Hierarchical_Clustering_ML_9.ipynb

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  "metadata": {
    "colab": {
      "name": "Hierarchical Clustering_ML_9.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN5OrcezzEAn7Nt9OarQzv1"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Altf3rM76H4L",
        "colab_type": "text"
      },
      "source": [
        "Author: Sreya Dhar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwAatuRShQc1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import make_blobs\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "import scipy.cluster.hierarchy as sch\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics.cluster import homogeneity_score, completeness_score, v_measure_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4SW_b1ghR8Z",
        "colab_type": "code",
        "outputId": "7fed61a3-fd2f-4641-e107-731b52cbbaf3",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
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                  "content-type",
                  "application/javascript"
                ]
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              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5e40375d-5745-462c-9754-cd4f174223f1\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-5e40375d-5745-462c-9754-cd4f174223f1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving assignment_shala_clustering.csv to assignment_shala_clustering (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jK8hRLUYhXSQ",
        "colab_type": "code",
        "outputId": "16a8bdee-a650-4adc-c3d6-d2df21c37fdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "print (uploaded['assignment_shala_clustering.csv'][:200].decode('utf-8') + '...')\n",
        "import pandas as pd\n",
        "import io\n",
        "\n",
        "df = pd.read_csv(io.StringIO(uploaded['assignment_shala_clustering.csv'].decode('utf-8')))\n",
        "\n",
        "df.dropna(axis=1)\n",
        "df = df.drop(['CustomerID'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100)\r\n",
            "1,Male,19,15,39\r\n",
            "2,Male,21,15,81\r\n",
            "3,Female,20,16,6\r\n",
            "4,Female,23,16,77\r\n",
            "5,Female,31,17,40\r\n",
            "6,Female,22,17,76\r\n",
            "7,Female,35,18,6\r\n",
            "8,Female...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfPsGk2ToXbK",
        "colab_type": "code",
        "outputId": "1e81b384-3524-4794-ef9f-7ddd16cbd030",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "label_1 = df['Gender'].astype('category').cat.categories.tolist()\n",
        "replace_map_comp_1 = {'Gender' : {k: v for k,v in zip(label_1,list(range(1,len(label_1)+1)))}}\n",
        "df_lc = df.copy()\n",
        "df_lc['Gender'] = df_lc['Gender'].astype('category')\n",
        "df_lc['Gender'] = df_lc['Gender'].cat.codes \n",
        "print(df_lc.info())\n",
        "df_lc.columns\n",
        "df_lc.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 4 columns):\n",
            " #   Column                  Non-Null Count  Dtype\n",
            "---  ------                  --------------  -----\n",
            " 0   Gender                  200 non-null    int8 \n",
            " 1   Age                     200 non-null    int64\n",
            " 2   Annual Income (k$)      200 non-null    int64\n",
            " 3   Spending Score (1-100)  200 non-null    int64\n",
            "dtypes: int64(3), int8(1)\n",
            "memory usage: 5.0 KB\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0       1   19                  15                      39\n",
              "1       1   21                  15                      81\n",
              "2       0   20                  16                       6\n",
              "3       0   23                  16                      77\n",
              "4       0   31                  17                      40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eXNGjnYqAIxK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing \n",
        "\n",
        "# Normalizing Data\n",
        "x = df_lc.iloc[:].values \n",
        "x \n",
        "min_max_scaler = preprocessing.MinMaxScaler(feature_range =(0, 1)) \n",
        "x_after_min_max_scaler = min_max_scaler.fit_transform(x) \n",
        "# print (\"\\nAfter min max Scaling : \\n\", x_after_min_max_scaler)\n",
        "\n",
        "Standardisation = preprocessing.StandardScaler() \n",
        "x_after_Standardisation = Standardisation.fit_transform(x) \n",
        "# print (\"\\nAfter Standardisation : \\n\", x_after_Standardisation) \n",
        "df_final=pd.DataFrame(data=x_after_Standardisation, columns=['Gender', 'Age', 'Annual Income (k$)', 'Spending Score (1-100)'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WDTQ77gATk_",
        "colab_type": "code",
        "outputId": "0fdf5d21-c2ad-49b4-bd74-fd9940261395",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "# Visualizing the correlation matrix \n",
        "plt.figure(figsize=(5,5))\n",
        "mask = np.zeros_like(df_final.corr(), dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "sns.heatmap(df_final.corr(), \n",
        "            vmin = -1,\n",
        "            cmap='coolwarm',\n",
        "            annot=True,\n",
        "            mask=mask)\n",
        "# plt.savefig('corr-matrix.png')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6wCPFvrQVNjr/QtbddXdqFi9m8tX/zaJ331mlzFpbbMmAH12KOnVi3gvPMeU31wPQfZ/92ODU0+m88SaM/86ZLHxnPADr7LgzG571bSo6VFGzfBkf/eFGPnv1laJel1leOClZ66ioYPB1/8mLh53G4o+msefT/2D68CeYP/69FUX6n3osy+fMY+S2h9Dv2C+z5X/9gDEnfx+AhRM/4JndjlrltJv/6NssnTGTkdsfChJVPboV7ZLqWnfX3em0YX/e/MYJdN16MJt87yLGn3PWKuU2vvAi3v/F1SwY9yabX/UL1t1lN+a9+DyLJ03kvf/8CZt8/+KVyi+fO4f3fvIjls38lM4DBjLomusYe/yRxbosK3FZLPInqQdwFzAAmAwcHxGz6ym3MXALsBEQwJcjYvKazp3JcuiSjpQUkrbK4L0nS+rV2P15IWkHSbemzy+XdNEayl5eZ7u3pIfbMr7uO23Hwvc+YNHkD4lly/jk7n/S97ADVirT9ysH8OGf7wdg6n0j6LXv7g2et//JR/PetTclGxEsmzmn1WNvrO57DmXmI8k/44Jxb1LZdR069Oi5UpkOPXpS2bUrC8a9CcDMRx6m+9C9AFj8wfssmfLBKuddNOFdls38NCkzeRIVnTqhqqq2vBQrIxFq1qOFLgEej4hBwOPpdn3uAK6NiK2BXYDpDZ04k6QEnAQ8k/60xvkJcMOaCkjaRtJI4NuSXpF0EkBEzAA+kbRnWwXXeYO+LP7okxXbiz6aSqcN+tYp02dFmaiuZtm8z6jq2R2ALgP6s+dz97LriDtZb48dAejQbR0AtvjP77Lnv+9hhz9fT8c+KyeBYqrq1Zul0z//m1r66XQ69uq9UpmOvXqzdMbnZZbNmEFVnTJr0n3vfVn47tvEsmUtD9jahYjmPVroCOD29PntwCpVe0