UrNnz9aVK1fk5uYmX19f/etf/1KLFi0UGBioH374QZUrV9bcuXM1aNAg+fj4SJLmz5+vK1euaMKECUX5dsosCo0AYAE7OzsKZgCAUoXcBACoiFq1alWoImNmZqaWLFmiqKgorVu3Tk2bNpUkxcbG6uzZs2rRooXi4uJM+8+dO1ddu3Y1FRqfe+65Iom/vGBuPwAAAAAAAErc33//rYEDB6pp06Zq2bKlunfvLkn65JNP1LZtW917773q3Lmz9u/fL0lavHixQkNDNXjwYLVo0UKtWrXS//73P0nStm3bFBgYaOr7k08+UUBAgAICAtS7d2+dOXPG1Ee3bt0UFhamFi1a6Oeff9aUKVM0d+5cU5FRkoKCgtSjRw9JN5Z+SkxM1PTp03X27FkNHDhQgYGBiouL09SpU/Xiiy9KulF0DAwMVGBgoBo2bGia7Z6enq6XX35Zbdq0UWBgoB599FFdvnxZkjRs2DA9++yzuu+++9SwYUMNGDBAaWlpxfehFzMKjQAAFINZs2bJxsbG9I8OSUpJSdGoUaPk5eUlNzc3hYWFKSEhwXpBAgAAAFa0adMmJSYm6tChQ9q/f79WrFihHTt26NNPP9X333+vffv2aebMmXrsscdMr9mzZ4/+9a9/6cCBAwoNDdXrr7+eo9+DBw9qwoQJ+vrrr/Xrr7+qffv2euqpp0zbd+/eberD399fp06dUnBw8B3jnTx5snx9fbVy5UrFxcWZFTalG5dRx8XFae/evfL399f06dMlSbNnz5arq6t+/vlnxcXFqUWLFnrllVdMr4uLi9OGDRv0+++/KyEhQatXry7oR1lqcOk0AABFbM+ePfrwww8VEBBg1j5u3Dh9+eWXWrVqlTw8PDR69GgNGDBAO3aU/Bo2AAAAgLW1bNlSv//+u0aOHKkuXbrogQce0Pr167V//361bdvWtN+lS5f0999/S5KCg4NVr1490/+/9957OfrdunWrevbsqbvuukuSNHLkSE2fPl2ZmZmSpPbt26tRo0bF9r6ef/553XXXXXr11VclSevWrVNSUpKpgJiWlqa6deua9u/fv78qVaokSWrTpo2OHTtWbLEVN2Y0AgBQhK5evaohQ4boo48+UpUqVUztSUlJWrBggd5++22FhIQoKChIixYt0k8//aRdu3bl2V9qaqqSk5PNHgA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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "*   It appears sales for KP781 product is highest among Male gender. This might be because KP781 has some extra features which is attarcting male gender customers or the market strategies are more leaned towards male.\n",
        "*   It appears sales for all three products is highes amoong the Married people.\n",
        "\n"
      ],
      "metadata": {
        "id": "o1HZfsw6ELwX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig,axes=plt.subplots(nrows=1,ncols=2,figsize=(16,3))\n",
        "sns.countplot(x=df['Fitness'],hue=df['Gender'],ax=axes[0])\n",
        "sns.countplot(x=df['Usage'],hue=df['Gender'],ax=axes[1])\n",
        "axes[0].set_title('Gender vs Fitness')\n",
        "axes[1].set_title('Gender vs Usage')\n",
        "axes[0].legend(prop={'size': 8})\n",
        "axes[0].set_xlabel('Fitness rating')\n",
        "axes[1].set_xlabel('Usage per week')\n",
        "axes[1].legend(prop={'size': 8})\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "id": "EPElhRtcY9z1",
        "outputId": "bbe1f5f5-910b-418a-ffba-9b828fef325c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x300 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "\n",
        "*  Almost **75%** of Male customers have **(2-4) Fitness rating**.\n",
        "*  Almost **84%** of Femal customers use treadmill for **2 to 4 times a week**.\n",
        "\n"
      ],
      "metadata": {
        "id": "5inlREwODc1g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Distribution of Product amaong diffrent Variables"
      ],
      "metadata": {
        "id": "RbSM065HfiaE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(16,8))\n",
        "axes = axes.flatten()\n",
        "\n",
        "sns.boxplot(x=df['Product'],y=df['Age'],ax=axes[0])\n",
        "sns.boxplot(x=df['Product'],y=df['Income'],ax=axes[1])\n",
        "sns.boxplot(x=df['Product'],y=df['Fitness'],ax=axes[2])\n",
        "sns.boxplot(x=df['Product'],y=df['Education'],ax=axes[3])\n",
        "sns.boxplot(x=df['Product'],y=df['Miles'],ax=axes[4])\n",
        "sns.boxplot(x=df['Product'],y=df['Usage'],ax=axes[5])\n",
        "\n",
        "axes[0].set_title('Product vs Age')\n",
        "axes[1].set_title('Product vs Income')\n",
        "axes[2].set_title('Product vs Fitness')\n",
        "axes[3].set_title('Product vs Education')\n",
        "axes[4].set_title('Product vs Miles')\n",
        "axes[5].set_title('Product vs Usage')\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "Ceki1itpLcXy",
        "outputId": "4f48d1d7-4c3c-48c0-c8e1-126879a5bb6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**\n",
        "\n",
        " The analysis clearly shows that preference for the KP781 model among the customers who are highly educated, having high income and are engaged in running activities greater then **150 miles per week**.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Lbro48b6PigL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Multi Varient Analysis**"
      ],
      "metadata": {
        "id": "-Ork1szTvUFp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1513
        },
        "id": "ciYnNRDjYqb0",
        "outputId": "a57d6493-2759-4e58-aa31-ace58734e072"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 42 Axes>"
            ],
            "image/png": 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sV7Fl0/jJWKRUyUQsZVNupgLzWz3ZWPfpzM9srM9oWB+kFBfEAXR0dOAHP/gBnn32WZhM8t9G/O53v4tLLrkEtbW1+Pa3v41f/epX2LRpEw4dOhTxvMuWLYPD4Rj519HRka6PQERxYn6qz2yK/kdKsY6TfinNT7MpP+p5SmMcJzGxXcWWTeMnY5FSJROxlE25mQrMb/VkY92nMz+zsT6jYX2QUlwQB7Bjxw50d3dj7ty5MBgMMBgM2LJlCx555BEYDAYMDQ2Ne825554LAGhraxt3LKSgoABmsznsHxGJgfmpvvJiIxrtVtljjXYryov5p2zZSml+2kqMaKq2yR5rqrbBVsIY0iK2q9iyafxkLFKqZCKWsik3U4H5rZ5srPt05mc21mc0rA9SigviAC666CLs2bMHu3btGvl39tln49vf/jZ27dqFvLy8ca/ZtWsXAGDy5MkZLi0RkT5MNJuwqrl23KJ4o92KVc21vH84xWQpMmJNS924SW9TtQ1rW+qEuD+gwy3hULcLre19OHTMBYdbUrtIwtNCu1J2YCyKRcv9KWNJPPG0iZZjT0TMh9RKR31qOeYZX6RUTjAYDKpdCBFdcMEFmDNnDh5++GEcOnQIzz33HL72ta/BarVi9+7duPXWWzFt2jRs2bJF8Tm50zeRuJif6jja78Enn7tRXJAHl3cIJaY8DPqGcNopRZhUVqh28UgQsfLT4ZbQ45Iw4PWj1JQPW4kYm1x19nuwdOPusI19mqptWNNShymM75hEbVcKlw3jJ2NRfXrpTzMZS9mQm6kQq030Ensiyua+NR35mar61EvMZ3N8kTK8QasCRqMRb775Jh5++GEMDg6isrISLS0tuOuuu9QuGhGRZjncEn40ZrIV0lRtw6OL6zlpIUUsReJNcB1uadzFBHB8d/s7Nu5mfCsgYrtSdmIsqktP/SljSTzR2kRPsSci5kNqpaI+9RTzjC+KhQviEfz5z38e+f/Kysq4vglORESx9bgk2cVw4Pikq8clcRJDmsX4JiJKDfanpBbGHmUbxjxlE95DnIiIVOH0+qMeH4hxnEhkjG8iotRgf0pqYexRtmHMUzbhgjgREanCbMqPerw0xnEikTG+iYhSg/0pqYWxR9mGMU/ZhAviRESkCluJcdzu3yFN1TbYSvjneKRdjG8iEonDLeFQtwut7X04dMwFh1tSu0iKsT+lVFOaD4w9ygaj88GQl8OYp6zBe4gTEZEqLEVGrFg0G8s37cG2tt6RxxvtVqxYNJv3pyNNY3wTkSg6+z3jNklrqrZhTUsdppQVqlgyZSxFRqxpqcMdG3dj65jPsLaljv0pxSWefGDskd6NzYciYx7WX3sOgsC4HGHMk97kBIPBoNqFyBZOpxMWiwUOhwNms1nt4hDRKMzPzHO4Jdz+4geomWxGfWUZfIFhFBhy0drRjwNHnPjZlWdx0kUAtJmfjG/KFlrMz2zicEtYsqFVdpO0pmobHl1cr5m+yOGW0OOSMOD1o9SUD1uJUTNlVwNzc7xE84GxR6kmQn5GyociYx7uXjgTZ59ajkFfgDFPusVviBMRkSp6XBLe3NeNN/d1RzzOiRdpFeObiETQ45JkF/8AYOvBHk31RZYiLshQchLNB8Ye6VGkfHBLQ1j20h68dduXMaeqXIWSEWUG7yFORESq4C7mpGeMbyISAfsiopOYD0QnMR8o23FBnIiIVMFdzEnPGN9EJAL2RUQnMR+ITmI+ULbjgjgREanCVmLU5C7mo3diP3TMBYdbUrtIJCCtxrdImGtEydNTX8Q+gZKlp3zQKuaxOJgPiWMc6wM31cwgETZOICJ5zE91dPZ7cMfG3dgqs4v55LJCFUsmb+xO7MDx8q5pqcMUAcurF1rNT63Ft0iYa9qh1fzMJnroi9gnxI+5KU8P+aBVzOOTRMlP5kP8GMf6wQXxDBKl0yOi8Zif6nG4JfS4JAx4/ULvYh5pJ3bg+CTo0cX1QpZbD7Scn1qJb5Ew17RFy/mZTbTcF7FPSAxzMzIt54NWMY/DiZSfzAflGMf6YlC7AERElN0sRdqYdEXaiR0Ath7sQY9L0sTnoMzSSnyLhLlGlHpa7ovYJ1CqaTkftIp5LC7mg3KMY33hPcSJiIgU4E7sRJnBXCOi0dgnEGkf85j0gHGsL1wQJyIiUoA7sRNlBnONiEZjn0Ckfcxj0gPGsb5wQZyIiEgB7sROlBnMNSIajX0CkfYxj0kPGMf6wgVxIiJSlcMt4VC3C63tfTh0zAWHW1K7SLIsRUasaakbNwkK7cTO+8WRFomYf8w1otQTMdeVYp9A6aTl3NAS5nF20WteMY71JScYDAbVLkS2EGknYSIKx/xUR2e/B0s37g7bnKSp2oY1LXWYUlaoYski407smcf8TA/R84+5pg3MT/GJnutKsU+ID3MzNr3khpYwj4/Tc35mQ14xjvWBC+IZpOdOj0jrmJ+Z53BLWLKhVXan7qZqGx5dXM+JBQFgfqYD849ShfkpNuZ69mJuRsfcIDXpNT+ZV6QlvGUKERGposclyU6WAGDrwR70uPTxp3VEImL+EWUH5jqRPOYGUeoxr0hLuCBORESqcHr9UY8PxDhORIlj/hFlB+Y6kTzmBlHqMa9IS7ggTkREqjCb8qMeL41xnIgSx/wjyg7MdSJ5zA2i1GNekZYY1C6AiNasWYNly5bhBz/4AR5++GEAgNfrxe23347nn38ePp8Pl1xyCZ544glMnDhR3cISEWmUrcSIy+un4NqG6TDk5WLA44e5MB/+oWE8vf1j2Ep4fzlRhTaScXqPt5mtmBvJRPJZnxtObwBOjx+WwnyUmgyYWl6kdrFgKzGiqdqGrRHu8cj8i445QFrBsVYs7DvEEc84qKTdupxe9A1KcHoDMBcaUF5kxESzKew5Wm9/rZef5MXTrpHiPHQOh0fChu+ci+2HerF+28dwS0Mjr9XT/JK5oA9cEB/jr3/9K/793/8ddXV1YY/feuuteP311/Hiiy/CYrFgyZIluPzyy7F9+3aVSkpEpG2WIiN+cPEZWL5pD7a39Y483mi3YmVzLScVgsqGneNT5dPewYjxfaq1WMWSHc+/NS11uGPj7rDFgKZqG9a21DH/omAOkJZwrBUH+w6xKB0HlbRbe+8glsnk2KrmWlSdGO+13v5aLz/Ji6dd5eL8opoJuOeyWbjr5b1h52i0W/HI4np8f0Mr3NKQruaXzAX9yAkGg0G1CyEKl8uFuXPn4oknnsCKFSswZ84cPPzww3A4HJgwYQKee+45XHHFFQCA/fv3Y8aMGXj33Xdx3nnnKTq/XncSJtID5mfmfdbnxo837g6bVIU02q1Y21InxDdp6SS1do7XYn5qJb5D33AZ8PpRasqHrYTfcIlGrRwQmRbzM5topS/SOzX6DuamMtHGQSXt5g0M47YXdkXMsYeumgOTIVfTYwfHvtQTIT/jadcup1c2zpdcaEdre59s/M+vtuGehTORm5Ojm/klc0Ff+A3xUW666SZ8/etfx8UXX4wVK1aMPL5jxw74/X5cfPHFI4/V1NSgqqoq6oK4z+eDz+cb+dnpdKav8EQUF+an+pzegOzkCQC2tfXC6Q1gaobLRNEp2Tk+FZNAPeSnVuLbUqSPC5RMyVQOiEwP+ZlNtNIX6V0m+g7mZmKijYNK2s0/NBw1x/oGJeTn5Wp67ODYlzwR8zOedu0blGTjvL6yDI9tbpM9x9sHe5Cbk4MvVJSkrtAqYy7oCzfVPOH555/Hzp07sXr16nHHjh49CqPRiLKysrDHJ06ciKNHj0Y85+rVq2GxWEb+VVZWprrYRJQg5qf6nB7uQq41mdo5Xg/5yfjWp0zlgMj0kJ/ZhH2RGDLRdzA3U09Juzm9gRjnCGh+7NB6+UUgYn7G066R4twXGFZ8Dj1gLugLF8QBdHR04Ac/+AGeffZZmEym2C9QaNmyZXA4HCP/Ojo6UnZuIkoO81N95kLuQq41mdo5Xg/5yfjWp0zlgMj0kJ/ZhH2RGDLRdzA3U09Ju5lN0f/o3mwyaH7s0Hr5RSBifsbTrpHivMAQfUlRb7HBXNAXLojj+C1Ruru7MXfuXBgMBhgMBmzZsgWPPPIIDAYDJk6cCEmS0N/fH/a6rq4uTJo0KeJ5CwoKYDabw/4RkRiYn+ozmwxotFtljzXarTEvMCjzbCVGNFXbZI+lcud4PeQn41ufMpUDItNDfmYT9kViyETfwdxMPSXtVl5sjJpj5cVGzY8dWi+/CETMz3jaNVKct3b0R4x/PcYGc0FfuCAO4KKLLsKePXuwa9eukX9nn302vv3tb4/8f35+Pt56662R1xw4cADt7e04//zzVSw5EZF2TS0vwsrm2nGTqEa7FSuba7nJl4AsRUasaakbNxHU087xqaKV+Ha4JRzqdqG1vQ+HjrngcEtqF0lozAHSGq30RUpoub9i36FNkdptfrUN935jFnoHJZgMuVgVIcdWNddiotmk+fbXevlJXjztOtFsko3zfZ0OrGyuTUtsiNjnMxf0JScYDAbVLoSILrjgAsyZMwcPP/wwAODGG2/EG2+8gaeffhpmsxk333wzAOCdd95RfE4RdhImInnMT3V093vg8g/BFxjGgMeP0sJ8FBhyUZKfh4qyQrWLRxE43BJ6XBIGvH6UmvLTvnO8lvPzsz43nN7ASF2ZTQZhFqA6+z1YunF32OZATdU2rGmpwxTmX1SZzgGRaTk/s4nIfZESeumvMtl3MDdTJ9Ru/R4JPv8w3vl7L9Zv+xhuaQhN1TY82FKHnNwc9A1KcHoDMJsMKC82YqLZJHserY4dWi+/SETKz3jatcvplY3zVMeG6H0+c0Ef+DdyCv385z9Hbm4uWlpa4PP5cMkll+CJJ55Qu1hERJrlcEu4fcxEJ6Sp2oZHF9dzYiEoSxEnfUpNLS/CVLULIcPhlsZdaADA1oM9uGPjbuZfDMwB0hpR+yIl9NRfse/QplCb/eTVD2Xj8Mcn4rBmcvSFTa23v9bLT/LiadeJZtO4X/TEe45YtNDnMxf0gQviEfz5z38O+9lkMuHxxx/H448/rk6BiIh0psclyS6GA8cnPD0uiRMNojRh/hGRVrC/IhEwDokyg7lGmcJ7iBMRkSqcXn/U4wMxjhNR4ph/RKQV7K9IBIxDosxgrlGmcEGciIhUYTblRz1eGuM4ESWO+UdEWsH+ikTAOCTKDOYaZQpvmUJEpENhG54UGlBeNH5jH7XZSoxYWDsJzXOnocJcAJd3CKUmA7qcXmzaeRi2EjH/FC60iYrT64e5MB+2Yt5DTm0ix3tnvwcOjx9Ojx+WwnyYC/OF2AzIVmLEghkVOHOyGfWVZfAFhmHKz8PO9j4cOOIUIv+Ya0Spc7jPjQFvYKQvKjEZME0jm2raSoxoqrZha4Q9R0Tor5Riv5a8ROsw0uuUPl5iMigeN9nO2pMNbRbPfDnSc+Otp0TqNd4+PxvajtKDC+JERDrT3juIZZv2YHtb78hjjXYrVjXXosparGLJwlmKjPjRP9RguUxZVzbXCjmREX3H82wkcrx/2jsYMb5PVblsliIj7l44E8s27cFjm9tGHg/Vndr5x1wjSh2R+yIlLEVGrGmpwx0bd4ctkDRV27C2pU71/kop9mvJS7QOI71uxaLZuP+1j/Dmvu6RxxfMqMDdC2fizpf3hj1/Ye0k3LVwJpbHGDfZztqTDW0Wz3xZ7rkX1UzAPZfNwl1j8iJaPSVar/H0+dnQdpQ+OcFgMKh2IbKF0+mExWKBw+GA2Rx9B2oiyiy95GeX04vbXtgVNoEJabRb8dBVc4T55uxnfW78eOPuiGVd21KHqQJ9e83hlrBkQ6vsJi9N1TYhdjzXq0j5KXK8d/Z78KPffhCxbA9ecZaqE3WR41nkstF4ehk/9epwnxtLo4y1a1rqNPNN8dC3AAe8fpSa8mEr0c63ANXo1/SWm4nWYbTXNdqtmFNVHrbAveRCO1rb+8blzJIL7djV3odtMrkUen8AHL80Rq05RybzM575cqTnRsoLQL6eUlGvsfp8zhcpWbyHOBGRjvQNSrITFQDY1taLvkEpwyWKzOkNRC2r0xvIcImiU7LjOWWWyPHu8Pijls3hUXdDIJHjWeSyEWnNQIyxdkCwsTYaS5ERX6gowZyqcnyhokRTCx3s15KXaB1Ge922tl7UV5aFPVZfWSabM/WVZbKL4aPfn+2sPdnQZvHMlyM9N1JeAPL1lIp6jdXnZ0PbUXrxlilERDoSaxFZpEVmZ4wFQdF2EOeO5+IROd5Fj2+R41nkshFpjeh9UbZgv5a8ROsw1ut8geGoP8d6fPT7x/rTe7azeLIhN+OZL0d6rpL4Dz9n+us1G9qO0osL4kREOmI2Re/WYx3PJHOhtnYQ547n4hE53kWPb5HjWeSyEWmN6H1RtmC/lrxE6zDW6woMuVF/jvV4rPeP9zmUWdmQm/HMlyM9N974z0S9ZkPbUXrxlilERDpSXmxEo90qe6zRbkV5sTh/Xmw2GaKWVaTFe+Dkjudy5HY8p/QrLzbiopoJWHKhHeuuORtPfHsu1l97DpZcaMdFNRNUjXdLYX7U+LbEWKRKN5HjWeSyEWlNaYyxtlSwsVavsqlfc7glHOp2obW9D4eOueBwp+a2BdHqcMGMCpSYDLLvG+11jXYrWjv6wx5r7eiXzZlIjwMn2zCb2lkvsqHN4rk+jPRcJfE/WibqNRvajtKLC+JERDoy0WzCqubacROW0C7iomyoCQBTy4uwMkJZVzbXCrWhJnByx/OxEy+5Hc8pMyaaTbjnsllobe/DDc+8j399dieuf/qv2NXeh3sum6VqvE8pK4wa32puqAmIHc8il41Ia6bFGGu1sqGm1mVLv9bZ78GSDa246N+2oPmJd3DRQ1tw84ZWdPZ7kj53pDpcMKMCdy+ciR+++IHs+0ar+1XNtThwxBn2+IEjTqxqrh33/EiPj27DbGlnPcmGNovn+jDSc//ePRBxLHlg0exx9ZSJes2GtqP0ygkGg7FudUUporedvon0RG/52eX0om9QgtMbgNlkQHmxUajFcOB4Ge97ZS9a5lWiwlwAl3cIJaY8dDt92LijA/d+Y7ZwZQZi73hOqRcpP0XeXd7hlnDfqx/i2obpMOTlYsDjR2lhPgJDw3h6+8e497JZQsSNyPEsctnoJL2Nn3qj1bFWrzLZr2U6NzM1Jo+twxKTAT988YOY7xup7lP1eLQycvwSX6bbTI2xM57rw7HPLTUZcM/v9mLGFAvqK8vgCwyjwJCL1o5+7Ot0YNXldbLnykS9Mt8oUfwbOSIiHZpoNgl/gds3KOGNvV14Y2+X7PHvX3SGkJ8h9A0gUp+S3eXVaqsel4SXWjvxUmun7PGbvlItRByJHM8il41IK7Q61uqVnvu1TI3JY+vwULdL0ftGqvtUPR7vc0gs2dBm8Vwfjn3u/iNOvLX/GN7af0z2+X2Dkuy5M1Gv2dB2lB68ZQoREakinh3PieSIvLu8yGUjouzBsZYyRa1xj+MtUfpxLCE94oI4ERGpIp4dz4nkiLy7vMhlI6LswbGWMkWtcY/jLVH6cSwhPeKCOBERqSKeHc+J5Ii8u7zIZSOi7MGxljJFrXGP4y1R+nEsIT3igjgRkUIOt4RD3S60tvfh0DEXHG5J7SJpWjw7nhPJEXl3eZHLNhr7NSJ941hLmZLqcU/p+KSV8ZZIy6KNJSuba+H2BTiXJM3JCQaDQbULkS3U2EmYiJSJlZ+d/R4s3bg7bNOepmob1rTUYUpZYSaLqjvx7HhO2SlWfoq8u7zIZWO/RqnA+a34jvZ78MnnbhQX5MHlHUKJKQ+DviGcdkoRJjHXdUut3EzFuJfI+CTyeEs0llbHzrHXbSUmA1a/sQ+v7zk68hzOJUkruCCeQVrt9IiyQbT8dLglLNnQKruDfVO1DY8urueEmyiNOH6mHvs1ShXmp9iY69lLq7nJmKVsoNX8HI25SlrHW6YQEcXQ45JkB3oA2HqwBz0u/lkYEWkL+zWi7MBcJ61hzBJpA3OVtI4L4kREMTi9/qjHB2IcJyISDfs1ouzAXCetYcwSaQNzlbROFwvikiThwIEDCAQCaheFiHTIbMqPerw0xnEiItGwXyPKDsx10hrGLJE2MFdJ6wxqFyAZbrcbN998M5555hkAwN/+9jecfvrpuPnmmzF16lTccccdis/15JNP4sknn8Qnn3wCAJg1axbuueceXHrppQCACy64AFu2bAl7zfe+9z388pe/TM2HISJh2UqMaKq2YWuE+6PZSsS7N1poYyGn1w9zYT5sxeJuLHS4z40BbwBOjx+WwnyUmAyYVl6kdrEi0lLdkvpEjW9biRELZlTgzMlm1FeWwRcYhik/Dzvb+3DgiFPIfo1ILZ39Hjg8/pE8Nhfma2azMOa6WDiHiC3RebfDLaF3UEJgOIjhYBBuXwCWIuNIHYfq3i35UVZoxKA0dHxjwEIDyosS29A9Ve3JuGAdpFvYZpijYj7eeh99ngklRsyvtmHHp324vnF62BjT5fRyfKGUSVf/oOkF8WXLluGDDz7An//8Z/zDP/zDyOMXX3wxfvKTn8S1ID5t2jSsWbMG1dXVCAaDeOaZZ/DNb34Tra2tmDVrFgDgO9/5Du6///6R1xQVqX9BS0TpZykyYk1LHe7YuDtsct5UbcPaljrhJmud/R4s3bg77J5uou72/WnvIJZv2oPtbb0jjzXarVjZXItTrcUqlkyeluqW1CdyfFuKjLhr4Uws37QHj21uG3k8VD7R+jUitYicx0ow18XBOYQyicy7O/s9uOd3e/FPX6zCU9s/DsvXpmobViyajftf+wi7Ovrx7D+fhztkcnpVcy2q4sjpVLUn44J1kG7tvYNYNibmL6qZgHsum4W7Xt6ruN7HnqfImIenrz0Hw0Hg0T8dDBtj5lfb8OUzJsDCJTNKUjr7h5xgMBhMtoBqOfXUU/Gb3/wG5513HkpLS/HBBx/g9NNPR1tbG+bOnQun05nU+U855RT89Kc/xQ033IALLrgAc+bMwcMPP5zw+fSwkzCRXinJz9BvJge8fpSa8mErEe+bC1ra7ftwnxtLN+4Om5yFNNqtWNNSJ8Q3aUO0VLd6o8XxU/T47nJ6cfsLu7AtQvkeumpOQt+Wo+yjxfxUqrPfgx/99oOIefzgFWcJv1jz2Ym+KFKur22pw1SBxlq9UmMOofXcVDrvDtXtWZVlaG3vi5ivc6rKUV9ZhvVjFsxHP0fp2Jeq9uTcMnvrIFP52eX04rYXdo2L+SUX2iPmi1y9RzrPLRdXY8cnn+Nthechike6+wdN30P82LFjqKioGPf44OAgcnJyEj7v0NAQnn/+eQwODuL8888fefzZZ5+FzWbD7NmzsWzZMrjd7qjn8fl8cDqdYf+ISAyJ5KelyIgvVJRgTlU5vlBRIuTgrqXdvge8AdlJGABsa+vFgFesfSG0VLdap4fxU/T47huUZBfIgOPl6xtkPJM8PeSnUg6PP2oeOzzibxjm9Aai5rpTsLFWrzIxh9Bbbiqdd4fqtr6yLGq+1leWocJcEPU5Sse+VLUn55bZUwdq5WffoCQb89HyRa7eI52ndqpFdjE80nmI4pHu/kHTC+Jnn302Xn/99ZGfQ4vg//mf/xm2kK3Unj17UFJSgoKCAvzLv/wLNm3ahJkzZwIAvvWtb+HXv/41/vSnP2HZsmX4r//6L1x99dVRz7d69WpYLJaRf5WVlXGXiYjSQ6/5qaXdvp0xFhJEKiugrbrVOj3kp+jxHWsRjItkFIke8lMp0fNYCT18Bj3IxBwim3JztFDd+gLDUZ/nCwzD5R2KcS5lY1+q2pNzy+ypA7XyM1JMx8qXsfWeqvMQxSPd/YOmF8RXrVqF5cuX48Ybb0QgEMAvfvELfPWrX8VTTz2FlStXxn2+M888E7t27cJ7772HG2+8Eddccw0++ugjAMB3v/tdXHLJJaitrcW3v/1t/OpXv8KmTZtw6NChiOdbtmwZHA7HyL+Ojo6EPysRpZZe81NLu32bC7VTVkBbdat1eshP0ePbbIq+jUys45S99JCfSomex0ro4TPoQSbmENmUm6OF6rbAEH1po8CQixJTXoxzKRv7UtWenFtmTx2olZ+RYjpWvoyt91Sdhyge6e4fNL0g3tjYiF27diEQCKC2thb/8z//g4qKCrz77ruYN29e3OczGo2w2+2YN28eVq9ejbPOOgu/+MUvZJ977rnnAgDa2tpkjwNAQUEBzGZz2D8iEoNe89NWYkRTtU32WFO1TajdvktNBjTarbLHGu1WlAq2IKelutU6PeSn6PFdXmyMWr7yYsYzydNDfiplKcyPmieWGIvNIjDH6Iv4y6/MyMQcIptyc7RQ3bZ29KMhSqy3dvSj2+lLydiXqvbk3DJ76kCt/Iw032vt6I+YC3L1nqrzEMUj3f2DpjfVTLcLL7wQVVVVePrpp8cd2759OxobG/HBBx+grq5O0fm0vrEJkZ7pKT87+z24Y+NubB2zE/PaljpMTnLzr9AGR06vH+bCfNiKk9tY9NPeQdy1aU/Yvefm261Y0VyLU63FSZU1HdJZtxRZrPxMdVymyqe9g7hz056w+/c22q1YKUh8t58o39j8W9lciyoByidqu1I4PY2fckTPYyWOj7V78XbbybFrvt2GFc2zNfMZAO33CZ39HizduDvsfqjzq214ME1zCL3n5mid/R7c+7u9+McvVuGpMZtmzq+24YFvzsaK1z/Cro5+PPvP5+GB1z4cl9OrFI59oTjsc0vwDw1j+6FerN/2MdzSUMw5oVwMe6Qh/Olvx1BRWgBfYBim/Dx0Ob34yhkTMClL5pbZOL/OZH629w5i+Zhx7KKaCbjnslm4++W9YfU+v9qG1c21mHbK+M2WD/cO4rDDg+ICA1zeIZSaDHBLAViLC/CTV/aGzyfT2LeNFc/YoPVxJBuls3/Q9IJ4pI0IcnJyUFBQAKNReWAvW7YMl156KaqqqjAwMIDnnnsOa9euxR/+8AecfvrpeO655/C1r30NVqsVu3fvxq233opp06Zhy5YtcZU3WyYlRFqjt/wMDfYDXj9KTfmwlSQ/2MtdyDVV27CmpQ5TEhyM2nsHsb2tBxVmE3yBYRQYctE94EPDF6xCLMjJSUfdUnTR8jMdcZkqnb2D8A4H4QsMY8DjR2lhPgoMuTDl5mCKAPHd0TuIbWPzz+lFo92GSpXLJ3K7Uji9jZ9jdfV78LnHjyAwksc5AE4pzMdEjcRiR+8g3m7rwUQBc10pPfQJmW4HvefmWA63hN5BCUPDQQwNBzHoC0AaGsZ7H3+OuZXlONzvxkSzCUEAp1qL4A8E4fL5YTblo7zYiIlmU8z3iPRLjZ98YxZyAFijLK7JvXbBjArcvXAm7hzzCyutxXayjvR78OdRvxQIXQ9ccMYELoinSJfTi75BCU5vAGaTYSTmP+tz49NeN/o9fhQYctHa0Y8DR5y475uzx8Vfe+8glm3aE/YLp9Avk97/5HNYiowZb794xgY9jCPZKl3X35peEM/NzR3ZSFPOtGnTcO211+Lee+9Fbm70u8PccMMNeOutt3DkyBFYLBbU1dVh6dKlWLBgATo6OnD11Vdj7969GBwcRGVlJZqbm3HXXXfF1Xll26SESEuYn9E53BKWbGiV3eW5qdqGRxfXxz0oHe5zY+nG3bI7ljfarVjTUodp5eO/nUDZJ1J+piMuU6XL6cVtL+yKGN8PXTVH0cV3uohcPpHblcbT8/iph1gUOdeVYjskRs+5GcvomFlyoR2t7X2ydR9P/CQTh5Fem6qyaZke8jsRIuRnPHUfqw+7rmE6bnjm/ajnULP82RpnFJ2mbxr39NNP484778S1116LL37xiwCA//3f/8UzzzyDu+66C8eOHcPPfvYzFBQUYPny5VHPtW7duojHKisr4/omOBGR3vS4JNkJBABsPdiDHpcU9yRiwBuQnVQBwLa2XgxE2M2cKCQdcZkqfYNS1PjuG5RUXYQSuXwitytlFz3Eosi5rhTbgeI1OmbqK8vw2Gb5fb/iiZ9k4jDSa1NVNi3TQ35rVTx1H6sPW3ppTcxzpFo85WeckRxNL4g/88wzeOihh3DVVVeNPHbZZZehtrYW//7v/4633noLVVVVWLlyZcwFcSIiiszp9Uc9PhDjuOw5Pak/J2WXdMRlqjhj/EIn1vF0E7l8IrcrZRc9xKLIua4U24HiNTpmfIHhqM9VGj/JxGGk16aqbFqmh/zWqnjqPlYf5fIOxTxHqsVXfsYZjRf9PiKCe+edd1BfXz/u8fr6erz77rsAgMbGRrS3t2e6aEREumI25Uc9XhrjuOw5C1N/Tsou6YjLVDGbon/nINbxdBO5fCK3K2UXPcSiyLmuFNuB4jU6ZgoM0Zc8lMZPMnEY6bWpKpuW6SG/tSqeuo/VR5WY8mKeI9XiKz/jjMbT9MhbWVmJdevWYc2aNWGPr1u3DpWVlQCA3t5elJeXq1E8IqKYtLLTta3EiKZqW9juziFN1TbYSuIvc6nJgEa7NWzH85BGuxWlgl4chm1KU2hAeZGyjZgo9WwlRiyYUYEzJ5tRX1kGX2AYpvw87Gzvw4EjzoTiMlXKi41R47u8WN08Ly824qKaCZgxxTKu7vZ1OlQtXzr6G6JE6CEWRe+LlGA70GihubPL50dZkRFSYBguX2BkHg0Aw8Egnr72HFhLCoCcINZdczZycnKws70Pv2v9DD/5xixUmAsw6B2Cf3gYXU4vJppNUeflycRhpNe2dvRHjAutxHay4q1XrVw7ZVKidRKp7ouMebh74UwMB4Nobe+DuTAf5sL8qH1Yt9M37vF0x3A8sRN67vuf9uH6xulhc98upzfucjIO9UHTm2q+8soruPLKK1FTU4NzzjkHAPD+++9j37592LhxIxYuXIgnn3wSBw8exL/927+pXFoxNk5IRHt7O3p65O+3FA+bzYaqqqoUlIgo9dTIT63tdN3Z78EdG3eHTTqaqm1Y21KX8A7in/YO4s5Ne8ImV412K1Y21+JUa3HSZU61aLurVwlYXr2Ilp+f9g5iuUybiBBDose3yHXXfqJsY+uOuSYerc5vldJDLIreFynBzxA/PeZmaO6849M+PLK4Hk9t/zhsDJtfbcNNX7FjyXM7saalbtzxC2sm4O6FM3H3y3tl2+HB/96P1/ccHXl87Lw8mbmw3GsXzKjA3Qtn4q6X96Z0fq01SutVa9dO0aQqP5Otk7F1X2TMw/prz8Hjm9vwdtvJcy6snYQf/UNNxD5s7e/34Y29XeMeT3f/HE9OHun34NPP3Xh088Fx/cbaOGJIT3GY7TS9IA4An3zyCX75y1/ib3/7GwDgzDPPxPe+9z24XC7Mnj1b5dKF0+KkpL29HTU1M+DxuJM+V2FhEfbv38dFcRJSpvNTqztdh34bPuD1o9SUD1tJ4r8NP9znxs/+sB/XN54OQ14uBjx+lBbmIzA0jKe2/R23X1KDaeVFKf4EiYu1u/pDV83hN8XTJFJ+djm9uP2FXRG/raJmm3T2e/DAax/iinmVqDAXwOUdQokpD91OHzbu6MBdC2epOmkWue4cbgm3v/gBakZ987/AkIvWjn4cOOLEz648S8j+MVtpcX6rlB5i8bM+N1a8/lHEvujOr8/EVIHGWjldTi+WvbQbM0f9RUuoHfZ1OrDq8jrhx181YklvuTl67rzkQjta2/tk52QNdiuub5iO9WMWwwFEfV2j3YrrGqbjhmfeD3t87Lw8mblwpNemcn6tVbHqQKvXTpGkIj9TVSej6768yIi7Xt4bthge8rXZE/EvF9iRk4ORscSUn4fHNx/EtFOKVeufleaPwy1hyXOtsp9NaX3pLQ6znZh/jx6H0047beSWKU6nExs2bMA//uM/4v3338fQkPyN/Um5np4eeDxunHv9vTBPPi3h8ziPfIL31t+Hnp4eLogTQbs7XVuKUjdBH/AG8PKuI3h51xHZ49/9sj0l75MqsXZX7xuUhL8g15u+QUl2QRdQv00cHj9+v7cLvx/1bZnRbr7oDFUXxEWuux6XhDf3dePNfd0Rj4vYP5L+6CEWnd5AzL5oaobLFK++QQmb9x/D5v3HIh4XffzVQyypbfTcub6yDI9tbpN93va2XtxxaY3snC3a67a19WLppTXjHh87L09mLhzptamcX2tVrDrQ6rVTOqWqTkbX/aFul+yCMQC8sbcLLfMqw35p9OrNDRGv5YDM9M9K86fHJUX8bErri3GoL5pfEAeArVu3Yt26ddi4cSOmTJmCyy+/HI899pjaxdIV8+TTcErVmWoXg0g3uNM14PRoqw5i7a4e6zilnshtInp8C1137B9JEHqIRdH7IiVE7q+U0kMsqW10HfoCw1Gf6/LKfzEu0dexfdTHHBovHXUS65xjcyhSzpw8nzj9cyrqi3GoL5pdED969CiefvpprFu3Dk6nE1dddRV8Ph9efvllzJw5U+3iERFFxZ2uAXOhtuog1u7qsY5T6oncJqLHt9B1x/6RBKGHWBS9L1JC5P5KKT3EktpG12GBITfqc0tMebKPJ/o6to/6mEPjpaNOYp1zbA5FypmT5xOnf05FfTEO9SX6iCCoyy67DGeeeSZ2796Nhx9+GJ2dnXj00UfVLhYRkWKhna7lZMuu8qUmAxrtVtljjXYrSgWaQAFAebExannLi/XfZqIRuU0shflRy2aJsUiVbiLXHftHEoUeYtEcY6wVabEiEpH7K6X0EEtqG12HrR39aIgQEw12K7qdPtnjrR39UWOp2+kb9zjbRwzMofHSUSfRztlot6K1oz/ssW6nTzP9cyrqi3GoL5pcEP/973+PG264Affddx++/vWvIy8v+m+liIhEYykyYk1L3bgBNbQrdrL3HutyerH/iBP/+/Hn2H/UiS6nN6nzpcO08iKsbK4dN4kK7Uou0oaaADDRbMKqCOVd1Vwr/P1L9UjkNplSVhg1vtXehV7kukt3/0iklB5icWqMsVb0DTUBsfsrpfQQS2obXYfrt32M6xqmj1v0nl9tw80XVmPpxt2yx/cfcWLFosj5sGnn4bDHY7WPwy3hULcLre19OHTMBYdbSsEnJTnMofHSUSfRzrmyuRb7Oh1hj2/c0RFxjJHrn2PlTDpzKhX1xTjUl5xgMBhUuxDx+stf/oJ169bhN7/5DWbMmIH/83/+D/7pn/4JkydPxgcffCDsLVO0uNP3zp07MW/ePCy486mk7iH+efsB/HHlddixYwfmzp2bwhISpYZa+ZmOXeXbewexbNOesM2EQpOSKmtxskVOqc96B+GUAggiBwMeP0oL85GDIMxGA6YKVtaQLqcXfYMSnN4AzCYDyouNmrgY17JY+SlqmxzuHcRhhwfFBQa4vEMoMeVh0BfANEshpgkQ36KXLx39I6WeFue38dJ6LB7uHcSAzFhbajQIketKHO334JPP3SguyBvVXw3htFOKMEnlXzDGI5OxpMfcPNLvwZ//dgwVpQUIDAcx2WLCcBAYGh5GWaFx5NuZPS4Jgz4/LIVGSEPDcHr98PmH8b+ffI6zq8phMOSMGfuOx1KhMU9x+3T2e7B04+6wDfaaqm1Y01Kn+i+99Uzr/XFIKvMzHXUid06vNISPx/TDHmkIU80mdDi8MfvnWDmTqZxKRX3pJQ6znfh/IyfjvPPOw3nnnYeHH34Yv/nNb7B+/XrcdtttGB4exh//+EdUVlaitLRU7WISEcWU6l3lu5zecYvhALCtrRfLN+3BQ1fNEWKhEDhe1h/LlBU4voAvUllHm2g2CVmubCZim3Q5vVgqcHyLXj4g9f0jUaK0HIud/Z6ouf7gFWcJv3jncEv40ZhFkpCmahseXVyvmfbRciypzeGW8GOFcTC6jh1uCfe/9hHePtiDJRfa8eTWQ7L5EDrHFypKFJVl7MIdAGw92IM7Nu7WVExqDXNovHTUydhzOtwSfigT80sutKO1vS9qTlmKjDFz5qdXnpWxnEpFfTEO9UGTt0wJKS4uxvXXX49t27Zhz549uP3227FmzRpUVFTgG9/4htrFIyLKuL5BSXZCAhxfFO8bFOdPObVUVqJ4iR7fopePiFLD4fFHzXWHx5/hEsWvxyXJLoICxxdLelzsr7JBonEw+nX1lWUR8yGeWGJMUraJFPNKcypWzvQNMqco8zS9ID7amWeeiQcffBCHDx/Ghg0b1C4OEZEqnN5AUsczSUtlJYqX6PEtevmIKDWcMRa8B7ziL4g7Y5RRC5+BkpdoHIx+nS8wnNA5UlUWIq2KFPNKcypWzsSadzKnKB10syAekpeXh0WLFuGVV15RuyhERBlnNkW/E1as45mkpbISxUv0+Ba9fESUGubC/KjHS03Rj4vAHKOMWvgMlLxE42D06woM0Zc/lMYSY5KyTaSYV5pTsXIm1ryTOUXpwKsdIiIdKS82otFuxbYI9wotLxbnXmflxUZcVDMBM6ZYUF9ZBl9gGKb8POxs78O+TodQZR0ttImK0+uHuTAftmLeQ05tn/W54fQG4PT4YSnMR6nJgKnlRaqWSfRcFL18omM/oJwe6ips495CA8qLxNi4VwlLYX7UXLfEWDAXga3EiKZqG7ZGuHd0aCNFLdBDPqjB4ZZgyM3B/GpbxHuIy8WBwy1hOBjE+mvPhqXQCHOhAfPtVrwtkw/zq20w5OXA4ZZitkkyMZntMZDtnz8eydSV0nFL6XtEivnWjv6IY8zoXIiVM+XF+unnM405lTguiBMR6chEswkrm2tx56Y9YROTRrsVK5trhbqAn2g24Z7LZmH5pj14bHPbyOMiljUkU7ufk3Kf9g5i+ZgN40IxdKq1WLVyiZ6LE80mrGquxXKZ8q0SoHwiYz+gnB7qqr13cNxm1aE8qVKxj1FqSllh1L5IC+1gKTJiTUsd7ti4O2yxpKnahrUtdZq58NdDPqghVG87Pu3DI4vrMRwMhuVjpDiQq+8LaybgroWzcP9rH4ado8FuxTVfOg2X/uJtnH1qecw2STQmsz0Gsv3zxyOZulI6bsXzHpFi/pNjrohjzIpFs8M2uo2WMxPNJl3085nGnEpOTjAYDKpdiGzhdDphsVjgcDhgNpvVLo4iO3fuxLx587DgzqdwStWZCZ/n8/YD+OPK67Bjxw7MnTs3hSUkSg0t5qecLqcXP3llL66YV4kKcwFc3iGUmPLQ7fRh444O3PuN2cIsdHX2e/Dj334Q8VtrD15xllADucMtYcmG1ojfTErl7ucULlJ+ftbnxo837pbdzKfRbsXaljrVvinucEu4/cUPUDPZPPIXEAWGXLR29OPAESd+duVZQsRL2DeITAaUF2vnm69qYD8wXqT81ENddTm9uO2FXRH7mIeumiN8vmhpXhBL6FtwA14/Sk35sJVo51twauSDHua2Y+utyJiH6xuno76yDABQdUoRKkoLxtVdtPq+sGYCvn3uqSM/Wwrz8ee/HcP6bR/DLQ0BUN4m8cSkHvrEZGT75x8rWn4mU1dKx61E32NszJeYDLhz0x7F891YOaPlfj7TmFPJ4zfEiYh0pG9Qwu/3duH3e7tkj3//ojOEufB1ePyyi+EAsK2tFw6PX6gF8Vi7o/e4Yv+JLaWW0xuIuLP9trZeOL0BTM1wmUJ6XBLe3NeNN/d1RzwuQrxMNJuE6RO0gP2Acnqoq75BKWof0zcoCZ8/WpoXxGIp0u7CiB7yQQ1j680tDYX9VeFbt31Ztt6i1ffm/cfw7XNPxQ3PvA8AWHfN2WHnBJS3STwxme0xkO2fPx7J1JXScSvR9xgb84e6XXHNd2PljJb7+UxjTiVPd5tqEhFls1g7dMc6nklOT/TdwkXbTTzW7uiilTcbiBxDjBd9Yrsqp4e60tKYGokePoMe6CEf1JBovcV6nS8wLPv/Ss6dqGyPgWz//PFIpq6U9vmpag+2q3pY98njgjgRkY7E2qE71vFMMsfYyEu03cRj7Y4uWnmzgcgxxHjRJ7arcnqoKy2NqZHo4TPogR7yQQ2J1lus1xUYcmX/X8m5E5XtMZDtnz8eydSV0j4/Ve3BdlUP6z55nAGd8OSTT+LJJ5/EJ598AgCYNWsW7rnnHlx66aUAAK/Xi9tvvx3PP/88fD4fLrnkEjzxxBOYOHGiiqUmIq1L9a7Q5cVGXFQzATOmWEbu42bKz8PO9j7s63SgvFicP5uyFOZH3JW80W6FJcZiZ6bZSoxYWDsJzXOnjdyHtdRkQJfTi007D3P3cxWYTYaoMaTmQo+txIimalvYxkAhTdU2YeIl7B7ihQaUF4lzD3ERy6aVdhWBHuqqvNgYtY8RaUyNRA+fIUTEPkEpPeSDGsbW29h7iAeGh9Hl9I6Lg2j13WC3orWjH8DxHAj9/2hj2yQV8/Vsj4Fon39+tQ2GvBw43Km7xYOSNkv1dViqJBMrSvv8WO9RYjLgULdrXN2MrbMSkyGh8yRKrs0AyLajqO2bKonEid7rJF7cVPOEV199FXl5eaiurkYwGMQzzzyDn/70p2htbcWsWbNw44034vXXX8fTTz8Ni8WCJUuWIDc3F9u3b1f8Hlrc2ISbalK2UCM/07Ur9Ke9g1gus7P4yuZanDpqZ3ERfNo7KLsruYhlBbRVt3oSLT9FjiGRywYA7b2DWCYTz6uaa1GlcvlEL9tymXYVoWxqiJafnf0e3LFxd9jFWlO1DWtb6jBZoD0iotFDe4veFykhcp+gVKbbQYvXnnJC/cj7n/bhkcX1eGr7x4riQK7/abBbcV3DdHx/QyvOPrUcDyyajftf/RBv7T8W8XypnK/roU9MhpI2SfY6KPQ+sdosXddhSsXKz2RiRem4Fek9Viyajftf+yjsvuCRHl8wowJ3L5yJu17eq/g8idbx2DYrMuZh/bXn4PHNbXi7Lb3vLap44kTtmBcRF8SjOOWUU/DTn/4UV1xxBSZMmIDnnnsOV1xxBQBg//79mDFjBt59912cd955is6nxUkJF8QpW2Q6P9O1K3Rnvwc//u0HEb8V8OAVZwkz4DncEm5/8QPFu5KrTemu7ZR6sfLzsz43nN7AyI70ZpMBU8uLVCjpSV1OL5a9tBszR/21Rii+93U6sOryOlXjReR4FrlsWuu3MiFWfoa+jRTKT1uJ9r6NFPbNZJMB5cXa+WZyZ78HD7z2Ia6YVzny100lpjx0O33YuKMDdy2cJcy8IBKR+wSl1BgTtHjtGYnDLaHPLeHul/fi7TjiYHT/U1xggDEvFw6PhOKC499svf/VDzF9QknE/hxAyufreugTk+FwS+ge8KH9czcAoLWjH+u3fQy3NAQgueug0PljtRmQ+naNl5L8TCZWlI5bY9+jxGTAnZv2yG6S2Wi3Yk5V+bhNaBfMqMCK5lq4Rs3Fo50nkTqWa9clF9rR2t4XcWyQK2um2jeTlMRJutYetI63TJExNDSEF198EYODgzj//POxY8cO+P1+XHzxxSPPqampQVVVVdQFcZ/PB5/PN/Kz0+lMe9mJSBm18zNdu0I7PH7ZxXDg+M7iDo9fmAvfHpcU167kalO6azslL978nFpehKnpLlSc+gYlbN5/DJtHfets7HE140XkeBa5bFrrt9Ih3vy0FGl/sWei2aTZ/t3h8eP3e7vw+71dssdvvugMYeYFkYjcJyiViTFB7bltOlmKjDji8MouhgOR40C+/zn+DdlD3S68vudoxPfscUkAkPL5uh76xGRYiozocUm44Zn3ZY8ncx0EKLvGAlLfrrEkkp/JxIrScWvsexzqdkWc42xr68V1DdPHPf7Hfd2449IAvlBRoug8idSxXLvWV5aNW/COVdZ0ta+alMRJutYetI6bao6yZ88elJSUoKCgAP/yL/+CTZs2YebMmTh69CiMRiPKysrCnj9x4kQcPRp5EF29ejUsFsvIv8rKyjR/AiJSSu38TNeu0E6Pdnab1trO2Ep3bafkqZ2fqSB6vIhcPrHLpq1+Kx30kJ/ZREvzgkhE7hOUysRn0HtuproOlfTn7PPTI531Kmq7aiU/Y9WNLzAs+/jYOkt1HcudL1JZYh3PxrxlXyaPC+KjnHnmmdi1axfee+893Hjjjbjmmmvw0UcfJXy+ZcuWweFwjPzr6OhIYWmJKBlq52e6doU2x9iIUqTdprW2M7bSXdspeWrnZyqIHi8il0/ssmmr30oHPeRnNtHSvCASkfsEpTLxGfSem6muQyX9Ofv89EhnvYrarlrJz1h1U2CQX0IcW2eprmO580UqS6zj2Zi37MvkcUF8FKPRCLvdjnnz5mH16tU466yz8Itf/AKTJk2CJEno7+8Pe35XVxcmTZoU8XwFBQUwm81h/4hIDGrnZ2hXaDnJ7DRvKcxHo90qe6zRboUlxoVxJqWrDtIltGu7nNG7tlPy1M7PVBA9XkQun8hl01q/lQ56yM9soqV5QSQi9wlKZeIz6D03U12HSvpz9vnpkc56FbVdtZKf0eqm0W5Fa0f/uMfl6izVdSx3vtaOfjRE6ROUljUbsC+TxwXxKIaHh+Hz+TBv3jzk5+fjrbfeGjl24MABtLe34/zzz1exhESkVZYiI9a01I0bmEK7Qid6D68pZYVY1VyL1c2zse6as/HEt+di/bXnYHXzbKxqrhXqPqHpqoN0mWg2YVVz7biLsdCu7aLfv1TPHG4Jh7pdaG3vw6FjLjjcktpFGomX+WPiZb4g8SJyPItcNi30WyLmg9ZpuU6nlBViZYR8WinYvCASkfsEpfTwGdSmtA5D+fpBRx8+7R3Ewa6BsP8P5TEArI3Rn6vR52u1v4mn3OmsVyXn1sJYHo9Uxky0ulnVXIsDR5zjHl/dXIt+jx8fdTrw108+x8GuAQBIaR3LlWv9to9x84XVmB9HWaO9t1ZzT4lEY17PdQIAOcFgMKh2IUSwbNkyXHrppaiqqsLAwACee+45rF27Fn/4wx+wYMEC3HjjjXjjjTfw9NNPw2w24+abbwYAvPPOO4rfQ4s7fe/cuRPz5s3DgjufwilVZyZ8ns/bD+CPK6/Djh07MHfu3BSWkCg11MrPdOw0/2nvIO7atCds46H5ditWNNfiVGtxskVOqSP9Hvz5b8dQUVoAX2AYBYZcdA/4cMEZEzBZwIv0w72DOOzwoLjAAJd3CCWmPAz6AphmKcQ0wepWT6LlZ2e/B0s37g7bKKap2oY1LXWqL/R09A5iW1sPKsymk/Ht9KLRbkOlAPFytN+DTz53o7ggb1Q8D+G0U4owSeW6E7lsQHr67lRQIx+0OL+Nh8h9jFIdvYP4TGbsmmopFKIvUqrL6UXfoASnNwCzyYDyYqOmFpIzPYfQY27GGhtC+brj0z48srgeT23/GK3t/SP/P3pz1lAeFxvzYvbnmerztdrfJFrudNarknOrOZanKj/TFTNydeORhvCnUddtpvw8TCkzweH245HNB8Pya361DQ+21KFIQX4lWy4Asu8RT/tqNffixToJxwXxE2644Qa89dZbOHLkCCwWC+rq6rB06VIsWLAAAOD1enH77bdjw4YN8Pl8uOSSS/DEE09EvWXKWFqclHBBnLKFFvNTTme/Bz/67QdhE5KQRrsVD15xljADmMMtYcmGVtkdr5uqbXh0cb0QC0whXU4vbnthV8S6feiqOZq6MNeSSPkpcgyJHi8i153IZROZWvWml/FTjh5iUUvzAj1TY0zQW27GysefXnkWfvjiB3j7YA+WXGhHa3sftrf1hv2/3OtEyWOt9jdaLbfaUpGfmax7ufdacqEdUywmvL7niPD5FQ1jeLxsqRPeMuWEdevW4ZNPPoHP50N3dzfefPPNkcVwADCZTHj88cfx+eefY3BwEC+99FJci+FERJng8PhlJyQAsK2tFw6PODtI97gk2UEWALYe7EGPS6w/yeoblKLWbd+gWOXNBiLHkOjxInLdiVw2kbHeUk8PdaqleYGeiT4maEGsfOwbPHm8vrJspL5H/7/c60TJY632N1ottx5ksu7l3qu+sgwTzSZN5Fc0jOHxsqVOxN+Sm4iIFHPGuLAd8Ipz4euMURaRygoATm8gqeOUeiLHkOjxInbdiVs2kbHeUk8PdaqleYGeiT4maEGsfBxdh77AsOz/yxElB7Ta32i13HqQybqXe69YuZXqMqQLY3i8bKkTLogTEemIuTA/6vFSU/TjmWSOURaRygoAZlP0ITPWcUo9kWNI9HgRu+7ELZvIWG+pp4c61dK8QM9EHxO0IFY+jq7DAkOu7P/LESUHtNrfaLXcepDJupd7r1i5leoypAtjeLxsqRPeMoWISEcshflotFtljzXarbDEuDDOJFuJcdxO1yFN1baRTVJEUV5sjFq35cVilTcbiBxDoseLyHUnctlCupxe7D/ixP9+/Dn2H3Wiy+lVu0iaqDet0UOdamleoGeijwlaECsfy4tPHm/t6EfDifoe/f9yrys05gnRn2eiv3G4JRzqdqG1vQ+HjrngcCd/24NY5S4xGRS9ZzrKpnfxxkykuYuSupd7r9aOfnQ5vRHza361DUPBYNg509HOyZ5TD2N9qmVLnXBTzQzS4sYm3FSTsoUW8zOST3sHceemPdg26n5ujXYrVjbX4lRrsYolG09LZQW0V169iJafIreJyGUDgPbeQSyXKd+q5lpUqVy+zn4P7ti4G1vH7Gy/tqUOk1XeALC9dxDLNu0Ju2emKPWmRpvqafyUI3IsKiV6X5QtMt0OeszNWH1cKF/f/7QPjyyux1PbP0Zre//I/4/tt1c21+K+Vz/E5v3HZM+Xaensbzr7PVi6cXfYfYGbqm1Y01KX9Ma6kcq9YtFs3P/aR3hzX3fU90xn2USVqvxUGjOR5i4rm2vxgII2knuvImMenr7uHAwPA4/+6WDYuedX23BD43T867M74ZaGsGBGBe5eOBN3vrw3pe2cqtjhODmeHuY/sXBBPIO0OCnhgjhlCy3mp5zP+tz46R/24/rG02HIy8WAx4/SwnwEhobx1La/44eX1GBqeZHaxQRw/FsKy17ajZlTLKivLIMvMIwCQy5aO/qxr9OBVZfXYaLZpHYxR3T2e3DXy3silveBRbW6nbSrLVJ+ihxDnf0ePPDah7hiXiUqzAVweYdQYspDt9OHjTs6cNfCWarGi8Mt4fYXP0DNZPO4ujtwxImfXXmW6rvHO9wSelwSBrx+lJryYSsxql6mLqcXt72wS3YDqUa7FQ9dNUe1mFOrTfUyfkYjYiwqdbjPjZ9FmRfcfkkNpgkyL9AzNcYEveWm0j7O4ZbQPeDDZ30enFJihBQYxuduCZPMJkiBYTi9fphN+TAacvHwm38LWwwPUbM/T0d/43BLWLKhVXaTvKZqGx5dXJ+S9xhd7hKTAXdu2hO20Cr3npkom4hSmZ+xYibW3GVOVTke29wW9nikuh/9XsUFBuzq6Ef3gBfzqycAANzSEAy5Ofj7MReOOL34+R8PAgCWXGhHa3ufbBkSbedUxY7I1xNq0/L8RwnerIyISEec3gBe3nUEL+86Inv8u1+2Y2qGyxRJ36CEzfuPyV6IhI6LNPlwePxRy/tDj58L4hkmcgw5PH78fm8Xfr+3S/b4zRedoWq89LgkvLmvW/ZCNXRc7QmvpUi8SXffoCR7MQcA29p6VY05LbSpVokYi0oNKJgXUPqJPiZogdI+zlJkRI9LwrVP/zXiudZdczYqzAUR5w9q9ufp6G96XJLsoiEAbD3Yk5LxYWy5D3W7IrbV6PfMRNn0LlbMxJq7XNcwfdzjkep+9Hsd6nbhR7/dDQD46R/+Nu4c6645e+T/6yvLxi26x3qvWFIVOyJfT6hNy/MfJXgPcSIiHXF6tLMjtNMbSOp4pmmpbrOFyDEkerxky+7xqSZ0zLFNSYbofVG2YDskL54+LtZzfYFhuLxDMd5PrHloMtQYH5S+J8eu9IsVy77AsOzjsepeSZ7Feg+l75XI+ys9p8hzO0ovLogTEemIOcbmWCLtCG02Rf8jpVjHM01LdZstRI4h0eMlW3aPTzWhY45tSjJE74uyBdshefH0cbGeW2DIRYkpL8b7iTUPTYYa44PS9+TYlX6xYrnAIL8sGKvuleRZrPdQ+l6JvL/Sc4o8t6P04oI4EZGOmE0GNEbY6bvRbhVqQC8vNkYta3mxWH+eZSnMj1peS4yLXUo9kWNI9HjJlt3jU03kmGObkpzSGPOCUoHmBXom+pigBfH0cdGe22C3orWjH91On7D9eaqpMT4ofU+OXekXa+7S2tE/7nElda8kz0JaO/ojliHRdk5V7Ig8t6P04qaaGZTpjU3a29vR0yN/TyWl9