Hierarchial_Clustering_Customer_Data.ipynb

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        "# New Section"
      ],
      "metadata": {
        "id": "L0hopx2xcGuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "from scipy.stats import zscore\n"
      ],
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        "id": "u3b4G8aicJd3"
      },
      "execution_count": 1,
      "outputs": []
    },
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          "data": {
            "text/plain": [
              "   Cust_ID Name  Avg_Mthly_Spend  No_Of_Visits  Apparel_Items  FnV_Items  \\\n",
              "0        1    A            10000             2              1          1   \n",
              "1        2    B             7000             3              0         10   \n",
              "2        3    C             7000             7              1          3   \n",
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              "5        6    F             4000             3              0          1   \n",
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              "7        8    H             2500             3              0          1   \n",
              "8        9    I             2000             2              0          2   \n",
              "9       10    J             1000             4              0          1   \n",
              "\n",
              "   Staples_Items  \n",
              "0              0  \n",
              "1              9  \n",
              "2              4  \n",
              "3              4  \n",
              "4              3  \n",
              "5              8  \n",
              "6              2  \n",
              "7              1  \n",
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      ],
      "source": [
        "# reading the CSV file into pandas dataframe\n",
        "custData = pd.read_csv(\"Cust_Spend_Data.csv\") \n",
        "custData.head(10)"
      ]
    },
    {
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          "data": {
            "text/plain": [
              "   Avg_Mthly_Spend  No_Of_Visits  Apparel_Items  FnV_Items  Staples_Items\n",
              "0            10000             2              1          1              0\n",
              "1             7000             3              0         10              9\n",
              "2             7000             7              1          3              4\n",
              "3             6500             5              1          1              4\n",
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      ],
      "source": [
        "custDataAttr=custData.iloc[:,2:]\n",
        "custDataAttr.head()"
      ]
    },
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              "   Avg_Mthly_Spend  No_Of_Visits  Apparel_Items  FnV_Items  Staples_Items\n",
              "0         1.886498     -1.240347       1.527525  -0.741433      -1.380131\n",
              "1         0.787567     -0.620174      -0.654654   1.280656       1.725164\n",
              "2         0.787567      1.860521       1.527525  -0.292080       0.000000\n",
              "3         0.604412      0.620174       1.527525  -0.741433       0.000000\n",
              "4         0.421257      1.240347      -0.654654   1.730009      -0.345033\n",
              "5        -0.311364     -0.620174      -0.654654  -0.741433       1.380131\n",
              "6        -0.860829      0.620174      -0.654654   1.505333      -0.690066\n",
              "7        -0.860829     -0.620174      -0.654654  -0.741433      -1.035098\n",
              "8        -1.043984     -1.240347      -0.654654  -0.516756      -0.690066\n",
              "9        -1.410294      0.000000      -0.654654  -0.741433       1.035098"
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              "      <td>1.886498</td>\n",
              "      <td>-1.240347</td>\n",
              "      <td>1.527525</td>\n",
              "      <td>-0.741433</td>\n",
              "      <td>-1.380131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.787567</td>\n",
              "      <td>-0.620174</td>\n",
              "      <td>-0.654654</td>\n",
              "      <td>1.280656</td>\n",
              "      <td>1.725164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.787567</td>\n",
              "      <td>1.860521</td>\n",
              "      <td>1.527525</td>\n",
              "      <td>-0.292080</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.604412</td>\n",
              "      <td>0.620174</td>\n",
              "      <td>1.527525</td>\n",
              "      <td>-0.741433</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.421257</td>\n",
              "      <td>1.240347</td>\n",
              "      <td>-0.654654</td>\n",
              "      <td>1.730009</td>\n",
              "      <td>-0.345033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.311364</td>\n",
              "      <td>-0.620174</td>\n",
              "      <td>-0.654654</td>\n",
              "      <td>-0.741433</td>\n",
              "      <td>1.380131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-0.860829</td>\n",
              "      <td>0.620174</td>\n",
              "      <td>-0.654654</td>\n",
              "      <td>1.505333</td>\n",
              "      <td>-0.690066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-0.860829</td>\n",
              "      <td>-0.620174</td>\n",
              "      <td>-0.654654</td>\n",
              "      <td>-0.741433</td>\n",
              "      <td>-1.035098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-1.043984</td>\n",
              "      <td>-1.240347</td>\n",
              "      <td>-0.654654</td>\n",
              "      <td>-0.516756</td>\n",
              "      <td>-0.690066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-1.410294</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.654654</td>\n",
              "      <td>-0.741433</td>\n",
              "      <td>1.035098</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c6ca36e-a57c-48f5-af2d-739c805d7dbb')\"\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-3c6ca36e-a57c-48f5-af2d-739c805d7dbb 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-3c6ca36e-a57c-48f5-af2d-739c805d7dbb');\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": 5
        }
      ],
      "source": [
        "custDataScaled=custDataAttr.apply(zscore)\n",
        "custDataScaled.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "scrolled": true,
        "id": "ShXeJf0eX7xx",
        "outputId": "d6d7724f-4da0-463a-f4c1-95b5f1c651a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f030fbdef10>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 30 Axes>"
            ],