nbAB0i4tEkzpgfEQsbOnHRk5KktYGhwOnAiQX795X0lKS7JY2X9BdJSo9NlnRF+kE7traGVbfGIOkNSQPS5/dLelnSm5JWbWNZfXwDJI2TdHP62kckdUmPbS7pMUmvpbFspsS16XuPlXRCwfWMlPSApImSrpL0dUkvpuU2S8v1lnSPpJfSxyqJQ9I6wHYR8Vo9x86U9K80xsuBPwJ/APYEXiooej/w9dVc81mSRksa/a/lxa+JLJk6nSe33J9ndz+acZdcxZDbfkGHdbqiDpV06d+P2c+/yrN7HMOcF8aw9c9/WPT4iqXzgIH0P+sc3v/VtVmHYiUkULMehX/36aPRn5NA34io/RY6FehbT5ktgDmS7pX0avo5WdnQibPoUzoCeDgi3pE0U9KOEfFyemwHYDDwMfAsyQfrM+mxTyPii5LOAS4CzmjgfU6LiFnph/VLku6JiJmNjHEQcFJEnCnp78AxwJ+BvwBXRcR9kjqTJPWjgSHA9kCv9L1GpefZHtgamAVMBG6JiF0kfRc4H7gQ+DVwXUQ8k7a/jkhfU2gn4I26QUo6DzgIODIilkhamsZQERGLgAkFxUcD/1XfxUbETcBNAP9ca6tmfYda/PE0Om/Yb8V2lw3XZ8nH0+qUmU7nDfux+KNpqLKSqnXXWdEcVzMr+Tnv1TdZOHEKXQcNZO4rb7B8wcIVAxs+ufdh+p9yTHPCa7beRx5Nr698FYAF48fRsU8fFqTHOvbqw9JPZ6xUfumnM+jYu8+K7arevVlWp0x9qnr1ZrMrf86kq37G0o8/arX4rfw19+bZwr/7+kh6DFi/nkOX1jlPSKovig7AXiSf6x+Q9EGdCty6priyaL47Cfhb+vxvrNyE92JEfBgRNcAYkk60WvemP1+us391LpD0GvA8SSfboCbEOCkixhS+X1pb2TAi7gOIiMVpVXQo8NeIqI6IacBIYOf0tS9FxCcRsQR4D6gdNja24BoOBH4raQwwDFg3rU0W6gfU/WQ7GfgScGx6foAfATsC50l6UNL2BeWnAxs04d+gSea+PJaum29Cl002RFVV9Dv2y0wb/sRKZab/8wn6fyOp5a9/1CHMHPk8AB17rQcVya9ilwH96br5JiycNCV9zZP03HsXAHrut/tKAyeKYcb99zLuzFMZd+apzHl2FD0PPhSArlsPpnrBfJbPWvl7zvJZM6lesICuWw9OYj74UOY8+8wq5y1U2XVtNr/qWj66+Q8seGNs21yIla226lOKiAMj4gv1PB4ApknqB5D+rK+v6ENgTERMjIjlJK01X2zofYtaU0pHbOwPbJtm1kogJNUOR1pSULy6TnxL6tm/nJUTa+f0ffYl+bDfPSIWSnqq9lgj1Y2jSxNeu7rz1BRs1/D5NVQAu0XE4jWcZxGrxj+WpIbWH5gEEBEfAV+TdCVJ0929wGZp+c7pedpEVFfz5vd/xi7DboXKCj684x7mj5vAoJ+ez9xX3mD68CeZctvdbH/rNewzdgTLZs/l1XTkXY89d2bQT88nli8namp444LLWTZ7LgDj/+OXDLn1ara+5ics/XQWr5/9k7a6hAbNe/45uu26O1/489+pWbKYyVf/fMWxrW++jXFnngrAB9f/kgGXXEpFx07MffF55r3wHADdh+7NRhd8jw7durP5/1zLwvfeZcIPv0/vo46h0wb96Xfyt+h38rcAePfiC1k+J7tBHVY6MrpPaRhwCnBV+vOBesq8BHSX1Dvt196fpMVmjYrdfHcscGdEnF27I+2Y36uZ55sMHJae54vAwHR/N2B2mpC2AnZrdsSpiPhM0oeSjoyI+yV1IkmqTwNnS7od6AHsDVwMNHZk4SMkTXnXptcxpKCWVmsc8IM6+14Ffg8Mk3RIRHwsaXBEvEmS9F4GuhaU34J6mgBb04wRoxg5YtRK+9792W9WPK9ZspRXv3HhKq+b+sAjq733aPGUj3n+4G+2bqAtMOXXv2JKPftrExLAwnfG89Zpq8Y855lRzHlm1Cr7p/75dqb++fZV9ps1RhZDwkmS0d8lnQ68DxwPIGkn4NsRcUZEVKd9/o+n4wNeBm5e7RlTxW6+Owm4r86+e2j+KLx7gB6S3gTOA2rvZHwY6CBpHMk/3vPNPH9d3yRpFnwd+DdJe+t9wOvAa8ATwA8jYmoTznkBsJOk1yW9BXy7boGIGA90S5sQC/c/Q9K/NlzJcPajJD0HnEaS7C4oKL4fMLwJcZlZCchi9F1EzIyIAyJiUNrMNyvdPzoizigo92hEbBcR20bEqRGxtKFzK8ptjooyJel7wGcRcUsjyl4eEZfX2TcKOKK+G9wKNXegQynou2t2w8mLZccnn806BGu+ZlV5ho2ubtbf7Fd3qszlpHlZ3adkTfd7Vu6jWpOnCjck9QZ+1VBCMrPSUxPNe+SVpxkqEelAiDsbWfapOtszSEa+mFmZKbfGLiclM7MS5vWUzMwsN/LcFNccTkpmZiXMzXdmZpYbTkpmZpYbNWW2HLqHhJuZWW64pmRmVsLcfGdmZrnhpGRmZrnhIeFmZpYbjVkbqZQ4KZmZlTA335mZWW64+c7MzHLDNSUzM8sNJyUzM8sNN9+ZmVluuKZkZma5UVOTdQSty0nJzKyEuaZkZma54aRkZma54YEOZmaWG9HsqlI+pydyUjIzK2Hl1nznRf7MzCw3XFMyMythHhJuZma5UW7Nd05KtpLNjxiQdQhtpv93Tss6hDY15vvX8u+dds46jDa1x+iXsg4hdzz6zszMcsM1JTMzy41odlXJQ8LNzKyVufnOzMxyw813ZmaWGzVlVlVyUjIzK2GuKZmZWW44KZmZWW7UlFlWclIyMyth4WmGzMwsL5q/dEU+OSmZmZUwT8hqZma54ZqSmZnlRpndpuRF/szMrGkk9ZD0qKR305/rrabcNZLelDRO0g2SGpxwz0nJzKyERU0069FClwCPR8Qg4PF0eyWS9gD2BLYDvgDsDOzT0ImdlMzMSlhE8x4tdARwe/r8duDI+kIDOgMdgU5AFTCtoRO7T8nMrIQ1d+47SWcBZxXsuikibmrky/tGxCfp86lA37oFIuI5SU8Cn5Csk/HbiBjX0ImdlMzMSlhzR9+lCWi1SUjSY8D69Ry6tM55QtIqQUjaHNga6J/uelTSXhHx9JriclIyMythbTWjQ0QcuLpjkqZJ6hcRn0jqB0yvp9hRwPMRMT99zb+A3YE1JiX3KZmZlbCaiGY9WmgYcEr6/BTggXrKfADsI6mDpCqSQQ4NNt85KZmZlbCIaNajha4CDpL0LnBguo2knSTdkpa5G3gPGAu8BrwWEQ82dGI335mZlbAsFvmLiJnAAfXsHw2ckT6vBs5u6rmdlMzMSliZzTLkpGRmVspa4UbYXHFSMjMrYV7kz8zMcsM1JTMzyw0nJTMzy40yy0lOSmZmpcw1JbM16H3Kd+g6ZGdi6RKm/v6XLJk8YZUynQZuzvrf/gHq2IkFY15ixu2/B6DnMd+g2/6HsnzeXABm3nUbC8a8BJWV9D3rQjoP2BwqK5n39OPMfuCuol5XXRHBNX/7J8+OfYfOHau44ltHs/UmG6xS7tzrb2fG3M+orq5hh0ED+PHXD6OyooIb73+MkWPGI4ke63blim8dTZ/u6xb9OrrvvjsDL/oBVFQw/f4H+Oj221c6rqoqBl1xBV233orlc+fyzo9/wpJPknk4Nzz1VPoc8VWoqWHStb9gzvPPA9DvxBPpe9SRgJh2//188te/ArDWoEFs9uNLqFhrLZZ8/Anv/vSnVC9YUNTrLUfltvJsgzM6SLo0XaTpdUljJO3algFJulzSRenzKyWtdv6lJp73NElj0+t4Q9IRrXHeRr53F0kjJVWm2w9LmiPpoQZeV285SQMlvSBpgqS7JHVM93dKtyekxwek+7eVdFubXFyBrkN2puP6GzD5e6cx7eZf0+f08+ot1/e085l286+Z/L3T6Lj+Bqy1/U4rjs3+53188ONz+eDH5yYJCVhn171Qhyre/9F3+OAn59P9gC/TodcqkxIX1TNvvMsH02fywH9fyH988wh+/pf6b1S/+uwT+Ptl53H3Fecze/4CHh39BgCnHDKUv19+Hndddi57bbclNz34VBGjT1VUsOmPfshbF3yXMccdT69DDqbLwIErFel7xBEs/2werx51NB//3/+xyfnnA9Bl4EB6HXwQY44/gbfOv4BNL/kRVFSw1mab0feoI3n95FMY87Wvsd7QoXTun8zHufl//Afv//ZGXjvxJGY99SQbfPObRb9ky781JiVJuwOHAV+MiO1IppOYUozAACLiPyPisZaeR1J/kplth6bXsRvwegvP2ZRa5mnAvekdzgDXAo35i1xduauB6yJic2A2cHq6/3Rgdrr/urQcETEW6C9p4ybE3GRdd9ydeU8/DsDiCeOpXGttKrv3WKlMZfceVHRZi8UTxgMw7+nHWXunPRo8d0WnzlBRgTp2JJYvo2ZRtt+wR44Zx2G7DUES2222EZ8tXMSMOZ+tUm7tLp0BWF5dw/Ll1dQuvFm7H2DRkqU0vB5n61t78GAWTZnCko8+IpYv59NHHqXHPiuvwbbePnsz/aHhAMx8/Am67bIzAD322YdPH3mUWLaMJR9/zKIpU1h78GC6DBjAZ2+8Qc2SJVBdzbxXXqHH/vsB0HmTjZn3yisAzHnhRXqm+61lamqiWY+8aqim1A/4NCKWAETEpxHxMYCkyelSt2MlvZhOU46k3pLukfRS+tgz3X+5pD9KekrSREkX1L5JWht7R9IzwJYF+2+TdGzB+10h6ZX0PbcqeL9H09rcLZLel9SrznX0AT4D5qfXMT8iJqWv31zSY5JeS8+9mRLXpjWqsZJOSMvuK+lpScOAtyRVpuVeSmtgq5tS4+sUTFgYEY+n8axRfeXS5YT3J5lXClZeYKtw4a27gQMKlh9+EDixofdsiQ49erJs5owV28tnzaBDj56rlpn16edlZq5cpvshX2WTq39P37O/R0XXtQH47IWnqVmymE1//39s+ps7mf3QPdQsmN+Wl9Kg6bPnsX6Pbiu2+67Xjelz5tVb9pzrbueAH1zFWp07cuCOg1fs/+19j3LoD6/lXy+8zneOWGXGljbXqU9vlk77fM21pdOn0bFP7zpl+nxeprqa6vnz6dCtGx379GbJSq+dTqc+vVn43nusO2QIHbp1o6JTJ9bbcw869eg+kAAAABmsSURBVE1qtQvfm7gi6fU88IAV+61lMpr7rs00lJQeATZKE8bvJNVdynZuRGwL/Ba4Pt33a5Jv8TsDxwC3FJTfCjgE2AW4TFKVpB1JPiyHAF8mWTJ3dT6NiC8CvwcuSvddBjwREYNJPojrqw28RrLi4SRJf5J0eMGxvwA3RsT2wB4kC1IdncazPUnt8Np0enaALwLfjYgtSGomc9Nr3Rk4U9JK7R9p09qmETF5DdfVFD2BORGxPN3+ENgwfb4haU02PT43LQ8wGtirvhNKOkvSaEmj75pQtIrwKuY89hCTvvst3r/kHJbPnkXvb5wJQOfNtoSaGiae83UmffcU1vvKMVT1qW+Zl3z63fdO4dFf/JCly6t5afzEFfvPO+ogHr7mYr6063bc9cTzGUbYehZNnsxHd9zBNr/9DVv/5gYWvPMOUZ2srfDelVey/nHHst2dd1C51lrULFuWcbTlIaPl0NvMGpugImJ+mjT2AvYD7pJ0SUTclhb5a8HP69LnBwLbfP4FnXUlrZ0+H57WupZImk6yWuFewH0RsRAgrYWszr3pz5dJEgfAUJJ1O4iIhyXNruc6qiUdSpI4DgCuS6/rl8CGEXFfWm5xGsNQ4K9pc9s0SSPT184DXqytZQEHA9vV1uaAbsAgoPY4QC9gzhquqVimA6v2xLPyYl/vnHRok35bux10ON32PxSAJRPfoapnbxanxzr06M3yWTNXKr981kyqenxeke3Q8/My1XM//2ea+8TDbPjDKwBYd8/9WPDay8k39XlzWfTOm3TadBDLpk9tSqgtdteTL3DvqNEADB64IVNnzV1xbNrsuWscqNCpqop9t9+Kp8aMZ7dtNl/p2Jd33Z7zb7iz6LWlJdNn0LGgttKxT1+WTp9Rp8x0Ovbty9Lp06Gyksq112b53LksnT5jpZpOxz59WJK+dvoDw5j+QPJnvPE55ySvBRa9/z5vnZf0SXXeeGPWGzq0Ta+vvchzgmmOBgc6RER1RDwVEZcB55HUflYcrud5BbBbRAxJHxvWLvIELCkoX03TR//Vvr7Jr43EixHxPyQ1s2Maes1qFHZmCDi/4FoHRsQjdcovIlmnfo0k7ZoOJBkj6atrKDoT6F7Qp9Uf+Ch9/hGwUXq+DiRJsjYrdE5jaVVzH31wxcCE+aOfY929kg/WzptvRc3CBVTPmbVS+eo5s6hZtJDOm28FwLp7HcCCl58DWKn/ae2d92DJlMkALPt0OmsN3h4AdepE5823YunHH7b2pTTohP125a7LzuWuy85lvyFb89DzY4gIXn9vCmt36Uzv7uusVH7h4iUr+pmWV1fzzNh3GLB+kpDfn/Z5sn5qzPgV+4tp/ltv0WWjjem0wQaoQwd6HXwQs0aNWqnM7FFP0+ewrwDQ84D9mftSMvhk1qhR9Dr4IFRVRacNNqDLRhsz/803Aahabz0AOvbtS4/992PGww+vtB+J/qefxrR77inGZZa9jNZTajNr/GCXtCVQExHvpruGAO8XFDmBZB2NE4Dn0n2PAOeTdNIjaUhEjFnD24wCbpP0P2k8hwP/24RreBY4Hrha0sHAevVcxwbA+hHxSuF1RMRnkj6UdGRE3C+pE1BJsjLi2ZJuB3oAewMXkzQ/FhoBfEfSExGxTNIWwEcRsSJxRcTstO+pc21NrD4R8UIa1xqlSw8/CRwL/I2VF9iqXXjrufT4E/F54/EWwBsNnb8lFrz6Il2H7MyA6/9ILFnC1P/91YpjG//PjXzw43MBmPan36ZDwjuycMzoFaPsen/tdDptsikAy2ZMY9otNwAw55EHWf/bP2CTa5Nfi3kjH2XpB4WV0eIbuu0WPDP2Hb566XV07ljF5aceveLYCVfcyF2Xncuipcu48Ld/Ydny5dREsNOWAzl2n6R1+oZ7H+H9qZ9SIdGvZ3cu/caavoe0kepqJl57Ddv85gZUWcm0YcNYNHEiG519NvPHjWP2qFFMe+ABBl15BTvcdy/L583jnZ8kK2EvmjiRTx97jB3+8XeiupqJ11wDNUkz3ZbXXE2Hbt2I5cuZdPU1VM9PvpP2OuQQ1j8uaVSY+eRTTB/W4NI61gjlVlPSmjq80iau3wDdgeXABOCsiPhU0mTgLuBLJDWYkyJiQjrI4EaStdk7AKMi4tuSLgfmR8Qv0nO/ARwWEZMlXUryYTqdZLXCVyLiF+kw5oci4u70/XZK33sn4BcRsa+kPiTNh31JPowPAwbUDs5I32sT4E8kzVeLgRnAtyPiPUmDSJJgL2AZcBxJ89s16bUF8F8RcZekfYGLIuKw9LwVwH+RJFKl5z0yIj5v10nK3UrSHPhYuv00SYJbm6Qmc3pEjKjn37/ecpI2JUlIPYBXgW9ExBJJnYE7gR2AWcCJETExPddvgRENLbLV1Oa7UtL/O6dlHUKbGvP9a7MOoc3tMfqlrENoS80ag3nyTz9p1t/sHT/rl8GYz4atMSmt8YUFSaJVI2p6HJ2A6ohYrmQI++8josEaRzFJ+iLwvYjI5MaM9N9oJMmQ+OVrKuukVLqclEpes5LENy79uFl/s3/+7w1ymZTKYUaHjYG/p7WWpcCZGcezioh4RdKTkioL7lUqpo2BSxpKSGZWesqt+a7ZSSkiBrRiHM2W9nftkHUcDYmIP2b43u8C7zZY0MxKTp7vOWqOcqgpmZm1W5EOMCkXTkpmZiUsz1MGNYeTkplZCXPznZmZ5YYHOpiZWW44KZmZWW7URHkNdGhw7jszM7NicU3JzKyEufnOzMxyw0nJzMxyw0PCzcwsN2o8o4OZmeWFm+/MzCw3osyGhDspmZmVMNeUzMwsN5yUzMwsN8ptRgcnJTOzEuaakpmZ5YYX+TMzs9xwTcnMzHLDQ8LNzCw3vBy6mZnlhvuUzMwsN8qtT8mL/JmZlbCImmY9WkLScZLelFQjaac1lDtU0tuSJki6pDHndlIyM7OmegM4Ghi1ugKSKoEbgS8B2wAnSdqmoRO7+c7MrIRl0XwXEeMAJK2p2C7AhIiYmJb9G3AE8NaaXuSkZCvZ4q8Pr/G3rLVJOisibirmexZTMa9vj9HHF+NtVvD/XT48/cBezfqblXQWcFbBrpta+Xo3BKYUbH8I7NrQi9x8Z1k7q+EiJa2cr6+crw3K/Poi4qaI2KngsVJCkvSYpDfqeRzRlnG5pmRmZquIiANbeIqPgI0Ktvun+9bINSUzM2sLLwGDJA2U1BE4ERjW0IuclCxruW+zb6Fyvr5yvjYo/+trNklHSfoQ2B0YLmlEun8DSf8EiIjlwHnACGAc8PeIeLPBc0eU141XZmZWulxTMjOz3HBSMjOz3HBSMjOz3HBSMjOz3HBSsqKRVCnpyazjaEuS+kq6VdK/0u1tJJ2edVytQdLukm6U9LqkGZI+kPRPSedK6pZ1fC0hqb+kiyQ9IOklSaMk/U7SVyT5c7KI/I9tRRMR1UBNqX+ANeA2kiGwG6Tb7wAXZhZNK0mT7Bkk13Yo0I9kks3/ADoDD0j6anYRNp+kPwF/BJYCVwMnAecAj5Fc6zOS9s4uwvbFQ8KtqCQ9AOwAPAosqN0fERdkFlQrkvRSROws6dWI2CHdNyYihmQdW0tI6hURn7a0TB5J+kJEvLGG4x2BjSNiQhHDarc8zZAV273po1wtkNQTCABJuwFzsw2p5RqTbEoxIQEUJiRJPdJ9swqOLwWckIrENSUrOkldSL55vp11LK1N0heB3wBfIFlzpjdwbES8nmlgrUDSCcBLETFR0nYkXy7WAs6PiHuyja75JG0MXAMcAMwBBKwLPAFcEhGTs4uu/XFSsqKSdDjwC6BjRAyUNAS4MiJKsj+iPpI6AFuSfLi9HRHLMg6pVUgaDewZEUvSZtibgFeAERGxXbbRNZ+k54DrgbvTfs/aBeqOAy6MiN2yjK+9cVKyopL0MrA/8FRBn8sbEfGFbCNrHZKOrmf3XGBsREwvdjytRdJlwNnAH4BKkjnNfkPSTHkacCvJ/+lqVyLNK0nvRsSgph6ztuE+JSu2ZRExt86KlTVZBdMGTieZpLJ26Pu+wMvAQElXRsSdWQXWEhFxhaT9gEkkTZIPR8TlAJIOiYgrs4yvhV6W9Dvgdj5flG4j4BTg1cyiaqeclKzY3pT0NaBS0iDgAuDfGcfUmjoAW0fENEjuWwLuIFlxcxRQkkkp9R3gcmAJcBEk92EBwzOMqTWcTPJl4gqS1VIhWfdnGEkN0IrIzXdWVJLWAi4FDibpcxkB/CwiFmcaWCuR9FZEbFOwLeDNiNimcJh4qZLUue7/laQehaPVzFrCScmsFaXNQBsD/0h3HQN8CFwMPBQR+2UVW2uQNBw4Il0rB0n9SK5rx2wja750YMrpwJGsXFN6ALi1XAaqlAonJSsKSQ+S3rtTn3IZfZfWjI4Ghqa7ZgN9I+Lc7KJqPZLOBL4MHEvS7zIMuCgiHsk0sBaQ9FeSoeC3k3yBgGTp7lOAHhFxQlaxtUfuU7Ji+UX682hgfeDP6fZJwLRMImoDERGSJgK7kQwpngSU7D08dUXEzekMB/cDA4CzI6LU+wR3jIgt6uz7EHhe0jtZBNSeOSlZUUTESABJv4yInQoOPZje/1LSJG1BkmBPAj4F7iJpiSjp5rpakr5fuEnSRDkG2E3SbhHxq2wiaxWzJB0H3BMRNQDpJKzHkdR0rYiclKzYukraNCImAkgaCHTNOKbWMB54Gjisdo40Sd/LNqRWtU6d7XtXs78UnUgyEevvJNUmoe4kw/pPzCyqdsp9SlZUkg4lmQlgIsk37k1ImoBGZBpYC0k6kuQDbE/gYeBvwC0RMTDTwKxJ0nkLiYiZWcfSXjkpWdFJ6gRslW6Oj4glWcbTmiR1BY4gacbbn+QepftKeSAAgKSbgV/XN5t2es0nAEsi4i9FD64NSTooIh7NOo72xEnJik7SHiSd5CuajyPijswCaiOS1iPplzghIg7IOp6WSOco/AmwLclEszNI1lEaRDJ56R+BP5TTFwwASR9ExMZZx9GeOClZUUm6E9iMpJO8Ot0d5bKeUrmTtDawE8kif4uAcaU+27ukYas7BOwfEeXQ51kynJSsqCSNA7YJ/+JZTqSDG74BzK97CLgrIvoWP6r2y6PvrNjeILlP6ZOsAzFLPQ8srL1toZCkkq4FliLXlKyoJD0JDAFeJJnYEyifGR3MrGVcU7JiuzzrAKzlJK0VEQuzjqOtSDosIh7KOo72qCLrAKx9SZtIJgNV6fOXSFYvtRIgaQ9Jb5HcLIyk7dNJaMtNKa8PVdKclKyo0gk97wb+N921Ick8alYargMOAWYCRMRrwN6ZRtQ21HARawtOSlZs55LMejAPICLeBfpkGpE1SURMqbOrut6Cpe3srANor5yUrNiWRMTS2o10LRuPtikdU9Kbn0NSlaSLgHFZB9XaIuJFSGZ0yDqW9sZJyYptpKSfAF3SP/h/AA9mHJM13rdJarsbkiyENyTdLldeDr3IPCTciipdEuB0kuXQAUZExC0ZhmTtnGd0yBcnJSsKSUcA/SPixnT7RaA3SdPdDyPi7izjs8ZJlxo5n1XnLizZ+8w8o0O++D4lK5YfsvLaNB2BHYG1gT+RjMiz/LufpEnrQaAm41hai2d0yBEnJSuWjnVGbT0TEbNIVv1080jpWBwRN2QdRGuKiC+t4Vg5DnfPNTffWVFImhARm6/m2HsRsVmxY7Kmk/Q1kuUqHmHlaaJK9gZoSWpoguDGlLHW4ZqSFcsLks6MiJsLd0o6m2QePCsN2wLfJFnAsLb5LtLtUvWkpHuAByLig9qdkjoCQ4FTSJZGvy2b8NoX15SsKCT1IemPWMLn0wrtCHQCjoyIaVnFZo0naQLJ0iNLGyxcIiR1Bk4Dvg4MBOaQLGBYSVIj/F1EvJpdhO2Lk5IVlaT9gcHp5psR8USW8VjTSLofOCsipmcdS1uQVAX0AhZFxJys42mPnJTMrNEkPQVsRzKRrpcesVbnPiUza4rLsg7AyptrSmbWJJL6Ajunmy+Wa1OeZcNz35lZo0k6nmS05HHA8SSjKo/NNqrWI2kTSQemz7tIWifrmNob15TMrNEkvQYcVFs7ktQbeCwits82spZL1/o6C+gREZtJGgT8ISIOyDi0dsU1JTNrioo6zXUzKZ/PEa/1lQMe6GBmTfGwpBHAX9PtE4B/ZRhPa1oSEUulZNFZr/WVDSclM2u0iLhY0tEkMx0A3BQR92UZUyuqu9bXOXitr6Jzn5KZNVq6dMUnEbE43e4C9I2IyZkG1gqUVJHOIFnrS8AI4BbPeVdcTkpm1miSRgN71E4zlM4P92xE7LzmV+abpEqSGUa2yjqW9q5cOijNrDg6FM57lz7vmGE8rSIiqoG3JW2cdSztnfuUzKwpZkj6akQMgxUrCn+acUytZT3gzXRV5AW1Oz2FUnG5+c7MGk3SZsBfgA1I+l2mACdHxIRMA2sFkvapb399K9Ja23FSMrMmk7Q2QETMzzqW1uQplLLnpGRmjSapE3AMMICC5v+IuDKrmFpLOoXStcBTJLXAvYCLI+LuLONqb9ynZGZN8QAwF3iZgqUrysSlwM51p1ACnJSKyEnJzJqif0QcmnUQbaScp1AqGU5KZtYU/5a0bUSMzTqQNlDOUyiVDPcpmVmjSXoL2ByYRNJ8JyAiYrtMA2sldaZQerqMplAqGU5KZtZokjapb39EvF/sWFpbOU+hVEqclMysQZJ6rOl4RMwqVixtpVynUCo17lMys8Z4mWQZB9VzLIBNixtOm1hlCqU0MVkROSmZWYMiYmDWMRRBOU+hVDLcfGdmRnlPoVRKnJTMzAqU6xRKpcI3hplZuybp8DqjCr8PPCtpWDoiz4rINSUza1A5j76T9DqwW0QslHQY8CvgJGAH4LiIOCTTANsZD3Qws8Yo59F3EREL0+dHA7dGxMvAy5LOyTCudslJycwaVOaj75T2Iy0EDgB+V3CsczYhtV9OSmbWJJLWAwZR8IEdEaOyi6jFrgfGAPOAcRExGkDSDsAnWQbWHrlPycwaTdIZwHeB/iQf5LsBz0XE/pkG1kKSNgT6AK9FRE26rx9QFREfZBpcO+OkZGaNJmksycqsz0fEEElbAT+PiKMzDs3KhIeEm1lTLC6YsLRTRIwHtsw4Jisj7lMys6b4UFJ34H7gUUmzgZKfIdzyw813ZtYskvYBugEPF05kWqpWcy/WZxGxrOjBtGNOSmbWaJI2rm9/OQwGkDQZ2AiYTXI/VndgKjANODO9d8namJvvzKwphvP5TbSdgYHA28DgLINqJY8Cd0fECABJBwPHAH8iuXdp1wxjazdcUzKzZpP0ReCciDgj61haStLYiNi2zr7XI2I7SWMiYkhWsbUnrimZWbNFxCuSyqUG8YmkHwF/S7dPAKZJqgRqsgurfXFSMrNGk/T9gs0K4IvAxxmF09q+BlxGMrIQ4Nl0XyVwfFZBtTduvjOzRpN0WcHmcmAycE/tvUtmLeWkZGYGSNoCuAgYQEErUqlPoVRqnJTMrNHK+YNb0mvAH0iW6aiu3e+h4MXlpGRmjVbOH9ySXo6IHbOOo71zUjKzRivnD25JlwPTgfuAJbX7S3lV3VLkpGRmjVbOH9ySJtWzOyKilFfVLTlOSmbWaP7gtrbmpGRm7Zqk/SPiCUn1rgkVEfcWO6b2zDfPmlmTSNqDVUff3ZFZQC23D/AEcHg9xwJwUioi15TMrNEk3QlsRrIUeu3ou4iIC7KLysqJk5KZNZqkccA2UUYfHHWmTlpFRPyqWLGYm+/MrGneANYHPsk6kFa0TvpzS2BnYFi6fTjwYiYRtWOuKZlZo0l6EhhC8mFdOCT8q5kF1UokjQK+EhGfpdvrAMMjYu9sI2tfXFMys6a4POsA2lBfoHBZ96XpPisiJyUza7SIGFm4LWkocBIwsv5XlJQ7gBcl3ZduHwncnmE87ZKb78ysSSTtQLLO0HHAJJKlK36bbVStQ9KOwNB0c1REvJplPO2Ra0pm1qB0dvCT0senwF0kX2r3yzSw1jeGZBBHBwBJG0fEB9mG1L64pmRmDZJUAzwNnB4RE9J9E8tpeiFJ55OsPDuN5B4skdyDtV2mgbUzrimZWWMcDZwIPCnpYeBvJB/a5eS7wJYRMTPrQNqziqwDMLP8i4j7I+JEYCvgSeBCoI+k30s6ONvoWs0UYG7WQbR3br4zs2aRtB7JYIcTIuKArONpKUm3ktxAO5yV78HyjA5F5KRkZgZIuqy+/RFxRbFjac+clMzMCkhaKyIWZh1He+U+JTMzQNLukt4Cxqfb20v6XcZhtTtOSmZmieuBQ4CZABHxGuB574rMScnMLBURU+rsqq63oLUZ36dkZpaYkq6qG5KqSO5bGpdxTO2OBzqYmQGSegG/Bg4kaUUaAXzXN9MWl5OSmZnlhvuUzMwASZtKelDSDEnTJT0gqWzm9isVTkpmZon/A/4O9AM2AP4B/DXTiNohN9+ZmQGSXq87I7ik1yJi+6xiao+clMzMAElXA7NJZkAP4ARgPeBagIiYlV107YeTkpkZIGnSGg5HOa0dlWdOSmZmlhse6GBm7ZqknSWtX7B9cjry7gZJPbKMrT1yUjKz9u5/gaUAkvYGrgLuIFnw76YM42qXPM2QmbV3lQWDGE4AboqIe4B7JI3JMK52yTUlM2vvKiXVfkE/AHii4Ji/uBeZ/8HNrL37KzBS0qfAIuBpAEmbkzThWRF59J2ZtXuSdiOZyeGRiFiQ7tsCWDsiXsk0uHbGScnMzHLDfUpmZpYbTkpmZpYbTkpmZpYbTkpmZpYb/w/u8ZXDmoqRIAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oGbYDPCAbb_",
        "colab_type": "code",
        "outputId": "5f751b3e-a38a-4789-ba0b-71a361164878",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.scatter(df_final['Annual Income (k$)'], df_final['Spending Score (1-100)'])\n",
        "X1 = df_final.drop(['Gender', 'Age'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KXzqxauHAkqf",
        "colab_type": "code",
        "outputId": "7563d12e-5790-4299-b14b-fedbc55faac0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "dendrogram = sch.dendrogram(sch.linkage(X1, method='ward'))\n",
        "plt.title(\"Dendograms\")\n",
        "plt.axhline(linestyle='--', y=3) \n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H0bmLF7O2sbHoZnAI1jc/Lkly+ta3L7glHIYz11yTsydPLroZAHAfwQ8ALMDaxkbWNzezurKy6KYwZ6s/JPA5LtY3N5NE8APAUjkw+Kmqlyd5VpK7WmtP2X7s4UleleS6JO9M8tzW2vvm10wA6M/qykrO33DDopsBzMjpW29ddBMA4DKjjPFzc5Jn7nnsa5P8WmvtiUl+bft3AAAAAJbIgcFPa+11Sd675+HnJPnR7Z9/NMk/nXG7AAAAAJjSpHf1uqa1dsf2z3cmuWZG7QEAAABgRqa+nXtrrSVpw6ZX1dmqulBVFy5dujTt5gAAAAAY0aTBz0ZVfWySbP9/17AZW2vnWmunWmunTpw4MeHmAAAAABjXpMHPa5K8aPvnFyX5v2fTHAAAAABm5cDgp6pekeT1Sa6vqtur6ouTfHuSz66qP07yWdu/AwAAALBErjxohtba84dMesaM2wIAAADADE09uDMAAAAAy0nwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdunLRDQAA6NG5ixeztrGx6GZwiNY3N5Mkp2+9dcEt4TCdueaanD15ctHNABhKxQ8AwBysbWzcFwRwPKyurGR1ZWXRzeAQrW9uCniBpafiBwBgTlZXVnL+hhsW3QxgTlR3AUeBih8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOjUVMFPVX1FVb25qv6gql5RVR81q4YBAAAAMJ2Jg5+qelSSL0tyqrX2lCRXJPmCWTUMAAAAgOlM29XryiQPqqorkzw4ycXpmwQAAADALEwc/LTW3p3kO5P8aZI7ktzdWvufe+erqrNVdaGqLly6dGnylgIAAAAwlmm6en1MkuckeXySk0keUlUv2Dtfa+1ca+1Ua+3UiRMnJm8pAAAAAGOZpqvXZyX5/1prl1prf5vk1Uk+bTbNAgAAAGBa0wQ/f5rkU6vqwVVVSZ6R5K2zaRYAAAAA05pmjJ83JPmpJG9Kctv2us7NqF0AAAAATOnKaRZurb0syctm1BYAAAAAZmja27kDAAAAsKQEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnpgp+quphVfVTVfW2qnprVT1tVg0DAAAAYDpXTrn89yb55dba51fVA5M8eAZtAgAAAGAGJg5+quqqJP8oyU1J0lq7J8k9s2kWAAAAANOapqvX45NcSvIjVXVrVf23qnrI3pmq6mxVXaiqC5cuXZpicwAAAACMY5rg58okn5TkB1prNyT5QJKv3TtTa+1ca+1Ua+3UiRMnptgcAAAAAOOYJvi5PcntrbU3bP/+U9kKggAAAABYAhMHP621O5P8WVVdv/3QM5K8ZSatAgAAAGBq097V68VJfmL7jl7vSPKF0zcJAAAAgFmYKvhpra0nOTWjtgAAAAAwQ9OM8QMAAADAEhP8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnrlx0AwCGOncuWVtbdCtmY/17tv4//eWLbccsnDmTnD276FYAAAAjODLBz7lbzmXttk4uAPdYv3PrgvD0zR1cEA5w5qlncvZGF4lMYG0tWV9PVlcX3ZKpnV/t5O97fX3rf8EPAAAcCUcm+Fm7bS3rd65n9dqjfwG41+rXdnJBOMD6nVsXiYIfJra6mpw/v+hWsOP06UW3AAAAGMORCX6SZPXa1Zy/6fyim8EYTt98etFNAAAAgGPL4M4AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdmvquXlV1RZILSd7dWnvW9E0CAIDj7dzFi1nb2Fh0MzjA+uZmkuT0rbcuuCUc5Mw11+TsyZOLbgYsxCwqfv5tkrfOYD0AAECStY2N+0IFltfqykpWV1YW3QwOsL65KUjlWJuq4qeqHp3kc5N8S5J/N5MWAQAAWV1Zyfkbblh0M+DIU5HFcTdtxc/3JHlJkr+bQVsAAAAAmKGJg5+qelaSu1prtxww39mqulBVFy5dujTp5gAAAAAY0zQVP5+e5POq6p1JXpnkM6vqx/fO1Fo711o71Vo7deLEiSk2BwAAAMA4Jg5+Wmtf11p7dGvtuiRfkOTXW2svmFnLAAAAAJjKLO7qBQAAAMASmuquXjtaa+eTnJ/FugAAAACYDRU/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdOrKRTcAAGCezl28mLWNjUPf7vrmZpLk9K23Hup2z1xzTc6ePHmo2wQAlpeKHwCga2sbG/eFMIdpdWUlqysrh7rN9c3NhYRcAMDyUvEDAHRvdWUl52+4YdHNmLvDri4CAJafih8AAACATgl+AAAAADp1qF293nHpA3nef339/R571id8bF74tOvywXvuzU0/8sbLlvn8Gx+df3HqMbn3Qw/KXe/+vMuWf8GnPi7P/sSTufgXH8xXvGr9suX/98/4e/msJ12TP7m0ma9/9W2XTX/xZz4x//CJV+fNF+/ON//cWy6b/pJnXp8bH/fw3PKu9+Y7fvkPL5v+0mc/KU8+eVV+64/fk//863982fRv/WdPzRNOrORX37KRH/rNd1w2/buft5qTD3tQfu73LubHf+ddl03/gRfcmIc/5IH5yQt/lp+65fbLpt/8hZ+SBz3wivzY69+Zn//9Oy6b/qp//bQkybnX/Ul+7a133W/aRz3givzoF31KkuT7fu2P89tvf8/9pn/Mgx+YH3zhjUmS//TLb8ub3vW++03/2Ks+Kt/zBVtl8//+596ct1z8y/tN/3snHnLfz1/36t/POy594H7Tn3Tyo/OyZz85SfLlr7w1d9z91/eb/kmP+5h8zTP/fpLk3/zYLXnfX91zv+mf/nFX58ue8cQkyYte/sb89d/ee7/pz/gHj8zZf/SEJLnsvElGP/fe+4F78iU/fstl0517h3TuPeYz8qY9x2+Uc+/b/tknJHHuzfzce9Lz8tJ3/kaenPR/7k34ujfquXfpd96XD/3VvXneGz98jjj35vO6d8f2+D4XH//BY3Huved3/+J+51Xide8ovufunLfPe+Prj8y55z23j3Nvt17OPe+5zr3den/dG0TFDwAAAECnqrV2aBs7depUu3DhwkTLnr75dJLk/E3nZ9cg5s5xYyqnT2/9f/78IlvBbo7JzOwMwnscBhxetOP0XB+nfe2dYwmz4++J46CqbmmtnRo0TcUPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB06spFN4DBzt1yLmu3rS26GVNbv3M9SXL65tOLbciUzjz1TM7eeHbRzQAAAICxqPhZUmu3rd0Xmhxlq9euZvXa1UU3Yyrrd653EcIBAABw/Kj4WWKr167m/E3nF92MY++oVysBAABwfKn4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6ZXBnAACAI+rcxYtZ29hYdDOW2vrmZpLk9K23Lrgly+vMNdfk7MmTi24Gc6LiBwAA4Iha29i4L9hgsNWVlayurCy6GUtrfXNTeNg5FT8AAABH2OrKSs7fcMOim8ERpRKqfyp+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOrUUd/U6d8u5rN22tu8863euJ0lO33x63/nOPPVMzt54dlZNAwAAADiylqLiZ+22tfuCnWFWr13N6rWr+86zfuf6gQESAAAAwHGxFBU/yVawc/6m81Ot46BqIAAAAIDjZGmCHwA4TOcuXszaxsbCtr++uZkkOX3rrQtrw5lrrsnZkycXtn0AAOZvKbp6AcBhW9vYuC98WYTVlZWsrqwsbPvrm5sLDb4AADgcKn4AOLZWV1Zy/oYbFt2MhVhkpREAAIdn4oqfqnpMVf1GVb2lqt5cVf92lg0DAAAAYDrTVPx8KMlXttbeVFUPTXJLVf1Ka+0tM2obAPN07lyyNuadENe378B4+vT42ztzJjl7dvzlAACAiU1c8dNau6O19qbtn9+f5K1JHjWrhgEwZ2trHw5yRrW6uvVvXOvr44dMAADA1GYyxk9VXZfkhiRvGDDtbJKzSfLYxz52FpsDYFZWV5Pz5+e/nUkqhAAAgKlNfVevqlpJ8tNJvry19pd7p7fWzrXWTrXWTp04cWLazQEAAAAwoqkqfqrqAdkKfX6itfbq2TQJJnPulnNZu232XUnW79zqCnP65tMzX3eSnHnqmZy9cQnHPZlk/JdZm2Y8mVkxLg0AAHCETRz8VFUl+eEkb22tfdfsmgSTWbttLet3rmf12gnGH9nHrNe3206otJTBz874L5OM5zIri9x28uHgSfADcGScu