Customer_Segmentation_using_Clustering.ipynb

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      "source": [
        "## Customer Segmentation using Clustering \n",
        "\n",
        "- K-means clustering model "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3aa362bd",
      "metadata": {
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      },
      "source": [
        "### Importing Dependencies "
      ]
    },
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      "execution_count": 1,
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23b9790a",
      "metadata": {
        "id": "23b9790a"
      },
      "source": [
        "## Data Collection and Analysis"
      ]
    },
    {
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      "execution_count": 24,
      "id": "e8b7dac5",
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      "source": [
        "customer_dataset = pd.read_csv(\"Mall_Customers.csv\")\n"
      ]
    },
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              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
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              "      <td>1</td>\n",
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              "      <td>19</td>\n",
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        "customer_dataset.head()"
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              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "195         196  Female   35                 120                      79\n",
              "196         197  Female   45                 126                      28\n",
              "197         198    Male   32                 126                      74\n",
              "198         199    Male   32                 137                      18\n",
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          "metadata": {},
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      "source": [
        "customer_dataset.tail()"
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            "text/plain": [
              "(200, 5)"
            ]
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          "execution_count": 27
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      ],
      "source": [
        "# shape of dataset\n",
        "\n",
        "customer_dataset.shape"
      ]
    },
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      "execution_count": 28,
      "id": "b07f4799",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ],
      "source": [
        "# information about dataset\n",
        "\n",
        "customer_dataset.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "a332d2fd",
      "metadata": {
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            "text/plain": [
              "CustomerID                 int64\n",
              "Gender                    object\n",
              "Age                        int64\n",
              "Annual Income (k$)         int64\n",
              "Spending Score (1-100)     int64\n",
              "dtype: object"
            ]
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      ],
      "source": [
        "# datatypes of columns dataset\n",
        "\n",
        "customer_dataset.dtypes"
      ]
    },
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            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
            ],
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              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1bf39d9-07b0-45c9-a2d0-b806d1321fda')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b1bf39d9-07b0-45c9-a2d0-b806d1321fda 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-b1bf39d9-07b0-45c9-a2d0-b806d1321fda');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# statistical measures of dataset\n",
        "customer_dataset.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "30412c14",
      "metadata": {
        "id": "30412c14"
      },
      "source": [
        "### Data Visulalization "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "3dbf9e4a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "3dbf9e4a",
        "outputId": "f16a9f70-9370-4cb5-f345-92dd6a796597"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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FcqMP5od/61vfsjuUlL3wwgu6+eabj5i3DQDIPK7oAgDgQTt37tSf/vSnvluAH3nkEX3mM5+xOyzP6+rq0gsvvKBDhw5pz549uu+++3TOOefYHRYA+A7NqAAA8KBYLKbrrrtOe/fuVV5enq666iqdffbZdofleYZhaNWqVbr22muVnZ2tT33qU7rmmmvsDgsAfIdblwEAAAAAnsKtywAAAAAAT6HQBQAAAAB4CoUuAAAAAMBTPN+Mat++mJJJpiEfLS9vvDo6DtodBnyEnIOVyDdYiXyDlcg3WMnp+RYMBjRxYmTAMc8XusmkQaE7CNYLrEbOwUrkG6xEvsFK5Bus5NZ849ZlAAAAAICnUOgCAAAAADyFQhcAAAAA4CkUugAAAAAAT6HQBQAAAAB4CoUuAAAAAMBTKHQBAAAAAJ5CoQsAAAAA8BQKXQAAAACAp1DoAgAAAAA8hUIXAAAAAOAplhS6K1eu1Ny5c/Wxj31M//M//9P387fffluXXnqp5s2bp0svvVS7du1KaQwAAAAAgMFYUuieffbZWrNmjYqLi4/4+U033aTLLrtMmzZt0mWXXaYVK1akNAYAAAAAwGAsKXSnTZumoqKiI37W0dGhHTt2aMGCBZKkBQsWaMeOHers7BxyDAAAAACAoYyx64NbW1t1/PHHKxQKSZJCoZAmTZqk1tZWGYYx6Fhubq5dIQMAAAAAXMC2QtcqeXnj7Q7BsQoKjrU7BPgMOZeiWEy6807pwQelpiaptFS68krp+uulSMTu6FyDfIOVyDdYiXyDldyab7YVukVFRdqzZ48SiYRCoZASiYT27t2roqIiGYYx6NhIdXQcVDJpZGAJ3K2g4Fi1tR2wOwz4CDmXolhMOYvma+z2bX/72TvvSDffrJ7HHld0fT3FbgrIN1iJfIOVyDdYyen5FgwGBr2wadvjhfLy8jRlyhRt2LBBkrRhwwZNmTJFubm5Q44BgJeFV686ssjtZ+z2bQqvXmVxRAAAAO4TMAwj45c7b7/9dj399NNqb2/XxIkTlZOTo/r6eu3cuVPLly/X/v37ddxxx2nlypX68Ic/LElDjo0EV3QH5vTfzsB7yLnU5FaWK9TUOOh4orRMnVsbLIzIncg3WIl8g5XIN1jJ6fk21BVdSwpdO1HoDszpSQvvIedSk1+Yo0AyOei4EQyqfXfUuoBcinyDlcg3WIl8g5Wcnm+OvHUZAPD3ksUlpsYBAABAoQsAjtK16POmxgEAAEChCwDOEgiYGwcAAACFLgA4Sfa6X5saBwAAAIUu4G2xmMK1NcqtLFd+YY5yK8sVrq2RYjHLP1+hkPWf70LBlmZT4wAAAJDG2B0AgAyJxZSzaP4Rz2QNNTUqUlujrGc2Kbq+XopEvPv5LpUsLhny8UI0owIAABgeV3QBjwqvXnVEkdnf2O3bFF69ytOf71Zdi5eYGgcAAACFLuBZ2WvXmBp3++e7VXzpMvVMrRhwrKeiUvGlyyyOCAAAwH0odAGPsnuup92f71qRiKLr6xWrqlaitExGMKhEaZliVdWKrtvA7d4AAAApYI4u4FF2z/W0+/NdLRJRvKpa8apquyMBAABwJa7oAh5l91xPuz8fAAAA/kWhC3iU3XM97f58AAAA+BeFLuBVds/1POrzxVxTAAAAWCRgGIZhdxCZ1NFxUMmkpxdxVAoKjlVb2wG7w4CPuCrnYjGFV69S9to1CrY0K1lcoq7FS3qvQlOgO1e/7RZqaVaC7QaLuOr4Btcj32Alp+dbMBhQXt74AccodH3K6UkL73FNzsViylk0f8BnAPdMrVB0fT1FkxOx3WAj1xzf4AnkG6zk9HwbqtDl1mUA6Ce8etWAxZIkjd2+TeHVqyyOCKlguwEAgP4odAGgn+y1a0yNwx5sNwAA0B+FLgD0E2xpNjUOe7DdAABAfxS6cIdYTOHaGuVWliu/MEe5leUK19ZIsZjdkcFjksUlpsZhD7YbAADoj0IXzne4yUyktkahpkYFkkmFmhoVqa1RzqL5FLtIq67FS0yNwx5sNwAA0B+FLhyPJjOwUnzpMvVMrRhwrKeisvdRNXActhsAAOiPQheOR5MZWCoSUXR9vWJV1UqUlskIBpUoLVOsqlrRdRt4RI1THbXdxHYDAMDXeI6uTzn9mVj95RfmKJBMDjpuBINq3x21LiCMiptyDu5HvsFK5BusRL7BSk7PN56jC1ejyYxPmW1ARgMzAAAA36LQhePRZMaHzDYgo4EZAACAr1HowvFoMuM/ZhuQ0cAMAADA3yh04Xw0B/Idsw3IaGAGAADgbxS6VmPe4OhEIopXVatza4Pad0fVubVB8arq1Itc1rurBFuabR13NXIdAABAY+wOwFcOzxvsf0vlB/MGs57ZpOj6eq5