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nd0q3j5Hs8+mi0nydPqyfHp5Xpcvunqt7/3WiRuZlRCAxAMQXnnOAY6OPAIhm1Ch4gWUlAABOWGv145dXqrxqt26/uOSEBoYP16d3im49v1hTS3J19R/n66nFmz1KCgAAAABAfGLmMADAiT+9t1Fvr67RHZeNUe8Ub25HxhhNGZWrEbnpuvvNtSqv2q2fTh+rgJ9lJgAAAAAA+CRmDgMAetx7a2v1x3fW67aLRimtl/e/pxzUt7d+fMVYbdm9VzPum6+axqDnbQAAAAAAEOsYHAYA9KidDUF9468VumnKcPVP7xWxdlJTknTL1GKNysvQ5ffM1YptDRFrCwAAAACAWMTgMACgx4RCVrc+tVTnl+RqzICsiLdnjNFVEwfpmtOH6F//tFCvVu6MeJsAAAAAAMQK1hwGAPSYR+dXac/eVn39vIE92u7pw7LVP72Xvvvccm2qa9FXzimSMaZHMwAAAAAAEG2YOQwA6BFbdu3VXW+s1VcmFynJ1/MDs8Nz0vWjK0r1l0Wbdftzy9XWHurxDAAAAAAARBMGhwEAEWet1fdfWKGLx+ZrQJ9UZzn6p/fSHZeN0UfVTfriw4vVFGx1lgUAAAAAANcYHAYARNzrK6u1obZZl44rcB1FvVOS9Z+fGqW0lCR9+vfztHX3XteRAAAAAABwgsFhAEBE7W9r10/+tlLXnVmo5KTouO0k+Yy+MKlQk4Zn69P3ztPSzbtdRwIAAAAAoMdFx7/SAQBx65H3q1SQFdC4gVmuo3yMMUYXjy3QFyYV6osPL9aLFdtcRwIAAAAAoEcluw4AAIhfe/Ye0B/+uV53XDbGdZSjOmVoX/VPL9HP/75KH+1s0m0XjpLPwYZ5AAAAAAD0NGYOAwAi5vf/XK9TC/s63YSuK4Zmp+nHV47V2x/V6Mt/LmejOgAAAABAQmBwGAAQETWNQT25aLOmnzzIdZQuyUr167sXj1aSka783fvaUNvsOhIAAAAAABHF4DASTihktaG2WfPX12lDbbNCIes6EhCX7n17nc4pzlG/tBTXUbosOcmn688u0tTRubrqD/P0WuVO15EAHIZ7OABEL2o0ED76EVxgzWEklFDIak7lTs2cXaFga0gBv0+zZkzQtNJ81hgFPFTdGNRzS7fp158Z7zrKCTm/JE9D+/XW915YoQ8279ZtF45SchK/TwVc4h4OANGLGg2Ej34EV/iXLhJKVX3LoUIrScHWkGbOrlBVfYvjZEB8+eM763VOcY769I6dWcOfNCI3Qz+7cqzmr6/XtQ8sUE1T0HUkIKFxDweA6EWNBsJHP4IrDA4joVQ3Bg8V2k7B1hCDPoCHdrUc0DNLtuqScQWuo4QtM9Wvb11UoiH90nTpb+dq3ro615GAhMU9HACiFzUaCB/9CK4wOIyEkpcZUMD/8cs+4PcpNyPgKBEQfx6eu1GnD+sXU2sNH4vPZ/SZUwbpy5OH6eYnl2rWax+pnbW/gB7HPRwAohc1Gggf/QiuMDiMhFKYnaZZMyYcKrida/gUZqc5TgbEh5b9bXpswSZdMjb2Zw1/0vhBffTT6WP19ke1uvaBBapu5Df4QE/iHg4A0YsaDYSPfgRX2JAOCcXnM5pWmq+SWyarpimo3IyACrPTWNwd8MhTi7eopCBDBX1SXUeJiL69U/SdaSV6oWKbLvnte7pzxkmaMirXdSwgIXAPB4DoRY0Gwkc/gisMDiPh+HxGRTnpKspJdx0FiCvtIasH527UjecUuY4SUT6f0VUTB6mkIFO3Pf2hrpo4SN+8aJT8SbwZB4g07uEAEL2o0UD46EdwIaL/kjXG+IwxmZFsAwAQHV5fuVMZgWSNzMtwHaVHjCnI1H9NH6fFVbt09R/na9uefa4jAQAAAADQLZ4PDhtj/mKMyTTGpElaIWmlMeabYR7zIWNMjTFmhTcpAQBeu//dDbqoNN91jB6VmerXbReO0piCTF1293t6c1W160gAAAAAAHRZJGYOj7HWNkqaLukVScMkXRfmMR+RNC3MYwAAImTFtgZt3b1Ppxb2cx2lx/mM0eUnDdAt5xfrO88u069eWa229pDrWAAAAAAAHFckBof9xhi/Dg4Ov2StbQ33gNbadyXtCjsZACAiHpy7UReMzlNSAm+WUJKfqZ9NH6d5G+r1r39aqLrm/a4jAQAAAABwTJEYHL5PUpWkNEnvGmOGSmqIQDsfY4y50RhTbowpr62tjXRzAOIMNeTE1Tfv1+srqzVlVI7rKM5lpvr1rQtHaWCfVF1693uq2LLHdST0EGoIgHBQQwCEgxoCIByRGBx+2Vo70Fp7ibXWStos6foItPMx1tr7rbVl1tqynBwGKAB0DzXkxD25aLNOG9ZXGQG/6yhRweczurpssP719KH64sOL9Ez5FteR0AOoIQDCQQ0BEA5qCIBwRGJw+NnDP+gYIP5rBNoBADjW1h7SYws26YLRibURXVecWthP37tktGa9vkY/eblS7SHrOhIAAAAAAB/j2eCwMabEGPMZSVnGmKsOe31RUsCrdgAA0eOt1TXq0ztFw/qnuY4SlQb17a0fXzFWi6t26/pHFqt5f5vrSAAAAAAAHOLlzOFRki6T1EfS5Ye9Jkr6SjgHNsY8KWm+pFHGmK3GmBvCzAoA8MCj86o0dVSu6xhRLT2QrG9NG6Vkn9G//GGedjYEXUcCAAAAAECSlOzVgay1L0p60RhzprV2vlfH7Tj2NV4eDwAQvqq6Fq3Y3qgbzxnuOkrUS/b5dMPZw/Tysu2afu/7+vMNp2lkXobrWAAAAACABOfZ4LAx5lvW2l9LutYY838Gc621t3jVFgDAvScWbtbk4v5KSY7E8vXxxxijK04aqH5pKfrsffP1wOfLVFbYz3UsAAAAAEAC82xwWNKqjv+We3hMAEAU2t/WrmeWbNEdl45xHSXmnD0iRxm9/Lrh0XLd9dkJOq+EZTkAAAAAAG54uazEyx3/fbTzc8YYn6R0a22jV+0AANx7tbJag/v1VkGfVNdRYtJJg/to5qdGaubsCv10+lhdNn6A60gAAAAAgATk+XuBjTF/McZkGmPSJK2QtNIY802v2wEAuPP4gk2aMjLHdYyYNjIvQ9+eVqIfvlipZ5ZscR0HAAAAAJCAIrFQ5JiOmcLTJb0iaZik6yLQDqJMKGS1obZZ89fXaUNts0Ih6zpSt8R6fqCnbKht1prqJp3KerlhG5qdptsvGa1fvrJaTy3e7DoOEPW4V3uPc4p4xzXuHc4l0H2R7jf0S3jByzWHO/mNMX4dHBz+nbW21RjD1RnnQiGrOZU7NXN2hYKtIQX8Ps2aMUHTSvPl8xnX8Y4r1vMDPenJRVt0TnGOkpPYiM4LA/uk6ruXjNYv/rFK1kqfO22I60hAVOJe7T3OKeId17h3OJdA90W639Av4ZVI/Mv+PklVktIkvWuMGSqJNYfjXFV9y6GCJEnB1pBmzq5QVX2L42RdE+v5gZ5yoC2kZ5dsYUkJjxVkper2S0brN699pGfKWWICOBLu1d7jnCLecY17h3MJdF+k+w39El7xfHDYWnu3tXagtfYSe9AmSed53Q6iS3Vj8FBB6hRsDammKegoUffEen6gp7y+sloD+7IRXSQUZKXqOxeP1i9eWa2XP9zuOg4QdbhXe49zinjHNe4dziXQfZHuN/RLeMWzZSWMMf/PWvu4MWbmUb5llldtIfrkZQYU8Ps+VpgCfp9yMwIOU3VdrOcHesoTCzfpXGYNR8zAPqn61rQS/eDFFUr1J+mCMXmuIwFRg3u19ziniHdc497hXALdF+l+Q7+EV7ycOdy7478ZR3khjhVmp2nWjAkK+A9eUp1r3RRmpzlO1jWxnh/oCVt27VXl9kY2oouwIf166z8vHKXbnvlQ89bXuY4DRA3u1d7jnCLecY17h3MJdF+k+w39El4x1nqzV5wx5puSnrTWbvXkgGEoKyuz5eXlrmMknFDIqqq+RTVNQeVmBFSYnRZTi6DHen50SZf+h1JDjuw3r36kDXXNuu6MQtdREkLl9gb97u11euz60zVuUJbrODiIGuIY92rvcU57FDXEAa5x73AunaOGxKBI9xv6JbrhqBeGZ8tKSBogab4xpkrSk5KettbWenh8RDmfz6goJ11FOemuo5yQWM8PRFJbe0izy7fotgtHuY6SMEoHZOn6ScP0pUcW6emvTtKw/swAALhXe49zinjHNe4dziXQfZHuN/RLeMGzZSWstf8haYik70saJ2mZMWaOMeYLxhiWlQCAGPbu2lr17Z2iwf16H/+b4ZlTh/XT9JMH6roHF6q2ab/rOAAAAACAOOPlmsOyB71jrf2apEGS/kfSrZKqvWwHANCznliwmY3oHDm/JE9nFGXrCw8tUsv+NtdxAAAAAABxxNPB4U7GmHGSfiLpXkn7Jd0eiXYAAJFX0xjUwo27dObwbNdREtZVJw9UflYv3fTEB2prDx3/LwAAAAAA0AWeDQ4bY4qNMXcYYyolPSGpRdKF1tozrLW/9aodAEDPenrJFp1R1E8Bf5LrKAnLGKMvnTVMe/a16gcvVsqrzWQBAAAAAInNy5nDcyT1kvRZa+14a+3PrbUbPDw+AKCHhUJWTy7aonNH5rqOkvCSfT7dMnWE3l9fpwfe4/YKAAAAAAiflxvSDbfWft9au+JY32eMme9VmwCAyFqwoV7JPqPhOWmuo0BS75Rk3XbhKN33zga9vpLl/AEAAAAA4YnImsPHEXDQJgDgBDy+cJPOHZUjY4zrKOjQP72Xbr1gpL759Idaub3RdRwAAAAAQAxzMTjMQokAEAN2tRzQu2vqdPbwHNdR8AkjctN13ZlDdf0ji1XbtN91HAAAAABAjHIxOAwAiAHPLtmiiUP7Kj2Q7DoKjmDS8P46a0S2vvLncu1va3cdBwAAAAAQgzwbHDbG9Orqt3rVJgAgMqy1+suiLZoyklnD0eyqiYMU8Pv03eeWy1remAMAAAAA6B4vZw7PlyRjzGPH+b7rPGwTABABi6t2q609pJL8DNdRcAw+Y/Rv5wzXkk279ci8KtdxAAAAAAAxxsv3CqcYY66VNMkYc9Unv2itfa7jvys8bBMAEAGPL9ikKaNy2YguBgT8SfqPC0bqRy9XalR+hiYN7+86EgAAAAAgRng5c/irkiZL6iPp8k+8LvOwHQBABO3Ze0Bvra7R2cUMMsaK3MyAbpoyQl//y1Jt3b3XdRwAAAAAQIzwbOawtXaupLnGmHJr7YNeHRcA0LOeWbJVE4f0UWbA7zoKumHswCxdOq5AX3m0XM//+1kK+JNcRwIAAAAARDkvZw7LGJMraagx5pmO1487PgcAiAHWWj2+YJPOK6F0x6KLx+YrO72Xvv3sMjaoAwAAAAAcl2eDw8aYsyQtlmQl/bnjJUmLOr4W7vGnGWM+MsasM8Z8J9zj4X+FQlYbaps1f32dNtQ2KxRiQAFIVAs27FLISqPy2IguFhljdMPZw/Thlj16lA3qEId4ZgHQXdQNAIg8am1s83JDujslTbfWLj3scy8ZY56XdJ+k00/0wMaYJEn3SvqUpK2SFhtjXrLWrgwnMA524DmVOzVzdoWCrSEF/D7NmjFB00rz5fOxERWQaB6dV6WpJWxEF8sC/iTdesFI/fjlSo0dmKWywn6uIwGe4JkFQHdRNwAg8qi1sc/LZSUyPzEwLEmy1lZICncK2mmS1llrN1hrD0j6q6QrwzwmJFXVtxzqwJIUbA1p5uwKVdW3OE4GoKfVNAY1d12dJrMRXczLywzoy5OLdNMTH6imKeg6DuAJnlkAdBd1AwAij1ob+7wcHDbGmL5H+GQ/D9oZKGnLYR9v7fjc4e3caIwpN8aU19bWhtlc4qhuDB7qwJ2CrSEGE5BwqCHSk4s264yifuqd4uWbSuDKxCF9dU5xjm56/AO1toeO/xcQFmpI5PHMgnhGDYkM6gYSBTUELlFrY5+Xg8P/I+k1Y8y5xpiMjtcUSa90fC2irLX3W2vLrLVlOTk5kW4ubuRlBhTwf/wyCPh9ys0IOEoEuJHoNaStPaQnFm7W+aPzXEeBhz598kC1W6tfvbLadZS4l+g1pCfwzIJ4Rg2JDOoGEgU1BC5Ra2OfZ4PD1tr7Jf1Y0k8lVUnaKOknkn5mrb0vzMNvkzT4sI8HdXwOYSrMTtOsGRMOdeTOtWEKs9McJwPQk15fWa3+6b3o+3HG5zP62rnD9fKy7Xpl+Q7XcYCw8MwCoLuoGwAQedTa2Ofpe4ettX+T9LdjfY8x5nZr7S+6eejFkoqNMcN0cFD4c5KuPbGUOJzPZzStNF8lt0xWTVNQuRkBFWansWg4kGAeen+jzh+d6zoGIiAj4NfNU4t1+/PLNSo/Q0U56a4jASeEZxYA3UXdAIDIo9bGPhcLS14tqVuDw9baNmPM1yW9KilJ0kPW2sr/z96dx0dV3/sff38nC5OEJISQlS3sYABR41JxBxV3qtal7W1rvbW2tYvU/mpba1uvrfVaba/Xtmpb17YqbkBdsOKCC6gEZEkEZAtLICGJEJLAkGW+vz9IELkJmcks58zM6/l4zINk5uSc9xzO+Zwzn5x8TyTCJSKPx2hkXn8aBkCCWr1jjzbWteh708Y4HQURMiqvvy4/doiue3yp5t0wlXGlEbM4ZwEQLOoGAEQetTa2hXPM4UD16VcH1tqXrLVjrbWjrLW/DncoAEhUD79bpWkT8pXsceKQgGg5a3y+Bg/w6uZnV8la63QcAAAAAIALONEJ4BMpALjEJy2temnVDp01nhvRxTtjjK6ZOkIrt+3WP9/f4nQcAAAAAIALxMyVwwCA8PvHe5t1wogcZaelOB0FUdAvOUnfO2uM/vuVtVq1rdHpOAAAAAAAhznRHH7agWUCAA7T2u7XY4s365yjCp2OgigqGpCma04u0Tf/Xq7de1udjgMAAAAAcFDYm8PGmCHGmOeNMXXGmJ3GmGeNMUO6XrfW/ibcywQABG/u8moNzknT8NwMp6Mgyk4cmatjhuboB08tl9/PaE8AAAAAkKgiceXww5LmSSqSVCzpX53PAQBcwlqr+xdu0HkTuWo4UV11wlDVNPr0pzfXOx0FjHEl4wAAIABJREFUAAAAAOCQSDSH86y1D1tr2zsfj0jKi8ByAAB99ObHdfJbadLgbKejwCHJHo9uOHO0Hnq3SovW1zsdBwAAAADggEg0hxuMMV82xiR1Pr4sqSECywEA9NEfX1+vCyYVyRjuEZrIcvv307dOH6XvPvGhahp9TscBAAAAAERZJJrDX5d0haQaSTskXS7pmggsBwDQB0s379LWXXt10shcp6PABSYOztbZRxXo+r8vVWu73+k4AAAAAIAoCntz2Fq72Vp7sbU2z1qbb62daa3dEu7lAAD65t7X1umCSUVK8nDVMA646OhipSQZ3f7iR05HAQAAAABEUXK4ZmSMufUIL1tr7X+Fa1kAgL6pqG5URXWjvj51hNNR4CIeY/TN00bp1rkVOm54ji6ZMtjpSAAAAACAKAjnlcMt3Twk6VpJPw7jcgAAffSHBR/r/ElFSk2OxKhCiGUZ/ZL1vWljdOvcSq3escfpOAAAAACAKAhbd8Bae3fXQ9KDktJ0YKzhJyWNDNdyAAB9U1HdqA+37Na0CflOR4FLDc/N0H+cNFzXPVauxr1tTscBAAAAAERYWC8dM8YMNMbcLmmlDgxZcay19sfW2p3hXA4AIHh3/3utLphcpH7JSU5HgYtNHT1Ik4cM0A1PLFOH3zodBwAAAAAQQWFrDhtj7pK0RFKTpEnW2l9aa3eFa/5wP7/famNdsxZvqNfGumb5+9hUCNd8wsVteYC+WLZllyqqGzVtfIHTURADrj5hmHbvbdNdr6xxOgrwGa2tHSqv+kQvrNyupVWfqLW1w+lIAFyuvd2vFVt3aX7FDq3Yulvt7X6nI7kWn3uAwLCvhC7Ydcg6j6yw3ZBO0g8l7Zd0i6SfGWO6njc6cEO6rDAuCy7j91vNr6zRrNnL5Wvzy5vi0T1XTNGM0kJ5PKb3GYR5PuHitjxAX1hr9duX12jmMUMYaxgBSfIY3XDWaN06t0Klxdm66OhipyMBam3t0JyV23Xr3IqDx+TbLpmomZOLlZrKX0QA+L/a2/2as6Jat8z5tG7cPnOiZh49WMmcE30Gn3uAwLCvhC7Ydcg6j7xwjjnssdamWWszrbVZhzwyaQzHv6qGloM7qiT52vyaNXu5qhpaevnJyMwnXNyWB+iLNz+u0/bd+3T62DynoyCGZHlT9IPpY3XLnApVVDc6HQfQyu2NBxvD0oFj8q1zK7RyO9sngO5V7mg82BiWDtSNW+ZUqHIHdeNwfO4BAsO+Erpg1yHrPPL4dSnConaP7+CO2sXX5tfOJp8j8wkXt+UBgtXht/r1i6t1ZdlQJfFbVQSpJDdD15xcomsfXaKde6h7cFZND8fkWrZNAD3Y0dh93ahppG4cjs89QGDYV0IX7DpknUcezWGERUGWV96Uz25O3hSP8jO9jswnXNyWBwjWk0u2qF+yR8cNz3E6CmLUiSNzdfrYPF37aLl8bYzvCucU9XBMLsjimAyge0XZad3WjcJs6sbh+NwDBIZ9JXTBrkPWeeTRHEZYlORm6J4rphzcYbvGgCnJzXBkPuHitjxAMBr3tenuf3+sL504XIeMAw8EbeaUwcpOS9GNTy3n5g9wzKTibN12ycTPHJNvu2SiJhdnO5wMgFuVFmXp9pmfrRu3z5yo0iLqxuH43AMEhn0ldMGuQ9Z55Blr4+9DXllZmS0vL3c6RsLx+62qGlq0s8mn/EyvSnIz+jQ4eLjmEy5uy4OQBPQfFy815Na5Fdqxe5++fspIp6MgDrS2+3XHy6t15rh8/fi88U7HcUpC1RA3am3t0Mrtjard41NBlleTi7O5GR1iCTXEAe3tflXuaFRNo0+F2V6VFmVzM7oe8LnH9aghLsG+Erpg1yHrPCx6XGHJ0UyB+ObxGI3M66+Ref1dMZ9wcVseIBAV1Y3614rtuvOyyU5HQZxITfboxrPH6lfzKjUkJ01fOmm405GQgFJTk1RWMtDpGABiSHKyR0cPzdHRQ51O4n587gECw74SumDXIes8sviVKQDEmfYOv3787EpdUTZUmd4Up+MgjmR5U/Sjc8fr7lc/1qsf1TodBwAAAAAQIprDABBnHn53kzzG6PSxeU5HQRwqzPZq1tljddPTK7Sk6hOn4wAAAAAAQkBzGADiyIa6Zt33xgZ9feoIbkKHiBmV11/fPmOUrnusXJXbG52OAwAAAADoI5rDABAn2jv8uvGp5br02MEqzPY6HQdxbvKQAfrq50r0lb99oPU7m5yOAwAAAADoA5rDABAnfr9gnYwxmj6hwOkoSBAnjszVlccP1Rf/8r421bc4HQcAAAAAECSawwAQBxatr9cTH2zR9aeNlIfhJBBFp47J08xjBuuqBxZrQ12z03EAAAAAAEFwfXPYGPMFY0ylMcZvjClzOg8AuE3tHp++9+SHuv70URqQnup0HCSgM8fl6/PHDtZVD7yntTUMMQEAAAAAscL1zWFJFZIulfSW00EAwG18bR36z0fLNX1CgSYNznY6DhLY6WPzdeXxQ3XVg4u1bMsup+MAAAAAAALg+uawtXa1tXat0zkAwG38fqubnl6hrLRkXXx0sdNxAE0dPUjfOHWkvv7IEr2+ptbpOAAAAACAXri+ORwoY8x1xphyY0x5XV2d03EAxJhYrCF3vLxG63c267pTR8kwzjBc4phhOZo1faxuenqlHltU5XScqInFGgLAPaghAEJBDQEQClc0h40xC4wxFd08Lgl0HtbaB621Zdbasry8vEjGBRCHYq2G3Pf6Or1SWaNZZ49VarIrSjlw0JiCTN164VH6y9sb9bPnV6m13e90pIiLtRoCwF2oIQBCQQ0BEApXdBSstdOttRO7ecx1OhsAuM0f31ivJz7YqpvPG69Mb4rTcYBuFWR59cuLS/VxbZOufHCxahp9TkcCAAAAABzGFc1hAEDvrLX67cur9eQHW/TT8ycoJz3V6UjAEaWnJusH08dqXEGmzr/3bb22mnGIAQAAAMBNXN8cNsZ83hizTdLnJL1ojHnF6UwAEG2+tg59/8nlemNNnW658CgNzKAxjNjgMUaXTBms7545Wj99fpVufnalmve3Ox0LAAAAAKAYaA5ba5+31g6x1vaz1hZYa891OlOi8/utNtY1a/GGem2sa5bfb52OlFBY/4ln6yd7ddmfF6m+eb9+ev4EZTGUBGLQ+KIs/ebzk7Szyafpdy/kKmL0CcfA8GOdIt71to2zDwCIZB2gxiAWJDsdALHF77eaX1mjWbOXy9fmlzfFo3uumKIZpYXyeIzT8eIe6z+xWGs1b8V2/XJepS6YXKTzJxbJGP6fEbvSU5P1jVNHaVV1o26dW6m/v7dZP7/wKI3M6+90NMQAjoHhxzpFvOttG2cfABDJOkCNQaxw/ZXDcJeqhpaDhU2SfG1+zZq9XFUNLQ4nSwys/8RRvXufvvFYuX73ylrddM44XTCpmMYw4sakwdm649JJKh6Qppl/elc3P7tS23fvczoWXI5jYPixThHvetvG2QcARLIOUGMQK2gOIyi1e3wHC1sXX5tfO5u4C300sP7j3x5fm+56Za3O+8NbGpCeql9/fhJXVSIupSR5dOHkYv3u8qO1t7VDM/7wlm6avUIf1zY5HQ0uxTEw/FiniHe9bePsAwAiWQeoMYgVDCuBoBRkeeVN8XymwHlTPMrP9DqYKnGw/uNX7R6fHl1UpX+8v0XHDBug22dOUl5mP6djARGX6U3R1ScM04WTi7Rgda2uevA9jcrL0JdOHK5zSwuVlprkdES4BMfA8GOdIt71to2zDwCIZB2gxiBWcOUwglKSm6F7rpgib8qBTadrzJyS3AyHkyUG1n988bV16JXKGn3j0XJNu3uhNtQ161cXl+qbp42iMYyEk+lN0eePGaL/uXKKTh41SI8trtLxv16gb/9jqV5cuUN7fG1OR4TDOAaGH+sU8a63bZx9AEAk6wA1BrHCWBt/d0osKyuz5eXlTseIW36/VVVDi3Y2+ZSf6VVJbgaDqUcR6z8kAa2oSNUQa6021rfovY0NenNtnRZvaFDJoHSdNCJXnxuVq/RU/pgDONTuva1aUrVLy7fu0uodTRpXmKmpo3JVNmKgpgwZoJyM1GhHcrSGgGNgJLBOo4oa4oDetnH2AcQQakiERLIOUGPgIj1ueDSHASSSqJxQ+f1W9S37taVhrzbVt2hdbbMqtjeqcvse9Uv2aHxRliYWZ2nykAHKTkvp83KARNLa7tfa2iat2bFH6+uatWFnszK9KRpXmKlxBZkamZehYQPTNSQnXQXZ/dQvOSLDUfChDEAoqCEAQkENARCKHmsIl6kBQIj+9OZ6zfmwWrv3tqmhuVUdnb90y0lPUdGANBVnezWuMFPnTSxUTvqnVzq2d/jV0LzfqdhAzCnO9qo426uzxufLb6WdTT5t/WSvNtY3a/HGBu1s8qmuab/8nb/3zkhNUk5GqnLSU5SdlqIsb4oyvSlK75ektJQklQzK0BVlQ519UwAAAADgoLi8ctgYUydps9M5omCQpHqnQ7gA64F10KW39VBvrZ3R20yCrSH5V94+Kq1kyoCu7/3793bYjtZP7zpgrUfG+Lv94Wgih7sykCOCGTzG0y89ySQl93qFzea7Zi6Tv/3Qk6Ej1ZGI1JAocfNxgmzBc2suiWxHEmwNcTpvd8gUGDIFhkyB6crkpvMQN6wnN2SQyOG2DBI5esrQYw2Jy+ZwojDGlFtry5zO4TTWA+ugi1vXg1tykcNdGcjhvgxuyhFubn5fZAueW3NJZAsnN+YlU2DIFBgyBYZM7s1ADvdlIEffMniiEQYAAAAAAAAA4C40hwEAAAAAAAAgAdEcjm0POh3AJVgPrIMubl0PbslFjk+5IYNEjkO5IYPknhzh5ub3RbbguTWXRLZwcmNeMgWGTIEhU2DI1D03ZJDIcSg3ZJDIcaiAMjDmMAAAAAAAAAAkIK4cBgAAAAAAAIAERHMYAAAAAAAAABIQzWEAAAAAAAAASEA0hwEAAAAAAAAgAdEcBgAAAAAAAIAERHMYAAAAAAAAABJQstMBImHGjBl2/vz5TscA4D4mkImoIQB6QA0BEApqCIBQUEMAhKLHGhKXVw7X19c7HQFADKOGAAgFNQRAKKghAEJBDQEQrLi8chjoC7/fqqqhRbV7fCrI8qokN0Mej+nxebflBJyUyNtlrLz3WMkJAEA4tbf7VbmjUTsafSrKTlNpUZaSkwO/RorjJxD/Ynk/D7XGARLNYUDSgYPB/MoazZq9XL42v7wpHt1zxRSdM6FA/15d+3+en1Fa6MjBoqecTuUBpMTeLmPlvcdKTgAAwqm93a85K6p1y5yKg8e/22dO1MyjBwfUPOH4CcS/WN7PQ61xQBdHtxZjzEPGmJ3GmIoeXj/DGNNojFne+bg12hmRGKoaWg4eDCTJ1+bXrNnLVbmjsdvnqxpaXJXTqTyAlNjbZay891jJCQBAOFXuaDzYNJEOHP9umVOhyh2NAf08x08g/sXyfh5qjQO6OP2rhEckzehlmrettVM6H7dFIRMSUO0e38GC2sXX5teOxu6f39nki2a8g3rK6VQeQErs7TJW3nus5AR6Y63VpvoWvfpRrRZ8VKvNDS2y1jodC4BL9XQuX9MY2PGP4ycQ/2J5Pw+1xgFdHB1Wwlr7ljGmxMkMgCQVZHnlTfF8prB6Uzwqyu7++fxMrxMxe8zpVB5ASuztMlbee6zkBHpirdW8Fdt13+vrtWtvq0pyM2QlVdW3aFD/fvrOWaN10eQiGePuP/8EEF1F2WndHv8KswM7/nH8BOJfLO/nodY4oIvTVw4H4nPGmBXGmJeNMaU9TWSMuc4YU26MKa+rq4tmPsSBktwM3XPFFHlTDuwSXeMMlRZld/t8SW6Gq3I6lSeeUEP6LpG3y1h577GSM5ZRQyKncW+bvvrQB/qfBev0hbKhuveqY/TDc8bppnPG6d6rj9Glxw7W719dq689vESNe9ucjgv0CTUkMkqLsnT7zImfOf7dPnOiSouyA/p5jp+IFdSQvovl/TzUGgd0MU7/KV7nlcMvWGsndvNaliS/tbbZGHO+pP+x1o7pbZ5lZWW2vLw87FkR37ruULqzyaf8zE/vUNrT827LiYAEtKKoIcFL5O0yVt57rOR0OWpIlNU0+nTlg4s1cXC2rj5+mJJ62Gbb/X49+cFWrapu1FPfPElF2WlRTgoEhBrigPZ2vyp3NKqm0afCbK9Ki7KDulETx0+4CDUkQmJ5Pw+1xiGh9LhRu7o53M20VZLKrLX1R5qOYgigB5xQAQgFNSSKGpr369I/L9LUUYN00dHFAf3Miyu3a+G6Oj33ranKy+wX4YRA0KghAEJBDQEQih5riKt/nWCMKTSdg8cZY07QgbwNzqYCAABAJO1v79B1jy/VMUNzAm4MS9IFk4t1Ykmu/vPRJfK1dUQwIQAAABAfHG0OG2OekLRY0jhjzDZjzLXGmOuNMdd3TnK5pApjzApJ90q6yjp9qTMAAAAi6o6X1shjpC+UDQn6Zy89drAy+iXrF/MqI5AMAAAAiC/JTi7cWnt1L6/fJ+m+KMUBAACAw95cu1Mvrtqh38ycJI8Jfrw/Y4yuPWWEbplToRdX7tAFk4sikBIAAACID64eVgIAAACJo8nXph8/u1LXnTpS/b19v4YhPTVZ3z5jlH4+t0INzfvDmBAAAACILzSHAQAA4Ar/PX+tSouzNHFwdsjzGp2fqamjc3Xr3IowJAMAAADiE81hAAAAOG71jj3614rtuur4YWGb52XHDlH55l1atKE+bPMEAAAA4gnNYQAAADjKWqtfzqvUpccOVqY3JWzz7ZecpC+dMFy3zKlQe4c/bPMFAAAA4gXNYQAAADhq4cd12r57n84aXxD2eZeV5CgjNVlPLdka9nkDAAAAsY7mMAAAABzj91vd8fIaXX7cUCV5TNjnb4zRVccP1e8XfKyW/e1hnz8AAAAQy2gOAwAAwDGvVNaow291fElOxJYxMq+/xhVm6eF3N0VsGQAAAEAsojkMAAAAR/j9Vr9f8LFmThksY8J/1fChLj1msP769iY17muL6HIAAACAWEJzGAAAAI5YsLpWHX6rY4cNiPiyigekacqwAXr4Ha4eBgAAALrQHAYAAEDUWWt13xvrdeHk4ohfNdzl4snFemRRlZp8XD0MAAAASDSHAQAA4IAlVbtU37xfJ5QMjNoyiwakaeLgbP39vc1RWyYAAADgZjSHAQAAEHUPLNygGaWF8niic9VwlwsnF+lv72ySr60jqssFAAAA3IjmMAAAAKKqqr5F5Zt36bSxeVFf9vDcDA0fmKHnP6yO+rIBAAAAt6E5DAAAgKh6+N1NOnNcvvolJzmy/PMnFer+NzfI77eOLB8AAABwC5rDAAAAiJrm/e167sNqTZ+Q71iGCUVZSk4yemPtTscyAAAAAG5AcxgAAABR89zSbZpYnKXc/v0cy2CM0YyJRXrgrY2OZQAAAADcIKDmsDFmqjEmo/PrLxtj7jHGDA9HAGPMQ8aYncaYih5eN8aYe40x640xK40xx4ZjuQAAAIgua60eWVSlaRMKnI6