u3bh6uvvpqbapLu6WnjofbeQWxv60GF2QRfYBim/Dx0OTxosNtQJdgu2R29g9gmU9ZGuw2VgpUV4A7kIaENVpxeP8yF+bAVp/f+ctHys713EMtl2mRVc63q8S56vHT3e+DyD8EXGIbTc7wtCwy5KM3PwwTeyzYikWOus9+DOzbuxtZR99RsqrZhbUsdJqepTfU0fkbS5fSib1CC0xuAudCA8iKjpu4nqqV5QTSZHntSLdNjgp5ys7PfA4fHD5cvgMDQMLYf6sX6bR/DLQ1F7OPk+sMGuxXXNUzH9ze04oIzbFh66QzZNhGhP0+1VIwP8eZgrPd0uCX0DkoIAvjJKx+G3Q863WOX2jKdnx29g2jvO55Hpvw87Gzvw75OB+65bBZWvPYR/jjqfu/R6n50DFgK82HMy8XyTXtG2thWYsRj35oLS2E++t1+lJoM6HJ68fruTtxy8Rm46+W9KZ2jRIuxImOe4ngVeW5H6cOvBOhUe3s7ampmwONxp+R8fp+UkvMQUWa8secI3h41oM+vtqHBLv8bdDUFIVNWu5hlBQBTXi7u/PpMBAEMePwoLcxHzonHs0VnvwdLN+4ed9GypqVOlQ3BjHm5WHJhNZZeWgOXdwglpjwM+oZgFKBNRI8Xz9Aw7v7d3rCNlkKLMxRZlbUYD1015+QCqcmA8mIxFkinlBXi0cX16HFJGPD6UWrKh61EW4uGomnvHcSyTXvG5YnWLpK1Mi+IRLSxJxGijwmi+vTEQtX2MXPFV5c0IicHsEZY5BrbHxYZDcjPy0HvoISXbvwSyouNyBkOCjuHSLVkx4dEcjAHwKW1k3HNl06DLzCMAkMuugd8yBlzviJjHq5vnI4bv/wFFOTnoqzQyLErhTr7PVi+aS/ebjvZdvOrbbj3slnIB/DVWZPwT1+sCmujSOcZGwMLZlRg9eW18PqH4ZH8KDEZcefL48fMlSfGzFTPUSLFtVsawpINrYrjVeS5HaUPvyGeQZn8LeDOnTsxb948nHv9vTBPPi3h8xzZ8y72vvIfaLzlcUydUZ/wefgNcRKdXr5F09nvwY9++4HsTuKNdisevOIsYS4cu5xe3PbCrohlfeiqOUJNQhxuadzEKqSp2oZHF9frfuKuVh1Eyk+R20TksgHA4T43lm7cHTH/1rTUYVp5kQolI63Ry/gpR2vjlBwtzQsiEb0/VUKNz6CH3Exn/OohrjIlkbqK9prVl9fijd1HwhZoY51PbzKVnw63hCXPtcrWdYPdioW1k/GZw4vHNreFHRvbDkpiwBsYFmLMZG6TUvyGuM6ZJ5+W1K1OnEc+SV1hiCjtHB6/7CQEALa19cLh8Qtz4ds3KEUta9+gJNRCQ49Lkp1YAcDWgz3ocUm6n1yJVgeilWc0kcsGAAPeQNT8G/AGMlwiIvFobZySo6V5QSSi96dK6OEzqCGd8cs2US6Ruor2morSAtkF2mjno8T0uKSIdb29rRfXN0xHhcw4NrYdlMSAf2hYiDGTuU1KcUGciEhHnB5/1OMD3ujHM8kZY8Et1vFMc8aoO5HqNl1EqwPRyjOayGUDtNVXEKlFa+OUHD3kuuj9qRJ6+AxqSGf8sk2US6Suor3GFxiO+3yUmFhtF60tRreDkhiQhqLffCJTYyZzm5TigjgRkY6YC/OjHi81RT+eSWZT9CEo1vFMM8eoO5HqNl1EqwPRyjOayGUDtNVXEKlFa+OUHD3kuuj9qRJ6+AxqSGf8sk2US6Suor2mwBD9Hu2s+9SJ1XbR2mJ0OyiJAf9Q9F90ZGrMZG6TUuLP4oiISDFLYT4a7dawHbJDGu1WWGJcWGRSebExalnLi8X6UzZbiRFN1bawXcxDmqptsJWIVd50EK0ORCvPaCKXDQBKTYao+VcqwEKfwy2hxyXB6fXDXJgPW4SN04jSRWvjlBwtzQsiEb0/VUIPnyGadPTXXU4vSox5ccdvl9OLvkEJLl8A1mIjpKFhODwBmAsNKM7Pg9MrocRkRF7u8Y0FI91nOB1tkkg9iTAWJhK/0V7TPeDTdT6IIBQ3Q8FgxDhvsFvR7fTiM4d33LGx7WArMWLBjAqcOdmM+soy+ALDMOXnYWd7Hw4cccJWYoQ3MBw1X4uNeejs92DA6x/JyfKi8I0rR8d7SYEBxrxc9HsklJiUx36i/a0IuUaZpf7VDhERpcyUskKsbK7FnZv2hE1GQrt7i3Sf0IlmU9SyinZfVkuREWta6nDHxt1hE6ymahvWttRlxYTJUmTEikWzsVymzVYsmp3xOhCtPGPL9sCi2bLx/YDKZQOAaeVFUfNP7Q01O/s9WLpxd9gFXFO1DWta6oTqx0jftDZOydHSvCAS0ftTJfQ8h0hHf93eO4hlm/bgmNOH//i/Z+Oul5XFb+h1re39eGRxPX7+5t/C7mkcipk1v9+HLX/rwSOL6zEcDIY9J11tkkg9iTIWJhK/0V7zlTMm4MtnTNBlPohgdNwUGfPw2LfqgSDC7iXeYLfi5gurMcViwn2vfhj2erl5tKXIiLsWzsTyTXvCNuAM5aKlyAgLEHG8eWBRLb71n+/hVGsRrm2Yju9vaIVbGkKj3YpVzbWoshbLxnuD3YrrGqZj8f97D2efWq4o9hOJV1FyjTIrJxgMRr/RD6VMJnf63rlzJ+bNm4cFdz6V1Kaan7z3B7y3/j403vI4ps6oT/g8n7cfwB9XXocdO3Zg7ty5CZ+HKF0ymZ/p5HBLuO/VD3Ftw3QY8nIx4PGjtDAfgaFhPL39Y9x72SxhJpmH+9y453d7MXOKZeSbBgWGXLR29GNfpwP3fXO26otyckLfHhjw+lFqyoetJHu+PeBwS7j9xQ9QM+rbIaE2O3DEiZ9deVZa6iJSfqpVHiU+63Pj7ijxff83Z2OqivF9uM+Nh/6wH9c1nj6ur3hq299x+yU1quWfwy1hyYbWiN/ae3RxfdbknBboZfyU43BLWL5pDy6fOw0V5gK4vEMoMeWh2+nDpp2HRxYBRKbVsXY00fvTeGRyDpGJ3ExHf93l9OK2F3Zhe1svllxox0edDtm2HzvOj31da3uf7AZ/jScW2G545n0UGfNwfeN01FeWAQCqTilCRWlBytskkXoScSxMJH6jvSab59Tpyk+5uCky5uG7TafjgjMr4B8aRrExD8VGAwqNebjv1Q8xfUJJzPzq7Pfgx7/9IOK3vx+84iwUG/Mijpm/3dGBL1SU4rHNbWiwW1FfVT6ysB56/dgF6ZDRz48n9pXGl4i5RpnBb4gTEelIj0vCS62deKm1U/b4TV+pFmZAH/AGsHn/MWzef0z2+A8vEXOzMktR9kzWx+pxSXhzXzfe3Ncd8Xgm60a08ozmVBDfUzNcptEGvAFs2nUEm3YdkT3+3S/bM1yik3pckuxFCQBsPdijartSdulxSXh9z1G8vueo7PFbF5wpfCxqdawdTfT+NB56m0Oko7/uG5RGFrLrK8vw2Oa2iG0/+vxyr5Ozra0XSy+tAQC4paGw571125fT0j6J1JOIY2Ei8RvtNXrLBxHIxY1bGsLDbx7Ew28exFu3fRlfqCgBABzqdkUc30LnGvnlhccvuxgOHM8ph8cPjzQUdcxcd00lAGB7Wy+ub5g+7vWR4n308+OJfaXxJWKuUWZwQZyISEe0tKu206OdstJxosWXaOUZTfT4Frl8IrcrZRc9xKLIua6UHj6DXqUjR5zek7+k8QWib9I3+vzxvM7lHYp5vlRKpJ700P9Q5sUTN3E9V0E/nJ8XfbPU0Xk5NkdjnX/081Md+8y17MUFcSIiHdHSrtrmGBt5iVRWOk60+BKtPKOJHt8il0/kdqXsoodYFDnXldLDZ9CrdOSIedSmzgWG6Atso88fz+tKTHkxz5dKidSTHvofyrx44iau5yroh40xFsRH5+XYHI11/tHPT3XsM9eyFxfET1i9ejVeeukl7N+/H4WFhfjSl76EtWvX4swzT95/+4ILLsCWLVvCXve9730Pv/zlLzNdXCIiWbYSI26/2I5F9dPgkobg9BzfJbvEmIeXWw8LtWt7qckQdSfyUpOYQ9RnfW44vQE4PX5YCvNRajIIe+/SVO+Wnuiu7eliKzHi/55Xhesbp8PjHx6J98L8XKzf9rGq8W42GdA8Z3LYPbrNhfnwn7hHt1nl+BY5/0SLMzldTi/6BiU4vQGYCw0oLzJqYoNFio+txIiFtZPQPOp+qKUmA7qcXmzaKdaYGonIua6UOcZnULs/jYeW5hBKxNNfh+YkbsmPskIjBqWhcX3oZ31uBAH85rvnwVyYD0NeTsS2b6q2ocRkwKFuF1w+P8qLjLh4RgVqJpthKczHumvORk5ODna292H9to/hlo5/K3y+3QZjXh6WXGgPezyZ8WX0fKukwABjXi76PRJKTMfnXomMa9FeM7/aBkNeDhxu+Vs5xCoPb/+gT11OL/xDw3j+O+eh5MRYtXTjbrilIVzfOB1fOt2KPreEjzodKC4wwGwyYPXltagoLYAvMAxTft5Ivpx9ajkMuTlobe+DuTBfUT8cGApifrUt4n3AWzv6w/4/dA//80+3YtAXwIbvnIvth3rD8nLsa+PJ00jXQWPncGZTftz5meprrEydm8JpZ/aQZlu2bMFNN92Ec845B4FAAMuXL8dXv/pVfPTRRyguLh553ne+8x3cf//9Iz8XFWl3AkNE+mMpMuIb9dNwx6Y9YRsJjd4BXBTTyosi7kS+srlWyE2+Pu0dxPIIdXuqtTjKKzMvHbulJ7JrezpZioy4Yf7pEdtEzXifWl6EWxacGbFsai+AiJx/liIjHlg0W7ZsDyyarXo/1t47iGUy7bqquRZVgvUDlBxLkRE/+ocaIfsYpUTOdaWmxvgMavenSmlpDqGU0nlBaE6y74gTz/7zeePmqf949lT861eqx9XP12ZPxIpFtbjrZfnx4Od/PIBXPjiCRxbX45dbDuFHl9Tg/tc+DLsveIPdikcW1+P7G1pRX1WGaxpOQ/MT21FfVTby+NyqMqxIcHyRm281nNi4c/H/ew9nn1qOB1vqsGLRbCyXieFI7xupbhvsVlzzpdNw6S/extmnlo+b1ykpTzJzQRJTpLnJc985D0f6PfiPt/8+Li/u+8ZsvPXRUbw56h79DXYr1l97DnJzgEsfeXtkYfqfG06L2g/f+8qH2NXRj2f/+Tw88NqH455zz2Wz8K3/95cTuVuL7/3qfTyyuB5Pbf84rFyNo/LVLQ2NPP//rnsvrusNuTxYMKMCdy2cOe4zXFQzIeK8Uy4/03GNlYlz03g5wWAwqHYhRHTs2DFUVFRgy5YtaGpqAnD8G+Jz5szBww8/nNA5M7HTd8jOnTsxb948LLjzKZxSdWbsF0TwyXt/wHvr70PjLY9j6oz6hM/zefsB/HHlddixYwfmzp2b8HmI0iWT+ZlOh/vcWLpxd9hkKKTRbsWaljphLn4P97lxz+/2YuYUy7idzfd1OnDfN2cLU1bg+Le6fhylbte21AlzUZ7u3dKV7tqeKpHyU+R4F7lsgNj51+X0YtlLuyOWbdXldap9G7vL6cVtL+yK2K4PXTUn674prpfxU46W+v1IDve5ce/v9mKGgLmulMj9lVJqxFImczPavGD0nGTdNWdj/faPx9XDH29rwk9e+XDc40sutOOjTkfEtv/BxWfgfz7qQmt7H+qrytHa3idbx/PtNty1cAZe3X1kzLfFrfjRP9Tgfz7qwoEjTvzsyrPims9Em2812K2oryrHY5vbsPryWrz1URdqppjHfY5Y7+twS+ge8KH9czcAoLWjf9w320PzOqXlScVckJKTyvyMNjeZb7fia7WTsWzT3nHHGu1WzDkRE2NfM++0U/DwmwdHHntlSQN+8ebfxo0lAPDse59i8/5jUfN1f6cD3z+Rr/s6Hbhn4Szc9fIevB0hX2/76hn4fFAayfWfXDYL5sJ8RTEbKQ+WXGiP2EdcVDMB3zr3VACImp/pvMZK9/UbjcdviEfgcDgAAKecckrY488++yx+/etfY9KkSbjssstw9913R/yWuM/ng8/nG/nZ6XSmr8BEFBe95ueANyA7yAPHd/AeGLXhkNoGvAFs3n8Mm0d9K2G0H14iTlmB45s1RatbpzeAqRkuUyTp3i1d6a7tiVKanyLHu8hlA8TOv75BKWrZ+gYl1Rad+walqO2qZtkyRa/jpxwt9fuRDHgDeGv/MbwlYK4rJXJ/pVQmYknN3Iw2Lxg9J6kwF8jWQ2AoKPt4fWUZHtvcFrHtb7+kZuQ51zdMH7ewF/J2Ww+koeFxx99u68W1A76Rx+OdH0Wbb21v68X1DdMBABWlBXhzfzfe3N8d8TyR3tdSZESPS8INz7wve3z0vE5peVIxF6T4pDM/o81N3m7rxbUn2n2sbW29uE7mmNxrDHm5smPJumvOHsnPWPm6+NxTR3Lt9kuGZBfDj79/D65tOC0s5n90yRAqFf4lTaQ8CJVPzlv7j+Fb554qm2ejcyWd11jpvn6j8aLf9T5LDQ8P45ZbbkFDQwNmz5498vi3vvUt/PrXv8af/vQnLFu2DP/1X/+Fq6++OuJ5Vq9eDYvFMvKvsrIyE8UnIgX0mp9KdgAXhZbKCmirvFrfLV1pforcJiKXDRC7fM4YvyyIdTydRC5bpuh1/JQjcp4oxc8ghkx8BlFzc/ScxOUdkn9OhPrxBYajnnvA4x95TqznRnrv0a+Ltx1izbeUli3W+yqd1yktj5L3pNRKZ37GmntEi79Ix8Y+PqAgR2PFeVj8xegTx54rnvlVpDyIp3yjjc6VdF5jaf36TYu4IC7jpptuwt69e/H888+HPf7d734Xl1xyCWpra/Htb38bv/rVr7Bp0yYcOnRI9jzLli2Dw+EY+dfR0ZGJ4hORAnrNTyU7gItCS2UFtFVere+WrjQ/RW4TkcsGiF2+WBvkqbmBnshlyxS9jp9yRM4TpfgZxJCJzyBqbo6ek5SY8uSfE6F+QrdkiKS0MH/kObGeG+m9R78u3naINd9SWrZY76t0Xqe0PErek1IrnfkZa+4RLf4iHRv7eKmCHI0V52HxF6NPHHuueOZXkfIgnvKNNjpX0nmNpfXrNy3igvgYS5YswWuvvYY//elPmDZtWtTnnnvuuQCAtjb5P7soKCiA2WwO+0dEYtBrfpae2AFcTqPdilKBFmu0VFYAI7urywntri4KW4kRTdU22WPx7M6uFqX5KXIMiVw2QOzylRcbo5atvFi9+BW5bJmi1/FTjpb6/UhEznWl9PAZMhFLoubm6DlJt9MnWw+GvBzZx1s7+qPWW2BoGK0d/WiwW0f+G+m53U7fuMdDrwMSmx9Fm2+NPnf3gC+peZnSeZ3S8mhhLqg36czPaHOT+XYrup1e2WONo2Ii1uOBoeGYORotB0fHXyh3lTw39Px45leR8iBWfyJXF2NzJZ3XWFq/ftMiLoifEAwGsWTJEmzatAmbN2/G9Ony91kabdeuXQCAyZMnp7l0RKRXDreEQ90utLb34dAxFxxuKanzTSsvwsrm2nGDfWgHcJE2ndJSWQFgaozyirSxmqXIiDUtdeMmVfHszq4FIseQyGUDxC7fRLMJq5prMd8eHr/z7Tasaq5V9R7dobLJ1ZvaZaPU01K/H4nIua6UHj6DHmIpUaPnJEs37sY9l83C6ubZWHfN2Xji23Ox/tpzcOCIEysXja+ffZ2OqPX29PaPsX7bx7iuYTo+6nTghsbpmD/mufNPPNfh8Ye956rm2fjnxtOxftvHEedHsebpkeZbDXYrrmuYPnLur5wxIal5mdJ5ndLy6GkuSNHnJndfNgtTygrHHWuwW/GTb8zG/iPh9zJvqj4+1zpw4vEiYx6WXGiHRxrCLRefgQ3fORdLLrSjyHj8Ly72n8jR+dU2rN/2ccQcvKHxePw12q2497JZuOnZnbiuYbpsmUOxGvo53vlVpDw4cMQZtT85IFMX939zNj7pHRzJ/3ReY2XL9ZtIcoLBYFDtQojgX//1X/Hcc8/hd7/7Hc4888yRxy0WCwoLC3Ho0CE899xz+NrXvgar1Yrdu3fj1ltvxbRp07BlyxZF75HJnb537tyJefPmYcGdT+GUqjNjvyCCT977A95bfx8ab3kcU2fUJ3yez9sP4I8rr8OOHTswd+7chM9DlC6ZzM+Qzn4Plm7cHbZ5RlO1DWta6jClrDDh83b3DsIHwCUNYcDjR2lhPkqMeSgAUKFwM5JM6egdxGcOD4oLDHB5h1BiysOgL4CplkLFG6dkUne/By7/EHyB4ZG6LTDkoiQ/DxVJtFm6ONwSelwSBrx+lJryYStJ72aY6RItP4/1e+AdGh4X76a8XExQuU0O9w5iQAogiJyRsuUgiFKjAdMEiO/PegfhGw6Oi+eC3BxMVbl87b2D2NbWg4lmE3yBYRQYctHt9KLBbkOVAHXX5fSib1CC0xuA2WRAebExaxfD1Rg/M+lw7yAOjxunhjDNYhIij5Xo6h2EhPHzAiOAiRr5DFobf+Vkus8VLTcdbgm9g8cXlO/93d6wDfXm22246Sunw1Zqgn8oODJvyc/LgcvtRXlpEdyj4rcwPxc//u0H+OLpNjR8wYoCQy6KjHkwGfLw7t97USEzdtz/6kdhm1rOr7bhvm/MAgBYi8fPj5TO04/0e/Dnvx1DRWkBpKFhTCgpQJExD77AEMwmY9jcK9l5mdLXj35ecYEBxrxcODwSigu0OxfUm3Tk58m5yfH4KMzPxbEBH4oK8pCXk4ucnOObFBcZ81BsNKCs6PgtOMbGlEcawp/+dgyTzSZMLjNhxWsfjcvXuxbOwJF+L444vThzYgm2HuzBnGllmFJuwo5P+sJzcMCHL04/BV5/AAPeACabTTh4bBCGnByYjHkoL8qHRxo6XmZjHga8fjg8yc2vRufl6HJ85YwJyMnNkZ3Djc6bQmMedrb344HXPoJbOr7/QGhhGoDsuS84YwImp2A80sv1mxaI//dlGfLkk08CAC644IKwx5966ilce+21MBqNePPNN/Hwww9jcHAQlZWVaGlpwV133aVCaYlI6xxuadwkGzi+g/QdG3fj0cX1CQ18nf0e/GjTHtmdxhvtVjx4xVlJLban0uE+N+6IUtY1LXVCfevL4ZZwu0ybAccnSIm2WTpZivQ9gXK4JdwmaJt81ufG0ijxvbalTtVvBH7W58aPBS1fZ78HywTvxyaaTVm7AJ5NupzeqHn80FVzhI8DkXNdKS2Ov2MdjjEmiDbnSYdQGy3Z0Bq2uAYAb7f1YBhB1FeV47HNbWi0WzGnqhwA0Nreh+1tvVhyoX3k/0P+95P+kedc3zAd67d/HLGO51SVhy2Iv32wBz955UPZ+FE6T3e4Jfw4jthMdl6m9PXyz9PGL78ocRPNJpgMuVj5xr64+svRPzvcEn50IqaXXGhH67a+cTn1dlsP7n/to5F8bbBbUV9Vjvfb+2SfD2DkOaH8vq5hOq59+q8RypXcHE9JXtZMHv9LiFDeONzS8X5KJv///LdjeGP3Ebzdlr7xSO/XbyLhgvgJsb4oX1lZqfib4BTZvn37kj6HzWZDVVVVUudob29HT8/4TkyNslB26nFJsoM0cHyw7XFJCQ2EDo9fdhICANvaeuHw+FVfSAoZ8AailnUgjt3EMyFdbUaJE7lNnDHi2+kNYGqGyzSayOXTUj9G+tY3KEWNxb5BSfgFcZFzXSmR+3qltDbnSZdobbm9rRfXNxy/bem2tl5cd+L/H9t8fL+u+sqykf8fLfT4HZfWRK3j0PlGixQ/SmNOD7FJ+pJsTI5+faScA8LzdfT/K3n+trZeLL20Jq5yxSOVdTBWRWmB7GK40nOTWLggThnhcfQCyMHVV1+d9LkKC4uwf/++hBei29vbUVMzAx6PW/WyUPZyev1Rjw/EOB7xvJ70nDcdtFRWIH1tRokTuU1Ej2+Ryydy2Si7OGMsUsY6LgI95JPIfb1SemiHVIjVlr7AsOz/y/089nGXd0jxuUeTq3ulMaeH2CR9STYmR78+Us7IHY/13LHPGZuvqcyVVNbBWLE+J3NeW7ggThnhdw8ACGLOt5ZiwvSamM+PxHnkE7y3/j709PQkvAjd09MDj8eNc6+/F+bJp6laFspeZlN+1OOlMY5HPG9hes6bDloqK5C+NqPEidwmose3yOUTuWyUXcym6JdKsY6LQA/5JHJfr5Qe2iEVYrVlgSFX9v/lfh77eIkpT/G5R5Ore6Uxp4fYJH1JNiZHvz5Szsgdj/Xcsc8Zm6+pzJVU1sFYsT4nc15bxJ/Fka6UVFQltclnKpknnyZMWSj72EqMaKq2YWuEe5vZShL7UytLYT4a7VZsi3D/REuMC7JYQpt8OL1+mAvzYZPZhEipUpMhallLBVtoSFebUeJEbhNzjPhWeyFN5PKlux+jzErluJFp5cXGqLFYXiz+5xA515WylRixYEYFzpxsRn1lGXyBYZjy87CzvQ8Hjjg1Mf5qbc4TSdiGwoUGlBeFb0Y3Ns8/63PD6Q3A6fHDUpiPYmMe5lfbZG9H0GC3AgDWXXM2AKCsyIh+t4Srzp6Kq887Dfl5ufjNd8+DuTAf/qFh3PTsTnT0ebC304HVzbNhzMvFfLtN9nYGjXYrWjv6xz0+v9qGoWAQh465wvqmaPOL0a8pMRmEnYdQ9hidf6cUj4/dImMerm+cji+dboXDI4XF+9jcLTEZsLB2Ek6bUIIJpQWYb7eOu+c/cDxfQznVYLdiQunxzWSj5Xfo+Y12K7qdvpFjo3Ml1pxByZwi2euDaK/vHvAx53VEGyMvEZHOWIqMWNNShzs27g4bUEO7Vye6WDClrBArm2tx56Y9YRddjXYrVjbXJnXf3c5+z7gNhpqqbVjTUpfQeaeVF0Utq2ibS1mKjFixaDaWy5R3xaLZmlng0RNLkREPLJotG0MPqNwmU2PEt9qb2IlcvnT2Y5RZqR43Mm2i2RQ1FkW/fzggdq4rZSky4u6FM7Fs056w+9M22q1Y1VyrifFXa3MeOe29g+M2PL6oZgLuuWwW7np5b1ieL5hRgbsWzhz3eb82eyIe+OZs3PWy3Lhdiwf/ex9+v7dr5PELaybgnoWzZJ//qxu+iO/9agfOm27FY5sP4oHX9+GRxfUYRjCsjKE6XvHaR2Gfp9FuxTVfOg2LHt8OtzQU1jdFmqePfc2CGRVYsWg27np5b0rn80RKjR1ni4x5WH/tOQgGj29+WWTMwyOL6/HU9o/D+s8FMypw98KZuHNM7jZV2/DAotm479UPsX7bxydyCmE51XBiU8zvb2gdya/7X/0Qf+ty4Vc3fBF3v7xXNr//77r30Gi34p7LZuFb/+8vo44dn7PHmjMonVMke50d7fVfOWMCvnzGhJRfw5M6coKxdpOklHE6nbBYLHA4HDCbx+9qm0o7d+7EvHnzsODOp5L6FvQn7/0B762/D423PI6pM+pVP8/n7Qfwx5XXYceOHZg7d25C50hV3aSiLCSOTObnaKHfcg94/Sg15cNWktw35w73ubHy9Y9wxbxKVJgL4PIOocSUh26nDxt3dGD512cmdNEVabdtIPEdtQ/3ufGzP+zH9Y2nw5CXiwGPH6WF+QgMDeOpbX/H7ZfUCHWB6HBLuP3FD1Az6htqBYZctHb048ARJ3525VmcBKVJpPzscnqx7KXdmDnFMq5N9nU6sOryOtUWrESP73T1FangcEtYvmkPLp87bVzZNu08jJUaWQDLFpHyMx3jRqZ19ntw18t7IvYxDywS/xc0h/vcuOd3eyN+hvu+OVuosVaOHmKpy+nFQ/+zH99p+gICQ8GRMcGQl4P/3HoIt321JuXjVSrntl1OL257Yde4TSuXXGhHa3tfXI9/1OmIGI8zpljCFu0inQc4vpC2urkWyzftHflWeOibsPWVZQCAqeWF477F7vD44fUP4Z2/92L9to/hlk7ey3hsPCl5zYIZFVjRXAuXN5Cy+TzpXyryM1LfWGTMw11fn4EzJpYiLzcH//Y/B8Z9yztWbs2pKsdjm9vCcionB5hQWoBgEDji8MKQmwMAePa9T7F5/zGsu+ZsPPvepxHz+56Fs+Dw+uEY9GNACowbT8cudoc0Vdvw0yvPwg9f/CCucSDZ6+xor0/1NTypg98QJyJSkaUotYPngDeA3+/tCvt2zWg3X3RGQudNdrduOQPeAF7edQQv7zoie/y7X7bHXc506nFJeHNfN97c1x3xOCdCmdU3KGHz/mPYvP9YxONqLYiLHt/p6itSoccl4fU9R/H6nqOyx29dcCZzTQPSMW5kmsPjj9rH/NDjF35BfMAbiP4ZLhF/Y1A9xFLfoIQX3v8ML7z/mezx6xpOF/ovDvoGJdmFs/rKsrAFbKWPR4rHb517qqLzAMC2tl64pKGwW6S4paGw579125dH6jU05z7U7cLlT74je86x8aTkNX/c1407Lg3gCxUlsseJ0iVS3+iWhrB8096R2w/J3fIkVm5d1zB95Fyjn7fumrNxwzPvh/0cyucKc0HU8eaWBX5847Htssd+6PFH7ef7BuMfB5K9zo72+lRfw5M6uCBORKQjTk96drtPdrdu2XOmqazpko46oOQ4vdEXcmIdTyfR41vk8jHX9EEP7Shyniili8+gh1gSeLxSIlL5fIHhlDwe6Xis5ycS34nEkx5ikPQnVlxGy594czHS46N/dnmHxj49TLTjsXI5Vh/JHKREcEGciEhHzDE2m0t05+tkd+uWPWeaypou6agDSk6szeDU3CxO9PgWuXzMNX3QQzuKnCdK6eIz6CGWBB6vlIhUvgJDbkoej3Q81vMTie9E4kkPMUj6Eysuo+VPvLkY6fHRP5eY8qKeM9rxWLkcq49kDlIiomcBERFpSqnJgEa7VfZYo92K0gQvuEK7bctJdEftdJU1XdJRB5Sc8mJj1BgqL1avTUSPb5HLx1zTBz20o6UwP2qeWGJcwItA5FxXSg+xJPJ4pUSk8rd29KfkceB4PbR29Mf1fLPJEHdsJBJPeohB0p9ocdlwIp9aO/rRkIJcjPT46PN0O31Rzznok/+Wd2g8jZZj5cXMQUo9LogTEenItPIirGquxerm2Vh3zdl44ttzsf7ac7C6eTZWNdcmvHFWaLftsRORZHbUnlZehJXNteMmTqHdykXb5CsddUDJmWg2YVWEGFrVXKvq/VjTlYupLJ+o+cdc0wc9tOOUssKoeSL6/cMB8fsiJfQQSyKPV0pEKv++TgdWLKrF/DFts/+IUzZ39nU6sCJKTh044gx7/ECE84SeP7W8CGta6sa9//wosREpnuZX23DvN2ahd1CCwy0peo2WYpD0J1os33xhNdZv+xjrt32M6xqmj8uhA0ecWNVcK/valc212NfpCHs8Uo7uP+LEikWz0Wi3YunG3bh74SzZfH1gUS2sJUasbp6NIuPxb4oXGfOwunk27l44E11OL+76+kysvrx25DhwMscmmk1C5aDDLeFQtwut7X04dMw1rs8gbRD/KwFEMvbt26fKa4m04o09R8I2UJlfbUODXf636kpNKSvEo4vrU7qjdh6O73K+9NIauLxDKDHlYdA3hOh/cKeeHACX1k7GNV86bWTn9O4Bn9rFympV1mI8dNUc9A1KcHoDMJsMKC82CrO4kI5cTJVcyOVfQIhvS6Sjv6HM00M7mvJycefXZyIIYMDjR2lhPnJOPK4lIvdFSughlkTuc5UIH2/9KDYacNTpxf9Z9x6+WT8V//LlLyAvNwduaQhdDg+MANa21MHpDYy0WYkxD4OShFXNtRiUhkYetxTmY0pZIX525VmybfzgFWfB4fGPez4ADA0N49LZk3Dt6LmZ04vAUOR7JI+Op36PBJ9/GO/8vReXPboNbmkITdU2rGmpC/ullx5ikPQnUlwCwKtLGjHg9cNcmI+HrpoD16hcDMXu6str8WmvG/0ePwoMudh/1Inh4SC+03Q6bllwRlhflQfg0tpJ+KcvVo3kmsMjoSAvdySnB33h+R3qJ6785TvocUmYX23D6zc3os8tobzIiHt+9yGWbdo78nmaqm144/vz4fRIKC4IzzFRcrCz34OlG3eHbfIp12eQ+LggTpricfQCyMHVV1+d9Ln8Pv4Wj/Tnsz43lm3ag+1jdhN/+2APlm/ag7UtdZiaxLfBUrmjdpfTi6UyZQWOf5PgoavmCLOoCRz/JsCPx0x+QpqqbXh0cT0vilQy0WwSKlaA9Odisjr7PbgjSv49eMVZqk/qU9nfkHq03I4Ot4QfarzfF70vioeWY0lrc55IQmV84PWPwj7LY5vb8NjmNjTYraivKsdjm9tGPteMyeYxZymOeP5IbTylrFB2TOpyeqOOZdHqNfQ+P3n1w3E5vvVgD+7YuHtcjms5Bkm/IsXl2McmjklFh1vCHS/tCYv/ddecjbt+tzdiTl3XMB03PPN+2OOh8bDKejK3HW4JS97YNy633j7Yg3tf+RA/vfIs/PDFD/B22/jcu+d3eyOOr2rnoMMtjVsMByL3GSQ2LoiTpvjdAwCCmPOtpZgwvSahcxzZ8y72vvIfCATE3s2dKBFOb0B2AgMA29p64fQGMDXDZYqkb1CKWta+QUmoi8MelyS7KAIcnwT1uCROgGiE6Lno8Pijls/h8au+IE6kNj30+6L3RdlCa3OeaKJ9lu1tvbi+YTqAzHyuZOtVDzlOlCi5+K8wF0TNqaWXjl+DkcuVWLnVN6jN3GOfoS9cECdNKqmowilVZyb0WueRT1JbGCKBOD3+qMcHvNGPZ5LTG/2XUrGOZ5ozRt2JVLekPtFzUfTyEYlAD/0+c10MWpvzRBOrrL7AyVuVpPtzJVuveshxokTJxb/LOxT1NZGOj82VWLkVKzdFzT32GfqilVuWERGRAubC/KjHS03Rj2eS2RT9d7KxjmeaOUbdiVS3pD7Rc1H08hGJQA/9PnNdDFqb80QTq6wFhpNLDOn+XMnWqx5ynChRcvFfYoq+k1Ok42NzJVZuxcpNUXOPfYa+aGfkJSJSmcMtocd1fCMhc2E+bMXJ38Ms1ec0mwxotFuxLcJ935K9MOlyek9uXlhoQHlR4psXlhcbo5a1vFisPzezlRjRVG3D1gj3kg1tYEOZ19nvgcPjh9Pjh6UwH+ZRm22pJd25mCxLYX7U8lliLKJlQir7G6JE2EqMWFg7Cc1zp6HCXACXdwilJgO6nF5s2nlYE/2+6H1RPLTcJ2htzjPa6Hq3FBpgNOTihe+eh6KC47mwdONu9LiO783UYLeitaMfwPHPVWzMg8OdvlsIJFuvnNuRnkS7rpQ7Jhf/3U5f1JzqdvrGPS6XK7Fyq7w4sdxLx/V4PJT0GWqXkZTTzgyIiEhF6dhNOh3nnFpehJXNtbhz056wiUyj3YqVzbVJbZzV3js4bmOuRrsVq5prwzZRUWqi2RS1rKJd5FqKjFjTUoc7Nu4OmwQ1VduwtqWOEx2VfNo7iOUycbmyuRanJhCXqZLOXEyFKWWFUcun9i8UUt3fECXCUmTEj/6hJmIfo4V+X/S+SCmt9wkTzSasaq7Fcpl2WCXgnCdErt4bTmysd+3Tf8XcqjI8+8/n4dv/+RecOakU1zVMx/c3tKLRbsU9l81C85PvYOZkc1Jz22iSrVfO7UgvIl1Xrm2pQxCIeM25tqUOS0fF/9KNu/H8d8/HT17ZKztmrP39vrD3bbRb8cCi2eNyJVZuTTSb4s69dFw7xyvW53JLQ/ixymUk5XKCwWBQ7UJkC6fTCYvFAofDAbN57G7bqbVz507MmzcPC+58KuF7bQPAJ+/9Ae+tvw+NtzyOqTPqdXGeVJXl8/YD+OPK67Bjxw7MnTs34fOQGKLlp8MtYcmGVtkNNEK7asc7YU7HOYHj3+JZ/tJuzJhiQX1lGXyBYRQYctHa0Y99nQ6surwuoYuuLqcXt72wK+KO4w9dNSfu8zrcEm5/8QPUTDaPK+uBI0787MqzhLwQCf3Wf8DrR6kpH7YS/tY/3SLlZ2e/Bz/67QcR4/LBK85SbfLZ5fTiof/Zj+80fQGBoSAGPH6UFubDkJeD/9x6CLd9tUaIBZDQt+tD8WwR4Nv16ehvKH0yOb/NND3EYme/Bw+/eSBiX/SDi89UPedj0UM7hIR9y91kQHlx+r7lnmxuRqv3BrsV9VXleGxz28hC2dBwEL0uH0zGPHQ7fWHfHE9mbqu0rMnUK+d2lGmpHDujXVeuvrwWb+w+grfbIl9zAhiJ/+ICA/Z85kBZUT4mlB7/y6gSUx5M+Xl4fPNBTDulOK5rzFi5pTT30nXtnCi5cgMQqowUG78hTkQUQzp2k07XDtV9gxLe2n8Mb+0/FvF4IhdefYNS1B3HEzlvj0vCm/u68ea+7ojHRZw0WIp4kSQKh8cfNS4dHr9qCz19gxJeeP8zvPD+Z7LHr2s4XYgFnCllhcIthqWjvyFKhB5i0eHxR+2Lrm04Xbg+YCw9tEPIRLNJM2WNVu/b23pxfcN0AMfbwOn1o9vpww3PvC/7/GTmtkokW6+c25GWRbuurCgtkF0MB07m5RcqSkbi/1C3C7e98MG45756cwNe3nUkYhki9cOxcktp7qXr2jlRcuU+1O0SqowUGzfVJCKKIR27Sadrh+pYO3bHOp7J83KXbkqW0yNuDKUrF7MB645EoYdYFLmfVEoP7aBFserVFxge+X+XdyjsZzlaiDUiLYp2TRVvXkY6l8s7FKMM6e2HtXDdqIUyUjguiBMRxZCO3aTTtUN1rM2xEt08Kx3n5S7dlCxzjI0f1YyhdOViNmDdkSj0EIsi95NK6aEdtChWvRYYTi4llJjywn6Wo4VYI9KiaNdU8eZlpHOVmPJilCG9/bAWrhu1UEYKxwXxE1avXo1zzjkHpaWlqKiowKJFi3DgwIGw53i9Xtx0002wWq0oKSlBS0sLurq6VCoxEWVKaDdpOYnuQJ+OcwJAebERjXar7LFGuxXlxeKcN111QNnDUpgfNS4tMRaC0qm82IivzZ6IddecjVdvbsCG75yH125uxLprzsbXZk9MOBdTzeGWcKjbhdb2Phw65oLDLaldpLT1Y0Tx0kMsWgrz0TxnMl5Z0oA3fjAfv/nuefj9D+bjlSUNaJ4zWdV+Uik9tIMWRav3BrsVrR39AI63QbfTh9aO/rDnFxnzsORCO9ZdczbWXXM2hoNBIcYYIr2Jdk3VPeDD/BjXW6Pngoa8HNlzdTt9EfuDi2omwFyYLzufTNU8UwvXjVooI4XjgvgJW7ZswU033YS//OUv+OMf/wi/34+vfvWrGBwcHHnOrbfeildffRUvvvgitmzZgs7OTlx++eUqlpqIMiG0m/TYAS6ZHegtRUasWDR73MSi0W7FCpmdupWaaDZhZXOt7HlXNtcmfH/FiWYTVkU476oEz5uOeqXsMqWsMGq8q3lf3IlmE5ZeOgPrt3+Myx7djsX/7y9Y+Og2PLX9Yyy9dIYQ95Dt7PdgyYZWXPRvW9D8xDu46KEtuHlDKzr7PaqWKx39DVEi0jWmZtKUskLcsuBMrP3v/fjaL97GP/7HX3DpL97Gg/+9H7csEH9DTYB9gloixX+D3YrrGqZj/baP0Wi34oFFtfjJKx9iX6cDK5tr0VRtQ5ExD48srkdrex9ueOZ93PDM+1jw861CjDFEehPpmqrBbsUUiwl3L5wZ8ZrTIw2FzQUv/cXb+Nev2Mctom/aeVi2P7ioZgLuuWwWlm7cPW4+2d47iNtf/CAl80wtXDdqoYwULicYDAbVLoSIjh07hoqKCmzZsgVNTU1wOByYMGECnnvuOVxxxRUAgP3792PGjBl49913cd5558U8Zyp3Eo5l586dmDdvHhbc+RROqToz4fN88t4f8N76+9B4y+OYOqNeF+dJVVk+bz+AP668Djt27MDcuXMTPg+JQUl+pnIHeodbwu0vfoCayeZxO3UfOOLEz648K6Fzf9bnxt2/24uZUyyyO4Df/83ZmFpelFCZAaDL6UXfoASnNwCzyYDyYmPSF6KprFfSp0j56XBLWL5pDy6fOw0V5gK4vEMoMeWh2+kbmbirFUud/R786LcfyG5I1mi34sErzlJ1IcrhlrBkQ6vs5j9N1TY8urhe9TxMR39DqZfJ+W2midzHKPVZnxs/3rg7Yl+0tqUuqXlBJrFPiE+yudnl9GL5S7sxY4oFF5wxAU5vABNKjSjMz0OvS0JRwfFceGnHYdz61TPwaa8br+/uxNJLZ8ArDeGul/fgbZm4E2WMIVJTOsZOh1tC94AP7Z+7AQCtHf3Iz8vBBx39steGB444ceGMiVj20p6w8xQZ83D3wpk4+9RyDPoCYddnY/thc2E+lm7cLTufbLRbMaeqHI9tbgt7PJk+QAvXjVooIx3HG65F4HA4AACnnHIKAGDHjh3w+/24+OKLR55TU1ODqqqqiAviPp8PPp9v5Gen05nmUhORUonkZyp3oO9xSXhzXzfe3Ncd8Xgi7+X0BrB5/zFs3n9M9vgPLwlgatxnPWmi2ZTyi89U1ivpg9L87HFJeH3PUby+56js8VsXnKlabDk8ftkFKADY1tYLh8ev6oJ4j0uSvXgBgK0HexLug1IpHf0NJS+b5rci9zFKOb2BqH2R05vcvCCT2CdEl+rc7BuU8Nb+Y3hr/zHUV5bhhmfej/jcy+dNGzl+01eqAUB2MRwQZ4whyqRMjJ2WIiN6XFJYrq675mz8/I8HI14b/tMXq8Y95paGsOylPXjrti9jTlV52LGx/fChblfE+eS2tl5c1zB93OPJ9AFauG7UQhnpOC6IyxgeHsYtt9yChoYGzJ49GwBw9OhRGI1GlJWVhT134sSJOHpUfpK8evVq3HfffQmVob29HT098h2LEvv27Uv4tRSfVNS1zWZDVdX4wYjSJ5n8TIV07ULt9HB3a9I+pfkp8m7uoueiyHVHYlN7/MwkPeSJ6H0RpU6qc9PpDYz8vy8wHPW5o48PeP2I9SfojDvKNpkaO8eOW/Hk7lhK8jTWOBnp/OwDSARcEJdx0003Ye/evdi2bVtS51m2bBluu+22kZ+dTicqKytjvq69vR01NTPg8biTen8A8Pu4cUm6eBy9AHJw9dVXJ32uwsIi7N+/j4viGZRofqZKunahNsfYHIu7W5MWKM1PkXdzFz0XRa47Epva42cm6SFPRO+LKHVSnZtm08mlggJD9K3HRh9XElOMO8o2mRo7x45b8eTuWEryNNY4Gen87ANIBFwQH2PJkiV47bXXsHXrVkybNm3k8UmTJkGSJPT394d9S7yrqwuTJk2SPVdBQQEKCgriLkNPTw88HjfOvf5emCefFvfrAeDInnex95X/QCAQiP1kSojfPQAgiDnfWooJ02sSPo/zyCd4b/196Onp4YJ4BiWan6kS2oV6a4T79ya6C7XZZECj3YptEe4VOvrihkhUSvMzXXmUCpbC/Ki5aImxSJVuItcdiU3t8TOT9JAnnBdkj1TnZnmxcSR2Wjv60WC3yt5+p8FuRWtHP4DwvNB67hClUqbGzrHjVrTcbaq2oXvAN+7x0DEleRptnGwc1Tckcm6idOMM6IRgMIibb74ZmzZtwp///GdMnx5+r6N58+YhPz8fb731FlpaWgAABw4cQHt7O84///y0lMk8+bSEN8R0HvkktYWhiEoqqpLauJS0I7RBhtPrh7kwH7bixO8PFtqF+o6Nu8MmEMnuQj21vAgrm2tx56Y9YRe/jXYrVjbXJr1xVirrgChZ6cqjVJhSVhg1F9W8fzggdt0RiUIPeZLueQHpR2iO5/L5YSnMhzQ0jJ98Yzbue+VDrN/2MR5ZXA8AYQtrDXYrrmuYju9vaB2XF1rPHSItGjtuhXI3BwgbA0K5GAwG8dw/n4t+jx+m/DzsbO/DgSNO3HvZLPQOSvh7z2DYNZ/ctWCkXF+xaDYeeO2jsPKxDyCRcEH8hJtuugnPPfccfve736G0tHTkvuAWiwWFhYWwWCy44YYbcNttt+GUU06B2WzGzTffjPPPP192Q00i0pfOfs+4HbSbqm1Y01KX8MLWlLJCPLq4PuW7UBsA3Pn1GQgiBwMeP0oL85GDYNIdfjrqgChZOQAurZ2Ma750GnyBYRQYciN+2yXT8gAsudCOpZfWwOUdQokpD4O+APLULtgJItcdkSjSNVZnUh7k5wWi9EWkvtAcb8enfXhkcT0e/MMBbG/rRZExD9c3TseNF3wBBfm5uP8bs+H1D8EbGILZlA9jXi4cHgmvLmkclxd6yB0iLRqbe+bCfDx01Ry4vIGwXHRLQ1i6cQ/ebjt5bTffbsOyr9XgqNOD/7v+r3BLQwCABTMqcPfCmbjz5b3jrgXXttRFzPWfXXkW+wASFhfET3jyyScBABdccEHY40899RSuvfZaAMDPf/5z5ObmoqWlBT6fD5dccgmeeOKJDJeUiDLN4ZbGLQQDx3fIvmPjbjy6uD6pb4qnclLQ5fTix5v2yP5ZXKPdioeumhO2M7hS6awDokQ53BJ+LBOXwPEJuppx2eX0YmkacjFVRK47ItGkeqzOpM/63FH7orUtdfyWeJYbPcdbcqEdT23/eCRe3NIQHtvchsc2t6HBbkV9VTke29w2ZpwojnhuLecOkZbJ5d5E88n/d7gl/Pi3u8MWwwHg7bYeDL+xD1+vnYzrG6fjsc1tAIAzJ5uxTGYs2XqwB0tPXAt+oaJEUTmIRBH9DvtZJBgMyv4LLYYDgMlkwuOPP47PP/8cg4ODeOmllyLeP5yI9KPHJckuGgHHJwE9LnE2r+0blGQveoHjfybXN5hYWbVUB5Q9RI7LdOViqohcd0SUOk5vIGpf5PRyv6FsN3o8qK8sixgv29t6UV9ZBoDjBJHW9bikcYvhIdvbejHRbBrJdyB638D+gLSK3xAnEsS+ffuSPofNZuPGnGng9PqjHh+IcTyTYl3YJnrhq6U6oOwhclymKxdTReS6I6LUcXqY6xTd6PHAFxiO+tzRxxk7RNoVax44ti+I1TewPyAt4oI4kco8jl4AObj66quTPldhYRH279/HRfEUM5vyox4vjXE8k8ym6N16rOORX6edOqDsIXJcpisXU0XkuiOi1DEXMtcputHjQYEh+h+Qjz7O2CHSrljzwLF9Qay+gf0BaREXxIlU5ncPAAhizreWYsL0moTP4zzyCd5bfx96enq4IJ5ithIjmqptYTtnhzRV22ArEee+aOXFRjTarWG7iIc02q0oL06srFqqA8oeIsdlunIxVUSuOyJKHbPJELUvUvuXc/FwuCX0uCQ4T2wSZyvmvWmT5XBLMOTmYH61DW8f7EFrRz8a7FbZWyM02K1o7egHwHGCSOuizQMb7FZ0Ob3odHhHHmvt6I84lijtD9iHk2i0MwMi0rmSiiqcUnWm2sUgGZYiI9a01OGOjbvDJg2hXbVFGsgnmk1Y2VyLOzftCZuwNNqtWNlcm/AmflqqA8oeIsdlunIxVUSuOyJKnanlRVH7Iq1sqNnZ7xm3uXdTtQ1rWuowpaxQxZJpV6hOd3zah0cW12M4GMT6bR/jkcX1ABC2KN5gt+K6hun4/oZWjhNEOhCaB47tVxvsVtx8YTVyc4AVr5+8peuBI06saq7FXS/vTWjeyD6cRMQFcSIK097ejp4e+Q024qG3+5lPKSvEo4vr0eOSMOD1o9SUD1uJeL/VdrglPPjf+3Fdw3QsvbQGLu8QSkx56Hb68NP/3o+VzbUJl1krdUDZRdS4TGcupoqodUdEqXWqtRhrW+rg9AZGct1sMmhmMdzhlsYtpADHN3K7Y+NuPLq4nv1WnMbW6fc3tOL6xum4vmE6DLk5WPHN2fANDWPAE0BpoQEmQy6cHj9eXdLIcYJIJ6aUFeKxxfXoHvDB4fGjyJiHYqMBZUXHb3/y6pLGcfPDROaN7MNJVFwQJ6IR7e3tqKmZAY/HnfS59Hg/c0uR+BcAPS4Jr+85itf3HJU9fuuCM5P6DFqoA8o+IsZlunMxVUSsOyJKvanlRZiqdiES1OOSxi2khGw92IMel8R+LE5j69QtDeGxzW0jP79125dRM8msRtGIKIOizQPlHk9k3sg+nETFBXEiGtHT0wOPx41zr78X5smnJXwe3s9cPbF2DOcO4ESZwVwkIkoN9qepxzolokxhf0Oi4oI4EY1jnnwa72euUbF2DOcO4ESZwVwkIkoN9qepxzolokxhf0OiylW7AERElDqhHcPlKN0BnIiSx1wkIkoN9qepxzolokxhf0Oi4jfEiXRm3759sZ+Uhtem63x625wz3UI7ht+xcXdCO4ATUWowF4mIUoP9aeqxTokoU9jfkKi4IE6kEx5HL4AcXH311Umfy++ThCmLHjfnTLcpZYUJ7QBORKnFXCQiSg32p6nHOiWiTGF/QyLigjiRTvjdAwCCmPOtpZgwvSahcxzZ8y72vvIfCAQCqpcF4OacyUhkB3AiSj3mIhFRarA/TT3WKRFlCvsbEg0XxIl0pqSiKuENMZ1HPhGmLERERERERERERKnGTTWJiIiIiIiIiIiIKCtwQZyIiIiIiIiIiIiIsgIXxImIiIiIiIiIiIgoK/Ae4hkUDAYBAE6nM+rzXC4XAMBx+O8YDgwl9F6u7s+Ov9fRT1BQUJDQOfR6HpHKItp5RCoLAAwc/fT4+VyumHkDAKWlpcjJyUnovZTmJxElhvlJJKZkchNgfhKlE8dOInExP4nEpSQ/c4KhTKS0O3z4MCorK9UuBpFuORwOmM3mhF7L/CRKL+YnkZiSyU2A+UmUThw7icTF/CQSl5L85IJ4Bg0PD6OzszPpb+KM5XQ6UVlZiY6OjqQuaLSEn5mfWU4yuaU0P7XUDixr+mipvKKUNd35Kcrn1CLWXWL0Um/JzkuzJT/5GcSQbZ8hE3PbTNJD+43GzyO+dH4mveVnKukxlsbKhs8IaPdzKskt3jIlg3JzczFt2rS0nd9sNmsqQFOBnzk7ZOIzx5ufWmoHljV9tFReLZV1rHjyU8ufU22su8Rke71lW37yM4iBnyG2dF97JkMP7TcaP4/4RPtMIudnKolW7+mQDZ8R0Ofn5KaaRERERERERERERJQVuCBORERERERERERERFmBC+I6UFBQgHvvvRcFBQVqFyVj+Jmzg4ifWcQyRcKypo+WyqulsiYjWz5nOrDuEsN6U04PdcXPIAZ+Bm3T22fn5xGfHj+TFmRDvWfDZwT0/Tm5qSYRERERERERERERZQV+Q5yIiIiIiIiIiIiIsgIXxImIiIiIiIiIiIgoK3BBnIiIiIiIiIiIiIiyAhfEMygYDMLpdIK3bScSD/OTSFzMTyJxMT+JxMTcJBIX85NIfVwQz6CBgQFYLBYMDAyoXRQiGoP5SSQu5ieRuJifRGJibhKJi/lJpD4uiBMRERERERERERFRVuCCOBERERERERERERFlBS6IExEREREREREREVFW4II4EREREREREREREWUFLogTERERERERERERUVbggjgRERERERERERERZQWD2gUgfXG4JfS4JDi9fpgL82ErNsJSZIx5jKJj3RERUTp0Ob3oG5Tg9AZgLjSgvMiIiWaT2sUSHsdl0ho9xCw/A2UjkWIm1WUR6bNR6mTD3JKxqw9cEKeU6ez3YOnG3Xj7YM/IY03VNqxtqUMQkD22pqUOU8oKVSitdkSqV9YdERElo713EMs27cH2tt6RxxrtVqxqrkWVtVjFkomN4zJpjR5ilp+BspFIMZPqsoj02Sh1smFuydjVD94yhVLC4ZbGdQoAsPVgD/78t2NY+lv5Y3ds3A2HW8pkUTUlWr2y7oiIKFFdTu+4CxYA2NbWi+Wb9qDL6VWpZGLjuExao4eY5WegbCRSzKS6LCJ9NkqdbJhbMnb1hQvilBI9LmlcpxBSUVqAt9vkj2092IMeFzuNSKLVK+uOiIgS1TcojbtgCdnW1ou+QY4vcjguk9boIWb5GSgbiRQzqS6LSJ+NUicb5paMXX3hgjilhNPrj3jMFxiO+tqBKK/NdtHqFWDdERFRYpzeQFLHsxXHZdIaPcQsPwNlI5FiJtVlEemzUepkw9ySsasvXBCnlDCb8iMeKzBED7PSKK/NdtHqFWDdERFRYsym6NvIxDqerTguk9boIWb5GSgbiRQzqS6LSJ+NUicb5paMXX3hgjilhK3EiKZqm+yx7gFfxGNN1TbYSrgbbyTR6pV1R0REiSovNqLRbpU91mi3oryY44scjsukNXqIWX4GykYixUyqyyLSZ6PUyYa5JWNXX7ggTilhKTJiTUvduM6hqdqGr5wxIeKxtS11sBSx04gkWr2y7oiIKFETzSasaq4dd+HSaLdiVXMtJppNKpVMbByXSWv0ELP8DJSNRIqZVJdFpM9GqZMNc0vGrr7kBIPBoNqFyBZOpxMWiwUOhwNms1nt4qSFwy2hxyVhwOtHqSkfthLjSKcQ7RhFx7pLv2zITyKtYn6mT5fTi75BCU5vAGaTAeXFRl1csKQbx+WTmJ/aoIeY5WeID3NTH0SK+1SXRaTPlml6zs9smFtmc+zqifZv4kNCsRRF7giiHaPoWHdERJQOE80m3V2kZALHZdIaPcQsPwNlI5FiJtVlEemzUepkw9ySsasPvGUKEREREREREREREWUFLogTERERERERERERUVbggjgRERERERERERERZQUuiBMRERERERERERFRVuCCOBERERERERERERFlBS6IExEREREREREREVFW4II4EREREREREREREWUFLogTERERERERERERUVbggjgRERERERERERERZQUuiBMRERERERERERFRVuCCOBERERERERERERFlBS6IExEREREREREREVFW4IL4CatXr8Y555yD0tJSVFRUYNGiRThw4EDYc7xeL2666SZYrVaUlJSgpaUFXV1dKpVYXQ63hEPdLrS29+HQMRccbkntIhEREVGcOJ4TEWUO+1zKBMYZUWTMDwoxqF0AUWzZsgU33XQTzjnnHAQCASxfvhxf/epX8dFHH6G4uBgAcOutt+L111/Hiy++CIvFgiVLluDyyy/H9u3bVS59ZnX2e7B04268fbBn5LGmahvWtNRhSlmhiiUjIiIipTieExFlDvtcygTGGVFkzA8aLScYDAbVLoSIjh07hoqKCmzZsgVNTU1wOByYMGECnnvuOVxxxRUAgP3792PGjBl49913cd5558U8p9PphMVigcPhgNlsTvdHSAuHW8KSDa1hHUhIU7UNjy6uh6XIqELJiJKjh/wk0ivmZ+pxPKdUYX4SxaZGn8vczD4c27WD+Zl5zA8ai98Qj8DhcAAATjnlFADAjh074Pf7cfHFF488p6amBlVVVREXxH0+H3w+38jPTqczzaVOvx6XJNuBAMDWgz3ocUnsREgT9JifRHrB/Ew/jueUKOYnUfwy0ecyN4lju7iYn+pjftBYvIe4jOHhYdxyyy1oaGjA7NmzAQBHjx6F0WhEWVlZ2HMnTpyIo0ePyp5n9erVsFgsI/8qKyvTXfS0c3r9UY8PxDhOJAo95ieRXjA/04/jOSWK+UkUv0z0ucxN4tguLuan+pgfNBYXxGXcdNNN2Lt3L55//vmkzrNs2TI4HI6Rfx0dHSkqoXrMpvyox0tjHCcShR7zk0gvmJ/px/GcEsX8JIpfJvpc5iZxbBcX81N9zA8ai7dMGWPJkiV47bXXsHXrVkybNm3k8UmTJkGSJPT394d9S7yrqwuTJk2SPVdBQQEKCgrSXeSMspUY0VRtw9YI912ylfBPTEgb9JifRHrB/Ew/jueUKOYnUfwy0ecyN4lju7iYn+pjftBY/Ib4CcFgEEuWLMGmTZuwefNmTJ8+Pez4vHnzkJ+fj7feemvksQMHDqC9vR3nn39+pourGkuREWta6tBUbQt7vKnahrUtdbznEhER/X/27jw+ruq+//9b0mg2STO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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix=df.corr()\n",
        "plt.figure(figsize=(12,6))\n",
        "sns.heatmap(correlation_matrix,annot=True,cmap=\"YlGnBu\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "0e76bv2-97eh",
        "outputId": "44ff573f-3937-4959-bb29-e2ef0b45746a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-78d437092379>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix=df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "\n",
        "\n",
        "*   Age and income are psoitive corelated\n",
        "*   Education and income are also having postive co relation, which means higher the education higher the income levels\n",
        "*   High corelation between fitness and miles as well, whcih tells us people who have do more activity on tredmill are high rated in fitness\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "XqW8yC-m-y5l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Computing prababilty-Marginaland conditional Probility**"
      ],
      "metadata": {
        "id": "h4jI00rzdWz1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Marginal_Probability=pd.crosstab(index=df['Product'],columns='Marginal probability')\n",
        "np.round((Marginal_Probability),2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "RTX43_GU1wQj",
        "outputId": "7d66b9d3-ae22-4485-ebf4-5e5cc3cd31e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "col_0    Marginal probability\n",
              "Product                      \n",
              "KP281                      80\n",
              "KP481                      60\n",
              "KP781                      40"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>col_0</th>\n",
              "      <th>Marginal probability</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KP281</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP481</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP781</th>\n",
              "      <td>40</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-226f4927-1483-4246-b174-af2f5e643b0d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-226f4927-1483-4246-b174-af2f5e643b0d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-226f4927-1483-4246-b174-af2f5e643b0d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4eb6e00e-4dbd-40b8-918d-36a397882eb4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4eb6e00e-4dbd-40b8-918d-36a397882eb4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4eb6e00e-4dbd-40b8-918d-36a397882eb4 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",
              "summary": "{\n  \"name\": \"np\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"KP281\",\n          \"KP481\",\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marginal probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 40,\n        \"max\": 80,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          80,\n          60,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Probability_of_each_product=pd.crosstab(index=df['Product'],columns=' probability')/len(df)\n",