OZgLr3XWSxSUKNTUOOZ7J17sWuQ4AACCJK7qWYt6gPVjv7mO2AZlfG5iR6wAAAL0odC3EvEF7sN7dx2wDMr82MCPXAQAAelHoWsjX8wZtxHp3IbMNyHzawIxcBwAA6MUcXQv5dt6gzVjvLnW4AVm8qtqe17uQ7bkeiym8epWy165RsKVZyeISdS1e0nsFPZVfLph9PQAAwGFc0bWQX+cN2o31Dr+wNdcPN8KK1NYo1NSoQDLZ1wgrZ9H84bs+m309AABAPxS6FvLrvEG7sd7hF3bmutlGWDTSAgAA6UShayWfzhu0HesdfmFjrptthEUjLQAAkE4BwzAMu4PIpI6Og0omPb2Io1JQcKza2g7YHQZ8xFc558O5qvmFOQokk4OOG8Gg2ndHM/b6o/kq32A78g1WIt9gJafnWzAYUF7e+AHHaEYFAOl0eK5p/9twP5hrmvXMJkXX1w9drJp9vU3MNsKyvZEWAADwFG5dBoA08utcVbONsGgaBwAA0olCFwDSyK9zVc02wqJpHAAASCcKXQBIo2BLs63jtjHbCIumcQAAII2Yows4nQsbE/mZr+eqRiKKV1UrXlVtz+sBAAAO44ou4GSHGxNFamsUampUIJnsa0yUs2i+FIvZHSGOwlxVAAAA+1HoAg7m1sZEfsZcVQAAAPtR6AIO5tbGRL7GXFUAAADbBQzDMOwOIpM6Og4qmfT0Io6K0x/+jF75hTkKJJODjhvBoNp3RzMXQFubJlx9hca+8rKUSEihkHpmnqH37v+FVFAworci5zCsNM5HJ99gJfINViLfYCWn51swGFBe3viBxyyOBcAIpNK4KGPa2pQ3/VRl/f5FBRIJBSQFEgll/f5F5U0/VWpry9xnw3+Yjw4AANKIQhdwMDsbE024+goF4/EBx4LxuCZcfUXGPhv+w3x0AACQThS6gIPZ2Zho7CsvmxoHRoL56AAAIJ0odAEns7MxUSJhbhwYgWBLs6lxAACA/ih04Q6xmMK1NcqtLFd+YY5yK8sVrq3xx7y9SETxqmp1bm1Q++6oOrc2KF5Vnfnuu6GQuXG7+TlnXMjW+egAAMBzKHThfDSpsUXPzDNMjduKnHEdO+ejAwAA76HQhePRpMYe793/CyXD4QHHkuFw7yOGHIqccR8756MDAADvodCF49GkxiYFBerY/Ia6z5wtIxSSIckIhdR95mx1bH5jxM/RtRI540J2zkcHAACeEzAMw7A7iEzq6DioZNLTizgqTn/4c3/5hTkKJJODjhvBoNp3R60LCKmLxRRevUrZa9co1NKsRHGJuhYv6b06l8HChZyBm45xcD/yDVYi32Alp+dbMBhQXt74gccsjgUYMZrUuNRR82Rl4TzZZNFkU+MAAABwNwpdOB5NatzJznmyiRNONDUOAAAAd6PQhePRpMad7JwnG3pnl6lxAAAAuBuFLpyPJjWuFGxpNjVu6rNb3zU1DgAAAHcbY3cAQEoiEcWrqhWvqrY7EqQoWVzSOzd3iHEvfrbt+jUAC7Y0K2lRAzAAAAAn4YougIywc261b+d1H9UALGBhAzAAAAAnodAFkBF2zq3267xuOxuAAQAAOAmFLoDMOGputaycW+3Ted12NgADAABwkoBhGIbdQWRSR8dBJZOeXsRRcfrDn+E95Fzm5RfmKJBMDjpuBINq3x21LiAbkW+wEvkGK5FvsJLT8y0YDCgvb/zAYxbHAgDIkOGabHm6CRcAAEA/FLoA4BG+bcIFAABwFApdAPAIvzbhAgAAOBqFLgB4hU+bcAEAAByNQhfA4GIxhWtrlFtZrvzCHOVWlitcW5P681j7vV6h0Mhfj5GLRBSvqlbn1ga1746qc2uD4lXVFLkAAMBX6LrsU07voAYHiMWUs2j+gM9l7Zlaoej6+qGLJ7OvB0zgGAcrkW+wEvkGKzk93+i6DGDEwqtXDVikStLY7dsUXr0qo68HAAAARotCF8CAsteusXUcAAAAGC0KXQADCrY02zoOAAAAjBaFLoABJYsmmxsvLjE1DgAAAIwWhS6AASVOONHUeNfiJabGAQAAgNGi0AUwoNA7u0yNx5cuU8/UigHHeioqFV+6bJSRAQAAAEOj0AUwoGDru6bGFYkour5esapqJUrLpGBQidIyxaqqFV23gUcLAQAAIGModJG6tjZNWHi+8osmKn/SccovmqgJC8+X2trsjgwZkJY5tpGI4lXV6tzaICUS6tzaoHhVNUUuAAAAMopCF6lpa1Pe9FOV9fsXFUgkFJAUSCSU9fsXlTf9VIpdD2KOLQAAANyKQhcpmXD1FQrG4wOOBeNxTbj6CosjQqYxxxYAAABuRaGLlIx95WVT43Cho+bYGsyxBQAAgEuMsTsAuEQiYW4c7nR4jm28qtruSAAAAICUOeKK7nPPPaeFCxfqoosu0oUXXqinn35akvT222/r0ksv1bx583TppZdq165d9gbqZ6GQuXEAAAAAsIjtha5hGPrOd76jO+64Q4899pjuuOMO3XDDDUomk7rpppt02WWXadOmTbrsssu0YsUKu8P1rZ6ZZ5gaBwAAAACr2F7oSlIwGNSBAwckSQcOHNCkSZO0b98+7dixQwsWLJAkLViwQDt27FBnZ6edofrWe/f/QslweMCxZDis9+7/hcURAQAAAMDAbJ+jGwgE9MMf/lBLly5VOBxWLBbTT3/6U7W2tur4449X6PAtsaFQSJMmTVJra6tyc3NtjtqHCgrUsfkNTbj6it7GU4mEFAqpZ+YZvUVuQYHdEQIAAACAJAcUuocOHdJPfvITrV69WpWVldq6dauuvfZa3XHHHWl5/7y88Wl5Hy8qKDh2hC84Vnrpv474UZaklEvcWEy6807pwQelpiaptFS68krp+uvp4OsTI845v2JfSQvyDVYi32Al8g1Wcmu+2V7ovvnmm9q7d68qKyslSZWVlTrmmGM0btw47dmzR4lEQqFQSIlEQnv37lVRUdGI3r+j46CSSSMTobtaQcGxams7YN0HxmLKWTRfY7dv+9vP3nlHuvlm9Tz2uKLr6/kC73GW55xbsa+kBfkGK5FvsBL5Bis5Pd+CwcCgFzZtn6NbWFio3bt363//938lSTt37lRHR4dOOOEETZkyRRs2bJAkbdiwQVOmTOG2ZZcKr1515Bf3fsZu36bw6lUWRwQ4E/sKAACAeQHDMGy/3Pn444/r/vvvVyAQkCQtW7ZM55xzjnbu3Knly5dr//79Ou6447Ry5Up9+MMfHtF7c0V3YFb/dia3slyhpsZBxxOlZerc2mBZPLCe038j6BTsK+lBvsFK5BusRL7BSk7Pt6Gu6Dqi0M0kCt2BWZ20+YU5CiSTg44bwaDad0ctiwfWc/qB0inYV9KDfIOVyDdYiXyDlZyeb46+dRn+kCwuMTUOl4rFFK6tUW5luRQKKbeyXOHamt5mSxgQ+woAAIB5FLqwRNfiJabG4UKHmypFamt6b8VNJhVqalSktkY5i+ZT7A6CfQUAAMA8Cl1YIr50mXqmVgw41lNRqfjSZRZHhEyjqdLosK8AAACYR6ELa0Qiiq6vV6yqWonSMhnBoBKlZYpVVSu6bgOPS/Gg7LVrTI37FvsKAACAaTSj8imnTyz/O7GYwqtXKXvtGgVbmpUsLlHX4iW9V7dS+eJv9vUYMdNNldhmMMF1xzi4GvkGK5FvsJLT842uyxS6f8fpSXuEw3M9B7oNtmdqhaLr64cufMy+HqNi6jE5bDOY5KpjHFyPfIOVyDdYyen5RtdluJrZuZ7MFbWHmaZKbDMAAACYQaELxzM715O5ovYw01SJbQYAAAAzKHTheMGWZlvHMUpHNVXSCJoqsc0AAABgBoUurBOLKVxbo9zKcuUX5ii3slzh2pphn6eaLC6xdRwmRCKKV1X3zsVNJNS5tUHxquph59eyzVxqlPs4AABAulHowhqHmwtFamsUampUIJlUqKlRkdoa5SyaP+QXYTNzPdMxDuuxzVzIxD4OAACQbhS6sISZ5kJm5nqm4/WwHtvMfWggBgAAnIRCF5Yw1VzoqLmexgjmeqbl9bAe28x1aCAGAACchOfo+kkspvDqVcpeu0ahlmYlikvUtXhJ79WxDBcO+YU5CiSTg44bwaDad0czGoMv9dvmwZZmJS3c5kdz+nPYPMOmbZ6WfTyNsZNvsBL5BiuRb7CS0/NtqOfoUuj6xeH5cwPdWtgztULR9fUZ/RKcW1muUFPjoOOJ0rLehkVIH5u3+dGcfqD0BBu3ee7UUxQaoht2orhEndt3DP4GaY6dfIOVyDdYiXyDlZyeb0MVuty67BN2z5+juZD17N7msJ6d2zxxwommxslXAACQThS6PmH3/DmaC1nP7m0O69m5zUPv7DI1Tr4CAIB0otD1ieAQtxSmMm4azYUsZ/s2h+Xs3ObB1nfNjZOvAAAgjSh0fSJZXGJqPC0iEcWrqtW5tUHtu6Pq3NqgeFU1RW6GOGKb26mtTRMWnq/8oonKn3Sc8osmasLC86W2Nrsjyxg7t7nZz/Z9vgIAgLSi0PUJ5sj6j6+3eVub8qafqqzfv6hAIqGApEAioazfv6i86ad6tti1c5ub/Wxf5ysAAEg7Cl2fYI6s//h5m0+4+goF4/EBx4LxuCZcfYXFEVnDzm1u9rP9nK8AACD9KHT94qg5smKOrPf5eF702FdeNjXuWnZuc7Of7eN8BQAA6cdzdH1qVM/EisUUXr1K2WvXKNjSrGRxiboWL+m90uL1L6F+XnYz2to04eorNPaVlxVIJGSEQuqZeYbeu/8XUkHB0K81sc7zJx2nwBDjhqT2vftHvDhwD6c/9w/eQr7BSuQbrOT0fBvqOboUuj414qSNxZSzaP6Az7nsmVqh6Pp67xZ8fl52Mw7Pkx3oFuJkOKyOzW8MXuyaXOf5RRMVSCQGHTdCIbW37ht+GeBaTj8xw1vIN1iJfIOVnJ5vQxW63LqMlIRXrxqw6JCksdu3Kbx6lcURWcfPy26GmXmyZtd5YnKxqXEAAAC4G4UuUpK9do2pcTfz87KbYWaerNl1Hjh0yNQ4AAAA3I1CFykJtjSbGnczPy+7KUPcOjzcuNl1Htyz29Q4AAAA3I1CFylJFpeYGnczPy+7KaHQqMfNrnPXb7NYTOHaGuVWliu/MEe5leUK19ZIsZjdkQEAALgChS5S0rV4ialxN/PzspvRM33mqMfNrnNXb7PDjbgitTUKNTUqkEwq1NSoSG2NchbNp9gFAABIAYUuUhJfukw9UysGHOupqOx95ItH+XnZzeipmDbqcbPr3M3bjOZnAAAA5lHoIjWRiKLr6xWrqlaitExGMKhEaZliVdWKrtvg7cfr+HnZTch+om7042bXuYu3Gc3PAAAAzBtjdwBwkUhE8apqxauq7Y7Een5e9lGyvYmXS7eZ7esNAADAA7iiCyAjTDWE8vE8Vdc30gIAAHAACl0AGWGmIZSf56m6upEWAACAQ1DoAsgIMw2h/DxP1c2NtAAAAJyCQhdAZhzVEEojaAjl63mqLm6kBQAA4BQBwzAMu4PIpI6Og0omPb2Io1JQcKza2g7YHQZ8ZCQ5l1tZrlBT46DjidIydW5tSFdo8CCOcbAS+QYrkW+wktPzLRgMKC9v/MBjFscCAMNinioAAADMoNAF4DjMUwUAAIAZFLoAnId5qgAAADCBQhdwulhM4doa5VaWK78wR7mV5QrX1nj6WbKSpEhE8apqdW5tUPvuqDq3NiheVZ16kevX9QYAAACNsTsAAEOIxZSzaP4Rz5QNNTUqUlujrGc2Kbq+nqubA2G9AQAA+BpXdAEHC69edUSx1t/Y7dsUXr3K4ojcgfUGAADgbxS6gINlr11jatyvWG8AAAD+RqELOFiwpdnUuF+x3gAAAPyNQhdwsGRxialxvzZkMr3eAAAA4GoUuoCDdS1eMvrxww2ZIrU1CjU1KpBM9jVkylk039PFrqn1BgAAANej0AUcLL50mXqmVgw41lNRqfjSZYO+1s8NmcysNwAAALgfhS7gZJGIouvrFauqVqK0TEYwqERpmWJV1Yqu2zDkI3J83ZDJxHoDAACA+wUMwzDsDiKTOjoOKpn09CKOSkHBsWprO2B3GMig/MIcBZLJQceNYFDtu6OWxUPOwUrkG6xEvsFK5Bus5PR8CwYDyssbP/CYxbEAsAgNmQAAAOBXFLqAR9GQCQAAAH5FoQt4FA2ZAAAA4FcUuoBX0ZAJAAAAPjXG7gBgoVhM4dWrervttjQrt7hEXYuX9F7Zo+jxpkhE8apqxauq7Y4EAAAAsAyFrl/EYspZNP+I56qGmhoVqa1R1jObFF1fT7ELAAAAwBO4ddknwqtXHVHk9jd2+zaFV6+yOCIAAAAAyAwKXZ/IXrvG1DgAAAAAuAWFrk8EW5pNjQMAAACAW1Do+kSyuMTUuOvFYgrX1ii3slz5hTnKrSxXuLZGisXsjgwAAABAmlHo+kTX4iWmxl3tcCOuSG2NQk2NCiSTfY24chbNp9gFAAAAPIZC1yfiS5epZ2rFgGM9FZW9jxjyKBpxAQAAAP5CoesXkYii6+sVq6pWorRMCgaVKC1TrKpa0XUbPP1oIRpxAQAAAP7Cc3T9JBJRvKpa8apqFRQcq862A3ZHZAkacQEAAAD+whVdeJ7vG3EBAAAAPkOhC8/zdSMuAAAAwIcodOF5fm7EBQAAAPgRhS6876hGXIaPGnEBAAAAfkQzKvhDv0Zc8IlYTOHVq5S9do2CLc1KFpeoa/GS3iv4/HIDAADA0yh0AXhPLKacRfOPeH5yqKlRkdoaZT2zSdH19RS7AAAAHsatywA8J7x61RFFbn9jt29TePUqiyMCAACAlSh0AXhO9to1psYBAADgbhS6ADwn2NJsahwAAADuRqELwHOSxSWmxgEAAOBuFLoAPKdr8RJT4wAAAHA3Cl0AnhNfukw9UysGHOupqOx9xBAAAAA8i0IXgPdEIoqur1esqlqJ0jIZwaASpWWKVVUrum4DjxYCAADwOApdAM4UiylcW6PcynLlF+Yot7Jc4doaKRZL7fWRiOJV1erc2qD23VF1bm1QvKraHUWu2WUHAADwuTF2BwAAfycWU86i+Uc8CzfU1KhIbY2yntmk6Pp6dxSso+HnZQcAAEgTrugCcJzw6lVHFHr9jd2+TeHVqyyOyDp+XnYAAIB0odAF4DjZa9eYGnczPy87AABAujii0H3//fd100036bOf/awuuOACfe9735Mkvf3227r00ks1b948XXrppdq1a5e9gQKwRLCl2dS4aWbnyJp4ve3LDgBIP3ovAJZzxBzd2tpajRs3Tps2bVIgEFB7e7sk6aabbtJll12miy66SI899phWrFihhx56yOZoAWRasrhEoabGIcczxuwcWZOvt3XZAQDpR+8FwBa2X9GNxWKqq6vTNddco0AgIEnKz89XR0eHduzYoQULFkiSFixYoB07dqizs9POcAFYoGvxElPjZpidI2v29XYuOwAg/ei9ANjD9kK3qalJOTk5uvfee7Vo0SJdfvnl2rJli1pbW3X88ccrFApJkkKhkCZNmqTW1labIwaQafGly9QztWLAsZ6KSsWXLsvYZ5udI2t23M5lBwCkH70XAHvYfutyIpFQU1OTTjnlFN1www367//+b33jG9/QPffck5b3z8sbn5b38aKCgmPtDgE+k3LOFRwr/dfvpDvvlB58UGpqkkpLpSuv1Njrr1dBJm/xGmYObKileejlMPt6O5fdYzjGwUrkGwZl9rwwAPINVnJrvtle6BYVFWnMmDF9tyifdtppmjhxorKzs7Vnzx4lEgmFQiElEgnt3btXRUVFI3r/jo6DSiaNTIQ+OrGYwqtXKXvtGgVbmpUsLlHX4iW9V2lS+QJr9vWHFRQcq7a2AyYWBBiZUeXc0m/3/ukvnpTimcvd3GHmyCaKS9Q5xHKYfX0fG5bdSzjGYVhpOp9KPsu3NK43v0jbeeEwX+Wbm3lkX3F6vgWDgUEvbNp+63Jubq5mzpypl156SVJvp+WOjg6deOKJmjJlijZs2CBJ2rBhg6ZMmaLc3Fw7wzXncDOCSG2NQk2NCiSTfc0IchbNH77zntnXAxiW2TmyzLEFXIDz6eiw3kaF84IPsa84gu2FriTdcsst+slPfqILLrhA3/72t3XHHXfouOOO080336xf/vKXmjdvnn75y1/qlltusTtUU+xucgNgeGbnyDLHFnA+zqejw3obHc4L/sO+4gwBwzAcdF9v+jnp1uXcyvKhb10pLVPn1oaMvb4/p9+GAO9xVc45ZIoBRs9V+QbLpfN8Kvkn39K93nyFW+V9xUv7itPzbahblyl0LZRfmKNAMjnouBEMqn13NGOv78/pSQvvIeeQcf2+SIZampXgFwwYRDrPp5J/jm9pWW/8ItA0v+Sbm6X7GGMnp+ebo+fo+kmyuMTWcQDwrKPmQ4n5UBgC59PRMb3emLcIn+AY4wwUuhaiyQ0AZAbzoTASnE9Hx+x6Yz+FX3CMcQYKXQvR5AYAMiN77RpT4/AXzqejY3a9sZ/CLzjGOAOFrpUiEUXX1ytWVa1EaZmMYFCJ0jLFqqoVXbdh+LkpZl8PAB4VbGk2NQ6f4Xw6OibXG/spfINjjCPQjMqnRjWxnAYSMMHpzQzgbl7qcAmXoPnZiLGfpgfnU1jJ6flGMyqYRwMJAA7GfChYiuZno8J+CsBKFLpICQ0kADgZ86FgJc6Jo8N+CsBKFLpICQ0kADjaUfOhxHwoZBDnxFFi3iIACzFH16dGer+9lx587ToemRvt9Dke8BbyDcMycWzlnAjLMSccVnJRvg01R5dC16dG+iWQBhI2OTwPbKBb5HqmVii6vt5xB5zBUHjASuQbhmTy2Mo5EZby0HcBuIDL8i0jzai6urrU3d096qDgLjSQsAfzwAAg/cweWzknwkp8F4CVvJRvKRe6K1eu1Ouvvy5Jev755zVjxgxNnz5dzz77bMaCg3PQQMIezAMDgPQze2zlnAgr8V0AVvJSvqVc6D7xxBP66Ec/Kkm67777VFtbq3/7t3/T3XffnbHg4CA0kLBFsKXZ1DgA4O+ZPrbS/AwW4rsArOSlfEu50P3rX/+qY445Rvv27VNTU5PmzZunM888Uy0tLZmMD0eLxRSurVFuZbnyC3OUW1mucG2NNc/si0QUr6pW59YGte+OqnNrg+JV1amf0O2M3U4mljtZXGJqHLCUX/dxuA7HVrgJ+epTNp1TvZRvKRe6J554oh5//HGtWbNGs2bNkiR1dnYqOzs7Y8HhKEc9oD7gpgfUuzl2M0wuN/PA4Bp+3cfhSqaPrUflu8h3ZBDfBXzIxnNq16LPmxp3kpQL3Ztuukn/8R//oVdeeUXXXHONJOnFF1/sK3qReW6eHO7m2M0wu9zMA4Nb+HUfhzuZPbaS77AS3wX8x9ZjTCBgbtxBeLyQi6TzcQZWP3rDr49iSMty8xxduIDT9nHyDcMycWx1Wr7DB1z0XFOYZ+cxxm3Ht7Q8R/fll18e8OdZWVkqLCxUcXHx6CPMIC8Vuul8QL3VXwLTGbub+HW5B0Lh4W1Oy3XyDZnktHyHv3B88z47jzFuO74NVeiOSfVNbrzxRu3du1eSlJOTo2g0KknKy8tTe3u7Pvaxj+muu+7SiSeeaDpgDCxZXDLkb1icPDnczbGb4dflTguPXMn2C3IdfkK+A8gkO48xXjq+pTxH93Of+5wuv/xybdmyRS+++KK2bNmiK664QosXL9bmzZtVXl6uW265JZOx+p6bJ4f7tZFC1yVfMDXuWzQ2ch2/7uPwJ/IdQCbZeYzx0vEt5UL3oYce0nXXXdfXZTk7O1vXXnutfvGLXygcDmv58uVqaHDO/dqe5OLJ4b5tpDDczABvT5EfNRq9uI9v93H4EvkOIJPsPMZ46fiWcqEbDof1xhtvHPGzP/7xjzrmmGN63yiY8lthlLLX/drUuK0iEUXX1ytWVa1EaZmMYFCJ0jLFqqoVXbfBs7eiZq9/2NS4X2WvXWNqHDbw6T4Onzoq30W+A0gnO8+pHjq+pdyMqq6uTrfccovmzp2roqIi7d69W88995y+973vaeHChXruuef029/+VrfffnumYx4RmlENjEYG1nDbhP5MGknOsd5gFse4EWA+vGnkG6xEvsFKTs+3tDSjWrhwocrLy7Vp0ybt3btXJ554ov7zP/9TJ510kiTp05/+tD796U+nJ2IMyEuTw/2CbTY6rDfAIofnw/efKvDBfPisZzYpur6eYhcA4EopF7qSdNJJJ/UVtrBe1+IlitTWDDkOZ2GbjQ7rDbBGKvPh41XVFkcFAIB5Kd+6HI1G9fOf/1xvvvmm4vH4EWNr1jh3vpyXbl0e6DfvH+ipqBzRffNOvw3BM9K4zdxuRDnHeoNJHONSk1tZPuTdE4nSMnVupdHkcMg3WIl8g5Wcnm9puXX5uuuuU3d3t84777y+BlSw2OHJ4cylchG22eiw3gBLBFuaTY0DAOBUKV/Rraio0B/+8AdlZWVlOqa08tQV3TRy+m9n/o7ZZik0W7Gd63IOrka+pcbXV3TTeF4g32Al8m0E7Pz+55Hvnk7Pt6Gu6KZc6H7xi1/UypUrVVZWltbgMo1Cd2BOT9ojDHUb69SK4ZulmH090sJVOQfXI99SE66tGXI+fKyq2ptzdNN8XiDfYCXyLUV2fv/z0HdPp+fbUIVuyg+//Yd/+Ad97Wtf049//GM98sgjR/wBMimVZimZfD0AeFV86TL1TK0YcKynorL3yoMHcV4AvM/O/ZxjjDOkfEX38ssvH/gNAgE99NBDaQ0qnbiiOzCn/3amP7O31vn61jwHcVPOwf3ItxHwyO11I5Hu8wL5BiuRb6mx8/ufl757Oj3f0tKM6t///d/TFhAwEmabpbi+2YoPv4QCsFAkorhXb1EehOvPCxgdzqf2sGm927mfc4xxhiFvXe5/sTeZTA76B8ikZHGJreO2OjzHI1Jbo1BTowLJpEJNjYrU1ihn0XwpFrM7QgBwHVefFzA6nE/tYeN6t3M/5xjjDEMWupWVlX1/P+WUU/Txj3/8iD8f/AzIpK7FS2wdtxNzPAAg/dx8XsDocD61h53r3c79nGOMMww5R7e1tVVFRUWSpJaWlkHfpLi4OP2RpQlzdAfm9PvtjzBU57qKSkXXbRh91+VUXm8j5ngAo0O+YUhpPi+Qb87H+dQetq53O7//ufi759Gcnm+j7rr8QZEr9Razg/0BMioSUXR9vWJV1UqUlskIBpUoLVOsqjq1A4XZ19uIOR4AkAEuPi9gdDif2sPW9W7nfs4xxhGGvKJbVVWlQCAw7JvccccdaQ0qnTx3RTdNE/qd/tsZ9OI30MDouCrfaJDjeiPON7a55Tif2sPV6539VJLz823UV3RPOOEElZWVqaysTMcee6yeeeYZJRIJFRYWKplM6re//a2OO+64jASNAdBIwXe6Fn3e1DgAh+O47j9sc1swZ9Ierl3v7KeeMGSh+0//9E99f3bt2qWf/vSn+td//Vd9+9vf1p133qmf/OQnevvtt62K1fdopOBDw91RkcIdFwCci+O6/7DN7RFfukw9UysGHOupqOy9Soe0c+t6Zz/1hiEL3f5ee+01nXbaaUf87LTTTtP27dvTHhQGlr12jalxuE/2ul+bGgfgbBzX/YdtbhPmTNrDpeud/dQbUi50TznlFN11113q6uqSJHV1denuu+/WlClTMhYcjmR6Qn8spnBtjXIry6VQSLmV5QrX1nD7hYPRPAPwNvZxk/qd1/ILc1xxXmOb2ygSUbyqWp1bG9S+O6rOrQ2KV1U7ttg6gpu/w7lwvbOfekPKhW5NTY22b9+uadOm6cwzz9S0adO0bds2/eAHP8hkfOjH1MOnj5prIOYauAIPHAe8jX3cBJfOoWObY8T4Dmc59lNvSLnQLSkp0dq1a/Wb3/xG//Zv/6ann35aa9euVWlpaSbjQz9mJvQz18CdXNvEAUBK2MdHz63nNbY5Rsqtue5m7KfekHKh+4GsrCzl5uYqkUioqalJTU1NmYgLAzAzoZ+5Bu7k1iYOAFLDPj56bj2vsc0xUm7NdTdjP/WGIZ+j29/vfvc73XjjjWprazvyDQIBvfnmmxkJLh14jm6v/MIcBZLJQceNYFDtu6MZCBimeeQ5bk5/Dhu8xVX55pF93GpOOq/xHF1kkpNy3VfYTyU5/3w61HN0Uy50zznnHH31q1/VxRdfrOzs7LQGmEmOK3Rt2mlc/cBueILTD5TwgH7H11BLsxI+/VLiF6bPa2k8H3N8QybxHc4mdhe6dn/+YU4/vg1V6KZ86/L+/fu1ePFiVxW5jmNj4wzmGgDwNJq1+I6p85pLG1nBn/gOZwO7jxF2f75HpFzoXnLJJVq3bl0mY/E8O5sJMNcAgJfRrMV/zJzXyBe4Cd/hrGf3McLuz/eKlG9dvuyyy/T666+ruLhY+fn5R4ytWePcSfBOunXZ9ltPuK0PNnL6rS9wN9uPr7DHKG/tS3e+cHxDxvEdzlJ2n1Ps/vz+nH58S8sc3UcffXTQsYsvvnh0kVnASYWuk5oJOD1p0Y9D5miYZXnOeWS9ITVOOr5iBGzaT9OdL5xTMSzmhLuK3ecUuz+/P6fnW1oKXbdyUqHLb2cwYofnaAx0+0rP1ApF19e7pmizNOc8tN6QGicdX5EiG/dTrujCUmnOdfIt8+w+p9j9+f05Pd/S0ozKMAz9+te/1pe//GVdcMEFkqTNmzdr48aN6YnSB2gmgJFijsbosN78h+Or+9i5n5IvsBLnJPex+xhh9+d7RcqF7j333KNHHnlEl156qVpbWyVJhYWF+tnPfpax4LyGZgIYKR4SPzqsN//h+Oo+du6n5AusxDnJfew+Rtj9+V6RcqH76KOP6sc//rHmz5+vQCAgSSopKVFTU1PGgvOcSETR9fWKVVUrUVomIxhUorRMsapqRddt4FZK/J1gS7Opcb9ivfnQUcdXcXx1PFv3U87HsBDnJBey+xhh9+d7RMpzdGfPnq3f/va3GjdunGbMmKFXX31VBw8e1Pz58/XCCy9kOs5Rc9Ic3bRIUzMDp99vj15OmqNhlpU556X1htHhGOd8adlP7Ww65+YuuGbWG43+Row54bCcj5qfpWWO7pw5c1RTU6Pu7m5JvXN277nnHn36059OT5QYHg+P9h3maIwO6w1wPtP7qZ3nxKM+W246H5tZb3wPGRXOSbAU+2mflAvd6upqtbW1qbKyUgcOHNDUqVP17rvv6vrrr89kfOiHZgb+wxyN0WG9Ac5ndj+185zo5vOxmdjdvNx24pwEK7Gf/s2IHy/U0dGhlpYWFRUVqaCgIFNxpY2Xbl1O560vTr8NAf145DYxnqMLK3GMcwkT+6mdUxTcPD3CTOxuXm7b+ehWUtjLb7fKp+05uvv379fzzz+vvXv3atKkSZozZ44mTJiQtkAzwUuFbjofHu30pIX3kHOwEvnmfek8J7rps80yE7ubl9tLOL5hKOneT52eb2mZo/vyyy9r7ty5+vd//3e98cYb+uUvf6mzzz5bL7/8ctoCxdCSxSWmxgEA8Ao7z4nJosmmxu1kZr3xPQRwPvbTv0m50L3tttt066236uGHH9Y999yjX//617rtttt0yy23ZDI+9EMzAwAAetl5TkyccKKpcTuZWW98DwGcj/30b1IudPfu3at58+Yd8bPPfOYzam9vT3tQGBjNDAAA6GXnOTH0zi5T43Yys974HgI4H/vp36Rc6F500UVas2bNET/71a9+pYULF6Y7JgyGh0cDANDLxnNisPVdU+O2MrPe+B4COB/7aZ+Um1F98Ytf1Ouvv668vDwdf/zx2rNnjzo6OnTaaacpEAj0/b+ji2G7eakZVTo5fWI5vIecg5XIN4v4tLs53YdhJ45vsJLT822oZlRjUn2TL3zhC/rCF76QtqAAAICLxWLKWTT/iOc1hpoaFamtUdYzmxRdX+/ZYrdr8RJFamuGHAcA2GvYQrehoUFZWVm6+OKLJfU+R/f73/++3nrrLZ1++um64YYbFPHoiQwAAAwsvHrVEUVuf2O3b1N49SrFq6otjsoa8aXLlPXMpgGX329z4ADAqYado/v973//iIZT3/ve97Rr1y5deumleuutt1RbW5vRAAEAgPNkrx16qtJw46521Bw4+XgOHAA41bCF7s6dOzVt2jRJ0v79+/XCCy/ozjvv1JIlS3TXXXfpueeey3iQAADAWYItzabGXS8SUbyquncubiKhzq0NvVewUylyYzGFa2uUW1mu/MIc5VaWK1xbI8VimY8bsBr5PjqsN9OGvXU5kUho7NixkqTXXntNBQUF+tCHPiRJKioq0v79+zMbIQAAcJxkccmQDZmSxSUWRuMiPp7bDB8i30eH9ZYWw17RPemkk/Tkk09KkjZu3Kgzzjijb2zPnj069thjMxcdAABwpOEaLtGQaWCpzG0GvIJ8Hx3WW3oMW+hef/31uummmzRjxgw9//zzuvrqq/vGNm7cqIqKgR9IDAAAvCu+dJl6pg78HYCGTIPz9dxm+A75Pjqst/QY9tbladOm6bnnntOuXbt04oknavz4vz2naM6cOTr//PMzGiAAAHCgww2Z/PgcXTN8P7cZvkK+jw7rLT2GvaIrSePHj1d5efkRRa4kffjDH9bxxx+fkcDgQEyK9x87t7nZzyZfrcc6959+DZnad0dH1pBJsnc/tylfh5u7zNxmeAn5Pjqst/QIGIZh2B1EJnV0HFQy6elFHJWCgmPV1nYg9RcMMCn+Az1TK5gU70Vp3uYjyjmzn02+Ws9h63zExzhYz8793MbjW7i2RpHamkHHY1XVnn3+MNLDTcc38n10nLTenJ5vwWBAeXnjBx6zOBa4FJPi/cfObW72s8lX67HOMVJ27ud25itzm+En5PvosN7Sg0IXKWFSvP/Yuc3Nfjb5aj3WOUbKzv3c1nw9PLc5VlWtRGmZjGBQidIyxaqqFV23gbtN4C3k++iw3tKCW5d9aqS3IeQX5iiQTA46bgSDat8dTUNkcIp0b/OR5JzZzyZfree0de70W61g735u5/ENMIt8g5Wcnm/cuuwkLm3WYvukeJeuNzezc5ub/Wzb89WHWOcYKTv3c/IVQMbx3dV2FLpWOtz8IlJbo1BTowLJpEJNjYrU1ihn0XxHJ37X4iWmxk1x8XpzMzu3udnPtjVffYp1jpHquuQL5sZN5Bz5CiCj+O7qCBS6FnJzsxY7J8W7eb25mZ3b3Oxn08TBeqxzjNhwM6eGGTeTc+QrgEziu6szUOhayNXNWmycFO/q9eZmdjZCMPvZNHGwHuscI5S9/mFT46ZyjnwFkEF8d3WGMXYH0N+9996rH/3oR3riiSd08skn67XXXtOKFSv0/vvvq7i4WLW1tcrLy7M7zFELtjSbGrddJKK4Dc87c/16s1MspvDqVcpeu0bBlmYli0vUtXhJ79WKVL7I2bTN08LNsWN0+uW7WpqVO9J8h6XScmw3s59zjACQIXx3dQbHXNH94x//qNdee03FxcWSpGQyqaqqKq1YsUKbNm3StGnTdOedd9ocpTk0vxgd1tsouXl+iJtj9yu7t9lRny9yxvE4tgPwKo5vzuCIQre7u1u33nqrbr755r6fNTQ0aNy4cZo2bZokafHixXrqqadsijA9aH4xOqy30XHz/BA3x+5Xdm8zuz8fI8exHYBXcXxzBkcUuvfcc48uvPBClZT87bcbra2tmjx5ct+/c3NzlUwmFY1GbYgwPWh+MTqst9Fx8/wQN8fuV3ZvM7s/HyPHsR2AV3F8cwbb5+hu375dDQ0Nuv766zPy/oM9QNgWBcdK//U76c47pQcflJqapNJS6corNfb661Vg8RyygoJjLf28UXPYenONYeZ/hFqaLc+BlD/PgbFjGHZvM7s/HyPnsWM7+QUrkW8Ox/HNEQKGMVx//8z66U9/qoceekhZWVmSpN27dysvL0+XX365Hn/8cW3YsEGS1NnZqbPPPlvbt28f0ft3dBxUMmnrIqaX2eZChxUUHKu2tgMZDBR2y60s752rOIhEaZk6tzZYFs9Ics5psWN4dm8z05+fpmMr/IlzKqxEvsFKTs+3YDAw6IVN229d/vrXv64XX3xRzz77rJ599lkVFhbqgQce0Ne+9jV1dXVpy5YtkqS1a9fq3HPPtTlam9nd7AWu4ub5IW6O3a/s3mamPp9jKwAAnmN7oTuYYDCoO+64Q7fccos++9nPavPmzbruuuvsDstWNFvBSLh5foibY/cru7eZmc/n2AoAgPfYfutypnnp1uV03hro9NsQkCYOuh1zxDnnoNiRIru3Wb/PD7U0K5Hi59t92zXcj3MqrES+wUpOz7ehbl2m0HWR/MIcBZLJQceNYFDtu6MpvZfTkxbeQ87BSiPJt3QeW+FPHN9gJfINVnJ6vjl6ji5Sx8OnASD9OLYCAOA9FLouYnezFwDwIo6tAAB4D4Wui9jd7AUAvIhjKwAA3kOh6yaRiKLr6xWrqlaitExGMKhEaZliVdWKrttAgx4AGA2OrQAAeA7NqHzK6RPL4T3kHKxEvsFK5BusRL7BSk7PN5pRAQAAAAB8g0IXAAAAAOApFLoAAAAAAE+h0AUAAO7U1qYJC89XftFE5U86TvlFEzVh4flSW5vdkQEAbEahCwAA3KetTXnTT1XW719UIJFQQFIgkVDW719U3vRTKXYBwOcodAEAgOtMuPoKBePxAceC8bgmXH2FxREBAJyEQhcAALjO2FdeNjUOAPA2Cl0AAOA+iYS5cQCAp1HoAoDT0GDHX2IxhWtrlFtZrvzCHOVWlitcWyPFYnZH5myhkLlxuA/7CkaKnPG1gGEYht1BZFJHx0Elk55exFEpKDhWbW0H7A4DPkLOpehwg52B5h4mw2F1bH5DKiiwITB3cU2+xWLKWTRfY7dv+7uhnqkViq6vlyIRGwJzvgkLz1fW718cdLz7zNl6r26jJbG4Jt/cjH2lD/mWInImLZyeb8FgQHl54wceszgWAMAQaLDjL+HVqwb8EiZJY7dvU3j1Kosjco/37v+FkuHwgGPJcFjv3f8LiyNCJrGvYKTIGVDoAoCD0GDHX7LXrjE17msFBerY/Ia6z5wtIxSSIckIhdR95mzufPAg9hWMFDmDMXYHALhCLKbw6lXKXrtGwZZmJYtL1LV4ieJLl3HbC9KLBju+EmxpNjXuewUFlt2eDHuxr2CkyBlwRRcYzuE5HpHaGoWaGhVIJhVqalSktkY5i+bT0ADpRYMdX0kWl5gaB/yCfQUjRc6AQhcYBnM8YKWemWeYGoe7dC1eYmoc8Av2FYwUOQMKXWAYzPGAlWiw4y/xpcvUM7ViwLGeisre6REA2FcwYuQMKHSBYTDHA5aiwY6/RCKKrq9XrKpaidIyGcGgEqVlilVVK7puAz0AgA+wr2CkyBnf4zm6PuX0Z2I5SW5luUJNjYOOJ0rL1Lm1wcKI3Imcg5XIN1iJfIOVyDdYyen5xnN0AROY4wEAAAC4C4UuMAzmeAAAAADuQqELDIc5HgAAAICrUOgCqYhEFK+qVufWBrXvjqpza4PiVdUUufCeWEzh2hrlVpYrvzBHuZXlCtfW8LxoL2ObAwA8aIzdAQAAHCIWU86i+Uc8NzrU1KhIbY2yntmk6Pp6frnjNWxzAIBHcUUXACBJCq9edUTB09/Y7dsUXr3K4oiQaWxzAIBXUegCACRJ2WvXmBqH+7DNAQBeRaELAJAkBVuaTY3DfdjmAACvotAFAEiSksUlpsZt16+pkkIhmiqlwPXbHACAQVDoAgAkSV2Ll5gat9XhpkqR2hqFmhqlZLKvqVLOovkUu4Nw9TYHAGAIFLoAAElSfOky9UytGHCsp6JS8aXLLI4odTRVGh03b3MAAIZCoQsA6BWJKLq+XrGqaiVKy2QEg0qUlilWVa3oug2OfswMTZVGycXbHACAoQQMwzDsDiKTOjoOKpn09CKOSkHBsWprO2B3GPARcg6ZlF+Yo0AyOei4EQyqfXfUuoDgKxzfYCXyDVZyer4FgwHl5Y0feMziWAAASDuaKgEAgP4odAEArkdTJQAA0B+FLgDA9WiqBAAA+qPQBQC431FNlURTJQAAfI1CF+4QiylcW6PcynLlF+Yot7Jc4doano0J4G8iEcWrqtW5tUFKJNS5tUHxqmqKXHgP50QAGNYYuwMAhhWLKWfR/COekRlqalSktkZZz2xSdH09X2QBAP7AOREAUsIVXTheePWqI07o/Y3dvk3h1assjggAAHtwTgSA1FDowvGy164xNQ4AgFdwTgSA1FDowvGCLc2mxgEA8ArOiQCQGgpdOF6yuMTUOABgCH5tbOTS5eacCACpodCF43UtXmJqHAAwiMONjSK1NQo1NSqQTPY1NspZNN/xRd+ouXi5OScCQGoodOF48aXL1DO1YsCxnopKxZcuszgiAPAGvzY2cvNyc04EgNRQ6ML5IhFF19crVlWtRGmZjGBQidIyxaqqFV23gccoAMAo+bWxkauXm3MiAKQkYBiGYXcQmdTRcVDJpKcXcVQKCo5VW9sBu8OAj5BzFonFFF69Stlr1yjY0qxkcYm6Fi/pvcrjoy/A5Ftq8gtzFEgmBx03gkG1745aF5BF0r3c5BusRL7BSk7Pt2AwoLy88QOPWRwLACBTXDzvEPbwa2Mjvy43APgJhS4AeISb5x3CHn5tbOTX5QYAP6HQBQCPcPW8Q9jCr42N/LrcAOAnFLoA4BHBlmZT4/AhvzY28utyA4CP0IzKp5w+sRzeQ85lXm5luUJNjYOOJ0rL1Lm1wcKI7EO+IeP6NX4LtTQr4dPGb7AexzdYyen5RjMqAPCBrku+YGocQIqOavwmGr8BgONQ6AKAVwx3g463b+ABLEPjNwBwPgpdAPCI7PUPmxoHkBoavwGA81HoAoBH0IwKsAb7GgA4H4UuAHhEsrjE1DiA1LCvAYDzUegCgEd0LV5iahxAatjXAMD5KHQBwCPiS5epZ2rFgGM9FZW9jz0BYBr7GgA4H4UuAHhFJKLo+nrFqqqVKC2TEQwqUVqmWFW1ous28GxPIF2O2tfEvgYAjhMwDG8/b6Kj46CSSU8v4qg4/eHP8B5yDlYi32AlX+VbLKbw6lXKXrtGwZZmJYtL1LV4Se9VbAp8S/gq32A7p+dbMBhQXt74AcfGWBwLAAAA3CgWU86i+Uc8QzjU1KhIbY2yntmk6Pp6il0AjsGtywAAABhWePWqI4rc/sZu36bw6lUWRwQAg6PQBQAAwLCy164xNQ4AVqLQBYB0i8UUrq1RbmW58gtzlFtZrnBtjRSL2R0ZAIxasKXZ1DgAWIk5ugCQTsxhA+BRyeIShZoahxwHAKfgii4ApBFz2AB4VdfiJabGAcBKFLoAkEbMYQPgVfGly9QztWLAsZ6Kyt5HDAGAQ1DoAkAaMYcNgGdFIoqur1esqlqJ0jIZwaASpWWKVVUrum4D0zIAOApzdAEgjZjDBsDTIhHFq6oVr6q2OxIAGBJXdAEgjbou+YKpcQAAAJhHoQsA6WQY5sYBAABgGoUuAKRR9vqHTY0DAADAPApdAEgjmlEBAADYj0IXANJouGZTNKMCAADIPApdAEijrsVLTI0DAADAPApdAEij+NJl6plaMeBYT0Wl4kuXWRwRAACA/1DoAkA6RSKKrq9XrKpaidIyGcGgEqVlilVVK7pugxSJ2B0hAACA542xO4B9+/bpO9/5jhobG5WVlaUTTjhBt956q3Jzc/Xaa69pxYoVev/991VcXKza2lrl5eXZHTIADC0SUbyqWvGqarsjgVViMYVXr1L22jUKtjQrWVyirsVLeq/gZ/qXG3Z+NgAADhUwDHsf6hiNRvXnP/9ZM2fOlCStXLlS7733nm6//XbNmzdPNTU1mjZtmlavXq2mpibV1NSM6P07Og4qmeS5lUcrKDhWbW0H7A4DPkLOwUqW5lssppxF8zV2+7a/G+qZWqHo+vrMFZx2fjb6cHyDlcg3WMnp+RYMBpSXN37gMYtj+Ts5OTl9Ra4knX766Xr33XfV0NCgcePGadq0aZKkxYsX66mnnrIrTAAABhRevWrAQlOSxm7fpvDqVZ78bAAAnMz2Qre/ZDKpX/3qV5o7d65aW1s1efLkvrHc3Fwlk0lFo1H7AgQA4CjZa9eYGnfrZwMA4GS2z9Ht77bbblM4HNaXvvQl/eY3v0nLew52KRu9tyIAViLnYCXL8q2lecjhUEtz5mKx87NxBNYzrES+wUpuzTfHFLorV67UO++8ox//+McKBoMqKirSu+++2zfe2dmpYDConJycEb0vc3QH5vT77eE95BysZGW