23.11.5487_dbscan.ipynb

23-11-5487_dbscan.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/nurikhsanGIT/df690e5e207832fe92acb57f9ae721db/23-11-5487_dbscan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Lembar Kerja Praktikum:**\n",
        "* Mata Kuliah :BDDM\n",
        "* Pertemuan Ke : 24\n",
        "* CPMK: Mahasiswa Mampu Menerapkan Metode Analisis Data (CPMK 21)\n",
        "* Sub CPMK: Mahasiswa dapat menjelaskan konsep dasar dan cara kerja algoritma clustering (un-supervised learning) (SCPMK 1682111)\n",
        "* Indikator Keberhasilan: Mahasiswa penyelesaian contoh kasus clustering dalam bahasa pemrograman"
      ],
      "metadata": {
        "id": "wG9gEW9DJRhb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Identitas Praktikan:**\n",
        "* Nama: nur ikhsan cleviriadi\n",
        "* Nim: 23.11.5487\n",
        "* Kelas: 23BDDM02"
      ],
      "metadata": {
        "id": "asQRO8MtJchP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Petunjuk Praktikum:**\n",
        "* LINK DATASET: https://s.amikom.ac.id/DATASET-MALLCUSTOMER"
      ],
      "metadata": {
        "id": "-LInCQPPJiHu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Tujuan Praktikum**\n",
        "1. Mengidentifikasi core point, border point, dan noise\n",
        "2. Mengimplementasikan DBSCAN pada data numerik\n",
        "3. Menganalisis Pengaruh Epslion dan Mint Points\n",
        "4. Mencari Epsilon dan Mint points yang optimal"
      ],
      "metadata": {
        "id": "EGEPW_EOKynn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import Library**"
      ],
      "metadata": {
        "id": "GLdsEQZpLrQi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.datasets import make_moons\n",
        "from sklearn.preprocessing import StandardScaler\n"
      ],
      "metadata": {
        "id": "pc4dbR-4LXEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MEMBUAT DATA SINTETIS**"
      ],
      "metadata": {
        "id": "v6uDM-hBLyP1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create syntetic data\n",
        "X, y = make_moons(n_samples=300, noise=0.1, random_state=42)\n",
        "X = StandardScaler().fit_transform(X)\n"
      ],
      "metadata": {
        "id": "yIyznS9OLaif"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**VISUALISASI AWAL**"
      ],
      "metadata": {
        "id": "eXZz2D0KMApI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X[:,0], X[:,1], s=30)\n",
        "plt.title(\"Data Awal (Belum Clustering)\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "k7uYaOZELiqH",
        "outputId": "0e02f125-2cb6-414a-b0b6-9bd1c5f47372"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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bIyU9PR3bt2/3uN1//vOfBkuAjTJp0iQIIXDfffe5A7K6Nm7ciHfffVfy/unp6SgrK/MomHjkyBF88cUXHrfztmTYNYridDoBSH8ve/TogfT0dLz88ste26j0HvurUaNGyM7OxsKFC3H48GH35UVFRfj222+93mfjxo2w2WzIzMw0tG1kfRx5oZD07bffYvv27aiurkZJSQlWrFiBZcuWoX379vjqq68QHR0NoPas9JprrsGLL76IM2fO4Pzzz8fSpUu9jiq4RjP+/ve/Y8iQIWjcuDFuuukmXH/99YiKisJNN92Ehx9+GBUVFXjzzTeRmJiII0eOyLZzwYIFEELg5ptv9nr9DTfcgMjISMyfP9+dZHr11Vfjo48+Qrdu3dz5CpdffjmaNWuGnTt3Nsh3ufHGG/H555/j1ltvxaBBg7B3717MmTMHF110kddOTY1bbrkFX3zxBYQQDc78y8rK3HV0qqursWPHDsyePRtNmjRxjxQBwL/+9S+sXLkSGRkZGDVqFC666CKcOHECP//8M5YvXy5b6+PBBx/EtGnTkJOTg5EjR6K0tBRz5szBxRdf7E6oNVLv3r0xa9YsPProo+jSpQvuu+8+dOzYEeXl5SgoKMBXX32F//3f/5W8/5AhQ/C3v/0Nt956Kx5//HGcOnUKs2fPRqdOnTwSlZ977jmsXr0agwYNQvv27VFaWorXXnsNbdu2dddhSU9PR/PmzTFnzhzExsaiWbNmyMjIQGpqKt566y0MHDgQF198MR544AGcf/75OHToEFauXIm4uDh8/fXXhr5PkydPxtKlS3HVVVdh9OjROHv2LP7973+ja9euXisCL1u2DFdddZV7apJIUlDWOBEZxLVU2vUXFRUlkpOTxXXXXSf+7//+Tzgcjgb3OXjwoLj11ltF8+bNRXx8vLjzzjvdSzbrLxt+/vnnxfnnny8iIiI8lk1/9dVX4pJLLhHR0dHiggsuEC+88IJ45513JJdWu3Tr1k20a9dO9jX169dPJCYmupf0zpo1SwAQo0eP9rhddna2ACDy8/M9Lq+pqRFTp04V7du3F3a7XVx22WXim2++8bpc19tr9ubnn38WAMT333/vcXn9pdI2m020aNFC3HzzzWLjxo0NHqekpESMGTNGpKSkiMaNG4vk5GSRlZUl3njjDfdtvC2VFkKIDz74QKSlpYmoqCjRvXt38d1330kulX7ppZc87rty5UoBQHzyyScel6tZal/Xxo0bxbBhw0SbNm1E48aNxXnnnSeysrLEu+++K86ePeu+nbf3denSpaJr164iKipKdO7cWXzwwQcNliXn5+eLW265RbRp00ZERUWJNm3aiKFDh4qdO3d6PNaXX34pLrroIvcS5Lrv1aZNm8Rtt90mWrZsKex2u2jfvr246667PL4nruf1tiRbaqn0mDFjGty2ffv2Yvjw4R6X5efni8suu0xERUWJ9PR08dZbb4mnnnpKREdHe9zu5MmTIioqSrz11lsNHpeoPpsQfmRXEVHYysrKQps2bdx5RERqDR48GFu3bvXILZsxYwZefPFF7N6923Rbd5D5MOeFiHwydepUfPzxxwFJkiXrqr+p465du7B48WKP7R3OnDmDadOm4ZlnnmHgQqpw5IWIiAzTunVr995h+/btw+zZs+F0OrFp0yZ07Ngx2M0ji2LCLhERGWbAgAH48MMPUVxcDLvdjszMTEydOpWBC/mFIy9ERERkKcx5ISIiIkth8EJERESWEnI5LzU1NTh8+DBiY2O5uRcREZFFCCFQXl6ONm3aKG48GnLBy+HDh33aa4SIiIiC78CBA2jbtq3sbUIueImNjQVQ++JdW9cTERGRuTkcDqSkpLj7cTkhF7y4pori4uIYvBAREVmMmpQPJuwSERGRpTB4ISIiIkth8EJERESWwuCFiIiILIXBCxEREVkKgxciIiKyFEODl7y8PFxxxRWIjY1FYmIiBg8ejB07dije75NPPkGXLl0QHR2Nbt26YfHixUY2k4iIiCzE0OBl1apVGDNmDNatW4dly5bhzJkzuP7661FZWSl5n7Vr12Lo0KEYOXIkNm3ahMGDB2Pw4MHYsmWLkU0lIiIii7AJIUSgnuzo0aNITEzEqlWrcM0113i9zd13343Kykp888037st69eqF7t27Y86cOYrP4XA4EB8fj7KyMhapIyIisggt/XdAK+yWlZUBAFq0aCF5m8LCQowfP97jspycHCxcuNDr7Z1OJ5xOp/vfDofD/4YShblKZzXeXrMXC9bvR2l5FRJjozEsox1G9klFM3vIFeYmIosJWMJuTU0Nxo0bh6uuugpdu3aVvF1xcTGSkpI8LktKSkJxcbHX2+fl5SE+Pt79x00ZifxT6azG3a8XYsbynSh2VKFGAMWOKsxYvhN3v16ISmd1sJtIRGEuYMHLmDFjsGXLFnz00Ue6Pm5ubi7KysrcfwcOHND18YnCzdtr9uK3Iw7U1JtQrhHAb0cceHvN3uA0jIjoTwEJXsaOHYtvvvkGK1euVNzmOjk5GSUlJR6XlZSUIDk52evt7Xa7exNGbsZI5L8F6/c3CFxcakTt9UREwWRo8CKEwNixY/HFF19gxYoVSE1NVbxPZmYm8vPzPS5btmwZMjMzjWomEdVRWl7l1/VEREYzNHgZM2YMPvjgAyxYsACxsbEoLi5GcXEx/vjjD/dt7r//fuTm5rr//cQTT2DJkiV45ZVXsH37dkyePBk//fQTxo4da2RTiehPibHRfl1PRGQ0Q4OX2bNno6ysDP369UPr1q3dfx9//LH7Nvv378eRI0fc/+7duzcWLFiAN954A5deeik+/fRTLFy4UDbJl4j0MyyjHSJs3q+LsNVeT0QUTAGt8xIIrPNC5B/XaqP6SbsRNuCi1nH4+OFMLpcmIt1p6b+5txEReWhmj8THD2diXHYnJMdFI8IGJMdFY1x2JwYuRGQKHHkhIiKioDNthV2icCVVsXbIFSn4aMMBVrIlItKAIy9EBpPKIbEBsDeOwOnqGuaWEFHY48gLkYlIVawVAKrO1DS4fd1Kto9ndVT9PNyPiIjCBRN2iQwmV7FWitZKttyPiIjCCYMXIoP5WpFWy/24HxERhRMGL0QG87UirZb7cT8iIgonDF6IDCZXsVaK1kq23I+IiMIJgxcig43sk4qLWsc1CGBsAKIbRzS43LXaaGQf5Y1MXbgfERGFEy5BIDKYq2KtkXVehmW0w4zlO71OHblGcbgaiYhCBeu8EIUApf2I3hlxBR6ct4H7FRGRaXFvI6Iwo7Qf0UcbDnA1EhGFDI68EBnAbFM0vabmo9ghnbSbHBeNdROzAtgiIiJPrLBLFESljirc+OoalJY73Ze5CsYt3VoclCkarkYiolDCaSMiHVU6qxsELi7BnKLhaiQiCiUMXoh09PaavV4DF5dgFYyTqzVjA5CW0Ay9puYjLXcRek3Nx8z8XdxSgIhMi9NGRDpSE5gEY4pmZJ9ULN1aLLmz9bo9x92XGzHFZbYcICKyNo68EOlITWASjCkaqdVImektcbq6xtBVSNw0koj0xuCFSEdqAhMtZf/11MweicezOmLdxCzsyRtU+9+jlYbvicRNI4lIbwxeiHQ0LKMd5LYxSoy1ayr7bzSlkaISR5XfIyPcNJKI9MbghUhHI/uk4uI2cV4DmMRYO755rE9AcjwqndWYmb9LMQlXaaRIAH5P7XCZNhHpjcELkQpqgwFXbsmT13nmloy/rhNW/rUfEuOMz3fRkmOiZsdrf6d2uEybiPTGCrtECqT2DbIBSIi1w2YDjpY7TbOCZmb+LtlNGsdld8LjWR0BnHttWw47ZB/Tnwq8WtpDROGLexsR6Ugq4VQAKC13osThNNUKGi05Jq6RIoXBF7+mdkb2ScVFreMajPC4NoU0Uw4QEVkDgxciBXLBQH01Athy2IGuk74LWrE3rTkmzeyRSFKYzvJnakdp00jWeSEirXjUIFLgy6iDQPD2M0qMjZbdhNFbIDIso53s1I6/y7tdy7Q5PUREeuDIC5ECf0YdglHLRC4JVyoQ4dQOEVkJgxciBWpW5MgJdC0TXwIRTu0QkZVwtRGRAqnVRlpE2IA9eYP0bZgM7iVERFajpf9m8EKkQv1goFWMHQBwrMKpKqDxZ6kxEVE44FJpIp25Ek7zn+qLcdmdEGGz4ViFE02jIhGjMJKhR8IrERGdw/FjIpW8TR9VOKvduSRCCOwoKfcYiVGT8MopHiIibQydNlq9ejVeeuklbNy4EUeOHMEXX3yBwYMHS96+oKAA/fv3b3D5kSNHkJycrOo5OW1ERlGqFPtovw6IioyQDULqByoJsXYIUVuhV9R7vItax8kmy5o16DFru4jI3LT034YeSSorK3HppZfiwQcfxG233ab6fjt27PBoeGJiohHNI9JEqXLtpxsPYt3ELMlaJt5GbkocTsnHcy2x9vZ43h5Lqq5MIIMJLe0iIvKVoUeRgQMHYuDAgZrvl5iYiObNm+vfICI/+Ls7stQ2A1JcS6y9BS9vr9mLrYcdqP9QNQLYevhc0KMmmHA9nh7BjdRrVArGiIi0MOUpUPfu3eF0OtG1a1dMnjwZV111leRtnU4nnM5zZ68Oh/wGc0S+Uqpc2zQqEr2m5ksGAFq2GXCRCog+WLevQeDiIv68/vGsjorBxOyC3SjYUarbSImafZWMDl44bUUU+ky12qh169aYM2cOPvvsM3z22WdISUlBv3798PPPP0veJy8vD/Hx8e6/lJSUALaYwolSsbpKZzWKHVWSmzT6ss2AVHXfo+Xep5vqX68UTMxb+7viSIkW/o5O+cs10jRj+U7Zz4KIrM1UwUvnzp3x8MMPo0ePHujduzfeeecd9O7dG9OnT5e8T25uLsrKytx/Bw4cCGCLKZxIVa51/dPbFE7dAEDrNgNyS6xtChV/XdcrBQsVzmrVO1CrofQa/dlqQQ25kaYthx0Y9d5PDGCIQoCpghdvrrzyShQVFUleb7fbERcX5/FHZASpEvpyUxF1AwAt2wwoLbFWWiPout7fYEHrSIkv+yrpSWlqbu3u4xyBIQoBpg9eNm/ejNatWwe7GUQAzhWrWzcxC3vyBmHdxCycOi3fEboCALk9hxJj7UiMtaveUygxzi77nK7r/d2XSWvwE+wNHtUEW4HeKJOI9Gdo9lpFRYXHqMnevXuxefNmtGjRAu3atUNubi4OHTqE9957DwAwY8YMpKam4uKLL0ZVVRXeeustrFixAkuXLjWymUR+UUrkdQUArpEbPZJJ78loL1tz5p6M9gBqg4mlW4t92pfJl5ESPV+jL5Q+CyBwicNEZBxDjyQ//fSTR9G58ePHAwCGDx+OefPm4ciRI9i//9yc+unTp/HUU0/h0KFDaNq0KS655BIsX77ca+E6omBzrWqpkJmCqB8AuEZu/O04pYKS+iMcUsFEiaNKcrWSi68jJXq9Rl8My2gnGdTVZXTiMBEZixszEvlAzU7Taqrk+tsGX0c4ek3Nlx2hiLFHYv3ELMstLXZ9LlsOy5dM4EaZROZjmgq7RKFKqeBcjD0SD12TZuhUiT8jHHIjFBE24KFr0iwXuADnRppGvfcT1u4+7vU23CiTyPpMn7BLZEZKq1pi/gwszBgAVDqrcbq6BhFe1lsHKrHWSM3skXjz/p7o2iZ4icNEZCzzHVmJLCDYxdh8JTfdFRlhwyN90zG6X7opgy4tgp04TETG4i+YLMOXHA+199H62GpXGJmN3HRXjRCIiowImY49mInDRGQsJuySJUiNGMglxaq9jy+PPTN/l2zOyLjsTqbsNJUSdYORyMq9iIgI0NZ/M+eFLEHNbsW+3seXxw52MTZfmW26Kxh7EVU6qzEzfxd6Tc1HWu4i9Jqaj5n5u1h1l8hCGLyQJajZrdjX+/jy2FJbBchVxjVDp6k0neXaGTtQ7fMlcPQHN24kCg0ckyVL8GXEQO19fB2N0JJT4W1qytVpLt1abFgtmPqUirhVOqvdRfcC0T41gaOe029qgiUzTvcRkSeOvJAl+LJbsdr7+LMTstrRlECPMEjxd2dsvQV6GsuXUTYiMh8GL2QJSrsVpyU0axBA3NGjraodjpU2LyxxVHkNSrRMQZil0/R3Z2y9+RM4+sJsOT9E5BsGL2QJcgmyUZERKNx9vEEAkb+tBF2S5ZNq5Qq2uQh4D0q0jKaYqdP0Z2dsvSkFpXpXwg10sERExmDwQpYgNWLQK60lnGdqvE537Cgpx7VdEiWTagHg7tcL8VpBEapVbLlcPyjRMppi9k5Tz/ZpSUwe2ScVXZLjUD9+sQHokqz/qq1AB0tEZAwm7JJleEuQ7TU1X3J35BoBfLrxINZNzPKahDkzf5fs/kRSj+lKItUymqK0l1CwO0292ieVmDxt2U5MW7YTibF23NurvUcNF6lSU0aUoFK7GzcRmRtHXsjS/JmOUdqfSOkxtYxWmL0ujF7tU9qwsrTc6TH99vaavdhRUt4gABWoHTnTO1HYlyXuRGQ+/KWSaXmrvHpHj7YAakdU1ORhyAUYvuZxJMTaAWgbrTD7Xjt6tU9NQFh3+i3QS6UBbhtAFAoYvJApSU0//HtlkerHUJruUNqfSIoQte3TOgVh9k5Tj/apDQhdgYmZEpmJyDo4bUSmpDT9oETNdIfSEmkpR8udeHvNXk5BeKElsdc1uqPX4xFR+Ai/oyspMnL3ZrX3La86ozlwcQUiap9bbuTEBuCsxPMLnJvOMPtoSqApVfCtKyHW7nOiMDdzJApv3FWaPBi5e7OW5/NFhA3YkzeowePLdXJS109ftlNyFZPUc6kVyh2vls8zMdaObx7rgwfnbQjY942IzIu7SpPPjNy9Wcvz+aL+FIOaCrjeCrY9ntURSXHGTGeE+saAdafSkuLssrc9Wu7ERxsOaJ56M8tWC0QUPAxeyIORuzdrfT6t6k8x+NPJGVXMLBw6XldAuH5iNhJjpQMY1/SbVAApNXpilq0WiCh4GLyQByN3b/bl+bSon5zrTydnVF2WcOt4j1U4Za8vcVRpHm3iCiUiYvBCHozYvTnCZpPsoPRaTZIUZ29wpu5PJ2fUSqJw63iVPl8BaJ4u4wolImLwQh58mS4ZltGuwd40dZ2tEZLTIb4uV67frnsy2je43N9OTut0hhrh1vGq+Xy1TpdxfyIiYvBCHnyZLhnZJxWNZHooV26DludTS65dZuzkzNgmI7k+XzlS02VSGzwOuSLF1FstEJHxuFSaGvBlKW/qhEU+Ly1Wsw2A1GVy7TLjkloztslolc5qdJ30nabvh9L79M6IK/DRhgPu70xCrB3pCTEoKq3AsQpnSC0/JwoXWvpvBi/kldYApuuk71Ahk7cQY4/Elik5AW+bGWuqmLFNRus1NV92K4bkuGism5jl/vfM/F2yxevGZXdyFwb0NyAsdVRh3MebsW7PcdSI2vv1SmuJGXd3R6LCknki0g+DFwYvfvGlMwhU8BKOIxehQEswAmgLdrQ+dl2ljipc89JKVJ2paXBddOMIrH66v0cAE46BJ1GgsEgd+cWXWiRKq0X0Kr4WDnVSQpHWXCotq7L8WX4+7uPNXgMXAKg6U4NxH292/zvUCwwSWQmDF2rAl85AqSKt0vVqhVudlFChdem5llVZ/iw/X7fnuOx9617PwJnIPDjOSQ340hn4usFeINpG5qBlE0st36fE2GjZKSZXnSFv0zpK1Z3rXq8mcOYGnUSBwZEXasCXWiRGVaTVo20UPFLLnZWmWLR8n/ypM6S0RL/u9QycicyDwQs14EstEqMq0urRNj352hmHI39yRLR8n/ypM9QrraXsa6h7PQNnIvMwNHhZvXo1brrpJrRp0wY2mw0LFy5UvE9BQQEuv/xy2O12dOjQAfPmzTOyieSFr6MoRlSk1attemDCpjb+5oio/T41s0firML8T/1REVcQWlRaIXmf6MYRmHF3d/e/gx04E9E5hgYvlZWVuPTSSzFr1ixVt9+7dy8GDRqE/v37Y/PmzRg3bhz+8pe/4LvvvjOymWFHafQgUKMovghm25iwqU0gk6uVEsLrjorUDUJLyxtuHGkD0Du9ZYNl0sEMnInIU8DqvNhsNnzxxRcYPHiw5G3+9re/YdGiRdiyZYv7siFDhuDkyZNYsmSJqudhnRd5ga6TEkp1MbQWWgt3abmLZBNi5aouS5H6Pp2ursFrBUWqar34UxcmlL7PRGajpf821a+tsLAQ2dnZHpfl5ORg3LhxkvdxOp1wOs+dPTkcDqOaFxLUjB7otWLCW6DkmmZZurU46KM4WjFhUxulVUBac0Tkvk9dkuPQOSkWO0rKvQbldUdF/Fk1pGXFFBEZx1QJu8XFxUhKSvK4LCkpCQ6HA3/88YfX++Tl5SE+Pt79l5KSEoimWlYgh/JDbZqFCZva6J0jIvd92l7sQNaFSaqmExmEElmfqYIXX+Tm5qKsrMz9d+DAgWA3ydQCeeAOtYJyTNjURu8cEaXv06cbD6pK8GUQSmR9phqzT05ORklJicdlJSUliIuLQ5MmTbzex263w263B6J5IUHvoXxAOg+gROZ5AOud4Y7sk4qlW4sl84WYsOnJlVytV46IXoF3oAoqEpFxTBW8ZGZmYvHixR6XLVu2DJmZmUFqUejR+8Atl4fQKMKGapmMTaud4erdGYcDPXNElALvGlGbVK30eTAIJbI+Q1cbVVRUoKioCABw2WWXYdq0aejfvz9atGiBdu3aITc3F4cOHcJ7770HoHapdNeuXTFmzBg8+OCDWLFiBR5//HEsWrQIOTnqdiTmaiN5eq82klu54Zot8PYFi7ABj/brgKjICAYCpIrcd60uNd9lrhoiMh8t/behwUtBQQH69+/f4PLhw4dj3rx5GDFiBH7//XcUFBR43OfJJ5/Eb7/9hrZt2+If//gHRowYofo5Qy14MeIgq+UxlW6rtHw4MsKGGiEaBEpdkuMghJBcHWK1lUhkPKnA2xulJc9EZD6mCV6CIZSCl0DXZPHl+btN/k6xlse47E5+1eUgcqkbTMsFzQDr7hBZjZb+2/KrjUJZsJcaq3l+NSs3vK0A+XTjwZBaiUSBUXfLAKVNFa2WEE5E6jF4MbFgLzVW8/y+Lh9mrQ3yF5c8E4UvBi8mFuwOXs3z+1rLgx0P+Yt1d4jCF4MXEwt2B6/m+X3dKNGsHY/SppVkHtwokSh8cTmHCbmSEitkOsxAdPBqa8L4UsvDjLU2Qm0vplDHujtE4YurjUxGzXJQM6028uf5zVZrw5/dhomIyD9cKm3h4EWpEFeMPRIPXZOmawcvF0QAMFWAYSSlmjVcemt+ZguIiUg9Bi8WDl4C3YEGu5aMmaTlLlKsWbMnb1DgGkSa8LtMZG2s82JhgV5hFOxaMmYS7ARp8g+/y0Thg8GLyQS6Aw12LRkzMesKKFKH32Wi8MHgxWQC3YEGu5aMmXDprbXxu0wUPjgBbDKBXkKcGBstm2MTTlMlXHprbb58l41O8GUCMZExmLBrQoE84HF5MIUKrd/lQJQCYAIxkXpa+m/+ckzIl6JvvjJjsTgiX2j9LqtJ8PXnN2j04xOFM468kORIz5ArUvDRhgMc8ibL0DJqqaUsgS+joawbRKQN67wwePEbh7wp1Kmt6+Prb4F1g4i0YZ0X8htrZlCoU0pGj7DZ3CMuvvwWWDeIyDgMXsgr1sygUDcsox0kqhIAAM7WCPdUkS+/BdYNIjIOx/3JKy01M7TmA3D5qDS+N4Ezsk8qZubvQrVEZCIA9+cgR+p6JsMTGYdHQ/JKbc0Mb/kAxY4qzFi+E0u3FjfIB9B6+3DC9yawmtkjcVYuKQVwB5C+1EJi3SAi43DaiLxSO+StNR+AuTTS+N4EXlKccl6K3G8BACqc1ZiZvwuVzuoG17nKHqybmIU9eYOwbmIWHs/qyMCFyE8MXsgrtaXyteYDMJdGGt8b/VX+GVj0mpqPtNxF6DU13yPQUBOkS/0WXCqc1ZixfCfufr2wQQCj9PxE5BsGL+SVa8h7XHYnJMdFI8JWW5diXHYnj+kLrfkA3H9GGt8bfbmm4WYs34liRxVqxLlpOFegoSZIr/tbiJEYMfE2Oqbm+YnINxy7JElqKv1qzQfgXkrS+N7oS22FW7m8FKB22wE1ibuu0THX74UVdomMw+AljBixkmVYRjvZ/WTqLwfVevtwwvdGX2qm4Vz5J96CdKnidHLqBjhKzz9j+U4AYPIuecWVh/L4DoQJo1ayaF0OyuWj59Q/OLWKsaNVjB3HKpxh/97owd9pOKmREzl1R8fUjNRwJRl5w5WHypjzEiaMWsmiNjfG19uHKm/5EKXlThwtd6JVjB1JcfawfW/04kuFW1eC7ZX/XI5py7yPgkmpPzqmZpqPK8nIG648VMajYYiRGmr8YN0+VUPovtCyCzaHQmtJHZwEgGMVTozL7sR8CD9pnYZzBZRbDzugdcM3b6Njcs9fl7+/Pwo9aqc8wxlHXkKI3OqG0nKn7H0DsZKFqy/O4bJo46ld7u/iCijVBi4RNsiOjiktsa6rRCZRm8IPVx4qC59T3TAgN9SoJCHWDsDYkRGuvjiHByfjaa1wKxdQ1hdhg+LoWN3nn75sp2xQFE6jjqSMKw+V8RcTQrQcfOtLT4gxPEmMQ6Hn8OAUGFqmNLUEjGoTqF3P/8bqPagIo5FF8g9XHirjtFEI8edsvai0wvAkMY42nMMdh81HbcAY8+eIipZA/tRp+cBF6XoKL0OuSEGrGHuDy7ny8BwGLyHEn7P1YxVOw/MwfFn9Eaq05mOQ8ZT2MAJqP58RvS/A22v2air5z+8+qVXprMaD8zbgqJc8xVYxdrwz4gpOMyJAwcusWbNwwQUXIDo6GhkZGfjxxx8lbztv3jzYbDaPv+ho/rDVUHPwlZIYG234yAhHG87hknHzcQWUUj+hCBvQJTkO+dtKNCed87tPaskljh+rcOKjDQcC3iYzMjx4+fjjjzF+/HhMmjQJP//8My699FLk5OSgtLRU8j5xcXE4cuSI+2/fvn1GNzMkjOyTii7JDQ++NgAJMXbZg/KwjHaGnh1WOqtxuroGEbaGrQjX0QbuOGwuroDyyes6ITG29vcSYav9/STF2TEuuxOu7ZKIHSXlmqdWOdJGanElojqGHyWnTZuGUaNG4YEHHgAAzJkzB4sWLcI777yDCRMmeL2PzWZDcnKy0U0LSUJ4/9a3bBaFVjFRDQ689Q+eRiSJyZVZj4yw4ZG+6RjdL52dNgWdUoJvr6n5PiWda135ROGLuYHqGPqLOX36NDZu3Ijc3Fz3ZREREcjOzkZhYaHk/SoqKtC+fXvU1NTg8ssvx9SpU3HxxRd7va3T6YTTeW5u0OFw6PcCLObtNXuxo6S8wXCjALCztByP9uuAgd1aSx48jSrdL1dmvUYIREVG8OBtUiwq6MmfjkXLyicKX1yJqI6h00bHjh3D2bNnkZSU5HF5UlISiouLvd6nc+fOeOedd/Dll1/igw8+QE1NDXr37o2DBw96vX1eXh7i4+PdfykpKbq/DqtQGm78dONB2WkKo/IwOAxqTSwq2BATb8lozI9Sx3SnTpmZmcjMzHT/u3fv3rjwwgvx+uuv4/nnn29w+9zcXIwfP979b4fDEbYBjB7DjUacHXIY1JpYVLAh1t8gLXwZueTmteoYGry0atUKjRo1QklJicflJSUlqnNaGjdujMsuuwxFRUVer7fb7bDbG66HD0dmHW40a7uoVjD2w7Iqdiyklq9FP5kfpY6h00ZRUVHo0aMH8vPz3ZfV1NQgPz/fY3RFztmzZ/Hrr7+idevWRjUzZJh1uNGs7SLz74dlNlziTmr5U/STKxGVGf5OjB8/HsOHD0fPnj1x5ZVXYsaMGaisrHSvPrr//vtx/vnnIy8vDwDw3HPPoVevXujQoQNOnjyJl156Cfv27cNf/vIXo5tqeWY9KzRru8i//bDCdcSMibekhhW3Q7FSgr7hrbn77rtx9OhRPPvssyguLkb37t2xZMkSdxLv/v37ERFxbgDov//9L0aNGoXi4mKcd9556NGjB9auXYuLLrrI6KZanlmHG83aLqNY6QDg635YHDEjkme1XD+j97bTm01IFQaxKIfDgfj4eJSVlSEuLi7YzaEwI1XTxjXKZLYDQFruIsXgJcIGS7yWYLJSwEqB0WtqvmyuX3JcNNZNzApgi+TNzN8lm4yutIu6HrT039zbiEhHRm9uqTelqR9XZVnmd0jjknLyxshcv0pnNWbm79K0v5YSq5W04NGHSEdWm+dWWvp7T0Z75nco4JJy8saoXD+jpnesNs3FkRciHVntAMA9d/wX7DNWI87CyX9GrUwzanTXagUYOfJCpCOr1bQJt2RqIwQzYLVakmW4MWJlmr+ju1L5WXf0aIvXCoosU4CR3+ogYYJfaDK6AqsR3xsu/fVPIAJWqc/9dHUNp6zCjD/Bslyw2zkpFl2S47C92BolLThtFARM8AtdRk7DlDqq0P/lAkxb5vm9mb6M35tgMroIo9zxYs6q3ZZKsiT/+TO9IzfltKOkHNd2SbRMgr65WhMmmOAXuoyahql0VuPGV9d4rXorAGw9zO9NINUdCSlxVKFRhA1CCI8d3fU6Y5U7XtQoVLowW44V+c+f0V01m/e6qvmaHYOXINAyZ8npJesxYhrm7TV7Zcv1C5hvJVOo8jb0Xl0jYAMQGWFDjRC6/k59LSQImC/Hivznzyomqy0okMPeLwjUfoGYjEcuaob/rXTgsTKpkRCB2pEQvYt5+fq52gBUOKuRlruIJz0hxJ/RXastKJDDnJcgUDtnabWCZ2QcNR2YlQ48VhbopdFKn2tkhM1rzo1AbfDCnLrQ4+vGjaG0SS6DlyBQ+wXS4yDJGhChQU1gYqUDj5UFeuhd6XjxSN90jyTLGHskvN2cJz0USnWdGLwEgdovkL8HSa5qCh1yHRgAJMbaLXXgsbJAF/NSOl6M7pfucRYeY4+EVIoMVyCFN6MK5wWDdVoaQtTOWfo7P8lVTcFhRJK1VJIeUBu4fPNYH0sdeKzM6Fo+9WnNcQilpEzSX6jUdeKu0ibm7y6fVtvVNBQYuas0V56Zg9l3DufvPrxZ+Tihpf829ysJc/5u7MUzsMAzcrQrVM6YrM7fWj5Gdy6BHhki8winFaoceTE5fw50PAMLPL7nJCcQozZmHxki4/g7Wh9sHHkJIVrOtusHOk2jalcdeItOeQZmDI52kZxA5KFxs83QJndC6++mjVbCb7GBAjn36O1sq0JiNZEVl8VZRSgVgSL9zV+/T7Zzmb9+ny6dC6cYQ5PStFA4nTwxeDGIL3OP/gQ7Umd0QG2lzWb2SJw6Xc0zMIMx34DklDqkt3hQcz2FN6WRu6ZRkZInrUBonTyx9zKI1uFhfxOt5IYLBWoLV22ZkuPnqyIl/iZZU2iz2QC5LEObTC0fMq9AjbIrTQsBtceacDh5YpE6g2itjuvvVgDhNFxoZqFUBIr0p7Q8IrSWT4SHQBYDVVOYNFQq6CrhkdQgWoMJfxOtmGthHkbkG1i5dgOdkxBrl90dvGVMFGbm7+LnHGRafm+BLAaqdJxPiosOm2Tt0HklJqM1mPB35IS5FqErnGo3WJXazu7eXu0xfdlOyfL9ETabx++Yn3Pgaf29BXKFj5rjfLgka3PayCBad+/0d7+UIVekICrS+8cZFRmBIVekyN6fzIu7i5ublmmDkX1ScXEb78P6ibF2HKtw8nMOMq2/t0BM2bs22P1gnffVajacC3zDZfNdBi8G0bp7p79blX+04QCcZ2q8Xuc8U4OPNhxQ3XYyF6Uzuw/W7Qtsg8iDls5OLifKJpFo6XqsN1bvCfkOyQy05isavVFn3eDY25Rj5J8dR3WNCKvNdxm8GERr4qa/W5UvWL9fcihagDvJWpnymZ0zpA9SZqe1s3MN67t2gV43MQuPZ3XEUZlcGKC2blOod0hmoHUkxd8TTyVKZTDO1ogGx/5wGK1j8GIgqYOUt3lrf1epcLVR6GoVY1e8TSgfpMxOr9+emjP0UO+QzEDrSIq/J55KlMpgSJ20egucQwmzv0zEn0QrrjYKXR0SY2RXqAChVfbbavT67cklY7qEWol3M9K6+MHX7RjUJnn7c+IZyietDF5CBFcbha7dRysUb+PLQYrLr/Wh9Nu7o0dbVcufXQUOtxx2yD5fKHdIZuBLoUmtJ55aVjQpBcdyQvmkldNGIcLooUsKHqVcCED7QSqQhbVCndxvr0tyHPK3lah6n11n8DEKgWMod0hmEIhCk1qSvOVyamx//nkT6ietDF5ChNIPDqjdLr3X1Hyk5S4Km+V0oUBNZ6X1IMXl1/qR++316dAK24vLVb/PzeyReOiaNEMTQEmZlnxFX2hJ8pYPjmNxYZietNqECK2C1A6HA/Hx8SgrK0NcXFywm+MTvYfzvQ1RAue+4Cx+ZW4z83fJFjZLjLVj5V/7afoMe03Nlx2KTo6LxrqJWRpbSnVVOqtx6ZSlqJZJYvH2PvP3GvrSchfJ5jZF2IA9eYPc/5brEwCEzPSvlv7bWq8sDBhRTTWQ5atJf645+K2HHQ0CmMRYO755rI/m7wRXpxnv7TV7ZQMXwPv77GsCKFmH1iRvpZyacKioW19Apo1mzZqFCy64ANHR0cjIyMCPP/4oe/tPPvkEXbp0QXR0NLp164bFixcHopmmYMRwvtY6FGQurs7syes8pyXGX9cJK//aD4lx2nMgjC6sRep+V1Lvs9HTFuTJVcHW32l1tY9jdG2YcGB48PLxxx9j/PjxmDRpEn7++WdceumlyMnJQWlpqdfbr127FkOHDsXIkSOxadMmDB48GIMHD8aWLVuMbqopGBFo8Czb+up2Zr9OzsGwjHZYsH4/uk3+zqcDLQ+exlPzu+L7HHx6Ja9r3SaCCyz8Y3jwMm3aNIwaNQoPPPAALrroIsyZMwdNmzbFO++84/X2//d//4cBAwbg6aefxoUXXojnn38el19+Of79738b3VRTMCLQ4Fl26NDrQMuDp/GUfleRETa+zyag12i3XttEMKdJHUODl9OnT2Pjxo3Izs4+94QREcjOzkZhYaHX+xQWFnrcHgBycnIkb+90OuFwODz+rEzpgFcjoPlMm2fZoUOvAy0PnsZTWuL6SN90vs8moNdot17bRPA7oY6h79KxY8dw9uxZJCUleVyelJSE7du3e71PcXGx19sXFxd7vX1eXh6mTJmiT4M1MKrAl5oqm1oTeH0pukTmpOYAqTZxz5+KzqRM6Xc3ul968BpHbnqNdnN6PrAsX+clNzcXZWVl7r8DB4zfPdnIAl9Sw/n1aTnT5ll26OAB0lzkEjT5u7MGvabVOT0fWIb+elq1aoVGjRqhpKTE4/KSkhIkJyd7vU9ycrKm29vtdtjtyhvX6cnIpcf1l0nKLafzdqYtNyLEs2zrU1MqfGb+Li6pDQC1ZQ2kfnfcnsEc9NpahVu0BJahIy9RUVHo0aMH8vPz3ZfV1NQgPz8fmZmZXu+TmZnpcXsAWLZsmeTtg8Hopcd150KVRmDqnmmz5Hvok8ujAGq/f/y8jeUabcmYmo8th33LP+Jv1Tz0Sl5nEnxgGT5tNH78eLz55pt49913sW3bNowePRqVlZV44IEHAAD3338/cnNz3bd/4oknsGTJErzyyivYvn07Jk+ejJ9++gljx441uqmqBXLoXstQJEu+hz4104r8vI1TN+iokAkwlE5i+Fs1D72m98JhmlCvejh6MPzdvPvuu3H06FE8++yzKC4uRvfu3bFkyRJ3Uu7+/fsREXEuhurduzcWLFiAZ555BhMnTkTHjh2xcOFCdO3a1eimqqa1OqI/tAxF6pnMSeZUd1pRLrGbn7cxpIIOb+ROYvhbNRe9ktdDOQneiOrv/ghIwu7YsWOxb98+OJ1OrF+/HhkZGe7rCgoKMG/ePI/b33nnndixYwecTie2bNmCG264IRDNVC2QS4+1DEUymTM8uA6QSvh5608u6KhP7iSGv1WyGrONFlp+tVEwBHJuU8tQJLPdrUOP4Vd+3oGnNqhQOonhZ0dWY7ZtZhi8+CDQc5vN7JEY2ScVwzLaITE2GqXlVViwfj/eXrPXo7NjMTpr0CtZk5934KkJKtScxPCzI6sx22ih9TOIgsTfuU0tyyTVzjXKFcXqkhyH09U16DU1n8syg0yvpfZaiw9yaa7/lIpIxtgj8dA1aYrvKQtHktUEMtdTDZsQQuUMrjU4HA7Ex8ejrKwMcXFxwW6OV96CEeDcgav+6M3L3+3ArJVF8PZBRdiAcdmd3J2dtw7qjh5tkb+tBDtKylU9Hxmr19R82YNAclw01k3MUvVYagMSrd858k7P95HBJGkVzO/MzPxdsotH6vZDvtLSfzN4CQItX4JKZzUunbIU1TJZgkqdXSC+dKReWu4i2aTPCBuwJ2+Qrs+p13eAHS7fAwqOYJ+ABOL5tfTfzHkJAi2JT2+v2SsbuADKc41mS7QKd61i5CtCGzH8qsd3gIXVanFDPQqGYK/2MVsdG/7agkBL4pOaTkWpszNbolU4U+rgbTAmWVOP74CR22KQdhwBCi9mqA1kpjo2HHkJAi3LJNV0KkqdHZdlmsfba/biWIVT8vqEWLshyZp6fAc4gmceHAULPzwJ9cTgJQi0LJNU6lQiI2yKnR2XZZqHUpEzmw2GnDXr8R3gwdM8gj2FQIGn9gTETCX8jcTgJQi0FLmT63RsAB7pm67Y2XHDMONoPVAodfBHy6VHZfyhx3eAI3jmwVGw8KPmBCScRuQYvASBUuITAHeHOGP5TkTYbKj/nY2wARe3icPoful+Px/nx33jy4EiWAGAHt8BjuCZB0fBQpPcyZCaE5BwGpHjUmmTkVqOZgPQKMKGszUCSXFMzDMDX5Yf+7tkudJZjdkFuzFv7e/uXY1j7JEY0fsCjO6nPArnj2Av1aRz9KwVRJ6ClQit5vcFQLZtVv9eaOm/eaQxGanIWQA4WyMwpn8H/DWnc1DaRp58yf73p7JqpbMad8xei23F5R6XVzir8e+VRVixvRSfPGJcAFF3R2uucAkuLbvNk3rB3DlZ7Wo+udU+4TQix2kjk5HrEAWAOat2h9S8pZX5cqDwZ/rm7TV7sb1e4FLXtgDVemCNk+BjHpsxgjntokceUzjlpfGIYzJKHWJ1jWA9DZPwda8PX2slLFi/3+sWES7iz9u45r45OmIORkxDcBTMGMGspaLHqEk4jcjxG24ySh0iEJhiRKQs0AcKNQev0vKqoA17U0NGTkOYqWBYqAjmtIseGx+G04afnDYyGT3qbVBgBHroXs3Bq2lUZNisNrCCcFr9EQqCOe2ix2q+cFpZytVGKgQy+1yPjRgpcAL53ZiZvwvTl+2UnDqyofbgVSGTE+U6OHKKITCsvvoj3OixGtDX4wFX83FXaV2DFyO+UEpf8Je/24FZK4u8dlLcBTp8Sa02crmodRy2HXHI5sXUFU4HxWAJxg7i5Dt/jvdS9wVqK6E/0jddsZxBuO9XxeBFx+DF30i8PrVr+cM9AifvlOq8ZL2ySjFnqi4Gw8biyIv1+BpAyPUVQO3I6MVtePyWw+BFx+BF74OP2mAo3CNw8o3SAdQbdqDG0fvkh8xLqa8A+JkrYZE6Hemdfa52KR5XEpAvpFYbyGECuHGCtfqDJz+Bp+Z3ZPRy63DC1UYK9M4+D6cKiBR43lYbSK1gcAmlwlVmE4zVH+G0OZ+ZqP0d8RivD4bgCvSu5aHHWn4iOfVH7ZSmLkKpcJUZ+TqK6uvoidoy86Qvub6iLh7j9cGRFwV61/LgzrwUaCwlbz3+jJ7oUWaetHP9zuQGOnmM1w+DFwV6D/uyI6FAC6fCVWZW6azGzPxd6DU1H2m5i9Braj5m5u/yGoj4U9yOU9PB4fqdjenfAZFezlB5jNcXVxsFAZPpiMKL1voh/qxy5PLs4OMx3jdcbWRyXElEFF7kRlK2HnZgdsFu/DWns/tyf0ZPwmlzPrPiMd54nDYiIjKYXB6KADBn1W6P6SN/VjlyalodLdN4ZD4MXoiIDKY0klJdIzzyWPxJ7GeOkzIuJ7c+fotNwsg5Us6/EgWXUokEwLN4mb/F7ThtIS8Yy8mNOA6H87GdCbsmYORuotyplCj4ZubvwrRlO2VvU3+TxnDumIwW6KRmozb4DbVju5b+m9NGJuDPsshgPjaZG+f0zWNkn1Svy2frqp/H4ho9WTcxC3vyBmHdxCz31iHkn0AvJzfiOBzux3YGLyZgZFEpFqwKT1rn9BnoGKuZPRKP9E2XLGDGVUCBpfe2L/XV/z3JVd719Tgc7sd2Q4OXEydO4J577kFcXByaN2+OkSNHoqKiQvY+/fr1g81m8/h75JFHjGxm0Bl5FsCCVeFJy1kZkxd9pyXoG90vHRe34SogMzCy0rm335PSlgG+HIfD/dhuaPByzz33YOvWrVi2bBm++eYbrF69Gg899JDi/UaNGoUjR464/1588UUjmxl0Rp4FGH2GQeak5aws3IeffaU16FNaBQSAo18BYuRycqnfkxxfjsPhfmw3bPJ027ZtWLJkCTZs2ICePXsCAF599VXccMMNePnll9GmTRvJ+zZt2hTJycmqnsfpdMLpdLr/7XA4/Gt4EBhZVIoFq8KTlrMyNYHOyD6pTB6tx5cVK1KrgLwlX7oCoaVbiy2ZfGlmrkDSiO+03O/JG1+Pw+F+bDds5KWwsBDNmzd3By4AkJ2djYiICKxfv172vvPnz0erVq3QtWtX5Obm4tSpU5K3zcvLQ3x8vPsvJSVFt9cQKEaeBbBgVXjSclamFOiUOKo4reSFnjkHagIh5iXpy6iEaC3TNf4ch8P92G5Y8FJcXIzExESPyyIjI9GiRQsUFxdL3m/YsGH44IMPsHLlSuTm5uL999/HvffeK3n73NxclJWVuf8OHDig22sIFCOLSrFgVXjSMqevFOg0s0dyWskLPXMOlAKh+ev3MYC0CKXfU4QNuhyHw/3YrvnVTZgwAS+88ILsbbZt2+Zzg+rmxHTr1g2tW7dGVlYWdu/ejfT09Aa3t9vtsNvtPj+fWRhZVIoFq8KPliJnSsPPgHTCoWuEIRy/W0qF57TkHCgGQg4njpY7A1pUjXyj9Hsal91Jt88qnI/tmkdennrqKWzbtk32Ly0tDcnJySgtLfW4b3V1NU6cOKE6nwUAMjIyAABFRUVam0oUtrSclSkNP586LX9WH+qrGqTouWJFKdCx2ZQDSDKHcJ/OCRTNIy8JCQlISEhQvF1mZiZOnjyJjRs3okePHgCAFStWoKamxh2QqLF582YAQOvWrbU2lSisqT0rU0pezHpllW4jDKHE3xL+dSmdrSvVQQ/XANKMjEwGpnMM3R5g4MCBKCkpwZw5c3DmzBk88MAD6NmzJxYsWAAAOHToELKysvDee+/hyiuvxO7du7FgwQLccMMNaNmyJX755Rc8+eSTaNu2LVatWqXqOa24PQCRmc3M3xWwYXCr0auEv1Kp99JyJ0rLnZL317ucPVEwaOm/DQ0B58+fj7FjxyIrKwsRERG4/fbbMXPmTPf1Z86cwY4dO9yriaKiorB8+XLMmDEDlZWVSElJwe23345nnnnGyGYSkQw9RxhCjV45B0pn62+v2RvWy2KJ6uPGjERhpP5IQUKsHekJMSgqrcCxCqfkyAE3CQyuUNyEj6g+Lf03gxeiMCHVAdbHDtGcGEBSqDPNtBERBYaajk1t2XIuvzWncF4WS1Qfd5Umsji1e+xoKVvO5bdEZGYceSGyOLV77GhdTlvsqEJa7iI0jao9TFQ6q5EUx6kKsj5OwVkfR16ILE7tHju+1GOpEUCFsxoVzmoIsCQ9WZ/W3cDJnBi8EFmc2j125CrCahHuexqRtSmNVI567ydufmkBDF6ILE7tDtJSZct9wZwYsiqlkcq1u48rjshwh+/gY/BCZHFq99jxtt9RUpwdvdNbIjHWrjmoYUl6siKt39v6I42cdjIHZiYRWZyWCrhKy217Tc2X3ceornDd04isTWk3cG/q7p6uNkGejMWRFyKL07KDtBK1eTEsSU9W5Wvul2v1ndQ2DQCnUwOJIy9EIUCvAmZSozh1cU8jsjI133Epam5fd1pK7ZJsLt3WjtsDEJGH+gdS1nmhUFD3e13iqHJ/f0+drkZibDTSEpqhcPdx+Nshunb4VrsfFfetOofbAxCRz4JVhp5nn2QUbwFChbPaI0AAoGrvLzl1p1PV5sb4kkOj5rcS6r8njrwQUdDx7JOMNDN/l2SuSoQNGJfdCY9ndWzQ4WsJYup/V5WS310jNGpv56LmtwJ4D8TM/nviyEsYC/Vom0ITV3CQkZRqu8xYvhNAbT5M3VFHpcDClfjr7Tirtnik2tu5qPmtAAj53xNXG4UQ1h8gq1K7xQGRL5QCBFcAU/84qVRDaVx2J+zJG4R1E7PweFZHjxNEtcUj1d7ORc1vJRx+TwxeQojaiJzIbLSefRLVpVTxVk1NIm/HSamq1GpW3KktHqn2di5qfivh8Hti8BJCwiHaJutRU0pd69knkYuaEWe1tV3qHyf9qaGkNvDRGiCp+a2Ew++JSRAhJByibZJntpwnb8mFro5l6dZidwcwLKOdbEIlC+KRFDUjzlpqu9Q/Tnpbfafmd+YKfPS6nYva30qo/5642iiEaM1ap9ASzBU7Ugfz09U1eK2gSNUqDyuujqDgU3vcc31H5Srk1r29lGB/V7naqBanjUKI1rlTCi3BynmSG7afs2q3qqlMPbc4IOvyZbdmtSPOrhGUcdmd/DpOBju3UM1vJRx+Txx5CSHBPiOg4PJ35M3XKSe5GhpKImzAnrxB2u9IIcfX45cRdVLkvu8c4TYOR17CVDhE2yTNn5wnf5bZyyWKKwmFxEHSh68jGlpHnP09TjK30BzYm4WYYJV2p+BLjI2WPSOUCxT8KRLn68GaU5lUl5rVkt6+g0OuSMEH6/ahtNzpcbnccmZ/jpP+/M5IPxx5IQoR/uQ8+bPMXulgHRlh86lOBoUXX0Y0Kp3VeHDeBhytF7gAQKsYO94ZcYXuI87MLTQHBi9EIcKfglr+DIUrHcwf6ZvOqUyS5ErSVeItSHaNGHqLu49VOPHRhgM6tNCTP78z0g+PHEQhQk29CKmk3IRYO0ocDc9eXeRGV6RqaLgO5qP7pbuH6YnqkkqerU9qRMPXqSZ/aK3LQsbgaiOiMCG3yqJVjB3HKpyK9VjkHpsHc9JKzUo1uVVAabmLFO/L1WzWwV2liagBuaTco+VOJMQ2DGDUDoX7myjO4Cc8Ka1UcwXOUt8DJs+GL+a8EIUJuY7CdXEwclO4G3r4UrNSrf5uzXUxeTZ88ZSGKEwodRTHKpxBWWbvzzJtsjZ/R06U8q2YPBu6OPJCFCbMttOsa5WJXM4Dd0MPbf6OnLAwZ/jiJ0sUJsy0c7PaVSYAK5YGSyDykPQYOWFhzvBkWPDyz3/+E4sWLcLmzZsRFRWFkydPKt5HCIFJkybhzTffxMmTJ3HVVVdh9uzZ6NiRX0oif0lVIrXBe0fh6rw+WLcPR8udsNkAIYDEODvuyWivuhPz1gmmJTRTFbgATLoMBm/BpSsPaenWYt1GNbjsmHxl2FLpSZMmoXnz5jh48CDefvttVcHLCy+8gLy8PLz77rtITU3FP/7xD/z666/47bffEB2t7gDGpdJEDcmNdCTG2vHNY32QGBfd4PZbD3svAKZ2EzstIyzeqFmmTfqTm87jZ0JGMcXGjFOmTMGTTz6Jbt26qbq9EAIzZszAM888g1tuuQWXXHIJ3nvvPRw+fBgLFy40qplEYUEqKRbwXolUrnIpoLxZnprnVcKkS9+4col6Tc1HWu4i9Jqaj5n5uzSt2vJnuwiiQDBNwu7evXtRXFyM7Oxs92Xx8fHIyMhAYWGh5P2cTiccDofHHxF50toZqdkpWk0n5uuO066zeyZdaqPXsnPunExmZ5rgpbi4GACQlJTkcXlSUpL7Om/y8vIQHx/v/ktJSTG0nURWpLUzUts5GdHJ1Z2WYOCijZpl52qYbWUaUX2agpcJEybAZrPJ/m3fvt2otnqVm5uLsrIy99+BA/pvxEVkdVo7I7Wdk96dXKhMFekxdeMLvaZ7zFj8LVjvqdXaFC40ndY89dRTGDFihOxt0tLSfGpIcnIyAKCkpAStW7d2X15SUoLu3btL3s9ut8Nut/v0nEThQusyabnby91Py/N60yutJd68v6elR1wCtVLHG71GwsxW/C2Y76mV2hRONL2zCQkJSEhIMKQhqampSE5ORn5+vjtYcTgcWL9+PUaPHm3IcxKFC62dkev2SquNlDoxqeeVsudopeUP+MGsGKzXXj9alzAbXRNG7XsayD2yWBk6uAxbKr1//36cOHECX331FV566SV8//33AIAOHTogJiYGANClSxfk5eXh1ltvBVC7VPpf//qXx1LpX375hUuliXSg9cBuRJ0XuY7VJcJWOwIz4+7uHsu3raLX1HzZ15kcF411E7MMee5gLHGW261czXJ6NdS8p/lP9VXdDj2CnGB+zqFKS/9tWPAyYsQIvPvuuw0uX7lyJfr161f75DYb5s6d656KchWpe+ONN3Dy5En06dMHr732Gjp16qT6eRm8EJmX0gG/rujGEVj9dH/LBTBpuYsUp9v25A0y5LkDEUjUF4iASc17Oi67k6p26PUeBfNzDlWmqPMyb948CCEa/LkCF6A2WKmbQ2Oz2fDcc8+huLgYVVVVWL58uabAhYjMp25SY4nKwAUAqs7UYNzHm41rmEGCuVInGHv9BKImjJr3VG07uCIrNFh7cpmITM3fCrvr9hzXv1Ey9JhOCPYeUoHe6ycQNWHUvKczlu9U1Q41QY6a9y7Yn3O4M02dFyIKPf5U2AXg8/18oVeBt5F9UnFR67gGS40DuVInkEt4AzECoeY9VdsOPVdkBftzDmcMXojIML5W2HWRqjViBL2mE4IxdVOXXkGYWoGoCaPmPZVrBwCUOKrQa2o+mkbJv/9aV2QF63MOd4Yl7AYLE3aJgqf+tIu/Iye901tiwahe+jROQaisHgn0iqNgJAlraUd9NkByzy5uOhlcWvpvhoZEpAtf8lsSY+1wVJ1B1ZmaBtdFN47AjLu769tIGaGyn49eOR1qaa0JY5T67ShxVHkNUlyX1Q9ilKZ7AllDhpRx5IWIdCF3xu+N6yx3yBUpGPfxZqzbcxw1Inh1XpRGXmLskYixR5q+4/JnCW8oddB6fp5mGV0KdRx5IaKA05LfUvcst5k9MmBTQ3XV76ibRkXKTilUOqtR8We+iJnLwPtaZTfUyt0rjZSdOl2NLVNyVD0Wq+maDxN2iUgXaqZVzJLU6C2ptcJZ7TVwceWA1r9OayJvoMglrtpQ+zq9rUDSK2HZLPRcBRWIWjakDYMXItKFUmeQHBeNPXmDsG5iFh7P6hjUs3i5Jdw21E4puAItuXaaseOSWsIL1AZgFc5qryuQQq2D1nMVVKjkQ4USBi9EpItALJnVi1xHLVAbvLgCrVOn5ZcWm63j8raEN8YeCW8fTd1RlVDroPWsw8JquubD4IWIdDHkihRERXo/pERFRmDIFSl+P4dexde0dNRW7LhcVXbXTczCnrxBiLFHSubyuEZVrPg65ehZh8VKgXm4sE72FRGZ2kcbDsDpZckzADjP1OCjDQf8SmqUSiidvmwnZubvwtkagaQ4datjlJJagdpVO4mx0UhLaCZZs8YqHZeaYO3Rfh3w75VFkre5o0dbvZtlOL22ShjZJxVLtxZLrjZiNd3A48gLEeliwfr9kmf3AudyJnwdPZHKUxEAqmsEBNRXklWqxloj4M4LKdx9HFGREZYuAx9qoyqBxmq65sM6L0SkCzX1RX6dnONzvQyluh31n0uuUmqpowo3vroGpeVOVY9nA5CZ3hJ7jlZasv6Jmqq7C9bvD4kKw2RdWvpvjrwQkS7UnN37sxxXS8Ko3OqYSmc1Hpy3Accq1AUuQO3ozp6jle4cEjOsmNJCTfJqqCXsUmhj8EJEulCT1OjPclytUxtSna2vO11bufOWmvZ4tF8H9OuciKxXVim+H2rf/0DuaE3hyxqnDURkemqSGmcs3yn7GHIBwrCMdpq2H5DqbH3d6doKeSFK5f3rJq9q2YtKbWKy2av0htL2B+GOnxYR6ULNBn2+lq4HpIMjb+Q6W19GUKQez9UZzl+/D6UOJ2w2QAggIdaOe3u1D2inqDVwUDsCpSUxWWlacNR7PwUtb8jsgRVpw0+KiHSjtDRVafQkLaEZKp3VXjsRb8FRhM2Gs3+uNHJR6mzVLJOuS+rxvHWGruUPpeVOTF8W2E5R6/47akagklUuPVfzmDUCWLv7uPvfWgIHPUZMuD9RaGHOCxEFjFzpegAo3H1cdplz/eJr/5l0PZ68TtvyVaXcnN7pLVU9ntLIhUBg9wTSmk+kNAIVYYPmxGSto1pqErW97UOldkl8XaG2/UG448gLEQWMa/Rk1Hs/eZyFu9Tt8NWcBftShEwpN+fN+3v6Pcrg4uoU9TijVxp90LpayJ8pPLn7aBnVApTfI7UjJnq/P2RuHHkhooBqZo/EnqOVktcbfRasV8ExtZ2dHp2imtEHrYXojCh5r1T8T4rce6RmxMSI94fMjSMvRBRwwT4L1qNsvNpRBlen6E/ehprRB7l8Im/BiBEl77UkVdclFzio+a4Y8f6QuXHkhYj84ktdj1A4C1YzyuDqFP3N21Az+qB1F2UjSt5LPWbv9JZed7V2tU8ucFDzXTHi/amLtWvMh8ELEfnM1045FHbpVUo+tuFcp+hPZWFA3eiDL8FI/QRoPSoHN7NHYmSfVAzLaIfE2GiUlldh99EKJMTaGwQwagIHNd8Vo94fQL+EYdIXp42IyGe+Lj8NhV166y7dVqrzomZkQG4KS21yrV67KPvD2xLyEocTEbba9wUAjlU4VU+bjeyTiiVbirHtiMNjSbwNQJfk2u+K0r5M/rw/XGJtTgxeiMhnvnbKagraWYHaztDfHB8r5WvIdfbHKpyyG2ZKkdo/2HW5ke+Pv4EnGcMaRwgiMiV/OmUzjBIEir/Lkq00UqV3Z//2mr3YUVKO+g8pAGwvLkfG1HxUOqvRKMIGIbQVLFQj2Mnl5B1zXojIZ6GQeBsI/ub41M3XSPwzdyTizymqoxVOvL1mr2lyL/Tu7OWCIQGgwlkNAaD6zxtFRth0ST524XfcnBi8EJHPQiHxNhD8Weni4kqETYy1w2arHcUQqM0nMVPyqN6dvZZgRwCoEQLjsjvpknwM8DtuVgxeiMhnenTK4UCvZclvr9mLrYe955NsPey5ailYy3v17uy1Bjt6Fznkd9ycbEIqE8qiHA4H4uPjUVZWhri4uGA3hyjk6bFpHtVSei+v/OdylJY7Je+fGGvHj3/P9rriBzjX4Rq5WaTezz0zf5fsZp7eRNiAPXmDNLRaHr/jgaGl/2bwQkQhweodjJpOv+uk7xokrtZlA7D3X4NkO/wIG3xa8aOFnp+F1PsiJzkuGusmZvnQcgomLf23Yb/of/7zn1i0aBE2b96MqKgonDx5UvE+I0aMwLvvvutxWU5ODpYsWWJQK4koFHjr4FyFxJZuLTZ0pEEvauqJuOrISLH9ObWhdsWPUQGfnivJvC2rbxoVico/E3XrYx5KeDDs13z69GnceeedyMzMxNtvv636fgMGDMDcuXPd/7bb7UY0j4hCiBkLiWkNDNQEHErj5K7r1az4sVLAVz8YUhqlYh5K6DPsmzllyhQAwLx58zTdz263Izk52YAWEVGoMlshsUpnNe6cU+hRFbbYUYXpy3ZiyZZifPJIw8BATcCRGGdHiUMm5yWu9mRPTV0ZMwZ8aikVOQRqc2WsOoVIykz3KRYUFCAxMRHnnXcerr32Wvzv//4vWrZsKXl7p9MJp/Pcj9nhcASimURkIkYXEtM6ijK7YDd+O9LwWCRQGxjMLtiNv+Z09rhOTcChVEn2noz2ANRVnDVbwAdoe5+lpqasNKJEvjPVUukBAwbgvffeQ35+Pl544QWsWrUKAwcOxNmzZyXvk5eXh/j4ePdfSkpKAFtMRGZgZCExXzbmm7f2d9nH9Ha9miXGapftqrmd2SrH6rUBor+bYJI1aApeJkyYAJvNJvu3fft2nxszZMgQ3HzzzejWrRsGDx6Mb775Bhs2bEBBQYHkfXJzc1FWVub+O3DggM/PT0TWZGQhMV86wwqFjtbb9WoCDrX1YtTczmyVY/UKOtSMKJH1aRo7e+qppzBixAjZ26SlpfnTngaP1apVKxQVFSEry/uyN7vdzqReojBn5N4/Rk2vzMzf5TEdonazSrUreZRud0ePtvj3yiLJ+9/Ro63m1+QPvd5ns40okTE0BS8JCQlISEgwqi0NHDx4EMePH0fr1q0D9pxEZD1G7lLtS2cYY49UHH3xloMRTptV1qdX0OHvJphkDYblvOzfvx+bN2/G/v37cfbsWWzevBmbN29GRUWF+zZdunTBF198AQCoqKjA008/jXXr1uH3339Hfn4+brnlFnTo0AE5OTlGNZOIQoSr4183MQt78gbptreNL9MrI3pfoPi4wc7B+HTjQb+u15te01jciyg8GBa8PPvss7jsssswadIkVFRU4LLLLsNll12Gn376yX2bHTt2oKysDADQqFEj/PLLL7j55pvRqVMnjBw5Ej169MD333/PaSEiChpfOsPR/dJxYXIsJO7mFswcDLNNr+gVdHAvovDA7QGIiGT4ulePa9nvtGU7ZR9fzT48RlTC7TU1X3Z6JdAl9vXcE8nqW0WEK+5txOCFiHTkT2fob5Bg1CaLRu5/5Ov7xaAjvDF4YfBCRCbhb5BgVJBhVFAUzB2tydq09N+mKlJHRBRq/M3BMKpuidqaMVqxSBwFAkdeiIgM5s90SFruIsngBVCXM2Ok+q8NgGx7A51LQ9ahpf/m2B0RkcH8qd9i5rolUlNEclgkjvTAaSMiIhMzc90SqSkiOSwSR3pg8EJEZGJmrlsil4/jTbCDLQodDF6IiEzMqMRaPWiZAjJDsEWhgzkvREQmZ9Y9j5TycVyjRazXQnrjt4iITINFysxB7ecwLKOdYYXuiORwqTQRmQKLm5mDls+BnxnpiUXqiMhyWNzMHLR8DmbOx6HQxpEXIjIFs20UGK74OVCwcOSFiCxHaeUKi5sFBj8HsgIGL0RkCkrFy1jcLDD4OZAVMHghIlMIVCXZSmc1ZubvQq+p+UjLXYReU/MxM38XKp3Vujy+1Zm5oi+RC4MXIjKFQFSSda2OmbF8J4odVagRQLGjCjOW78TdrxcygIG5K/oSuTBhl4hMw+g6LzPzd7EuiQrBqrfDOj/hTUv/zeCFiMIGV9KYF2vGEFcbERH9qW6Oi1zgAnAlTTCxzg9pweCFiEJW/RwXJVxJEzxyO1TXiNrriVwYvBBRyJI6m/eGK2mCi/VlSAsGL0QUsuTO5uviSprgY30Z0oLBCxGFLDVn69yLxxxYX4a04C+ViEJWYmw0VxdZxMg+qVi6tVhytRFHxagujrwQUcji2bx1cIdq0oJ1XogoZLF2CJF1sM4LERF4Nk8UqjjyQkREREHHkRciIiIKWQxeiIiIyFIYvBAREZGlMHghIiIiSzEsePn9998xcuRIpKamokmTJkhPT8ekSZNw+vRp2ftVVVVhzJgxaNmyJWJiYnD77bejpKTEqGYSERGRxRgWvGzfvh01NTV4/fXXsXXrVkyfPh1z5szBxIkTZe/35JNP4uuvv8Ynn3yCVatW4fDhw7jtttuMaiYRERFZTECXSr/00kuYPXs29uzZ4/X6srIyJCQkYMGCBbjjjjsA1AZBF154IQoLC9GrVy/F5+BSaSIiIusx7VLpsrIytGjRQvL6jRs34syZM8jOznZf1qVLF7Rr1w6FhYVe7+N0OuFwODz+iIiIKHQFLHgpKirCq6++iocffljyNsXFxYiKikLz5s09Lk9KSkJxcbHX++Tl5SE+Pt79l5KSomeziYiIyGQ0By8TJkyAzWaT/du+fbvHfQ4dOoQBAwbgzjvvxKhRo3RrPADk5uairKzM/XfgwAFdH5+IiIjMRfPGHk899RRGjBghe5u0tDT3/x8+fBj9+/dH79698cYbb8jeLzk5GadPn8bJkyc9Rl9KSkqQnJzs9T52ux12u939b1cKD6ePiIiIrMPVb6tKxRUGOnjwoOjYsaMYMmSIqK6uVrz9yZMnRePGjcWnn37qvmz79u0CgCgsLFT1nAcOHBAA+Mc//vGPf/zjnwX/Dhw4oNjXG7ba6NChQ+jXrx/at2+Pd999F40aNXJf5xpFOXToELKysvDee+/hyiuvBACMHj0aixcvxrx58xAXF4fHHnsMALB27VpVz1tTU4PDhw8jNjYWNpvN79fhcDiQkpKCAwcOhOTqpVB/fUDov8ZQf31A6L9Gvj7rC/XXGIjXJ4RAeXk52rRpg4gI+awWw/aDX7ZsGYqKilBUVIS2bds2aCAAnDlzBjt27MCpU6fc102fPh0RERG4/fbb4XQ6kZOTg9dee03180ZERDR4Pj3ExcWF5BfSJdRfHxD6rzHUXx8Q+q+Rr8/6Qv01Gv364uPjVd3OsOBlxIgRirkxF1xwQYO5rejoaMyaNQuzZs0yqmlERERkYdzbiIiIiCyFwYsCu92OSZMmeaxoCiWh/vqA0H+Nof76gNB/jXx91hfqr9Fsry+g2wMQERER+YsjL0RERGQpDF6IiIjIUhi8EBERkaUweCEiIiJLYfBCRERElsLgpZ7ff/8dI0eORGpqKpo0aYL09HRMmjQJp0+flr1fVVUVxowZg5YtWyImJga33347SkpKAtRqbf75z3+id+/eaNq0qccGmHJGjBjRYPfwAQMGGNtQP/jyGoUQePbZZ9G6dWs0adIE2dnZ2LVrl7EN9dGJEydwzz33IC4uDs2bN8fIkSNRUVEhe59+/fo1+AwfeeSRALVY2axZs3DBBRcgOjoaGRkZ+PHHH2Vv/8knn6BLly6Ijo5Gt27dsHjx4gC11DdaXt+8efMafFbR0dEBbK02q1evxk033YQ2bdrAZrNh4cKFivcpKCjA5ZdfDrvdjg4dOmDevHmGt9NXWl9fQUFBg8/PZrOhuLg4MA3WKC8vD1dccQViY2ORmJiIwYMHY8eOHYr3C+ZvkMFLPdu3b0dNTQ1ef/11bN26FdOnT8ecOXMwceJE2fs9+eST+Prrr/HJJ59g1apVOHz4MG677bYAtVqb06dP484778To0aM13W/AgAE4cuSI++/DDz80qIX+8+U1vvjii5g5cybmzJmD9evXo1mzZsjJyUFVVZWBLfXNPffcg61bt2LZsmX45ptvsHr1ajz00EOK9xs1apTHZ/jiiy8GoLXKPv74Y4wfPx6TJk3Czz//jEsvvRQ5OTkoLS31evu1a9di6NChGDlyJDZt2oTBgwdj8ODB2LJlS4Bbro7W1wfUlmGv+1nt27cvgC3WprKyEpdeeqnqyuh79+7FoEGD0L9/f2zevBnjxo3DX/7yF3z33XcGt9Q3Wl+fy44dOzw+w8TERINa6J9Vq1ZhzJgxWLduHZYtW4YzZ87g+uuvR2VlpeR9gv4bVLVVc5h78cUXRWpqquT1rt2wP/nkE/dl27ZtE4D63bCDYe7cuSI+Pl7VbYcPHy5uueUWQ9tjBLWvsaamRiQnJ4uXXnrJfdnJkyeF3W4XH374oYEt1O63334TAMSGDRvcl3377bfCZrOJQ4cOSd6vb9++4oknnghAC7W78sorxZgxY9z/Pnv2rGjTpo3Iy8vzevu77rpLDBo0yOOyjIwM8fDDDxvaTl9pfX1afptmA0B88cUXsrf5n//5H3HxxRd7XHb33XeLnJwcA1umDzWvb+XKlQKA+O9//xuQNumttLRUABCrVq2SvE2wf4MceVGhrKwMLVq0kLx+48aNOHPmDLKzs92XdenSBe3atUNhYWEgmhgQBQUFSExMROfOnTF69GgcP3482E3Szd69e1FcXOzxGcbHxyMjI8N0n2FhYSGaN2+Onj17ui/Lzs5GREQE1q9fL3vf+fPno1WrVujatStyc3M9NkUNltOnT2Pjxo0e731ERASys7Ml3/vCwkKP2wNATk6O6T4rwLfXBwAVFRVo3749UlJScMstt2Dr1q2BaG5AWOnz80f37t3RunVrXHfddfjhhx+C3RzVysrKAEC23wv2Z2jYxoyhoqioCK+++ipefvllydsUFxcjKiqqQW5FUlKSaec4tRowYABuu+02pKamYvfu3Zg4cSIGDhyIwsJCNGrUKNjN85vrc0pKSvK43IyfYXFxcYPh58jISLRo0UK2rcOGDUP79u3Rpk0b/PLLL/jb3/6GHTt24PPPPze6ybKOHTuGs2fPen3vt2/f7vU+xcXFlvisAN9eX+fOnfHOO+/gkksuQVlZGV5++WX07t0bW7duRdu2bQPRbENJfX4OhwN//PEHmjRpEqSW6aN169aYM2cOevbsCafTibfeegv9+vXD+vXrcfnllwe7ebJqamowbtw4XHXVVejatavk7YL9GwybkZcJEyZ4TaCq+1f/QHLo0CEMGDAAd955J0aNGhWklqvjy+vTYsiQIbj55pvRrVs3DB48GN988w02bNiAgoIC/V6EAqNfY7AZ/foeeugh5OTkoFu3brjnnnvw3nvv4YsvvsDu3bt1fBWkh8zMTNx///3o3r07+vbti88//xwJCQl4/fXXg900UqFz5854+OGH0aNHD/Tu3RvvvPMOevfujenTpwe7aYrGjBmDLVu24KOPPgp2U2SFzcjLU089hREjRsjeJi0tzf3/hw8fRv/+/dG7d2+88cYbsvdLTk7G6dOncfLkSY/Rl5KSEiQnJ/vTbNW0vj5/paWloVWrVigqKkJWVpZujyvHyNfo+pxKSkrQunVr9+UlJSXo3r27T4+pldrXl5yc3CDRs7q6GidOnND0fcvIyABQO7qYnp6uub16adWqFRo1atRgdZ7c7yc5OVnT7YPJl9dXX+PGjXHZZZehqKjIiCYGnNTnFxcXZ/lRFylXXnkl1qxZE+xmyBo7dqx7AYDSCF+wf4NhE7wkJCQgISFB1W0PHTqE/v37o0ePHpg7dy4iIuQHqHr06IHGjRsjPz8ft99+O4DaLPP9+/cjMzPT77aroeX16eHgwYM4fvy4R0dvNCNfY2pqKpKTk5Gfn+8OVhwOB9avX695VZav1L6+zMxMnDx5Ehs3bkSPHj0AACtWrEBNTY07IFFj8+bNABDQz9CbqKgo9OjRA/n5+Rg8eDCA2qHr/Px8jB071ut9MjMzkZ+fj3HjxrkvW7ZsWcB+b1r48vrqO3v2LH799VfccMMNBrY0cDIzMxssqzXr56eXzZs3B/23JkUIgcceewxffPEFCgoKkJqaqnifoP8GA5IWbCEHDx4UHTp0EFlZWeLgwYPiyJEj7r+6t+ncubNYv369+7JHHnlEtGvXTqxYsUL89NNPIjMzU2RmZgbjJSjat2+f2LRpk5gyZYqIiYkRmzZtEps2bRLl5eXu23Tu3Fl8/vnnQgghysvLxV//+ldRWFgo9u7dK5YvXy4uv/xy0bFjR1FVVRWslyFL62sUQoh//etfonnz5uLLL78Uv/zyi7jllltEamqq+OOPP4LxEmQNGDBAXHbZZWL9+vVizZo1omPHjmLo0KHu6+t/R4uKisRzzz0nfvrpJ7F3717x5ZdfirS0NHHNNdcE6yV4+Oijj4Tdbhfz5s0Tv/32m3jooYdE8+bNRXFxsRBCiPvuu09MmDDBffsffvhBREZGipdfflls27ZNTJo0STRu3Fj8+uuvwXoJsrS+vilTpojvvvtO7N69W2zcuFEMGTJEREdHi61btwbrJcgqLy93/8YAiGnTpolNmzaJffv2CSGEmDBhgrjvvvvct9+zZ49o2rSpePrpp8W2bdvErFmzRKNGjcSSJUuC9RJkaX1906dPFwsXLhS7du0Sv/76q3jiiSdERESEWL58ebBegqzRo0eL+Ph4UVBQ4NHnnTp1yn0bs/0GGbzUM3fuXAHA65/L3r17BQCxcuVK92V//PGHePTRR8V5550nmjZtKm699VaPgMdMhg8f7vX11X09AMTcuXOFEEKcOnVKXH/99SIhIUE0btxYtG/fXowaNcp94DUjra9RiNrl0v/4xz9EUlKSsNvtIisrS+zYsSPwjVfh+PHjYujQoSImJkbExcWJBx54wCMwq/8d3b9/v7jmmmtEixYthN1uFx06dBBPP/20KCsrC9IraOjVV18V7dq1E1FRUeLKK68U69atc1/Xt29fMXz4cI/b/7//9/9Ep06dRFRUlLj44ovFokWLAtxibbS8vnHjxrlvm5SUJG644Qbx888/B6HV6riWBtf/c72m4cOHi759+za4T/fu3UVUVJRIS0vz+C2ajdbX98ILL4j09HQRHR0tWrRoIfr16ydWrFgRnMarINXn1f1MzPYbtP3ZcCIiIiJLCJvVRkRERBQaGLwQERGRpTB4ISIiIkth8EJERESWwuCFiIiILIXBCxEREVkKgxciIiKyFAYvREREZCkMXoiIiMhSGLwQERGRpTB4ISIiIkv5/2jgizpjrH31AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CLUSTERING MENGGUNAKAN DBSCAN**"
      ],
      "metadata": {
        "id": "Jx7D5yq3MIZ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dbscan = DBSCAN(eps=0.3, min_samples=5)\n",
        "labels = dbscan.fit_predict(X)\n"
      ],
      "metadata": {
        "id": "Fv6VovvHMN1V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**VISUALISASI HASIL CLUSTERING**"
      ],
      "metadata": {
        "id": "7yERFKkTNFbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X[:,0], X[:,1], c=labels, cmap='rainbow', s=20)\n",
        "plt.title(\"Hasil Clustering DBSCAN\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "xKHh81uxNLNT",
        "outputId": "6c2e4d84-3a86-48f0-c404-a3af0b7c2dde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**AMBIL CORE POINT**"
      ],
      "metadata": {
        "id": "KOnds97wN5WC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "core_indices = dbscan.core_sample_indices_\n",
        "core_mask = np.zeros(len(X), dtype=bool)\n",
        "core_mask[core_indices] = True\n"
      ],
      "metadata": {
        "id": "d7VYGQRKNe_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"core indices\\n\",core_indices)\n",
        "print (\"core mask\\n\",core_mask)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJMaamVkqkPE",
        "outputId": "e47bedbf-b35c-4975-d837-d31d03c4365e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "core indices\n",
            " [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
            "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
            "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
            "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
            "  90  91  92  93  94  95  96  98  99 100 101 102 103 104 105 106 107 108\n",
            " 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126\n",
            " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 145\n",
            " 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163\n",
            " 164 165 166 167 168 169 170 171 172 173 175 176 177 179 180 181 182 183\n",
            " 184 185 186 187 188 189 190 192 194 195 196 197 198 199 200 201 202 203\n",
            " 204 205 206 207 208 209 210 211 212 213 214 216 217 218 219 220 221 222\n",
            " 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 242 243\n",
            " 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261\n",
            " 262 263 264 265 266 267 268 269 270 271 272 273 275 276 277 278 279 280\n",
            " 281 282 283 284 285 286 287 289 290 291 292 293 294 295 296 297 298 299]\n",
            "core mask\n",
            " [ True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True False  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            " False  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True False  True  True  True False  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True False\n",
            "  True False  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True False\n",
            "  True  True  True  True  True  True  True False  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            " False False  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            "  True  True  True  True  True  True  True  True  True  True False  True\n",
            "  True  True  True  True  True  True  True  True  True  True  True  True\n",
            " False  True  True  True  True  True  True  True  True  True  True  True]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IDENTIFIKASI BORDER POINT**"
      ],
      "metadata": {
        "id": "S93khbrdN90e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "border_mask = (labels != -1) & (~core_mask)\n"
      ],
      "metadata": {
        "id": "mbx82ns_N0ad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"border mask\\n\", border_mask)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0WKqunsgrFZF",
        "outputId": "130cb10e-e841-47bb-ea6f-f9a333004e51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "border mask\n",
            " [False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False  True False False False  True False\n",
            " False False False False False False False False False False False  True\n",
            " False  True False False False False False False False False False False\n",
            " False False False False False False False False False False False  True\n",
            " False False False False False False False  True False False False False\n",
            " False False False False False False False False False False False False\n",
            "  True  True False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False False False False False False False False False False  True False\n",
            " False False False False False False False False False False False False\n",
            "  True False False False False False False False False False False False]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "IDENTIFIKASI NOISE"
      ],
      "metadata": {
        "id": "4FcB-LegQLj4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "noise=X[labels==-1]"
      ],
      "metadata": {
        "id": "VWyNI9W6Pgbu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "noise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iKtKSmQFPm_X",
        "outputId": "45936277-29a5-4f1a-b8f7-ceaa5c3fdbf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.59615919, -0.78652881],\n",
              "       [ 0.15489551, -1.84913949]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(7,7))\n",
        "\n",
        "# Noise\n",
        "plt.scatter(X[labels == -1, 0], X[labels == -1, 1],\n",
        "            c='black', s=20, label='Noise')\n",
        "\n",
        "# Core points\n",
        "plt.scatter(X[core_mask, 0], X[core_mask, 1],\n",
        "            c='red', s=20, label='Core')\n",
        "\n",
        "# Border points\n",
        "plt.scatter(X[border_mask, 0], X[border_mask, 1],\n",
        "            c='blue', s=20, label='Border')\n",
        "\n",
        "plt.legend()\n",
        "plt.title(\"DBSCAN: Core vs Border vs Noise\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "Y1WXV9VkOMqC",
        "outputId": "ea395612-64df-43eb-d506-41c229de77fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_core_points = np.sum(core_mask)\n",
        "count_border_points = np.sum(border_mask)\n",
        "count_noise_points = np.sum(labels == -1)"
      ],
      "metadata": {
        "id": "cz0pxNlerieb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Jumlah Core Points:\", count_core_points)\n",
        "print(\"Jumlah Border Points:\", count_border_points)\n",
        "print(\"Jumlah Noise Points:\", count_noise_points)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djbvUnFprq30",
        "outputId": "8b0cb430-d9e1-4f6a-8a05-b0bb45d17330"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah Core Points: 288\n",
            "Jumlah Border Points: 10\n",
            "Jumlah Noise Points: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ANALISA PENGARUH EPSILON DAN MIN POINTS**"
      ],
      "metadata": {
        "id": "Fk1au81zVCyO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52GdaohZEJLr"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from itertools import product\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/sample_data/Mall_Customers.csv')"
      ],
      "metadata": {
        "id": "4OEFO7UeExcZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IPg3pZX5FXrc",