3gxaxsbi27G1NY3N5Mkp2+9dcEtmd6Za67J2ZMnF90MGFsvrycH6en15iDH9fVomoqfT0/ywiS3VdXO6KBf31r7xembBZNZvXY15286v+hmjGxeVUQzc1jjvywr49IAHDlrGxtZ39zM6srKopsylaPe/h07F5TH8UKLo6+X15OD9L5/O47z69HEwU9r7beS1AzbAgAAU1tdWcn5G25YdDPI8aggoG9eT/pxnF+Pph7cGQAAAIDlJPgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAAPFfg8sAACAASURBVDol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6deWiGwAAwOXOXbyYtY2NsZZZ39xMkpy+9daxljtzzTU5e/LkWMsAAEeDih8AgCW0trFxX5AzqtWVlayurIy1zPrm5tgBEwBwdKj4AQBYUqsrKzl/ww1z3ca41UEAwNGi4gcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTBnQGgI6PeAnzc23673TcAwNEk+AH6de5csrY2+fLr61v/nz49XTvOnEnOnp1uHVxm1IBjmHGDj0GWMQzZuQX4Qbf0HueW3zvP1bLtKwAABxP8AP1aW9sKb1ZXJ1t+0uV22wmPBD8zN2rAMcyky+1Y5jBk1rcAd7tvAICjS/AD9G11NTl/fnHbn7ZaiH3NOuAYhzDk8CxDdVeynBVeAAAHEfwAAEtt0dVdyXJXeAHAUTXtlzvjmNUXQeNYli+NBD8AwNJbZHVXosKLo+cwL6b2s4gLrUGW5eILuL9pv9wZx2FsY7dl+tJI8APA7AwbUHu/gbINfg0wc4d5MbWfRW8/Wa6LL+Byi/5yZ14WHXjvJviBOTp3y7ms3Tb8rlLrd25dDJ+++fTQec489UzO3uiimCNi2IDawwbKNvg1wNz0ejE1rmW6+AJYBMEPzNHabWtZv3M9q9cOvugd9viOnWBI8MORMs6A2ga/BgCAuRL8zNBB1R3jGKUSZByqRhZn9drVnL/p/ETLzur4AwDAUbAs41MlyzNG1Q5jVTEpwc8MHVTdMY5ZrGOHqhE4xoaNuZMYdwcAWDrLMj5VshxjVO0wVhXTEPzM2DTVHfOiagSOsWFj7iTG3QEAlpLxqS63LFVHHE2CH4DejTPmTmLcHQAO3Ty798yzu46uN8BRIPgBGNd+3af22q871SC6WC2Ngy5CRr2QcFEAcLB5du+ZV3cdXW+Ao0LwAzCu/bpP7TXKPDt0sVoqB12EjHIh4aIAYHRHrXuPrjfAUXEkg59hd88adicsd7QCZm7c7lOj0MVq6Ux7EeKi4OjotcJrlO4zR3XfAIDRHMngZ9jdswbdCcsdrQCAg/Ra4TVK95ll27dpx3qZxXguQi4AerKQ4Gdvxc6gSp2DqnRGvXuWO1oBAKPotcJrFt1nDnPfph3rZdrxXJYxwAOAaSwk+NlbsbO3UkeVDszBOAMSJ+MPSrzD4MQATGmRY70sa4AHAJNaWFev/Sp2VOl82LDxjMYxbOyjcRkr6YgbZ0DiZLxBiXcYnBiOtGFdbA7qOjNNt5hZjUGjaw4AwGBHcoyf42TYeEbjmGbZHaqwOjGPAYl3Mzgxo9hdfba7sky12MIN62KzX9eZabvFzGIMGl1zAACGE/wcAaOOZzRPqrCAmdldfbZTWXYEq8V6rVQZt4vNLLrF9Dq2Dos16SDRkw4OvWx/ywCwQ/DD0pm0e9ukXdp0YTtixhmraJxxilSbHK691WdHsFpMpQost0kHiZ5kcGh/y8zTQSHmUfySAThcXQU/gwKD/cIAF/zLadLubZN0aVumLmzO3xGNM1bRqOMUHcFqE5aDShVYboc1SLS/ZebpoBDTlwzAQboKfgYFBsPCgGW64F8mw6ptDqqmmXUIcVjd25apC5vzdwyzHqvoCFabcHTs902tb2nZMeg82e/8cF7A8TJNiCmYBLoKfpLRA4NluuBfJsOqbfarpjn2IcQMOX+hP/t9U+tbWnYMOk+GnR/OCwCYr0nHidtt0jHjdpvVFz3dBT9Mb9xqGyEEcN/YS3vHVTJ2UpLJv6n1Le3xMup54rwAOFqWJURI5lsxOsl+LuuA+pOOE7fbNMsms/2i51gEP6OOnXJsx0wBDtewAar3G4x62QOUQWMvGTsJjpRJuyXqdrYYsxjwN3H8mK3jEnCMaxlChGT+FaOT7OcyD6h/WOPEDTPLL3qORfAzytgpk3RX2hsoCZOAkQwboHrYYNRHJUDp4E5dcJxN0i1Rt7PFmXbA38TxY/aOS8AxiUWHCMnhVIwexn6qfB3f0gQ/u0OUvQHKLMKTg7ovTdJdaW+gNEmYdNCty0e9RbmAibkb5Tbqo9w+fdkrVw7LOANUzztA2XtsBx1Hxw2OhXE/sPvwvVjLfGfBcSs/JqnymHdFh6qqyRyXgAOOkqUJfnaHKLsDlGUfOHi/QGmUMOmgW5ePcovyZX+OFmkWwZpQbdsot1E/6PbpR6VyZa+jGIyMM+bO3mO79zge1eMGczTqXbh6u+Dbu9/HYZ+ZzLiVH+NWeRxGRYeqKliMWYSu3ovub2mCn2RwiDKvgYNH6aaVHM5F/7S3Lje48nDTBmvLFKrNuypuJNPeRv2odv05isHIuGPu7HdsF3jcprkV+qLe8Ke9MN69/N5l9y532OFDT2HHtMdplLtw9XjBt3e/p9nnYX/fw/62F3FejVq1Mm61yrLsy7yf63lWfhxWRccyV1UNc5wC2nFfR3b0sv+9mjZ07fH9d1pLFfwcpoO6aSXLc9E/76qVcQKFozhQ9jTB2jKFamNVxQ3qlnUUqlSW2WEGI7OqMOpgzJ2Dxhy545577ntz3+3ue+/N+ubmZR8GD+OD3rQXxruX373soOUOO3zoKeyYRYBx0AVhr10N9tvvcfZ52N/3oL/3RZ1Xo1atjFOtskz7skzPNVtmEbDPMqBdduO8juzoaf97Nk3oOs570TKEh4fxRcixDX6S+Yz7Mw/zrloZJ1AYNlD2He+/47757/6bu7N+5/p9AdGoodPe7Q56/pcpUFqEkaviBlV7HIUqlUFGCUGS8UOs3evdvc5lCMOOYoXRHB10kblxzz1LV24/7YXxoOWHLTdt+DBOhdGw7Q36dnm/dSyLWQUYTO4o3MJ+0ouPgyoWF1HRNMq+OPcXa1YB+6xf35a5+s3YZB82bRepZX7PnpdlCA8P44uQYx38HCXzrloZp5vdsHk3PrAx9gDXo9xxbdR1scdB3bKOSvXHQSFIMlkQsnu9O+tcpkBlSbteLaOjcOF42MYJc8apMBpmFusYx3HqxnDc7RzrUULJZTsvjkJF06zMqxvocezumyxnNWFP1W89m6aL1LjHY5zuo8lyvy/v9ze3XzXOLAP8eX+eFfzMwU4Vy8LGYcl43bdmZdIxmkYNtZalAosxzKvL0l6TBiEddIVitoYFJsv8YWWQcYOYcSqMhpnFOkY1r24M41Y/jbOOZT6HxglXDtugi5hhx3cZu7cchWB6FkHFvLqBHtTdd5hZvAbsXs80z8UynIezchSr3w5jbKtRqqEOczDicY/T3vetUd8DRu0+urPeZL7n/bzef3vpJiv42WNYYDJOWDKoiuWwq1WO6l3SpjGL8YdmMej3LM6hpTCL0GZeXZbm1f1rXOPcOeuoGdYVLulj/4YYFJgs+xv5MIcZxCzCPLppzav6aRbn0KSDfo8S5owTrizC3mO93/HdO+9R7YZ4mGYVVBz03O+sa9yL7Um+iZ90W/N6Lnab9PXpsEPlWVbQzbr6bZzn4jAu2kephtqZdsc992Tjnnsum75MYxMmsznXdxun2/nu7U8aus76M9wydJOd9kuaqYKfqnpmku9NckWS/9Za+/Zp1rcMBgUmk4Qle6tYxqlWmdUdxw7zLmnjmHQw6YPmncX4Q7MY9Hucc2gp7tQ1zLDQ5o47ko2N5O67t6bvhAPDwoC9VTWDQptxA4V5df8a1zh3zlrmMYUGGdQVLlma7nCTdP8Y9Y1ynItMxjfsw931b3jDfR+Gd3/4nLbSZpx17Gx71tVPw5afV5e8SQKsac77WT3383DY3RDHMe7zNs+ubPMOUne2sde0x2GUQGHnQnvvhfWo7wG7zTq02a8de83ionac98NBx2/3DRVGfT53zLL6bdznYh4X7YOO6Y79notZj0046HXhntbysN/8zfseG+U9dR6ffaYJ6GYVus7zM9xh3sU1mf5LmomDn6q6Isn3J/nsJLcn+d2qek1r7S2TrnNZTBPazMKw8OGO99+RjQ9snRy7A4yFBwRjmnQw6VEql2Yx/tAsBv0e9Rxa+sqsQV2sTp/eCn6e/vQPPzZOGDCrQGFQ24aFSvMMV0btLjaLMYUOu8Jo2PFfAtN0/9hv3nlZ5gvjwzbsw9365mY27703K1dccd+8yzzO0CzMs0veorreHbQfi/hbGOe5OMwP8uMe/1lfiO9nnkHqsCqscda7N8DYMWgdOxfaT7/qqvttMzm814VZvT5Ne1E77vvhoO0NCi2GrWOZv6SZNjAZ95iOc86O0/Vq0OvCoPfT/do2jknDnEkDukm7oU26H+Ose9rX5MP+kmaaip9PSfL21to7kqSqXpnkOUmOfPCzDIYFGH/03j/K0x/34Qvu177rtUmWICAY0ywGk57X9obZmX93Zc6kA27Pum1j2blw3xse7DeGzl7Tjo0zr0BhbS157Wu3QqmdcOW1W38ji65QSTL987Z7/3Ys0/4dsmm6fxx2Fc/axkZee/fdefpVV93vzf21d9+dZDlDiXna70J8muN0FLu3HcU2DzLqfiz738Lu9iW57GJl7wf5vfs87v6Ne/wHvZb90Qc/eL9QY7/tjWqex2nQusdd797jdNA6pn0P2Jl/7wXiOOOoLMvf+rTPxTj7Mc5xGveYTmvQ3/r65mbuvvfeXLUnMJnF3+88n4thx3Qe59u4f7+zCCvncV7M6jVu2tfkw3xdqNbaZAtWfX6SZ7bW/tX27y9M8r+01r50z3xnk+xckVyf5A8nby4AAAAAezyutXZi0IS5D+7cWjuX5Ny8twMAAADA/X3EFMu+O8ljdv3+6O3HAAAAAFgC0wQ/v5vkiVX1+Kp6YJIvSPKa2TQLAAAAgGlN3NWrtfahqvrSJP9Ptm7n/vLW2ptn1jIAAAAApjLx4M4AAAAALLdpunoBAAAAsMQEPwAAAACdEvwAAAAAdOpQg5/aslpVTxkw7SF7fn/wPut5wJDH965j4Paq6mFV9dD92jnK9gYsN7TNByxXu37et22HadT9ntG2DnW/x9neqPMedPz3nlfbjz1k0Lx7lxv2dzOuqnrEkHZ89IDzfti8K3t+f1hVPWzI9gatd9BjQ9cx6n4MW8fe9u7XjgHz7Lhx0DlQVSd3/fwx2/M+tao+epLt7dfmUfdv1/myOuA5Ornn98vaPGyf91vvPvuxd3uXrfug5217ns/Z/v+FVfW/VdWXVNVNu6Z/UlV9SlV9Q1U9f5S2LaOq+ldVdW773+ceMO9/rap/UlVXHFb7lkFVfWNVfdn2/1+1/djzq+qrq2qlqj5717z/ZPvfv6+qL19cqw9PVd1UVS/Z/veiXY8/d/vf91bVcxfZxp7sfV0f972MLTXmZ8B93tcHfm7pxe79O+g9eRbP0e51DPtsMOJ69rZ573XZWNdPw86XeR3/cc/PIesY6bPofse1Lr/OPfDz04DtjXwdsM86Lrs+rD2f97Yfm/r1cNBzP6/X2f3WO+z4zcOwdszi2E18V68JfVuSdyR5alX9WWvtO6rqBUkenOSjquqxrbWv2p731VX1hiS3JvmF1trfVtX/meThSX6zqm5orb04SfZZx6DtvSzJRyV5bFX9VmvtB7bX8Snby1SS5yb5yn2293nb23tCko9orf2Hfdr80iQP3W7Ho1pr37jP9oa17ZG75j3TWvvuYW3Y5/HL2rHPvJft9z778UtJfjPJL7bW1ncO9JA2D5v3sv0eZ3sTtG3Q9oY9F4PmvWzf9jn+lx3n7fUOPGeHrPuy83if5+K7k2wm+Y0kT2mtfd/24/86yb3b+/LoJF+7/fh/SXJPkrcnuS7JV+0z77cleV+ST6+q397+e/rGJBe3Jte1rbVv2We9lz22Pe+wdbwsW69R/yPJqdbaj+zTtsvWMai9+7Rt4POW5CuSfGj7+X9ukq/ZXse57eV3r/s/JHlAkp9PctOuYz1oe5ft27DneNz9S/ItSX51+xz6j0m+YUh7M6TNA/d50Hq323bZc7fP9gate9jz9ops/S1Vkk/L1h0kr03yiNba11bVN+XDnp7kAdvPy0t3HqyqFyd5UJL3J3lQa+27