+5RaXKNTUOOh4orhEnRmKxc7Pxt9wfIOVyDdYyen55ug5upJ01113qaGhQffdd5+ysrIkSeXl5erq6tKWLVskSWvXrtW5555rZ5gAAPydrsVLTI279bMBAHAy27suv/XWW1qwYIFOPPFEZWdnS5JKSkp03333adu2bbrpppuOeLxQfn7+iN6fK7oDc/pvZ+A95Bys5JiuyxWVmX1+sp2fjT4c32Al8g1Wcnq+DXVF1/ZCN9ModAfm9KSF95BzsJLl+cZzdH2N4xusRL7BSk7PNwpdCt2/4/Skhff4qvCA7TjGwUrkG6xEvsFKTs+3oQpdxzSjAoC0GeB2zlBToyK1Ncp6ZpOi6+spdgEAADzMEc2oACCdwqtXDThnUZLGbt+m8OpVFkcEAAAAK1HoAvCc7LVrTI0DAADA3Sh0AXhOsKXZ1Dh8KBZTuLZGuZXlyi/MUW5lucK1NVIslvnXm/1sAADwd2hG5VNOn1gO77Ey53IryxVqahx0PFFaps6tDZbEAnuMKN+GekTP1Irh53Sbeb3Zz4YjcE6Flcg3WMnp+TZUMyqu6ALwnK7FS0yNw1/Mzuk283rmkwMAkBkUugA8J750mXqmVgw41lNR2fuIIeAws3O6zYwznxwAgMyg0AXgPZGIouvrFauqVqK0TEYwqERpmWJV1Yqu28CtoDiC2TndZsaZTw4AQGZQ6ALwpkhE8apqdW5tUPvuqDq3NiheVU2Ri7+TLC6xbdzse7teW5smLDxf+UUTlT/pOOUXTdSEhedLbW12RwYAcDkKXQCAr5md021m3NfzydvalDf9VGX9/kUFEgkFJAUSCWX9/kXlTT+VYhcAYAqFLgDA18zO6Tbzej/PJ59w9RUKxuMDjgXjcU24+gqLIwIAeAmFLgDA38zO6Tbzeh/PJx/7ysumxgEAGArP0fUppz8TC95DzsFK5Jvz5U86ToEhxg1J7Xv3WxWOKeQbrES+wUpOzzeeowsAANIvFlO4tka5leXKL8xRbmW5wrU1Uiw2/GtDIXPjgNVonga4CoUuAAAYuVhMOYvmK1Jbo1BTowLJpEJNjYrU1ihn0fxhi92emWeYGgcsRfM0wHUodAEAwIiFV6/S2O3bBhwbu32bwqtXDfn69+7/hZLh8IBjyXBY793/C9MxAulC8zTAfSh0AQDAiGWvXWNqXAUF6tj8hrrPnC0jFJIhyQiF1H3mbHVsfkMqKEhfsIBJNE8D3IdCFwAAjFiwpdnUuCSpoEDv1W1Ue+s+te/dr/bWfXqvbqM1Ra6Z+cV+5tf1lkiYG/frevMztrnt6LrsU07voAbvIedgJfIt83IryxVqahx0PFFaps6tDRZGNAKH5xcPdOt1z9QKRdfXj+jRTr7JtzSvNzfJL5qowBDFrBEKqb1138CD5Jv/eGhfcXq+0XUZAACkVdfiJabG7WR2frFf+Xm9mWme5uf15ldsc2eg0AUAACMWX7pMPVMrBhzrqahUfOkyiyNKnen5xT7l5/Vmpnman9ebX7HNnYFCFwAAjFwkouj6esWqqpUoLZMRDCpRWqZYVbWi6zY4+ra8tMwv9iFfrzcTzdN8vd58im3uDBS6AIC/cXPzjH6xKxRyV+xuFYkoXlWtzq0Nat8dVefWBsWrqh1d5EpSsrjE1LjtbNpPXb/ezBpl8zTfrzcfYps7A4UuAKDX4eYZkdoahZoaFUgmFWpqVKS2RjmL5ju7YDwqdrkpdljOzfOL7dxPXb3ebMR68x+2uTNQ6AIAJLm7eYabY4f13Dy/2M5cd/N6sxPrzX/Y5s5AoQsAkOTu5hlujh02cPH8Yltz3cXrzVasN/9hmzsCz9H1Kac/E8txYjGFV69S9to1CrY0K1lcoq7FS3p/I8fBKiXknPPlF+YokEwOOm4Eg2rfHbUuoBFwc+xwPyuPb+Q6OJ/CSk7PN56jC5jh5nmLwAi4uXmGm2MHRoJcB4DUUOgCw2DuH/zCzc0z3Bw7MBLkOgCkhkIXGAZz/+AXbm6e4ebYgZEg1wEgNRS6wDB46Dd8w83NM46KXW6KHRgJN++nAGAhmlH5lNMnljtJbmV573M5B5EoLVPn1gYLI3Incg5WIt9gJfINw0pjU0vyDVZyer7RjAowgflQAABg1GhqCdiCQhcYBvOhAADAaNHUErAHhS4wHOZDAQCAUaKpJWAPCl23icUUrq1RbmW58gtzlFtZrnBtDbe9ZFokonhVtTq3Nqh9d1SdWxsUr6qmyIXzcIwAAEehqSVgjzF2B4ARODzHo//tLx/M8ch6ZpOi6+spvAA/4xgBAI6TLC4ZsqllsrjEwmgA/+CKroswxwPAUDhGAIDz0NQSsAeFroswxwPAUDhGAIDz0NQSsAeFroswxwPAUDhGAIAD0dQSsAVzdF2EOR4AhsIxAq4Tiym8epWy165RsKVZyeISdS1e0nuFiy//8JLDTS3jVdV2RwL4Bld0XYQ5HgCGwjECrnK4eVqktkahpkYFksm+5mk5i+bTKRwAYAqFroswxwPAUDhGwE1ongYAyCQKXTdhjgeAoXCMgIvQPA0AkEkBwzAMu4PIpI6Og0omPb2Io1JQcKza2g7YHQZ8xFU5x7xB13NVvvlUfmGOAsnkoONGMKj23VHrAjKBfIOVyDdYyen5FgwGlJc3fsAxmlEBQH+H5w32v6Xyg3mDWc9sUnR9PcUukAY0TwMAZBK3LgNAP8wbBKxB8zQAQCZR6AJAP8wbBKxB8zQAQCZR6AJAP8GWZlPjAFJE8zQAQAYxRxcA+mHeIFzHzc3TIhHFq6oVr6q2OxIAgMdwRRcA+mHeIFzlcPO0SG2NQk2NCiSTfc3TchbNl2IxuyMEAMAWFLoA0A/zBuEmNE8DAGBgFLoA0B/zBuEiNE8DAGBgFLp+EospXFuj3MpyKRRSbmW5wrU13NoGHO3wvMHOrQ1q3x1V59aG3jmEVhW5/fbV/MIc9lUMiuZpAAAMjGZUfnF4Hlf/W9w+mMeV9cwmRdfXc6UKcAL2VYwAzdMAABgYV3R9gnlcgDuwr2IkaJ4GAMDAKHR9gnlcgDuwr2IkaJ4GAMDAKHR9gnlcgDuwr2JEaJ4GAMCAKHTdZpRNaoabp8U8LsAZ2FcxYnY2T6NxGtyEfAV8hULXTQ43qYnU1ijU1KhAMtnXpCZn0fwhD9Rdiz4/5FsPNw7AGsy5hGuYOCcBliNfAd+h0HURU01qAoGh33y4cQCWYM4l3ILGaXAT8hXwHwpdFzHTpCZ73a+Hfu0w4wAswpxLuASN0+Am5CvgPxS6LmKmSQ0NbgAXsXPOJZAizisu5sO5qq7P137bTKGQL7YZYBaFrouYaVJDgxsAQDpxXnEpn85VdXW+HrXN5JNtBphFoesiZprU0OAGAJBOnFfcya9zVd2cr37dZoBZFLouYqZJDQ1uAADpxHnFnfw6V9XN+erXbQaYRaHrJmaa1Bz1WtHgBgBgBo3TXMn1c1VHy8X56tttBpgUMAzDsDuITOroOKhk0tOLOCoFBceqre2A3WHAR8g5WIl8g5XclG+5leW98zwHkSgtU+fWBgsjwnB8vc1iMYVXr1L22jUKtjQrWVyirsVLeq/AO/iXE17i9ONbMBhQXt74gccsjgUAAAA2cfNcVb/y7TbzaeM0pA+FLgAAgE+4ea6qX/l1m9GEC2ZR6AIAAPiFi+eq+pZP+6zQhAtmMUfXp5x+vz28h5yDlcg3WIl8g5X8km/5hTkKJJODjhvBoNp3R60LyKecnm/M0QUAAADgGsniElPjAIUuAAAAAEfxbRMupA2FLgAAAABH8WsTLqQPhS4AAAAAZ6FxGkyi0AWAdIvFFK6tUW5lufILc5RbWa5wbY07nvnn5tgBAN4SiSheVa3OrQ1q3x1V59YGxauqKXKRkjF2BwAAnnL4Aff9n/33wQPus57ZpOj6eueeoN0cOwAAQD9c0QWANHLzA+7dHDsAAEB/FLoAkEZufsC9m2MHAADoj0IXANIo2NJsatxObo4dAACgP8cXum+//bYuvfRSzZs3T5deeql27dpld0gAMCg3P+DezbEDAAD05/hC96abbtJll12mTZs26bLLLtOKFSvsDgkABuXmB9y7OXYAAID+HF3odnR0aMeOHVqwYIEkacGCBdqxY4c6OzttjgwABubmB9y7OXYAAID+HF3otra26vjjj1coFJIkhUIhTZo0Sa2trTZHBgCDcPMD7t0cOwAAQD8BwzAMu4MYTENDg2644QbV19f3/ez8889XbW2tPv7xj9sYGQAAAADAqcbYHcBQioqKtGfPHiUSCYVCISUSCe3du1dFRUUpv0dHx0Elk46t5W1TUHCs2toO2B0GfIScg5XIN1iJfIOVyDdYyen5FgwGlJc3fuAxi2MZkby8PE2ZMkUbNmyQJG3YsEFTpkxRbm6uzZEBAAAAAJzK0Vd0Jenmm2/W8uXLtXr1ah133HFauXKl3SEBAAAAABzM8YXuRz7yET388MN2hwEAAAAAcAlH37oMAAAAAMBIUegCAAAAADyFQhcAAAAA4CkUugAAAAAAT6HQBQAAAAB4CoUuAAAAAMBTKHQBAAAAAJ5CoQsAAAAA8BQKXQAAAACAp1DoAgAAAAA8hUIXAAAAAOApY+wOINOCwYDdITgW6wZWI+dgJfINViLfYCXyDVZycr4NFVvAMAzDwlgAAAAAAMgobl0GAAAAAHgKhS4AAAAAwFModAEAAAAAnkKhCwAAAADwFApdAAAAAICnUOgCAAAAADyFQhcAAAAA4CkUugAAAAAAT6HQBQAAAAB4yhi7A0Bm7du3T9/5znfU2NiorKwsnXDCCbr11luVm5ur1157TStWrND777+v4uJi1dbWKi8vz+6Q4XJLly5Vc3OzgsGgwuGwvve972nKlCl6++23tXz5ckWjUeXk5GjlypU68cQT7Q4XHnHvvffqRz/6kZ544gmdfPLJHN+QMXPnzlVWVpbGjRsnSbr++uv1yU9+kpxDRrz//vv6/ve/r5dfflnjxo3T6aefrttuu41zKtKuublZ//iP/9j37wMHDujgwYN69dVX3ZtvBjxt3759xh/+8Ie+f//gBz8wqqurjUQiYZxzzjnG5s2bDcMwjPvuu89Yvny5XWHCQ/bv39/399/85jfGwoULDcMwjMsvv9yoq6szDMMw6urqjMsvv9yW+OA9DQ0Nxle/+lXj05/+tPHnP/+Z4xsy6oM864+cQ6bcdtttxr/8y78YyWTSMAzDaGtrMwyDcyoy7/bbbzduueUWwzDcm2/cuuxxOTk5mjlzZt+/Tz/9dL377rtqaGjQuHHjNG3aNEnS4sWL9dRTT9kVJjzk2GOP7fv7wYMHFQgE1NHRoR07dmjBggWSpAULFmjHjh3q7Oy0K0x4RHd3t2699VbdfPPNfT/j+AarkXPIhFgsprq6Ol1zzTUKBAKSpPz8fM6pyLju7m498cQTuuSSS1ydb9y67CPJZFK/+tWvNHfuXLW2tmry5Ml9Y7m5uUomk323JABm3HjjjXrppZdkGIZ+9rOfqbW1Vccff7xCoZAkKRQKadKkSWptbVVubq7N0cLN7rnnHl144YUqKSnp+xnHN2Ta9ddfL8MwVFlZqW9/+9vkHDKiqalJOTk5uvfee/XKK68oEonommuuUXZ2NudUZNSzzz6r448/Xh//+MfV0NDg2nzjiq6P3HbbbQqHw/rSl75kdyjwuH/5l3/R888/r29961u644477A4HHrV9+3Y1NDTosssuszsU+MiaNWv0+OOPa926dTIMQ7feeqvdIcGjEomEmpqadMopp2j9+vW6/vrr9c1vflPxeNzu0OBx69at0yWXXGJ3GKZR6PrEypUr9c477+iHP/yhgsGgioqK9O677/aNd3Z2KhgM8ptnpNXChQv1yiuvqLCwUHv27FEikZDUe/Leu3evioqKbI4QbrZ582bt3LlTZ599tubOnavdu3frq1/9qt555x2Ob8iYD45bWVlZuuyyy7Rt2zbOqciIoqIijRkzpu+W0dNOO00TJ05UdnY251RkzJ49e7R582ZdcMEFknrz0K35RqHrA3fddZcaGhp03333KSsrS5JUXl6urq4ubdmyRZK0du1anXvuuXaGCQ+IxWJqbW3t+/ezzz6rCRMmKC8vT1OmTNGGDRskSRs2bNCUKVMcf8sLnO3rX/+6XnzxRT377LN69tlnVVhYqAceeEBf+9rXOL4hI+LxuA4cOCBJMgxDGzdu1JQpUzinIiNyc3M1c+ZMvfTSS5Kkt99+Wx0dHTrxxBM5pyJjHn30Uc2ZM0cTJ06UJFd/hwsYhmHYHQQy56233tKCBQt04oknKjs7W5JUUlKi++67T9u2bdNNN910xKMQ8vPzbY4Ybtbe3q6lS5fqr3/9q4LBoCZMmKAbbrhBH//4x7Vz504tX75c+/fv13HHHaeVK1fqwx/+sN0hw0Pmzp2rH//4xzr55JM5viEjmpqa9M1vflOJRELJZFIf+chH9M///M+aNGkSOYeMaGpq0ne/+11Fo1GNGTNG1157rebMmcM5FRkzb9483XjjjTrrrLP6fubWfKPQBQAAAAB4CrcuAwAAAAA8hUIXAAAAAOApFLoAAAAAAE+h0AUAAAAAeAqFLgAAAADAUyh0AQAAAACeQqELAIBLXH755Zo+fbq6u7vtDgUAAEej0AUAwAWam5u1ZcsWBQIB/fa3v7U7HAAAHI1CFwAAF6irq9Npp52miy++WHV1dX0/37dvn77xjW+ooqJCl1xyie6++2598Ytf7BvfuXOnrrzySs2YMUPz5s3Txo0bbYgeAABrjbE7AAAAMLzHHntMX/nKV3Taaafp0ksvVXt7u/Lz83XrrbfqmGOO0UsvvaSWlhZ99atf1eTJkyVJ8XhcV111lZYtW6b7779f//M//6Mrr7xSJ598sk466SSblwgAgMzhii4AAA63ZcsWvfvuuzrvvPNUXl6u0tJSbdiwQYlEQk8//bS++c1v6phjjtFJJ52khQsX9r3u+eefV3FxsS655BKNGTNGp5xyiubNm6ennnrKvoUBAMACXNEFAMDh6urqNGvWLOXm5kqSFixYoEcffVTz58/XoUOHVFRU1Pd/+/+9paVFr7/+uqZNm9b3s0QioQsvvNC64AEAsAGFLgAADtbV1aUnn3xSyWRSs2bNkiR1d3dr//796ujo0JgxY7R792596EMfkiS1trb2vbaoqEjTp0/Xgw8+aEvsAADYhVuXAQBwsGeeeUahUEj19fWqq6tTXV2dNm7cqGnTpqmurk6f+cxndO+99+qvf/2rdu7cqccee6zvtZ/61Ke0a9cu1dXVqaenRz09PXr99de1c+dOG5cIAIDMo9AFAMDBHn30US1atEiTJ09WQUFB358lS5boiSee0IoVK3TgwAHNmjVL3/nOdzR//nxlZWVJksaPH68HHnhAGzdu1Cc/+UnNnj1bd955J8/hBQB4XsAwDMPuIAAAQHrU1taqvb1dK1eutDsUAABswxVdAABcbOfOnfrTn/4kwzD0+uuv65FHHtFnPvMZu8MCAMBWNKMCAMDFYrGYrrvuOu3du1d5eXm66qqrdPbZZ9sdFgAAtuLWZQAAAACAp3DrMgAAAADAUyh0AQAAAACeQqELAAAAAPAUCl0AAAAAgKdQ6AIAAAAAPIVCFwAAAADgKf8/Wd/LuyrKI6MAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (16,9))\n",
        "plt.scatter(x = customer_dataset.Age, y = customer_dataset['Spending Score (1-100)'], c = \"red\", linewidth = 3)\n",
        "plt.title('scatter between Age and Spending ')\n",
        "plt.ylabel(\"Spendings\")\n",
        "plt.xlabel(\"Age\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "7a78de40",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "7a78de40",
        "outputId": "2875daef-d1b0-4500-b94b-ae7e61c83d5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Age', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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iv/32iy9/+ctx7LHH9tlwAAAA0J2S35kdMGBArFmzZrvb1q5dG42NjRUfCgAAAIop+Z3ZU089Nc4666z45Cc/GXvttVcsX7487rzzzvjoRz/al/MBAADA65RcZs8///wYPXp0zJ8/P1auXBmjRo2Kc845R5kFAABgpyv5Y8bXXntt7LfffnHnnXfGvffeG3feeWe89a1vjWuvvbYv5wMAAIDXKbnMLliwIA4++ODtbjv44INjwYIFFR8KAAAAiim5zNbU1ERnZ+d2t3V0dLzuNgAAAOhrJZfZiRMnxg033NBVXjs7O+Ob3/xmTJw4sc+GAwAAgO6U/AugZs6cGeeee24cccQRsddee8WKFSuiubk5brnllr6cDwAAAF6n5DK75557xj333BOLFy+OFStWxJgxY2L8+PFRW1vym7sAAABQESWX2YiI2traOPTQQ+PQQw/to3EAAACgZ95WBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQnPpqD7CrGbHbwKhrbMi9rqOtPda8smmbnAFR19hYRk5brHllc+51pK2pqSEaGgbmXtfevinWrWvvg4l2LcObGmNAw4Dc6za3b45X17X1wURAbwxrGhQDG/K/BNrUviVa122s8CyDY2BDXRmzdETrur9VdBaA1CizFVbX2BArb/m33OtGnfe5iPi/ZbausTFW3DQzd86YC66NCGX2jaahYWD8x53H5V532icXRYQy25MBDQNi+l0fzL3uq6feFxHKLPQ3Axvq4+S7f5573T2nHB2tFZ+lLk69+3e51911ymEVnwUgNT5mDAAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDk9KsyO3fu3DjggAPimWeeqfYoAAAA9GP9psw+9dRT8fjjj8fee+9d7VEAAADo5/pFmW1ra4vZs2fHVVddVe1RAAAASEC/KLM33HBDnHTSSbHPPvtUexQAAAASUF/tAX73u9/FkiVL4tJLLy07Y8Rug6Kmvi73umxLR1nrutPcPKxfZFQypxK2ztK5pS1q6xtzry93XX+37WPUsaUt6so4xnLXFZul2jn9aZZKcb90zyzsbP3pedSfZumvtnRkUV9Xs9PWvZF0bsmitj7/fVTuOvLLtnRGTX3+9xzLXZeyqpfZ3/72t7Fs2bI45phjIiLipZdeirPPPju+/OUvxxFHHFFSRk19Xay6+X/n/rubzz89Vq1q/b9/7sVFYWtOJTIqmVMJlbpffnfLP+Zef9h58yt+PJVSycf6h3d8MHfGR868r1+ed/1plkpxv3TPLOxs/el51J9m2RU1Nw+L2364Mve6T39k1C59v1RCc/OweOJb+e/bQz7lvt1ZmpuHxcs3PJR73ehpk3bJx6i2tiZGjhza7deqXmY//elPx6c//emuP0+ePDluueWWGDt2bBWnAgAAoD97Y70PDQAAwC6h6u/M7ujBBx+s9ggAAAD0c96ZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSU1/tAeg7I3ZrjLrGAbnXdbRtjjWvtPXBRL23+26NUV/GMW1p2xxr++kx0Xd2a2qIxoaBude1tW+KV9a198FE/cOwpgExsKEx97pN7W3Rum5zhWcZGAMbGsqYpT1a122q6Cz9SX+6X4Y1DYqBDflfLmxq3xKt6zZWPAfoH3bfbUjUN+Z/X2xLW2esfWVDH0zEjkbsNijqGvPvux1tW2LNK2nsu8rsLqyucUA8/81P5F6370XfiYj+WfzqGwfEf982Jfe693x6QfTXY6LvNDYMjGv+87jc6z4/dVFE7LpldmBDYxw/719yr/vJh78erVHZMjuwoSFOuGdO7nX3nnxZtMauW2YHNjTElLu+m3vdglPPqPj9MrChPv7xrrtzr5t/6inRukPOSXf9OHfOj089abscoH+ob6yNP859Ofe6t104ug+moTt1jfWx8psP5F436qJj+mCavuFjxgAAACRHmQUAACA5yiwAAADJUWYBAABIjjILAABAcpRZAAAAkqPMAgAAkBxlFgAAgOQoswAAACRHmQUAACA5yiwAAADJUWYBAABIjjILAABAcpRZAAAAkqPMAgAAkBxlFgAAgOQoswAAACRHmQUAACA5yiwAAADJUWYBAABIjjILAABAcpRZAAAAkqPMAgAAkBxlFgAAgOQoswAAACRHmQUAACA5yiwAAADJUWYBAABIjjILAABAcpRZAAAAkqPMAgAAkBxlFgAAgOQoswAAACRHmQUAACA5yiwAAADJUWYBAABIjjILAABAcuqrPcDatWtj+vTp8de//jUaGxvjTW96U8yePTtGjBhR7dEAAADop6r+zmxNTU2cc845sWjRopg/f37su+++8bWvfa3aYwEAANCPVb3MNjU1xbve9a6uPx966KGxfPnyKk4EAABAf1f1Mrutzs7O+P73vx+TJ0+u9igAAAD0Y1X/mdltffGLX4zBgwfH6aefvtP+zubmYf0mxyx9l7FtTseWtqirbywrozdru5ulP+SYpe8yKpVjlr7L2DanrWNLNNblvyzuuK5SOeXa9n5p6+iIxrq6MmYpb12xWaqdsyvP0tbRGY11+d+f2HFde0cWDXU1uXO2XbelI4v6MjLKXdedSt2/ldTRkUVdGce347rOjixqy8gpd92OKn3uZluyqKnPP1e564pndkZNff7n0Y7rKpVTrv60120r29IRNfX5rys9res3ZXbOnDnxl7/8JW655Zaord15bxivWtXa9f9786BtzalEhln6fpYHvn1iWRnHnLOw3x6TWcxilt7PcuLdt+Rev/CU8143y5S7b8+ds+CUs/vkfply1//JP8upH+u3j5FZup9l6t3P5F7/n6eMfd0sF9/zfO6c//fkfbeb5Sv3rMidMePkMRW/X/qT5uZh8eD3VuVeN/mfml93v/z6O/lz/uETzf323H3xuvzny97/Oqbij3Nz87B46fqncq/b85KDXne/vPyN/8mdM/qzE/rlY1Qpzc3DYuWNP8y9btRnPhItLetj5Mih3X69X5TZ66+/PpYsWRK33XZbNDb2/l0vAAAAdm1VL7N//OMf49Zbb403v/nN8bGPfSwiIvbZZ5+48cYbqzwZAAAA/VXVy+zb3va2+MMf/lDtMQAAAEhIv/ptxgAAAFAKZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJKjzAIAAJAcZRYAAIDkKLMAAAAkR5kFAAAgOcosAAAAyamv9gAAAFBpTU1DoqEh//s27e2dsW7dhorn0HdG7DY46hrrcq/raOuINa/8rQ8m2nWM2G1Q1DXmr4wdbVtizSsb+2Ci7SmzAADschoaauM/716de93UU/Z4Xc6CH+TPmfL/7NHzN1ERdY118dLX/px73Z6XvqUPptm11DXWx8q59+ZeN+rCE/pgmtfzMWMAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBQAAIDnKLAAAAMlRZgEAAEiOMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJKdflNlnn302pk6dGscdd1xMnTo1nnvuuWqPBAAAQD/WL8rsrFmz4rTTTotFixbFaaedFldeeWW1RwIAAKAfq3qZbWlpiaVLl8aUKVMiImLKlCmxdOnSWLNmTZUnAwAAoL+qr/YAK1asiNGjR0ddXV1ERNTV1cWoUaNixYoVMWLEiJJzaocNKevvr62t2SFnaK9zaocNr8gsdcOaep1TN2yPisxSP2xUr3MaK5ARETFgaO9zBpaZsWPOoArMEhExeOjoXucMqUBGRMTQIb3PGVaBjIiI4RXI2W1wZWbZvQI5Iys0y6hBvX9ejxq8e2VmGdxUgVkqs2eOGjysArP0PuO1nN5fS0YNrsx1rRI5owYPrtAsgyowy8CKzNJcgZzmwQMqNEtjBWYp7yXdjrOMGFzX65zdKpARETF0cHnvuWybM7gCGRERgyqQM3BIZWYZUIGchqGVmaV+WO9z6oZX5nypG97750Dd8IaKzFI7vPfP6dphldlfaof1fq+rHdb7vfu1nMpcS7ZVk2VZVlZqhSxZsiQuu+yyWLhwYddtJ5xwQlx33XVx0EEHVXEyAAAA+quqf8x4zJgx8fLLL0dHR0dERHR0dMTKlStjzJgxVZ4MAACA/qrqZXbkyJExbty4WLBgQURELFiwIMaNG5frI8YAAAC8sVT9Y8YREcuWLYsZM2bEq6++GsOHD485c+bEW97ylmqPBQAAQD/VL8osAAAA5FH1jxkDAABAXsosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJJTX+0BKmnOnDmxaNGiePHFF2P+/PkxduzYiIj4+c9/HjfccENkWRZZlsWFF14YH/jAB3Jl/OIXv4gbbrghtmzZErvttlt8+ctfjn333bfbjLVr18b06dPjr3/9azQ2Nsab3vSmmD17dowYMSIef/zxuPLKK2Pz5s2x9957x3XXXRcjR47MnfO5z30uHnnkkVi1alU89thjMWTIkFwZr7zySlx55ZWxatWqqK+vj7e//e0xa9asGDhwYK6cpqam+PjHPx4bN26MiIjm5ua4+uqrY5999sl1PFtdfvnl8cMf/rCsYxoxYkQccMABMXbs2Kitfe3fab761a/GAQcckCtj3bp1MXv27Hjqqaeivr4+jj/++LjwwgtzzfLcc8/F1Vdf3fV9LS0t0dzcHPfcc0+uWe666674zne+E7W1tVFXVxdXXHFFTJw4Mff9cvfdd8edd94ZnZ2dse+++8ZXvvKVaGpq6jbnggsuiBdeeCFqa2tj8ODB8YUvfCHGjRsXzz77bMyYMSPWrVsXTU1NMWfOnHjzm9/cbUaxnELPsVIz9txzzx7Po1JnKXR7noyt5s6dG9/85jfLOqZx48bF5MmTo7GxMQYMGBAREZdeemkceeSRuTI2b94cX/rSl+Lhhx+OAQMGxKGHHhpf/OIXc80ybNiw+MxnPtP1Pa2trbF+/fr4zW9+k2uWPPtusZw8e+9WOz4WefbdYjml7ruFMhoaGnLtu4Vy9t9//5L33WLHs1Up+26xnFL33