Customer Segmentation/Customer Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Source: https://www.kaggle.com/shwetabh123/mall-customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Important libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import *\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import linkage,dendrogram,cut_tree,cophenet\n",
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "import plusmodules as pm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Mall_Customers.csv')\n",
    "df = df.rename(columns={'Genre':'Gender'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                 int64\n",
       "Gender                    object\n",
       "Age                        int64\n",
       "Annual Income (k$)         int64\n",
       "Spending Score (1-100)     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical summary of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
       "count  200.000000  200.000000          200.000000              200.000000\n",
       "mean   100.500000   38.850000           60.560000               50.200000\n",
       "std     57.879185   13.969007           26.264721               25.823522\n",
       "min      1.000000   18.000000           15.000000                1.000000\n",
       "25%     50.750000   28.750000           41.500000               34.750000\n",
       "50%    100.500000   36.000000           61.500000               50.000000\n",
       "75%    150.250000   49.000000           78.000000               73.000000\n",
       "max    200.000000   70.000000          137.000000               99.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for missing values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Gender                    0\n",
       "Age                       0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In any algorithm that involves distance calculation, scaling is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cl=df[[\"Annual Income (k$)\",\"Spending Score (1-100)\"]]\n",
    "ss=StandardScaler()\n",
    "cls=ss.fit_transform(cl)\n",
    "cls=pd.DataFrame(cls,columns=cl.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dendrogram's  for different linkage methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarity between the clusters (inter-cluster distance) can be measured using various types of linkages \n",
    "\n",
    "Some of the types are:  \n",
    "\n",
    "**Single:** It is defined as the minimum distance between the points of two clusters. \n",
    "\n",
    "**Complete:** It is defined as the maximum distance between the points of the two different clusters \n",
    "\n",
    "**Average:** It is defined as the average of all the pairwise distances between the two clusters \n",
    "\n",
    "**Centroid linkage:** It is defined as the distance between the centroids (means) of the two clusters. \n",
    "\n",
    "**Ward Linkage (ward minimum variance method):** Rather than measuring distance directly, it examines cluster variance. Ward's method is said to be the best for quantitative variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linkage method : single\n"
     ]
    },
    {
     "data": {
      "image/png": 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iJkmqXVvvGQPfNTbTzV+4Ay87bd9WYk2nBW42mG7L5nQN0iI6XbakaiayRU2SJGkWm27L5nRNt0V0umxJ1Uxli5okSdIs12bLZttsSR2+Jlptm2qZ7VLrqy1qkiRJkhrTRKttEy2zXWt9tUVN0qQuvfFSVty8YvIJx1l7Z3mgxIlfPnHK3z348Qdz5F5HTvl7kiSpe2ZCq23XWl9tUZM0qRU3r2Ddneum/L19dt6HfXae+lP81t25blqJoSRJ0qiwRU1SX/beeW/OP/D8VmJNpwVOkiRplNiiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkd43vUJEmS1Hmrr9rAjStvm/L3Nq6/G4DPn/WdKX93r6W78MT9d5vy96ZjOss3U5atbaNSV0zUJEmSRsConJxuyY0rb2Pj+s3MX7jDlL43f+GO04q3cf1mgE4v30xZtraNSl0xUZMkSRoBo3JyujXzF+7Ay07bt5VY00lcB9XW8g1j2do2CnXFRE2SJGlEjMLJqaTCh4lIkiRJUseYqEmSJElSx5ioSZIkSVLHmKhJkiRJUseYqEmSJElSx5ioSZIkSVLHmKhJkiRJUseYqEmSJElSx5ioSZIkSVLHmKhJkiRJUseYqEmSJElSx5ioSZIkSVLHmKhJkiRJUseYqEmSJElSx5ioSZIkSVLH9JWoRcSBEbEuIm6KiNMn+PwtEbEmIr4bEV+NiD3qL6okSZIkzQ6TJmoRMQc4GzgIWAwcExGLx012PbAkM58CXAZ8qO6CSpIkSdJs0U+L2lLgpsy8OTPvBy4CDu+dIDO/lpk/rQa/DSyst5iSJEmSNHv0k6jtBtzSM7y+GrclrwG+NEihJEmSJGk2m1vnzCLilcAS4Llb+HwZsAxg0aJFdYaWJEmSpJHRT4vaBmD3nuGF1bhfEhEvAt4JHJaZ9000o8w8NzOXZOaSBQsWTKe8kiRJkjTy+knUrgH2jIjHRcS2wNHA8t4JIuLpwDmUJO2/6i+mJEmSJM0ekyZqmfkgcCpwJfB94JLMXB0RZ0bEYdVkHwZ2AC6NiFURsXwLs5MkSZIkTaKve9QycwWwYty4M3r+flHN5ZIkSZKkWauvF15LkiRJktpjoiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR1joiZJkiRJHWOiJkmSJEkdY6ImSZIkSR3TV6IWEQdGxLqIuCkiTp/g89+OiO9ExIMRcUT9xZQkSZKk2WPSRC0i5gBnAwcBi4FjImLxuMl+CJwAXFh3ASVJkiRptpnbxzRLgZsy82aAiLgIOBxYMzZBZv6/6rOfN1BGSZIkSZpV+un6uBtwS8/w+mrclEXEsoi4NiKuvf3226czC0mSJEkaea0+TCQzz83MJZm5ZMGCBW2GliRJkqQZo59EbQOwe8/wwmqcJEmSJKkB/SRq1wB7RsTjImJb4GhgebPFkiRJkqTZa9JELTMfBE4FrgS+D1ySmasj4syIOAwgIn4zItYDRwLnRMTqJgstSZIkSaOsn6c+kpkrgBXjxp3R8/c1lC6RkiRJkqQBtfowEUmSJEnS5EzUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWNM1CRJkiSpY0zUJEmSJKljTNQkSZIkqWP6StQi4sCIWBcRN0XE6RN8/vCIuLj6/OqIeGztJZUkSZKkWWLSRC0i5gBnAwcBi4FjImLxuMleA9yVmb8O/DnwwboLKkmSJEmzRT8takuBmzLz5sy8H7gIOHzcNIcDn6r+vgx4YUREfcWUJEmSpNkjMnPrE0QcARyYma+tho8H9svMU3um+V41zfpq+N+raTaOm9cyYFk1uDewrq4FkSRJkqQZZo/MXDDRB3PbLEVmnguc22ZMSZIkSZpp+un6uAHYvWd4YTVuwmkiYi7wK8AddRRQkiRJkmabfhK1a4A9I+JxEbEtcDSwfNw0y4FXV38fAfxTTtanUpIkSZI0oUm7PmbmgxFxKnAlMAc4LzNXR8SZwLWZuRz4JPCZiLgJuJOSzEmSJEmSpmHSh4lIkiRJktrV1wuvJUmSJEntMVGTJElqgO+UlTQIEzVJkqRmPBwgIlo93zJBlEaDiVpHtbmTbfsAUsX0IFKjtn/PYaw/60x9/C3rNcrb3ygvW9Mi4iXAiojYJTN/3lLMPQGG+eTtUVqH443ysql+ddSXGZmoRcQuEfHolmI9LCIe1kasKt7TI+LX2trJRsQBwPHV343Xh4h4UUQ8bRgHkTZ2sBHxiIh4RPV3G7/nPhExv+nfMyLeHhEHRcRzoN2TgIjYMyK2ayNmRMytXkPS+PqLiIURMa/JGD2xWl9/URn7u4V4j25zX13FbK1uVvF+KyLmNRmv7boyzH1LFb+RulklaR8BEviNalzT+5QDgH+NiJOajLOV+I3XzwliNrpvGdX6OexYox6vzmPDjEvUIuJw4GLgixFxTAuxLgAujIgDImLHhuO9BPhrYKcm4/TEexFwEfBXEfGEpq/4RcTBwCeARU3G6Yn3xog4PiJeBq2ccBwKfBb4ckQc0MLveRDwV5QXzDftJsrL7t8TEe9rIR4AEfFc4GTKb7o0InZpMNbBlO39CxHxvMz8eVMnVhGxL3Ad8JqImN9EjHH+L7A7La2/at95HnBeROzfwrb3TOBa4OCI2L7JWD0xnwe8hhbqZhXvN4CDgSsi4nciYnFDoVqtK23Hi4g3NX1cqI7lH6DadwGnVbEaOyZExIHAh4EvAb9ajWvzxHQf4CAarp9tH9dpv362tnxDOEdqO17j2/oW4v42dR4bMnPG/AN+E1gFPAk4BPgmsGPDsfYFDgW+AbwXWNRQvEOB7wL7VsPR8G95SLV8zwTeCryxGr9NQ/F2AL4OvLAafgTwiIaX8QDKic1K4P1Nrbue3/N64LeAEygn4Y0tXxXv34ClE3xW2zoE9h83vCvwL8CHgJ2qcY3W1SrGKcA5wPsmWuYa5n9Qtf29BFgG/Ah4aoPLswC4GjgTeAPwmIbiLAQe3ub6A54KrK22vdcB3wOOBXZo8Pd8EnAz5eT4d5qM1RNzTvX/a5usmxPEPQJ4D+Wi10Ezta4Mo25W83xhk8cFygXwjwDPrYYfRjl/OKnBOvG86vjzjGrf8iPggCbr4VbK8vIm6mfP/Fs5rg+xfrZ53vLitmK1vWxVvEa39a3EHXv1WS3HhpnWorYI+LfM/B4lSZsD/J+IODEiHtdArFWZ+Z3MvAL4W+Bw4CURsU2dV6qqq/a/A2yXmd+JiB2AD0TEJ6srU9vVFauKtwtwJPCmzPw28BPg0IjYJksrQq1X4ar5/QzYnJlfjYidgc8Al0TEOyLiWTXH26lalq9k5grKyfdC4A1VS0atIuKRlAPl2zLzm8A/A3cDb4/S1bPu9fdISnfVuzJzZUTMi4jTI+JDEfGcrKklKCL+BPibiPiDsXGZeSulru4NnF6Na+KK9Aci4p0R8eYqxjnAucBPgVdGxFNqjLUDJVF7V2ZemZnnAh+jnPw35UHgVuBe4Ncp+5X9IuKJdQWIiPOAvwC+HhF7QGvrb1dgbWauyMyPUS4EHV/Fbar7122UiyNBSWSeHKVb8K/VHSgizo2IV2XmzwAy8xM0VDereCdHxJFVCx6ZeRlwPnAV8Nqx8QPGaLWuDKNuVseaRZn51aaOC1HuD3sC8O7M/EZ1HHqA0gvo8dU0TbRyPRJ4XWZel5m3A38MHBMRjfe2qI7nRMRcgMz8HKU1vbb6Wc2/teP6kOpnm8v3aoDM/IeWzpHaPidrfFvfQtyxrvcPg/qODTMtUVsLzKs2ouuBLwLLgedSWhjq9APg5xHx4mp4Z+A7wKuBZ9W5gWbpDnEq8A8R8W3gq8AdlGU8jdLaVqc7KC1oX6/if5ySSP3varjWE7cs7gFuiYj3U06Cl1fxHk45SZ1bxwEsIj5OuYLxL/GLm6rvovy+c4GjB40xgXuAMzPzK1HuObqAsu7WAm8HDqszWGb+FHgXcH1EXEqpL48A/hv4TFTd9moI9W1K0rlrRLytJ/6dlGb9Q3qTuLpU2/cTgfXAfmMXYTLzOso2fxelJbiuk56fUq7+fr0nidgGeHYN855QVSf/mdL1+ALKVejLKVfDBxYRn6b0Nng5sBo4sSf2ncBJNLT+KF0QN1WJ5zaZ+WXKSc+7I+LZNdXN8TZTtrcPAV+gXNH/B+AxdQap6uILgHMj4lVj46u6eQU1182I+BTlAuFBwNER8YIq3g8py/mPwPMHOSFvu64Mo25GxLmU1oM7xtZLtQ3+AeWkauDjQpSu738HfBz4eEQ8qaeufxM4KSIOrPnkfm+A6qLI1T37r5XAfOBR1XRNdeG+ArihOil+cCxOZt5CTfWzitPacX1I9bPN5Tubsk8ZG96myXOkts/J2tjWtxD374G1EbFDZt4f1b3u1bHhcgY4NnQ+UYvSv/M5UR5AsRp4N/A14IbM/OPMvBz4NPDyGPAesp5YT83Ma4HvAydHxArgOZn5mirWi7c6o6nHW5KZD2TmqZR7gb6YmR/OzI9SsvFXjV2tqiHe/sBTMvMn1bhHVB9/kJIE13Z/XM/y7VeN+gSlC+RjgEsy818oB7ZnUXaMAx3AIuKTlIT6aOBblAMmAJm5iXLytjQi3jlInJ54SyPit4CnZ+bd1eg5wO9n5psz80LKMr8qanjIQUS8ICKWRcRJmXkTcDalBeHSzHxPZr4P+DPKCUEdCczdlPsl/w1YFBGnRMRxEbEwMzcCxwB71HnVNiKeAOycmYdl5qco+6hnRMSRAFVr+tXAETHgQ3d61t+TM/O7VR0Z2yfeAGyqpjsmqhvJB4z1nIhY0jN6R2A/YPvq/2uAvWLAe9YiYgGlG+DYPby3AU+JiPMj4rkRsXNm3kGN66+qmydHxGururGe0rK1KCLmZOaXKPffHjForHHxTgaoLgTNARZTWiqfDtwI/EqUFuhaZOZ/UPaVJ1N6c/Qma6spXaMGrpsAEfGHwPzMPBR4PeWCwtKeeHdS9nN7ANNqOWy7rgypbu4H7J6Zz8vM/wYeGxE7RMRO1XHwvZQLQtM+LkTEsyn3h706M