qup0ts6fG5P8RWvt+7fnfVWSX0/y062192w/9u+S/P0kH53k51prPzFs3t2q6l+01n5yv8er6su223Ff25KstNbObk+7PskvbM/7yu3nd/f23pLkz5N8fVVVa+2bD9jeZfsypA0D17HPc3HZ87nPc/zibL23/FCSp7XWfnrXc/GR2TrWH2yt/eeqesl2Ez49ye5z6ANJPra19nVV9dXbjz0xyXcl/387Zx/tZXHc8c8qoEGEo0ehjS+IJ6QRq/SYaluroEGFQIJGo2hVBOILhmAw9SUqVmI0oCFW21qtTVLTxGpzNAn1YLBKDPGlMbFa35K02hi0TYhNEYQG5eVO/5hdnnl+v9nfvY9ebgT2e85zfBzmNzszOzs7u8/u5RJgAPBApI8GVqM59MLY1qWAtMrtQD8N6AJGAZtE5OqGdvQG7+ej3RuBASLyZx10HiYi18V/Tz4E2B94FlgFfN/0r2dfm26R93rg11YP7/eddDbt2tj0xqkbmx1iq4kdbX7uUEfkaoNbgDep5/XcXObVQznd2uzO5ZsOMZurUT3fe7Vhbqzn5i3PnzkZXt2Sq329Ps3NkbnawIvZnB1tsdVh7Hn5NNdPbi7z8noH3Tz7cnNyj33UsBbJ1UO5MeLVv249S7P1k1eX5/q/TTcvBrvxfY/XAZnxlOuPthwSZXo1XG6d21Y/NYyhXI2as89bH7r1XqZuzclt4vtcnvVq0Vxft43rDnJz/efl9aPo+fyU083zW67vcjGURV9f9QrAK6iCXZE2CRgdF1r/Y3i/LSJXAcuBVNy9gibILwP/bXhzMrz2+gF/DzyBFhoJ56MO3RktHDu1Nz62dy2woRud1wDPxYns1920Z3Wzu99/A0yIz+93o0OO7umR4/XsztmxBFgIjAghfKYbnXO8nt1N2muqm9dezhdevHi2gd//Xj9DPmY92V4c5+xbISJXosXIBw3vMGAv4N/RQjHhtajXg4ae410WdXjITNYD0ST7EPWY9eR6tE4y3oy2/JGxJaebJ8PTN6dHzm8D0EXlb1P3/W3Aq9QXpOvQnLoOLeg7tefZ1knnJvaF2I7E95y+OZ1zNntyoe67Cd2058nO+e1WEbk+LmKvjLRHgcvj+7OGtwudKEHHqm2vK+aG9KVsHHBoHOv2i8pjwD8Bp4YQ0ibKTsAcdKE8tIV3keUNIdwX4kkL4IzEaOiXWjparLXq9lIsHJfGJ+FfHN2eEZHHY5F9jWnv25n2dnZs8XTI2eL9PufPnI93RQvVqcBxhr47unFzQ3wHeBl4HPhOSwwtBx4LIZwD/DLSnhaRtcA8tDhKWBTlfQ74rvl9Tq5Hf0+092tUH8t2Ba7uoR09sXm3bnhfA4YAf059TCadlxqdH4t9dxEaNwCIyAJ0rlwlIi93Y59nB+imUase3u9dnU1s1sYI/jh1Y5N8bCU9vgqkU3A5OwbR7uc1wLNOHZGrDVZGHR+gyuu5uczO6++PtN0yunl2e7kJ8jHbpnOHPOTZnct7uXnL82cuX3h1S64u92IrzXutNqfa4CfUx4gXszk7vNhKcXxDi1zPvlw/5XKZl9dzunm1T25OztUGnowmtUiuNsiNEa/+zdWzufWTNya9eLH9b2tDT7clwOdpXxvkfNEP+Ar+OmA81ZjO6ZyLWS+HgN+vuTWDVz81iSHQGO9PvUbNrXO89WGu3vPyYU5uJ9+39nUuz3p1fG4d6M2pVq7NQ7n+u5X2vN7d/GRzWS4OPfts39lxk2KodV2VRV+f+LkWTRpLqIJ5JlWhdofhvRNARJ5Cd4ABbheRVfH9HsM7k8oRX8u0l/Bl4BDgi9TtnyMiqwFCCI867d1teG9Dd9hAd1kT7nJ0vltEXonvDxher72k2986ui2PvPcaHdY7OuR0s3r8s8N7r+H9ioi8ln4X/3uPKRatHYtFZD3wzfh00jnHa+1Oxwdz7d3nyPBsa9pezm9evHi2gR+zXj9DPmY92V4c5+z7etThWyGEFw3v9cCxaCG5wNC/hO547wvc3olXRJbEHe4jze8Xoqct0nsnuYk23NA6yVgS270thLDc0e26jIwvtOg7hjq+iOac4ehkDvCP8Tc1v4nIghDCQehC4kuG/kQI4XngUCP3s1HunoBN4p4v7ndsy/k42XcUmtgXdsM7FzjYvOf0BV28dlmdczZ7ciPafBfbew44zPDlZH82+mdo1CfxLjPvz8T/PmZo95j3m8z7fabJJSLyfHx/Mv73CfSrUCuGoLG1Cf1aB5ofZqKF6y8M72DgTOAW9DQS6AJgBRqb3zK8D6P9dgtabCTsAqwLenz4+1H3RehmRSseFpFfAH8V4rU3EXnI2GwLgU+LyNOw+QtfwtJoy53AZKPDz0II50fdE5ahBfgfo4sZog2z0HluguH9EbB3tCN9THoB6B/l2gXJo1HXBUFPCSTcAXwivq+MNt0VQhgFrAkhjBeRNGa+0eqc6LfkhysM/UdRP4uA5oqVIYRjRSTNLwPQr3FdIYRpInJ7pC9HN2Q+ii7gAH6Gflm7GbBXC7uiHQOpNiL/C11or6a+KbkJXeBcRpX3fhrbeXf8XUK/+P8XUP8IlU57XBFCWCEiXxeRh9GYa4OIfA/4Xgv5NuBkdGGRNtI2AZ9C4/7nhve7aN0xGfhhpL2Izk9T0EVBwo7omDkR3bAFzRv9gGMwp47QRfGGEMIVqG9BC+8XQgg3AT8wvM+hsXWT0RfgP6Ie/0o1NyxG+6kf9WJ5A7ro2JHKz/8L/CoW4GsM78+B1yN9o6H/Ei3C/5pqU/EnaD6+CPg7w/tglH9Y1A80BjcCbwD/YHgnAQuDXuFMY+94dEzdIiJ2Y/MD6AZ4az5bgfpzLtX464f67n5gH8O7K7Ap8r4RaUfH332V+gfi/kFPJA0GnjH06cDTaBzPiLRl6MZIP+qbAV1o//8eVT58Fa2z9qYeQ9+JupxqZOyAbjSdF0Kwuf/JqPMXqNcXyyL9T4H/NHZMRBdSTxreVeiJisuN3QHtu6cw8xM6RuagdUSaLwah/f9e4BHD+wKaXwaFEE4TkTsj/RG0L4YAP440r+4Bvy6bi55qFMyc3KH28WR0V4uMMbRcbZBbj3j1r7cOBL9+zq0D1gMTox1pc2UlmrPOBK4yvC8BxwU9lfJ0pK1DczfU59uuyAdVbQgak/eh8ZjqksXAEfH9fsO7K3BACOEIqljeG60TjgwhvCgiiyN9ObqJdjP1NYpXa+XWyrbuTPHp9ilV/w/CXwfY2jy3zmlbh3eoL9vq1g5yczp7a7DcmsFb/3hrRtActBSdn9J8+QZwAtqHqw3vofHfdgohXCQiqc2n0M2dwVSxNQW4MObvh4yMfdAYPYHKx4PRzdHB1OecVdEfO6IfsxI2oRtjto58E833pwCv0wP09YmfjwHnoQM8FUszgBuCHnkca3jPCPp3Du4KIZweadMTjfqC4gr0eNvcKD9hLvDh+O9pITYf2APYyWxugBZO86KMdCT5ckObangvB8aGEPYQkecM/QMhhClB/55AkrEwhHBe5P2h4Z2YeKm+YJ+M7greAZxreEeEECbEoij5bTdgeKRZv40FJseC234xPC1+7VmEFgugxfvkSLN/T2KC0e1PIu34EMJVQe9w7mt4P5joIYSTDH2Kae/sSDsp0UL9CPpwNHl/0rS3v7HZ9vVeiR5CmBVpJ0S551Of6CZG+sygx3ITPoQe+zwTLViIv5sS7fudFt7fRRNB8tvkJBc9WplwUYqXUH29mWFidorVLer3GXRcJFxn4mVlpE1C42Ie1ckHgGsTr6H9oem7Ey0dLZwOpP61b190sTMWTdAA5wAHoBPSZt3iQm0aMNT482y0yJkef5cwCS3WDkK/iCTadLT4ep/hPR0tBH6Lqj8ALjO+SBPr7eii6BEz+YAW30PRBfLKqO9dwEeoivKEBejXgB+b8TvLjPXNf/8i6PHMUVHO3Bb6ZGCcGeuT0HiZjibzhI9GX1xMNc6GmPh+j5F7CerjqS1j5GC0WBlFHCNBj5QeArzPjAXQHHd8bGte5L0ePZY9JtS/RF4Z9ToDmG14T4q2zDO8F0S7LqW+2D3RxPjoKONC4C+Aj5v8neiz0WOqScbHgBvRfjmW3sXcUH3lTjn1VOD9kWZzyzpgVPxSt5+hDRWRz6F5KmEHdCPnEqq8YH9vc2T/yHsxet0mIRgZ47qx42Jjx8e74f204Z1o6HOiLlMN3eq8V4tua9FCJS1Skh3noPkzYaSxI51QGAaMjHLtR4yZRrdjDP1qqi/DxwDpqt+H0IK2O5ubIF0LG0rd78OA4aJXbvZroe8jIvPRAh50zHp9ug69hjafyp/D8H28AV2030i1yNgPPX6+kvrCYx2wZ4tcgBFUC267kdIEN6IbVTtR1QwpvvtT7+vZaLzvSzWekj8HtPDugOblkVR+HgOMjV9D7d+IONTQR0TaRjQGV1HfyBqBFr6rqH80GYEW4jca+hGARLl2kbkJOKqFvhfq//ktunVF/vnUN352QRdQkwx9r0g7DI3lhMFo/phv6IvQDaGB1PPpIuIVTnRTr0Yz8w3A8+im2Ltb6GuBISJyDdVmzmIj19aza9CYvcb4YjG6KJoGnGV41xreNw395ij7MnQeJ/pgo+jVP+vPQVR+fpehDUFrdful/TBgveipxqTb6Mh/CPWv/Qehi6s/oO77w4DDox42tpIONi66gA0x3yf6JjSu7qIehwEdeyuocuQmqvFrx+MY4MgYbyNb6CkOUx0w3tTOow3vBej8P51qnXNKlHcC9fpkNjpXP9ZSl5+N+u0HVPn3SFPPHmh4EZFXReTuEMLJUe4FaO10HPoRIPE9LSJp4+oAI+KsoH977RKqmmgamg8uvo8mfgAABK5JREFUtjLQU09pnpsd5dqTifuZ97XAHrFPN29sisj+aN0xzPBuFJHj0E3PdCJmoIicG2m23l+foQ+Msp9B5yOI17Ij756G93VD/z/DOwP4S0fuDPTUlz3tPAuthY41PjoLOD/68iOGdxo6P55CXGPEzbOEzX/3R0TeEJF7ReSbmPWhiPxURF6OHw1GRfJks+Y72sibavp0AmyOi0+g64/ZRu5KEflGbO+YSLObOnb+PcK0N8jIeElE7hGRNVQxezpaEz9OVX8BfNjodnT8/QvpH1McR6T8ux9Vnl2D1nut1+bXAru35MhET/kw4WbgV2ietTXO8+iHn52p1mD291buehF5L3ATVWytAXZxdFuDxtHVVLm+I/p64+ddtB+N9GjgH6PM8XpHOXN072hljjcnN3ec0zvK1YT35Whb63GyJkfpc8ejvaOmTY+2e0fQc/Qm7Y1D73T2xD6P7h0Tt764tYXu8TexL/2+Va4XL7mYzR2P9+Izx+vFVo63N/zp0VPMLpX2Kz099XGuPc++3Pj16DleT27uKkXOnx49x+uNhVx/5I6ENxkjTXJZE97eyNVN5oDegHddzKOBf40sd7Xs36T9apHltR8EcteQPBlN7GjKe6uIXCc9uDon1bWg1ab4ztnh0XN+a6JbE5ubIGdHTmeP3kRGdz7efPUq4/dGMt4CPD/nYtPjbRLfuSuZbfScbQ3pPW4P/1pgU3oTXveqZobehBf865C9yXsu9dNWHn/O90381hv91ETG24rDDuO3J3GYToLl6mRvfZCrOXK11mCH7tbJwb8yvDt6oqp2PTHDm/hbrzO6MjzeDnK9OEzXpB+kfk3auz6du1LdhL6leHM+yl1b7bHfGtKbXMt2dfPkduhTt723q1uH9ryc5eVCS2/Nex69Sa7OyfViuTsdWnXOQ0T67EF34j+FniQ4L0d7C7yHo8d2TwMmdaIDh5h/H98Nb05uTsYwdNdzDjCrKW+kj0JPQVne44Fx8f2KHC2+H2jeN+vcQbZH8+wYa/59tnl36Q3ba2Kfx+va3ITexL4Ocr0YysVsrv/b4qWXeLekP70+beLjnFzPvtx4eru8ubGe86c3Rlxez0e5/uilMdIklzXh7Y1c3WPe8pSnPOUpT3m2t8erATx6h/pkrHl/q/XsaPOe5uqR6Mn8T7bIbePN8XeQ4fG6crf1Z0v5rQm9Qww10c2Tm9Mh197b1W27jKFOT4jO6BPEo9vpL1cfLiLHe7S3yPsUelSqI31L8fZ1e+9k3Yov2nhLzBbdtpr2uuHtjVzdI96CgoKCgoLtCVtBHdG0nn27tUGb3G0dW8pvW6oObBIXvyHdtrsY6oi+3GWivkt3cI7WG7zbenvvZN2KL7YO3Yovtg7dtldflKc85SlPecqzPT1b41xdaoMtHwNbYz+9k3Xbnp8+PfFTUFBQUFBQUFBQUFBQUFBQUNB36Os/7lxQUFBQUFBQUFBQUFBQUFBQ0EcoGz8FBQUFBQUFBQUFBQUFBQUF2yjKxk9BQUFBQUFBQUFBQUFBQUHBNoqy8VNQUFBQUFBQUFBQUFBQUFCwjaJs/BQUFBQUFBQUFBQUFBQUFBRso/h/0m3Rrq2YJ50AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgKioWo3A22v",
