4. Species Segmentation with Cluster Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Species Segmentation with Cluster Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iris flower dataset : https://en.wikipedia.org/wiki/Iris_flower_data_set\n",
    "\n",
    "\n",
    "There are 4 features: sepal length, sepal width, petal length, and petal width.\n",
    "\n",
    "Start by creating 2 clusters. Then standardize the data and try again. Does it make a difference?\n",
    "\n",
    "Use the Elbow rule to determine how many clusters are there.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "0           5.1          3.5           1.4          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('iris_dataset.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this exercise, try to cluster the iris flowers by the shape of their sepal. \n",
    "\n",
    "<i> Use the 'sepal_length' and 'sepal_width' variables.</i> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'sepal width')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cluster the iris flowers by the shape of their sepal\n",
    "plt.scatter(data['sepal_length'],data['sepal_width'])\n",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('sepal width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering (unscaled data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.copy()\n",
    "kmeans = KMeans(2)#create a k-means object with 2 clusters\n",
    "kmeans.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = data.copy()\n",
    "cluster['cluster'] = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc74856ddf0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tT0huPYYiFvW1mADPw/dXOPXdiICfrAcnQDhcbzqy7rq9wqlDeSGNQICs8XfUmwaFoq754i5XhVO3EEQDgoXjk8yspYhDOfYE2L/+KsapV2CaaBv+qB8RhoFeRRUk26qV9aNBoahjTANKfk080b99lXJPtUVZLgHGgQcl3C6iUSsBVn2gaci8/IS7zBZ1UKxaoWgAhAbOvMT73C0aPK4jY1GOPQH+MX+tyAu+G+lyETz3fGRuFXdhqhEC//U3Ybo9MZul24N/9C31o0GhqGOEgB7Xhyvyk+/G5pb0Hl1/w56NDeXYExDp159dD/0TMz8f6XYjnU6C5w2n7NF/1auOwM23Ehg5Cun2QFYWZlYWvptvIXj1yHrVoVDUJUfdHKb7SMu52zwSe5ak981hjrw6QaoLRVKoOPZ9xbNGo2hbNmPmF1Sdo7w+CAbxmn6K9Cxwpv+EUqbHCKcbmaIT9k9rNAiBIoGnpUy6IlFtyRSbqjj2usBmw2x3YIM6dW31z+RediGcdBK5V1+CVnlC1TTx/PMhCo7pTsGJR+N8aWp8G5s24nn4fnJGXonruWcRZXVwQ4fDOKe/Rs61V8Gtt6KvXZP6cygaNTYX5BxY9069KZBUj/3JJ59k3rx5CCEYPnw4V111Vcz+iRMnMmPGDHJzreo9I0aM4JJLLklaRNr22BsY+wdzybt0BGCFOkoAIdj51rtETzwJTJOCo7uhb/hjz34gckJfSma9D4DtmyXkXTDUytceDiPdHsyCAnbM/wzpTdEkrN9P/pCBVrZJv8+q5GS3UzppCuHB56TmHHVAOr/3e5MpOiFztGa6zup67NUuUFqyZAmLFy/m7bffJhqNctZZZ3HKKafQseOePCErVqzg8ccfp1evXrWUr0hE7nXXAFTkWxeAlJLckVeyfeU63FP+U+HU9z7OvugLbEsWEz3mOHJuGoXm81W0KQJ+tEiErEfuT9mcgft/U7GtWY3YnUI4GkVEo+SMvp7i/oPA4dh3AwqFIqVUOxRz7LHH8tJLL2Gz2SguLsYwDDye2EiNFStWMHnyZIYMGcKECRMIVcrdragF4TBiVymVI3wFoG0rAsD5+itV/rv7+SmIbdvQ/4jPyy6iERxzUpciwTlzxh6nHoPEtvz7lJ1HoVAkR1IpBex2O0899RTPP/88gwYN4oADDqjY5/P56NKlC2PHjqV9+/aMGzeOSZMmMWbMmKRF7OuRoj7wenMa9PwJSbRAqhxBueb8xKGXAnC1bI6rbfMq29CzslJ33QWJdWimSUG7lpCO9i0nLd/7BGSKTsgcrY1ZZ42iYgKBAKNGjeKss87iwgsvTHjMypUrufPOO5k1a1bSItQYe2IKjumOvv63mF67BKJHdmPngoXYP/6QvAuHxfXqJbD9+1WYbdqSd8FQ7As/R0SjFftNtxv/2DsI3PiXlOh0vPs2OTf+Gc3v36NBCMwOB7N98XdJpxCub9L5vd+bTNEJmaM103Xud1TMunXrWLVqFQBut5sBAwbw888/V+zftGkTb775ZsVrKSU2m8otlgpKZs9BZmVVTIpKQObmsXP6bAAip51B8OJLre17/fjuuRezTVsASv89BaNjJ8ysbMysbKTLTeT0AQRG3ZgyneHBQwhefhXS6cTMzoacHMwDWlEybXraOnWFojFTrQfesGEDTz31FK+++ioAH330Eeeff37FfpfLxaOPPspxxx1Hu3btmDZtGv379687xU0Is01bitdtxPnma+Su+J7SXscRPu/8mGPKnpyE/5bbcE95Bul04r/+Jmi+J+2BbNmSHZ8vwfbVYvQ/1hPt2Ruj86GpFSoEvgkPEvjz9di/+pLcTgexvdsxoOupPY9CoUiKah37KaecwvLlyzn33HPRdZ0BAwYwePBgRo4cyejRo+nWrRsTJkzguuuuIxKJ0Lt377hwyExF//knbN8txWzVmshJp9TKUTmnv4b9i8+JdutG8Oo/g1aLpQO+AIRCiKDfGnuv1IbMyibasxfY7Qink7hBLSkRkTBEo7XPThkM4vn3v9A2bCB0zrlETjsj7hCz3YGE2h1ojamn8WNuqBR+/8jGlmzIPxpcBTVvI7BN8McnOpodDjo9iqPSU7E0YeNCnV0bBN7uJi26Vj1nolCkGrXyNNEYlmGQc+3VOD+Yi9Q00ASyoBk7Z8+xFiwlw47tND+2J6Jk555tDgfbP/kS85DOSTWh/bGeghOPRQQDFXHqMjub4q+WQXkMuuvF58m+exzSpoPQEKZByfP/I9LPemrSCreQN/RMtMJCkCZCSsInnUrp1JfBbk9Kh/2TBeRdNCxmQtc4uCM7Fn4DCYbd0nn8ct27Nj66wYXQQQiBEZGc+niQw4ZHq//ncn580c4XdzvRbICwnPig5wMc1M/KHe4rFMwa6sZfqCHLx8fanhRl0NQgenImjyGd7VmZTNGa6TrVytNa4Jr6X5zz5yGCATS/D62sDG3TRnJHXpl0G3kXD0eU7LRyqJf/EA6Tf97g5Ns47+wKp87udsrKyB8+BAB9zWqy7xmHCAXRfD60sl0Iv5+8qy5DlJYAkHP9SPT1v6H5ytD8fkQggOOzT3D/5+nkdVw6Akwz5lr0X38h+7bMSkbm3yr48Hor93ekTBDeBUZQ8MktLnZtSG4uYMcajS/ucWKEBBGf1U7UL5h7lZtQqXXMh9e7KF2vEfFZ+6IBwYbPbHz/n1p4dYWiFijHngD3C/9FBPwx24RhYFuxHFFYmFQb9u+WJo5BL9wCZWXVN2Ca6L+vT9jG7nzszumvWcMrlZCahmPu+4jSEuyLFyGM2Co0IhjAnSD1QCJsn35sxdQn0OF8a3pSbaQLv7xri7sOsHrca2cnN+H/83QbZoLOvdDgt7k2QqWwebGONGLPZAQFK19SC7UU9YNy7AkQwWDiHZqGCFWxrzL7GuFKpo19xLHvRgQCYCQoHWYa1jWEwlVHpVR1jZXQdpVWfX4j+eGLdCAatAo7VEYaEA0m12OPBgQyURum1YYREiT89gCMJG8dhWJ/UY49AcFzzkMmWAZvNm+BWUURjsoYBx4UN4kpAenxxEStVInNhszNS9iG2dJaIBY+6+y4vPEAwpSET++P9Hox2neI2y/tdsJnJ5fDJTzgzIRfDhIIH39CUm2kC+37G4gE89+6AzoMSO5LquNZUWzxJkea1iSqxyvJbR//pazZJR3PzqwvQkXmohx7AgI334LR7kBMTxYA0ulEerIonfTfpOOyS6e+bFVBKn+9+/euf09JWkfplKkgRGwbQlD6/P8AiBx/AqGzh2J6rFh3qWlItxvfLbdhtm1nnW/iZMzsbKTLBYDp8WC2ao1v7J3JiXA48N31t4oY+QodTqdljwyioLNZkfcbIUGAzSM5/P8ieLslF7XS+niDTmdHsXksiwhNYnNLjr4lRE5by0KnTwxiz5boLuu1zSPJaiU5ZqwqHKGoH1RUTFWz44EAztlvYV/4OeZB7QleegVm6zY1alvbspms8Xdg+/47jI4dKZvwEOahh9WsjV9/IfueO3Cu/Zng4V3x3f8QZtu9InOkxP7ZJzjfnol0OgmNuJhoz94xbYjCQlzTXkT/ZR3R408gOOwCqJTvpzpsSxaT9Y+/oRVuIXxqP3x33wvl2Twrk+4RB1u+1lj9ph2Xy0G7QX5aH2/UaB2VlLDhM511b9vQnXDYiAgte8Z+MfgLBSun2dn5i0ab4w06D4tgr5nJK0h3e+5NpmjNdJ3VRcUox54Jb7DPhzeyiyJnHiQYekFKq8i23Y7ZqnX966tERtiUutdZtgm2r9Zo1dvEkfg7MCn2V+eO1QJfoaBNH9MK0axD1HufWuosba+iATFNsu69B/fU/4JNp4Vh4h91A/5xd1cMCdm+/YbcUX9C27IZpCR6eBdKp7yI2eHgBhbfdAmXwfQzPJT8smeks8OZUc56sX5nT3esFbw12ENoR/njiAZH3xLi2NtUybnGjhpjT2Pcjz+C+4XnrJS4ZWWIgB/3M//GNeUZAMS2beSdfw76b78igkFEKITth+XknzMIIurD21C8OXC3U98T+f/bHBtfjK/fcMfpp2eVO/XyH1PwzWNO1r2nUj00dpRjT1ekxPPMxLh4ei3gx/P0EwC43nglLuRQmCaibBeOj+bXm1TFHoI7YOea3U59bwQ/Tq0/x77uXZ1ogAQ6YPE/VO25xo5y7OmKaSJKE8eQa9uLAawFTAni0UU0irZpY53KUyRm18aqP1JGPQbF7FhdlQ5BoFhl3GzsKMeerug6RqdDEu6Kdj0SsMIdzays+AOEIHr0MXWpTlEFBYeaVSxQkrhb1F+AwMGDEqyiKtfh7aESkjV2lGNPY8rufwTpdu+JHxfCilOf8CAAobOGYB7YHunc82htut2E+55CtHvP+heswOaALpdGIGZpmfX3yY/U3+Rp8yNMWhxpxOvQ4NRHE5UxVDQmlGNPYyL9zmDn9LeJnNoP2rUjfPoAds6eQ2T3ik+Hg53vz8d/3U0Y7TsQPeRQ/OPuofSFaQ0rvIlz2j9DHH93CHuOidAkWW0kZ74coNPgqnrRdcPwDwN0uTSC7pQIXdKsi8mIj/zkqYCpRo+KY8/weNZ0JFO0Kp2pJ1O0ZrrOJpm2V1+7htzLLqR5p3Y0O+pIXFP+k1RSrb2xLf+evPOH0LxTOwqO74Xz9VdiE3uVlpJ39gBatMqnRctcCo7tgfbjihRfCTjmzyX/tBMhL4/8Aadi//TjlJ+jMbHpS50ZZ7mZ0jGb107x8Mv76blUY8vXGi/1ymJSy2z+0yqb2cPdhPcKgJLSyvv+8jFZPJQH71zkpnhV6j+uRcs1Zp/v5r+dspl2vIefXrfF3OZGCL560MELR2bxXOdsPrrJhb8wdhJhb5v/pzu1svlv83VeP83DlI7ZTB/g4Y9PVUjm/tDoeuzaH79TcGofRFkZovzSTLeH4CWX4Xvg0bjjE30j6it/pOCs08Hvr5gHMz0e/LfcRmC0lYO82WEd0HZsr9gvATTNKiKdotWfjndmkXvjtVYWx93ncbspmTqNSL/4CkbpQkP1hjYu0nnvYjfRwB7HY3NLTn4kyOEXxifgaiidO9fBKydk707+U75VktVacsUyHwCL73ewfIqDqL98v5DYPXDBRz7yO6bms1K8UmPGWR6i/j06bB4r703v0dY6iHdGuNm0WMcoz34pbNYk8P8t8uHIrrnNE7H2HRsLbnTFtTFo6p7iJalG9dgzDM+kpxCBYIVTByv22/2/FxDFxUm1kfXI/RAIxAQ3aH4/nicehWAQx8wZMU4dyj8WpknW+DtScRkAZP/97hinDlaq3uy/352yczQmvpzgjHEOYKXZ/fIfzn1mUa5vPr/TXcmpW3/7Ngs2fKoR3gXLJu/l1AGkIBqEpU+kLgZ9ySOOuFj3qF+w9Akn0SBs+0Fj81d7nDqAjArCpYKfp1tFQ1Jh8y//nriNRX9X8fa1pdE5dtvXSxDR+FWX0ulEX7smuTa+/y7mi2FvtE0bcXyWeDhEALbvvk1a6z4xDLQ/fk+4S1+X3HU0Nbb/lPh2Dm0XRHz1LGYfbFuRaAGTxe+f2Cj5VUtYQk8agsJvU/eRLfpeB5lYh2+TYNuPWsLkaFG/oPAba6hkf21uGrDrj8Qadq5rdO6p3mh0ljM6H2rVKa2ECIUxD0yuXqlxcMeE24VhIFu2rIgjr4zcx//WGF1HNmuecJd5QKvUnKORkd028TyKzU2tMyvWBTkHmhBfchyAFt1MstvIxIuZhCT/kNTFoOcenLgt0wB3S0lue5nw+0d3SQo6W/+7vzbXdHA1S2wLzwFp9JiVYTQ6x+6/8S/gjH2EM50uwqf1w2zTNrk2br09roCF6XYTvOgSZHYOwav/DA5HgkhlKCuPMU8Fvr/81SrMsfd53B58Y1M33NOYOPa28lzre2HzSHreEEak0Z3e9x+hBFslNg8cOiyKu4VVlGN3Pvfd2Fxw1M2pW756zK0J7OWWHH5RBEe2lXs+u52JZou90zU7dLnEeipOhc2P+ku4PL99rI5jxiaykyIZ0uh2Tw1G1yMpeek1jPYHI212K0f5+RdQ+szzSbcR6XsypRMnY7RqjbTbkW43wSuupuz+R6wDNI3tCxZielvuKUDhclM65QXMw7uk7FqC116P79bbMXNzweHAzM+n7J57CV18acrO0Zg4ZGiUvvcFcTUz0RwSe7ak901hjvpLehW4aHWMyWlPBNEcu+8ea+L0os/31MLt968gh18YQXdZjjS7ncnA5wIc0Dt1Pfa2fQ36TQyS1cpEs1sFQ7peEabv/ZZDFQLOnRngoNOjaHaJsEladDM5b7Yfj9dyxJVt7sihxjbvfm2Eo28N4ciVaA6JM19y/D0hulysKk7VlkYXFVOBlIhdpUiXGxKUudvNPmfHpUSUliA9WWCvosL8ju1ooVDd5kE3DLxOSVFYgwTDTOlGQ0ccSBPCu8CexT7zjze0TrBytts84MpPvN8IQ35WDqXhXTUqBlITpIRwqaUj0dg+QDRgDdE4qgjE2G3zNh1yKN5RO5uahtWGM5c6f8JKh/c+GVQ+9soIgczNq/2/l+zE9dJUHJ9/itHhYALXjMJIVP2ooBlV9aFEURHuqVOwf72E6OFdCIwchXlQ+z0HGAY51/4Jx7z3QEK43xns+u+L8V9Eug4FOZABN2I6IDRw1v6trxeMMKyeYWPd2zac+XDkFRFaHx8f2qc7LKe/q6h251n8gJ3lzzowI4Lm3QzOfDFA9gGxxwixb3t98lcnP0+3I6PW8MyZLwbiHPxum+9PIQ9Nr/oLTlEzGm+PPUkSfSOKoiIKTu+L2LkTLRhA6jo4HJRMfZlIv/5Jtav99isFA05FBPyIUMga0nE4KHnzbaJHWQm6mndqh9hVGhMLL91uin/ZZDnzanSmK5mitaF0GmGYOdTD9lWaFdIoJDYXHHNbiF43xEd01VbnG6d72PbD7q6vAKzJ0Mu/LyM7yQfMF3t58G2MbUPo8KdfynAkKOal3vvUouLYU4jn8UfQirehBa0YcmEYiECAnJtvSHoFa/bf70aUliBC5eOVkQiaz0fOraMBcD31RIxTh/JyCIEAnvvvTeXlKNKMNTNte5w6WDHqAcGSh5wEt6fmHNtXU+7UdxfawPot4f0rkgsR+vUDvdypx7YhDfjoOldqhCrqBOXYE+Cc9z4iQQUirbQE7ff1SbVh/+xjRIIvAf3nn8Dnw/36K1Wff9aM5MUqMo5f37fFLj4qR7PDpsWpGR39bmJVi3sExSuS+9gv+09Vc1OCPz5TS/7TGeXYEyBzq6g8bJjI7Jzk2kiUJx2sIRaHA5mzj3aSPIciM3E1kwgt8dCjMzc1Q5JZrap6srSibJLBVVCVFok9wTCMIn1Qjj0B/j9fj1k5ftxmJ3LsccgWLZJqI3DlNZiVYuGlw0HonPPAbmfXQ/+s8n9L73+45qIVGcMRl0fQ4zrUErtH0rpPanKjHPPX3U+c8c75iEuTC0U85eGq88cfdYuKMU9nlGNPQOjiS63FSE4nZk4upsdD9PAulE6emnQbgdG3EB54FtLlwszJRbrdRI4+hrJHHgfA6NmL4KVXsieS2foJnTcc46RT6uKyFGnCAb1MTrg3hO6SOHKseHtPK8mQ6QG0FI1w6A445Z+78wztucMKDjM56f7kHLvbC0ePDUGlu7TNCQbd/6RizNMZFRWzj9lxbctmbMu+x2zThuiR3alNILG2/jdsP63C6HAwxmGHxx+wfTvZ/xgPhknZXeOhinQBmTKLD5mjtaF1hkph81c6jhxofaxRZez2/ug0wlamyLLNgl43hWjZreZthMtg8X1OQqVw7NjQPgt1NLRNkyXTdTbOOPZAAOe899GKthI5rk/CMnD6jytwLPocs3kLQoMGg6fmyULMVq0J78/CI9PE+f672L9aRLTLEfhv/iu4YqMJRDRKtNdRYJpophkfE28YOD75CAo3YG97MJGTTmmQRUrRAPw6z0agSND6OANv95qvgCzbAt885iS4XXDYiEhcXU4poXCpxtbvdLJaSToMjKJXvbYsIaYJyyfb2fyVTrMuBkfdHMFWKYDDv1Xw61wb2VnQ/ARBduvYTkU0DN89bWfbDzoHHGXQ87rIfsVnV4UzFzr0r3ropWwzfDzGTWgbdBhs5+gx8RP6237U2LRIx91c0mFQNC4/S2A7RHwCGRX4NurQreY2d2TDyQ/t39DLbptLEzoMjMbZ3DTgj090dq7TaHaYSbuTqv6iq0uSuc+rs3k6kFSP/cknn2TevHkIIRg+fDhXXXVVzP5Vq1Zx11134fP5OProo7n33nux2ZL/JNSkx66vWkn+uWdBJGxFrmg64dNOp/S5l6yJSdMk54Y/43z/HTBNpM0ONp2SGe8k/AKos2/uwkKaH9sDEdireoKus2P+pxhHdgfA+do0csaOQeqatcrVlJRNeIDgVdcAILZtI3/IALTCLWiRCKbNjtm+AzvfnrNfi69qSvEqjdnnujEiAiNifa8ceFqUgc8FEw4dJLLpiql2Prs9dmC54FCTCz/zo2lWz/L9y9xs/krHNKwVkDa35Lx3/EnnH/cXwsvHZpenorUQOgyf78d7pPUB/ek1G5+OdSF0EAhMQ3LChBDdrrKc5o7VgtdPy8Lcy4fqTvi/xWXkJJdqKCV8P9nGont2fyNZ8eOaE65ebcWPSxM+vMHFr+9bzlKzgbDB0Bn+CmdUHzavTKL3fm+bIy3te9s8sE3w1hA3/kINI2zpyG1vcu7bfpxVxDHsL4l0VnefJ2Pz+tAJKYhjX7JkCYsXL+btt99mxowZ/O9//+OXX36JOWbs2LGMHz+eefPmIaXkjTfeqMUlJIGU5F75f4gd29HKyhChECLgx/HxR7imvQSA863pOOa8iwgEEKEQmq8MraSE3MsuqnEVpf0h/+Jh1uIk9ooCNgzyLhgKgLZ5EzljxyBCQTS/Hy0QQISCZP/tTrTffgUg+/Zb0NevRysrg/Jr0deuJutvd9XbdUgJc690E9whiJQJzJAVc/3HxzZWTUsuvCIahM/GOSHGGoIdqzWWPGR1D5dPsbN5sU7Ub50jUiYIbBN8MDL58It3LvbslV/c+pEGvHOB1UbZZsGnY10YIUHUL4j4wQgJFv3NSclv1jDb2xd4yp36njaMELw9vP66ZYZBJadu/TZDMPs861rWvGXj1zk2ogGBERJEfIJwieD9y9xIs/5sXh2Vbb5b7942/+x2J7vWa9b9FbauZcdajUV/q7987Mnc59XZPJ2o1rEfe+yxvPTSS9hsNoqLizEMA89ewxobN24kGAzSs2dPAIYNG8bcuXPrRKy+bi36li1xmURFwI/rfy8A4Hr5RTS/P+5/RWkJth9/qBNdibD9uCJeJ6AVF0NpKY7330mcktswcL49yxrGmfNeXG55EQ7Xa5z7znUC35a9F6hYRAOClf9LzrGvfNleRZZaUfGhWfWyPa7YAtJyRNb5q6f4x0R5zgXBYkG41IofT2RzacC6t+2YUfBtjr9WEJTUY27wn17Z/RgUr6PoO2vfypftCWPhw6VWHvX6snl1VGdzacKvc2yY0diDzLBg7awk4zJTQDL3eXU2TyeSGi+x2+089dRTPP/88wwaNIgDDtiTbGLr1q14vd6K116vl8LCwhqJ2NcjRQyFDtAS33B2TLzeHKgic4umaRRkO8AbHyPuTbBtv6lihEsA3gI3OPWExwgpyXbpZHtzqKoboJlm3WhOgCysOiGThl6ljr23u/Zxlwmp4fXmVFF2AoQmKMjLJs9bxQExYqs8CwUFObidiY+RUuB2OfF699VDFPVmc9c+fYSlo6pDhCbIy85iR33ZPAF72ykZm1c1GCzNurX53m0nc59XZ3NvLe1VHbWxQdID4aNHj2bkyJGMGjWKN954gwsvvBAA0zQRe0WLSCljXidD0mPs3gNplpOL7ostzSJdbnzDLiRQtAvXeReQ9d33aIHYXrtpt1N8YOe4RFp1NcZe0LET+i/rYj48EpDZORQbdrQT+9FMiLgPl7TZ2XHSGRjFPnJPOhVHpRWs0mYjNOBMdtXXjL4X7DlZRHyxt7XNJek0LERRUfW5TQ46F7gtm/ium6Tj0AhFRSE6nevgu4kOjFDsMVmtTUIuH0VJJMHK6+ih5JfKvXYrnNBn+PCeKEBkxenQbJKWJ/kp3mHiKsgiuKNyz02S1UZSVFQ/ZZg6XACMSWyvgkNNior8dDzPzpbv4kvKaXYT24G+erN5ZSq/99XZfFuxSbuT3Gz4TEeae44RNkn7AVGKiqqOpd8f4j73Sdzn1dm8Nvaqsc7d59zfMfZ169axatUqANxuNwMGDODnn3+u2N+qVSuK9rqibdu20bJlyxqJTxpNo/TZFzCzspBOawzSzMoi2vVIAuUTjsGLLyPa+yjM8pWf0ulEuj2UPvsC1GBCd38peek1qwpS+WurxKWg9Pn/WboP7ojvltuRLjdS15GahnS78V93I0aXIwAoe+xfyObNMT1ZFddqelviu++hersOocHAZ4PYsiS607oae5akeVeTI6+Kd+qJcOXDUWPCVI6HdreQnHCvFW3R64Yw+YeY2LOsc+guyyH3fyaQdJTpWS8FrAm6va0uYODz1mxq3sFWoWbdJRG6RGjWZGHP68I072J9eQ56MVDug/a0ITQ46+XY2rN1ie6A7qP2ttceHUPetDosXS6O0LK3scdeTiuf+oBng2i2+rN5dVS2OZqMs/kpjwVxNZcVxTbsWRKPV9L3vvpbBJXMfV6dzdOJaqNiPv30U5566ileffVVAEaNGsX555/P4MGDK445++yzuffeeznqqKO45557aN++Pddcc03SImoaxy6KinC9+Trals1ETuhL+IwBsdkQTRPHxx9i//xTTG9LQsMvrLKcXJ3Gs5aWknXf37B/uxTjkM74/n5fXN52fdVKa8xcSsJDhhLt1iO2DZ8P16wZ5Py+jtKDDyU0dBi46389t79IsPpNG74tgrYnGBx0hlHlYpqqbLplqcbXjzgJ7oBDzonS4/pITOSmGYVf59rYskQju53ksOERXM1qpjNcCl/e56TwW42CQyR9/h4ku9JbX7xKY+0sG263k9an+/B2ix3y8hfBlxNcFK/U8HY36DM+hKugZjpSwcYvNT69zUVkh06r4yOc9nQwJqOiNOH3j3U2fq7j9koOHR4lq1I5ufqw+d5U9d7vtrmU0GlINM7mER+smWVn+08aLboaHDI0iq0Ob/OqdFZ3nydj8/rQWV2PPalwx6effpo5c+ag6zoDBgzgpptuYuTIkYwePZpu3brx008/cffdd1NWVkbXrl158MEHceyjuEVl6mSBUiiE7edVmM2aY7arutZppi9USEf2pbX0d0GoRNDscDNhUYeIzypi7DlA1tkHRkrYsUajID8LvIkLWAS3Q+kfGrntzYQ5wqVpFXLWHJL8TjJhG/6tgrLNgoJOJvYEn0EzCttXadhzJHkdqr7WfdnTCMH2nzVczSQ57Rp8rWGd3qfJ2DxZMuXzVKeOva5JtWN3vvI/su++HRCISIRIr96UPv9ywjwvmf4GpyOJtPq2COZc4aZ4lWbF/2pwyqNBOp+3Z2n6txPtfPOoE2EDMwztTo7Sf3Kwyqo9taHoB425V7kJbBMIIXDmmQz4b4BWR1s9SDMKn451svpNO7rDivU+4tIIfe8PVUyubVyoM/9al7Xwx4Ss1pIzXwzQ7DCrjYgfPrrRxfr5NjQHmBHodVOYY/4arnBGv86xseBmF2bUOmdBZ5MzXwwkdM5VvfcrX7Gx8O7yIckItOxlMOj5IO4WDfeRrqv7tDqb15RM+TypfOzl2BcvIueOv6KVlaGV7UKEgti/+Zq8yy9qaGlNFinhnQvdFC3XMIJWnHC4VPDxX1wULbduwXXv2PjmMWtiKrLLihP+4zMbC25KXd7vSBnMPs/Drt+tXOgRH5Rt0nhnhIfgTuuYrx50sOYtO0ZIEC7XseoVO99NtB4vfIWC9y5x49+qEfFZsc4lvwpmnevGKB8S/nSs5dSNUPm1BAXfT3Swero1ELv9Z435o1yEdlq2MIKC4pUab5/vqTJCpDKbFut8cYeLSFl5GyFB4Tc671/e+NIuJmNzRSyNzrG7Jz0NgdiJLhGNYFvxA9ov6xpIVdOmeIVG6XoNacQ+OxshWD7FGrL79mlHXIywGRKsn2+rcLr7y7p3bZgJVvBLA9bOtCMlrHjeERf1EA0Ilk22dP70uh1ZuQ1pOdbf5tuI+GDd27a4SJNoQPDt01YbK16wY1TKwyUNgX+rYMvXyX0kv59kj1lhC2BGBdtWaJT8UkfFURuI6myuiKfROXZt86aE8bnSbkeri3gkRbX4i0TCiVZpCso2Wu9WoCixMxI2CG1PjaMKFGkJe3jRAPi2Wo/4kfi1bQCEdloafJtEnNMGazglsFUQLqtaa2Cbta9sg4j7kgNAgH9rch9J3+ZEi7Gs5fj+osb1sa7O5op4GtcdAIRPOx3pjF9oIiIRjK5dG0CRwtvDjOuhghVed9Dp1hh7276GFQ5X+RiHJOeg1IwZtzoucVIxexa0Od6KfqhqzLZlT6vL2O6kPeFueyOEVejZ45U48hLs1yRtT7DaOLCfgc0df4wZgQOOSi4f+4GnRSvC8iq30bxranK6pwvV2VwRT6Nz7IFrb8DMz0fuFZUjPR58t9+ZdPUjRWpxN5f0vD5cEacMVgywxys54jIrRviYsSEc2dbCFAsrRrjv/aGUxQi3OsakzQmxTtXmlnh7GLQ7yXIQJz0UwubeU+FIaFZ89YnlMdUdBkYpONREd8W20WFglOZHmAgNTn44VH6O8jZsElsWHHen1cbhIyJktTZjHLPNIznyqnBc1sOq6HFtBGe+RHPEtnHM7aGUTjanA9XZXBFPo4yKEUVFuCc9hfPDeZgtDyBw3Y2EzxiY8NhMnx1PRxJpldLKG7Jssp3gDkHHs6L0GBWOiQ/ftVHw7ZMONi3SyTnQpPfoCG1SVFFoN2YUVk6zs+plO7qmc8jwIF2viMT05Let0Fj6pIPtKzVadDc56i/hmJ58NAA/PG+viJzpekWYwy+KxixJ37xE49unHJT+qtH6eIPeo8Pktt9zj4dKYfmzDta9bcORK+k+MkKnc6IJQ/j2FXP9/SQ76z+04WlpLfppf0bD9mDr6j5NxuY1IVM+TyrcsZZk+hucjmSKVqUz9WSK1kzX2TgLbSgyDiMMXz/i4MeXHET81ph23/tDMT3h5c/ZWTzBaUV7aNBxUJQBzwcrVkpGfPDlP5z8/IYdM2KNM590f4icA1PbKfjsTic/vmBHRkGzQ49RYfrcs2eSoGyz4Iu7nKz/0IamQ+dhEU74ewhH+UiflPDji3aWPuEgUCQoOMzkxAmhiuEegN8/0fhwlJtg+cSwt4fBkOmBisVQ0oTvJtpZ9oyDUAl4e3joe1+QA3qnduhh02Kdhfc4KV5pLXLqfVOYbiMjKUspUF8kY/NUsO0Hjc/vclK4VMeRI+l2TYSj/hLOvJQC9YHqsVdPpuiExFrn/cmK7Y4Gyz2GkNiz4eLPfWS3kfz0ho0FN7qonHyr1XEGw94JICXMPMdN0fd6RYSE0CTOAskli304a1F3JJHOT251lqdpjdXR++Ywx98VJuKDacdnEdi2J7JFc0iaH24yfL4fIeDbp+0VMfm7sbklQ14P0Pp4g+KfNF4/eXfqa1FxDldzydWrrERjn9/lYNXLsaGXNo9k+Fw/zQ5PjXPf+r3GrKGeOJ09RoU57o7k6qJWpqHu0+psXpna6Cz5RfDG6VlEfLHnOOS8CP3+VTcB9WqBkiJtKf3dijeucOpQHocMy5+1Fv4kLqog2PKVTnCH5YS2/aDHhL1J0yre8NPrqcvbbeUqj8+D/v0kaxB+zSw74V2x4YpmWLBzncamL3WMCCx9Ij4DYDQg+Kq8wMXndzor2t37HMFiwa8f6IRKYOVL8fH0RhCW/quGtQL3wZJHHEQrJU+MBgTLnnHExcinM8nYPBV8928H0Ur+OxoQrJlhx59mYZfKsSvqnB1rtIRhhmZYsHWZFeAeikuVu4etyzR2/Jz4Vo0GBEXfV5GNrIZEw1WmwK8olbdtuZaw2IJpWHlfgsUCMxq3G7BWnALs+ClxDDrApoU6pes1tAT2kqaoWKmbCopX6iDjdQjNGm7KFJKxeSooWqYjo/F20Z2Up4tOH9JLjaJRkt8xcRy7Zpe0KI+5duTunaI2lhZdTfI6mgldoc0tUxa3bXNQlb9FlI+hNutixoRt7kazWfleXM1keergePI7Wt8aeR1NqrrWVsca5BxoYiYaCREypeF9BZ0T6zAN6jRjYapJxuapoFkXM+FaCyMEuQenV9ilcuyKOifvYEm7vvELanQHdP+z1RU+7q5EY5SSFj0MPF4rBj3/EDMmbhsh0R1WnuxUYSUlq/zhlXS90jrHocMjMXHuYH1BZbcxadvXQHdAz1HhOOdvc0uOHWd565MeCFW0u/c5HLmSToMNXAVw6AWRuEVMNhccdXPtxr4TcczYUFxqXJvbiqe3Z6XsNHVOMjZPBb1uCqNXGjHUXZKDz6rb1L21QTl2Rb0w8LkgXS6JWItMhKRlb4Ohs/zklq8qPfKKKMfeEUKz7ykK0eZEg+FzrMFeIeCct/wcMjSK5rAca5sTDIbN8e1X/vDK9H8mSOdhURCWBqFZi6hOLnfGjmw4f46fticZCE2i2a0P9nlv+ytiqo+5LczRt4Rw5ltt5HYwGfBsoCJCw9vdZODzAezZe641/xCT/1u4p0LTKY+E6D4ybB0joNnhBoNfDdCiW+p6hq2PNRk0NUD+IQa7v1h63RTmhL+lzhnWF9XZPBU0O9TknOl+WhxpgJAVi8pOf7puqjztDyoqJkOiTTJFJ+xbq5TWOHZVRTrAGuu27WPOS5b7w9ouTklGZ1I6TECwz9BA06j+WjUbMcUvYs4hoUXzHIq31+17X53OZEmH+zSZa9lfnaZh3X91HRaq4tgVVbJthcb3kxzsXKfRpk+UHqMiZLWq/y/SPz7VWT7FTrBY0PHsKF2viCRc/l6VM41GYdbZbrZ+a31qs9pIzn/PR3bb5DVIE9a8ZWPVK3ZsOnQ638ahw6MJ45D35dRh318s1dk84oeV/7OzdrYNZ76k258itD89vncpRGocbnXUxznqC2Uv5dgbPes/0pl3tZW3WpqCbT9qrHrFwQXzfTFL3Ouab5+2880/nRURJdt+1Fn5sp0R8/0Jqwsl4r8HZ2OGYPcMp28TvNQrmytXluGJr6GSkA/+7GL9h7YKHRu/drHunShnvRxMWe+rOptHgzDjTA8lv2kY5SF6mxbZrPjxFI4JK5ouaoy9ESMlfHKLi2hAVFSAN8OCUCl89WCiuPG6IbgTvn7EGRMmaAQFvk0aK19JLgZ9+RRbjFO3sP5+5wJPon+Jo/A7zVoktZeOqF+wcaGNTV+mpguWjM1Xv2mndP0ep75bx/f/dqRdPLQiM1GOvRET2CoqlqzHYAo2fFZ/z5Jbv9UTxrFHA4Lf5iX30PjD1KrGRUTSscqbFloLiOJ1wMYvUmOPZGz+2wd6wlh4zQ5bvk7zZ3xFRqAceyPGniWrLLXmLKi/YRhXM5l44Y+QeFomp8PjrSoaRCb80qhKR6JjbU4rtXAqSMbmnpYyYTz0bo0Kxf6iHHsjxp4NBw+Kojsqxfd6JD1GpS72uzq8PUw8rcyY2G+w4rK7X5PcmPLA53aHlMU7vt5jkgs363h2FWleNThkaBVLF2tIMjY/8soIeuURKCFx5klaH6cKRyj2H+XYGzmn/StI6z4GusuKU9adkq6Xhzni0vpz7ELAkNcD5B9irdp05FgxwH3vD3LAUcnFZXtaQI8b917YY/14exkcfXNyTtmZC2e/5sftNbFnSxw54GpmMvjlAO4WqespV2fzFkeanPJ4EHuWxF5ui7wOknNm+Pc7hFOhABXHnhZxt8mwvzpL1wt2bdBodriZsmGHqqhKq5RWPpVQicDbw8Ce3JxnDNEoLLzLQWiH4IQJIbJb1bwN04CiZRr5eVnY2++qs5Sr1dk8GoCty3Qc2ZLmXc0qo3Iy5R6FzNGa6TpVHLsCgNz2ktz2dfeYLyVs+Upn4yIdbwc4oB8VucV3IwT7zHUiTSvWfet3OlmtTQ4ZEo0LhQwWCfI7WiGDoe0a2a1qvhJT0+GA3iZeLySqb26E4Jc5NkrWaRQcZtJhYDR+6CQJqrO5zW3lpVdkDnvf5+7mkk5DI3H3eTqgHLtivzGjMOdKFxu/sBENgt0FiGzOfi2Q9JhxNACzz/ewfZVGxG85vUV/g3Nn+2nexXLea2fZ+Gi0C6TV6176TwdHXB6m732pi/32bRHMGOQhVCKI+K1C1+4WkvPn+Ov8SUeR3lS+z21OWPR3Z43u8/pCjegp9pufp9usm90vwLQcYsQnmHu1CzPJ+/37ZxxsW6FZRQyklWc9tNNaUAQQKoGPRrswggIjJJBRQTQoWPmyg02LUxci+OlYJ75CUaEjUiYo2yBYeE/9xf0r0pPK93k0IGp8n9cXyrEr9pufXrEnjMuO+gXbfkjuFvv5dRtGsHIbgtL1GmWbBL8vsCUcC48GYPWM1Dx4ShPWf2iLKaIBYEYFv7ynHm6bOqm4z+uL9FKjaFxUXTsjeZIY/aiX+pxqQaiiKlJxn6cY5dgV+83h/xdJWHzC5pF4k0wze9iFUSul794IK/1qdhvJQf2iyASPuzY3HDosNTHoQoP2Z0TjFg9pdknHwak5hyJzScV9Xl8ox67Ybw4bEaXdSVHrptck9ixrBeag5wNJx2X3vC6Mt5uJPctqw5YlcebBgGetxUfOPOj3ZBDdJdFdEmGT2NxWfHiiYsW15ZRHQ2S1kuV50K3fOe2siveKpk3l+9zmkTW+z+sLFcee4fGs6YKUsGWJzqYvdbztnRzQbxfOvBq2YcKGz3UKv9XJbm3SaUg0rpKPb4tg3Ts2ogFB+/7RioiZ2lCVTY0w/DrXxs61Gs3Kwx3rKtY9GdL9vd+bTNFaW5173+euZpJDhkZqfJ/XBBXHnqGYBhR9ryFNaNnLrJ0DCYexffctOOxEe/SqumpDHSIENO9qYEahTWcQCW52KaF4pbVAqWUPI85pCw283Q00G2S1NhOWZ8tqJek+sm5XzeoOOOSc/R962bBQY9tynfb9oxQc0jAdl+psrqgZQkDr44y0C2+sTFJuZOLEicyZMweAU045hdtuuy1u/4wZM8jNzQVgxIgRXHLJJSmW2vjYvERj7pVuouXRILpDMvC/Qdr2Tf6mcXw4j5xR11jdXVMic3Ioffl1ot171pHqxCybbGfx/U50B8go5B7s4exXAxXFJUp/F7x7sZuyjRqabsUE970vyBGXWQ5USvjqAQfLnnGgO61ec8ueBme+FEjLBSD7omwTvH5qFqGd1vu66G9OWnQzGD4/UK/fudXZXNF4qfY2W7RoEV988QUzZ85k1qxZ/Pjjj8yfPz/mmBUrVvD4448ze/ZsZs+erZx6EoRK4d2LPAS2aUTKrHjp4HaN9y51E9iW3BS7tnEDuX+6HK20BG3XLjRfGfqWzeSdPwQCgTq+gj388anOVw86MYKCcKkVx779J433LrEqJUsJ74zwULJOI+oXhHdZMcBf3O1iyzfWLbhmho3lUxwYIasNIygoXKrz4XXufZ06LZlx5m6nvudn2w86H93oqjcNydhc0Xip9h32er2MGzcOh8OB3W6nU6dObNq0KeaYFStWMHnyZIYMGcKECRMIhdREU3Wse8eeOJWtCWtmJTce43r9FTAS9O4NA+cHc/ZPYA1YNjk+vlcagp1rNXasFRQt0/AX7ik8sZtoEH54zsqj+/1/HHFtmBHBxs91gtvrVn8q8ReCb3Oi+DfButn1N/KZjM0VjZdq77TOnTtX/P3bb78xZ84cXn311YptPp+PLl26MHbsWNq3b8+4ceOYNGkSY8aMSVrEviYB6gOvN6fez2mLgJlgJXw0JNCCLrze+N5dnE5fCYTjG9EMg9yIH+rpuqIlibfrdoFbZqOZJJ47kILoTjter51IFW1oNkGWLYdm3pTJjSHV733h5qr3mVFR6/PV9P9KkrB5XdEQn6fa0Jh1Jt2FWLNmDddeey233XYbHTp0qNielZXFlClTKl5fffXV3HnnnTVy7E0xKia/p4Zm92BGY3tUNrekoHeAoqLYnnginY7j+pLz/PNoPl/MdiklO3oci1FP19X2NAeFy61hlL0xohK9bRmuCERD2VTuxdrckranhSgqitD2FCelr9qRleyhe0wiWb6Eybr2lzp57w8AoWXH9ZRBkt1OUlTkS/hv+6I2Ol2dqrd5XdDYo2Lqm9pGxSQ12LZ06VKuvPJKbr31Vs4777yYfZs2beLNN9+seC2lxGZTwTbV0bKnSYcB0ZgFDzaPpG3fKG36JDd5Gj59ANGevTE9e/Lfmh4PwQsuwuh8aMo1V0X3kWE8LeWeBUbCciAn3BvC7rFi0I+9LYTNvTuPOuguSXZbky6XWA7m6L+GceZJtPICFUKz4tRPeTSU9hXh90bT4JjbQuzJGY/1W8AZ/66/eY9kbK5ovFTrgTdv3swNN9zAE088QZ8+feL2u1wuHn30UY477jjatWvHtGnT6N+/f52IbWz0nxxk7SwbK6dZ4+2HXxTh0POjyS+R13VKXp+J641Xcb7xGricBC67kvDZQ+tUd2WceTBigY8VUx389oFOs4NsHHZlICYlba8bI3i7myyfYidQLOh4dpQjL49UhN9lt5Zc9Jmf5c/a2fiFTm57SY/rwrTskV4r+pLh6Fsi5HeSfPkPB4FtgmaHmZzyzxDeI+v3WqqzuaLxUu0Cpfvuu48ZM2Zw0EEHVWy76KKLWLBgAaNHj6Zbt27MmzePp59+mkgkQu/evbn33ntxOJKfoGmKQzExmOUf+H3EwqWFziSIBuGAtjkUF6e/1kyxaabohMzRmuk6qxuKUStPG/ANFoWF5Iz9C4758wBJ+NR+lD32JGbbdnHHpvuNuOJFGwvvdmGEAAQtukU5d3YAR8POi++TdLfpbjJFJ2SO1kzXmZIxdkUdEIlQMPgMHB/OQxhRhGHg+GQB+WeeDsHkijOnC79+oPPZWFf55Kk1jrTtB53XTlbP/ApFQ6AcewPh+GAuYnsxIrpnFaAwDMSuXTjfnd2AymrOwrsTFaGwClQULVe3mEJR36hPXQOhr1uLSLA6VPOVoa9d0wCKao+/UKOqhNRbvsmgkBaFopGgHHsDYRx+ONIVv1zezMrG6HJEAyiqPdltTaqqiNHmhPROlqRQNEaUY28gwqcPwGzTBmnfEz0kbTZk8+aEzjy7AZXVnJMfTjQnIMnrZNL88MwLV1QoMh3l2BsKXWfnux8QHD4C05OF9HgIDR3GjjkLoAahoulAu5NM+k8O4sgt77kLaHuSwYWf+htamkLRJFFLRBsQWdCMsicnUfbkpIaWst90Pi9K5/OsiWArRKv+VlkqFIpYmmaPXUoc775N3pCB0LUrnvvvRexIzxSC+g/LyfnTZdClCzmj/oT+06qGlpQQf5Fg0d8dvHqihxdPg9/mN+1J081LNN67xMUrJ3j4eIyT0vVpVu1Y0ahpkguUPA/+A8/kfyP81lCBdDgwWx7Ajk8WIXPrsM5VDbEv+oK8i8+HYBAhJVLTwOVi51vvEu19dEPLqyCwTfD6qR6COwVm2HJgNo/kmL+G6HVj+uYlqatFKuvetfHRDS6iAQCB0CU2Dwyf56tVJaVMWUwDmaM103WqBUqVEMXFeCY9VeHUAUQ4jNhWhOulqQ2oLJ7s229FBAKI8u9eYZoIv5/su8c1sLJYlk22xzh1gKhfsOQRJ+GyBhTWAEgTPrvdSTSwZ7GWNARRH3z1YKJ4f4Ui9TQ5x25b9i3SEf8B04JBHAs+bABFVWAY6Kt/SrjLtuy7ehazb/74xBbj1Hej26H4x6Y1JOMvsipAVUaagk2LmpYtFA1Hk3PspvcAMOJrPkpNw0iQo6XB0DSkJ/GSfDM/v361VENW68Rx7EYEPN6mFe7oyK56qMXTosFHPRVNhCbn2I0ju2G274DUK/WenE6CI0c1jKhECEHgT3/GdMcuYpJuD4Frb2ggUYnpeX0EW6W1VppN0uJIk7yOTcuZ2bOg09AoujP2um0eSa/RCUpmKRR1QJNz7AhByRuziPbohXS5ICcHMzePXU9OItq9Z0Ori8E/7m5C549AOp2Qm4t0ughcchmBG//S0NJiaHO8wUkPBbFnS+zZEpsLWh1jcNb/mmbI46mPBmk/wHLujhyJzSXpdUOYQ4fHPykqFHVBk4yK2Y32x+801yIUtTwI7HVXA3J/ETu206KsmG25XmRefkPLqRIjBDtWa7TpnEXYlbkRB6nCv1Xg2yLI72hi34/0xZkSwQGZozXTdVYXFdOkFyiZBx5kFXxO8zdYFjSDQ9sj01yn7oQW3UzyvNRJjdJUse5dnWX/caDr0OUKG4eeX/OedOFSjV/et6HZofOwKM0OjZ9L8LSUeFo2eL9J0QRp0o5d0fR4e7iLDZ/tue03Lnbxw3MG57+f/LDRZ3c4+ekVO9EgCB2+n+Sgzz0huo9M35h9RdOi6Y2xK5osGz7Xyp36nhhzEBR+o7PuveRCEbd8o/HTq3YrTl0KZFRgBAVfTnDiK1SrSxXpgXLsiibD8ilVJ1db8XxyidfWvW0rX1Eai9Bh/QfqAViRHijHrmgy6PuYH9eSnDvX7CASfWoEaHY1nq5ID5RjVzQZet9cdRx5r5tCSbVx6PnRhF8C0oAOA1U4oyI9UI5d0WTwdjfpcmkEa5Xs7t615JDzorQ7MbkVss2PMDn29hC6U2JzS2weie6SnDEpiKugrpQrFDVDDQoqmhSnPR6i+zURvvu3A6fDzuFX+fF2r1nag143RDjk3Cjr59vQHZKDB0VxNasjwQpFLVCOXdHkaH6EyRn/DuL12ikqql0um5y2kiOvVOGNivREDcUoFApFI0M5doVCoWhkKMeuUCgUjQzl2BUKhaKRoRy7QqFQNDKUY1coFIpGhnLsCoVC0chQjj0DMA0IbLd+KxQKRXUk5dgnTpzI4MGDGTx4MI888kjc/lWrVjFs2DAGDhzIXXfdRTSqcmakAinh26fsPH9oNv9sDVMPz2b5lPSt9KRQKNKDah37okWL+OKLL5g5cyazZs3ixx9/ZP78+THHjB07lvHjxzNv3jyklLzxxht1JrgpsewZO9/800l4l8AIQ6hEsPg+J6teUQuGFQpF1VTr2L1eL+PGjcPhcGC32+nUqRObNm2q2L9x40aCwSA9e/YEYNiwYcydO7fOBDclvn3SYRV02ItoQPD1Y84GUqRQKDKBart+nTt3rvj7t99+Y86cObz66qsV27Zu3YrX66147fV6KSwsrJGIfRVlrQ+83pwGPX8iTAOC2xPv8xdqaal5b9Jd326UztSTKVobs86kn+nXrFnDtddey2233UaHDh0qtpumiRB7epVSypjXyVBcXIZpNkyRgnSuVp5zYBa7/oh/qMo/xKCoyN8AipIjnW26N0pn6skUrZmuU9PEPjvESU2eLl26lCuvvJJbb72V8847L2Zfq1atKNqrJP22bdto2bJlsroV++CEe0PY3LFfeDa35IS/JVcUQqFQNE2qdeybN2/mhhtu4LHHHmPw4MFx+9u2bYvT6WTp0qUAzJ49m5NPPjn1Spsgnc6OMvD5AC26GThzoWUvg7P+F+CgfiruUaFQVE21QzHPPfccoVCIhx56qGLbRRddxIIFCxg9ejTdunXjscce4+6776asrIyuXbty+eWX16nopkT70w3an+4vfyRL3+EXhUKRPggpZYNX4FVj7NWTKTohc7QqnaknU7Rmus6UjLErFAqFInNQjl2hUCgaGcqxKxQKRSMjLdama1rN4t4b2/mTJVN0QuZoVTpTT6ZozWSd1WlPi8lThUKhUKQONRSjUCgUjQzl2BUKhaKRoRy7QqFQNDKUY1coFIpGhnLsCoVC0chQjl2hUCgaGcqxKxQKRSNDOXaFQqFoZCjHrlAoFI2MtEgpUJ88/PDD7NixIya/PMDEiROZMWMGubm5AIwYMYJLLrmk3vVddtllbN++HZvNemsmTJhAjx49KvavWrWKu+66C5/Px9FHH829995bcWw66UwXewIsWLCAiRMnEggEOPHEE7n77rtj9qeLTavTmQ42nT59Oi+//HLF6w0bNjB06FDGjx9fsS1d7JmM1nSwKVgFip599lkATj75ZG6//faY/TW2qWxCLFq0SB533HHy9ttvj9t37bXXym+//bYBVO3BNE3Zt29fGYlEqjxm8ODB8rvvvpNSSnnHHXfIadOm1ZO6PSSjMx3sKaWUv//+u+zbt6/cvHmzDIfD8uKLL5affPJJzDHpYNNkdKaLTXezevVq2b9/f1lcXByzPR3sWZmqtKaDTf1+vzzmmGNkcXGxjEQicvjw4XLhwoUxx9TUpk1mKGbnzp088cQTjBo1KuH+FStWMHnyZIYMGcKECRMIheq/rugvv/wCwNVXX80555wT09sA2LhxI8FgkJ49ewIwbNgw5s6dW98yq9UJ6WFPgPnz53PWWWfRqlUr7HY7TzzxRMyTRbrYtDqdkD423c3f//53xowZQ7NmzSq2pYs9K5NIK6SHTQ3DwDRNAoEA0WiUaDSK0+ms2F8bmzYZxz5+/HjGjBlT8ci1Nz6fjy5dujB27FhmzpxJaWkpkyZNqneNpaWl9OnTh3//+9+88MILvPbaayxcuLBi/9atW/F6vRWvvV4vhYWFaaczXewJsH79egzDYNSoUQwdOpRXXnmFvLy8iv3pYtPqdKaTTQEWLVpEMBjkzDPPjNmeLvbcm6q0potNs7OzufnmmznzzDM55ZRTaNu2Lb17967YXxubNgnHPn36dFq3bk2fPn0S7s/KymLKlCl06tQJm83G1VdfzaefflrPKqFXr1488sgj5OTk0KxZM4YPHx6jwzRNhNiTrlNKGfM6XXSmiz3B6g19+eWXPPDAA7z++ussX76cmTNnVuxPF5tWpzOdbArw2muvcdVVV8VtTxd77k1VWtPFpj/99BMzZszg448/5vPPP0fTNJ577rmK/bWxaZNw7O+//z4LFy5k6NChPPXUUyxYsIAHHnigYv+mTZt48803K15LKRtksuebb77hyy+/rFJHq1atKCoqqni9bds2WrZsWa8aoXqd6WJPgBYtWtCnTx+aNWuGy+XijDPOYPny5RX708Wm1elMJ5uGw2G+/vpr+vXrF7cvXey5m31pTRebfvHFF/Tp04fmzZvjcDgYNmwYS5YsqdhfG5s2Ccc+depU3n33XWbPns3o0aPp168fd955Z8V+l8vFo48+yh9//IGUkmnTptG/f/9617lr1y4eeeQRQqEQZWVlzJw5M0ZH27ZtcTqdLF26FLBm0k8++eS005ku9gQ47bTT+OKLLygtLcUwDD7//HO6du1asT9dbFqdznSy6c8//0yHDh3weDxx+9LFnrvZl9Z0senhhx/OokWL8Pv9SClZsGAB3bp1q9hfK5umbGo3Q5gxY0ZFVMw111wjly9fLqWUcu7cuXLw4MFywIABcty4cTIUCjWIvieeeEIOGjRIDhgwQL7wwgtxOletWiXPP/98OXDgQHnLLbekrc50saeUUk6fPr1Cy7333isNw0hLm1anM11s+t5778m//OUvMdvS0Z5SVq81XWw6efJkOXDgQHn22WfLO+64QwaDwf2yqaqgpFAoFI2MJjEUo1AoFE0J5dgVCoWikaEcu0KhUDQylGNXKBSKRoZy7AqFQtHIUI5doVAoGhnKsSsUCkUjQzl2hUKhaGT8P1z4Rf0bOEpNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['sepal_length'],data['sepal_width'],c=cluster['cluster'],cmap='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standardize the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-9.00681170e-01,  1.03205722e+00, -1.34127240e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.14301691e+00, -1.24957601e-01, -1.34127240e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.38535265e+00,  3.37848329e-01, -1.39813811e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.50652052e+00,  1.06445364e-01, -1.28440670e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.02184904e+00,  1.26346019e+00, -1.34127240e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-5.37177559e-01,  1.95766909e+00, -1.17067529e+00,\n",
       "        -1.05003079e+00],\n",
       "       [-1.50652052e+00,  8.00654259e-01, -1.34127240e+00,\n",
       "        -1.18150376e+00],\n",
       "       [-1.02184904e+00,  8.00654259e-01, -1.28440670e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.74885626e+00, -3.56360566e-01, -1.34127240e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
       "        -1.44444970e+00],\n",
       "       [-5.37177559e-01,  1.49486315e+00, -1.28440670e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.26418478e+00,  8.00654259e-01, -1.22754100e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.26418478e+00, -1.24957601e-01, -1.34127240e+00,\n",
       "        -1.44444970e+00],\n",
       "       [-1.87002413e+00, -1.24957601e-01, -1.51186952e+00,\n",
       "        -1.44444970e+00],\n",
       "       [-5.25060772e-02,  2.18907205e+00, -1.45500381e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.73673948e-01,  3.11468391e+00, -1.28440670e+00,\n",
       "        -1.05003079e+00],\n",
       "       [-5.37177559e-01,  1.95766909e+00, -1.39813811e+00,\n",
       "        -1.05003079e+00],\n",
       "       [-9.00681170e-01,  1.03205722e+00, -1.34127240e+00,\n",
       "        -1.18150376e+00],\n",
       "       [-1.73673948e-01,  1.72626612e+00, -1.17067529e+00,\n",
       "        -1.18150376e+00],\n",
       "       [-9.00681170e-01,  1.72626612e+00, -1.28440670e+00,\n",
       "        -1.18150376e+00],\n",
       "       [-5.37177559e-01,  8.00654259e-01, -1.17067529e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-9.00681170e-01,  1.49486315e+00, -1.28440670e+00,\n",
       "        -1.05003079e+00],\n",
       "       [-1.50652052e+00,  1.26346019e+00, -1.56873522e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-9.00681170e-01,  5.69251294e-01, -1.17067529e+00,\n",
       "        -9.18557817e-01],\n",
       "       [-1.26418478e+00,  8.00654259e-01, -1.05694388e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.02184904e+00, -1.24957601e-01, -1.22754100e+00,\n",
       "        -1.31297673e+00],\n",
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       "       [-7.79513300e-01,  1.03205722e+00, -1.28440670e+00,\n",
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       "       [-7.79513300e-01,  8.00654259e-01, -1.34127240e+00,\n",
       "        -1.31297673e+00],\n",
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       "        -1.31297673e+00],\n",
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       "       [-1.26418478e+00, -1.24957601e-01, -1.34127240e+00,\n",
       "        -1.18150376e+00],\n",
       "       [-9.00681170e-01,  1.72626612e+00, -1.22754100e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.50652052e+00,  3.37848329e-01, -1.34127240e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-6.58345429e-01,  1.49486315e+00, -1.28440670e+00,\n",
       "        -1.31297673e+00],\n",
       "       [-1.02184904e+00,  5.69251294e-01, -1.34127240e+00,\n",
       "        -1.31297673e+00],\n",
       "       [ 1.40150837e+00,  3.37848329e-01,  5.35295827e-01,\n",
       "         2.64698913e-01],\n",
       "       [ 6.74501145e-01,  3.37848329e-01,  4.21564419e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 1.28034050e+00,  1.06445364e-01,  6.49027235e-01,\n",
       "         3.96171883e-01],\n",
       "       [-4.16009689e-01, -1.74477836e+00,  1.37235899e-01,\n",
       "         1.33225943e-01],\n",
       "       [ 7.95669016e-01, -5.87763531e-01,  4.78430123e-01,\n",
       "         3.96171883e-01],\n",
       "       [-1.73673948e-01, -5.87763531e-01,  4.21564419e-01,\n",
       "         1.33225943e-01],\n",
       "       [ 5.53333275e-01,  5.69251294e-01,  5.35295827e-01,\n",
       "         5.27644853e-01],\n",
       "       [-1.14301691e+00, -1.51337539e+00, -2.60824029e-01,\n",
       "        -2.61192967e-01],\n",
       "       [ 9.16836886e-01, -3.56360566e-01,  4.78430123e-01,\n",
       "         1.33225943e-01],\n",
       "       [-7.79513300e-01, -8.19166497e-01,  8.03701950e-02,\n",
       "         2.64698913e-01],\n",
       "       [-1.02184904e+00, -2.43898725e+00, -1.47092621e-01,\n",
       "        -2.61192967e-01],\n",
       "       [ 6.86617933e-02, -1.24957601e-01,  2.50967307e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 1.89829664e-01, -1.97618132e+00,  1.37235899e-01,\n",
       "        -2.61192967e-01],\n",
       "       [ 3.10997534e-01, -3.56360566e-01,  5.35295827e-01,\n",
       "         2.64698913e-01],\n",
       "       [-2.94841818e-01, -3.56360566e-01, -9.02269170e-02,\n",
       "         1.33225943e-01],\n",
       "       [ 1.03800476e+00,  1.06445364e-01,  3.64698715e-01,\n",
       "         2.64698913e-01],\n",
       "       [-2.94841818e-01, -1.24957601e-01,  4.21564419e-01,\n",
       "         3.96171883e-01],\n",
       "       [-5.25060772e-02, -8.19166497e-01,  1.94101603e-01,\n",
       "        -2.61192967e-01],\n",
       "       [ 4.32165405e-01, -1.97618132e+00,  4.21564419e-01,\n",
       "         3.96171883e-01],\n",
       "       [-2.94841818e-01, -1.28197243e+00,  8.03701950e-02,\n",
       "        -1.29719997e-01],\n",
       "       [ 6.86617933e-02,  3.37848329e-01,  5.92161531e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 3.10997534e-01, -5.87763531e-01,  1.37235899e-01,\n",
       "         1.33225943e-01],\n",
       "       [ 5.53333275e-01, -1.28197243e+00,  6.49027235e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 3.10997534e-01, -5.87763531e-01,  5.35295827e-01,\n",
       "         1.75297293e-03],\n",
       "       [ 6.74501145e-01, -3.56360566e-01,  3.07833011e-01,\n",
       "         1.33225943e-01],\n",
       "       [ 9.16836886e-01, -1.24957601e-01,  3.64698715e-01,\n",
       "         2.64698913e-01],\n",
       "       [ 1.15917263e+00, -5.87763531e-01,  5.92161531e-01,\n",
       "         2.64698913e-01],\n",
       "       [ 1.03800476e+00, -1.24957601e-01,  7.05892939e-01,\n",
       "         6.59117823e-01],\n",
       "       [ 1.89829664e-01, -3.56360566e-01,  4.21564419e-01,\n",
       "         3.96171883e-01],\n",
       "       [-1.73673948e-01, -1.05056946e+00, -1.47092621e-01,\n",
       "        -2.61192967e-01],\n",
       "       [-4.16009689e-01, -1.51337539e+00,  2.35044910e-02,\n",
       "        -1.29719997e-01],\n",
       "       [-4.16009689e-01, -1.51337539e+00, -3.33612130e-02,\n",
       "        -2.61192967e-01],\n",
       "       [-5.25060772e-02, -8.19166497e-01,  8.03701950e-02,\n",
       "         1.75297293e-03],\n",
       "       [ 1.89829664e-01, -8.19166497e-01,  7.62758643e-01,\n",
       "         5.27644853e-01],\n",
       "       [-5.37177559e-01, -1.24957601e-01,  4.21564419e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 1.89829664e-01,  8.00654259e-01,  4.21564419e-01,\n",
       "         5.27644853e-01],\n",
       "       [ 1.03800476e+00,  1.06445364e-01,  5.35295827e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 5.53333275e-01, -1.74477836e+00,  3.64698715e-01,\n",
       "         1.33225943e-01],\n",
       "       [-2.94841818e-01, -1.24957601e-01,  1.94101603e-01,\n",
       "         1.33225943e-01],\n",
       "       [-4.16009689e-01, -1.28197243e+00,  1.37235899e-01,\n",
       "         1.33225943e-01],\n",
       "       [-4.16009689e-01, -1.05056946e+00,  3.64698715e-01,\n",
       "         1.75297293e-03],\n",
       "       [ 3.10997534e-01, -1.24957601e-01,  4.78430123e-01,\n",
       "         2.64698913e-01],\n",
       "       [-5.25060772e-02, -1.05056946e+00,  1.37235899e-01,\n",
       "         1.75297293e-03],\n",
       "       [-1.02184904e+00, -1.74477836e+00, -2.60824029e-01,\n",
