{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "64b1abbc",
   "metadata": {},
   "source": [
    "# K-Means Workflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb239b02",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:55.959119Z",
     "iopub.status.busy": "2026-08-31T06:02:55.958876Z",
     "iopub.status.idle": "2026-08-31T06:02:56.786396Z",
     "shell.execute_reply": "2026-08-31T06:02:56.786132Z"
    },
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
    ]
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee64b505",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5619400e",
   "metadata": {},
   "source": [
    "## K-means in a nutshell\n",
    "\n",
    "K-means partitions the data into \"k\" groups by minimizing the **within-cluster sum of squares**. It alternates between two steps until convergence:\n",
    "\n",
    "1. **Assign** each point to the nearest cluster center.\n",
    "2. **Update** each center to be the mean of its assigned points.\n",
    "\n",
    "This process is fast and simple, but it assumes clusters are roughly spherical and similar in size.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f4980f0",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:56.787800Z",
     "iopub.status.busy": "2026-08-31T06:02:56.787676Z",
     "iopub.status.idle": "2026-08-31T06:02:56.925862Z",
     "shell.execute_reply": "2026-08-31T06:02:56.925620Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d4e0ddb",
   "metadata": {},
   "source": [
    "## Create Some Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0435c89",
   "metadata": {},
   "source": [
    "`make_blobs` generates synthetic Gaussian clusters for quick experiments. It returns the feature matrix and the cluster labels used to create the points. Because we generate the data ourselves, we know which group each point came from, so we can check whether k-means recovers those groups. Real data come without these labels; the next section clusters a customer data set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "901541bf",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:56.927278Z",
     "iopub.status.busy": "2026-08-31T06:02:56.927162Z",
     "iopub.status.idle": "2026-08-31T06:02:56.929219Z",
     "shell.execute_reply": "2026-08-31T06:02:56.929025Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create Data\n",
    "data = make_blobs(n_samples=200, n_features=2, \n",
    "                           centers=4, cluster_std=1.8,random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0919594c",
   "metadata": {},
   "source": [
    "## Visualize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aafca2c3",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:56.930251Z",
     "iopub.status.busy": "2026-08-31T06:02:56.930180Z",
     "iopub.status.idle": "2026-08-31T06:02:56.965895Z",
     "shell.execute_reply": "2026-08-31T06:02:56.965680Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11629c4d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[0][:,0],data[0][:,1],c=data[1],cmap='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d43242a",
   "metadata": {},
   "source": [
    "## Creating the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "982a776a",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:56.966960Z",
     "iopub.status.busy": "2026-08-31T06:02:56.966894Z",
     "iopub.status.idle": "2026-08-31T06:02:57.359400Z",
     "shell.execute_reply": "2026-08-31T06:02:57.359158Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef64c8c6",
   "metadata": {},
   "source": [
    "K-means starts from randomly placed centers, so different runs can end in different clusters. `n_init=10` runs the algorithm 10 times from different starting centers and keeps the best result, and `random_state` fixes the random starts so that the result is the same every time the page is built."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7bcda63",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:57.360611Z",
     "iopub.status.busy": "2026-08-31T06:02:57.360540Z",
     "iopub.status.idle": "2026-08-31T06:02:57.361844Z",
     "shell.execute_reply": "2026-08-31T06:02:57.361672Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4, n_init=10, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4dac765",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:57.362715Z",
     "iopub.status.busy": "2026-08-31T06:02:57.362663Z",
     "iopub.status.idle": "2026-08-31T06:02:57.394193Z",
     "shell.execute_reply": "2026-08-31T06:02:57.393930Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\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",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global 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-global 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-global 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-global 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-global 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-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global 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-global 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-global 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: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global 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-global 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-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global 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-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global 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-global 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-global 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-global 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-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global 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-global 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-global 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-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global 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-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global 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-unfitted-level-0);\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-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\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",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\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",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\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",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\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-global 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-unfitted-level-0);\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-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\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-global 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-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "    border: none;\n",
       "    padding: 0;\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".copy-paste-icon:focus-visible {\n",
       "    outline: 1px solid currentColor;\n",
       "    outline-offset: 1px;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
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       ".features summary {\n",
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       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
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       "\n",
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       "\n",
       ".features details > summary .arrow::before {\n",
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       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
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       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
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       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
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       "  align-items: center;\n",
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       "\n",
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       "\n",
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       "\n",
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       ".features .features-container table tr:nth-child(even) {\n",
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       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4, n_init=10, random_state=101)</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 sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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/1.9/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\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy n_clusters to clipboard\"\n",
       "                 onclick=\"copyToClipboard('n_clusters',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_clusters;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_clusters,-int%2C%20default%3D8\">\n",
       "            n_clusters\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_clusters;\">\n",
       "            n_clusters: int, default=8<br><br>The number of clusters to form as well as the number of<br>centroids to generate.<br><br>For an example of how to choose an optimal value for `n_clusters` refer to<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">4</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy n_init to clipboard\"\n",
       "                 onclick=\"copyToClipboard('n_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_init;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_init,-%27auto%27%20or%20int%2C%20default%3D%27auto%27\">\n",
       "            n_init\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_init;\">\n",
       "            n_init: &#x27;auto&#x27; or int, default=&#x27;auto&#x27;<br><br>Number of times the k-means algorithm is run with different centroid<br>seeds. The final results is the best output of `n_init` consecutive runs<br>in terms of inertia. Several runs are recommended for sparse<br>high-dimensional problems (see :ref:`kmeans_sparse_high_dim`).<br><br>When `n_init=&#x27;auto&#x27;`, the number of runs depends on the value of init:<br>10 if using `init=&#x27;random&#x27;` or `init` is a callable;<br>1 if using `init=&#x27;k-means++&#x27;` or `init` is an array-like.<br><br>.. versionadded:: 1.2<br>   Added &#x27;auto&#x27; option for `n_init`.<br><br>.. versionchanged:: 1.4<br>   Default value for `n_init` changed to `&#x27;auto&#x27;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy random_state to clipboard\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Determines random number generation for centroid initialization. Use<br>an int to make the randomness deterministic.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">101</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy init to clipboard\"\n",
       "                 onclick=\"copyToClipboard('init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-init;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=init,-%7B%27k-means%2B%2B%27%2C%20%27random%27%7D%2C%20callable%20or%20array-like%20of%20shape%20%20%20%20%20%20%20%20%20%20%20%20%20%28n_clusters%2C%20n_features%29%2C%20default%3D%27k-means%2B%2B%27\">\n",
       "            init\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-init;\">\n",