ik0YM1IadzVq9Y4/TUQAAAADHBHrl8J8l7TXGHC3ph5I2SHosTBkekTTjCK+fJ2lM5+O6ziwAAACIMUuqdqmtw6+jirKcjqLkJI+mTcjX397Z5HQUAAAAwDGBNofbrbVW0iWS7rPW/lFSZjgCWGvfkvTJESa5RNJj9oD3JA0wxhSFY9kAAACInscWV+nM8fkyxjgdRZI0bXyB5lfU6JOWVqejAAAAAI4ItDncZIz5iaQvS3rRGOORlBK5WJ8xWNLWQ77f1vncZxhjrjPGlBtjyuvq6qIUDUC8oIYACAU1pHeftLTqzbV1OnV0ntNRDspKS9HxJTl6askWp6MgwVFDAISCGgIgFIE2h6+UtF/StdbaGklDJN0VsVR9YK190FpbZq0ty8tzz4cOALGBGgIgFNSQ3j23bJuOHZ6j/t5kp6N8xvQJBXps8WZ1+K3TUZDAqCEAQkENARCKgJrD1toaa+09klYYYwZKapb0QkSTfapa0tBDvh/S+RwAAABigLVW/3x/i84Y674PrCPz+ivLm6LXVtc6HQUAAACIuoCaw8aYbxpjaiStlLS081EeyWCHmCfpK+aAkyQ1Wmt3RGnZAAAACNGyLbu0v92v8YVhuWVF2E2bkK9HF1c5HQMAAACIukD/ru8mSROttfXhDmCMeULSGZIGGWO2SfqFOsczttbeL+klSedLWi9pr6Rrwp0BAAAAkfPEB1t12thBrrkR3eFOHJGrf76/RVX1LSoZlOF0HAAAACBqAm0Ob9CBxmzYWWuv7uV1K+k7kVg2AAAAImtva7teqajRnZdPdjpKj1KTPTp9XJ4eW1ylWy8qdToOAAAAEDWB3pDuJ5IWGWMeMMbc2/WIZDAAAADEvpdW1Wh8UaZy0lOdjnJEZ47L17PLquVr63A6CgAAABA1gTaHH5D0uqT39OmYw0sjFQoAAADx4aklWzR19CCnY/SqIMurUXkZemElt7YAAABA4gh0WIkUa+2siCYBAABAXNn6yV59XNus7501xukoATlzfL4eXVSly48b4nQUAAAAICoCvXL4ZWPMdcaYImPMwK5HRJMBAAAgpj2zdJtOHpWr5KRATzmddezQHNXs8amiutHpKAAAAEBUBHqmfrU6xx3Wp0NKlEcqFAAAAGKb32/1zNJtOiUGhpTo4vEYnTkuT4+/t9npKAAAAEBUBNQcttaO6OYxMtLhAAAAEJvKN+9ScpLRiEEZTkcJyhnj8vXSyh1q8rU5HQUAAACIuICaw8aYdGPMLcaYBzu/H2OMuTCy0QAAABCrni7fqqmjBskY43SUoOSkp2rikGzN+bDa6SgAAABAxAU6rMTDklolndz5fbWk2yOSCAAAADHN19ah+ZU1mhpDQ0oc6qxx+Xp08WZZa52OAgAAAERUoM3hUdba/5bUJknW2r2SYusyEAAAAETFqx/ValRefw3MSHU6Sp+UFmfJ19ah8s27nI4CAAAARFSgzeFWY0yaJCtJxphRkvZHLBUAAABi1tPlW3XyqFynY/SZMUZnjc/Xo4uqnI4CAAAARFSgzeFfSpovaagx5h+SXpP040iFAgAAQGyqa9qvZVt26/iSgU5HCcmpY/L05to61TdzPQQAAADiV0DNYWvtvyVdKulrkp6QVGatfSOCuQAAABCD5i2v1nHDc+RNSXI6Skj690vWCSNy9NSSLU5HAQAAACImoOawMeY1a22DtfZFa+0L1tp6Y8xrkQ4HAACA2PLMsm0xPaTEoaaNL9Df39uiDj83pgMAAEB8OmJz2BjjNcYMlDTIGJNjjBnY+SiRNDgaAQEAABAb1tU2aeee/ZpYnO10lLAYmddfWd4Uvb5mp9NRAAAAgIjo7crhb0paKmm8pGWdXy+VNFfSfZGNBgAAgFjyzNJtmjp6kDwe43SUsJk2IV8Pv7vJ6RgAAABARByxOWyt/R9r7QhJN1lrRxzyONpaS3MYAAAAkiS/3+r5D6s1dfQgp6OE1Ukjc7V6xx6t39nsdBQAAAAg7JKP9KIx5tLOL6sP+foga+1zEUkFAACAmPLexgb19yZr2MB0p6OEVUqSR2eOy9cj727S7Z+f5HQcAAAAIKx6G1bioiM8LgxHAGPMDGPMWmPMemPMzd28/jVjTJ0xZnnn4z/DsVwAAACEzzNLt2nqqPi6arjLtAkFmrtiuxr3tTkdBQAAAAirI145bK29JpCZGGO+aq19NNiFG2OSJP1R0tmStklaYoyZZ6396LBJn7LW3hDs/AEAABB5+1o79OpHtbrz8slOR4mIgRmpmjJ0gGYv2aJvnDbK6TgAAABA2PR25XCgvt/HnztB0npr7UZrbaukJyVdEqZMAAAAiIJ/f1SjMQX9lZOe6nSUiDm3tFAPvVul9g6/01EAAACAsAlXc7ivt6QeLGnrId9v63zucJcZY1YaY54xxgztNoAx1xljyo0x5XV1dX2MAyBRUUMAhCLRa8jT5dt0cpwOKdFlVN6B5vcrlbVOR0EcSvQaAiA01BAAoQhXc9iGaT7d+ZekEmvtZEmvSup2+Apr7YPW2jJrbVleXl4E4wCIR9QQAKFI5Bqyc49Py7fuVllJjtNRIm5GaaH+vHC9rI3kqS8SUSLXEACho4YACIXTVw5XSzr0SuAhnc8dZK1tsNbu7/z2r5KO6+OyAAAAEGbPf1itE0bkqF9yktNRIu644Tn6pKVVS6p2OR0FAAAACItwNYff7ePPLZE0xhgzwhiTKukqSfMOncAYU3TItxdLWt3HZQEAACCMrLV6euk2TY3zISW6eDxG508s0h/fWO90FAAAACAsko/0ojFm1pFet9be0/nvDX1ZuLW23Rhzg6RXJCVJeshaW2mMuU1SubV2nqTvGWMultQu6RNJX+vLsgAAABBeldv3qMnXpvFFWU5HiZpTx+TpuQ+rtaZmj8YXJs77BgAAQHw6YnNYUmakA1hrX5L00mHP3XrI1z+R9JNI5wAAAEBwni7fqlNGD5LH9HWEsdiTmuzRjNIC3ff6et33xWOdjgMAAACE5IjNYWvtr6IVBAAAALGjtd2veSu26xcXlTodJeqmTyjUjbOXa2Nds0bm9Xc6DgAAANBnAY05bIwZa4x5zRhT0fn9ZGPMLZGNBgAAALd6fc1OFQ9IU0GW1+koUZeWmqRzjirQva8x9jAAAABiW6A3pPuLDgzt0CZJ1tqVOnDzOAAAACSgp5Zs0SmjE+NGdN2ZMbFQb6zdqQ11zU5HAQAAAPos0OZwurX2g8Oeaw93GAAAALhfXdN+LanapRNH5DodxTHpqck6b2Kh7vn3WqejAAAAAH0WaHO43hgzSpKVJGPM5ZJ2RCwVAAAAXOu5ZdtUVpKjtNQkp6M46tzSQi3e+IkqqhudjgIAAAD0SaDN4e9IekDSeGNMtaTyfSI/AAAgAElEQVQfSLo+YqkAAADgStZaPblkq04fk+d0FMd5U5I0c0qxfvPSaqejAAAAAH3Sa3PYGJMk6dvW2umS8iSNt9aeYq3dHPF0AAAAcJVlW3arrcOvcYWZTkdxhTPH52tzw169sWan01EAAACAoPXaHLbWdkg6pfPrFmttU8RTAQAAwJWeeH+LTh0zSMYYp6O4QrLHoy+eOEy/+lelWtv9TscJmrVWfr91OgYAAAAckhzgdB8aY+ZJelpSS9eT1trnIpIKAAAArtPka9MrlTX678snOx3FVY4ZOkCvrd6pv769Ud8+c7TTcXq0t7Vdb6+r1zvr6rVqW6M2f9Kixn1t8lspLSVJBVn9dFRRlj43epCmT8hXUXaa05EBAAAQYYE2h72SGiSddchzVhLNYQAAgAQxb8V2lQ7O0oD0VKejuIoxRl/53HD9Yl6lLjq6WEMHpjsd6TOWbt6lRxdV6bU1tRqTn6mjirJ00ZRiFWV7leVNkcdIvja/6pr3a1N9ixZ8VKu75q/R5CHZ+sZpo3QaV4oDAADErYCaw9baayIdBAAAAO72j/e26KKji52O4UoFWV5dMKlQP3pmhZ74xkmuaKa+u75ed/97rbbv9unsowp0zxemKCstpdtp01KTNGxguoYNTNfpY/PU2u7X4o0N+vmcCuX2T9UvLyrV0UMHRPkdAAAAINICag4bY7ySrpVUqgNXEUuSrLVfj1AuAAAAuMjKbbvV0LJfk4dkOx3Ftc6fVKzbXqjU44s36ysnlziWY21Nk277V6U2NbTo88cM0ckjc+XxBNesTk326PSxeTp19CC9ta5O1zyyRBdNLtLN501QWmpShJIDAAAg2nq9IV2nxyUVSjpX0kJJQyRxYzoAAIAE8djizTprfL48Lrgi1q2SPEbfPG2U7n71Y62tif6pcpOvTb+cV6krH1isMQWZuvOyyTpl9KCgG8OH8niMzhiXrzsvnayN9S06/963taZmTxhTAwAAwEmBNodHW2t/LqnFWvuopAsknRi5WAAAAHCLxn1tml9Ro9PG5DkdxfWKB6Tp6hOG6vq/L1WTry1qy/13ZY2m371QWz5p0Z2XT9a5pYVK9gR6qt+7/t5kffuM0TpvYqGufOA9/WvF9rDNGwAAAM4J9IZ0XWe2u40xEyXVSMqPTCQAAAC4yTPlWzVl6ABuRBeg08fma0Ndi77/5HL95StlSgrhyt3e1DXt18/nVGhVdaOuO22kjiqO7LAfp47J09CB6br9xY+0qa5F35022hXjKwMAAKBvAr2c4EFjTI6kn0uaJ+kjSXdGLBUAAABcwe+3enTxZk2fUOB0lJjylZOGq65pv26dWyFrbdjnb63V0+Vbdc7vF8qb4tFvPj8p4o3hLiW5GfrFRaWau6JaP31+lTr84X9/AAAAiI6AmsPW2r9aa3dZaxdaa0daa/OttQ+EI4AxZoYxZq0xZr0x5uZuXu9njHmq8/X3jTEl4VguAAAAevfWujole4zGFvR3OkpMSU7y6AfTx+i9jQ267YWPwtog3lTfoqv/8p7uX7hBPzp3vK48fphSk8M3hEQgctJT9dPzJ2hVdaN+8OSHau/wR3X5AAAACI+AziKNMbnGmP81xiwzxiw1xvzBGJMb6sKNMUmS/ijpPElHSbraGHPUYZNdK2mXtXa0pN+LK5YBAACi5m/vbNLZRxUwdEAfpKcm6+YZE/TOunrNmr1C+9s7Qppfy/52/ff8NZr5x3c1Jj9Tv7p4okYMyghT2uClpybrR+eM19Zd+/TdJ2gQAwAAxKJALzF4UtJOSZdJulxSvaSnwrD8EyStt9ZutNa2di7nksOmuUTSo51fPyNpmuHTCQAAQMRtrGtWRXWjTh41yOkoMau/N1k/PX+Ctu/ep8v+vEgb65qDnsf+9g79/b0qnX7XG1pV3ahfz5yo8ycVRXQs40ClJnt04/Sx2tHo06zZKxhiAgAAIMYE2hwustb+l7V2U+fjdknhGHhusKSth3y/rfO5bqex1rZLapQU8lXLAAAAOLK/vr1JZ47Lj/qQBfHGm5Kk708bo7LhAzXzj+/qzpfXqKF5f68/V9e0X396Y71OvfMNPbesWjdOH6tvnzFauf37RSF14LoaxBvrmvWz51dFZIxlAAAAREZygNP92xhzlaTZnd9fLumVyETqG2PMdZKuk6Rhw4Y5nAZArKGGAAhFPNaQXS2tmrdiu+66fLLTUeKCMUbnlhaqbHiO5iyv1ul3vamTR+XqtLF5GluQqQHpKWpt96t2j08V1Y16Z329Vu9o0gkjcvSD6WMdHT4iEKnJHt149ljd8fIa3fXKWv2/GeOdjhRT4rGGAIgeagiAUJhAfrNvjGmSlCGpayAxj6SWzq+ttTarTws35nOSfmmtPbfz+590zvCOQ6Z5pXOaxcaYZEk1kvLsEYKXlZXZ8vLyvkQCEN8C+vtbagiAHiRUDbl3wTot37ZL3zh1lNNR4lKTr01LN+/Sx7VNqtmzXy3725XkMcpJT9HgAWkaX5Sl0uIs9UtOcjpqUPbsa9NtL3ykb5w6Ql+bOsLpOG6TUDUEQNhRQwCEoscaEtCVw9bazPBl+YwlksYYY0ZIqpZ0laQvHjbNPElflbRYB65Yfv1IjWEAAACExtfWoUcWbdJPzp/gdJS4lelN0Rnj8nXGuHyno4RVVlqKfjxjnH71r49UmO3VjIlFTkcCAADAEQQ6rISMMZdKOkWSlfS2tXZOqAu31rYbY27QgSEqkiQ9ZK2tNMbcJqncWjtP0t8kPW6MWS/pEx1oIAMAACBCZi/ZqlH5/TUkJ93pKIhBeZle/fCccbr5uVXKy/TquOE5TkcCAABADwJqDhtj/iRptKQnOp+63hhztrX2O6EGsNa+JOmlw5679ZCvfZK+EOpyAAAA0Lu2Dr/uX7hB3zyd4STQdyMGZei6U0fqusfK9dy3T9bwXHePmQwAAJCoAr319FmSzrXWPmytfVjS+Z3PAQAAII7M+bBagzL7aWxBpEYVQ6I4ZliOLjmmWF996APt3tvqdBwAAAB0I9Dm8HpJh97ycmjncwAAAIgT7R1+/e/r63XR5GKnoyBOnD2hUKWDs/XNx5eqtd3f+w8AAAAgqgJtDmdKWm2MedMY86akjyRlGmPmGWPmRSwdAAAAombu8u3K9CartDjL6SiII188fpg6rNXPnl8l7isNAADgLoHekO7WQ742kk7VgRvD/SLsiQAAABB1bR1+/X7Bx7pm6ggZY5yOgzji8Rh954zR+q8XPtL9CzfoW2eMdjoSAAAAOgV05bC1dqGkPZIulPSIDow3fL+1dmHnawAAAIhhs5ds1cCMVB1VxFXDCD9vSpJmnT1Wf3tnk+ZX1DgdBwAAAJ2OeOWwMWaspKs7H/WSnpJkrLVnRiEbAAAAomBfa4f+sGCdvj99jNNREMdy+/fTjdPH6sfPrtTgAWmaNCTb6UgAAAAJr7crh9fowFXCF1prT7HW/q+kjsjHAgAAQLT89e2NGl3QX6Py+jsdBXFuZF5/XTt1hK59dImqd+9zOg4AAEDC6605fKmkHZLeMMb8xRgzTQfGHAYAAEAc2Nnk01/e3qgry4Y6HQUJ4vgRA3VOaYG+9tAHavK1OR0HAAAgoR2xOWytnWOtvUrSeElvSPqBpHxjzJ+NMedEIyAAAAAi586X1+j0cXkqyPI6HQUJ5PyJRRo5KEPXP75UbR1+p+MAAAAkrEBvSNdirf2ntfYiSUMkfSjpxxFNBgAAgIj6cMsuvbm2TjOnDHY6ChKMMUb/