59HuRj6luqzyMzvUs5djouaHoRWJYbXR8Tf9oweS5RWArcDH62Ga784EuW+n3uAzwGXRsQe2dObo476WcVp7bg+pPrZ5vJdBmxfJaFE6e2TPbHeW1esav5tn5M1vq1vIe5rKNveN4Bre5K1sZa1tQxwbOh0ohYRh1BW7CHAWyPi7Mz8XmZ+BvhBRLywmnR7yg5jkJO23lhvi4g/zcwPU66evAN4aTXpTpRuSwMZF++N1VUAMvOVlL60Y7al7PQHPeiPxTsIeFNEnFPFu6eaZB3lfrxarjaMW75TI+Kj1ZWFfwK2A86oJl1EDfUwSveEO4FlmfnzzHwL8JMoN+BTnSz+BDiOsuN9woDxxpbvYMrvObb+7gLW9CRKDwM2Mvj6O4jSKjEPOCEijqyStd8D/nTc5D+e7klilBtvPwZQtbjekJkXA5cCb6J0Y7u1mvwnlHVZ50N25gKLI+KFEXEG5Z6LpwKvjojPVOVaQdkhviGmeaV43Pp7a8/6G9u2fwJsrhLE91Lq1rSM2xbeEOUKI8DnKS1pF1DuwfsopXvUtE+qIuJ4ysHvzOoK97OBoyh95FdRTvjHHuZTy/rrqZu/QllPB1OSmfmU++/GWiaT6p1SNcY7LiKOqz66mtID4TxKi+/fURLDOYPGrOKOdXl/OuUk+LnAX0TENRHxDihdiRiwbvb4JvB/qvneQ2mheHr0XPTJzFWUeyvfGVO8mNd2XRlG3azcRTmGEhG/R7l49mlK1/QnV/vsYyndZQc5LnwwM6+v/n4PsHNEPJxyMQ3KQykeBO4fIAYAUR6Ycypln3xfRHwWyv6rignwh8AD1cWoJqwC3pOZf0Cp8383lqyNTTBI/YR2j+tD2ne2uXxBefpoVsMnA39OeTDfiyPiUZn54yrWoNtC6+dklba29fFWAmdk5kmUbeG6Kll7YOxcsDo2XMV0jg3Zwo110/lH6XP9JX7x8IlFwH8B51fDb6PcN/Z5Sjezp9Yc6w7gk+OmewPlxvjfaGDZbgfOGzfdiZSr7E9q6LccH+8VwJXV9NO+SXYL8TYCZ1fDz6a0Bn2hquDTXnfV/F5JOTl7fDX8MMrB8RvAYT3TzaWcKL5jbNqGf88Talp/21fr5ZBq+FTKzuYZ46Y7ntLt7IkDxJpPOVj9SjV8CeXE9KOUVqa/piRr21SfLwUWDLJ81Xwe1TPPNwB/WW3Xe1XjFlTb+j49v/mhTa0/SnfVB6r6Oe3tfSuxPlYNfwL4nZ7p5w34O45ffw8f+7sa/ivgzT3DA62/LdTN4yn36QTwTuBvKE++W13Dtj5RvGMo9xPOpXSNPbJn+kcPWjd75jVWP58DvL76+3OUi4TLeqabdt2cIObDev5+NvCVnuHfrv6fQ7kIMKfjdaXteMdTEvptgK9QehtcVpVjf8oFmDdU9XR7BjguVOtgXs/fCyn7rwW99ZDqIRQ11Y1fo/RQmV8t12fHff5IygWTX60r5gRl2Lbn7w9S7hMde9DGywasn20f19uun60tH+Vc5MxqXtdTWg2/BjyZso++mOo8pao3g/6Wba+71rb1cXH3o/R6AJjbM/5jwI09w8+v/p/WsaHLLWpB6Xp1GzzUJ/9TlGbL91JaEc6i6vaYmf9Wc6zzgGdGxFnwUBfBnYHjMvP7A8TaUrwLKMs2Fm9vyoH5pCzdveqON/ZbntUz3T8Cx2TmT7OqVTXGOx94QUR8IDP/NTNfT2kNevGA6w7KSeC/U66mVCEzKTuj2wEi4kPAHpl5H+XE+e6JZtSnSX/PiHgG5QEjdaw/qFqxIuJplETpcOAvI+LvqvFPpdzLeGKWLljT9TPKSe8rq+HllP7liyknqFcAd2R11TQzV2a5eX3aqhamc4FvRcSemfkXlHv7rqO6KZfyez+K0sIMpWvdVdMNyeTbw/cpLRqvHnB731Ks50XEuzPztZn5hYiYU32+aYBY8Iv1d0w1v/v45bq+C+VqMNXnA68//mfdPJRyEe2TmfknlO38/cBLatjWJ4r3Usr++uzMPCQzL+3pcnLHIIEi4l0RcVg1r7GWgp8CL65aMXag3Kj+sYh4fvX5tOtmb7zKz3r+voOqHkXExZQnzJLloSbfqP6firbrStvxvkTpvvwwytNAd6ckCxsz8yrKMWPPLP6bAY4Lmfmznm03gB8Dd2bm7VWL7wci4hFZrujXIjP/MzM3Z+lqfArwiLGWtShdrOcBb8/MH9UVc4L6+UDPvusPKRdfr4yIb1KOf1B6CEynfrZ9XG+7fra5fFdQ6v6DlPuk1gFvycwbqn30jylPSCTLPfCD/pZtr7vWtvUxEXEB5TkLF0fEM7O0wo7dl/Y6yovu10bEP/GLbWF6x4apZnZN/6O6gl79/Z5qwX6XcrI41jXoPEo/2zZifRx4VDXNQI9inUK8HShXGh7Z5vK1+HvWdpW7irUTpWve68aN/yNKV84LgL9tcfm2p+wwBnpE+Lh4b6qWcSXwoZ7xKyndu+bSc/VvwLhPBW4BXkZpyfoA1WOK6XnlwKDbQzWPT1K6qG1DuQr2zz2f/T7loT5jN+mf09L6m0fp1jLtetpnrE9Q7serbVsYt/6OrYbnVPuULwAfrylGP3XzWsqFn7biXUO5kFbX7/gp4D+q3+3l4z77KOXK7XbV8D5Nxqs+fwzwr5SHB50/U+rKsOJRjguXAa+phl9Jud/2HdXwRcAf172MPfEvqPad11Hug20kTk+8+ZQLomup3ntZ8/wnrJ+UxHROz/Am4NM1rb/Gj+vj5t12/Wxl+Xpi/cHYOhv3+ecpF3ln3LL1xGttW6e0yH2++vt91XL9j9ciUR7w9tmB49VZ8WpY+EMpVysv7hn3RsoT7j5I1Q2E8gSVXWdKrGnE+7WZ9FsOI94E8X9pB1uNO5tyNe+snnHTSi6muHy71fh7XtQz7pHAq4AX9Yz7EHBUA7/nAZRWpVeMG1/nO9r2oNyA/+iecZfT03WTX3SDfP8g67DN9TfsbaGa94uANZQWwbH1eUYd63GKdfOIGpZlKvGOHDRez/zeRrkP7TDKFenek9PH84uukL3dvwb5XbcWbxvKFf2fA39dR7w26sqw4wFPo3qwTTX8VMrV/vPoSSams0/ZSsygtPz/O/BDypX82n6/SWK/mfIOtdoTw0nqZ1R19DX0XEQYdF3S8HF9CzHbrJ+tLV9PrON6xm1H6S7e+EWZptddm9s6pcfPe6q/303pjfY5yjtYd6/Gnz4u7vSPDXWvnAEWfPvqR13GVrJtSqb8Tap+oV2PZbz6422lHGM72JOq4ZcDf9rz+bQ2lA78nhf2fPZqysF/afX59fS0NtT8e+5PuYL6Rqp7gmqcd1992KvPoufvKa/DUd63TFKW36rW3+9R3S8y3d9wWHVzGNtCNd8Pj9W9qgxHU05Oj+qZbu6gsfqId+S4aV9Qx3pso650JR7lRPuhE+9q3JwmY1bzPYEB7heeRrydKK28T6l5vlOpn70X3Wr5XWnouD5JzDbrZ2vL1xPrhGr4mZR3/jWyfG2vu6a39Wpb+Ajl3Og6SkvdRsq9Z8dREtGXVtM+oba4dVe6AX+E8TfH9h6U5wIHUrq7PG0mxTJe/fG2Uo7nVDvY3+eXT6oG3UCH/Xv+Tc9n7wYupBwoB3pQSR/l2LP6Lf90bOde03znU25iHrvxfG71//mU9xRSxXxCTfFGdt/S5/o7i3KQqeNqYqt1cwjx5lO6s8zrGbcj5UD8OUqLwl8Cv9livL/qjTfo/qytutKVePzixPvNwME945uM2ejvt4WY2zUwz37q50fH1c9al52GjuuTxGyzfra2fD3bwuv55Qd7NHXBotV11+S2Xm0L76LcEvJ4yr2hH+z5/B30dMevK25UM+qcKO8aORe4JzNfGRFPovwwK7PGm2PbjmW8+uNNEH9PypWVJ1AeDfzprLGiD/H3fCAzj46Ix1Puo1qTmQM/6nkK5ZiXgz/sYmxeO1GW6auZ+bGe8X9EeTrggZT74o7ZwiwGiT2y+5ZJylLb+uuZZ6t1s414PXXzHzPznHGfHUFJDFdk5ktnYrw+y1R7XRl2vJ7jwuOB72XmBU3GGxVdqZ9NH9cnid1m/Wx8+cbHyvLO0sa0ve6a2tarbeHjlKfvnhMRu1NeNXVJZn4lIi4BVmXm+7c6o6nG7WqiBhDlxa8fpjz9cBvguZn5nzM9lvHa09QOdoi/53Mo3U+en5nrm