       "            init: {&#x27;k-means++&#x27;, &#x27;random&#x27;}, callable or array-like of shape             (n_clusters, n_features), default=&#x27;k-means++&#x27;<br><br>Method for initialization:<br><br>* &#x27;k-means++&#x27; : selects initial cluster centroids using sampling             based on an empirical probability distribution of the points&#x27;             contribution to the overall inertia. This technique speeds up             convergence. The algorithm implemented is &quot;greedy k-means++&quot;. It             differs from the vanilla k-means++ by making several trials at             each sampling step and choosing the best centroid among them.<br><br>* &#x27;random&#x27;: choose `n_clusters` observations (rows) at random from         data for the initial centroids.<br><br>* If an array is passed, it should be of shape (n_clusters, n_features)        and gives the initial centers.<br><br>* If a callable is passed, it should take arguments X, n_clusters and a        random state and return an initialization.<br><br>For an example of how to use the different `init` strategies, see<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.<br><br>For an evaluation of the impact of initialization, see the example<br>:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_stability_low_dim_dense.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;k-means++&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy max_iter to clipboard\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=max_iter,-int%2C%20default%3D300\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=300<br><br>Maximum number of iterations of the k-means algorithm for a<br>single run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy tol to clipboard\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Relative tolerance with regards to Frobenius norm of the difference<br>in the cluster centers of two consecutive iterations to declare<br>convergence.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy verbose to clipboard\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Verbosity mode.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy copy_x to clipboard\"\n",
       "                 onclick=\"copyToClipboard('copy_x',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy_x;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=copy_x,-bool%2C%20default%3DTrue\">\n",
       "            copy_x\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy_x;\">\n",
       "            copy_x: bool, default=True<br><br>When pre-computing distances it is more numerically accurate to center<br>the data first. If copy_x is True (default), then the original data is<br>not modified. If False, the original data is modified, and put back<br>before the function returns, but small numerical differences may be<br>introduced by subtracting and then adding the data mean. Note that if<br>the original data is not C-contiguous, a copy will be made even if<br>copy_x is False. If the original data is sparse, but not in CSR format,<br>a copy will be made even if copy_x is False.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><button type=\"button\" class=\"copy-paste-icon\"\n",
       "                 aria-label=\"Copy algorithm to clipboard\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></button></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-algorithm;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=algorithm,-%7B%22lloyd%22%2C%20%22elkan%22%7D%2C%20default%3D%22lloyd%22\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-algorithm;\">\n",
       "            algorithm: {&quot;lloyd&quot;, &quot;elkan&quot;}, default=&quot;lloyd&quot;<br><br>K-means algorithm to use. The classical EM-style algorithm is `&quot;lloyd&quot;`.<br>The `&quot;elkan&quot;` variation can be more efficient on some datasets with<br>well-defined clusters, by using the triangle inequality. However it&#x27;s<br>more memory intensive due to the allocation of an extra array of shape<br>`(n_samples, n_clusters)`.<br><br>.. versionchanged:: 0.18<br>    Added Elkan algorithm<br><br>.. versionchanged:: 1.1<br>    Renamed &quot;full&quot; to &quot;lloyd&quot;, and deprecated &quot;auto&quot; and &quot;full&quot;.<br>    Changed &quot;auto&quot; to use &quot;lloyd&quot; instead of &quot;elkan&quot;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lloyd&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-cluster_centers_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=cluster_centers_,-ndarray%20of%20shape%20%28n_clusters%2C%20n_features%29\">\n",
       "            cluster_centers_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-cluster_centers_;\">\n",
       "            cluster_centers_: ndarray of shape (n_clusters, n_features)<br><br>Coordinates of cluster centers. If the algorithm stops before fully<br>converging (see ``tol`` and ``max_iter``), these will not be<br>consistent with ``labels_``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](4, 2)</td>\n",
       "           <td>[[-9.47,-6.56],\n",
       " [-0.01, 2.13],\n",
       " [ 3.72, 7.01],\n",
       " [-4.14, 7.95]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-inertia_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=inertia_,-float\">\n",
       "            inertia_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-inertia_;\">\n",
       "            inertia_: float<br><br>Sum of squared distances of samples to their closest cluster center,<br>weighted by the sample weights if provided.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float</td>\n",
       "           <td>1318</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-labels_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=labels_,-ndarray%20of%20shape%20%28n_samples%2C%29\">\n",
       "            labels_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-labels_;\">\n",
       "            labels_: ndarray of shape (n_samples,)<br><br>Labels of each point</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](200,)</td>\n",