       "        -2.61192967e-01],\n",
       "       [-2.94841818e-01, -8.19166497e-01,  2.50967307e-01,\n",
       "         1.33225943e-01],\n",
       "       [-1.73673948e-01, -1.24957601e-01,  2.50967307e-01,\n",
       "         1.75297293e-03],\n",
       "       [-1.73673948e-01, -3.56360566e-01,  2.50967307e-01,\n",
       "         1.33225943e-01],\n",
       "       [ 4.32165405e-01, -3.56360566e-01,  3.07833011e-01,\n",
       "         1.33225943e-01],\n",
       "       [-9.00681170e-01, -1.28197243e+00, -4.31421141e-01,\n",
       "        -1.29719997e-01],\n",
       "       [-1.73673948e-01, -5.87763531e-01,  1.94101603e-01,\n",
       "         1.33225943e-01],\n",
       "       [ 5.53333275e-01,  5.69251294e-01,  1.27454998e+00,\n",
       "         1.71090158e+00],\n",
       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 1.52267624e+00, -1.24957601e-01,  1.21768427e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 5.53333275e-01, -3.56360566e-01,  1.04708716e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 7.95669016e-01, -1.24957601e-01,  1.16081857e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 2.12851559e+00, -1.24957601e-01,  1.61574420e+00,\n",
       "         1.18500970e+00],\n",
       "       [-1.14301691e+00, -1.28197243e+00,  4.21564419e-01,\n",
       "         6.59117823e-01],\n",
       "       [ 1.76501198e+00, -3.56360566e-01,  1.44514709e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 1.03800476e+00, -1.28197243e+00,  1.16081857e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 1.64384411e+00,  1.26346019e+00,  1.33141568e+00,\n",
       "         1.71090158e+00],\n",
       "       [ 7.95669016e-01,  3.37848329e-01,  7.62758643e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 6.74501145e-01, -8.19166497e-01,  8.76490051e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 1.15917263e+00, -1.24957601e-01,  9.90221459e-01,\n",
       "         1.18500970e+00],\n",
       "       [-1.73673948e-01, -1.28197243e+00,  7.05892939e-01,\n",
       "         1.05353673e+00],\n",
       "       [-5.25060772e-02, -5.87763531e-01,  7.62758643e-01,\n",
       "         1.57942861e+00],\n",
       "       [ 6.74501145e-01,  3.37848329e-01,  8.76490051e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 7.95669016e-01, -1.24957601e-01,  9.90221459e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 2.24968346e+00,  1.72626612e+00,  1.67260991e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 2.24968346e+00, -1.05056946e+00,  1.78634131e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 1.89829664e-01, -1.97618132e+00,  7.05892939e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 1.28034050e+00,  3.37848329e-01,  1.10395287e+00,\n",
       "         1.44795564e+00],\n",
       "       [-2.94841818e-01, -5.87763531e-01,  6.49027235e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 2.24968346e+00, -5.87763531e-01,  1.67260991e+00,\n",
       "         1.05353673e+00],\n",
       "       [ 5.53333275e-01, -8.19166497e-01,  6.49027235e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 1.64384411e+00,  3.37848329e-01,  1.27454998e+00,\n",
       "         7.90590793e-01],\n",
       "       [ 4.32165405e-01, -5.87763531e-01,  5.92161531e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 3.10997534e-01, -1.24957601e-01,  6.49027235e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
       "         1.18500970e+00],\n",
       "       [ 1.64384411e+00, -1.24957601e-01,  1.16081857e+00,\n",
       "         5.27644853e-01],\n",
       "       [ 1.88617985e+00, -5.87763531e-01,  1.33141568e+00,\n",
       "         9.22063763e-01],\n",
       "       [ 2.49201920e+00,  1.72626612e+00,  1.50201279e+00,\n",
       "         1.05353673e+00],\n",
       "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
       "         1.31648267e+00],\n",
       "       [ 5.53333275e-01, -5.87763531e-01,  7.62758643e-01,\n",
       "         3.96171883e-01],\n",
       "       [ 3.10997534e-01, -1.05056946e+00,  1.04708716e+00,\n",
       "         2.64698913e-01],\n",
       "       [ 2.24968346e+00, -1.24957601e-01,  1.33141568e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 5.53333275e-01,  8.00654259e-01,  1.04708716e+00,\n",
       "         1.57942861e+00],\n",
       "       [ 6.74501145e-01,  1.06445364e-01,  9.90221459e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.89829664e-01, -1.24957601e-01,  5.92161531e-01,\n",
       "         7.90590793e-01],\n",
       "       [ 1.28034050e+00,  1.06445364e-01,  9.33355755e-01,\n",
       "         1.18500970e+00],\n",
       "       [ 1.03800476e+00,  1.06445364e-01,  1.04708716e+00,\n",
       "         1.57942861e+00],\n",
       "       [ 1.28034050e+00,  1.06445364e-01,  7.62758643e-01,\n",
       "         1.44795564e+00],\n",
       "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 1.15917263e+00,  3.37848329e-01,  1.21768427e+00,\n",
       "         1.44795564e+00],\n",
       "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
       "         1.71090158e+00],\n",
       "       [ 1.03800476e+00, -1.24957601e-01,  8.19624347e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 5.53333275e-01, -1.28197243e+00,  7.05892939e-01,\n",
       "         9.22063763e-01],\n",
       "       [ 7.95669016e-01, -1.24957601e-01,  8.19624347e-01,\n",
       "         1.05353673e+00],\n",
       "       [ 4.32165405e-01,  8.00654259e-01,  9.33355755e-01,\n",
       "         1.44795564e+00],\n",
       "       [ 6.86617933e-02, -1.24957601e-01,  7.62758643e-01,\n",
       "         7.90590793e-01]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "x_scaled = preprocessing.scale(data)\n",