WIZefbdQjnr168ved8tNkuefbdYTp59NyIK7gF5zt9CGXnP3e5y9tlnn1znb3cZkyZNyn3u9rQ3lnL+FsrIe+4Wyslz/naXMWTIkNznbqFZ8py/hTLynLuFrjt5XzMUyslznS6Ws1Up1+pCGXmu04Uy8l6nu/v+c889N9d1utDfmfc6XSgnz3X6hRdeKDh73nOmS7YL+e1vf5stX748O/roo7M//OEPWZZlWWdnZzZx4sSuPz/99NPZoYcemnV0dJScsW7duuzwww/P/vznP2dZlmXz5s3LzjrrrIJzrF27Nvv1r3/d9eevfOUr2eWXX551dHRk73//+7Pf/va3WZZl2Y033pjNmDEjd06WZdl///d/Z6tXr87Gjh2brV+/PnfG888/nz311FNZlmVZR0dHNm3atGzu3LllzfLqq6923X7nnXdmn/nMZ3JnZFmWPfDAA9nll19e9jFlWdbj2lIyzj333OyOO+7o+trKlSvLytnW+eefn33729/OlbFmzZrssMMOy1atWpVlWZbdf//92fHHH597lj/96U/ZEUcckbW0tGRZ9tp594UvfKFgzraP589+9rPswx/+cJZlWXbGGWdk8+bNy7LstefAGWecUTCjWE53z7E8GaXe56XMUuj2PBlZlmVLlizJzj777LKPKcuyktb2lPHFL34xu/baa7POzs4sy7Kuc6ecY9rqmmuuya6++upcGXn33UI5effeLHv9Y5F33y2Uk2Wl77uFMvLuu8VmKXXfLZaRZaXvu8VySl1bLCPPvtvTMW1VbN8tlJF33y2Uk3ffzbLu94C852+h+yLvudtdTt7zt9Asec/dYntjqedvoYy8526hnDznbyl7fSnnbnc5ec/f7jLynruFrjt5XzMUyslznS6Wk2WlX6sLZeS5ThfKyHudLuX7e7pOd5dRznW6u5xyrtOFZs97zmy1S33MeOLEiTFmzJjX3V5bWxutra0R8dq/AIwaNarrX+FKyfjLX/4Se+yxR+y3334REXHUUUfFr371q1izZk23GU1NTfGud72r68+HHnpoLF++PJYsWRIDBgzo+heyj33sY3HfffcVPJ5CORER7373u0t6Z6FQxj777BMHHnhgRLx2/4wfP74rO+8sw4YN67p9/fr1Be/bYhlr166NuXPnxuWXX172MeVRKOO5556LZ555Jj7xiU90fa25ublXs7S0tMRDDz0UH/rQh3JlZP//v5Rt2LAhIl47d/fcc8/cszzzzDMxbty4rncujzrqqJg/f37BnB0fz5qammhpaYmlS5fGlClTIiJiypQpsXTp0oLPgUI5EYWfp6VmlPP4F5ql0O15Mtra2mL27Nlx1VVXlX1MeXWXsWHDhpg3b15MmzatK3OPPfbo1SxtbW0xf/78OOWUU3Jn5Nl3C+Xk3Xu7eyzy7ruFciJK33cLZeTdd4vNUuq+Wywjz75bLCeP7jLy7rulzNLTvlsoI+++Wygn775bSDnnb3fynLuFlHP+difPuVtM3vO3r5Rz/hZTyrlbSDnn747ynLuFrjt5XzMUu37luWYWyyl1/yrnWlpqRt7sUr6/p+t0sYw81+lCOXmv04VmL+d15la71MeMu1NTUxPf+MY34oILLojBgwfHhg0b4rbbbsuVsd9++8Xq1atj8eLFMX78+K4n9YoVK4p+tDEiorOzM77//e/H5MmTY8WKFbHXXnt1fW3EiBHR2dnZ9XZ6qTnlKpSxadOmuPvuu+OSSy4pO+dTn/pULF26NHbfffe4/fbbc2fMnj07Lr744u02rXJnOeOMM6KjoyPe+973xkUXXRSNjY0lZ/zpT3+K0aNHx8yZM+Ppp5+OPfbYI6ZPnx5ve9vbypolImLevHkxadKkkjbDbTNGjBgRs2fPjpNPPjmGDx8enZ2d8d3vfrfHjB1z/v7v/z6efPLJeP7552OfffaJBQsWxN/+9rei593MmTPjoYceiizL4tvf/nasWLEiRo8eHXV1dRERUVdXF6NGjerxObBjTjmKZeR5XhTKyTNjd997ww03xEknndTjRzxLmeXSSy+NLMtiwoQJcckll8Tw4cNLznj++eejqakp5s6dG4888kgMGTIkpk2b1uPHI4sd/4MPPhijR4+Ogw46KFdGufvujjl77rlnrr23u8einH23nMc0b0ap+26xnFL33UIZeffdYrOUuu92l1HOvtvT/VvKvttdRjn7bnc55ey7Ea/fA8o5f/PsI8UUyyn1/C2Ukfc1Q3c5ec/fQrPkfc2wY04552+x+zbPa4Ydc8o5f3fMyHPuFrruDBw4MNdrhp6uX6Vep4vllLqv9zRLKc+vQhlDhw7NdZ0u5bre03W6WEae63ShnAMOOKDsjrTt7EuWLCnrdWZE7FofM95q248BtLe3Z5/4xCeyRx99NMuyLHv00Uezo446qsePlez4UYKHHnoo+9jHPpadfPLJ2de//vVs4sSJ2dNPP93jLFdddVV2/vnnZx0dHdl9992XfepTn9ru6+PHj8/Wrl2bK2dbeT4i011Ge3t7dt5552WzZ88uKaPYLB0dHdlNN92UzZo1K1fGwoULs3/913/t+lpvjmn58uVZlmVZa2trdt5552XXX399roxFixZl48aN6/pI16JFi7JjjjmmrFm2OuGEE7L7778/d0Zra2s2derUbNmyZVmWZdnChQuzKVOmdH28I88sCxYsyE455ZTs1FNPzW6++eZs7NixWWtra48599xzT3bOOedkTz75ZHbCCSds97Xjjz8+W7JkSUnHtTVnW3k+rlMoo9B9njen2O3FMh577LHsn//5n7sek94c09Zzd/PmzdmVV16Zfe5zn8uVsWTJkmzs2LHZj3/84yzLsuzxxx/P/uEf/qGkx3nHWbY655xzsu985zulHk5XRrn7bnezlLr3Fnos8u67pTymPe1RPWWUuu+WMktP+26hjLz7brFZSt13C2Xk3XdLuV962ncLZeTdd4vNknff7W4PyHv+9rSPlHp9LZZT6vnb0yylvmboLifv+VtolryvGbrLyXv+9nS/lPqaobucvOdvoVlKPXcLXXceeeSRXK8ZSr1+9XSdLjZPqdfqYrOUep0ulPGb3/wm13W6lPulp+t0sYw81+liOeV2pG1n783rzF2+zC5evPh1Py/wwQ9+MHviiSdKztjRqlWrsoMPPjjbsGFD0YyvfOUr2Zlnnplt3rw5y7Ise+KJJ7ITTzyx6+stLS3ZoYce2uPx7JizrVIvTN1lbNmyJZs2bVp22WWXlVSQepoly177OZGejmnHjFmzZmVHHnlkdvTRR2dHH310Nnbs2Ox973tf9sc//rFXszzwwAPZ6aefnitj8eLF2eTJk7f7nvHjx3f97EjeWX73u99lkyZNytrb24uu7y7jJz/5yes27d7MstUTTzyRHXnkkT3Os9Xb3/72bNWqVdmECROyLVu2ZFn22rkzYcKEHmfZMWfNmjVdf85b/HbM6Ok488zS0+2FMm666aZs0qRJXefuuHHjsiOOOCL75S9/2atZfv/732dHH310roxVq1ZlBx544HbP5eOPPz5bvHhxWbO89NJL2SGHHFLy/bFtxuOPP17Wvltolq2K7b233nprt4/FrbfemmvfLZSz7WPa075bLCPPvlvKLFlWfN8tlHH88cfn2ndLnaXYvlso47bbbsu17/Y0Syn7bqGMhQsX5tp3S71f8u67W/eAcl83bJuxrbw/I7pjTjmvGwrNkmWlvWboLqfc1w3FZinlNUN3OeW+buhuljyvGbrLKfd1Q3ezbFXs3G1paen2uvPEE0/kes1QKKe761ex63ShnBtvvLHka3WpsxS7ThfLyHOd7mmWUq7TxTLyXKdLvV9K7Ug7zr569eqyX2fu8mV25cqV2WGHHdb1r1R/+tOfsne+8509vhu64wvtrT/M39HRkV1++eXZNddcU3T9v/3bv2Wnn3569re//a3rto6OjuyYY47J9YtIusvZVikXpkKzXHrppdkll1zSdeL0pLuclpaW7U60f//3f8+mTp1a9vFkWfnHtG7dumzjxo1Zlr32L8fTp0/Prr322lwZnZ2d2ZQpU7Jnnnkmy7Is+81vfpMdeeSRRS/axY7p85//fDZnzpyix1Io48knn8ze8573ZKtXr86yLMsefvjh7D3veU9Zs2w9fzdt2pSdffbZ2/2iim2tX7++618es+y1i/sRRxyRdXZ2Zqeffvp2P5hf7KJfLGernspssYxSzqOecrb9V9ZCM+Y5nt4c04YNG7p+yUVnZ2d2/fXXZxdccEGujM7OzuzMM8/sujj/+c9/zg4//PDslVdeyZ2TZVl28803ZxdffHHBYymW8fLLL+fad4vNknfv3WrbXwCVd9/tLmdb5f7SmHL23e5y8u673WXsqNxjyrvvdpdRzr7bXc5Wpe673WWUs+8WmqXUfTfLsoJ7QJ7zt5R9pJTHudgspZ6/hTLynrul7o3FjqtQRt5zt1BOnvO3p+Mp9dwtlJPn/C02S55zt9B1J89rhkI5K1asKPk63dM82+rpWt1dxksvvVTydbrYHHmu0z0dTynX6UIZ5fSjQrOUc53ubva858xWNVmWZcU/iJyOa665Jn7605/G6tWrY/fdd4+mpqZYuHBh/PjHP45vfetbXT+wfPHFF8f73//+XBkzZ86Mxx57LNrb22PSpElxxRVXdP1q7h398Y9/jClTpsSb3/zmrl9Zv88++8SNN94Yjz32WMyaNWu7X7Ff6OciiuVceOGFsXjx4nj55Zdj1KhRMXbs2G5/7qRQxkc/+tE499xzt/uV9O94xzti1qxZuWa5+OKL4/LLL4/29vaIiNh7771j5syZ3f5K7mLHs60DDjig6K/YL5RzzjnnxJVXXhk1NTWxZcuWOOyww+KKK67oNqfYLE8++WRcffXV0dbWFoMGDYqZM2fG+PHjc81y4403xqZNm2LSpEnxgx/8IN761rd2u76njDvuuCN+8IMfRENDQzQ2NsaMGTMK/mxFsZxzzjknli9fHu3t7XHCCSfEtGnTuv0h/9WrV8cFF1wQGzdujNra2thtt93isssui4MOOiiWLVsWM2bMiFdffTWGDx8ec+bMibe85S3dzlIsp9BzrNSMxsbGks6jnnJGjx5dcMY8x7OtyZMnxy233FLw1/0Xyhk+fHhcdNFF0dHREZ2dnfHWt741Pv/5z8eoUaNyzfL888/HFVdcEevWrYv6+vr47Gc/G0cddVTuxygi4rjjjouZM2fGe9/73m7X95SRZ98tlpNn793Wto9Fnn23WE6p+26hjOXLl+fadwvlZFlW8r5b7Hi21dO+Wyhnw4YNJe+7xWbJs+8Wyyl13y2WkWffLZZT6r4b8drPoxXaA0o9f4tl5Dl3C+UsXbq05PO3UMbatWtznbvFjmlbxc7fQhkvvvhirnO32Cylnr/FMvKcu8VySj1/i2XkPXe7u+7kec1QKOeggw4q+Trd0zzb6ula3V3GW97ylpKv08XmyHOd7ul4SrlOF8vIc50ullPOdbq72fOeM1vtUmUWAACAN4Zd6j/NAwAAwBuDMgsAAEBylFkAAACSo8wCAACQHGUWAACA5CizAAAAJEeZBYB+5Iwzzoh3vvOd0dbWVu1RAKBfU2YBoJ944YUX4tFHH42ampp44IEHqj0OAPRryiwA9BPz5s2LQw45JE4++eSYN29e1+1r166N8847L97xjnfEKaecEl//+tfj4x//eNfXly1bFmeeeWYcfvjhcdxxx8W9995bhekBYOeqr/YAAMBrfvSjH8UnP/nJOOSQQ2Lq1KmxevXq2GOPPWL27NkxaNCgeOihh+LFF1+Ms88+O/baa6+IiPjb3/4WZ511Vlx88cXxrW99K5555pk488wzY+zYsbH//vtX+YgAoO94ZxYA+oFHH300li9fHscff3wcfPDBse+++8aCBQuio6MjfvrTn8ZFF10UgwYNiv333z8+/OEPd637xS9+EXvvvXeccsopUV9fHwceeGAcd9xxcd9991XvYABgJ/DOLAD0A/PmzYtJkybFiBEjIiJiypQpcc8998SJJ54YW7ZsiTFjxnR977b//8UXX4zFixfHxIkTu27r6OiIk046aecNDwBVoMwCQJVt2rQpfvKTn0RnZ2dMmjQpIiLa2tri1VdfjZaWlqivr4+XXnop9ttvv4iIWLFiRdfaMWPGxDvf+c644447qjI7AFSLjxkDQJXdf//9UVdXFwsXLox58+bFvHnz4t57742JEyfGvHnz4thjj425c+fGxo0bY9myZfGjH/2oa+373ve+eO6552LevHnR3t4e7e3tsXjx4li2bFkVjwgA+p4yCwBVds8998RHPvKR2GuvvaK5ubnrf//0T/8U8+fPjyuvvDJaW1tj0qRJMX369DjxxBOjsbExIiKGDh0at99+e9x7771x5JFHxhFHHBFf+9rX/HdqAdjl1WRZllV7CACgdNddd12sXr065syZU+1RAKBqvDMLAP3csmXL4ve//31kWRaLFy+Ou+66K4499thqjwUAVeUXQAFAP7dhw4b43Oc+FytXroyRI0fGWWedFcccc0y1xwKAqvIxYwAAAJLjY8YAAAAkR5kFAAAgOcosAAAAyVFmAQAASI4yCwAAQHKUWQAAAJLz/wHU3NcpnhljrwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (16,9))\n",
        "sns.countplot(customer_dataset.Age)\n",