        "print(np.round((Probability_of_each_product),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XSlL8RYvaxP",
        "outputId": "1d2fec7c-000c-43ae-8567-f06e4e9fbb8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "col_0     probability\n",
            "Product              \n",
            "KP281            0.44\n",
            "KP481            0.33\n",
            "KP781            0.22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "\n",
        "*   The probability that the customer will purchase KP281 treadmill is **44%**\n",
        "*   The probability that the customer will purchase KP481 treadmill is **33%**.\n",
        "*   The probability that the customer will ourchase KP781 treadmill is **22%.**\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "0gFO4zkkdmA5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(index=df['Product'],columns=df['Gender'],margins=True,normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ulSbly9WwrP8",
        "outputId": "010da428-d4bd-4746-c099-5a4db2d1c696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender     Female      Male       All\n",
              "Product                              \n",
              "KP281    0.222222  0.222222  0.444444\n",
              "KP481    0.161111  0.172222  0.333333\n",
              "KP781    0.038889  0.183333  0.222222\n",
              "All      0.422222  0.577778  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-991d11f3-6ea9-4646-87ad-a0bd66b60f97\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Gender</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KP281</th>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>0.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP481</th>\n",
              "      <td>0.161111</td>\n",
              "      <td>0.172222</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP781</th>\n",
              "      <td>0.038889</td>\n",
              "      <td>0.183333</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>0.422222</td>\n",
              "      <td>0.577778</td>\n",
              "      <td>1.000000</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-991d11f3-6ea9-4646-87ad-a0bd66b60f97')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-991d11f3-6ea9-4646-87ad-a0bd66b60f97 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-991d11f3-6ea9-4646-87ad-a0bd66b60f97');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8acd88b1-d87e-4fe5-871f-25aac9474168\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8acd88b1-d87e-4fe5-871f-25aac9474168')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8acd88b1-d87e-4fe5-871f-25aac9474168 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",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"KP481\",\n          \"All\",\n          \"KP281\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Female\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16005400323208815,\n        \"min\": 0.03888888888888889,\n        \"max\": 0.4222222222222222,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.16111111111111112,\n          0.4222222222222222,\n          0.2222222222222222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Male\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19378194015237357,\n        \"min\": 0.17222222222222222,\n        \"max\": 0.5777777777777777,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17222222222222222,\n          0.5777777777777777,\n          0.2222222222222222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3454584834477339,\n        \"min\": 0.2222222222222222,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.3333333333333333,\n          1.0,\n          0.4444444444444444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **Probability** that the given customer is **male** would be **57%**\n",
        "\n",
        "The **conditional probabilty** that given customer is male, would purchase treadmill\n",
        "*   For KP281 is 22%\n",
        "*   For KP481 is 17%.\n",
        "*   For KP781 is 18%.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "The **Probability** that the given customer is **Female** would be **42%**\n",
        "\n",
        "The **conditional probabilty** that given customer is female, would purchase treadmill\n",
        "*   For KP281 is 22%\n",
        "*   For KP481 is 16%.\n",
        "*   For KP781 is 3%.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "cLUVEFIigiwW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(index=df['Product'],columns=df['MaritalStatus'],margins=True,normalize=True)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "R3xWbZJV3tK6",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "MaritalStatus  Partnered    Single       All\n",
              "Product                                     \n",
              "KP281           0.266667  0.177778  0.444444\n",
              "KP481           0.200000  0.133333  0.333333\n",
              "KP781           0.127778  0.094444  0.222222\n",
              "All             0.594444  0.405556  1.000000"
            ],
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              "      <th>KP281</th>\n",
              "      <td>0.266667</td>\n",
              "      <td>0.177778</td>\n",
              "      <td>0.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP481</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
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              "      <th>KP781</th>\n",
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              "      <th>All</th>\n",
              "      <td>0.594444</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"KP481\",\n          \"All\",\n          \"KP281\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Partnered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20610537078958605,\n        \"min\": 0.12777777777777777,\n        \"max\": 0.5944444444444444,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2,\n          0.5944444444444444,\n          0.26666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Single\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13940644310289543,\n        \"min\": 0.09444444444444444,\n        \"max\": 0.40555555555555556,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.13333333333333333,\n          0.40555555555555556,\n          0.17777777777777778\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3454584834477339,\n        \"min\": 0.2222222222222222,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.3333333333333333,\n          1.0,\n          0.4444444444444444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **Probability**that the given customer is **Married** would be **59%**\n",
        "\n",
        "The **conditional probabilty** that given customer is married, would purchase treadmill moder\n",
        "\n",
        "*   For KP281 is 26%\n",
        "*   For KP481 is 20%.\n",
        "*   For KP781 is 12%.\n",
        "\n",
        "\n",
        "\n",
        "The **Probability** that the given customer is **Single** would be **40%**\n",
        "\n",
        "The **conditional probabilty** that given customer is Single, would purchase treadmill\n",
        "\n",
        "*   For KP281 is 17%\n",
        "*   For KP481 is 13%.\n",
        "*   For KP781 is 9%.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DuXrhiCyjf0Y"
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    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(index=df['Product'],columns=df['Age_group'],margins=True,normalize=True)"
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          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "PySWPWj3laKX",
        "outputId": "e06e03a8-8638-4faa-d178-9080526798c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age_group  young-adults  middleaged-adult  seniorCitizen       All\n",
              "Product                                                           \n",
              "KP281          0.277778          0.127778       0.038889  0.444444\n",
              "KP481          0.177778          0.127778       0.027778  0.333333\n",
              "KP781          0.138889          0.055556       0.027778  0.222222\n",
              "All            0.594444          0.311111       0.094444  1.000000"
            ],
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              "      <th>Age_group</th>\n",
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              "      <th>KP281</th>\n",
              "      <td>0.277778</td>\n",
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              "      <td>0.444444</td>\n",
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              "      <td>0.333333</td>\n",
              "    </tr>\n",
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              "      <th>KP781</th>\n",