8bkS+dr9+slzq2StdToSAABAQgqoOXwoa+0ua+2D1tppkQgEAACAyGvv8Otnz1foyuOHKj31iPcoBiIiyWN0w5mjtWLrbt3z6sdOxwEAAEhIQTeHAQAAEPseXVSlJI/RKaMHOR0FCcybkqQfnjNOzy7bpr+/V+V0HAAAgIRDcxgAACDBbP1kr+59fb2uOblExnCvYTgrOy1F/+/c8fr9q+v0wsrtTscBAABIKDSHAQAAEojfb3XT0yt04eQiFQ1IczoOIEkqyPLqR+eO08/nVOiNtTudjgMAAJAwaA4DAAAkkL+9s0mN+9p0/sQip6MAnzE8N0M3Th+rG59arkXr652OAwAAkBBoDgMAACSIyu2Nuu+N9br+9FHyeBhOAu4zpiBT3z1rjL79j2V6b2OD03EAAADiHs1hAACABNDka9P1f1+q/zhpuAqyvE7HAXp0VFGWvnPmaF3/+FIt2sAVxAAAAJHkWHPYGDPQGPOqMWZd5785PUzXYYxZ3vmYF+2cAAAAsc7vt5r11AqNL8zS1NGDnI4D9Gri4GzdcNZofevvyxiDGAAAIIKcvHL4ZkmvWWvHSHqt8/vu7LPWTul8XBy9eAAAAPHhDws+1tZde/UfJw13OgoQsNLibM06e6xmPbVcc5dXOx0HAAAgLjnZHL5E0qOdXz8qaaaDWQAAAOLSM0u36qnyrfr+tDFKSWJEMcSWsQWZuvm8Cbr9hY/0wMINstY6HQkAACCuOPkJocBau6Pz6xpJBT1M5zXGlBtj3jPG9NhANsZc1zldeV1dXdjDAohv1BAAoXBrDXl9Ta1+/eJq/eic8RqQnup0HKBPhg1M1y8uKtUTH2zRzc+tUluH3+lIYefWGgIgNlBDAITCRPK378aYBZIKu3npZ5IetdYOOGTaXdba/zPusDFmsLW22hgzUtLrkqZZazccabllZWW2vLw8xPQA4pAJZCJqCIAexFQNeXtdnb77xIf64dljNTo/0+k4QMj2tXboT2+ulyT9+cvHKS+zn8OJghZTNQSA61BDAISixxoS0SuHrbXTrbUTu3nMlVRrjCmSpM5/u73ThLW2uvPfjZLelHRMJDMDAADEutfX1Oq7T3yo708bQ2MYcSMtNUk3nj1WJbkZuuDet/X+xganIwEAAMQ8J4eVmCfpq51ff1XS3MMnMMbkGGP6dX49SNJUSR9FLSEAAECMeWrJFv1w9gr98OyxGl+Y5XQcIKw8xuiy44bomqkj9K1/LNPvXlkbl8NMAAAARIuTzeHfSjrbGLNO0vTO72WMKTPG/LVzmgmSyo0xKyS9Iem31lqawwAAAIdp7/Dr9hc+0h8WrNPPLjiKK4YR16YMHaDbZ07Uog31uvi+d1S5vdHpSAAAADEp2akFW2sbJE3r5vlySf/Z+fUiSZOiHC3h+P1WVQ0tqt3jU0GWVyW5GfJ4eh7OqKfp29v9qtzRqB2NPhVlp6m0KEvJycH//iHYPJGef6TzAPEgEvtJa2uHVm5vVM0en4qyvJpUnK3U1CTX5YyVeSK+bd+9T9974kO1+61+dXGpMr0pTkcCIi4nPVU3nTNOb62r05f+8r4uO26IfjB9DNs/PqO38wmOuZ/qbV2E+joQL4LZ1oPtk8TqfhTpPkusrpdY4VhzGO7g91vNr6zRrNnL5Wvzy5vi0T1XTNGM0sJud7Sepp8+Ll/zVm3XLXMqDj5/+8yJmnn04KAaxMHmcer9hisPEA8isZ+0tnZozsrtunXupzXltksmaubk4j43iCORM1bmifhlrdUzS7fpNy+t1rmlhbpocjHbCRKKMUanj83XlKE5emrJFp1x15uadfZYXXH8UKUkOflHknCD3s4nOOZ+qrd1EerrQLwIZltvb/drzorqgPsksbofRbrPEqvrJZZwxpTgqhpaDu5gkuRr82vW7OWqamgJavqV2xsPFryu52+ZU6HKHcH9iV+weYIVrvcbrjxAPIjEfrJye+PBD3Jd87x1boVWhvBnw5HIGSvzRHyq3N6oL9y/WPcv3KAfnTtel0wZzAkyElZ2WoquO22UZp09VrPLt+rM372pp5Zs0f72DqejwUG9nU9wzP1Ub+si1NeBeBHMtl65I7g+SazuR5Hus8TqeoklNIcTXO0e38EdrIuvza+dTb6gpq/p6fnG7ucTrjzBCtf7DVceIB5EYj/pqabU7un7PCORM1bmifjycW2TbvjnMn35r+9r8tBs3XbxRI0YlOF0LMAVRub1183nTdDXp47QEx9s1Sm/fUP3LlhHDU1QvZ1PcMz9VG/rItTXgXgRzLa+ozG4Pkms7keR7rPE6nqJJQwrkeAKsrzypng+s6N5UzzKz/QGNX1hT89ndz+fcOUJVrjeb7jyAPEgEvtJUQ/zLMjq+zwjkTNW5onY197h15tr6/TIoipVbG/UjNJC3f2FKUoLcRxuIF5NKMrShKIsbW5o0YLVtXrwdxt1fEmOLjtuiM4an6/0VD4GJYLezic45n6qt3UR6utAvAhmWy/KTguqTxKr+1Gk+yyxul5iCVcOJ7iS3Azdc8UUeVMObApdY7eU5HZ/BVJP008uztbtMyd+5vnbZ05UaVF2RPMEK1zvN1x5gHgQif1kUnG2brvkszXltksmanJxcDUl0jljZZ6ITa3tfr2zrl4/e36VTvj1At3177UqLc7S/1x5jC6ZMpjGMBCA4bkZuvaUkbr3qmM0rjBLf3t7k8puX6CvPfSBHl9cpY11zbLWOh0TEdLb+QTH3E/1ti5CfR2IF8Fs66VFWUH1SWJ1P4p0nyVW10ssMfF4MlRWVmbLy8udjhEzuu76uLPJp/zMwO8Sefj0XXfhrGn0qTDbq9Ki7KBuRtfXPJGef6TzIKoC+o+jhgQvEvtJ193Fu+5IO/mwu4u7JWeszBNhEdEasntvqyqq9+jDrbv0/sZPtGzLLg3JSdexwwbohBEDVZSdFvQ8AfxfzfvbtWLrblVUN6py+x75rdUxw3J07PABKi3O1vjCTOVn9pMxYa+7nIc4oLfzCY65n+ptXYT6OkJGDXGJYLb1YPsksbofRbrPEqvrxWV6XGE0hwEkEk6oAIQiIjXkR0+v0Jtr69TS2q4RgzI0YlCGxuRnakJRpjK9KX0OC6B31lrVN+/Xx7XN2lTfoq279mpLw161+62G5aZr+MB0DR2YruJsrwqzvRrUv58GZqRqYEaqMr0pSgrugynnIQBCQQ0BEIoeawiDbQEAADho4cd1Ki3O0uCcNJnOc7a6pv2qa9rvcDIgseSkpyonPVWTBw9Q8/427Wj0aU1Nk95eV6/m/e3d/kzVby+IckoAAIDwissrh40xdZI2O50jCgZJqnc6hAuwHlgHXXpbD/XW2hm9zSQCNcQt/z/kcFcGiRxuyyAdOUdEakjWSVfkJecU9gt0+j7paE9TUvK+iC6jr8gWPLfmkhImm5ExHS2ftO5+6/HaIH4s2Brilrp4KDIFhkyBIVNgujI59VnmSJmc5IYMEjnclkEiR08ZeqwhcdkcThTGmHJrbZnTOZzGemAddHHrenBLLnK4KwM53JfBTTnCzc3vi2zBc2suiWzh5Ma8ZAoMmQJDpsCQyb0ZyOG+DOToW4bg7xYGAAAAAAAAAIh5NIcBAAAAAAAAIAHRHI5tDzodwCVYD6yDLm5dD27JRY5PuSGDRI5DuSGD5J4c4ebm90W24Lk1l0S2cHJjXjIFhkyBIVNgyNQ9N2SQyHEoN2SQyHGogDIw5jAAAAAAAAAAJCCuHAYAAAAAAACABERzGAAAAAAAAAASEM1hAAAAAAAAAEhANIcBAAAAAAAAIAHRHAYAAAAAAACABERzGAAAAAAAAAASULLTASJhxowZdv78+U7HAOA+JpCJqCEAekANARAKagiAUFBDAISixxoSl1cO19fXOx0BQAyjhgAIBTUEQCioIQBCQQ0BEKy4vHIYOBK/36qqoUW1e3wqyPKqJDdDHk9Av4QFgKigTgHdY98AAADxjHMdOIHmMBKK3281v7JGs2Yvl6/NL2+KR/dcMUUzSgspuABcgToFdI99AwAAxDPOdeCUuBxWAuhJVUPLwUIrSb42v2bNXq6qhhaHkwHAAdQpoHvsGwAAIJ5xrgOncOUwEkrtHt/BQtvF1+bXziafRub1dygVAHyKOgV0j30DQKhWbWvUgtU12lS/VylJRpOHDNB5kwqVn+l1OhoAcK4Dx3DlMBJKQZZX3pTPbvbeFA8nhABcgzoFdI99A0Bfbahr1pUPLNZ/PrZEVfV7VTwgTQPSU/XG2p0663cL9ct5lWrZ3+50TAAJjnMdOIXmMBJKSW6G7rliysGC2zWGT0luhsPJAOAA6hTQPfYNAH0xv2KHLvvTIo0vytQ9X5iiq04YptPH5mn6hAJ987RRuvsLR2tTfbMuuPdtbarnT7cBOIdzHTiFYSWQUDweoxmlhRr/vVO1s8mn/Ezu/gnAXahTQPfYNwAE65nyrbrj5TX6fzPGa8Sg7psrWWkpuv700Xptda0u//MiPXHdSRpbkBnlpADAuQ6cQ3MYCcfjMRqZ158xewC4FnUK6B77BoBAvb6mVne8vEY/OW+CBuek9Tr9tAkF8qYm6Ut/eV/Pf+dkDclJj0JKAPgsznXgBIaVAAAAAADEjQ11zZo1e4W+N21MQI3hLlNHDdJ5kwr19UeWMAYxACBh0BwGAAAAAMSF/e0d+s4/lumyY4f0aXiIGaWFKh6QplvnVkQgHQAA7uNoc9gY85AxZqcxptsjrzHmDGNMozFmeefj1mhnROD8fquNdc1avKFeG+ua5fdbpyMBQMRR+4DoYX8D0Js/vLpOmd5kTRuf36efN8boq58r0XsbP9GLK3eEOR2ARMO5C2KB02MOPyLpPkmPHWGat621F0YnDvrK77eaX1mjWbOXy9fmP3hXzRmlhQyeDiBuUfuA6GF/A9Cb1Tv26IkPtuiOSyfJmL7XBW9Kkq4/faRunVuhk0flKicjNYwpASQKzl0QKxy9ctha+5akT5zMgPCoamg5WPAkydfm16zZy1XV0OJwMgCIHGofED3sbwCOxFqrm59bpcvLhmhAeujN3NH5mTppVK5ue+GjMKQDkIg4d0GsiIUxhz9njFlhjHnZGFPa00TGmOuMMeXGmPK6urpo5oOk2j2+gwWvi6/Nr51NPocSAcGhhqAvqH3oQg2JPPY3xDNqSOjmrdiuZl+bzhzXt+EkunPZMUP09ro6Ld3M9UxwN2qIO3Hugljh9ubwMknDrbVHS/pfSXN6mtBa+6C1tsxaW5aXlxe1gDigIMsrb8pnNydvikf5mV6HEgHBoYagL6h96EINiTz2N8QzakhofG0duuOlNfrSicPlCWE4icOlpSbpyuOH6ZY5FYwTClejhrgT5y6IFa5uDltr91hrmzu/fklSijFmkMOx0I2S3Azdc8WUg4WvayydktwMh5MBQORQ+4DoYX8D0JN/vr9ZQwemaUJRVtjnPXVUrto7rOat2B72eQOIb5y7IFY4fUO6IzLGFEqqtdZaY8wJOtDMbnA4Frrh8RjNKC3U+O+dqp1NPuVnelWSm8Eg6wDiGrUPiB72NwDd2dvarj++sUE3nTsuIvM3xujqE4bpzvlrdN6kQvVLTorIcgDEH85dECscbQ4bY56QdIakQcaYbZJ+ISlFkqy190u6XNK3jDHtkvZJuspay9/zuJTHYzQyr79G5vV3OgoARA21D4ge9jcAh/vHe5s1pqB/RK/Em1CUpeIBaXrygy366skjIrYcAPGHcxfEAkebw9baq3t5/T5J90UpDgAAAAAgRvjaOvTgW5s065yxEV/WpccM1h8WrNOVxw+TN4WrhwEA8cPVYw4DAAAAANCdZ5dt0/Dc9KiM33ngyr8M/fP9LRFfFgAA0URzGOiF32+1sa5ZizfUa2NdM3cqBqKEfQ/A4agLALr4/VYPLNyo8ycVRW2ZFx9drPsXbtD+9o6oLRMAwDlgpLn6hnSA0/x+q/mVNZo1e7l8bf6DdxedUVrIIPJABLHvATgcdQHAoRasrlW/ZI/GF2ZGbZkj8/prSE66nl1arS+eOCxqywWARMY5YORx5TBwBFUNLQcLkCT52vya9f/Zu/P4qOp7/+Pv72SSDNlJyAYEQtgJm4ILgriB4lKlraL2tmqrtbVa22IX29rbW6/9dafWXrvYq1ZrteKGO25V3EBZZBdZww4JAZIQGEgy398fJLkBE8gwZ+acybyej8c8nJlMzvedcb6fc/jk5HtmLlZFdb3LyYCujbkH4GjUBQBt3ffOek0ZXiRjYtsY+MzIYv15zlo1cdYaAMQEx4DRR3MYOIadtcHWAtQi2BBSZV3QpURAYmDuATgadQFAi4+316piV71O7Zcb87EHF2UqPcWv2ct3xHxsAEhEHANGH81h4BgKswIKJB85TQLJPhVkBlxKBCQG5h6Ao1EXALR44N0NOndIgfy+2P9z1hiji0YU689vrZW1nD0MANHGMWD00RwGjqE0L10zpo1uLUQta9vE4orIQCJj7gE4GnUBgCTVHGjQy8t36JzBBa5lGNOnu3bvP6T5FXtcywAAiYJjwOjjgnTAMfh8RlPKizTk1jNVWRdUQWZApXnpLHoORBlzD8DRqAsAJOmphVs0uiRHOWkprmVoqUd/nbPOlaUtACCRcAwYfTSHgePw+YzK8jNUlp/hdhQgoTD3AByNugAkNmut/jFvo750el+3o2jioHx961+LtbG6Xn05ew0AoopjwOhiWQkAAAAAgOfNr9ijxqaQhhRluh1Fqf4knT04Xw++t8HtKAAARITmMAAAAADA8/75wUadPbhAxnjjT4knDy3U04u2qi7Y4HYUAABOGM1hAAAAAICn1Rxo0BsfV2rCwB5uR2mVl5Gq4b2y9eTCLW5HAQDghNEcBgAAAAB42nNLtmlk72xlBZLdjnKEycMK9ff3KhQKWbejAABwQmgOAwAAAAA87V8fbtLEgflux/iUwYWZSvIZvb2myu0oAACcENebw8aYB4wxlcaY5R183Rhj7jHGrDXGLDXGnBzrjAAAAAAAd6zaUavK2qBG9Mp2O8qnGGM0aVihHnyvwu0oAACcENebw5L+LmnKMb5+oaSBzbcbJf05BpkAAAAAAB7w+PzNmjCgh3w+b1yI7mjj+/fQ4s17tbG63u0oAACEzfXmsLX2bUm7j3oyvQYAACAASURBVPGSyyQ9bA+bJynHGFMcm3QAAAAAALc0NIX07OJtOtODS0q0SPH7dNagfD08d6PbUQAACNsJNYeNMd2NMSOdDtOBXpI2t3m8pfm5ozPdaIxZYIxZUFXFek8AwkMNARAJagiASFBDOvb26ioVZKaqOKeb21GO6bwhBXpy4RYdONTkdhQkIGoIgEh0ujlsjHnLGJNljMmVtEjS34wxM6IXLTzW2vustWOttWPz8737W2UA3kQNARAJagiASFBDOjZzwWaNH9DD7RjHVZAV0MCCDD23ZKvbUZCAqCEAIhHOmcPZ1tpaSZ/T4WUeTpM0KTqxjrBVUkmbx72bnwMAAAAAdFE1+xv07ppdOr0sz+0onXLe0MMXprPWuh0FAIBOC6c57G9e63eapBeilKc9z0m6xhx2uqQaa+32GI4PAAAAAIixF5dt18iSHGWk+t2O0ikje2erNtigRZv2uh0FAIBOC2cve6ekVyS9a62db4wpk7Qm0gDGmMcknS2phzFmi6SfSkqWJGvtXyS9JOkiSWsl7Zf05UjHBAAAAAB42xMLN+vcIQVux+g0nzE6b0ihHnxvg8b07e52HAAAOqXTzWFr7ROSnmjzeL2kz0cawFp79XG+biXdHOk4AAAAAID4sHn3fq2vqtf0STluRwnLxEH5+s7ji1VVd1D5maluxwEA4Lg63Rw2xvST9E1JpW2/z1p7qfOxAAAAAACJatbirTq9LFf+pHBWQnRfRqpfp5fl6rEPNunWSQPdjgMAwHGFs6edJalC0h8l/a7NDQAAAAAAR1hr9dTCrTqjfw+3o5yQSUML9cgHG9XQFHI7CgAAxxXOmsNBa+09UUsCAAAAAEh4y7fW6mBjkwYWZLgd5YT0zUtXfmaqXlmxQ5eM7Ol2HAAAjimcM4f/YIz5qTFmnDHm5JZb1JIBAAAAABLOU4u26Iz+eTLGuB3lhE0eVqgH3t3gdgwAAI4rnDOHR0j6kqRzJbX8fYxtfgwAAAAAQEQam0J6bsk2/fiioW5HicjYvrn65webtHxrjYb3ynY7DgAAHQqnOXyFpDJr7aFohQEAAAAAJK7311UrLz1FPXO6uR0lIkk+o0lDC3T/u+v1+ytPcjsOAAAdCmdZieWScqIVBAAAAACQ2J5etEXj+ue5HcMR5w4u1GsrK1VVd9DtKAAAdCic5nCOpFXGmFeMMc+13KIVDAAAAACQOA4catIbH1dqXFnXaA5nBPwa1z9PD8+tcDsKAAAdCmdZiZ9GLQUAAAAAIKG9/vFO9S/IUE5aittRHHNBeZF+/uJK3XzOAAWSk9yOAwDAp3T6zGFr7RxJFZKSm+/Pl7QoSrkAAAAAAAnk6UVbusxZwy165XRT//wMPb1oq9tRAABoV6ebw8aYr0p6UtJfm5/qJWlWNEIBAAAAABLHnvpDml+xR6eU5rodxXEXDi/SfW+vUyhk3Y4CAMCnhLPm8M2SxkuqlSRr7RpJBdEIBQAAAABIHC8u265RJdnqltL1ll4YWpyl5CSfXv94p9tRAAD4lHCawwettYdaHhhj/JL41ScAAAAAICJPLdyicWU93I4RFcYYXTSiWPe+uVbW8k9oAIC3hNMcnmOM+ZGkbsaYyZKekPR8dGIBAAAAABLB5t37tX5XvUb1znY7StScWpqrXfsO6YMNu92OAgDAEcJpDt8uqUrSMklfk/SStfbHkQYwxkwxxnxijFlrjLm9na9fZ4ypMsYsbr7dEOmYAAAAAABvmLV4q07vlyt/Ujj/PI0vPp/RxSOLdc8ba9yOAgDAEcLZ+37TWvs3a+0V1trLrbV/M8Z8K5LBjTFJku6VdKGkYZKuNsYMa+elj1trRzff/jeSMQEAAAAA3mCt1dOLtmpc/665pERbZw7oobWV+/TRpj1uRwEAoFU4zeFr23nuugjHP1XSWmvt+ub1jP8l6bIItwkAAAAAiAPLt9Yq2NCkQYUZbkeJOn+ST58Z1VMzXlvtdhQAAFodtzlsjLnaGPO8pH7GmOfa3N6UFOmCSb0kbW7zeEvzc0f7vDFmqTHmSWNMSQc5bzTGLDDGLKiqqoowFoBEQw0BEAlqCIBIJHINeWrRFp3RP0/GGLejxMRZg/K1akedFnH2MByUyDUEQOQ6c+bw+5J+J2lV839bbrdJuiB60Vo9L6nUWjtS0muSHmrvRdba+6y1Y621Y/Pz82MQC0BXQg0BEAlqCIBIJGoNaWwK6fkl2zQ+AZaUaJGc5NNlo3vqVy+vcjsKupBErSEAnOE/3gustRslbZQ0Lgrjb5XU9kzg3s3PtR2/us3D/5X06yjkAAAAAADE0Dtrd6lHRqqKc7q5HSWmzh5UoJeX7dC7a3ZpwsDEaYwDALypM8tK1Bljatu51RljaiMcf76kgcaYfsaYFElXSXruqPGL2zy8VNLHEY4JAAAAAHDZEws264wBeW7HiLkkn9HlY3rrrhdXKhSybscBACS44zaHrbWZ1tqsdm6Z1tqsltcZY7qHO7i1tlHSLZJe0eGm70xr7QpjzJ3GmEubX3arMWaFMWaJpFsV+UXwAAAAAAAuqgs26O3VuzSuLPGaw5J0Wr9cWUlPf7T1uK8FACCajrusRBjekHRyuN9krX1J0ktHPfefbe7/UNIPI04HAAAAAPCEl5ZtV3nPLGUGkt2O4gpjjL5wah/96uVVuqC8MGHfBwCA+zpzQbrOSozLywIAAAAAIvL4/M2aMCCx19sdVJip8l5Zuvv1NW5HAQAkMCebwyyWBAAAAAA4po3V9VpfVa/RJTluR3HdlWNL9NTCLVq5LdLL+QAAcGKcbA4DAAAAAHBMTy7YojMG5MmfxD9Hc9JSNO2UEn33iSVqbAq5HQcAkIBYVgIAAAAAEBNNIauZCzdr4sB8t6N4xtmD8pWcZPSnN9e5HQUAkICOe0E6Y8xLkh6VNMtau+8YLz3PsVQAAAAAgC7n/XW7lJHqV9+8dLejeIYxRl89s0x3zFquiYPzPbXcxsHGJm3Zc0BVdQd1sDGk5CSj/IxUleSmKZCc5HY8AIADjtsclvRXSVdJmmGMeUvSY5JetNYeavsia+1u5+MBAAAAALqKxz7cpDM5a/hT8jJS9eXx/fSNfy7Uy7dOVHZasis5GptC+mDDbr22cqfmrqvWhup69UhPUU5ailL9PjU0hbT3QIMq6w6qrEe6zhqUr8+M6qnhvbJdyQsAiNxxm8PW2mclPWuMSZP0GUnXSPqzMeZlSY9aa1+LckYAAAAAQJzbU39Ib6/epd9PG+12FE86tV+u1lTW6aZ/LtRDXzlVyTFck3l91T49Mm+jZn20VT0yUzW6JEdfOK2PSvPSleL/dI6GppDWV9Vr8eY9uv6h+eqRkaqbzxmgKeVF8vlYcRIA4klnzhyWJFlr90t6XNLjxpiRkh7S4UYxf0sCAAAAADimZz7aqpP65Cgj0Ol/hiacq07poxmvfaIfP7NMv/r8SBkTvUartVbz1u/Wn95aq+Vba3TWoHz95JJyFWUHjvu9yUk+DS7K1OCiTF0xpkSLNu/R719brXvfXKs7LxuuMX27Ry03AMBZnd4rG2MKJU3T4SUmiiXNlHRddGIBAAAAALoKa60e/XCTrjqlxO0onpbkM7rlnIH65eyPdecLK/WflwxzvEFsrdWc1VW6+/U1qqo7qAtHFOmGCWXtniHcGT6f0di+uTq5T3e9v65aNz68QJeMLNYPLxrKusQAEAc6c0G6r0q6WtJgSU9J+p619v1oBwMAAAAAdA2LNu3RgUNNGlac5XYUz+uWkqTvnT9Ev3pllW5/apl+/tnh8juwxIS1Vm9+Uqm7X1ujvQcadNnonjq9X55jy0D4jNGEAT00uiRHf3+/Qhfd847u+9JYDSjIcGT7AIDo6MweZpykX0gqsdbeSmMYAAAAABCOR+Zt1NmD86O6TEJXkhHw60cXDtXqyjp94W/zVFkbPOFtNYWsXlq2XRfd845+9vxKnT24QL/43Aid0b9HVNYHzkj16+az+2vS0EJd/pf39drKnY6PAQBwTmcuSPeVlvvGmF6S+rb9Pmvt29GJBgAAAACIdzX7G/Taykr97opRbkeJK91SkvTdyYP1zOKtuuDut3Xb+YN05Sl9On2huj31h/Tkws36+/sblZHq18UjizWmb3f5YtCgN8bonMEFKumeph8+vVQVu/rrqxPLoj4uACB84aw5/CtJV0paKamp+WkrieYwAAAAAKBdTyzcrJP75CirW7LbUeKOz2f0+ZN7a0zf7nrsg0269811+o/T+uiC8iL1z8844szfUMhq0+79mru+Wq8s36H5Fbs1pjRXN04s06DCTFfyDyjI0E8/U65fz16lqrqgfnjRUM4eBwCPCecysVMlDbbWHnQygDFmiqQ/SEqS9L/W2l8e9fVUSQ9LGiOpWtKV1toKJzMAAAAAAJwXClk9PHejrp/Qz+0oca00L10/vGio1lXt0zurq/TIvI2qO9iooqyAUv1JOtDQpJ21QaWn+jWkKFMje+foS+P6Ki0lnH/yR0ePjFT95JJh+s0rn2jfwSbdNXV4VJazAACcmHD2FOslJUtyrDlsjEmSdK+kyZK2SJpvjHnOWruyzcuul7THWjvAGHOVpJYzmAEAAAAAHvbu2l3y+4wGclEyR/TPz1D//MPv5b5go6rrD6qhySrV71NueorSU91vBrcnM5Cs2y8cot+88ol+9Mwy/eJzIziDGAA8IpxLnu6XtNgY81djzD0ttwjHP1XSWmvtemvtIUn/knTZUa+5TNJDzfeflHSeYS8CAAAAAJ53/7sbNGlYIY3AKMgI+NU3L10DCjJUkpvm2cZwi7QUv75/wRAt3rxXP31uhay1bkcCACi85vBzkv5b0vuSFra5RaKXpM1tHm9pfq7d11hrGyXVSMo7ekPGmBuNMQuMMQuqqqoijAUg0VBDAESCGgIgEl21hlTsqteSzXs1vn8Pt6PAI7qlJOl7FwzWe2t36TevfOJ2nC6jq9YQALFx3OawMaaPJFlrH2rvFv2InWOtvc9aO9ZaOzY/P9/tOADiDDUEQCSoIQAi0VVryAPvbdDZg/OV4g/nnCR0dS1nED+3ZJv+9vZ6t+N0CV21hgCIjc7spWe13DHGPOXw+FsllbR53Lv5uXZfY4zxS8rW4QvTAQAAAAA8qGZ/g575aKsmDytyOwo8KKtbsn4wZYjue3udZn20xe04AJDQOtMcbrs4VJnD48+XNNAY088YkyLpKh1evqKt5yRd23z/ckn/tixOBAAAAACe9eiHG3Vyn+7KTU9xOwo8qkdGqr53wRD913Mr9f7aXW7HAYCE1ZnmsO3gfsSa1xC+RdIrkj6WNNNau8IYc6cx5tLml90vKc8Ys1bSdEm3O5kBAAAAAOCcg41Nuv/dDbpwOGcN49hKctN0y7kDdPOji7R6Z53bcQAgIXXmcqajjDG1OnwGcbfm+2p+bK21WZEEsNa+JOmlo577zzb3g5KuiGQMAAAAAEBsPLNoq/rkpqlvXrrbURAHyntm6wun9dG1D3yoZ28Zr4LMgNuRACChHPfMYWttkrU2y1qbaa31N99veRxRYxgAAAAA0HU0haz+9NY6XTyi2O0oiCMTBuTrzIE99JW/z9eBQ01uxwGAhMJlYwEAAAAAjnhh6Talp/o1tJjziBCeqaN7KTc9Rd9+/COFQlxmCABiheYwAAAAACBioZDVPW+s0WWjesoYc/xvANowxuiGCWXavPuAfvPKJ27HAYCEQXMYAAAAABCxF5ZtV5LPaGTvbLejIE4lJ/n0rUkDNWvxVj2xYLPbcQAgIXTmgnRAQguFrCqq67WzNqjCrIBK89Ll83EmBID4Qi0D0B5qA5zSFLKa8eonuvrUPpw1jIhkBZJ12/mD9fMXV6okN02nl+W5HQnwLPbjcALNYeAYQiGr2St2aPrMxQo2hBRI9mnGtNGaUl5EwQUQN6hlANpDbYCTnl60RWkpfo3oxVnDiFyvnG666ewBuumRhXr6G+PVr0e625EAz2E/DqewrARwDBXV9a2FVpKCDSFNn7lYFdX1LicDgM6jlgFoD7UBTgk2NOm3r36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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#importing seaborn for statistical plots\n",
        "sns.pairplot(custDataScaled, height=2,aspect=2 , diag_kind='kde')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "5pekkpRqX7xy"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import AgglomerativeClustering "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "0PUKsf5EX7xz"
      },
      "outputs": [],
      "source": [
        "model = AgglomerativeClustering(n_clusters=3, affinity='euclidean',  linkage='average')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "jGdBVZdUX7xz",
        "outputId": "1b71ecf5-7175-4a55-a3c2-e8d4895593ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(linkage='average', n_clusters=3)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "model.fit(custDataScaled)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "MESJskwpX7x0",
        "outputId": "c4f5a454-48be-4e2d-e5f8-b55991e18a65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Avg_Mthly_Spend  No_Of_Visits  Apparel_Items  FnV_Items  Staples_Items  \\\n",