4rXhupJlVcAf5jl5eBExNmU7iZ/npmnVeOi7p36KO9b2tZ23Wwj3kR1sxr/NeA/M/O4ariWutl2vNmu7UR0puta/Rz19dfm8o3iRZkm4/VsC6dn5t9Eed/rb1Pu9/uPzDy+rlhjuvx4frI8dva7lBebvrzJk5s2YxmvPU3tEIb4e86r4s3oJA0gy6PaTwTeFREnVaO/CvxZT5K2TRMH/lHet7St7brZRrxxdfMEKHWR8kCTsZPS2upm2/Fmu1E+yW9C1+rnqK+/Npev7d9ypsertoWTKC9xf0Vmnkm5H/WtY0laTPWF1pOYW+fM6lY1Mx5MedfWDaMSy3gzn79nPTLzHyPiZOCzEbEd5R1tb4WHDvw/3/ocpmeU9y1tG9Vtoaqby4DPVDGvH+v61UTdbDueNBXWT6moujmeAnw6InajdK/8J2hmW+h010eAiNguM+8dtVjGm/n8PeszjPsPRnnf0rZR3hbavrfJe6nUZdZPqWhrW+h8oiZpdhn1+w80c830+yukOlk/paLJbcFETZIkSZI6ptMPE5EkSZKk2chETZIkSZI6xkRNkiRJkjrGRE2SJEmSOsZETZIkSZI6xkRNkiRJkjrm/wMEpGTmi9pLbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linkage method : ward\n"
     ]
    },
    {
     "data": {
      "image/png": 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AZkxyBgAAA7vh0ldn/86d8y5GkuTGPXuSjOfL0I8766wcf85j5l2MuXBbIwAADGz/zp1fTIrmbfPKSjavrMy7GEm6RHEsSes8GDkDAIA52LyyklMuuXjexRiVsYzezYuRMwAAgBEY/8jZeqeMn2a6d1O1AwAAAxv/yNl6p4xf73TvpmoHAADmYN0jZ1V15yQXJ7l9kpZke2vtpX0V7GaGnDLeVO0AC2nomdHmMfvZMs9wBrAIprmt8aYkP9lae3dV3SbJu6rqza21D/RUNoDRuexDl+WKq64YLN6e67sO/vlvHO5W6zPvemYeffdHDxZvKKszow01I9nQM5+tJoOSM4CNa93JWWvt2iTXTn7+dFV9MMmdkkjOgIV1xVVXZO/1e7P1hK2DxFs5YdgO/t7r9ybJQiZnyWLPjLbsM5wBLIJeJgSpqlOT3DvJOw/y3gVJLkiSk08+uY9wAHO19YSt2XHGjnkXYyaGHKEDAG5u6uSsqr4myWuSPKu1tv/A91tr25NsT5Jt27a1aeOxwax3ts1DmWYWzluyAWbonMXtdLO6ZW5Rb4sDWHazeHZzVs9negazH57XHdZUszVW1VekS8xe0Vp7bT9FYqGsd7bNQ1nvLJy3ZIPM0Ll6O12fVk5Y6f22ub3X7x30mSwAhrP67GafNq+s9P6M5o179gyaUCyyWWzzWzKL9nBLxtZWppmtsZK8PMkHW2u/2l+RWDhDzra5Hhtohs6NcDud2+IAFttGeHbTM5j92gjbfL3G1lamGTl7QJJzkzy0qq6c/Duzp3IBAAAslWlma3xHkuqxLAAAAEtrqmfOAAAA6IfkDAAAYAQkZwAAACPQy5dQAwCw/u+Emua7ncb0HU3AdIycAQD0ZL3fCbXe73Ya23c0AdMxcgYA0KMhvxNqbN/RBEzHyBkAAMAIGDkDDuqyD12WK6664qh/b8/13e0857/x/KP+3TPvemYeffdHH/XvsXzW81zPRnmmxzNLAMtLcgYc1BVXXZG91+/N1hO2HtXvrZxw9M9MJMne6/cmieSMI7L6XM/RPKOznud5ki8lPUMlL+upW7Jx6if5BDg0yRlwSFtP2JodZ+wYJNZ6Rto4tGUY+RzquZ55PNOzyM8sLXrySb8k8xypRWkrkjMWx64dye7Lj/73rntf9/+ORxz97572qGSbpILxMfLJmC1y8km/JPMcqUVpK5KzeZNQ9Gf35cl1u5M7nHZ0v3eHe64v3nW7u/8XcV0ugWUYWTLyCSwCyXw/FmVk6ZYsQluRnM2bhKJfdzgtOf8Nw8RaT2LMaBhZAmCZLMrI0qKTnI2BhALmwsgSAMtkEUaWFp3vOQMAABgByRkAAMAISM4AAABGQHIGAAAwApIzAACAEZCcAQAAjIDkDAAAYAQkZwAAACMgOQMAABgByRkAAMAITJWcVdUZVbW3qj5cVc/tq1AAAADLZt3JWVUdm+S3knxPknskeVxV3aOvggEAACyTaUbOvi3Jh1trV7XWPpfkVUnO7qdYAAAAy6Vaa+v7xapHJTmjtfbfJ6/PTXLf1trTDvjcBUkumLzcmmTv+osLAACwoZ3SWttysDc2zTpya217ku2zjgMAALCRTXNb48eS3HnN65MmywAAADhK0yRnf5/kblV1l6q6VZLHJvmTfooFAACwXNZ9W2Nr7aaqelqSP09ybJILW2v/0FvJAAAAlsi6JwQBAACgP1N9CTUAAAD9kJwBAACMgOQMAABgBCRnS66qapHjAQCztcjn9kWuW6If2Lc+6rfhkrNFb0RDxauqB1bVcW2gGWGq6m5VtXmoeJOYC7ntxBNPvOWKt8h1E29jx1vkc/vQ/aRJzIXddtbnUfytsc/WWFXPS3Jlkv2ttb8eIN6zknwyyX+01l43QLxB6zeJuZLk3CTfkeSXk/xTa+0DM4z34CTfk+S+SZ6d5OrW2r/NIM4zk1yfBd12c6jfs7LA+4L2It5Rxhts+y1BW3lWhj22DB1vofsta+I+OMkZSe6X2Z7bn5WB61dVW5Ocl+SBmWE/ac7bbub9sjXxBul3Dn0sWxP3welxfW6EkbN/THLnJC+sqhcNEG93uh3leVX14qo6ecbxhq5fWmt7WmsvSPLrSe6T5Ceq6ntmGPLtrbXnJHllkqck+dGq+rYZxPlAht12H05yUobbdkPXb+h94UPp9oWfXdD1OfS+PnT9ho73wYHjDdk+h24rQ6/LoY8tQ8cb+tzwngxbv1V/1Vp7bpI/zGzP7YPWr6pu1Vrb21p7fmbfTxq6ba5620D9siSD9juHPg+t6ref21ob5b90B7avXPP6jkn+OskvJTl+sqx6jPf8JCeveX18kouTvCTJfTZ6/SZ/74TJ/5vWLLtzuqtDr0ny4J7jPSPJY5I8ZM2y+yZ5TpKXJblnT3GOT3LMgNvuOw54Peu2eeKB2zHJJTOs39D7wn3X1nGA9Tl0exn6WDZ0/YaOd95B4i9E+5xDWxl6XQ59bBk63tDnhhcnefhQ9VsTZ3uSJx6w7FvS/7l98PpN6vbYtdspycnpuZ80dNtcE+cnkzwxySNnue3W/O1B+p0ZuJ+0Js5M+rmjHDmrqgvTVeqtVXVKkrTWrk3yvUm2JnnuZFkv92RW1fYkD0/yydX7U1trNyR5epKvSLej9mbo+k1i7kyyu6pObq3dVFXHTGJ8NMmfJvm/SR5SVbftKd4lSc5Md7vDkyfrOK21dyZ5Q5IbJu9NdU9wVf1ekt9N8tdVdbdJjBuSPC3JpvS/7X4+ySuq6umry2bcNi9K8utV9cdV9bCqumNr7fp09ZtF2xx6X7goyS8mubSq7jeJN8v1OXR7GfpYNnT9ho73W0nOXvP6mBnHuygDtc85tJWh1+XQx5ah4w19bvjNJD+V5OfWxJvZ9lsT9y5JHppke1U9cU3sdyXZmf7O7YPXr6p+J10n/1Vrt1Nr7SNJ/iw99ZOGbptr4v5eum23Nck5VXXqJHav225NvEH6nUP3k9bEnV0/d1bZ5BRZ6MVJLpv8/PtJfvaA9782yfuTPL2nePdN8mdrXt8lydfkS1e5jk/ytiQv2Ij1m/zN70tyWZLfSPLOJKdMlq+92n16kouSfGMP8W6Xbsf7qsnrOyV5XZKL13zmjCRvSvL1U8R5eborL8ck+dUkbz3g/dsmeWtf227yN7833S0cL07y7APeO7HntvncJK+Z/PzDSd6c5Kczudo2Wc+91W8O+8Lzk7xu8vOL0p2Ej8nkCtsM1ueg7WUOx7Kh6zd0vMuTXLTm9ebc/Op23/vDYO1zDm1l6HU59LFl0HiTvznkueGSJJdMft6R5HGTn1fbZq/b7yDxn5ru+aHr8+UjaA/P9Of2weuXriP//iR3mLx+cJLvTPKdaz7zLZmynzSPtjn5u89bPcZMXl+WLlFbWbPse6bddmv+1iD9zgzcT1oT93aZYT93VCNnVbUlyVVJHjdZ9G9J7llVO6rqQVV1Qmvtk5P3T+lplOeGdPenpqp+JN2J8eJ096ue1rqrGT+Y5LSq+oZpAs2pfkn3YPILW2tPT7fTv6aqTmmtfWH1A621K5O8L8kLqmrTegNNroy0JJ9Pd19xWmsfS3cP7ldW1fMny944KcszVq+mHGWcU9KdGC5orX2htfYTST5VVd84ef/Y1tqnkjw+3Tqeatut8el0B8/3Jjm5qn6oqh5fVSe11j6RfrfdpiR/mySttd9Jck26g9nDJvfE/3u6+k3dNicG2xcmWrq2mSQ3Jbl/uk7jk6vq1D7X59DtZeh9fQ71GzpeJfnGdG0mVfXUJL+W5A1V9fCqut0M9odB2ucc2so81uXQx5ah4yUDnRuq6vQkH22tnTtZ9NEk35YkrRud2DSD7bca+5hJ+7l3kn1JHpTkZVX192vO7W/KdOf20zOf+u1O8u4kD5iMgr44XWf/aVX1c5P470p3TJimnzSPtpkkV6S7lXA17v3TJdg/VVW/kCSttT/LFNvuAFdmmH7n0P2kQfq5o0nOqurcJC9M8r8mO+C3Jzkn3RXLK5P8WLr7fpPkU+mu9N1mmnhV9bJ0D+7evqp+NcnDJjF/Lcln0g23VpJ/T9eA2jTxMmD91mqt/XO6eqa19uwkf5HktVV1/KRs3zf56EvTPcy47npOOmqfSncLwB9VN0NPkuxPdxC6fVXdarLskiTvWLuzHomqekK6dfnbrbVPVtVXTLbT7ZKs3lr1+cnO/ol0J8uptl11tzuktfbWJLtba5emuyr0rHS3Xlw7+XgfbfO8Sdt8X5I7V9UPTLbV7dNNSnD2mo9/Mj20zYH3hfOq6qVJ3pHkkVX1qiTPTPLkdCMx90p3cE26djPt+hy8vWTYY9nQ9Rs63pPSrbt7JTm9qq5K13n5P+me53lKume1kq7js2Ha5xzaypMy7Loc/Dw7h3hDnht+MMkzWzdRxarfS/KoyXurCcwx6eHccDCtGxJ4RZJvaK3tTvKXSb453b6+ar3n9rnVr7X2tnSjdOenGzV7RLpO/UuT3KWqTph89GVZRz9p6LZ5oNbae1trn5n0v45P9yzU+enqfHJ1F4mSdW67g8Sbab9z6H7SWkP0c0eTnKWr5L586cD1riTfMmlQL0234z8k+eL9vxcn+eyU8fanux/1F9I9oHhsa+0TrbW/SvJPSe7WOv85+fynp4w3WP2q6n9U1SPXLPqvqjp28vefk2449s+r6h3pDkRJ8oV0M/h8ftp4rbXfTTecfWlV3b21dlO6Ot8ryepB4Jokf3W0sZK8Md32ueFL4VpLd/V536Q8v5RuGP2z6WfbXVtfuuJ516q6d5JHp7ui/rdJfry6Zzb6aJtvSHJdktWD2w+nOxn+eWvtRUn+X7orlmmt3Zh+6jfkvvCGdNtpT7p1+JYkL2+tfaS19ookH0t3VS+ttasz/fqcR3sZ8lg2dP2GjrczXXu8Kd39+3uT/ERrbXdr7efTdWQeNinIZ3qIN2T7HLqtDL0u53GeHTrekOeGNyW5uqpuU52vaN3zOy9Icu+quvUk1hd6Ojfc7Ny+poP5mSQPr6o/THdL3n9L8jtV9ZDJ++s9tw9av4P0W96S5GeS/FBr7YbJce3D6W5L3Tz5zHr7SUO3zS+r32TdfS7JL7Tumaykmxn2a5OsJhPr3XZD9zuH7icN2s8dU3L2+XRXXx6XJJOT+toVeft0V54yef/vWmv7poy3kuQJrbW/TPL6dAfW1Ss2jzgg3nt6iDdI/arqD9JdAX1qVX3/5O+1JF9Ys6P8bLr6X9Vae+bqZ1pr/9FHvMnfe0m6k9Gbq/suj8vTfbfFxybvr159OFqfT3fl+pzJ37lpsvyadLeNXJTkzq21f5q839e2e8Lk9Z+kmwThHkkekK7D88nVE1dPbfM+SU5vrb0s3RWgH5501pLkuCT/tfrhnuo39L5wepLvb61dle52ixOq6rsn75+e/vf10zN8exnyWHZ6hq3f0PHuXlVPn6zLM1tr71nz/tel6/Skx3inZ5j2OY+2MvS6HPrYMo/z+pDnhm9K8oOT8/XqeWBvugsGd203v21sqvrdwrn9PeluN7x9uln/3p7kHpPkZtpz+yD1u4W6vbu19v7JZyrdbH//3Fr71zWfOep+UgZum4foB36hqmrSH8ykP/gr6eo3Vb9s6H5nBu4nDd7PbT0+IDftv3TZ5kfT7ZhJcmy6qzJ/muT3ZhDv9HQdiketif/GJBfm5g/19TVN8iD1S/cFeA9K8sh0J4fvX1uXdEn5U5LsWLP8mFnEm7x/RrqO3E/1tU4PXJeTZb+V7irMr8xw231fuisiv5DJlNeZPBDac7zTJ/GesGa73TbJ25Nc2FdbOSDePPaFx09e/0y6mZvekcmD4DOKN3R7GepYNtf6DRjv8WuWbU539XSW63Pm7XOObWWodTn0sWVex7Khzg1ftu9Nlv9muhGRPrfdLfUl7ppJvyHJrdYsX3dfYsj6HaZuleSr0nW0X97XNhyybR5B/Y6b7PMX9hH3COL12u9csz4H6SfdUv0m7/faz+2t4D2ugO9K9yVy501ef3eSn+lrYx4k3nevjTdZduwM482sfuke9vzl1UaR5NbpphDdmeTRB3z2a6eNeZh4jznU3+9rna5Zl0+evP7+JC+ZcVv5YJIfmEV9DhFvbVu59QE7/kbfF1bj/cDk9UlJ7jfDeEO3l6GPZfOq39DxnjR5fb8k/3OG8QZrn3NsK0Ovy/PWLBvi2DJkvCHPDTfbfjP4+7d0bj9nzec2zSj+zOp3mLod2E+6V9/bctZt8yjrd59p4x5lvKn7nYdbn+m5n3SY+s2sn9tro+/rX5IHpruP9EeSfF/fG/MW4v14uts8Vpf3+iXQs65fuvuiX5TkuDXLbpPuodbXpsv4fzPJt/ZRx/XEm8G6fMBkXf7oASeNWbWV75jEe2aSR8yqXgdpKz+e3OxLDhdlX1hdn89I8tAB6jd0exn6WDZ0/ea1Pn8sN/8S1Vnv7zNvn3M87w21Lud1nh36WDb0ueEZfdfvCM/tvzHjc/tM6neEdfut9NRPOkzdem+b66zfNIMCg/Y7j2B99tpPWk/9+vg3pmfOvqi19o5035VRSR44mZWl2pSzxxxBvM8meWh1M1qlTbbCDOP1Xb/Pp7vXfXVK5rTWPt26h9j/KN0Q/Umttb9f8/40dTzqeH1rrf11unWZJN82QFv5q0m8/0o3s9KTZhFnTby1bfMRa9rmouwLq+vzpiRnDlC/odvL0Meyoes3r/XZkjyoqs6bLJ/1/j7z9jnH895Q63Je59mhj2VDnxtuSv/1O5Jz+51nfG6fVf2OpG536rGf9GVm3DbXU79p9vmh+51fZsb9pLn0c2tGx6leVdVxrbX9h/+keFV1r3TDrc9prf3RmuVvSfKvrbXHT15XHzvI0PGOoDwbdtuJJ5544s0jlnjiHeTvLey5fZHrNvl7+oEbeH0mGyQ54+hU1Xel+y6OX2qtXVTdd4I8tXXTfq5Op9rb1dGh4wEAs7XI5/ZFrluiH9i3wden5GwxVdUD03353cuSvKd1X5A5sx1k6HgAwGwt8rl9keuW6Af2bcj6Sc4WWFXdLd1MNndN8v7W2kWLFA8AmK1FPrcvct0S/cC+DVU/ydmS2Oj3vwMA87XI5/ZFrluiH9i3WdZPcgYAADACo5xKHwAAYNlIzgAAAEZAcgYAADACkjMAAIARkJwBAACMgOQMAABgBP4/dO2KcLIUTogAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linkage method : complete\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linkage method : average\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linkage method : centroid\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cophen=pd.DataFrame(columns=[\"Method\",\"Cophen coeff\"])\n",
    "for i in ['single','ward','complete','average','centroid']:\n",