              "      <td>0.138889</td>\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1055c320-68f2-4138-9f32-fbd6c21eba03 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1055c320-68f2-4138-9f32-fbd6c21eba03');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --bg-color: #3B4455;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26518319-f4b4-438a-b513-980c22b3466f 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",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"KP481\",\n          \"All\",\n          \"KP281\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"young-adults\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20660393313096723,\n        \"min\": 0.1388888888888889,\n        \"max\": 0.5944444444444444,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.17777777777777778,\n          0.5944444444444444,\n          0.2777777777777778\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"middleaged-adult\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1091493483577139,\n        \"min\": 0.05555555555555555,\n        \"max\": 0.3111111111111111,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.12777777777777777,\n          0.05555555555555555,\n          0.3111111111111111\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03191423692521127,\n        \"min\": 0.027777777777777776,\n        \"max\": 0.09444444444444444,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.03888888888888889,\n          0.027777777777777776,\n          0.09444444444444444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3454584834477339,\n        \"min\": 0.2222222222222222,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.3333333333333333,\n          1.0,\n          0.4444444444444444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **Probability** that a **young adult** would purchase threadmill is **59%.**\n",
        "\n",
        "The **conditional probabilty** that a young_adult, would purchase treadmill\n",
        "\n",
        "*   For KP281 is 27%\n",
        "*   For KP481 is 17%.\n",
        "*   For KP781 is 13%.\n",
        "\n",
        "The **Probability** that a **middle-aged adult** would purchase threadmill is **31%.**\n",
        "\n",
        "The **conditional probabilty** that a middle-aged adult, would purchase treadmill\n",
        "\n",
        "*   For KP281 is 12%\n",
        "*   For KP481 is 12%.\n",
        "*   For KP781 is 5%.\n",
        "\n",
        "\n",
        "The **Probability** that a **Senior citizen** would purchase threadmill is **9%.**\n",
        "\n",
        "The **conditional probabilty** that a Senior citizen, would purchase treadmill\n",
        "\n",
        "*   For KP281 is 3%\n",
        "*   For KP481 is 2%.\n",
        "*   For KP781 is 2%.\n"
      ],
      "metadata": {
        "id": "5icoPJ6vl3f8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(index=df['Product'],columns=df['Fitness'],margins=True,normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "RuXjpD8_rrbX",
        "outputId": "86c85779-e09f-4824-e204-d3900d9fa782"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fitness         1         2         3         4         5       All\n",
              "Product                                                            \n",
              "KP281    0.005556  0.077778  0.300000  0.050000  0.011111  0.444444\n",
              "KP481    0.005556  0.066667  0.216667  0.044444  0.000000  0.333333\n",
              "KP781    0.000000  0.000000  0.022222  0.038889  0.161111  0.222222\n",
              "All      0.011111  0.144444  0.538889  0.133333  0.172222  1.000000"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Fitness</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>All</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KP281</th>\n",
              "      <td>0.005556</td>\n",
              "      <td>0.077778</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.011111</td>\n",
              "      <td>0.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP481</th>\n",
              "      <td>0.005556</td>\n",
              "      <td>0.066667</td>\n",
              "      <td>0.216667</td>\n",
              "      <td>0.044444</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP781</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022222</td>\n",
              "      <td>0.038889</td>\n",
              "      <td>0.161111</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>0.011111</td>\n",
              "      <td>0.144444</td>\n",
              "      <td>0.538889</td>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.172222</td>\n",
              "      <td>1.000000</td>\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",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "  }\n",
              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"KP481\",\n          \"All\",\n          \"KP281\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0045360921162651445,\n        \"min\": 0.0,\n        \"max\": 0.011111111111111112,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.005555555555555556,\n          0.0,\n          0.011111111111111112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.059143405269153,\n        \"min\": 0.0,\n        \"max\": 0.14444444444444443,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.06666666666666667,\n          0.14444444444444443,\n          0.07777777777777778\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21403914875939423,\n        \"min\": 0.022222222222222223,\n        \"max\": 0.5388888888888889,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.21666666666666667,\n          0.5388888888888889,\n          0.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.044675326228943775,\n        \"min\": 0.03888888888888889,\n        \"max\": 0.13333333333333333,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.044444444444444446,\n          0.13333333333333333,\n          0.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09323848796425263,\n        \"min\": 0.0,\n        \"max\": 0.17222222222222222,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.17222222222222222,\n          0.011111111111111112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3454584834477339,\n        \"min\": 0.2222222222222222,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.3333333333333333,\n          1.0,\n          0.4444444444444444\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "\n",
        "\n",
        "*   The probabilty that given customer uses KP281 treadmill 3 times a week is 30%\n",
        "*    The probabilty that given customer uses KP481 treadmill 3 times a week is 21%\n",
        "*   The probabilty that given customer uses KP781 treadmill 5 times a week is 16%\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FbD8DTSYrx-8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**-------------------------------------------------Insights------------------------------------------------**\n",
        "\n",
        "\n",
        "*   Most of the customers are young adults and midaged adults\n",
        "*   The probaility of customer being a sinior citizen is less.\n",
        "*   There is not much difference in Distribution of data between Single and Partnered\n",
        "* Most of customers use treadmill 2-4 times a week and the have rated fitness 3 to 4\n",
        "*   The analysis clearly shows that preference for the KP781 model among the customers who are highly educated, having high income and are engaged in running activities greater then 150 miles per week.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "3UUmrABk8V_8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "------------------------------------------------------------**Customer Profiling**----------------------------------------------------------\n",
        "\n",
        "**KP281 Treadmill**\n",
        "\n",
        "\n",
        "*   Targetting young- adult (18-24) & Mid-aged adults\n",
        "*   Whether Married or un-married\n",
        "*   Equal representation of Male & Female\n",
        "*   Income (40,000-52,000)\n",
        "*   Usage 3-4 times a week\n",
        "*   Gerneral fittness rating 3\n",
        "\n",
        "**KP481 Treadmill**\n",
        "\n",
        "\n",
        "*   Suitable for both Young-adults and Middle aged adults \\\n",
        "*   Married or single\n",
        "*   Income(45,000-50,000)\n",
        "*   Average usage (3 times a week)\n",
        "\n",
        "\n",
        "**KP781 Treadmill**\n",
        "\n",
        "\n",
        "*   Trgetting Premium customers,mainly young adults.\n",
        "*   Having high income level(>60,000)\n",
        "*   often Highly educated and Fitness Enthusiats\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DMfVD7QsvzPI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**---------------------------------------------Recomendations-----------------------------------------**\n",
        "\n",
        "\n",
        "  **Imrovement of KP281 treadmill**.\n",
        "   \n",
        "\n",
        "*   Conduct a thorough analysis to identify the specific features or technologies that customers expect more in KP481 from KP281\n",
        "\n",
        "*   Based on customer feedback and market research, enhance the features or technology of KP481 to justify the higher price point\n",
        "\n",
        "*   Consider offering additional value-added services or accessories with KP481 to make it more appealing to customers in the mid-level market segment.\n",
        "\n",
        "**Building New Marketing Strategies for KP781**\n",
        "\n",
        "\n",
        "*   Highlight the premium features, high-quality construction, and advanced technology of KP781 in marketing materials to attract affluent customers\n",
        "*   Since the usage levels is high for this model. Branding this through reputed athelits will increase the sales.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Ez3qbQZgv3Rg"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fsbl36ieFTx_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}