              "0            10000             2              1          1              0   \n",
              "1             7000             3              0         10              9   \n",
              "2             7000             7              1          3              4   \n",
              "3             6500             5              1          1              4   \n",
              "4             6000             6              0         12              3   \n",
              "5             4000             3              0          1              8   \n",
              "6             2500             5              0         11              2   \n",
              "7             2500             3              0          1              1   \n",
              "8             2000             2              0          2              2   \n",
              "9             1000             4              0          1              7   \n",
              "\n",
              "   labels  \n",
              "0       0  \n",
              "1       1  \n",
              "2       0  \n",
              "3       0  \n",
              "4       1  \n",
              "5       2  \n",
              "6       1  \n",
              "7       2  \n",
              "8       2  \n",
              "9       2  "
            ],
            "text/html": [
              "\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2500</td>\n",
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              "      <th>9</th>\n",
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              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5ac0f46-ecc7-4e0c-916f-94ac33fdb2d8')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5ac0f46-ecc7-4e0c-916f-94ac33fdb2d8');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "custDataAttr['labels'] = model.labels_\n",
        "custDataAttr.head(10)\n",
        "\n",
        "#custDataAttr.groupby([\"labels\"]).count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "L2Lyj8nUX7x1"
      },
      "outputs": [],
      "source": [
        "custDataClust = custDataAttr.groupby(['labels'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "SDmSxN0aX7x2",
        "outputId": "b9156df3-4d6b-4166-f5da-c21ee663d7db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Avg_Mthly_Spend  No_Of_Visits  Apparel_Items  FnV_Items  Staples_Items\n",
              "labels                                                                        \n",
              "0           7833.333333      4.666667            1.0   1.666667       2.666667\n",
              "1           5166.666667      4.666667            0.0  11.000000       4.666667\n",
              "2           2375.000000      3.000000            0.0   1.250000       4.500000"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "      <th>No_Of_Visits</th>\n",
              "      <th>Apparel_Items</th>\n",
              "      <th>FnV_Items</th>\n",
              "      <th>Staples_Items</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>labels</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>7833.333333</td>\n",
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              "      <td>11.000000</td>\n",
              "      <td>4.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2375.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.250000</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41ed5ef4-3475-4685-bb3b-d2e9abee1104')\"\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",
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              "    <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",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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-41ed5ef4-3475-4685-bb3b-d2e9abee1104 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-41ed5ef4-3475-4685-bb3b-d2e9abee1104');\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": 12
        }
      ],
      "source": [
        "custDataClust.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "7nviRgs2X7x2"
      },
      "outputs": [],
      "source": [
        "from scipy.cluster.hierarchy import cophenet, dendrogram, linkage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "NIPKwsvFX7x3"
      },
      "outputs": [],
      "source": [
        "from scipy.spatial.distance import pdist  #Pairwise distribution between data points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Uc3YfPwYX7x4",
        "outputId": "15d2c31b-3407-4799-88bd-4b67b47c3dd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8681149436293064"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# cophenet index is a measure of the correlation between the distance of points in feature space and distance on dendrogram\n",
        "# closer it is to 1, the better is the clustering\n",
        "\n",
        "Z = linkage(custDataScaled, metric='euclidean', method='average')\n",
        "c, coph_dists = cophenet(Z , pdist(custDataScaled))\n",
        "\n",
        "c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "pRxfD4wzX7x4",
        "outputId": "d4fa152f-8e0e-4b7b-ef5f-3c1f55b4b981",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.title('Agglomerative Hierarchical Clustering Dendogram')\n",
        "plt.xlabel('sample index')\n",
        "plt.ylabel('Distance')\n",
        "dendrogram(Z, leaf_rotation=90.,color_threshold = 40, leaf_font_size=8. )\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "db4n_h5sX7x6",
        "outputId": "497ab514-30cd-4e86-c14d-ccf38185a55b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8606955190809153"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# cophenet index is a measure of the correlation between the distance of points in feature space and distance on dendrogram\n",
        "# closer it is to 1, the better is the clustering\n",
        "\n",
        "Z = linkage(custDataScaled, metric='euclidean', method='complete')\n",
        "c, coph_dists = cophenet(Z , pdist(custDataScaled))\n",
        "\n",
        "c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "P9qNTfEMX7x7",
        "outputId": "8d0f5c8f-d54d-49cb-ea72-22bcb37636c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.title('Agglomerative Hierarchical Clustering Dendogram')\n",
        "plt.xlabel('sample index')\n",
        "plt.ylabel('Distance')\n",
        "dendrogram(Z, leaf_rotation=90.,color_threshold=90,  leaf_font_size=10. )\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "TP2j0BuCX7x8",
        "outputId": "a343fc4a-8ae4-4a8c-aab6-bdc59de8dd89",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8453818941339526"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# cophenet index is a measure of the correlation between the distance of points in feature space and distance on dendrogram\n",
        "# closer it is to 1, the better is the clustering\n",
        "\n",
        "Z = linkage(custDataScaled, metric='euclidean', method='ward')\n",
        "c, coph_dists = cophenet(Z , pdist(custDataScaled))\n",
        "\n",
        "c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "dgyb-72VX7x8",
