14[kmeans].ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5bc35573-2da3-400a-b10c-d025d8e278b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3895ffd-f247-4ed0-b051-0406aa6293db",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gautam</td>\n",
       "      <td>39</td>\n",
       "      <td>155000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>David</td>\n",
       "      <td>41</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>38</td>\n",
       "      <td>162000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad</td>\n",
       "      <td>36</td>\n",
       "      <td>156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Angelina</td>\n",
       "      <td>35</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald</td>\n",
       "      <td>37</td>\n",
       "      <td>137000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tom</td>\n",
       "      <td>26</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jared</td>\n",
       "      <td>28</td>\n",
       "      <td>51000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stark</td>\n",
       "      <td>29</td>\n",
       "      <td>49500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ranbir</td>\n",
       "      <td>32</td>\n",
       "      <td>53000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dipika</td>\n",
       "      <td>40</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Priyanka</td>\n",
       "      <td>41</td>\n",
       "      <td>63000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nick</td>\n",
       "      <td>43</td>\n",
       "      <td>64000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alia</td>\n",
       "      <td>39</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sid</td>\n",
       "      <td>41</td>\n",
       "      <td>82000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Abdul</td>\n",
       "      <td>39</td>\n",
       "      <td>58000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age  Income($)\n",
       "0        Rob   27      70000\n",
       "1    Michael   29      90000\n",
       "2      Mohan   29      61000\n",
       "3     Ismail   28      60000\n",
       "4       Kory   42     150000\n",
       "5     Gautam   39     155000\n",
       "6      David   41     160000\n",
       "7     Andrea   38     162000\n",
       "8       Brad   36     156000\n",
       "9   Angelina   35     130000\n",
       "10    Donald   37     137000\n",
       "11       Tom   26      45000\n",
       "12    Arnold   27      48000\n",
       "13     Jared   28      51000\n",
       "14     Stark   29      49500\n",
       "15    Ranbir   32      53000\n",
       "16    Dipika   40      65000\n",
       "17  Priyanka   41      63000\n",
       "18      Nick   43      64000\n",
       "19      Alia   39      80000\n",
       "20       Sid   41      82000\n",
       "21     Abdul   39      58000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"csvs\\\\income.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c96cb599-c09c-410f-85dd-3c814f048654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x281a7ca2990>"
      ]
     },
     "execution_count": 5,
     "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(df['Age'],df['Income($)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52eb1134-2579-4aed-9f2a-067fe2aa60ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters = 3)\n",
    "km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d6745a1-27d2-4a43-afde-bd606347437a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    }
   ],
   "source": [
    "y_predicted = km.fit_predict(df[['Age','Income($)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02a67009-e79d-45fd-893b-6682369ad4d5",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>90000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>61000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>60000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gautam</td>\n",
       "      <td>39</td>\n",
       "      <td>155000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>David</td>\n",
       "      <td>41</td>\n",
       "      <td>160000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>38</td>\n",
       "      <td>162000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad</td>\n",
       "      <td>36</td>\n",
       "      <td>156000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Angelina</td>\n",
       "      <td>35</td>\n",
       "      <td>130000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald</td>\n",
       "      <td>37</td>\n",
       "      <td>137000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tom</td>\n",
       "      <td>26</td>\n",
       "      <td>45000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>27</td>\n",
       "      <td>48000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jared</td>\n",
       "      <td>28</td>\n",
       "      <td>51000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stark</td>\n",
       "      <td>29</td>\n",
       "      <td>49500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ranbir</td>\n",
       "      <td>32</td>\n",
       "      <td>53000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dipika</td>\n",
       "      <td>40</td>\n",
       "      <td>65000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Priyanka</td>\n",
       "      <td>41</td>\n",
       "      <td>63000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nick</td>\n",
       "      <td>43</td>\n",
       "      <td>64000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alia</td>\n",
       "      <td>39</td>\n",
       "      <td>80000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sid</td>\n",
       "      <td>41</td>\n",
       "      <td>82000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Abdul</td>\n",