    "    plt.figure(figsize=(15,5))\n",
    "    print(\"Linkage method :\",i)    \n",
    "    eucli_dist = euclidean_distances(cls)\n",
    "    dist_array = eucli_dist[np.triu_indices(200, k = 1)]\n",
    "    mergings=linkage(cls,method=i)\n",
    "    coeff, cophenet_dist=cophenet(mergings,dist_array)\n",
    "    cophen=cophen.append({\"Method\":i,\"Cophen coeff\":coeff},ignore_index=True)\n",
    "    #print(\"Cophenet coeff\",coeff)\n",
    "    \n",
    "    dendrogram(mergings,truncate_mode='lastp')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cophenetic Coefficients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Cophenetic coefficients is the correlation betwteen cophenetic distances and the actual distance between the observations\n",
    "* It measures how well the dendrogram represented the differences between the observations.\n",
    "* The value close to 1 represents the best linkage quality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>Method</th>\n",
       "      <th>Cophen coeff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>0.721888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>average</td>\n",
       "      <td>0.721728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ward</td>\n",
       "      <td>0.720913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>centroid</td>\n",
       "      <td>0.717398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>complete</td>\n",
       "      <td>0.666984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Method  Cophen coeff\n",
       "0    single      0.721888\n",
       "3   average      0.721728\n",
       "1      ward      0.720913\n",
       "4  centroid      0.717398\n",
       "2  complete      0.666984"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cophen.sort_values(by='Cophen coeff',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Silhouette score can also be used to determine the best cluster size. For various K values, we plot the Silhouette score. The K with the best Silhouette score represents the best number of clusters to use (K)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "K = [2,3,4,5,6]\n",
    "silhouette_scores = [] \n",
    "for i in K:\n",
    "    model = AgglomerativeClustering(n_clusters = i) \n",
    "    silhouette_scores.append(silhouette_score(cls, model.fit_predict(cls)))  \n",
    "plt.bar(K, silhouette_scores) \n",
    "plt.title('Silhouette Score for Values of K', fontsize = 15)\n",
    "plt.xlabel('Number of Clusters', fontsize = 15) \n",
    "plt.ylabel('Silhouette Scores', fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Silhouette score for K = 5 is the highest, as shown in the graph above. This implies that K = 5 is the best number of clusters to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XQJmZWa1DqcgBZWYD5fbTnll1CbXRL0E0HAeUmQ2UJ7Zdu+oSKtXvoVS0RtUFmJl106ZnPcimZz1YdRmVGKRwAregzGzATDrpPgDuP3TjiiuxsXJAmZn1oUFrLTVTekBJWhNYDPwpIg4oe3lmZoNoPARSo160oD4C3Ai4vW1m1qHxGExDSg0oSZOB1wBfAD5W5rLMzAbBeA6kRmW3oE4E/iew0XATSJoFzAKYMmVKyeWY2aC7bd42VZcwag6nVZUWUJIOAP4WEUskzRhuuoiYDcwGmD59uk+KZ2Zj8tSk+h775QDqTJl/yZcDB0naH1gP2FjSTyLi8BKXaWbj3OZz7gfg3pmbVlqHw2jsSguoiDgGOAYgt6A+7nAys7JtPucBoJqAcih1V33bwmZmfcLBVI6eBFRELAQW9mJZZma94FAqn1tQZjYQVgbGjDy8sKpSrEscUGbWt9yKGWwOKDPrC22H0fz55RZiPeOAMrNSVNa6WX/9apZrXeeAMsNdRQPle99L9x/4QLV12Jg5oErkLz2zCpx9drp3QPU9BxRwGP9Z0pyvLmm+ZjacOfwdgJl99Pm7gV2qLqGWHFBmZhVxMI1sjaoLMDMbjxxOrbkFZWZWIgfR6DmgzGygzFz4g6pLsC5xF5+ZWUncehobB5SZDZR3fv1U3vn1U6suw+HUBe7iM7OBsud5iwA45eNv7+lyHUjd5xaUmZnVkgPKzMxqyV18ZmYdcFde7zigzGygPDZh3dLm7XDqLQeUmQ2UI371na7P08FUDQeUmdkwHEzVckCZ2UB53+fSmSS+/+n3tjW9Q6i+SjuKT9J6kq6UdI2k6yUdX9ayzMyGvOyiK3nZRVe2Na3Dqd7KbEE9BuwdEX+XtDZwqaRfRcTlJS7TzGw1DqL+VFpARURAvnIYrJ1vUdbyzMyKHEr9r9R9UJLWBJYAzwG+GxFXlLk8M7Nd2bDqEqxLSg2oiHgKmCZpU+AcSTtFxHXFaSTNAmYBTJkypcxyzGxArdJamjixukKsq3pyFF9E3C9pAbAfcF3Dc7OB2QDTp093F6CZdWS1rryf/ayaQqzrSgsoSVsAT+RwmgC8CvhKWcszs/7mfUbWqMwW1NbA3Lwfag3g7Ig4r8TlmVnNVBI6xxyT7r/0pd4v27qqzKP4loN/EpmNR5W2hn73u+qWbV3lM0mYWVe4i866zQFlZh1zGFkvOKDMbEQOI6uKA8rMBsvkyVVXYF3igDKzVfR9i+knP6m6AusSB5RZH+n78DDrgAPKrGYcQmN01FHp/sQTq6zCusABZVYBh1CJli2rugLrEgeUWZc5fMy6o7Qr6pqZmY2FA8qsi9x6Mused/GZjZFDqWZ22KHqCqxLHFA2rjhMxoHZs6uuwLrEAWW14xAxM3BAAXDjQf5CrBNVXYD1tZOvmQXAES9yS6pscW65828ZUPmCgx+OiG+WW4qZ2djt8PDNVZcw8MoOpiEtj+KLiKeAt/SgFjMzq7E4t3fhBO138V0m6TvAWcDDQyMjYmkpVZmZ2bjXbkBNy/efLYwLYO+uVmNmZrXUy5bTkLYCKiL2KrsQM7NuWLbxtKpLGBhVhFJRWwElaSvgi8AzI+LVkl4A7BYRPyq1OjOzDn10pxOrLsG6pN1THc0BLgCemYdvBo4qoR4zMzOg/YCaFBFnA08DRMSTwFOlVWVmNkqnLT2c05YeXnUZfa/q7j1o/yCJhyVNJB0YgaSXAQ+UVpWZ2ShNfvSOqkvoa3UIpiHtBtTHgHOB7SVdBmwBvLG0qszMrOfqFE7QfkBdD+wJ7Eg6E81N+FIdZmZWonYD6ncR8WJSUAEgaSnw4lKqMjOzUtWttdTMiAEl6RnANsAESbuw8jyeGwPrl1ybmVnHfrfZblWXYF3SqgX1b8BMYDJwAisD6iHgP8ory8xsdP7j+V+qugTrkhEDKiLmAnMlHRwRP+tRTWZmVqJ+6N6D9g90mCxpYyU/lLRU0r6lVmZmNgrzrjqYeVcdXHUZtdUv4QTtB9S7IuJBYF9gIvA24MulVWVmNkoTn7iHiU/cU3UZtdPrS2V0Q7sBNbTvaX/g1Ii4Hl/41MysL/RbMA1p9zDzJZJ+DTwLOEbSRuTTHpmZWT31azANaTeg3k26JtQfIuKRfNqjd5ZWlZmZjVq/B9OQdgPqFfl+Z8k9e2ZWXxdNemXVJVRqUMIJ2g+ofy88Xg/YFViCr6hrZjXz+R0+XXUJPTdIoVTU7hV1DywOS9oWOLGMgszMrD2DGkxDRnvC1zuA53ezEDOzbph/+auZf/mrqy6jdIMeTtD+Jd+/Tb4WFCnUpgFLS6rJzGzUJjz9j6pLKN14CCdofx/U4sLjJ4EzIuKyEuoxMzMD2t8HNbfsQszMrLXx0nqC1pfbuJaVXXuriYidu16RmZkZrVtQbwC2Av7YMH5b4M5SKjIzG4Pztjqg6hK6bjy1mopaBdQ3gWMi4vbiSEkb5+cObPoqM7OKnLD9x6suwbqkVUBtFRHXNo6MiGslTS2nJDMzg/HbchrS6v+gNh3huQldrMPMrCsW/HYGC347o+oyrAtaBdRiSe9tHCnpPaRTHZmZmZWiVRffUcA5kg5jZSBNB9YBXl9iXWZmNs6NGFAR8Vdgd0l7ATvl0b+MiItLr8zMbBwb7/ufoP1/1F0ALCi5FjOzccuBtLp2T3XUsXzG81NJ/0cVwOyI+FZZyzMzAzj7mW+quoSOOZyaKy2gSOfsOzoiluZLxC+RdGFE3FDiMs1snDtp6geqLsG6ZLSX22gpIv4SEUvz44eAG4FtylqemRnAhCcfYcKTj1RdhnVBaQFVlP+pdxfgiibPzZK0WNLiu+66qxflmNkAm3/l/sy/cv+qy7AuKD2gJG0I/Aw4KiIebHw+ImZHxPSImL7FFluUXY6ZWW3Eud7/NJIy90EhaW1SOP1nRPy8zGWZmfUDB1L7SmtBSRLwI+DGiPhGWcsxM+sXDqfOlNnF93LgbcDekpblmzuGzcysLaV18UXEpYDKmr+ZWTNztp1ZdQmrcctpdErdB2Vm1mtzaxRQDqax6clh5mZmvTLxsbuZ+NjdVZdhXeAWlJkNlHlLDgFgr90X9myZbimVwy0oMzOrJQeUmZnVkrv4zMza4G683nNAmZkNw6FULQeUmQ2Uk7Z7/6he5zCqHweUmQ2Us7c5tOPXOJzqyQFlZgNl8j/+CMAdE7YddhoHUn9wQJnZQDnt6rcBw/8flMOpfzigzGxgOYz6mwPKzPreKkE0o8k460v+R10z62sOosHlFpSZ1Z5DaHxyQJnZYDn66KorsC5xQJlZrXXcejrwwFLqsN7zPigzq61Rde3ddFO6Wd9zC8rMamnU+52OOCLdL1zYrVKsIm5BmVnt+KAIA7egzKwCDiBrhwPKzHrCoWSdckCZGeAAsfpxQJnZYDn22KorsC5xQJn1Kbd4hrHPPlVXYF3igDKrKQfQKC1blu6nTauyCusCB5RZiRwyFTjqqHTv/4Pqew4os2E4XMyq5YAyK3AomdWHzyRhZma15BaUlc6tEjMbDQeUlcrhZD33xS9WXYF1iQNqQDkYbNzaffeqK7AucUANGAeTjXu//W26d1D1PQcUwC+Or7qCrpGqrsCsWgs4BYC9eGfFlXQu4jNVl1ArPorPzMxqyS0oM7OKueXUnFtQZmZWSw4oMzOrJXfxmdlAOYr9qi5hNe7CGx0HlJkNlGvYuqfLc/iUxwFlZgPlldwKwEVsX9oyHEq94YAys4FyLJcA5QWUw6l3HFBmZpnDp14cUGY27jiI+oMPMzczs1pyQJmZWS25i8/MBsoRHFh1CdYlpbWgJP1Y0t8kXVfWMszMGt3MJG5m0rDPe/9T/yizBTUH+A5waonLMDNbxQHcBMB57PjPcQ6l/lRaQEXEJZKmljV/M7NmjiZdsLAYUNafKt8HJWkWMAtgypQpFVdjZoPCrab+V3lARcRsYDbA9OnTo+JyzKyPRXwGZiyougzrksoDysxsNNxCGnz+Pygz6zsOp/GhtBaUpDOAGcAkSXcAn4mIH5W1PDMbLKMOodNO624hVpkyj+J7S1nzNrPB07VW0bbbdmc+VjnvgzKzypTSVXfWWen+0EO7P2/rKQeUmVWitP1IJ52U7h1Qfc8HSZhZz/kgB2uHW1BmViqHkY2WA8rMSuFgsrFyQJlZRxw81isOKLM+4nBow7x5VVdgXeKAMrPBMmn4a0FZf3FAmXWZWzkVmzMn3c+cWWUV1gUOqD7iLz6zNjigBoYDqoFDwMysHgYuoKTjO36NQ8nMrH4GLqAcNmZmg8GnOjIzs1oauBaUmY1z8+dXXYF1iQPKzAbL+utXXYF1ibv4zGywfO976WZ9zwFlZoPl7LPTzfqeA8rMzGrJAWVmZrXkgDIzs1pyQJmZWS0pIqqu4Z8k3QXcXsGiJwF3V7Dc0XK95XK95eu3ml1vubaLiC0aR9YqoKoiaXFETK+6jna53nK53vL1W82utxru4jMzs1pyQJmZWS05oJLZVRfQIddbLtdbvn6r2fVWwPugzMysltyCMjOzWnJAmZlZLY2bgJK0n6SbJN0i6ZNNnl9X0ln5+SskTa2gzMaaWtX8r5KWSnpS0iFV1NhQT6t6PybpBknLJV0kabsq6izU06re90m6VtIySZdKekEVdRbqGbHewnQHSwpJlR5m3Mb6nSnprrx+l0l6TxV1NtTUch1LelPejq+XdHqva2yopdU6/mZh/d4s6f4Kyhy9iBj4G7AmcCvwbGAd4BrgBQ3TfAD4fn78ZuCsPqh5KrAzcCpwSB/Uuxewfn78/irXcZv1blx4fBBwfp3rzdNtBFwCXA5Mr3O9wEzgO1XVOMqanwtcDWyWh7esc70N038I+HHV67mT23hpQe0K3BIRf4iIx4Ezgdc2TPNaYG5+PA94pST1sMZGLWuOiBURsRx4uooCG7RT74KIeCQPXg5M7nGNRe3U+2BhcAOgyiOK2tmGAT4HfAV4tJfFNdFuvXXSTs3vBb4bEfcBRMTfelxjUafr+C3AGT2prEvGS0BtA/yxMHxHHtd0moh4EngAmNiT6pprp+Y66bTedwO/KrWikbVVr6QjJd0KfBX4cI9qa6ZlvZJeDGwbEb/sZWHDaHd7ODh3+c6TtG1vShtWOzXvAOwg6TJJl0var2fVra7tz1zuTn8WcHEP6uqa8RJQViOSDgemA1+rupZWIuK7EbE98Ang2KrrGY6kNYBvAEdXXUsHfgFMjYidgQtZ2YNRZ2uRuvlmkFokP5C0aZUFtenNwLyIeKrqQjoxXgLqT0Dx19nkPK7pNJLWAjYB7ulJdc21U3OdtFWvpH2ATwEHRcRjPaqtmU7X75nA68osqIVW9W4E7AQslLQCeBlwboUHSrRcvxFxT2Eb+CHwLz2qbTjtbBN3AOdGxBMRcRtwMymwqtDJNvxm+qx7Dxg3B0msBfyB1MQd2pn4woZpjmTVgyTOrnvNhWnnUP1BEu2s411IO3Wf2yfbxHMLjw8EFte53obpF1LtQRLtrN+tC49fD1zeB9vEfsDc/HgSqYttYl3rzdM9D