        "outputId": "2bcac6ec-01c7-4d69-f37c-409124622ee5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aca13895-5e36-48bd-9001-46a6b410f9a8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</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",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aca13895-5e36-48bd-9001-46a6b410f9a8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-aca13895-5e36-48bd-9001-46a6b410f9a8 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-aca13895-5e36-48bd-9001-46a6b410f9a8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-24580cba-ce8f-44ce-88c3-eae4d4bdd01c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24580cba-ce8f-44ce-88c3-eae4d4bdd01c')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-24580cba-ce8f-44ce-88c3-eae4d4bdd01c button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9oDZxH7FbTl",
        "outputId": "fc345835-9f77-43f0-e7fe-13ce67170119"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.scatter(df['Annual Income (k$)'], df['Spending Score (1-100)'], s = 20) #Point size is 20\n",
        "plt.title('Raw Data')\n",
        "plt.xlabel('Annual Income (k$)')\n",
        "plt.ylabel('Spending Score (1-100)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "pdzj0JDjFg3A",
        "outputId": "8c6cca6c-3103-4ade-8969-e8eb97f4259f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[[ 'Annual Income (k$)', 'Spending Score (1-100)']]"
      ],
      "metadata": {
        "id": "jSBGMaTCForD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eps = 10\n",
        "min_samples = 5\n",
        "\n",
        "db = DBSCAN(eps=eps, min_samples=min_samples)\n",
        "labels = db.fit_predict(X)"
      ],
      "metadata": {
        "id": "7EhoJ18sSKWW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(X.iloc[:,0], X.iloc[:,1], c=labels, cmap=\"rainbow\", s=35)\n",
        "plt.title(f\"DBSCAN Result (eps={eps}, min_samples={min_samples})\")\n",
        "plt.xlabel(\"Annual Income \")\n",
        "plt.ylabel(\"Spending Score \")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "CQhwXCsNSVd7",
        "outputId": "77e512de-3cbd-4a59-a028-1d606707f60c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster\"] = labels\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "E6aHDN0aULFi",
        "outputId": "6b07c76d-1eda-4418-ff1e-f8a76d066265"
      },
      "execution_count": null,
      "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   \n",
              "\n",
              "   dbscan_cluster  \n",
              "0               0  \n",
              "1               0  \n",
              "2               1  \n",
              "3               0  \n",
              "4               0  "
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
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              "        async function quickchart(key) {\n",
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              "            document.querySelector('#' + key + ' button');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster\"].value_counts().sort_index()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "aaPHoXlKUPt-",
        "outputId": "6dc23f43-ccbd-4d18-a782-94c76968b91c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dbscan_cluster\n",
              "-1     17\n",
              " 0    113\n",
              " 1     11\n",
              " 2     32\n",
              " 3     27\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th>dbscan_cluster</th>\n",
              "      <th></th>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **experiment**"
      ],
      "metadata": {
        "id": "_PvZ5h6At5fC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "db_2"
      ],
      "metadata": {
        "id": "uPEcWj9_vRGr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eps = 2\n",
        "min_samples = 5\n",
        "\n",
        "db_2 = DBSCAN(eps=eps, min_samples=min_samples)\n",
        "labels2 = db_2.fit_predict(X)"
      ],
      "metadata": {
        "id": "75gc-9uvuA8D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(X.iloc[:,0], X.iloc[:,1], c=labels2, cmap=\"rainbow\", s=35)\n",
        "plt.title(f\"DBSCAN Result (eps={eps}, min_samples={min_samples})\")\n",
        "plt.xlabel(\"Annual Income \")\n",
        "plt.ylabel(\"Spending Score \")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "XUmrfMj8ufab",
        "outputId": "ea19b304-b733-40ed-a69f-08fea6dda5b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_2\"] = labels2\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "jK6v-Ktcu4ll",
        "outputId": "8367e01a-16f8-4eeb-c324-1865a9937a65"
      },
      "execution_count": null,
      "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   \n",
              "\n",
              "   dbscan_cluster  dbscan_cluster_2  \n",
              "0              -1                -1  \n",
              "1              -1                -1  \n",
              "2              -1                -1  \n",
              "3              -1                -1  \n",
              "4              -1                -1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e655e55-0de2-41db-9643-e2062f7db8bc\" 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",
              "      <th>dbscan_cluster</th>\n",
              "      <th>dbscan_cluster_2</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",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e655e55-0de2-41db-9643-e2062f7db8bc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0e655e55-0de2-41db-9643-e2062f7db8bc 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-0e655e55-0de2-41db-9643-e2062f7db8bc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-e0924f5e-db18-4053-8d05-b1f8b1019ff7\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0924f5e-db18-4053-8d05-b1f8b1019ff7')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-e0924f5e-db18-4053-8d05-b1f8b1019ff7 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_2\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "xlpx93k1u7-7",
        "outputId": "2394481b-a308-428a-b2ad-fab6f35e85ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dbscan_cluster_2\n",
              "-1    182\n",
              " 0      5\n",
              " 1      8\n",
              " 2      5\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbscan_cluster_2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>-1</th>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
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              "      <td>8</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "db_5"
      ],
      "metadata": {
        "id": "VreqiDIUvU9z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eps = 5\n",
        "min_samples = 5\n",
        "\n",
        "db_5 = DBSCAN(eps=eps, min_samples=min_samples)\n",
        "labels5 = db_5.fit_predict(X)"
      ],
      "metadata": {
        "id": "K_Qd5pf6vOkD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(X.iloc[:,0], X.iloc[:,1], c=labels5, cmap=\"rainbow\", s=35)\n",
        "plt.title(f\"DBSCAN Result (eps={eps}, min_samples={min_samples})\")\n",
        "plt.xlabel(\"Annual Income \")\n",
        "plt.ylabel(\"Spending Score \")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "JdxvFapnvfVL",
        "outputId": "3c68187d-83de-41d2-a7ba-b90f1dc389af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_5\"] = labels5\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "OdimMELwvk0z",
        "outputId": "4298a2ed-9d13-4f40-a058-6aac39c1bede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
              "0           1    Male   19                  15                      39   \n",
              "1           2    Male   21                  15                      81   \n",
              "2           3  Female   20                  16                       6   \n",
              "3           4  Female   23                  16                      77   \n",
              "4           5  Female   31                  17                      40   \n",
              "\n",
              "   dbscan_cluster  dbscan_cluster_2  dbscan_cluster_5  \n",
              "0              -1                -1                -1  \n",
              "1              -1                -1                 0  \n",
              "2              -1                -1                -1  \n",
              "3              -1                -1                 0  \n",
              "4              -1                -1                -1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f69a6d2-87b8-4662-8612-ca2cd09bc0be\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <th>dbscan_cluster</th>\n",
              "      <th>dbscan_cluster_2</th>\n",
              "      <th>dbscan_cluster_5</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "      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",
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              "        document.querySelector('#df-3f69a6d2-87b8-4662-8612-ca2cd09bc0be 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-3f69a6d2-87b8-4662-8612-ca2cd09bc0be');\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",
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              "      }\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-be2ae769-d0b2-4153-8136-557709a333b7 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 4,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          -1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_5\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "WJn5Wk32vqAz",
        "outputId": "39379c63-4f1d-4a01-d254-214da89cad77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dbscan_cluster_5\n",
              "-1    87\n",
              " 0     6\n",
              " 1    78\n",
              " 2    10\n",
              " 3     9\n",
              " 4    10\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbscan_cluster_5</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>-1</th>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>6</td>\n",
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              "      <th>3</th>\n",
              "      <td>9</td>\n",
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              "      <th>4</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "db_10"
      ],
      "metadata": {
        "id": "emPgpVxBv2fX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eps = 10\n",
        "min_samples = 10\n",
        "\n",
        "db_10 = DBSCAN(eps=eps, min_samples=min_samples)\n",
        "labels10 = db_10.fit_predict(X)"
      ],
      "metadata": {
        "id": "PEo8Soi6v-3r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(X.iloc[:,0], X.iloc[:,1], c=labels10, cmap=\"rainbow\", s=35)\n",
        "plt.title(f\"DBSCAN Result (eps={eps}, min_samples={min_samples})\")\n",
        "plt.xlabel(\"Annual Income \")\n",
        "plt.ylabel(\"Spending Score \")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "8zaX0-Y8wIxT",
        "outputId": "95b2bdeb-a85d-4c62-9970-2db6115ce608"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_10\"] = labels10\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "N7Oeo9KiwPi7",
        "outputId": "1f75b702-8dc7-41fd-eb8d-61fe7cc895a2"
      },
      "execution_count": null,
      "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   \n",
              "\n",
              "   dbscan_cluster  dbscan_cluster_2  dbscan_cluster_5  dbscan_cluster_10  \n",
              "0              -1                -1                -1                 -1  \n",
              "1              -1                -1                -1                 -1  \n",
              "2              -1                -1                -1                 -1  \n",
              "3              -1                -1                 0                  0  \n",
              "4              -1                -1                -1                 -1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-552dc78e-0e74-461a-a29d-c91f35fd5a1e\" 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",
              "      <th>dbscan_cluster</th>\n",
              "      <th>dbscan_cluster_2</th>\n",
              "      <th>dbscan_cluster_5</th>\n",
              "      <th>dbscan_cluster_10</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-552dc78e-0e74-461a-a29d-c91f35fd5a1e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-552dc78e-0e74-461a-a29d-c91f35fd5a1e 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-552dc78e-0e74-461a-a29d-c91f35fd5a1e');\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",
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              "\n",
              "\n",
              "    <div id=\"df-800a5725-bdb3-43c7-b98d-7a6b37bd2c2b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-800a5725-bdb3-43c7-b98d-7a6b37bd2c2b')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-800a5725-bdb3-43c7-b98d-7a6b37bd2c2b button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_10\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "LkPWphlYwVqr",
        "outputId": "ca3d0004-de14-40a0-80b7-1257000608e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dbscan_cluster_10\n",
              "-1    54\n",
              " 0    11\n",
              " 1    87\n",
              " 2    26\n",
              " 3    22\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbscan_cluster_10</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>-1</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
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              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "db_3"
      ],
      "metadata": {
        "id": "AZrgW7KkwdJS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eps = 10\n",
        "min_samples = 3\n",
        "\n",
        "db_3 = DBSCAN(eps=eps, min_samples=min_samples)\n",
        "labels3 = db_3.fit_predict(X)"
      ],
      "metadata": {
        "id": "c09n9bhTwbj7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(X.iloc[:,0], X.iloc[:,1], c=labels3, cmap=\"rainbow\", s=35)\n",
        "plt.title(f\"DBSCAN Result (eps={eps}, min_samples={min_samples})\")\n",
        "plt.xlabel(\"Annual Income \")\n",
        "plt.ylabel(\"Spending Score \")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "1z--rAKIwzzb",
        "outputId": "3355e285-7851-40e7-d0d7-470a519e3989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_3\"] = labels3\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "VZbLDY0rw_Q6",
        "outputId": "7181f04a-2baa-44ef-e5bf-daccdb90b7a5"
      },
      "execution_count": null,