       "      <td>39</td>\n",
       "      <td>58000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age  Income($)  cluster\n",
       "0        Rob   27      70000        0\n",
       "1    Michael   29      90000        0\n",
       "2      Mohan   29      61000        2\n",
       "3     Ismail   28      60000        2\n",
       "4       Kory   42     150000        1\n",
       "5     Gautam   39     155000        1\n",
       "6      David   41     160000        1\n",
       "7     Andrea   38     162000        1\n",
       "8       Brad   36     156000        1\n",
       "9   Angelina   35     130000        1\n",
       "10    Donald   37     137000        1\n",
       "11       Tom   26      45000        2\n",
       "12    Arnold   27      48000        2\n",
       "13     Jared   28      51000        2\n",
       "14     Stark   29      49500        2\n",
       "15    Ranbir   32      53000        2\n",
       "16    Dipika   40      65000        2\n",
       "17  Priyanka   41      63000        2\n",
       "18      Nick   43      64000        2\n",
       "19      Alia   39      80000        0\n",
       "20       Sid   41      82000        0\n",
       "21     Abdul   39      58000        2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster'] = y_predicted\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "088cc3fc-7185-46fb-8c86-58ece758d3ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x281aa5c2150>"
      ]
     },
     "execution_count": 14,
     "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": [
    "df1 = df[df.cluster==0]\n",
    "df2 = df[df.cluster==1]\n",
    "df3 = df[df.cluster==2]\n",
    "plt.scatter(df1.Age,df1['Income($)'],color='green')\n",
    "plt.scatter(df2.Age,df2['Income($)'],color='red')\n",
    "plt.scatter(df3.Age,df3['Income($)'],color='black')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Income ($)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b3d76397-56f1-410b-957b-e4aa61624f0f",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>0.213675</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>0.136752</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>0.128205</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>0.897436</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gautam</td>\n",
       "      <td>39</td>\n",
       "      <td>0.940171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>David</td>\n",
       "      <td>41</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>38</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad</td>\n",
       "      <td>36</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Angelina</td>\n",
       "      <td>35</td>\n",
       "      <td>0.726496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald</td>\n",
       "      <td>37</td>\n",
       "      <td>0.786325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tom</td>\n",
       "      <td>26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>27</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jared</td>\n",
       "      <td>28</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stark</td>\n",
       "      <td>29</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ranbir</td>\n",
       "      <td>32</td>\n",
       "      <td>0.068376</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dipika</td>\n",
       "      <td>40</td>\n",
       "      <td>0.170940</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Priyanka</td>\n",
       "      <td>41</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nick</td>\n",
       "      <td>43</td>\n",
       "      <td>0.162393</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alia</td>\n",
       "      <td>39</td>\n",
       "      <td>0.299145</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sid</td>\n",
       "      <td>41</td>\n",
       "      <td>0.316239</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Abdul</td>\n",
       "      <td>39</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name  Age  Income($)  cluster\n",
       "0        Rob   27   0.213675        0\n",
       "1    Michael   29   0.384615        0\n",
       "2      Mohan   29   0.136752        2\n",
       "3     Ismail   28   0.128205        2\n",
       "4       Kory   42   0.897436        1\n",
       "5     Gautam   39   0.940171        1\n",
       "6      David   41   0.982906        1\n",
       "7     Andrea   38   1.000000        1\n",
       "8       Brad   36   0.948718        1\n",
       "9   Angelina   35   0.726496        1\n",
       "10    Donald   37   0.786325        1\n",
       "11       Tom   26   0.000000        2\n",
       "12    Arnold   27   0.025641        2\n",
       "13     Jared   28   0.051282        2\n",
       "14     Stark   29   0.038462        2\n",
       "15    Ranbir   32   0.068376        2\n",
       "16    Dipika   40   0.170940        2\n",
       "17  Priyanka   41   0.153846        2\n",
       "18      Nick   43   0.162393        2\n",
       "19      Alia   39   0.299145        0\n",
       "20       Sid   41   0.316239        0\n",
       "21     Abdul   39   0.111111        2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "##between 0-1 income\n",
    "scaler.fit(df[['Income($)']])\n",