       "           <td>[3,2,1,...,2,0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.cluster.KMeans.html#:~:text=n_iter_,-int\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: int<br><br>Number of iterations run.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>5</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>4 features</div>\n",
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       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
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       "          <td>kmeans0</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>kmeans1</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>kmeans2</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>kmeans3</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
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       "            </div>\n",
       "          </details>\n",
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      ],
      "text/plain": [
       "KMeans(n_clusters=4, n_init=10, random_state=101)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(data[0])"
   ]
  },
  {
   "cell_type": "code",
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     "shell.execute_reply": "2026-08-31T06:02:57.397221Z"
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    {
     "data": {
      "text/plain": [
       "array([[-9.46941837, -6.56081545],\n",
       "       [-0.0123077 ,  2.13407664],\n",
       "       [ 3.71749226,  7.01388735],\n",
       "       [-4.13591321,  7.95389851]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
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   "id": "ddc25a87",
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     "shell.execute_reply": "2026-08-31T06:02:57.400204Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1, 2, 2, 0, 2, 1, 2, 1, 3, 1, 2, 2, 3, 1, 2, 1, 0, 3, 0, 1,\n",
       "       1, 0, 3, 0, 0, 1, 2, 2, 3, 0, 2, 1, 1, 3, 0, 0, 0, 1, 0, 3, 3, 3,\n",
       "       1, 2, 3, 1, 0, 1, 1, 3, 2, 1, 0, 3, 1, 1, 3, 2, 0, 2, 0, 3, 2, 1,\n",
       "       0, 2, 2, 0, 2, 1, 0, 1, 0, 2, 2, 1, 3, 1, 1, 0, 2, 0, 1, 1, 1, 3,\n",
       "       1, 0, 0, 0, 0, 1, 1, 0, 2, 3, 0, 2, 1, 0, 1, 1, 2, 1, 0, 2, 0, 0,\n",
       "       2, 3, 3, 2, 0, 2, 3, 3, 2, 3, 1, 3, 1, 3, 1, 2, 3, 1, 0, 3, 3, 3,\n",
       "       1, 0, 0, 3, 2, 3, 2, 1, 0, 2, 0, 3, 3, 2, 1, 0, 3, 3, 3, 3, 1, 2,\n",
       "       1, 3, 2, 2, 2, 1, 2, 1, 1, 3, 0, 3, 1, 2, 3, 1, 2, 1, 3, 2, 1, 3,\n",
       "       2, 2, 0, 2, 3, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 2, 2, 3, 0, 1, 2, 2,\n",
       "       0, 1], dtype=int32)"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
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  },
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     "shell.execute_reply": "2026-08-31T06:02:57.461256Z"
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    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x118af9490>"
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     "execution_count": 10,
     "metadata": {},
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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True,figsize=(10,6))\n",
    "ax1.set_title('K Means')\n",
    "ax1.scatter(data[0][:,0],data[0][:,1],c=kmeans.labels_,cmap='rainbow')\n",
    "ax2.set_title(\"Original\")\n",
    "ax2.scatter(data[0][:,0],data[0][:,1],c=data[1],cmap='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4409feb5",
   "metadata": {},
   "source": [
    "The two plots mostly agree, except where the original groups overlap. The colors need not match: k-means numbers its clusters in whatever order it finds them, so a group that is cluster 2 in the original labels may be cluster 0 in the k-means result. Cluster numbers are names, not rankings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-751ec4eb",
   "metadata": {},
   "source": [
    "\n",
    "## Section Practice\n",
    "\n",
    "Use this short exercise to check the main workflow from this section.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cell-4271d9b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:57.462975Z",
     "iopub.status.busy": "2026-08-31T06:02:57.462841Z",
     "iopub.status.idle": "2026-08-31T06:02:57.464317Z",
     "shell.execute_reply": "2026-08-31T06:02:57.464090Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Practice K-Means Workflow\n",
    "# Run one round of k-means by hand on six customers' monthly spend (one feature).\n",
    "#   1. Assign each value to the nearer of the two centers (0 or 1).\n",
    "#   2. Update each center to the mean of the values assigned to it.\n",
    "# Print exactly:\n",
    "# Assignments: [0, 0, 0, 1, 1, 1]\n",
    "# New centers: [22.0, 71.0]\n",
    "spend = [12, 20, 34, 58, 70, 85]\n",
    "centers = [20, 60]\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cell-d2d05a02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:57.465395Z",
     "iopub.status.busy": "2026-08-31T06:02:57.465338Z",
     "iopub.status.idle": "2026-08-31T06:02:57.467140Z",
     "shell.execute_reply": "2026-08-31T06:02:57.466927Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assignments: [0, 0, 0, 1, 1, 1]\n",
      "New centers: [22.0, 71.0]\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "spend = [12, 20, 34, 58, 70, 85]\n",
    "centers = [20, 60]\n",
    "\n",
    "assignments = []\n",
    "for value in spend:\n",
    "    if abs(value - centers[0]) <= abs(value - centers[1]):\n",
    "        assignments.append(0)\n",
    "    else:\n",
    "        assignments.append(1)\n",
    "\n",
    "new_centers = []\n",
    "for cluster in [0, 1]:\n",
    "    members = [v for v, a in zip(spend, assignments) if a == cluster]\n",
    "    new_centers.append(sum(members) / len(members))\n",
    "\n",
    "print('Assignments:', assignments)\n",
    "print('New centers:', new_centers)\n"
   ]
  }
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