      "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   \n",
              "\n",
              "   dbscan_cluster  dbscan_cluster_2  dbscan_cluster_5  dbscan_cluster_10  \\\n",
              "0              -1                -1                -1                  0   \n",
              "1              -1                -1                -1                  0   \n",
              "2              -1                -1                -1                  0   \n",
              "3              -1                -1                 0                  0   \n",
              "4              -1                -1                -1                  0   \n",
              "\n",
              "   dbscan_cluster_3  \n",
              "0                 0  \n",
              "1                 0  \n",
              "2                 0  \n",
              "3                 0  \n",
              "4                 0  "
            ],
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              "      <td>17</td>\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bbaa6225-a2c7-4a0a-a5bf-26cfdd8ff98c')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-bbaa6225-a2c7-4a0a-a5bf-26cfdd8ff98c button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_10\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dbscan_cluster_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -1,\n        \"max\": 3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"dbscan_cluster_3\"].value_counts().sort_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "lgwaSaWPxHmy",
        "outputId": "8e4e189c-5a6c-4103-fddc-a182ca94f9d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dbscan_cluster_3\n",
              "-1     10\n",
              " 0    126\n",
              " 1      3\n",
              " 2     33\n",
              " 3     28\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dbscan_cluster_3</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>-1</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>126</td>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#db\n",
        "siluet=silhouette_score(X, db.labels_)\n",
        "siluet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOKScM1ixZO_",
        "outputId": "0a1665b5-6781-4091-8de9-442c91fa54b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(0.41249187303464097)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#db_2\n",
        "siluet=silhouette_score(X, db_2.labels_)\n",
        "siluet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dg6R53vYy4k6",
        "outputId": "fed1c72a-5575-472d-ebc7-4399efddb592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(-0.3384933492468103)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#db_5\n",
        "siluet=silhouette_score(X, db_5.labels_)\n",
        "siluet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxjVRET-zHh6",
        "outputId": "913650bd-66e4-456a-c79e-93a717e27822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(0.1135163893571667)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#db_10\n",
        "siluet=silhouette_score(X, db_10.labels_)\n",
        "siluet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3dkoRrUzK4K",
        "outputId": "9514add4-e082-4695-95d4-8aba7710eab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(0.30375625528880634)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#db_3\n",
        "siluet=silhouette_score(X, db_3.labels_)\n",
        "siluet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9MsAQ9SzNEW",
        "outputId": "555af144-3469-4cbc-919b-d66228f8e515"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "np.float64(0.36270289706345077)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   model mana yang palin baik?\n",
        "- jawab : dari percobaan beberapa model diatas, model yang paling baik berdasarkan nilai silhoute score yang dihitung sebelumnya ada model db  (epsilon 10 dan min samples nya 5), model tersebut terbaik karena menghasilkan shiloutte score paling tinggi yaitu 0.4124...\n",
        "2.   apa pengaruh eps\n",
        "- jawab : parameter eps digunakan untuk menentukan batas jarak maksimum antar titik dalam suatu lingkungan dan berpengaruh terhadap hasil clustering. Nilai eps yang besar membuat jangkauan lingkungan semakin luas sehingga lebih banyak titik masuk ke dalam satu klaster, bisa menggabungkan klaster yang seharusnya terpisah. Sebaliknya, jika nilai eps terlalu kecil, klaster yang terbentuk bisa menjadi sangat sedikit atau banyak titik dianggap sebagai noise karena tidak memiliki cukup tetangga di sekitarnya\n",
        "3.   apa pengaruh min point\n",
        "- jawab : Parameter min point berfungsi untuk menentukan jumlah minimum titik dalam radius eps untuk membentuk klaster. Semakin besar nilainya, kriteria core point semakin ketat, klaster yang terbentuk lebih padat, dan titik noise semakin berkurang.\n",
        "\n"
      ],
      "metadata": {
        "id": "XHOm6_FHy3Sb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MENCARI NILAI EPSILON DAN MINPOINT YANG OPTIMAL**"
      ],
      "metadata": {
        "id": "JPmeIlc6VMvs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eps_values = np.arange(8,12.75,0.25) #parameter dalam range >=8 & <12.75 dengan kelipatan 0.25\n",
        "min_points = np.arange(3,10) #parameter dalam range >=3 & <10 dengan kelipatan 1\n",
        "\n",
        "DBSCAN_params = list(product(eps_values, min_points))\n",
        "DBSCAN_params[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "suVBvPxzWB0O",
        "outputId": "136bc1bf-a1d4-49fd-99f8-c3781ee4810d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(np.float64(8.0), np.int64(3)),\n",
              " (np.float64(8.0), np.int64(4)),\n",
              " (np.float64(8.0), np.int64(5)),\n",
              " (np.float64(8.0), np.int64(6)),\n",
              " (np.float64(8.0), np.int64(7)),\n",
              " (np.float64(8.0), np.int64(8)),\n",
              " (np.float64(8.0), np.int64(9)),\n",
              " (np.float64(8.25), np.int64(3)),\n",
              " (np.float64(8.25), np.int64(4)),\n",
              " (np.float64(8.25), np.int64(5))]"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#melakukan iterasi untuk menghitung silhouette score berdasarkan kombinasi parameter\n",
        "\n",
        "no_of_clusters = []\n",
        "sil_score = []\n",
        "\n",
        "for p in DBSCAN_params:\n",
        "    DBS_clustering = DBSCAN(eps=p[0], min_samples=p[1]).fit(X)\n",
        "    no_of_clusters.append(len(np.unique(DBS_clustering.labels_)))\n",
        "    sil_score.append(silhouette_score(X, DBS_clustering.labels_))"
      ],
      "metadata": {
        "id": "YhyK5I_5We0p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sil_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqeGH06PWzoZ",
        "outputId": "a00bc9e6-559e-4db8-921b-2eaeed421549"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " np.float64(0.3888444498053395),\n",
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              " np.float64(0.39201313128562093),\n",
              " np.float64(0.3392781429643879),\n",
              " np.float64(0.24232188104578023),\n",
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              " np.float64(0.17545298910606077),\n",
              " np.float64(0.46403608560805165),\n",
              " np.float64(0.41461622960696004),\n",
              " np.float64(0.39201313128562093),\n",
              " np.float64(0.3392781429643879),\n",
              " np.float64(0.24232188104578023),\n",
              " np.float64(0.23159654733166227),\n",
              " np.float64(0.17545298910606077),\n",
              " np.float64(0.4704714508042984),\n",
              " np.float64(0.419532558337873),\n",
              " np.float64(0.4321128619542937),\n",
              " np.float64(0.34133299752613233),\n",
              " np.float64(0.300305984909528),\n",
              " np.float64(0.23895668511168006),\n",
              " np.float64(0.19143138639469612),\n",
              " np.float64(0.475053701963407),\n",
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              " np.float64(0.43598431858196485),\n",
              " np.float64(0.35166437164696535),\n",
              " np.float64(0.2928486499658635),\n",
              " np.float64(0.2935222061383018),\n",
              " np.float64(0.20760239612573833),\n",
              " np.float64(0.480431771178107),\n",
              " np.float64(0.43096743883351124),\n",
              " np.float64(0.4422214837114166),\n",
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              " np.float64(0.39889488555584734),\n",
              " np.float64(0.41249187303464097),\n",
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              " np.float64(0.31460042298129),\n",
              " np.float64(0.36270289706345077),\n",
              " np.float64(0.39889488555584734),\n",
              " np.float64(0.41249187303464097),\n",
              " np.float64(0.4344252525531487),\n",
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              " np.float64(0.31460042298129),\n",
              " np.float64(0.36270289706345077),\n",
              " np.float64(0.39889488555584734),\n",
              " np.float64(0.41249187303464097),\n",
              " np.float64(0.40348780174661053),\n",
              " np.float64(0.42084049577844984),\n",
              " np.float64(0.36328873885121793),\n",
              " np.float64(0.3143272689586975),\n",
              " np.float64(0.36270289706345077),\n",
              " np.float64(0.39889488555584734),\n",
              " np.float64(0.41249187303464097),\n",
              " np.float64(0.40346443499452717),\n",
              " np.float64(0.40348780174661053),\n",
              " np.float64(0.4079173459073072),\n",
              " np.float64(0.34152262335726696),\n",
              " np.float64(0.36270289706345077),\n",
              " np.float64(0.3991445158830211),\n",
              " np.float64(0.4126986177259228),\n",
              " np.float64(0.40346443499452717),\n",
              " np.float64(0.40348780174661053),\n",
              " np.float64(0.4136956025809889),\n",
              " np.float64(0.3437932243993126),\n",
              " np.float64(0.29621585740509815),\n",
              " np.float64(0.3503754349709618),\n",
              " np.float64(0.3127556291422122),\n",
              " np.float64(0.40346443499452717),\n",
              " np.float64(0.40348780174661053),\n",
              " np.float64(0.4136956025809889),\n",
              " np.float64(0.3437932243993126),\n",
              " np.float64(0.29621585740509815),\n",
              " np.float64(0.35077635622133485),\n",
              " np.float64(0.31590301995172504),\n",
              " np.float64(0.3136438284634688),\n",
              " np.float64(0.4010964813515293),\n",
              " np.float64(0.38868493722939385),\n",
              " np.float64(0.38739688367949543),\n",
              " np.float64(0.29621585740509815),\n",
              " np.float64(0.35077635622133485),\n",
              " np.float64(0.3149648006663511),\n",
              " np.float64(0.3136438284634688),\n",
              " np.float64(0.40671748372320904),\n",
              " np.float64(0.41134745019497965),\n",
              " np.float64(0.42335234443339287),\n",
              " np.float64(0.29621585740509815),\n",
              " np.float64(0.35077635622133485),\n",
              " np.float64(0.3149648006663511),\n",
              " np.float64(0.31365323385894556),\n",
              " np.float64(0.3173128619854302),\n",
              " np.float64(0.31569450521116743),\n",
              " np.float64(0.42335234443339287),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3200802658739474),\n",
              " np.float64(0.3105587436000119),\n",
              " np.float64(0.3173128619854302),\n",
              " np.float64(0.31569450521116743),\n",
              " np.float64(0.42335234443339287),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3200802658739474),\n",
              " np.float64(0.3141506951327195),\n",
              " np.float64(0.3201903551320457),\n",
              " np.float64(0.31841228544650907),\n",
              " np.float64(0.4260237574720438),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3563908621481342),\n",
              " np.float64(0.3141506951327195),\n",
              " np.float64(0.31590301995172504),\n",
              " np.float64(0.31730391517513096),\n",
              " np.float64(0.401408271408564),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3455936875612336),\n",
              " np.float64(0.3563908621481342),\n",
              " np.float64(0.3141506951327195),\n",
              " np.float64(0.31590301995172504),\n",
              " np.float64(0.31730391517513096),\n",
              " np.float64(0.4143340576998346)]"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])\n",
        "tmp['No_of_clusters'] = no_of_clusters\n",
        "\n",
        "pivot_1 = pd.pivot_table(tmp, values='No_of_clusters', index='Min_samples', columns='Eps')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,6))\n",
        "sns.heatmap(pivot_1, annot=True,annot_kws={\"size\": 16}, cmap=\"YlGnBu\", ax=ax)\n",
        "ax.set_title('Number of clusters')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "-P4eB5zFW4PA",
