7.1_unsupervised_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5e1ea98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02b17278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris =load_iris()\n",
    "x=iris.data\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c932e481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 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-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 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: 1ex;\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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans=KMeans(n_clusters=3)\n",
    "kmeans.fit(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de344faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=kmeans.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6da09a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1,\n",
       "       1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2], dtype=int32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4a76bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center=kmeans.cluster_centers_\n",
    "center.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "74a5d76f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7c67ce3652b0>"
      ]
     },
     "execution_count": 19,
     "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(x[:,2],x[:,3],c=y_pred)\n",
    "plt.scatter(center[:,2],center[:,3],c='red',marker='x',s=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5abf912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.85566582597731"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e665662",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "93dafaae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=[]\n",
    "for k in np.arange(1,11):\n",
    "    kmeans2=KMeans(n_clusters=k)\n",
    "    kmeans2.fit(x)\n",
    "    y.append(kmeans2.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e8754008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[681.3706,\n",
       " 152.3479517603579,\n",
       " 78.851441426146,\n",
       " 57.38387326549494,\n",
       " 52.609242424242424,\n",
       " 39.054977867477874,\n",
       " 42.79241761769999,\n",
       " 36.474624875124874,\n",
       " 29.167879708011288,\n",
       " 27.564360139860142]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c118ddd5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7c67ce157b90>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ce157350>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ce2f7bc0>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ceeb9cd0>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ceec15e0>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ce374500>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ceec2a80>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ce1fdeb0>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ce366720>,\n",
       "  <matplotlib.axis.XTick at 0x7c67ce221160>],\n",
       " [Text(1, 0, '1'),\n",
       "  Text(2, 0, '2'),\n",
       "  Text(3, 0, '3'),\n",
       "  Text(4, 0, '4'),\n",
       "  Text(5, 0, '5'),\n",
       "  Text(6, 0, '6'),\n",
       "  Text(7, 0, '7'),\n",
       "  Text(8, 0, '8'),\n",
       "  Text(9, 0, '9'),\n",
       "  Text(10, 0, '10')])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(np.arange(1,11),y,'o-',)\n",
    "plt.grid()\n",
    "plt.xlabel('number of clusters')\n",
    "plt.ylabel('inertia')\n",
    "plt.xticks(np.arange(1,11))\n",
    "#plt.yticks(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "803f8ed3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e157f19b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Machine_learning_Puyan_Rajiyan",
   "language": "python",
   "name": "machine_learning_puyan_rajiyan"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}