    "df['Income($)'] = scaler.transform(df[['Income($)']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "688d417e-c75f-4b98-9222-0c278a2f1ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.213675</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.136752</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.128205</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.897436</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gautam</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.940171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>David</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.982906</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Andrea</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brad</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Angelina</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.726496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.786325</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tom</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arnold</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jared</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.051282</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Stark</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ranbir</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.068376</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dipika</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.170940</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Priyanka</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nick</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162393</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alia</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.299145</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sid</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.316239</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Abdul</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Name       Age  Income($)  cluster\n",
       "0        Rob  0.058824   0.213675        0\n",
       "1    Michael  0.176471   0.384615        0\n",
       "2      Mohan  0.176471   0.136752        2\n",
       "3     Ismail  0.117647   0.128205        2\n",
       "4       Kory  0.941176   0.897436        1\n",
       "5     Gautam  0.764706   0.940171        1\n",
       "6      David  0.882353   0.982906        1\n",
       "7     Andrea  0.705882   1.000000        1\n",
       "8       Brad  0.588235   0.948718        1\n",
       "9   Angelina  0.529412   0.726496        1\n",
       "10    Donald  0.647059   0.786325        1\n",
       "11       Tom  0.000000   0.000000        2\n",
       "12    Arnold  0.058824   0.025641        2\n",
       "13     Jared  0.117647   0.051282        2\n",
       "14     Stark  0.176471   0.038462        2\n",
       "15    Ranbir  0.352941   0.068376        2\n",
       "16    Dipika  0.823529   0.170940        2\n",
       "17  Priyanka  0.882353   0.153846        2\n",
       "18      Nick  1.000000   0.162393        2\n",
       "19      Alia  0.764706   0.299145        0\n",
       "20       Sid  0.882353   0.316239        0\n",
       "21     Abdul  0.764706   0.111111        2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df[['Age']])\n",
    "df['Age'] = scaler.transform(df[['Age']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95a5ad97-d002-4053-9c93-488adb8a7e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#with scaling\n",
    "km = KMeans(n_clusters=3)\n",
    "y_predicted = km.fit_predict(df[['Age','Income($)']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3490bf49-c8d8-4992-9708-a525a960fcad",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.213675</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.136752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.128205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.897436</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Age  Income($)  cluster\n",
       "0      Rob  0.058824   0.213675        0\n",
       "1  Michael  0.176471   0.384615        0\n",
       "2    Mohan  0.176471   0.136752        0\n",
       "3   Ismail  0.117647   0.128205        0\n",
       "4     Kory  0.941176   0.897436        1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster']=y_predicted\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43872726-0116-4a26-b23c-84b8c6531772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.85294118, 0.2022792 ],\n",
       "       [0.1372549 , 0.11633428],\n",
       "       [0.72268908, 0.8974359 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0efba11e-264c-4b9e-9d1e-5f3ce5c2d3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x281aa638390>"
      ]
     },
     "execution_count": 22,
     "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": [
    "df1 = df[df.cluster==0]\n",
    "df2 = df[df.cluster==1]\n",
    "df3 = df[df.cluster==2]\n",
    "plt.scatter(df1.Age,df1['Income($)'],color='green')\n",
    "plt.scatter(df2.Age,df2['Income($)'],color='red')\n",
    "plt.scatter(df3.Age,df3['Income($)'],color='black')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8f0b10-a60d-4ebc-8aaf-11a313a23e84",
   "metadata": {},
   "source": [
    "# Elbow Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "49edaba0-ba73-4dbf-9209-67457f404d35",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "C:\\Users\\jlhb83\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    }
   ],
   "source": [
    "sse = []\n",
    "k_rng = range(1,10)\n",
    "for k in k_rng:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df[['Age','Income($)']])\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "95aaa2e3-d56f-4967-9955-aa5bc61c774f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x281aa66ce10>]"
      ]
     },
     "execution_count": 24,
     "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.xlabel('K')\n",
    "plt.ylabel('Sum of squared error')\n",
    "plt.plot(k_rng,sse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c5fe1d0-a90a-4057-9a0a-2c0a5edfdf02",
   "metadata": {},
   "source": [
    "# elbow at k=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa4fb2c-3bc9-4226-8252-f4db2c28b37c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}