        "colab_type": "code",
        "outputId": "aeaf84c9-a418-4f9d-ca98-39d9dbff9dcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Compute agglomeration\n",
        "X1 = X1[[\"Annual Income (k$)\", \"Spending Score (1-100)\"]].to_numpy()\n",
        "\n",
        "# creating instance of our model\n",
        "agglo_clus = AgglomerativeClustering(n_clusters=5, affinity=\"euclidean\", \n",
        "                                     linkage=\"ward\")\n",
        "# fitting the initialized model\n",
        "agglo_clus.fit(X1)\n",
        "\n",
        "# getting the labels of the model\n",
        "labels_agg = agglo_clus.labels_\n",
        "\n",
        "# inspecting labels of top 10 data points\n",
        "labels_agg[:200]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
              "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 2,\n",
              "       4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 0, 1, 0, 1,\n",
              "       2, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 1, 0, 1, 2, 1, 0, 1, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 0, 1, 2, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
              "       0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsR2LvsiB7Ob",
        "colab_type": "code",
        "outputId": "a2f3bd60-d8d3-4725-9ba8-302a75ba1a2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "import matplotlib.cm as cm\n",
        "colors = cm.rainbow(np.linspace(0, 1, 5))\n",
        "colors"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.00000000e-01, 0.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n",
              "       [1.96078431e-03, 7.09281308e-01, 9.23289106e-01, 1.00000000e+00],\n",
              "       [5.03921569e-01, 9.99981027e-01, 7.04925547e-01, 1.00000000e+00],\n",
              "       [1.00000000e+00, 7.00543038e-01, 3.78411050e-01, 1.00000000e+00],\n",
              "       [1.00000000e+00, 1.22464680e-16, 6.12323400e-17, 1.00000000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ja4Sr2aQCA9E",
        "colab_type": "code",
        "outputId": "667b9e9b-c8bd-4801-bb87-bc6f3827c323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "# coloring data points based on cluster\n",
        "for i in range(5):\n",
        "  plt.scatter(X1[labels==i, 0], X1[labels==i, 1], s=50, \n",
        "              color=colors[i], marker=\"o\")\n",
        "print(\"Silhouette Coefficient: %0.3f\"\n",
        "      % metrics.silhouette_score(X1, labels_agg))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Silhouette Coefficient: 0.554\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "up5MdT5jEGCV",
        "colab_type": "code",
        "outputId": "19967d2f-14b3-4227-94c1-01ab88088bb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# Compute DBSCAN\n",
        "db = DBSCAN(eps=0.5, min_samples=10).fit(X1)\n",
        "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
        "core_samples_mask[db.core_sample_indices_] = True\n",
        "labels = db.labels_\n",
        "\n",
        "# Number of clusters in labels, ignoring noise if present.\n",
        "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
        "n_noise_ = list(labels).count(-1)\n",
        "\n",
        "print('Estimated number of clusters: %d' % n_clusters_)\n",
        "print('Estimated number of noise points: %d' % n_noise_)\n",
        "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_agg, labels))\n",
        "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_agg, labels))\n",
        "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_agg, labels))\n",
        "print(\"Adjusted Rand Index: %0.3f\"\n",
        "      % metrics.adjusted_rand_score(labels_agg, labels))\n",
        "print(\"Adjusted Mutual Information: %0.3f\"\n",
        "      % metrics.adjusted_mutual_info_score(labels_agg, labels))\n",
        "print(\"Silhouette Coefficient: %0.3f\"\n",
        "      % metrics.silhouette_score(X1, labels))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Estimated number of clusters: 4\n",
            "Estimated number of noise points: 21\n",
            "Homogeneity: 0.661\n",
            "Completeness: 0.745\n",
            "V-measure: 0.700\n",
            "Adjusted Rand Index: 0.660\n",
            "Adjusted Mutual Information: 0.691\n",
            "Silhouette Coefficient: 0.406\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mzsVHSBLMST",
        "colab_type": "code",
        "outputId": "6072dcda-57e3-4513-b408-232a149342d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# #############################################################################\n",
        "# Plot result\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Black removed and is used for noise instead.\n",
        "unique_labels = set(labels)\n",
        "colors = [plt.cm.Spectral(each)\n",
        "          for each in np.linspace(0, 1, len(unique_labels))]\n",
        "for k, col in zip(unique_labels, colors):\n",
        "    if k == -1:\n",
        "        # Black used for noise.\n",
        "        col = [0, 0, 0, 1]\n",
        "\n",
        "    class_member_mask = (labels == k)\n",
        "\n",
        "    xy = X1[class_member_mask & core_samples_mask]\n",
        "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
        "             markeredgecolor='k', markersize=14)\n",
        "\n",
        "    xy = X1[class_member_mask & ~core_samples_mask]\n",
        "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
        "             markeredgecolor='k', markersize=6)\n",
        "\n",
        "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kUB9Y2A89jzT",
        "colab_type": "code",
        "outputId": "1e0340f8-65f5-4114-db99-dc567e511bc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.scatter(df_final['Age'], df_final['Spending Score (1-100)'])\n",
        "X2 = df_final.drop(['Gender', 'Annual Income (k$)'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXyN-PsPCR-5",
        "colab_type": "code",
        "outputId": "96928eba-1530-411f-ed94-82dc7e436a84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "dendrogram = sch.dendrogram(sch.linkage(X2, method='ward'))\n",