    "x_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering (scaled data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_scaled = KMeans(n_clusters=2)#create a k-means object with 2 clusters\n",
    "kmeans_scaled.fit(x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  cluster\n",
       "0             5.1          3.5           1.4          0.2        1\n",
       "1             4.9          3.0           1.4          0.2        1\n",
       "2             4.7          3.2           1.3          0.2        1\n",
       "3             4.6          3.1           1.5          0.2        1\n",
       "4             5.0          3.6           1.4          0.2        1\n",
       "..            ...          ...           ...          ...      ...\n",
       "145           6.7          3.0           5.2          2.3        0\n",
       "146           6.3          2.5           5.0          1.9        0\n",
       "147           6.5          3.0           5.2          2.0        0\n",
       "148           6.2          3.4           5.4          2.3        0\n",
       "149           5.9          3.0           5.1          1.8        0\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = x.copy()\n",
    "cluster['cluster'] = kmeans.fit_predict(x_scaled)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc738cd7550>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['sepal_length'],data['sepal_width'],c=cluster['cluster'],cmap='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take Advantage of the Elbow Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WCSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[600.0000000000003,\n",
       " 223.73200573676343,\n",
       " 140.96581663074699,\n",
       " 114.5511767079852,\n",
       " 91.0024409856725,\n",
       " 80.51609408396625,\n",
       " 70.91337109630959,\n",
       " 62.45268372832474,\n",
       " 54.41887692898719]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WCSS = []\n",
    "\n",
    "num = 10 #arbitrary number for cluster \n",
    "for i in range(1,num):\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans.fit(x_scaled)\n",
    "    WCSS.append(kmeans.inertia_)\n",
    "WCSS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Within-cluster Sum of Squares')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,num), WCSS)\n",
    "plt.xlabel('The number of clusters')\n",
    "plt.ylabel('Within-cluster Sum of Squares')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the Elbow Curve, 2, 3 or 5 seem the most likely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_3 = KMeans(n_clusters=3)\n",
    "kmeans_3.fit(x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  cluster\n",
       "0             5.1          3.5           1.4          0.2        1\n",
       "1             4.9          3.0           1.4          0.2        1\n",
       "2             4.7          3.2           1.3          0.2        1\n",
       "3             4.6          3.1           1.5          0.2        1\n",
       "4             5.0          3.6           1.4          0.2        1\n",
       "..            ...          ...           ...          ...      ...\n",
       "145           6.7          3.0           5.2          2.3        2\n",
       "146           6.3          2.5           5.0          1.9        0\n",
       "147           6.5          3.0           5.2          2.0        2\n",
       "148           6.2          3.4           5.4          2.3        2\n",
       "149           5.9          3.0           5.1          1.8        0\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = x.copy()\n",
    "cluster['cluster'] = kmeans_3.fit_predict(x_scaled)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc708025160>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['sepal_length'],data['sepal_width'],c=cluster['cluster'],cmap='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_5 = KMeans(n_clusters=5)\n",
    "kmeans_5.fit(x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  cluster\n",
       "0             5.1          3.5           1.4          0.2        0\n",
       "1             4.9          3.0           1.4          0.2        2\n",
       "2             4.7          3.2           1.3          0.2        2\n",
       "3             4.6          3.1           1.5          0.2        2\n",
       "4             5.0          3.6           1.4          0.2        0\n",
       "..            ...          ...           ...          ...      ...\n",
       "145           6.7          3.0           5.2          2.3        4\n",
       "146           6.3          2.5           5.0          1.9        1\n",
       "147           6.5          3.0           5.2          2.0        4\n",
       "148           6.2          3.4           5.4          2.3        4\n",
       "149           5.9          3.0           5.1          1.8        1\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = x.copy()\n",
    "cluster['cluster'] = kmeans_5.fit_predict(x_scaled)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc72be31c70>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['sepal_length'],data['sepal_width'],c=cluster['cluster'],cmap='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original dataset has 3 sub-species of the Iris flower. Therefore, the optimal number of clusters has to be 3.\n",
    "\n",
    "This shows us that the Eblow method is imperfect but k-means is very useful in moments where we already know the number of clusters. "
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}