3. unsupervised_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\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\n",
       "..          ...     ...  ...                 ...                     ...\n",
       "195         196  Female   35                 120                      79\n",
       "196         197  Female   45                 126                      28\n",
       "197         198    Male   32                 126                      74\n",
       "198         199    Male   32                 137                      18\n",
       "199         200    Male   30                 137                      83\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/kennedykwangari/Mall-Customer-Segmentation-Data/refs/heads/master/Mall_Customers.csv'\n",
    "df = pd.read_csv(url)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Annual Income (k$)', 'Spending Score (1-100)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ks = range(2,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=4, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4, random_state=42)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4, random_state=42)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.4937945814354117)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = silhouette_score(x, kmeans.labels_)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For k=2, Inertia=183653.329, Silhouette Score=0.396\n",
      "For k=3, Inertia=106348.373, Silhouette Score=0.468\n",
      "For k=4, Inertia=73880.645, Silhouette Score=0.494\n",
      "For k=5, Inertia=44448.455, Silhouette Score=0.554\n",
      "For k=6, Inertia=40825.169, Silhouette Score=0.513\n",
      "For k=7, Inertia=33642.579, Silhouette Score=0.502\n",
      "For k=8, Inertia=26686.838, Silhouette Score=0.496\n",
      "For k=9, Inertia=24766.472, Silhouette Score=0.456\n",
      "For k=10, Inertia=23103.122, Silhouette Score=0.443\n"
     ]
    }
   ],
   "source": [
    "silhouette_scores = []\n",
    "inertia_scores = []\n",
    "for k in ks:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(x)\n",
    "    score = silhouette_score(x, kmeans.labels_)\n",
    "    silhouette_scores.append(score)\n",
    "    inertia = kmeans.inertia_\n",
    "    inertia_scores.append(inertia)\n",
    "    print(f'For k={k}, Inertia={inertia:.3f}, Silhouette Score={score:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(ks, inertia_scores, 'bo-', markersize=8)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Inertia vs Number of clusters')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(ks, silhouette_scores, 'bo-', markersize=8)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Silhouette Score')\n",
    "plt.title('Silhouette Score vs Number of clusters')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=5, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=5, random_state=42)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_k = 5\n",
    "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2,\n",
       "       4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 0,\n",
       "       4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 0, 1, 3, 1, 3, 1,\n",
       "       0, 1, 3, 1, 3, 1, 3, 1, 3, 1, 0, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
       "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
       "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
       "       3, 1], dtype=int32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_labels = kmeans.predict(x)\n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d49a1bfb60>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x['Annual Income (k$)'], x['Spending Score (1-100)'], c=cluster_labels, cmap='viridis', s=50)\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red', label='Centroids')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.title('Clusters of customers')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}