        "# range of age"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "6438cbbb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "6438cbbb",
        "outputId": "0bbc1211-b3a7-4a4f-edd1-4d985e27d94e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Annual Income (k$)', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (16,9))\n",
        "sns.countplot(customer_dataset['Annual Income (k$)'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "3577e85d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "3577e85d",
        "outputId": "fe81db34-2d21-4045-9974-73f3d99abc7e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Spending Score (1-100)', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (16,9))\n",
        "sns.countplot(customer_dataset['Spending Score (1-100)'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "aa327c11",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "aa327c11",
        "outputId": "2fe2f4ce-0921-4db3-a57e-9772d30283d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='Gender', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize = (10,7))\n",
        "sns.countplot(customer_dataset['Gender'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f6d281f4",
      "metadata": {
        "id": "f6d281f4"
      },
      "source": [
        "### Choosing the Annual Income Column and Spending Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "048bc978",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "048bc978",
        "outputId": "aa7071bc-9ab2-4236-ca84-15b9f63c3741"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a2082a0-7e34-4e4c-a200-44b1b7afcdef\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a2082a0-7e34-4e4c-a200-44b1b7afcdef')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "customer_dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "b41296f2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b41296f2",
        "outputId": "a0d40e5e-f3c3-4d2d-b565-58353239d252"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
              "       'Spending Score (1-100)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "customer_dataset.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "3932df1f",
      "metadata": {
        "id": "3932df1f"
      },
      "outputs": [],
      "source": [
        "# using iloc for picking 3rd and 4th columns\n",
        "X = customer_dataset.iloc[:,[3,4]].values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "2e925b40",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e925b40",
        "outputId": "1c1e8b73-6395-4d93-a9e6-2867da83fe93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 15  39]\n",
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            " [137  83]]\n"
          ]
        }
      ],
      "source": [
        "print(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a623e73",
      "metadata": {
        "id": "3a623e73"
      },
      "source": [
        "### choosing the number of clusters\n",
        "\n",
        "WCSS : within Clusters Sum of Squares"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "8f4ca1f2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f4ca1f2",
        "outputId": "ce08de7f-9ac7-412d-c70d-e5b70e52890b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# finding WCSS values for different number of clusters\n",
        "\n",
        "wcss = []\n",
        "\n",
        "for i in range(1,11):\n",
        "    # 1 and 11 will be excluded\n",
        "    kmeans = KMeans(n_clusters=i, init = 'k-means++', random_state = 42)\n",
        "    kmeans.fit(X)\n",
        "    wcss.append(kmeans.inertia_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "06665614",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "06665614",
        "outputId": "a74960c9-68a6-4d4b-aadc-840c5484e93b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plot an elbow graph\n",
        "\n",
        "sns.set()\n",
        "plt.plot(range(1,11), wcss)\n",
        "plt.title(\"The Elbow Point Graph\")\n",
        "plt.xlabel(\"Number of Clusters\")\n",
        "plt.ylabel(\"WCSS\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41398a7c",
      "metadata": {
        "id": "41398a7c"
      },
      "source": [
        "### Optimum number of clusters = 5"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a5ede7f",
      "metadata": {
        "id": "8a5ede7f"
      },
      "source": [
        "## Training the K-Means Clustering model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "1c0d54a0",
      "metadata": {
        "id": "1c0d54a0"
      },
      "outputs": [],
      "source": [
        "kmeans = KMeans(n_clusters =5, init = 'k-means++', random_state =0)\n",
        "\n",
        "# return a label for each data point based on their clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "6ac17ec4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ac17ec4",
        "outputId": "3896d78a-0ff6-4e46-f50b-c3bdf27e7fb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4\n",
            " 3 4 3 4 3 4 1 4 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 2 0 2 1 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 1 2 0 2 0 2\n",
            " 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0\n",
            " 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "Y = kmeans.fit_predict(X)\n",
        "\n",
        "print(Y)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d278730c",
      "metadata": {
        "id": "d278730c"
      },
      "source": [
        "## Plotting data in graph "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "295489c3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "295489c3",
        "outputId": "ee2ad5b5-2310-4f70-deee-629efbc3aa81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fac7a8f3be0>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plotting all the clusters and their centroids\n",
        "\n",
        "plt.figure(figsize = (16,9))\n",
        "plt.title(\"Customer Segmentation\", fontsize = 15)\n",
        "plt.scatter(X[Y==0,0], X[Y==0,1], s= 50, c = 'orange', label = 'Cluster 1')\n",
        "plt.scatter(X[Y==1,0], X[Y==1,1], s= 50, c = 'green', label = 'Cluster 1')\n",
        "plt.scatter(X[Y==2,0], X[Y==2,1], s= 50, c = 'red', label = 'Cluster 2')\n",
        "plt.scatter(X[Y==3,0], X[Y==3,1], s= 50, c = 'blue', label = 'Cluster 3')\n",
        "plt.scatter(X[Y==4,0], X[Y==4,1], s= 50, c = 'yellow', label = 'Cluster 4')\n",
        "\n",
        "# plot the centroids\n",
        "plt.xlabel(\"Annual Income\")\n",
        "plt.ylabel(\"Spending Score\")\n",
        "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s =100, c='black', label = 'Centroids')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}