        "outputId": "8e5efd70-78cd-4eca-c09f-efd9d6ed0c1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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khgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkxtxRrjzJ5cANwO3AbVW1cJTtSZIkSetqpAG595iqunoG2pEkSZLWmUMsJEmSpMaoA3IBpyQ5J8kREy2Q5IgkS5MsXbFixYjLkSRJkiY36oD8yKraGzgQODLJo8YvUFVHV9XCqlo4b968EZcjSZIkTW6kAbmqftb//CVwErDPKNuTJEmS1tXIAnKSuybZauw+8ATgglG1J0mSJE2HUZ7F4p7ASUnG2jm+qr40wvYkSZKkdTaygFxVlwF7jmr9kiRg6WJYtmToKrQmlp/f/Vx80LB1aM0sOBgWLhq6Cs0QT/MmSeuzZUtg+bKhq9Ca2H6P7qb1x/JlfhHdyMzEhUIkSaO0/QJYdPLQVUgbLnv7Nzr2IEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSY2RB+Qkc5Kcm+Tzo25LkiRJWlcz0YP8UuDiGWhHkiRJWmcjDchJdgQOAo4ZZTuSJEnSdBl1D/K/A/8A3LGqBZIckWRpkqUrVqwYcTmSJEnS5EYWkJM8CfhlVZ0z2XJVdXRVLayqhfPmzRtVOZIkSdKUjLIHeT/gyUkuBz4OPDbJx0bYniRJkrTORhaQq+rVVbVjVc0HngF8vaqeNar2JEmSpOngeZAlSZKkxtyZaKSqTgVOnYm2JEmSpHVhD7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktQwIEuSJEkNA7IkSZLUMCBLkiRJjblTXTDJLsAfV9VXk2wBzK2qG0ZX2gCWLoZlS4auYt0tP7/7ufigYeuYLgsOhoWLhq5CkiRtJKbUg5zkBcAS4AP9pB2B/x5VUYNZtgSWLxu6inW3/R7dbUOwfNmG8aVFkiStN6bag3wksA9wJkBVXZrkD0dW1ZC2XwCLTh66Co3ZUHrBJUnSemOqY5B/V1W3jD1IMheo0ZQkSZIkDWeqAfm0JK8BtkjyeOBE4HOjK0uSJEkaxlQD8quAFcAy4K+BLwCvG1VRkiRJ0lCmOgZ5C+BDVfVBgCRz+mk3j6owSZIkaQhT7UH+Gl0gHrMF8NXpL0eSJEka1lR7kDevqhvHHlTVjUm2HFFNkiRpQ+D1BWYfry0wJVPtQb4pyd5jD5I8BPjNaEqSJEkbBK8vMLt4bYEpm2oP8suAE5NcBQTYHnj6yKqSJEkbBq8vMHtsCD3gM2RKAbmqzk7yQOAB/aQfVNWtoytLkiRJGsZUe5ABHgrM75+zdxKq6iMjqUqSJEkayJQCcpKPAn8EnAfc3k8uwIAsSZKkDcpUe5AXArtVlZeXliRJ0gZtqmexuIDuwDxJkiRpgzbVHuTtgIuSnAX8bmxiVT15JFVJkiRJA5lqQH7jKIuQJEmSZoupnubttFEXIkmSJM0GUxqDnORhSc5OcmOSW5LcnuT6URcnSZIkzbSpHqT3HuAQ4FJgC+D5wHtHVZQkSZI0lKkGZKrqh8Ccqrq9qhYDB4yuLEmSJGkYUz1I7+YkdwHOS/J24OesQbiWJEmS1hdTDcjPpgvELwb+DtgJeOqoipKkSS1dDMuWDF3F7LD8/O7n4oOGrWO2WHAwLFw0dBWS1nNT7QV+SlX9tqqur6qjqurlwJMme0KSzZOcleR7SS5MctS6lytJdOF4+bKhq5gdtt+ju6l7T/jFSdI0mGoP8mHAu8dNO3yCaa3fAY+tqhuTbAqcnuSLVfWdNS9TksbZfgEsOnnoKjSb2IsuaZpMGpCTHAI8E7hPks82s/4AuGay51ZVATf2Dzftb7X2pUqSJEmjt7oe5DPoDsjbDnhnM/0G4PzVrTzJHOAc4H7Ae6vqzAmWOQI4AmDnnXeeWtWSJEnSiEw6BrmqflJVpwL7A//TX1Hv58COQFa38v6UcHv1y++T5EETLHN0VS2sqoXz5s1bm9cgSZIkTZupHqT3TWDzJDsAp9Cd1eLYqTZSVdcC38BzJ0uSJGmWm2pATlXdTHdqt/dV1V8Bu0/6hGRekm36+1sAjwe+vy7FSpIkSaM21bNYJMnDgUOB5/XT5qzmOfcCPtyPQ94E+GRVfX7typQkSZJmxlQD8suAVwMnVdWFSe5LN2RilarqfODB61ifJEmSNKOmFJD7g/NOax5fBrxkVEVJkiRJQ1ndeZD/vapeluRzTHAO46p68sgqkyRJkgawuh7kj/Y/3zHqQiRJkqTZYNKAXFXn9D9PSzKvv79iJgqTJEmShrDa07wleWOSq4EfAJckWZHk9aMvTZIkSZp5kwbkJC8H9gMeWlX3qKq7A/sC+yX5u5koUJIkSZpJq+tBfjZwSFX9eGxCfwaLZwHPGWVhkiRJ0hBWF5A3raqrx0/sxyFvOpqSJEmSpOGsLiDfspbzJEmSpPXS6k7ztmeS6yeYHmDzEdQjSZIkDWp1p3mbM1OFSJIkSbPBak/zJkmSJG1MDMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLDgCxJkiQ1DMiSJElSY2QBOclOSb6R5KIkFyZ56ajakiRJkqbL3BGu+zbgFVX13SRbAeck+UpVXTTCNiVJkqR1MrIe5Kr6eVV9t79/A3AxsMOo2pMkSZKmw4yMQU4yH3gwcOYE845IsjTJ0hUrVsxEOZIkSdIqjTwgJ7kb8CngZVV1/fj5VXV0VS2sqoXz5s0bdTmSJEnSpEYakJNsSheOj6uqT4+yLUmSJGk6jPIsFgH+C7i4qv5tVO1IkiRJ02mUPcj7Ac8GHpvkvP72xBG2J0mSJK2zkZ3mrapOBzKq9UuSJEmj4JX0JEmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkxtyhC5AkSdrgLV0My5YMW8Py87ufiw8atg6ABQfDwkVDV7FK9iBLkiSN2rIlsHzZsDVsv0d3G9ryZcN/WVgNe5AlSZJmwvYLYNHJQ1cxvNnQg70a9iBLkiRJDQOyJEmS1DAgS5IkSQ0DsiRJktTwID1NbDacjgZmzylpZvnpaCRJ0vSxB1kTmw2no4HZcUqa9eB0NJIkafrYg6xV83Q0naF7r6X1wWzY6zRb9jiBe52k9Zw9yJKkdTcb9jrNhj1O4F4naQNgD7IkaXq416kzG3qwJa0Te5AlSZKkhgFZkiRJahiQJUmSpIYBWZIkSWoYkCVJkqSGAVmSJElqGJAlSZKkhgFZkiRJahiQJUmSpMbIAnKSDyX5ZZILRtWGJEmSNN1G2YN8LHDACNcvSZIkTbuRBeSq+iZwzajWL0mSJI3C4GOQkxyRZGmSpStWrBi6HEmSJG3kBg/IVXV0VS2sqoXz5s0buhxJkiRt5AYPyJIkSdJsYkCWJEmSGqM8zdsJwLeBByS5MsnzRtWWJEmSNF3mjmrFVXXIqNYtSZIkjYpDLCRJkqSGAVmSJElqGJAlSZKkhgFZkiRJaozsID1JG6Cli2HZkqGrgOXndz8XHzRsHQALDoaFi4auQpI0jexBljR1y5bA8mVDVwHb79HdhrZ82ez4wiBJmlb2IEtaM9svgEUnD13F7DAberAlSdPOHmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaBmRJkiSpYUCWJEmSGgZkSZIkqWFAliRJkhoGZEmSJKlhQJYkSZIaIw3ISQ5I8oMkP0zyqlG2JUmSJE2HkQXkJHOA9wIHArsBhyTZbVTtSZIkSdNhlD3I+wA/rKrLquoW4OPAX4ywPUmSJGmdpapGs+LkYOCAqnp+//jZwL5V9eJxyx0BHNE/fADwg5EUJEmSJK1sl6qaN37i3CEqaVXV0cDRQ9chSZIkwWiHWPwM2Kl5vGM/TZIkSZq1RhmQzwb+OMl9ktwFeAbw2RG2J0mSJK2zkQ2xqKrbkrwY+DIwB/hQVV04qvYkSZKk6TCyg/QkSZKk9ZFX0pMkSZIaBmRJkiSpYUCWJEmSGoOfB3loSR5Id4W/HfpJPwM+W1UXD1eVhpZkH6Cq6uz+EukHAN+vqi8MXJoG0pyN56qq+mqSZwKPAC4Gjq6qWwctUNKsluQjVfWcoevQ1GzUB+kl+UfgELrLYF/ZT96R7p/gx6vqX4aqbQj9l4UdgDOr6sZm+gFV9aXhKptZSd4AHEj3BfIrwL7AN4DHA1+uqrcMWN6MSvIS4KSqumLoWoaW5Di698SWwLXA3YBPA4+j+1t62IDlDSrJI4F9gAuq6pSh65lpSe4LPJXu3P+3A5cAx1fV9YMWpsEkGX9a2wCPAb4OUFVPnvGiBpRkX+Diqro+yRbAq4C9gYuAt1bVdYMWOIGNPSBfAuw+vuen7ym6sKr+eJjKZl4fhI6k6w3bC3hpVX2mn/fdqtp7yPpmUpJldNtgM2A5sGPzoT6zqvYYtMAZlOQ64CbgR8AJwIlVtWLYqoaR5Pyq2iPJXLo9TfeuqtuTBPjeRva+OKuq9unvv4Dub8dJwBOAz21MnQv9384nAd8EngicS/cF6i+BF1XVqcNVN7skWVRVi4euYyYk+S5d+DsGKLqAfAJdBxxVddpw1c28JBcCe/anAD4auBlYQtfBsGdVPXXQAiewsY9BvgO49wTT79XP25i8AHhIVT0FeDTwT0le2s/LYFUN47aqur2qbgZ+NNYLVFW/YeN7X1xGt1fln4GHABcl+VKSw5JsNWxpM26T/svzVnS9yFv30zcDNh2sqmG0r/cI4PFVdRRdQD50mJIG8wLgwKp6M7A/XafLa+mGZb1r0Mpmn6OGLmAGLQTOAV4LXNd/UfpNVZ22sYXj3iZVdVt/f2FVvayqTu//btx3yMJWZWMfg/wy4GtJLgXGdiHvDNwPePFgVQ1jk7FhFVV1eZJHA0uS7MLGF5BvSbJlH5AfMjYxydZsfAG5quoO4BTglCSb0g0/OQR4BzBvyOJm2H8B36e78NFrgROTXAY8jG6Y1sZkkyR3p+tkydhehaq6Kcltkz91gzSXbmjFZnRDb6iqn/afl41KkvNXNQu450zWMqT+7+a7kpzY//wFG3fmuqDZg/C9JAurammS+wOz8viNjXqIBUCSTejGzrUH6Z1dVbcPV9XMS/J14OVVdV4zbS7wIeDQqpozWHEzLMlmVfW7CaZvB9yrqpYNUNYgkpxbVQ9exbyxLxEbjST3Bqiqq5JsQ9dj+NOqOmvYymZWksvpviyGbvfxflX1859Ki08AAAPXSURBVCR3A06vqr2GrG8m9XvangecCfwJ8LaqWpxkHvCpqnrUoAXOsD4I/hnw6/GzgDOqaqK9thu8JAfRfU5eM3QtQ+g7mN5N9xm5mm788RX97SVV9b0By5vQRh+Q1UmyI93QguUTzNuvqr41QFkaWJL7V9UlQ9eh9UOSLYF7VtWPh65lJiXZHdiV7iDF7w9dz5CS/BewuKpOn2De8VX1zAHK0iyR5A+A+9D1pl9ZVb8YuKRVMiBLkiRJjY39ID1JkiRpJQZkSZIkqWFAlqT1SJJTkyxcg+XflGT/NWzj8v6gVEnaKG3MpxyRpA1eVb1+6BokaX1jD7IkrYMkd01ycpLvJbkgydP76a9PcnY/7ej+intjPcDvSrI0ycVJHprk00kuTfLmfpn5Sb6f5Lh+mSX9GSLGt/2EJN9O8t0kJ/anWRu/zLFJDu7vX57kqH75Zf3l5UmybZJTklyY5Biac58neVaSs5Kcl+QDSeb0NZ+fZPP+9V+Y5EEj2cCSNAADsiStmwOAq6pqz6p6EPClfvp7quqh/bQt6C5HPOaWqloIvB/4DN2lmh8EHJ5k236ZBwDvq6pdgeuBF7WN9kMgXgfs318Kfinw8inUe3W//H8Cr+ynvYHu/MW7010yeue+jV2Bp9Odv3UvuothHFpVZwOfBd4MvB34WFVdMIW2JWm9YECWpHWzDHh8krcl+ZOquq6f/pgkZyZZBjwW2L15zmeb515YVT/vL05zGbBTP++K5vzjHwMeOa7dhwG7Ad9Kch5wGLDLFOr9dP/zHGB+f/9RfRtU1cnceZGHx9FdTfLsvo3HcedlYd8EPJ7ukrpvn0K7krTecAyyJK2Dqrokyd7AE4E3J/kaXWB8H7Cwqq5I8kZg8+ZpY1dqvKO5P/Z47O/y+JPUj38c4CtVdcgaljzW3u2s/n9AgA9X1asnmLct3WWVN6V7bTetYR2SNGvZgyxJ66C//PTNVfUx4F/pLqE6Foav7scFH7wWq945ycP7+88Exl+Z7DvAfknu19dx1yT3X4t2AL7Zt0GSA4G799O/Bhyc5A/7efdIMtZL/QHgn4DjgLetZbuSNCvZgyxJ62YB8K9J7gBuBf6mqq5N8kHgAmA5cPZarPcHwJFJPgRcRDdm+PeqakWSw4ETkmzWT34dsDaXBj+qX8+FwBnAT/s2LkryOuCUJJvQvb4jk/wpcGtVHZ9kDnBGksdW1dfXom1JmnW81LQkzTJJ5gOf7w/wkyTNMIdYSJIkSQ17kCVJkqSGPciSJElSw4AsSZIkNQzIkiRJUsOALEmSJDUMyJIkSVLj/wPVTQhFFYHbiAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.title('Agglomerative Hierarchical Clustering Dendogram')\n",
        "plt.xlabel('sample index')\n",
        "plt.ylabel('Distance')\n",
        "dendrogram(Z, leaf_rotation=90.,color_threshold=600,  leaf_font_size=10. )\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DyyE_6BnX7x9"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}