        "plt.title(\"Dendograms\")\n",
        "plt.axhline(linestyle='--', y=3) \n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7WJPNHbCYNp",
        "colab_type": "code",
        "outputId": "6e8059f3-867a-4a9e-89ed-c3b590ba44bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Compute agglomeration\n",
        "X2 = X2[[\"Age\", \"Spending Score (1-100)\"]].to_numpy()\n",
        "\n",
        "# creating instance of our model\n",
        "agglo_clus = AgglomerativeClustering(n_clusters=6, affinity=\"euclidean\", \n",
        "                                     linkage=\"ward\")\n",
        "# fitting the initialized model\n",
        "agglo_clus.fit(X2)\n",
        "\n",
        "# getting the labels of the model\n",
        "labels_agg = agglo_clus.labels_\n",
        "\n",
        "# inspecting labels of top 10 data points\n",
        "labels_agg[:200]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 0, 1, 0, 4, 0, 1, 0, 5, 0, 5, 0, 5, 0, 1, 0, 2, 0, 2, 0, 2, 0,\n",
              "       2, 0, 5, 0, 2, 0, 2, 0, 5, 0, 5, 0, 2, 0, 2, 0, 1, 0, 3, 0, 2, 0,\n",
              "       2, 0, 2, 4, 4, 4, 2, 0, 0, 3, 2, 2, 2, 3, 4, 2, 3, 4, 3, 3, 3, 4,\n",
              "       0, 3, 4, 4, 3, 2, 3, 3, 3, 4, 0, 0, 4, 2, 3, 0, 3, 2, 4, 2, 3, 4,\n",
              "       0, 2, 3, 4, 2, 2, 4, 4, 2, 4, 2, 4, 4, 2, 3, 4, 2, 4, 3, 2, 3, 3,\n",
              "       3, 4, 2, 4, 4, 4, 3, 2, 2, 2, 4, 2, 0, 0, 4, 0, 2, 0, 5, 0, 2, 0,\n",
              "       4, 0, 1, 0, 2, 0, 1, 0, 5, 0, 4, 0, 1, 0, 2, 0, 1, 0, 2, 0, 2, 0,\n",
              "       2, 0, 1, 0, 1, 0, 2, 0, 1, 0, 2, 0, 2, 0, 1, 0, 1, 0, 1, 0, 5, 0,\n",
              "       5, 0, 5, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 1, 0, 1, 0, 2, 0, 2, 0,\n",
              "       1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yF1rUqR6CojO",
        "colab_type": "code",
        "outputId": "9212dd72-3b76-4eab-e9a8-2cb9aad23c87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import matplotlib.cm as cm\n",
        "colors = cm.rainbow(np.linspace(0, 1, 6))\n",
        "colors"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.00000000e-01, 0.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n",
              "       [1.00000000e-01, 5.87785252e-01, 9.51056516e-01, 1.00000000e+00],\n",
              "       [3.00000000e-01, 9.51056516e-01, 8.09016994e-01, 1.00000000e+00],\n",
              "       [7.00000000e-01, 9.51056516e-01, 5.87785252e-01, 1.00000000e+00],\n",
              "       [1.00000000e+00, 5.87785252e-01, 3.09016994e-01, 1.00000000e+00],\n",
              "       [1.00000000e+00, 1.22464680e-16, 6.12323400e-17, 1.00000000e+00]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWUG9cWFCz79",
        "colab_type": "code",
        "outputId": "9cbc3dda-c249-4740-89e1-0e3575a29366",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# coloring data points based on cluster\n",
        "for i in range(6):\n",
        "  plt.scatter(X2[labels==i, 0], X2[labels==i, 1], s=50, \n",
        "              color=colors[i], marker=\"o\")\n",
        "print(\"Silhouette Coefficient: %0.3f\"\n",
        "      % metrics.silhouette_score(X2, labels_agg))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Silhouette Coefficient: 0.399\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNJvHYA2DQN2",
        "colab_type": "code",
        "outputId": "b7e9ad89-8e1a-41d8-d794-b26b85be2aba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# Compute DBSCAN\n",
        "db = DBSCAN(eps=0.5, min_samples=12).fit(X2)\n",
        "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
        "core_samples_mask[db.core_sample_indices_] = True\n",
        "labels = db.labels_\n",
        "\n",
        "# Number of clusters in labels, ignoring noise if present.\n",
        "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
        "n_noise_ = list(labels).count(-1)\n",
        "\n",
        "print('Estimated number of clusters: %d' % n_clusters_)\n",
        "print('Estimated number of noise points: %d' % n_noise_)\n",
        "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_agg, labels))\n",
        "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_agg, labels))\n",
        "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_agg, labels))\n",
        "print(\"Adjusted Rand Index: %0.3f\"\n",
        "      % metrics.adjusted_rand_score(labels_agg, labels))\n",
        "print(\"Adjusted Mutual Information: %0.3f\"\n",
        "      % metrics.adjusted_mutual_info_score(labels_agg, labels))\n",
        "print(\"Silhouette Coefficient: %0.3f\"\n",
        "      % metrics.silhouette_score(X2, labels))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Estimated number of clusters: 2\n",
            "Estimated number of noise points: 11\n",
            "Homogeneity: 0.194\n",
            "Completeness: 0.659\n",
            "V-measure: 0.300\n",
            "Adjusted Rand Index: 0.134\n",
            "Adjusted Mutual Information: 0.280\n",
            "Silhouette Coefficient: 0.223\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t1Q1Ekm-Dqjg",
        "colab_type": "code",
        "outputId": "a48cb053-24a8-4183-ca83-784a725479ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# #############################################################################\n",
        "# Plot result\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Black removed and is used for noise instead.\n",
        "unique_labels = set(labels)\n",
        "colors = [plt.cm.Spectral(each)\n",
        "          for each in np.linspace(0, 1, len(unique_labels))]\n",
        "for k, col in zip(unique_labels, colors):\n",
        "    if k == -1:\n",
        "        # Black used for noise.\n",
        "        col = [0, 0, 0, 1]\n",
        "\n",
        "    class_member_mask = (labels == k)\n",
        "\n",
        "    xy = X2[class_member_mask & core_samples_mask]\n",
        "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
        "             markeredgecolor='k', markersize=14)\n",
        "\n",
        "    xy = X2[class_member_mask & ~core_samples_mask]\n",
        "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
        "             markeredgecolor='k', markersize=6)\n",
        "\n",
        "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}