1hBPjFDP90qL6CHf8z9Sb92bgU+lcd9lvRLHmA94KfALcCVwLP7oOaXkH7RPUxq7V1f83r/C/grsCzfzq15vd8Crs+1LhgpEOpQb8O0lQZUm+v3S3n9XpPX7/OqrLfNmkXqSr0BuBZ4c53rzcPHAV+uet2O5uZTHZmZWS2Nl31QZmbWZxxQZmZWSw4oMzOrJQeUmZnVkgPKzMxqyQFlfUnSp/LZpJfnMzW/NI//4dBZxyWtkDRJ0lRJ15Vcz1RJby0MT5O0f5nLHKGWLfIZ+a+WtIekN0q6UdICSdMl/e8Wr58/2rMjSHpd1Wd9t8GxVtUFmHVK0m7AAcCLI+IxSZNI/6hIRFR1yYapwFuBocsvTCOdzml+BbW8Erh2aF1IOh94b0Rcmp9fPNKLI2Iswfo64DzS/wmZjYlbUNaPtgbujnyanIi4OyL+DCBp4TCn91lT0g9yq+vXkibk6aflk34ul3SOpM0a55NbYSvy4zUlfU3SVfk1R+T5fxnYI7fmPkH6Z8lD8/ChkjaQ9GNJV+aWTdOzTkv6RL4G1TWSvtyixu0lnS9piaRFkp4naRrpxLavzcv+DPAK4Ee57hmSzsuv31DSKXl5yyUdnMevyKGPpMNzzcsknSxpzTz+75K+kOu8XNJWknYnXZbka3n67Uf35zXLqv5PYd986/QGbEg6u8PNwPeAPQvPLSSfQYF0epdJpNbNk8C0PP5s4PD8ePnQ60mhcmKT+UwCVuTHs4Bj8+N1Sa2RZ5FOHnpeoY6ZFK51BHyxsMxNc+0bNLyvVwO/ZeU1szZvUeNF5NMxAS8FLh5m2cX38s86SZflOLEw3WYN6+35pBO6rp3Hfw94e34cwIH58VcL62QOFZ92y7fBubmLz/pORPxd0r8Ae5AugniWpE9GxJwRXnZbRCzLj5cAUyVtAmwaEb/J4+eSTnc1kn2BnbXyCsabkE4W+ngbrztI0sfz8HrAFODGwjT7AKdEvmZWRNw7XI2SNgR2z4+HXr9uixoa7UM67yR5efc1PP9K0glcr8rLmAAMXf/ocVJXHqT1+aoOl23WkgPK+lKkywYsJJ29+1rgHaRf78Mpnjn9KdKX7UieZGUX+HqF8QI+FBEXFCeWNKPF/AQcHBE3tZiuXWsA90fEtC7NrxmRTox6TJPnnoiIofOkPYW/S6wE3gdlfUfSjpKKlziYBtze6Xwi4gHgPkl75FFvA4ZaKitYefmHQwovuwB4v6S1cy07SNoAeIh0yYshjcMXAB9SbopI2qVJSRcC75S0fp5m8+FqjHS139skvTFPK0kv6mgFpOUdOTQwtG+r4CLgEElbDtWjdOG7kTS+b7NRc0BZP9oQmCvpBknLgReQztg8Gu8g7dRfTgq6z+bxXycF0dWk/TFDfkg6Qm2p0qHrJ5NaD8uBp/JBAx8lnZ37BUMHSZAuxb42sFzS9Xl4FRFxPnAusFjSMmCoO3C4Gg8D3i3pGtJZwTu9pPrngc0kXZfnsVdDPTeQLtL467zsC0kHqIzkTODf84EgPkjCxsRnMzczs1pyC8rMzGrJAWVmZrXkgDIzs1pyQJmZWS05oMzMrJYcUGZmVksOKDMzq6X/BuawgM08iwNoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2,6):\n",
    "    plt.title(f\"The Silhouette co-efficient with {i} clusters\",fontsize = 15)\n",
    "    pm.svisualizer(cls, i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should choose four clusters based on the plots above as there are no outliers, but we'll go with five."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mergings=linkage(cls,method='ward')\n",
    "clustercut = pd.Series(cut_tree(mergings, n_clusters=5).reshape(-1,))\n",
    "clustercut.value_counts().plot(kind='bar',color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cl = cl.copy()\n",
    "cl['cluster'] = clustercut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean of the clusters after segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.304348</td>\n",
       "      <td>20.913043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.095238</td>\n",
       "      <td>80.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.811765</td>\n",
       "      <td>49.129412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.538462</td>\n",
       "      <td>82.128205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89.406250</td>\n",
       "      <td>15.593750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Annual Income (k$)  Spending Score (1-100)\n",
       "cluster                                            \n",
       "0                 26.304348               20.913043\n",
       "1                 25.095238               80.047619\n",
       "2                 55.811765               49.129412\n",
       "3                 86.538462               82.128205\n",
       "4                 89.406250               15.593750"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl.groupby('cluster').agg('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cluster 0:** This cluster's annual income is lower, and their spending score is lower than their annual income.\n",
    "\n",
    "**Clusert 1:** Despite having a low annual income, the spending score is high.\n",
    "\n",
    "**Cluster 2:** With a medium income and a medium level of spending.\n",
    "\n",
    "**Cluster 3:** Class of people with high income and high spending score\n",
    "\n",
    "**Cluster 4:** Even though the conservative class has a high income, they spend very little."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(cl['Annual Income (k$)'], cl['Spending Score (1-100)'], c=cl['cluster'])\n",
    "plt.xlabel('Annual Income')\n",
    "plt.ylabel('Spending')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}