        "outputId": "d3a9ae77-5a90-4a53-e3dc-839f2a0826b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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AAHDASjN/FeU53wkAAAAAwCFm/gAAAADAAZvNZnZCpWHmDwAAAACqAWb+AAAAAMAhz5kvY/AHAAAAAA5wwBcAAAAAQJXCzB8AAAAAOMDMHwAAAACgSmHmDwAAAAAcsHnQfJnnfCcAAAAAAIeY+QMAAAAAB9jnDwAAAABQpTDzBwAAAAAOeNLMH4M/AAAAAHDAkwZ/nvOdAAAAAAAcYuavBPWjI/XvEdeo72XtFVM3UukZ2Vq/abfemrpIC39ab3aeomsH6d5b2uniTjGqVztYNpuUfCxLv/+RpKlfbdLW3cdpo61atC169VP9tWR1qff518yX5OPn66aiv9HmHNoqz/uvLtDMT5ZKkoaN6Kchw3ubG3QW2pxjpbZ585Zq+fJ12ro1USkpqTp58pQCAvwVFxejPn26a+jQqxUUFEhbFWqrCn1msclmdkKlYfB3jk5tG2vup2NVMyJESUdS9f3SDYqMCFHP7q3Up2c7TXzlaz370izT+to1j9K0FwYoOMhPh1NOafmaAyosNNQivqauv/ICDbyiiUZP/EkLlyXSRptHt52tXovGCq9bq8TbbF7mbuBAm3Noq5g/N+7RrM+WyWazyTAMs3Ps0OYcq7VNn/6t1q/fqvj4+mrZMl7h4cE6evSENmzYqk2bdujrrxfr008TVKdOTdqqSFtV6EPFMfg7i7+/r6a/+5BqRoToq3m/6h//fkfZOXmSTg8K//vJWD0+6gb9+vs2/fTLJlMan/l3DwUH+WnG/C2a8NoK5Rec/gVgs0kPDuukkbd31LOje+inX/cpN6+ANto8tu1srXt3V6srupn2/KWhzTm0OS87K1cvPDVDkbVC1axlA61YutnspCK0OceKbWPH3qPY2HoKDw+xuz419aRGjpyotWv/0uTJH+mll8bQVkXaqkKfWdjnz0MNurKLGsTUUmraKf3rsQ+LBn6StPaP3Up49WtJ0mMPXm9KX3iov1rEn/5Ly8sfrSn6H3FJMgzptY/XKSs7X2Eh/moSG04bbR7bBsCxD9/4Vgf3HdWox29UjeAAs3Ps0OYcK7a1a9es2ABBkiIiQjV69B2SpBUrzNlVhjbnWb0PFcfg7yyd2jWWJK3flKi0k5nFbv9p+em/tHXvfIHqRIW5tU2ScnPLPrNyPC3bhSXF0eYc2gBUpo1rdmrulyvU56pO6npJC7Nz7NDmHCu3OeLt7S1J8vW1xv6vZ6PNeVbvcyWbzctlF3djs8+zBAWd/mva8dRTJd5+7Hi6JMnLy0vtW8dp0ZIN7kqTJGVm5+v3P5LUpW20Hrq7c7HN8B64s6MCA3y09Ld9OpySQRttHtt2rv2bd+jo3kPKzcpRYGiQ6jaNVaNOLeVjgV9QtNHmLlmZOZry9ExFRAZrxMODTG05F23OsXKbI6dOZeqNN76QJF1++YUm19ijzXlW70PZMfg7S8rRk5KkuIa1S7z97OsbNYhyS9O5Hn/xF32Q0E+3DGyhXt0aaNO2oyosNNSySU3VqRWkOd/v0NOvraCNNo9vO9uWEo7AGBQRqr7/GqJGHVuaUPQ32pxDW/m9+8p8HT54XE9NGaaQ0BqmdZSENudYue2M5cvXacGCZSosNHTs2AmtX79VGRlZ6tGjo8aMGUZbFWyrCn3u5kn7/DH4O8vSX//U2AeuU4c2cWrXqpE2/rnH7vbhQ/8+pHJIiDkfwon703TT/XM1Zdxl6tGlvupGBRfdtmNPqlZvOKRTmXmlLIE22jyjTZKi4mLUK/4GNWjbTKFREcrPyVPKnoNaOeNbJW1N1Nzn3tP140eqQZumtNHm0W1rVm7TN1+vUq8r2+viy1q7/flLQ5tzrNx2tp0792vOnJ/srrv66p4aN+4ehYQEmVR1Gm3Os3qf+3nO4M9mWOF4wZUssOGtTj/2+5lPqke3Fjpw6Jge/M9HWv7bFkVGhOj/bu+jB+4doPz8Qvn5+eg/CV/oxbfnO/Uc9Ztc7nRfx1Z19OaE3sovNDT5nd+0cv0h5eUVqFPruho3opviGoTpq2+36bEpPzv9HLTR5q62f78ZW8nFpxmGofkJ72vX6k2KahSjoa+MdcnzOIM251SHtgENcpx6XEZ6lu69eYrycvP1wVePKCzi7/8xe378DC1esMa0c8LRZu22hsHNKppaJC8vX0lJKfrxx9/09ttfymaz6Y03HlOXLuYPXGlzXuX2XVDpfe5St+U4ly378F8JLlt2SUwfxm7ZskVTp07V1q1bJUlbt27ViBEjdPfdd+unn346z6OlnJwcnTx50u5iGM4fcn7IiFf06+9bVb9eTX390Rgd+fMjbVn+qkb939V686OF2rRlryTp+An37+MUEuSnt57uo8jwQN3/5GIt+GmXjqVm6eSpXC1ZtU/3jP1OmVl5umlAM3VtH00bbR7bdj42m03dbx0gSUrZc1DpKakmF/2NNufQ5thbL85VypE03f/IdXaDBCugzTlWbnPE19dHDRtG6667rtX77z+ltLRTGjPmRWVnO/dHDdrMb5Os3+cuHPClkixcuFCDBg1ScHCwMjMzNWfOHN1xxx1q166dCgsL1bdvX33//fe6/HLHM2UJCQmaMGGC3XXeoa3kG9bGqaaUYyd1xQ0TdHmPNup1UStFRgQrOSVNCxav1bo/dmv3729Jkv7cus+p5VfEZd0aqGZEoPYeTNPGrSnFbt+flK6NW1PUvUM9XdQxRr9tSKKNNo9sK4vI+nWL/p1+7IRCoiJMrLFHm3NoK9mKJZvl7e2lebN+1bxZv9rdtn/P6Z/dhXN/07rV2xVZM1SPJwyljTaXateumZo0aaAdO/Zp8+ad6ty5ldlJRWhzntX7UDamDv6efvppjRkzRs8++6xmzJih2267TSNGjNDEiRMlSePGjdOkSZNKHfyNGzdOo0ePtruudqvhFW776ZdNxU7kHhdbW9F1InT0eLrWb06s8HOUV3Tt0/tblbaPVXpGrqTT52hzJ9qcQ5vrZKX/PTvvF2itPtqcQ5tjBQWF+mPtboe3Hz6UqsOHUlUn2v0DZtqcY+W2sggMPH0E9WPHTpgbUgLanGf1PlfhgC+V5M8//9Qnn3wiSRo8eLBuv/123XjjjUW3DxkyRFOnTi11Gf7+/vL3t/9Fa7N5V36spFH/uFqS9NEXPyovz/lNS5115Ojpcw82bhCu4CBfncqw/59yH2+bWjU9fVLuA0nptNHmsW1lsX35WkmSX40ARcTUMbnGHm3Ooa1k/132rMPbzN53jTbnWLmtLI4fT9PWraf/SN6oUYzJNfZoc57V+1A2pg9jbTabpNPnzgsICFBY2N8nTw8JCVFaWppbe5o3jVFIcKDddd7eXhozcpCGD7lCOxOTNPn1/7q16YyfV+9XRlaeAgN8NPHfl6pGwN9jd18fLz02srti6oQoN69AC39278wkbbS5W/LuA9q1epMKC+z/EGMUFmrz4pVa/tkCSVKHq3rK28c1fxCijTYrtAHutnPnPs2bt1Q5ObnFbktMPKgHH5ys3Nw8tW/fTM2aNaKtCrRVhT4z2eTlsou7mTrz16hRI+3YsUPx8fGSpJUrV6phw4ZFt+/bt0/R0e49kMQ9t12he4ZcofWbduvQ4VT5+/moS4emqls7XDsTk3TVkOeUmWXOTq7H07L15MvLNemRnhrQq7G6tovWpm0pyssvVJtmtVQ3KlgFBYV69o2V2u/mmRjaaHO3k8nHNH/SB/IPrqHajesrKDxUORmZOrovqeiAG816dFK3W/rTRptHtwHuduxYmsaMeVHjx7+pFi0aq27dWsrLy9OhQyn666/dKiwsVHx8A7388iO0VZG2qtCHymHq4G/EiBEqOOuvqK1b2x829rvvvit1fz9XWPjTesXWr6X2rePUsU1j5eTma8fuQ3rt/W/09rRFys4x75xmkjTvh53avvu4ht3YWl3aRqt7x3qyyabk45mau3iHPpnzp/4o4eActNHmaW1RcTHqMLCXjuzcp9SDyTq0NVEyDNUID1HTi9qr1eXdFGfSzui00QZ4sqZNG+qhh27XmjV/avfuA9qyZbfy8vIVHh6i7t3bqk+fi3TDDb3l5+dLWxVpqwp9ZvKkff44z58JKnKeP8CTuOo8f0BV5Ox5/gBnVeZ5/oDzq7rn+WvQ9mmXLXv/H0+6bNkl8ZxhLAAAAADAIVM3+wQAAAAAK/OkzT495zsBAAAAADjEzB8AAAAAOGDGKRlcxXO+EwAAAACAQ8z8AQAAAIAD7PMHAAAAAKhSmPkDAAAAAAc8aeaPwR8AAAAAOMABXwAAAAAAVQozfwAAAADgiAdt9uk53wkAAAAAwCFm/gAAAADAAU864IvnfCcAAAAAAIeY+QMAAAAAB2w2m9kJlYaZPwAAAACoBpj5AwAAAAAHPOk8fwz+AAAAAMABDvgCAAAAAKhSmPkDAAAAAEc44AsAAAAAoCph5g8AAAAAHPGg6TIP+lYAAAAAAI4w8wcAAAAAjrDPHwAAAACgKmHmDwAAAAAc8aCZP48c/NVvcrnZCQDKYECDHLMTAMtoGNzM7AQAQEk8aFtJD/pWAAAAAACOeOTMHwAAAABUBsODNvtk5g8AAAAAqgFm/gAAAADAEc+Z+GPmDwAAAACqA2b+AAAAAMARL8+Z+mPmDwAAAAAsrqCgQE888YTi4uIUGBio+Ph4PfPMMzIMo8zLYOYPAAAAAByxyNE+J0+erLffflsff/yxWrVqpTVr1uiuu+5SWFiYHnjggTItg8EfAAAAAFjcr7/+qkGDBumqq66SJDVq1EjTp0/X6tWry7wMNvsEAAAAAEdsrrvk5OTo5MmTdpecnJwSMy666CL9+OOP2r59uyRp48aNWr58ufr371/mb4XBHwAAAAA44mVz2SUhIUFhYWF2l4SEhBIzxo4dq1tuuUXNmzeXr6+vOnTooFGjRmnIkCFl/lbY7BMAAAAATDBu3DiNHj3a7jp/f/8S7ztz5kx9/vnn+uKLL9SqVStt2LBBo0aNUr169XTnnXeW6fkY/AEAAACAIy484Iu/v7/Dwd65xowZUzT7J0lt2rTR3r17lZCQUObBH5t9AgAAAIDFZWZmysvLfvjm7e2twsLCMi+DmT8AAAAAcMQaZ3rQwIEDNXHiRDVs2FCtWrXS+vXr9dJLL+nuu+8u8zIY/AEAAACAxb3++ut64okndN999yk5OVn16tXT//3f/+nJJ58s8zIY/AEAAACAI17WmPoLCQnRK6+8oldeecXpZbDPHwAAAABUA8z8AQAAAIAj1pj4qxQM/gAAAADAAcOFp3pwNzb7BAAAAIBqgJk/AAAAAHDEIgd8qQwM/koQXTtI997SThd3ilG92sGy2aTkY1n6/Y8kTf1qk7buPk4fbbRZoO1c77+6QDM/WSpJGjain4YM721u0Floc46V2yTr9M2bt1TLl6/T1q2JSklJ1cmTpxQQ4K+4uBj16dNdQ4deraCgQFParN5HG220VZ0+VJzNMAzD7IjK1vTy951+bLvmUZr2wgAFB/npcMopbd5+VIWFhlrE11SDeqHKyy/U6Ik/aeGyxEos9ow+2mgrrx/n1a201j837tHo4W/KMCTDMCw1UKDNOVZukyq/r2FwM6cfe+utj2j9+q2Kj6+vunWjFB4erKNHT2jDhq3Kzs5VbGy0Pv00QXXq1HT6OSrCyn200Uabu/ouqPRed2kycJrLlr1z/jCXLbskDP7OMe/969UivqZmzN+iCa+tUH7B6ZfHZpMeHNZJI2/vqLT0HF104+fKzSuorGSP6KONtvKqrMFfdlau/nnbS8rJzlOzlg20YulmywwUaPO8Nsk1fRUZ/G3cuE2xsfUUHh5id31q6kmNHDlRa9f+pauuulQvvTTG6eeoCCv30UYbbe7qY/BXEncP/jjgy1nCQ/3VIv70XzJe/mhN0f/oSpJhSK99vE5Z2fkKC/FXk9hw+mijzcSfh7N9+Ma3OrjvqEY9fqNqBAeY2nIu2pxj5TbJen3t2jUr9j9rkhQREarRo++QJK1Ysd7dWUWs3Eebc2hzjpXbJOv3mcZmc93FzRj8nSU3t+wzF8fTsl1YUjIr99HmHNoqbuOanZr75Qr1uaqTul7SwrSOktDmHCu3SdbvO5e3t7ckydfX1+SSklm5jzbn0OYcK7dJ1u9D2TD4O0tmdr5+/yNJkvTQ3Z3l4/33aNxmkx64s6MCA3y09Ld9OpySQR9ttJn48yBJWZk5mvL0TEVEBmvEw4NMaXCENudYuU2yft+5Tp3K1BtvfCFJuvzyC02uKc7KfbQ5hzbnWLlNsn6fy3nZXHdxM472eY7HX/xFHyT00y0DW6hXtwbatO30AS5aNqmpOrWCNOf7HXr6tRX00UabBX4e3n1lvg4fPK6npgxTSGgN0zpKQptzrNwmWb9v+fJ1WrBgmQoLDR07dkLr129VRkaWevToqDFjhpmdZ+k+2mijrer0uZ3nnOnBeoM/wzBkM2H71zMS96fppvvnasq4y9SjS33VjQouum3HnlSt3nBIpzLz6KONNpPb1qzcpm++XqVeV7bXxZe1NqXBEdqcY+U2yfp9krRz537NmfOT3XVXX91T48bdo5CQIJOq/mblPtqcQ5tzrNwmWb8PzrPc0T79/Py0ceNGtWjh/H4UFTnaZ8dWdfTmhN7KLzQ0+Z3ftHL9IeXlFahT67oaN6Kb4hqE6atvt+mxKT87/RwVYeU+2mgrL2eP9pmRnqV7b56ivNx8ffDVIwqL+PsX0fPjZ2jxgjWmHRmSNs9rc1dfRY72ea68vHwlJaXoxx9/09tvfymbzaY33nhMXbpYY9Bq5T7aaKPtb5XbV4WP9nn9py5b9s7Zt7ts2SUxbZ+/0aNHl3gpKCjQpEmTir4+n5ycHJ08edLuYhQ6NxMREuSnt57uo8jwQN3/5GIt+GmXjqVm6eSpXC1ZtU/3jP1OmVl5umlAM3VtH+3Uc1SElftoo82d3npxrlKOpOn+R66z+59wK6DNOVZuk6zfdy5fXx81bBitu+66Vu+//5TS0k5pzJgXlZ2dY3aaJGv30UYbbX+zeh/Kz7TNPl955RW1a9dO4eHhdtcbhqEtW7YoKCioTJt/JiQkaMKECXbXRTS6WjXjril302XdGqhmRKD2HkzTxq0pxW7fn5SujVtT1L1DPV3UMUa/bUgq93NUhJX7aKPNnW0rlmyWt7eX5s36VfNm/Wrfted068K5v2nd6u2KrBmqxxOG0kabR/eVpl27ZmrSpIF27NinzZt3qnPnVmYn2bFyH23Ooc05Vm6TrN/nUibuklbZTBv8Pffcc3rvvff04osv6vLLLy+63tfXV9OmTVPLli3LtJxx48YVmyHseM1nTjVF1z69P1Np+zClZ+RKOn0ONHezch9tzqHNeQUFhfpj7W6Htx8+lKrDh1JVJzrCjVWn0eYcK7dJ1u8rTWDg6XMRHjt2wtwQB6zcR5tzaHOOldsk6/fh/Ewb/I0dO1ZXXHGFhg4dqoEDByohIcGp84b4+/vL39/+fzxtXs6df+TI0UxJUuMG4QoO8tWpDPv/6fXxtqlV09MnvT6QlO7Uc1SElftoo82d/rvsWYe3mb1/GG3OsXKbZP2+0hw/nqatWxMlSY0axZhcU5yV+2hzDm3OsXKbZP0+l/Kgk+OZ+q106dJFa9euVUpKijp37qzNmzebeqTPn1fvV0ZWngIDfDTx35eqRsDfY2NfHy89NrK7YuqEKDevQAt/TqSPNtpM/HkAcNrOnfs0b95S5eTkFrstMfGgHnxwsnJz89S+fTM1a9aIPtpoo61K9qFymH6qh+DgYH388ceaMWOGevfurYKCAtNajqdl68mXl2vSIz01oFdjdW0XrU3bUpSXX6g2zWqpblSwCgoK9ewbK7XfhJk/K/fRRhsAcxw7lqYxY17U+PFvqkWLxqpbt5by8vJ06FCK/vprtwoLCxUf30Avv/wIfbTRRluV7TMV+/xVvltuuUWXXHKJ1q5dq9jYWNM65v2wU9t3H9ewG1urS9tode9YTzbZlHw8U3MX79Anc/7UHyUc/II+2mgDYIamTRvqoYdu15o1f2r37gPasmW38vLyFR4eou7d26pPn4t0ww295efn3C4RntxHG220VZ0+U3nO2M965/mrDBU5zx8A93H2PH+AJ6rM8/wBgPVU4fP83fy5y5a988shLlt2SSwz8wcAAAAAVmN4ec7UnwcduwYAAAAA4AgzfwAAAADgiAcd8IWZPwAAAACoBpj5AwAAAABHPGfij5k/AAAAAKgOmPkDAAAAAEc86GifDP4AAAAAwBEO+AIAAAAAqEqY+QMAAAAARzxn4o+ZPwAAAACoDpj5AwAAAABHPOiAL8z8AQAAAEA1wMwfAAAAADjCzB8AAAAAoCph5g8AAAAAHDA8Z+KPwR8AAAAAOMRmnwAAAACAqoSZPwAAAABwxMbMHwAAAACgCmHmDwAAAAAcYZ8/AAAAAEBVwswfAAAAADjiQdNlHvStAAAAAAAcYeYPAAAAABzxoKN9MvgDAAAAAEc86IAvHjn4y+vZ0OyEUvku22d2QpVk9fcV5fftfs/5MAUqakCDbWYnACiDhsHNzE4AnOaRgz8AAAAAqAyGB232yQFfAAAAAKAaYOYPAAAAABzxoOkyD/pWAAAAAACOMPMHAAAAAI540NE+mfkDAAAAgGqAmT8AAAAAcMSDjvbJ4A8AAAAAHGGzTwAAAABAVcLMHwAAAAA44jkTf8z8AQAAAEB1wMwfAAAAADhgsM8fAAAAAKAqYeYPAAAAABxh5g8AAAAAUJUw8wcAAAAAjnjQSd6Z+QMAAACAaoDBHwAAAAA44uXCSzk0atRINput2GXkyJFlXgabfQIAAACAIxbZ7PP3339XQUFB0debN29Wnz59dNNNN5V5GQz+AAAAAMDioqKi7L6eNGmS4uPj1bNnzzIvg8EfAAAAADjiwlM95OTkKCcnx+46f39/+fv7l/q43NxcffbZZxo9erRs5ZiZZJ8/AAAAADBBQkKCwsLC7C4JCQnnfdx///tfnThxQsOGDSvX8zHzd476YQFaPqpsU6eDp67W6n2pLi4qLrp2kO69pZ0u7hSjerWDZbNJycey9PsfSZr61SZt3X3c7U1WbrPye2rlNsn6fYte/VR/LVld6n3+NfMl+fj5uqnob7Q5h7bK8/6rCzTzk6WSpGEj+mnI8N7mBp2FNufQ5jyr9M2bt1TLl6/T1q2JSklJ1cmTpxQQ4K+4uBj16dNdQ4deraCgQFPaqkKfaVw48zfukXEaPXq03XXnm/WTpA8//FD9+/dXvXr1yvV8DP7OkZFboFkbDjq8vUlUsNrHhCk9J1+bkk66sey0ds2jNO2FAQoO8tPhlFNavuaACgsNtYivqeuvvEADr2ii0RN/0sJlibT9j5XfUyu3SdbvO6Nei8YKr1urxNtsXuZu4ECbc2irmD837tGsz5bJZrPJMAyzc+zQ5hzanGelvunTv9X69VsVH19fLVvGKzw8WEePntCGDVu1adMOff31Yn36aYLq1KlJXzVRlk08z7V371798MMPmj17drmfj8HfOVKz8vTw3M0Ob596W0dJ0vzNScrKK3B4P1d55t89FBzkpxnzt2jCayuUX3D6Q8xmkx4c1kkjb++oZ0f30E+/7lOum/us2mbl99TKbZL1+85o3bu7Wl3RzbTnLw1tzqHNedlZuXrhqRmKrBWqZi0baMVSxz/D7kabc2hzntX6xo69R7Gx9RQeHmJ3fWrqSY0cOVFr1/6lyZM/0ksvjaHPQgyLHO3zjKlTp6p27dq66qqryv1Ya/yJsoqoE+KvS+NP/7V35nrHsyGuEh7qrxbxp//S8vJHa4oGV5JkGNJrH69TVna+wkL81SQ2nLYyMPs9LY2V2yTr9wHV1YdvfKuD+45q1OM3qkZwgNk5dmhzDm3Os1pfu3bNig2sJCkiIlSjR98hSVqxYr27s4pYvQ9SYWGhpk6dqjvvvFM+PuWfx2PwVw43to+Rt5dN25LTteFgmtufPze37DMrx9OyXVhSnJXbSmP2e1oaK7dJ1u8DqqONa3Zq7pcr1OeqTup6SQuzc+zQ5hzanGf1vnN5e3tLknx9rbHf8Lms3udSFjnJuyT98MMP2rdvn+6++26nvhU2+yyHG9ud3qHSrFmOzOx8/f5Hkrq0jdZDd3cutmnlA3d2VGCAj5b+tk+HUzJoKwOz39PSWLlNslbf/s07dHTvIeVm5SgwNEh1m8aqUaeW8rHALyjaaHOXrMwcTXl6piIigzXi4UGmtpyLNufQ5jyr953r1KlMvfHGF5Kkyy+/0OSa4qze53IW2uyzb9++Fdp3lcFfGXWNjVBczSDl5Bdq9sZDpnU8/uIv+iChn24Z2EK9ujXQpm1HVVhoqGWTmqpTK0hzvt+hp19bQVsZWOU9LYmV2yTr9W0p4QiMQRGh6vuvIWrUsaUJRX+jzTm0ld+7r8zX4YPH9dSUYQoJrWFaR0locw5tzrN63/Ll67RgwTIVFho6duyE1q/fqoyMLPXo0VFjxgwzO8/yfXAeg78yGtw+RpL0w7ZkpWblmdaRuD9NN90/V1PGXaYeXeqrblRw0W079qRq9YZDOpVpTp+V20pilfe0JFZuk6zTFxUXo17xN6hB22YKjYpQfk6eUvYc1MoZ3yppa6LmPveerh8/Ug3aNKWNNo9uW7Nym775epV6XdleF1/W2u3PXxranEOb86zeJ0k7d+7XnDk/2V139dU9NW7cPQoJCTKp6m9W73M7F57qwd3Y568Mgv281b9lHUnSV6Uc9t4dOraqo28+uEFN4yL00LM/qdsNn6nTNR/rH48tko+3lxIe6annHr6UtvOw0nt6Liu3Sdbq63jN5epwdS/Vahgtv8AA1QgPUWz75ro54SHFX9hGhfkFWvbh17TR5tFtGelZeumZmQqPCNL9Y65z+/OXhjbn0OY8q/edMWzYIG3bNl+bN8/R4sXvaezYe/TLL2t11VUj9fvv5h8x1ep9cJ6lBn8ZGRmaOnWqHn/8cb3xxhs6duzYeR+Tk5OjkydP2l2M/NxK7RrYOlo1/Hx0KC1Ly3YerdRll0dIkJ/eerqPIsMDdf+Ti7Xgp106lpqlk6dytWTVPt0z9jtlZuXppgHN1LV9NG2lsMp7WhIrt0nW75Mkm82m7rcOkCSl7Dmo9BT3nny+NLQ5hzbH3npxrlKOpOn+R65TWIS1/iJPm3Noc57V+87l6+ujhg2jdddd1+r9959SWtopjRnzorKzc8xOk2T9Prfxsrnu4mambvbZsmVLLV++XJGRkdq/f78uvfRSpaam6oILLtCuXbv0zDPPaNWqVYqLi3O4jISEBE2YMMHuurCeQxR+2e2V1jm4w+lN3GZtPCQzTw16WbcGqhkRqL0H07Rxa0qx2/cnpWvj1hR171BPF3WM0W8bkmhzwCrvaUms3CZZv++MyPp1i/6dfuyEQqIiTKyxR5tzaCvZiiWb5e3tpXmzftW8Wb/a3bZ/z+nP44Vzf9O61dsVWTNUjycMpY02j2yrCn2ladeumZo0aaAdO/Zp8+ad6ty5ldlJdqzeh7IxdfC3detW5efnS5LGjRunevXqacOGDQoLC9OpU6d03XXX6fHHH9cXX3zhcBnjxo3T6NGj7a5r88KySmtsUitIHeqHq9Aw9JXJRzWMrn16H7rS9ptLzzg96xke6u+WpjOs3HYuK72n57Jym2T9vrNlpf99VFm/QHPXuXPR5hzaHCsoKNQfa3c7vP3woVQdPpSqOtHuHzDT5hzanGf1vtIEBp4+F+GxYyfMDXHA6n0u4zm7/FnngC8rV67UO++8o7CwMElScHCwJkyYoFtuuaXUx/n7+8vf3/4Xrc3Hr9K6bu5Y/3Rf4nHtP5FVact1xpGjmZKkxg3CFRzkq1MZ9gMtH2+bWjU9faL1A0nptDlgpff0XFZuk6zfd7bty9dKkvxqBCgipo7JNfZocw5tJfvvsmcd3vb8+BlavGCNho3opyHDe7ux6jTanEOb86zeV5rjx9O0dWuiJKlRoxiTa4qzeh/KxvR9/mz/O29Gdna2oqPt9wWLiYlRSkrxTQjdxcfLpmvbnG760gKzHD+v3q+MrDwFBvho4r8vVY2Av8fuvj5eemxkd8XUCVFuXoEW/pxIWwms9p6ezcptkvX6kncf0K7Vm1RYUGB3vVFYqM2LV2r5ZwskSR2u6ilvH2/aaPPYNgBVw86d+zRv3lLl5BQ/NkVi4kE9+OBk5ebmqX37ZmrWrBF9FmJ42Vx2cTfTZ/6uuOIK+fj46OTJk9q2bZtat/77kLx79+5VzZo1zWu7IEpRwf5Ky8rToi1HTOs443hatp58ebkmPdJTA3o1Vtd20dq0LUV5+YVq06yW6kYFq6CgUM++sVL73Ty7ZuW2s1ntPT2bldsk6/WdTD6m+ZM+kH9wDdVuXF9B4aHKycjU0X1JRQfcaNajk7rd0p822jy6DUDVcOxYmsaMeVHjx7+pFi0aq27dWsrLy9OhQyn666/dKiwsVHx8A7388iP0WY2FTvJeUaYO/saPH2/3dXBwsN3X8+fPV48ePdyZZOem/53LbO7mJOUUFJrWcbZ5P+zU9t3HNezG1urSNlrdO9aTTTYlH8/U3MU79MmcP/VHCQdcqe5tZ1jxPT3Dym2S9fqi4mLUYWAvHdm5T6kHk3Voa6JkGKoRHqKmF7VXq8u7Kc6kndFpow0AztW0aUM99NDtWrPmT+3efUBbtuxWXl6+wsND1L17W/Xpc5FuuKG3/Px86YPL2AzDsPIB+5zSaMIisxNK5btsn9kJVVJez4ZmJ6CSjR3sOX9JAypqQINqduh0oIpqGNzM7IQq6gKzA5zW8NXKO5jkufY92NNlyy6J6fv8AQAAAABcz/R9/gAAAADAsjxoQyVm/gAAAACgGmDmDwAAAAAc8PKg6TIP+lYAAAAAAI4w8wcAAAAADnjQaf4Y/AEAAACAI540+GOzTwAAAACoBpj5AwAAAAAHbB409cfMHwAAAABUA8z8AQAAAIADHjTxx8wfAAAAAFQHzPwBAAAAgAPM/AEAAAAAqhRm/gAAAADAAZsHTZcx+AMAAAAAB9jsEwAAAABQpTDzBwAAAAAOeDHzJ+3fv18HDhwo+nr16tUaNWqU3nvvvUoJAwAAAABUHqcHf7fddpuWLFkiSTp8+LD69Omj1atX6/HHH9fTTz9daYEAAAAAYBabzXUXd3N68Ld582ZdeOGFkqSZM2eqdevW+vXXX/X5559r2rRpldUHAAAAAKgETu/zl5eXJ39/f0nSDz/8oGuuuUaS1Lx5cyUlJVVOHQAAAACYiKN9SmrVqpXeeecd/fLLL1q8eLH69esnSTp06JBq1qxZaYEAAAAAgIpzevA3efJkvfvuu+rVq5duvfVWtWvXTpI0b968os1BAQAAAKAqs9lsLru4m9Obffbq1UtHjx7VyZMnFRERUXT9P/7xD9WoUaNS4gAAAADATDYPOjN6hb4VwzC0du1avfvuu0pPT5ck+fn5MfgDAAAAAItxeuZv79696tevn/bt26ecnBz16dNHISEhmjx5snJycvTOO+9UZme5xHUJNu25yyJRDc1OqJJ8l+0zO6FKyutp3fVt0kzD7IQqa+xgD9r7HJKkb/f7m53g0IAGOWYnAJax79Q2sxOqpIbBF5id4DQO+CLpwQcfVOfOnZWamqrAwMCi66+77jr9+OOPlRIHAAAAAKgcTs/8/fLLL/r111/l5+dnd32jRo108ODBCocBAAAAgNmY+ZNUWFiogoKCYtcfOHBAISEhFYoCAAAAAFQupwd/ffv21SuvvFL0tc1m06lTpzR+/HgNGDCgMtoAAAAAwFQ2m+su7ub0Zp8vvviirrzySrVs2VLZ2dm67bbbtGPHDtWqVUvTp0+vzEYAAAAAQAU5PfirX7++Nm7cqBkzZuiPP/7QqVOndM8992jIkCF2B4ABAAAAgKrKy4P2+XN68CdJPj4+Gjp0aGW1AAAAAICleNIBX8o1+Js3b16Z73vNNdeUOwYAAAAA4BrlGvxde+21ZbqfzWYr8UigAAAAAFCVVNuZv8LCQld1AAAAAABcqEL7/AEAAACAJ7N50BFfnD7PnyT9+OOPuvrqqxUfH6/4+HhdffXV+uGHHyqrDQAAAABQSZwe/L311lvq16+fQkJC9OCDD+rBBx9UaGioBgwYoDfffLMyGwEAAADAFJzkXdJzzz2nl19+Wffff3/RdQ888IAuvvhiPffccxo5cmSlBAIAAAAAKs7pmb8TJ06oX79+xa7v27ev0tLSKhQFAAAAAFbgSTN/Tg/+rrnmGs2ZM6fY9XPnztXVV19doSgAAAAAsAJPGvw5vdlny5YtNXHiRC1dulTdu3eXJK1atUorVqzQv//9b7322mtF933ggQcqXgoAAAAAcJrTg78PP/xQERER+uuvv/TXX38VXR8eHq4PP/yw6GubzcbgDwAAAECV5EFnenB+8JeYmFiZHQAAAAAAF+Ik7wAAAADggBn75rmK04M/wzA0a9YsLVmyRMnJySosLLS7ffbs2RWOAwAAAABUDqcHf6NGjdK7776ryy67THXq1JHNk4bEAAAAACDJ5vT5EazH6cHfp59+qtmzZ2vAgAGV2WMZPjabrmlYV72iayk2OFAB3t5Ky83T7vRMLTqYrCVJR93eVD8sQMtH9SzTfQdPXa3V+1JdXPQ3K7edEV07SPfe0k4Xd4pRvdrBstmk5GNZ+v2PJE39apO27j7u9iart1n5faXNeYte/VR/LVld6n3+NfMl+fj5uqnob7Q5x8ptJXn/1QWa+clSSdKwEf00ZHhvc4POQptzrNwmWbuPNriT04O/sLAwNW7cuDJbLKNWgJ9e6NJKjUJq6EROnjanpiu7oEC1A/zVNjJU2QUFpgz+MnILNGvDQYe3N4kKVvuYMKXn5GtT0kk3llm7TZLaNY/StBcGKDjIT4dTTmn5mgMqLDTUIr6mrr/yAg28oolGT/xJC5e5/0BGVm6z8vtKW8XVa9FY4XVrlXibzcvcP3PS5hwrt53x58Y9mvXZMtlsNhmGYXaOHdqcY+U2ydp9tFUNnrSBo9ODv6eeekoTJkzQRx99pMDAwMpsMpWfl5emXNhKscE1NHX7Pn2+64AKzlrh/b28VD/InO83NStPD8/d7PD2qbd1lCTN35ykrLwCd2VJsnabJD3z7x4KDvLTjPlbNOG1FcovOP2e2mzSg8M6aeTtHfXs6B766dd9ynVzn5XbrPy+0lZxrXt3V6srupn2/KWhzTlWbpOk7KxcvfDUDEXWClWzlg20YqnjnxN3o805Vm6TrN1HG8zg9J8BBw8erNTUVNWuXVtt2rRRx44d7S5V1ZD4+ooNrqH5+w7rk5377QZ+kpRTWKhd6Rkm1TlWJ8Rfl8af/mvvzPWOZxzMYHZbeKi/WsTXlCS9/NGaosGVJBmG9NrH65SVna+wEH81iQ2nrYzMfl9LQxtgTR++8a0O7juqUY/fqBrBAWbn2KHNOVZuk6zdR1vVYbPZXHZxN6cHf3feeafWrl2roUOH6oYbbtCgQYPsLlWR9//285OkGbur1v+U3dg+Rt5eNm1LTteGg2lm59gxuy03t+wzK8fTsl1YUpyV287H7Pe1NLQB1rNxzU7N/XKF+lzVSV0vaWF2jh3anGPlNsnafbRVLTab6y7u5vRmn998840WLVqkSy65pDJ7THVBaJDC/X2Vkp2jQ5nZigupoR51aqpWgJ/S8/K16fhJ/ZaSKitu9Xxju3qSrDmTYHZbZna+fv8jSV3aRuuhuzsX27TygTs7KjDAR0t/26fDKe6d1bVy2/mY/b6Whray2b95h47uPaTcrBwFhgapbtNYNerUUj6+5h8UhDbPasvKzNGUp2cqIjJYIx621h+IaXOOldska/fRBjM5Pfhr0KCBQkNDK7PFdI1DgyRJR7NzdW+zWN3SOEZeZw/J46Xtaaf0xNotSs7ONamyuK6xEYqrGaSc/ELN3njI7Bw7Vml7/MVf9EFCP90ysIV6dWugTduOqrDQUMsmNVWnVpDmfL9DT7+2grYyssr7WhLaym5LCUeHDIoIVd9/DVGjji1NKPobbc6xatu7r8zX4YPH9dSUYQoJrWFaR0loc46V2yRr99FW9VjpgC8HDx7Uo48+qu+++06ZmZlq0qSJpk6dqs6dO5fp8U4P/l588UU98sgjeuedd9SoUSNnF2Mpob6nX44moUFqER6iOXuSNHvvIR3PyVPzsGA92CpeF4QFK6FLS/1j+cZi+wOaZXD7GEnSD9uSlZqVZ3KNPau0Je5P0033z9WUcZepR5f6qhsVXHTbjj2pWr3hkE5lmtNn5TZHrPK+loS284uKi1Gv+BvUoG0zhUZFKD8nTyl7DmrljG+VtDVRc597T9ePH6kGbZrSRluFrVm5Td98vUq9rmyviy9r7fbnLw1tzrFym2TtPtpQEampqbr44ot12WWX6bvvvlNUVJR27NihiIiIMi/D6cHf0KFDlZmZqfj4eNWoUUO+52xScvy4eedMc9aZUb2vl5d+PJSi1/7aXXTbumNpGrN6sz7p2VGNQ4J0eXQtLT6UYlLp34L9vNW/ZR1J0lelHFreDFZq69iqjt6c0Fv5hYYeevYnrVx/SHl5BerUuq7GjeimhEd6qmPrunpsys+0nYeV3tdz0VY2Ha+53O5rv8AAxbZvrobtmml+wvvatXqTln34tYa+MpY22iokIz1LLz0zU+ERQbp/zHVufe7zoc05Vm6TrN1HW9VllZm/yZMnq0GDBpo6dWrRdXFxceVahtODv1deecXZhxZZt26dIiIiiqI//fRTvfPOO9q3b59iY2N1//3365Zbbil1GTk5OcrJybG7rjAvV16+fuXuycz/++Ab8/cdLnZ7cnauViWnqmd0LXWqFW6Jwd/A1tGq4eejQ2lZWrbT/eceLI1V2kKC/PTW030UERagwffP1catf79vS1bt0869qVrwwQ26aUAzzf1hh37bkERbKazyvpaEtoqx2WzqfusA7Vq9SSl7Dio9JVUhUWX/a6Ir0eYcs9veenGuUo6k6T8JQxUWEeS25y0L2pxj5TbJ2n20oSQljWX8/f3l7+9f7L7z5s3TlVdeqZtuuknLli1TTEyM7rvvPt17771lfj6nB3933nmnsw8tctddd+nFF19UXFycPvjgAz3wwAO69957dfvtt2vbtm269957lZmZqbvvvtvhMhISEjRhwgS762Jvu0txQ+4pd09S5t8v/KHMko+smPS/6yP9zd+xX5IGdzi9GdmsjYcsdyAaq7Rd1q2BakYEau/BNLvB1Rn7k9K1cWuKuneop4s6xrh1gGXlNkes8r6WhLaKi6xft+jf6cdOWGYQI9HmLDPbVizZLG9vL82b9avmzfrV7rb9e05/5i2c+5vWrd6uyJqhejxhKG20eWwfbVWXlwtn/koay4wfP15PPfVUsfvu3r1bb7/9tkaPHq3HHntMv//+ux544AH5+fmVeWzm9ODvbNnZ2crNtT8ASlkOBrNjxw41bXp6/4O33npLr776qt3ItUuXLpo4cWKpg79x48Zp9OjRdtcNXLK2PPlFtqedUqFhyMtmU5ifr1JKOKhLmN/pQV9WQaFTz1GZmtQKUof64So0DH1lgSMHns1KbdG1T+9DV9p+c+kZp9/r8NDif2VxJSu3lcRK7+u5aKscWWedx9Qv0Px17my0OcfstoKCQv2xdrfD2w8fStXhQ6mqE+3+ATNtzrFym2TtPtpwrpLGMiXN+klSYWGhOnfurOeee06S1KFDB23evFnvvPOO6wd/GRkZevTRRzVz5kwdO3as2O0FBec/f1mNGjV09OhRxcbG6uDBg7rwwgvtbu/atasSExNLXUZJ06LObPIpSam5edqUelLtIsPUqWa4dp60P7S+t82mtpGnB7VbT6Q79RyV6eaO9SVJKxOPa/+JLJNr7Fmp7cjRTElS4wbhCg7y1akM+4GWj7dNrZqePtH6gST3vq9WbiuJld7Xc9FWObYvP/3HM78aAYqIqWNyjT3anGNm23+XPevwtufHz9DiBWs0bEQ/DRne241Vp9HmHCu3Sdbuo63qcuXMn6NNPEsSHR2tli3tj9zcokULff3112V+PqdP8v7II4/op59+0ttvvy1/f3998MEHmjBhgurVq6dPPvmkTMvo37+/3n77bUlSz549NWvWLLvbZ86cqSZNmjib6JRPduyXJN0WH6MW4X8fddHLJt3XopFiggKVkZevhQeS3dp1Lh8vm65tEy1J+tJiMwlWa/t59X5lZOUpMMBHE/99qWoE/P03D18fLz02srti6oQoN69AC38u/Y8N1antXFZ7X89GW9kl7z6gXas3qfCcP9AZhYXavHilln+2QJLU4aqe8vbxpo02AKj2vGyGyy7lcfHFF2vbtm12123fvl2xsbFlXobTM3/z58/XJ598ol69eumuu+5Sjx491KRJE8XGxurzzz/XkCFDzruMyZMn6+KLL1bPnj3VuXNnvfjii1q6dKlatGihbdu2adWqVZozZ46ziU5ZdyxNH27bq3uaxeq1bm209cQpHc/NVdPQYEXXCFB2QYGe3bBdqbnmHkL+iguiFBXsr7SsPC3acsTUlnNZre14WraefHm5Jj3SUwN6NVbXdtHatC1FefmFatOslupGBaugoFDPvrFS+908u2bltnNZ7X09G21ldzL5mOZP+kD+wTVUu3F9BYWHKicjU0f3JSk9JVWS1KxHJ3W7pT9ttAEALOShhx7SRRddpOeee06DBw/W6tWr9d577+m9994r8zKcHvwdP35cjRs3lnR6/74zp3a45JJLNGLEiDIto169elq/fr0mTZqk+fPnyzAMrV69Wvv379fFF1+sFStWlPmEhZXps10HtDXtlG5oFK0W4SFq5hOs4zl5+u7AEU3fdVD7M8zfbOum/50vbO7mJOVYYP/Ds1mxbd4PO7V993ENu7G1urSNVveO9WSTTcnHMzV38Q59MudP/VHCAVeqe9vZrPi+nkFb2UXFxajDwF46snOfUg8m69DWRMkwVCM8RE0vaq9Wl3dTXOdWtNEGAPgfV272WR5dunTRnDlzNG7cOD399NOKi4vTK6+8UqZJtzNshuHcmcrbtm2r119/XT179lTv3r3Vvn17TZkyRa+99pqef/55HThwwJnFVorLvl1h2nOXReLvp8xOqJJ8l+0zO6FKyuvZ0OwEuMDYwRb5TYRqYUCDnPPfCQBK0TB4oNkJTuv//XKXLfu7vpe4bNklcXqfv7vuuksbN26UJI0dO1ZvvvmmAgIC9NBDD2nMmDGVFggAAAAAZvFy4cXdnN7s86GHHir6d+/evbV161atXbtWTZo0Udu2bSslDgAAAABQOSrlPH+SFBsbq7CwMIWHh1fWIgEAAADAVOU9KqeVOT3bOHnyZH355ZdFXw8ePFg1a9ZUTExM0eagAAAAAABrcHrw984776hBgwaSpMWLF2vx4sX67rvv1L9/f/b5AwAAAOARvGyuu7ib05t9Hj58uGjwt2DBAg0ePFh9+/ZVo0aN1LVr10oLBAAAAACzmHFgFldx+nuJiIjQ/v37JUkLFy5U7969JUmGYaigoKBy6gAAAAAAlcLpmb/rr79et912m5o2bapjx46pf//+kqT169erSZMmlRYIAAAAAGaxykneK4PTg7+XX35ZjRo10v79+/X8888rODhYkpSUlKT77ruv0gIBAAAAABXn9ODP19dXDz/8cLHrzz7/nyRdddVV+uCDDxQdHe3sUwEAAACAKWyc6qHsfv75Z2VlZbn6aQAAAAAApai0k7wDAAAAgKfxpH3+POnIpQAAAAAAB5j5AwAAAAAHPGm2jMEfAAAAADjgxQFfAAAAAABVictn/h577DFFRka6+mkAAAAAoNJ50gFfKjT427Fjh5YsWaLk5GQVFhba3fbkk09KksaNG1eRpwAAAAAAVAKnB3/vv/++RowYoVq1aqlu3bqy2f4eEttstqLBHwAAAABUVZ60n5zTg79nn31WEydO1KOPPlqZPQAAAAAAF3B68JeamqqbbrqpMlsAAAAAwFI8aZ8/p2cxb7rpJn3//feV2QIAAAAAcBGnZ/6aNGmiJ554QqtWrVKbNm3k6+trd/sDDzxQ4TgAAAAAMJMnnefP6cHfe++9p+DgYC1btkzLli2zu81mszH4AwAAAFDledJmn04P/hITEyuzAwAAAADgQi4/yTsAAAAAVFXV9lQPo0eP1jPPPKOgoCCNHj261Pu+9NJLFQrzZHFdgs1OqJJuHhxrdoJDXyYGmZ3g0IEpf5md4FBez4ZmJ1RZVl7nbo7LMDsBlezb/f5mJ1RZAxrkmJ0AAEXKNfhbv3698vLyiv4NAAAAAJ6s2h7wZcmSJSX+GwAAAABgbeXe5+/uu+8+731sNps+/PBDp4IAAAAAwCqq9dE+p02bptjYWHXo0EGG4TlToAAAAADgyco9+BsxYoSmT5+uxMRE3XXXXRo6dKgiIyNd0QYAAAAApvKkmb9yH7n0zTffVFJSkh555BHNnz9fDRo00ODBg7Vo0SJmAgEAAAB4FC8XXtzNqef09/fXrbfeqsWLF+uvv/5Sq1atdN9996lRo0Y6depUZTcCAAAAACqowid59/Lyks1mk2EYKigoqIwmAAAAALAETzrVg1Mzfzk5OZo+fbr69OmjCy64QJs2bdIbb7yhffv2KTiYE5gDAAAAgNWUe+bvvvvu04wZM9SgQQPdfffdmj59umrVquWKNgAAAAAwlScd8KXcg7933nlHDRs2VOPGjbVs2TItW7asxPvNnj27wnEAAAAAgMpR7sHfHXfcIZvNg4a/AAAAAOCAGUfldBWnTvIOAAAAAKhaKny0TwAAAADwVNV6nz8AAAAAqC5s1f1UDwAAAACAqoWZPwAAAABwwJM2+2TmDwAAAACqAWb+AAAAAMABT5ot86TvBQAAAADgADN/AAAAAOCAF0f7BAAAAABUJcz8OeBjs+mahnXVK7qWYoMDFeDtrbTcPO1Oz9Sig8laknSUvirUtujVT/XXktWl3udfM1+Sj5+vm4qKs+LrJknRtYN07y3tdHGnGNWrHSybTUo+lqXf/0jS1K82aevu46Z01Q8L0PJRPct038FTV2v1vlQXF/3Nym1ns+I6Z+WfVdqcY+U2yfp9Z3v/1QWa+clSSdKwEf00ZHhvc4POYuU2ydp9tFmfJx3tk8FfCWoF+OmFLq3UKKSGTuTkaXNqurILClQ7wF9tI0OVXVBg6uDKyn1WbpOkei0aK7xurRJvs3mZNxFu1detXfMoTXthgIKD/HQ45ZSWrzmgwkJDLeJr6vorL9DAK5po9MSftHBZotvbMnILNGvDQYe3N4kKVvuYMKXn5GtT0kk3llm77QyrrnNnWPVnVaLNWVZuk6zf9+fGPZr12TLZbDYZhrU2QbNym2TtPtqqBgZ/HszPy0tTLmyl2OAamrp9nz7fdUAFZ63w/l5eqh8USF8Vazujde/uanVFN1MbzmXl1+2Zf/dQcJCfZszfogmvrVB+wekum016cFgnjby9o54d3UM//bpPuXkFbm1LzcrTw3M3O7x96m0dJUnzNycpizY7Vl7nzrDiz+oZtDnHym2Stfuys3L1wlMzFFkrVM1aNtCKpY4/X9zNym2StftogxnM/1OWxQyJr6/Y4Bqav++wPtm53+5/iCQpp7BQu9IzTKqzdp+V26zMqq9beKi/WsTXlCS9/NGaooGfJBmG9NrH65SVna+wEH81iQ13e19p6oT469L403/Bn7ne8QycGazQZtV1DkDJPnzjWx3cd1SjHr9RNYIDzM6xY+U2ydp9tFUd3i68uBuDv7N4/2//F0masdta/8MoWbvPym1WZuXXLTe37DNSx9OyXVhSfje2j5G3l03bktO14WCa2Tl2zG6z8joHoLiNa3Zq7pcr1OeqTup6SQuzc+xYuU2ydh9tMAubfZ7lgtAghfv7KiU7R4cysxUXUkM96tRUrQA/pefla9Pxk/otJVVmbfVs5T4rt51t/+YdOrr3kHKzchQYGqS6TWPVqFNL+fiasyO/lV+3zOx8/f5Hkrq0jdZDd3cuttnnA3d2VGCAj5b+tk+HU6w1S3Rju3qSrDfrJ5nfZuV17mxW+1mlzbPbJGv2ZWXmaMrTMxURGawRDw8yraMkVm6TrN1HW9XjSad6YPB3lsahQZKko9m5urdZrG5pHCMv21l7eMZL29NO6Ym1W5ScnUtfFWk725YSjugWFBGqvv8aokYdW7q9x+qv2+Mv/qIPEvrploEt1KtbA23adlSFhYZaNqmpOrWCNOf7HXr6tRVu7ypN19gIxdUMUk5+oWZvPGR2jh0rtFl9nTvDaj+rZ6PNOVZuk6zZ9+4r83X44HE9NWWYQkJrmNLgiJXbJGv30QYzMfg7S6jv6ZejSWiQWoSHaM6eJM3ee0jHc/LUPCxYD7aK1wVhwUro0lL/WL6x2H4y1bnPym2SFBUXo17xN6hB22YKjYpQfk6eUvYc1MoZ3yppa6LmPveerh8/Ug3aNHVrl9Vft8T9abrp/rmaMu4y9ehSX3Wjgotu27EnVas3HNKpzDy3Np3P4PYxkqQftiUrNYu2c1l9nbPqzyptntlm5b41K7fpm69XqdeV7XXxZa3d+tznY+U2ydp9tFVNnnS0T/b5O8uZP377ennpx0Mpeu2v3TqQka3M/AKtO5amMas3K6egQI1DgnR5dMmHg66ufVZuk6SO11yuDlf3Uq2G0fILDFCN8BDFtm+umxMeUvyFbVSYX6BlH37t9i7Lv26t6uibD25Q07gIPfTsT+p2w2fqdM3H+sdji+Tj7aWER3rquYcvdXuXI8F+3urfso4k6atSTrVgBqu0WX6ds+jPKm2e2WbVvoz0LL30zEyFRwTp/jHXufW5z8fKbZK1+2iDFZg6+PvXv/6lX375pULLyMnJ0cmTJ+0uhXnObaqUmf/3AS7m7ztc7Pbk7FytSj59MuZOtcKdeo6KsHKfldtKY7PZ1P3WAZKklD0HlZ7i3pNtW/l1Cwny01tP91FkeKDuf3KxFvy0S8dSs3TyVK6WrNqne8Z+p8ysPN00oJm6to92a5sjA1tHq4afjw6lZWnZTvPOUVcSq7RZeZ0rjdk/q6WhzTlWbpPM7XvrxblKOZKm+x+5TmERQW573rKwcptk7T7aqi4vm+su7mbqZp9vvvmm3nrrLcXHx+uee+7RnXfeqbp165ZrGQkJCZowYYLddbG33aW4IfeUuycpM6fo34cySz56YdL/ro/0d/8O4Fbus3Lb+UTW/3udSz92QiFREW57biu/bpd1a6CaEYHaezBNG7emFLt9f1K6Nm5NUfcO9XRRxxj9tiHJrX0lGdzh9GaVszYeMv2AJeeySpuV17nzMfNn9Xxoc46V2yTz+lYs2Sxvby/Nm/Wr5s361e62/XtOfx4vnPub1q3ersiaoXo8YahbuqzeZvU+2qoubw/a7NP0ff6+//57zZ8/X1OmTNETTzyh/v37695779WAAQPk5XX+iclx48Zp9OjRdtcNXLLWqZbtaadUaBjystkU5uerlBIOdhDmd/p/hrIKCp16joqwcp+V284n66zzmfkF+rv1ua38ukXXPr1/X2n79KVnnO4ND3Xv61aSJrWC1KF+uAoNQ19Z7CifVmqz8jp3Pmb+rJ4Pbc6xcptkbl9BQaH+WLvb4e2HD6Xq8KFU1Yl2/4DZym2Stftog9lMH/y1adNGV1xxhV544QXNmTNHH330ka699lrVqVNHw4YN01133aUmTZo4fLy/v7/8/e0/kL18/ZxqSc3N06bUk2oXGaZONcO186T94eu9bTa1jQyVJG09ke7Uc1SElfus3HY+25ef/mOBX40ARcTUcetzW/l1O3I0U5LUuEG4goN8dSrDfhDo421Tq6anTwJ/IMn89/TmjvUlSSsTj2v/iSyTa+xZqc3K69z5mPmzej60OcfKbZJ5ff9d9qzD254fP0OLF6zRsBH9NGR4b7c1nWHlNsnafbRVXRzwxQV8fX01ePBgLVy4ULt379a9996rzz//XM2aNXNrxyc79kuSbouPUYvwv49s6GWT7mvRSDFBgcrIy9fCA8lu7aoKfVZtS959QLtWb1Jhgf1Jy43CQm1evFLLP1sgSepwVU95+3i7tU2y7uv28+r9ysjKU2CAjyb++1LVCPj7b0W+Pl56bGR3xdQJUW5egRb+nOjWtnP5eNl0bZvT+x1+abFZPyu2WXWds/LPKm2e11YV+gCgspk+81eShg0b6qmnntL48eP1ww8/uPW51x1L04fb9uqeZrF6rVsbbT1xSsdzc9U0NFjRNQKUXVCgZzdsV2quOYdpt3KfVdtOJh/T/EkfyD+4hmo3rq+g8FDlZGTq6L6koh34m/XopG639Hdr1xlWfd2Op2XryZeXa9IjPTWgV2N1bRetTdtSlJdfqDbNaqluVLAKCgr17Bsrtd/kmb8rLohSVLC/0rLytGjLEVNbzmXFNquuc1b+WaXN89qqQh8Aa+Ak75UkNjZW3t6O/5Jms9nUp08fNxad9tmuA9qadko3NIpWi/AQNfMJ1vGcPH134Iim7zqo/RnmbrZl5T4rtkXFxajDwF46snOfUg8m69DWRMkwVCM8RE0vaq9Wl3dTXOdWbu86mxVfN0ma98NObd99XMNubK0ubaPVvWM92WRT8vFMzV28Q5/M+VN/lHAwGHe76X/nz5u7OUk5FttPzaptVlznrPyzSpvntVWFPgCobDbDcPMZfN3gsm9XmJ0AF7g5LuP8dzLJl4nWPSzygSl/mZ3gUF7PhmYnVFlxXYLPfyeTWPlnFXC3AQ1yzn8noBpoGDzQ7ASnvf7X9y5b9r9a9nXZsktimX3+AAAAAAAle+qpp2Sz2ewuzZs3L9cyLLnPHwAAAABYgZUO99SqVSu7Y6L4+JRvOMfgDwAAAACqAB8fH9WtW9f5x1diCwAAAAB4FFee5y8nJ0c5Ofb7Bpd0HvMzduzYoXr16ikgIEDdu3dXQkKCGjYs+zEU2OcPAAAAABzwshkuuyQkJCgsLMzukpCQUGJH165dNW3aNC1cuFBvv/22EhMT1aNHD6Wnl/2UW8z8AQAAAIAJxo0bp9GjR9td52jWr3//v8852rZtW3Xt2lWxsbGaOXOm7rnnnjI9H4M/AAAAAHDA24WbfZa2ief5hIeH64ILLtDOnTvL/Bg2+wQAAACAKubUqVPatWuXoqOjy/wYBn8AAAAA4ICXzXWX8nj44Ye1bNky7dmzR7/++quuu+46eXt769Zbby3zMtjsEwAAAAAs7sCBA7r11lt17NgxRUVF6ZJLLtGqVasUFRVV5mUw+AMAAAAAB1x5qofymDFjRoWXwWafAAAAAFANMPMHAAAAAA5YZeavMjD4AwAAAAAHvG2G2QmVhs0+AQAAAKAaYOYPAAAAABzwpNkyT/peAAAAAAAOMPMHAAAAAA540gFfmPkDAAAAgGqAmT8AAAAAcICZPwAAAABAlcLMHwAAAAA44Enn+WPwBwAAAAAOsNknAAAAAKBKYeYPAAAAABxg5g8AAAAAUKV45MzfzXEZZieU6svEILMTqiQrv26WXufejDW7wCErv6dwHu+r57H0ZxwAuBgzfwAAAACAKsUjZ/4AAAAAoDJ4M/MHAAAAAKhKmPkDAAAAAAe8OMk7AAAAAHg+T9pU0pO+FwAAAACAA8z8AQAAAIADnOoBAAAAAFClMPMHAAAAAA5wqgcAAAAAQJXCzB8AAAAAOOBJp3pg5g8AAAAAqgFm/gAAAADAAU862ieDPwAAAABwwJMGf2z2CQAAAADVADN/AAAAAOCAJ82WedL3AgAAAABwgJk/AAAAAHDAxj5/AAAAAICqhJk/AAAAAHDAgyb+mPkDAAAAgOqAmT8AAAAAcMCT9vlj8FeCRa9+qr+WrC71Pv+a+ZJ8/HzdVFScj82maxrWVa/oWooNDlSAt7fScvO0Oz1Tiw4ma0nSUdqqSJuV1zcrt51hxfe0qvTRRps7WP1zxOp9Z3v/1QWa+clSSdKwEf00ZHhvc4POYuU2ydp9tFmfJ20qyeCvFPVaNFZ43Vol3mbzMm81qBXgpxe6tFKjkBo6kZOnzanpyi4oUO0Af7WNDFV2QYFp//NBm/Osur5J1m2z+ntq5T7aaHM3q36OnGH1vj837tGsz5bJZrPJMAyzc+xYuU2ydh9tcDcGf6Vo3bu7Wl3RzewMO35eXppyYSvFBtfQ1O379PmuAyo46wfS38tL9YMCaatCbWdYcX07w4ptVn9PrdxHG21msOLnyNms3JedlasXnpqhyFqhataygVYs3Wx2UhErt0nW7qOt6rDZPGfwa/6fslAuQ+LrKza4hubvO6xPdu63++UuSTmFhdqVnkFbFWqDc6z+nlq5jzbaULV8+Ma3OrjvqEY9fqNqBAeYnWPHym2StftogxkY/FUh3v/bl0OSZuw+aHKNPdrgTlZ/T63cR5tzaINZNq7ZqblfrlCfqzqp6yUtzM6xY+U2ydp9tFUtNhde3I3NPkuxf/MOHd17SLlZOQoMDVLdprFq1KmlfHzN2en7gtAghfv7KiU7R4cysxUXUkM96tRUrQA/pefla9Pxk/otJVVmTEzTVnFWW9+s3Gb199TKfbTRZharfY6cy4p9WZk5mvL0TEVEBmvEw4NM6yiJldska/fRBjMx+CvFlhKO/hUUEaq+/xqiRh1bur2ncWiQJOlodq7ubRarWxrHyOvsY8/GS9vTTumJtVuUnJ1LWxVoO5vV1rezWa3N6u+plftoo80sVvscOZcV+959Zb4OHzyup6YMU0hoDVMaHLFym2TtPtqqHk714OGi4mLUK/4GNWjbTKFREcrPyVPKnoNaOeNbJW1N1Nzn3tP140eqQZumbu0K9T39djUJDVKL8BDN2ZOk2XsP6XhOnpqHBevBVvG6ICxYCV1a6h/LNxbb54M267VJ1l3frNxm9ffUyn200cZnXNXoW7Nym775epV6XdleF1/W2q3PfT5WbpOs3UcbzMY+fyXoeM3l6nB1L9VqGC2/wADVCA9RbPvmujnhIcVf2EaF+QVa9uHXbu8681cHXy8v/XgoRa/9tVsHMrKVmV+gdcfSNGb1ZuUUFKhxSJAujy75cNW0WatNsu76ZuU2q7+nVu6jjTZ3s+rniJX7MtKz9NIzMxUeEaT7x1zn1uc+Hyu3Sdbuo63q8qR9/kwf/L3xxhu64447NGPGDEnSp59+qpYtW6p58+Z67LHHlJ+fX+rjc3JydPLkSbtLXq5rNmux2WzqfusASVLKnoNKT0l1yfM4kplfUPTv+fsOF7s9OTtXq5JPN3WqFe6uLEm0uYLZ61tpzG6z+ntq5T7anENb5TP7c+R8zOx768W5SjmSpvsfuU5hEUFue96ysHKbZO0+2qouL5vrLu5m6mafzz77rJ5//nn17dtXDz30kPbu3asXXnhBDz30kLy8vPTyyy/L19dXEyZMcLiMhISEYrdfdd9QXX3/7S5pjqxft+jf6cdOKCQqwiXPU5KkzJyifx/KzHZwn9PXR/q7dwd12lzDzPXtfPhZcMzKfbQ5hzbXsPJnnGRe34olm+Xt7aV5s37VvFm/2t22f0+KJGnh3N+0bvV2RdYM1eMJQ93SZfU2q/fRBiswdfA3bdo0TZs2Tddff702btyoTp066eOPP9aQIUMkSc2bN9cjjzxS6uBv3LhxGj16tN11Hyf+7LLmrLPOk+QX6O+y5ynJ9rRTKjQMedlsCvPzVUoJO+6H+Z3+xZ5VUEhbFWg7HzPXt/PhZ8ExK/fRRpuVWPkzTjK3r6CgUH+s3e3w9sOHUnX4UKrqRLt/wGzlNsnafbRVTR50vBdzB3+HDh1S586dJUnt2rWTl5eX2rdvX3R7x44ddejQoVKX4e/vL39/+w9kXz+/Sm89Y/vytZIkvxoBioip47LnKUlqbp42pZ5Uu8gwdaoZrp0n7U/Y622zqW1kqCRp64l02qpA2/mYub6dDz8LVbOPNtqsxMqfcZJ5ff9d9qzD254fP0OLF6zRsBH9NGR4b7c1nWHlNsnafbTBCkzd569u3br666+/JEk7duxQQUFB0deS9Oeff6p27dpubUrefUC7Vm9SYUGB3fVGYaE2L16p5Z8tkCR1uKqnvH283domSZ/s2C9Jui0+Ri3Cg4uu97JJ97VopJigQGXk5WvhgWTaqkCbldc3K7dJ1n1Pq0IfbbS5i9U/R6zeB8AabDbXXdzN1Jm/IUOG6I477tCgQYP0448/6pFHHtHDDz+sY8eOyWazaeLEibrxxhvd2nQy+ZjmT/pA/sE1VLtxfQWFhyonI1NH9yUV7ezdrEcndbulv1u7zlh3LE0fbture5rF6rVubbT1xCkdz81V09BgRdcIUHZBgZ7dsF2puXm0VYE2K69vVm6TrPueVoU+2mhzF6t/jli9DwAqm6mDvwkTJigwMFArV67Uvffeq7Fjx6pdu3Z65JFHlJmZqYEDB+qZZ55xa1NUXIw6DOylIzv3KfVgsg5tTZQMQzXCQ9T0ovZqdXk3xXVu5damc32264C2pp3SDY2i1SI8RM18gnU8J0/fHTii6bsOan9GFm1VpM3K65uV286w4ntaVfpoo80drP45YvU+ANbgSfv82QzDzWd7dYN3tnxvdkKpvkzkELqe5ua4jPPfCcXwswBUDXzGOW9Ag5zz3wmoBhoGDzQ7wWlbTixw2bJbhF/tsmWXxNSZPwAAAACwMk+a+WPwBwAAAAAOmHEydlcx9WifAAAAAAD3YOYPAAAAABzwoIk/Zv4AAAAAoDpg5g8AAAAAHLDZPOfkCMz8AQAAAEA1wMwfAAAAADjAPn8AAAAAgCqFmT8AAAAAcMDmQVN/zPwBAAAAQDXA4A8AAAAAHPBy4cVZkyZNks1m06hRo8r1ODb7BAAAAAAHrLbZ5++//653331Xbdu2LfdjmfkDAAAAgCrg1KlTGjJkiN5//31FRESU+/EM/gAAAADAAZsLLzk5OTp58qTdJScnx2HLyJEjddVVV6l3795OfS8M/gAAAADABAkJCQoLC7O7JCQklHjfGTNmaN26dQ5vLwv2+QMAAAAAB1y5z9+4ceM0evRou+v8/f2L3W///v168MEHtXjxYgUEBDj9fAz+AAAAAMAE/v7+JQ72zrV27VolJyerY8eORdcVFBTo559/1htvvKGcnBx5e3ufdzkM/gAAAADAASsc7POKK67Qpk2b7K6766671Lx5cz366KNlGvhJDP4AAAAAwNJCQkLUunVru+uCgoJUs2bNYteXhsEfAAAAADjgZYWpv0rC4A8AAAAAHLDq2G/p0qXlfgynegAAAACAaoCZPwAAAABwwGYzzE6oNMz8AQAAAEA1wMwfAAAAADhg1X3+nMHgzwQ3x2WYnVAlTZpp3Sn3Sb+bXeBYXJdgsxMc4mcBqBq+TAwyO6HK4rXzPPzucs4/W5hdAInBHwAAAAA4ZPOgqT/2+QMAAACAaoCZPwAAAABwwIMm/hj8AQAAAIAjnrSppCd9LwAAAAAAB5j5AwAAAAAHOOALAAAAAKBKYeYPAAAAABzynKk/Zv4AAAAAoBpg5g8AAAAAHLAx8wcAAAAAqEqY+QMAAAAAB2w2z5kvY/AHAAAAAA6x2ScAAAAAoAph5g8AAAAAHOCALwAAAACAKoWZPwAAAABwiJk/AAAAAEAVwswfAAAAADjgSad68JzvBAAAAADgEDN/AAAAAOCQ5+zzx+APAAAAABzwpFM9MPgrwaJXP9VfS1aXep9/zXxJPn6+bir6G23lVz8sQMtH9SzTfQdPXa3V+1JdXPQ3K7edzcdm0zUN66pXdC3FBgcqwNtbabl52p2eqUUHk7Uk6agpXVZd5yTanEWbc6zcdoZVP0es3mb1PtrKx8o/q1ZuQ+Vh8FeKei0aK7xurRJvs3mZu7skbWWXkVugWRsOOry9SVSw2seEKT0nX5uSTrqxzNptZ9QK8NMLXVqpUUgNncjJ0+bUdGUXFKh2gL/aRoYqu6DA9P8xsto6dzbanEObc6zaZuXPESu3Wb2PNudZ9WdVsnabWZj5qyZa9+6uVld0MzujRLSVXWpWnh6eu9nh7VNv6yhJmr85SVl5Be7KkmTtNkny8/LSlAtbKTa4hqZu36fPdx1QgWEU3e7v5aX6QYFu7zqX1da5s9HmHNqcY8U2K3+OWLnN6n20VYwVf1bPsHIbKo7BH6q1OiH+ujT+9F+3Zq53PANnBiu0DYmvr9jgGpq/77A+2bm/2O05hYXalZ5hQhmAqsLKnyNWbpOs3UcbqhfPmfH0nO8EcMKN7WPk7WXTtuR0bTiYZnaOHbPbvP+3r4QkzdhtrYExgKrByp8jVm6TrN1HG1B1MfNXiv2bd+jo3kPKzcpRYGiQ6jaNVaNOLeXja/6OrrRVjhvb1ZNkvVk/yfy2C0KDFO7vq5TsHB3KzFZcSA31qFNTtQL8lJ6Xr03HT+q3lFQZ51+Uy1l5naONturcZuXPESu3Wb2Ptoqz2s9qVWkzi83GPn/VwpYSjngUFBGqvv8aokYdW5pQ9DfaKq5rbITiagYpJ79QszceMjvHjhXaGocGSZKOZufq3maxuqVxjLzO/vCLl7anndITa7coOTvXlMYzrLzO0eYc2pxjtTYrf45Yuc3qfbRVnNV+Vs9m5TZUnM0wDLP/+FHp3tnyfYUev27eT7J5ealB22YKjYpQfk6eUvYc1MoZ3yppa6K8fLx1/fiRatCmaSUV01YWk2ZW7qr64qDWuqF9jL7587BGztpYqcuuqMpsi+sS7NTjbm0co380b6S8wkL5enlpzp4kzd57SMdz8tQ8LFgPtopXw+BA7U7P0D+Wb7Tbmb6sbo6r2D4X1fnngTba3Nn2ZWKQU49zx+eIs6zcZvW+6t5Wkd9d1flz5J8t+lZysftk5P/ssmUH+VzqsmWXxPTBX1JSkt5++20tX75cSUlJ8vLyUuPGjXXttddq2LBh8vb2LvcyKzr4c8QwDM1PeF+7Vm9SVKMYDX1lrEuexxnVoa0yB3/Bft5a/e9equHno2Gfr9XSneaequBsld3m7ODvtvgY3duskSTpx0MpenbDdrvbawf46ZOeHeXv7a3nNmzX4kMp5X6Oig7+HKkOPw+uQJtzqkObs4M/d3yOOMvKbVbvq+5trvjdVR0+R6ry4C8z/xeXLbuGTw+XLbskph7wZc2aNWrRooW+/fZb5eXlaceOHerUqZOCgoL08MMP69JLL1V6enqpy8jJydHJkyftLnm5rpnGt9ls6n7rAElSyp6DSk8x54TbJaGtfAa2jlYNPx8dSsvSMgsN/CTrtGXm/31qifn7Dhe7PTk7V6uST7+XnWqFuyurTKy4zp1Bm3Noc47ZbVb+HLFym2TtPtoqn9k/q6WxchvKz9TB36hRo/TQQw9pzZo1+uWXXzRt2jRt375dM2bM0O7du5WZman//Oc/pS4jISFBYWFhdpdF733psubI+nWL/p1+7ITLnscZtJXd4A4xkqRZGw+ZvtP3uazSlpSZU/TvQ5nZDu5z+vpIf+vtBG61de5stDmHNueY2WblzxErt51+buv20eYafI5YmZcLL+5l6uBv3bp1uv3224u+vu2227Ru3TodOXJEERERev755zVr1qxSlzFu3DilpaXZXa78x80ua84667wwfoH+LnseZ9BWNk1qBalD/XAVGoa+sthRPq3Utj3tlAr/t1V4mF/JvyDPXJ9VUOi2rrKy0jp3LtqcQ5tzzGyz8ueIldska/fR5hp8jsAdTB381a5dW0lJSUVfHzlyRPn5+QoNDZUkNW3aVMePHy91Gf7+/goNDbW7+Pr5uax5+/K1kiS/GgGKiKnjsudxBm1lc3PH+pKklYnHtf9Elqkt57JSW2punjalnpQkdaoZXux2b5tNbSNP/6xuPVH65tlmsNI6dy7anEObc8xss/LniJXbJGv30eYafI5Yl82F/7mbqYO/a6+9Vv/85z+1cOFCLVmyREOGDFHPnj0VGBgoSdq2bZtiYmLc2pS8+4B2rd6kwoICu+uNwkJtXrxSyz9bIEnqcFVPefuU/2A0tLm/7Ww+XjZd2yZakvSlxWb9rNj2yY79kk7vQN8i/O8Dx3jZpPtaNFJMUKAy8vK18ECy29usvM7RRhttf7Py54iV26zeR1v5Wfln1cptqFymnufv2WefVVJSkgYOHKiCggJ1795dn332WdHtNptNCQkJbm06mXxM8yd9IP/gGqrduL6CwkOVk5Gpo/uSinZwbdajk7rd0t+tXbRVjisuiFJUsL/SsvK0aMsRU1vOZcW2dcfS9OG2vbqnWaxe69ZGW0+c0vHcXDUNDVZ0jQBlFxTo2Q3blZqb5/Y2K69ztNFG29+s/Dli5Tar99FWflb+WbVymxV40kneTT/VgyRlZ2crPz9fwcHOHZL+XBU51UPakaNav2CZjuzcp5PJx09v42wYqhEeorpNY9Xq8m6K69yqUjppK5/KONXDB7d0UO9mtfXJ7/v05LdbKry8yuSqNmdP9XC2zrXCdUOjaLUID1ENH28dz8nTumMnNH3XQe3PcH7z1IocLru6/zzQRps725w91cPZXPU5Uhms3Gb1vurY5uzvrur+OVKVT/WQXbDSZcsO8O7usmWXxBKDv8rmqvP8wVyVfZL36qIyBn+u4qrz/AGoXJUx+AM8Bb+7nFO1B3+rXLbsAO9uLlt2SUzd7BMAAAAArMxm7mFSKpXnfCcAAAAAAIeY+QMAAAAAhzzngC/M/AEAAABANcDMHwAAAAA44EmnemDmDwAAAACqAWb+AAAAAMAhZv4AAAAAAFUIM38AAAAA4IAnneePwR8AAAAAOMRmnwAAAACAKoSZPwAAAABwwMbMHwAAAACgKmHmDwAAAAAc4CTvAAAAAIAqhZk/AAAAAHDIc+bLPOc7AQAAAAA4xMwfAAAAADjA0T4BAAAAAFUKM38AAAAA4JDnzPwx+AMAAAAABzjVAwAAAACgSmHwBwAAAAAOebnwUnZvv/222rZtq9DQUIWGhqp79+767rvvyv2dAAAAAAAsrH79+po0aZLWrl2rNWvW6PLLL9egQYP0559/lnkZ7PMHAAAAAA5Y5VQPAwcOtPt64sSJevvtt7Vq1Sq1atWqTMtg8AcAAAAAJsjJyVFOTo7ddf7+/vL39y/1cQUFBfrqq6+UkZGh7t27l/0JDZQqOzvbGD9+vJGdnW12SjG0OcfKbYZh7T7anEObc6zcZhjW7qPNObQ5x8pthmHtPtowfvx4Q5LdZfz48Q7v/8cffxhBQUGGt7e3ERYWZnzzzTflej6bYRhGeUeo1cnJkycVFhamtLQ0hYaGmp1jhzbnWLlNsnYfbc6hzTlWbpOs3Uebc2hzjpXbJGv30Ybyzvzl5uZq3759SktL06xZs/TBBx9o2bJlatmyZZmej80+AQAAAMAEZdnE82x+fn5q0qSJJKlTp076/fff9eqrr+rdd98t0+M52icAAAAAVEGFhYXFZg5Lw8wfAAAAAFjcuHHj1L9/fzVs2FDp6en64osvtHTpUi1atKjMy2Dwdx7+/v4aP358uaZj3YU251i5TbJ2H23Ooc05Vm6TrN1Hm3Noc46V2yRr99GG8khOTtYdd9yhpKQkhYWFqW3btlq0aJH69OlT5mVwwBcAAAAAqAbY5w8AAAAAqgEGfwAAAABQDTD4AwAAAIBqgMEfAAAAAFQD1XrwV1BQoCeeeEJxcXEKDAxUfHy8nnnmGZ3vGDhLly5Vx44d5e/vryZNmmjatGmmtMyePVt9+vRRVFSUQkND1b1792KHen3qqadks9nsLs2bN3d769KlS4t12Gw2HT582O0tw4YNK7GlVatWRfdxxet2Rnp6ukaNGqXY2FgFBgbqoosu0u+//17qY1yxzpW3w6z1zdleV61zzrS4a537+eefNXDgQNWrV082m03//e9/7W43DENPPvmkoqOjFRgYqN69e2vHjh3nXe6bb76pRo0aKSAgQF27dtXq1atNaUlISFCXLl0UEhKi2rVr69prr9W2bdvs7tOrV69ir+M///lPU3qdfU9d0dKoUaMS18GRI0cW3ceZ1+58rbNnz1bfvn1Vs2ZN2Ww2bdiw4bzfvyR99dVXat68uQICAtSmTRt9++23532MK1ref/999ejRQxEREYqIiFDv3r2Lrf8l/Xz369fP7a3Tpk0r1hEQEHDex7mipaR1yWaz6aqrriq6jzOv2/l68/Ly9Oijj6pNmzYKCgpSvXr1dMcdd+jQoUPnXW55P+dc0WHWZ5yzva78nQ/XqdaDv8mTJ+vtt9/WG2+8oS1btmjy5Ml6/vnn9frrrzt8TGJioq666ipddtll2rBhg0aNGqXhw4eX6/waldXy888/q0+fPvr222+1du1aXXbZZRo4cKDWr19vd79WrVopKSmp6LJ8+XK3t56xbds2u5batWu7veXVV1+1a9i/f78iIyN100032d2vsl+3M4YPH67Fixfr008/1aZNm9S3b1/17t1bBw8eLPH+rlrnytth1vrmbO8Zlb3OOdPirnUuIyND7dq105tvvlni7c8//7xee+01vfPOO/rtt98UFBSkK6+8UtnZ2Q6X+eWXX2r06NEaP3681q1bp3bt2unKK69UcnKy21uWLVumkSNHatWqVVq8eLHy8vLUt29fZWRk2N3v3nvvtXsdn3/++VJbXdUrOfeeuqLl999/t+tYvHixJBVbB8v72p2vNSMjQ5dccokmT55c6nLO9uuvv+rWW2/VPffco/Xr1+vaa6/Vtddeq82bN7u9ZenSpbr11lu1ZMkSrVy5Ug0aNFDfvn2L/az369fP7nWbPn2621slKTQ01K5j7969532MK1pmz55t17F582Z5e3sXW9/K+7qdrzczM1Pr1q3TE088oXXr1mn27Nnatm2brrnmmlKX6cznnCs6zPqMc7ZXct3vfLiQUY1dddVVxt1332133fXXX28MGTLE4WMeeeQRo1WrVnbX3XzzzcaVV17p9paStGzZ0pgwYULR1+PHjzfatWtXobZzOdO6ZMkSQ5KRmppqesu55syZY9hsNmPPnj1F17nidTMMw8jMzDS8vb2NBQsW2F3fsWNH4/HHHy/xMa5Y55zpKIk71jfDcK7XVetcZbx27ljnJBlz5swp+rqwsNCoW7eu8cILLxRdd+LECcPf39+YPn26w+VceOGFxsiRI4u+LigoMOrVq2ckJCS4veVcycnJhiRj2bJlRdf17NnTePDBB8u8DFf2VsZ76qrX7sEHHzTi4+ONwsLCousq+tqd23q2xMREQ5Kxfv368y5n8ODBxlVXXWV3XdeuXY3/+7//c3vLufLz842QkBDj448/LrruzjvvNAYNGlTuZZ1RWa1Tp041wsLCnO6ozJZzvfzyy0ZISIhx6tSpousq+roZRum9Z6xevdqQZOzdu9fhfSr6OVdZHedy12dcScrS66rf+XCtaj3zd9FFF+nHH3/U9u3bJUkbN27U8uXL1b9/f4ePWblypXr37m133ZVXXqmVK1e6veVchYWFSk9PV2RkpN31O3bsUL169dS4cWMNGTJE+/btM621ffv2io6OVp8+fbRixYoKdVS05YwPP/xQvXv3VmxsrN31lf26SVJ+fr4KCgqKbYoTGBjo8K9lrljnnOk4l7vWt4r2VvY6VxmvnTvXuTMSExN1+PBhu3UpLCxMXbt2dbgu5ebmau3atXaP8fLyUu/evSu0/jnTUpK0tDRJKrYOfv7556pVq5Zat26tcePGKTMz0+nWivZW9ntaGa9dbm6uPvvsM919992y2Wx2t1X2a+cMV/2erQyZmZnKy8srts4tXbpUtWvXVrNmzTRixAgdO3bMlL5Tp04pNjZWDRo00KBBg/Tnn3+a0nGuDz/8ULfccouCgoLsrnfH65aWliabzabw8PASb3fV51x5Oxw9RnL9Z5yj5y5Lryt/b8E1fMwOMNPYsWN18uRJNW/eXN7e3iooKNDEiRM1ZMgQh485fPiw6tSpY3ddnTp1dPLkSWVlZSkwMNBtLeeaMmWKTp06pcGDBxdd17VrV02bNk3NmjVTUlKSJkyYoB49emjz5s0KCQlxW2t0dLTeeecdde7cWTk5Ofrggw/Uq1cv/fbbb+rYsaNTHc62nO3QoUP67rvv9MUXX9hd74rXTZJCQkLUvXt3PfPMM2rRooXq1Kmj6dOna+XKlWrSpEmJj3HFOudMx7nctb452+uqda6ir52717kzzuzrWNK65Gg/yKNHj6qgoKDEx2zdutWtLecqLCzUqFGjdPHFF6t169ZF1992222KjY1VvXr19Mcff+jRRx/Vtm3bNHv2bLf3uuI9rYzX7r///a9OnDihYcOG2V3vitfOGY4+8ypjf92KevTRR1WvXj27gUK/fv10/fXXKy4uTrt27dJjjz2m/v37a+XKlfL29nZbW7NmzfTRRx+pbdu2SktL05QpU3TRRRfpzz//VP369d3Wca7Vq1dr8+bN+vDDD+2ud8frlp2drUcffVS33nqrQkNDS7yPqz7nyttxLnd+xjnb6+rfW3ARs6cezTR9+nSjfv36xvTp040//vjD+OSTT4zIyEhj2rRpDh/TtGlT47nnnrO77ptvvjEkGZmZmW5tOdvnn39u1KhRw1i8eHGp90tNTTVCQ0ONDz74wLTWMy699FJj6NChTndURstzzz1n1KxZ08jJySn1fpXxup2xc+dO49JLLzUkGd7e3kaXLl2MIUOGGM2bNy/x/q5a58rbcTZ3rm+V0XtGZaxzFW1x1zqnczbrWbFihSHJOHTokN39brrpJmPw4MElLuPgwYOGJOPXX3+1u37MmDHGhRde6NaWc/3zn/80YmNjjf3795d6vx9//NGQZOzcudPUXsNw7j11RUvfvn2Nq6+++rz3K+9rd27r2cqzyaCvr6/xxRdf2F335ptvGrVr1y5TR2W2nC0hIcGIiIgwNm7cWOr9du3aZUgyfvjhB9NaDcMwcnNzjfj4eOM///lPmR/jipZ//OMfRps2bc57v/K+boZRem9ubq4xcOBAo0OHDkZaWprDZVTG51xldJzLnZ9xldFrGJX7Ox+uU603+xwzZozGjh2rW265RW3atNHtt9+uhx56SAkJCQ4fU7duXR05csTuuiNHjig0NNTpWT9nW86YMWOGhg8frpkzZxbbVOZc4eHhuuCCC7Rz505TWs924YUXVqijoi2GYeijjz7S7bffLj8/v1LvWxmv2xnx8fFatmyZTp06pf3792v16tXKy8tT48aNS7y/q9a58nac4e71raK9Z6uMda4iLWatc9Lp9UhSievSmdvOVatWLXl7e5frMa5qOdv999+vBQsWaMmSJeed0ejataskVeh1rGjvGZXxnla0Ze/evfrhhx80fPjw8963Ml47Zzj6zKvIOldRU6ZM0aRJk/T999+rbdu2pd63cePGqlWrlttft3P5+vqqQ4cOpnZkZGRoxowZuueee85738p83fLy8jR48GDt3btXixcvLnX2ylWfc+XtOJu7P+Mq2ntGZf/egmtU68FfZmamvLzsXwJvb28VFhY6fEz37t31448/2l23ePFide/e3e0tkjR9+nTdddddmj59ut0hlB05deqUdu3apejoaLe3nmvDhg0V6qhoy7Jly7Rz584y/VKqjNftXEFBQYqOjlZqaqoWLVqkQYMGlXg/V61z5e2QzFnfKtJ7rspY5yrSYuY6FxcXp7p169qtSydPntRvv/3mcF3y8/NTp06d7B5TWFioH3/8sULrnzMt0unB8/333685c+bop59+Ulxc3Hmf68xh6SvyOjrbe67KeE8r2jJ16lTVrl27TD+/lfHaOcPVn3nl9fzzz+uZZ57RwoUL1blz5/Pe/8CBAzp27JjbX7dzFRQUaNOmTaZ2fPXVV8rJydHQoUPPe9/Ket3ODGB27NihH374QTVr1iz1/q76nCtvh2TeZ5yzvedyxe98uIC5E4/muvPOO42YmBhjwYIFRmJiojF79myjVq1axiOPPFJ0n7Fjxxq333570de7d+82atSoYYwZM8bYsmWL8eabbxre3t7GwoUL3d7y+eefGz4+Psabb75pJCUlFV1OnDhRdJ9///vfxtKlS43ExERjxYoVRu/evY1atWoZycnJbm19+eWXjf/+97/Gjh07jE2bNhkPPvig4eXlVa7NOyqr5YyhQ4caXbt2LXG5rnjdzli4cKHx3XffGbt37za+//57o127dkbXrl2N3NzcEntdtc6Vt8Os9c3ZXletc860nOHqdS49Pd1Yv369sX79ekOS8dJLLxnr168vOlrbpEmTjPDwcGPu3LnGH3/8YQwaNMiIi4szsrKyipZx+eWXG6+//nrR1zNmzDD8/f2NadOmGX/99Zfxj3/8wwgPDzcOHz7s9pYRI0YYYWFhxtKlS+3WwTObP+/cudN4+umnjTVr1hiJiYnG3LlzjcaNGxuXXnqpKa+ds++pK1oM4/QRDBs2bGg8+uijxZ7T2dfufK3Hjh0z1q9fX7Sp+owZM4z169cbSUlJRcu4/fbbjbFjxxZ9vWLFCsPHx8eYMmWKsWXLFmP8+PGGr6+vsWnTJre3TJo0yfDz8zNmzZplt86lp6cXPefDDz9srFy50khMTDR++OEHo2PHjkbTpk2N7Oxst7ZOmDDBWLRokbFr1y5j7dq1xi233GIEBAQYf/75p9tftzMuueQS4+abby7xOZ153c7Xm5uba1xzzTVG/fr1jQ0bNti9Z2dval8Zn3Ou6DDrM87ZXlf+zofrVOvB38mTJ40HH3zQaNiwoREQEGA0btzYePzxx+1W9DvvvNPo2bOn3eOWLFlitG/f3vDz8zMaN25sTJ061ZSWnj17GpKKXe68886i+9x8881GdHS04efnZ8TExBg333xzubYLr6zWyZMnG/Hx8UZAQIARGRlp9OrVy/jpp58q1OFsi2GcPjR6YGCg8d5775W4XFe8bmd8+eWXRuPGjQ0/Pz+jbt26xsiRI+0GUO5a58rbYdb65myvq9Y5Z1oMwz3r3JnTWzh6jwoLC40nnnjCqFOnjuHv729cccUVxrZt2+yWERsba4wfP97uutdff91o2LCh4efnZ1x44YXGqlWrTGkpaXmSin4e9u3bZ1x66aVGZGSk4e/vbzRp0sQYM2ZMmfZbcUWvs++pq97HRYsWGZKK3bcir935WqdOnVri7We39ezZ0+5zxDAMY+bMmcYFF1xg+Pn5Ga1atTK++eabCr9uzrTExsaW+pjMzEyjb9++RlRUlOHr62vExsYa995773n/OOKK1lGjRhX9nNapU8cYMGCAsW7dOlNeN8MwjK1btxqSjO+//77Yczr7up2v98x+iSVdlixZUrSMyvicc0WHWZ9xzva68nc+XMdmGIYhAAAAAIBHq9b7/AEAAABAdcHgDwAAAACqAQZ/AAAAAFANMPgDAAAAgGqAwR8AAAAAVAMM/gAAAACgGmDwBwAAAADVAIM/AAAAAKgGGPwBAAAAQDXA4A8A4FbDhg2TzWYrdunXr5/ZaQAAeDQfswMAANVPv379NHXqVLvr/P39TaoBAKB6YOYPAOB2/v7+qlu3rt0lIiJCkmSz2fT222+rf//+CgwMVOPGjTVr1qyix+bm5ur+++9XdHS0AgICFBsbq4SEBLO+FQAAqgwGfwAAy3niiSd0ww03aOPGjRoyZIhuueUWbdmyRZL02muvad68eZo5c6a2bdumzz//XI0aNTI3GACAKoDBHwDA7RYsWKDg4GC7y3PPPVd0+0033aThw4frggsu0DPPPKPOnTvr9ddflyTt27dPTZs21SWXXKLY2FhdcskluvXWW836VgAAqDLY5w8A4HaXXXaZ3n77bbvrIiMji/7dvXt3u9u6d++uDRs2SDp9wJg+ffqoWbNm6tevn66++mr17dvX5c0AAFR1DP4AAG4XFBSkJk2aOPXYjh07KjExUd99951++OEHDR48WL1797bbLxAAABTHZp8AAMtZtWpVsa9btGhR9HVoaKhuvvlmvf/++/ryyy/19ddf6/jx4+7OBACgSmHmDwDgdjk5OTp8+LDddT4+PqpVq5Yk6auvvlLnzp11ySWX6PPPP9fq1av14YcfSpJeeuklRUdHq0OHDvLy8tJXX32lunXrKjw83N3fBgAAVQqDPwCA2y1cuFDR0dF21zVr1kxbt26VJE2YMEEzZszQfffdp+joaE2fPl0tW7aUJIWEhOj555/Xjh075O3trS5duujbb7+VlxcbswAAUBqbYRiG2REAAJxhs9k0Z84cXXvttWanAADgUfgzKQAAAABUAwz+AAAAAKAaYJ8/AIClsDcCAACuwcwfAAAAAFQDDP4AAAAAoBpg8AcAAAAA1QCDPwAAAACoBhj8AQAAAEA1wOAPAAAAAKoBBn8AAAAAUA0w+AMAAACAauD/AbU++r94QLIEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])\n",
        "tmp['Sil_score'] = sil_score\n",
        "\n",
        "pivot_1 = pd.pivot_table(tmp, values='Sil_score', index='Min_samples', columns='Eps')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(18,6))\n",
        "sns.heatmap(pivot_1, annot=True, annot_kws={\"size\": 10}, cmap=\"YlGnBu\", ax=ax)\n",
        "ax.set_title('Shilouette Score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "RvwAusKGXJKp",
        "outputId": "b76642c1-cd49-4eb5-8e51-8e84a1718bb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# install jika perlu: pip install kneed\n",
        "!pip install kneed\n",
        "from kneed import KneeLocator"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmsVIE48ZjKQ",
        "outputId": "0f35a1d4-1352-483b-85af-f6d30fbd5d26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kneed in /usr/local/lib/python3.12/dist-packages (0.8.5)\n",
            "Requirement already satisfied: numpy>=1.14.2 in /usr/local/lib/python3.12/dist-packages (from kneed) (2.0.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from kneed) (1.16.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import NearestNeighbors\n",
        "MinPts = 4  # min_samples DBSCAN\n",
        "\n",
        "# 1) hitung k-distance\n",
        "nbrs = NearestNeighbors(n_neighbors=MinPts).fit(X)\n",
        "distances, _ = nbrs.kneighbors(X)\n",
        "k_distances = np.sort(distances[:, MinPts-1])\n",
        "\n",
        "# 2) cari knee\n",
        "\n",
        "x = np.arange(len(k_distances))\n",
        "knee = KneeLocator(x, k_distances, curve=\"convex\", direction=\"increasing\")\n",
        "eps_opt = k_distances[knee.knee]"
      ],
      "metadata": {
        "id": "IueE_t-zZBgn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Index knee:\", knee.knee)\n",
        "print(\"Epsilon optimal:\", eps_opt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyP8mWLaZtXU",
        "outputId": "ccfb954f-a263-47fe-a8cd-804b00f2ab89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index knee: 176\n",
            "Epsilon optimal: 10.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) plot + titik knee\n",
        "plt.figure()\n",
        "plt.plot(x, k_distances, label=\"k-distance\")\n",
        "plt.axvline(knee.knee, linestyle=\"--\", label=\"knee index\")\n",
        "plt.axhline(eps_opt, linestyle=\"--\", label=f\"eps≈{eps_opt:.3f}\")\n",
        "plt.scatter([knee.knee], [eps_opt], s=80, zorder=3, label=\"knee point\")\n",
        "plt.title(\"Elbow/Knee Method untuk Epsilon DBSCAN\")\n",
        "plt.xlabel(\"Data points (sorted)\")\n",
        "plt.ylabel(\"k-distance\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "9M7yvweuZ6ch",
        "outputId": "c472b8d8-46d1-420d-9ac9-cd111ec31858"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eps = 8\n",
        "min_samples = 3\n",
        "\n",
        "db_grid = DBSCAN(eps=eps, min_samples=min_samples)\n",
        "labels_grid = db_grid.fit_predict(X)"
      ],
      "metadata": {
        "id": "M6hh2d5W5Qc0"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "plt.scatter(X.iloc[:,0], X.iloc[:,1], c=labels_terbaik, cmap=\"rainbow\", s=35)\n",
        "plt.title(f\"DBSCAN Result (eps={eps}, min_samples={min_samples})\")\n",
        "plt.xlabel(\"Annual Income \")\n",
        "plt.ylabel(\"Spending Score \")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ooFvpLMs55-x",
        "outputId": "56aa0496-cc77-49c6-b5b7-be6782e34808"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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