GRIP_Task_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Nimish Mohan Sane\n",
    "## Task 2: Prediction using Unsupervised ML-> From the given ‘Iris’ dataset, predict the optimum number of clusters and\n",
    "## represent it visually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</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 (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = datasets.load_iris()\n",
    "df = pd.DataFrame(dataset.data,columns = dataset.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,[0,1]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding the optimum number of clusters for k-means classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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DE+4PM7PfpK6s7NNQH2PddrWmikh6S3b4KObu23vuuPs2YHhqSspODfW1gFpTRSS9JRsKXWY2pudOOBled2pKyk4jh5YysbZC5xVEJK0lO83Fd4BXzeyV8P45wK2pKSl7TZtcy8OzV9LS1kl5cSRfZSEi0q9kjxSuAf4dmAs8CXzC3XVO4SA1TIrR3tXN62pNFZE0lWwoPASUAJcCPwT+y8y+lrKqstSUuirKi/KZqSEkEUlTybak/s7MZgGnAdOAPya4ovneFNaWdYoK8vikWlNFJI0l25L6EjCbYLbUxcBp7j45lYVlq57W1GWb1ZoqIukn2eGjBUA7cDxwInC8mZWmrKosptZUEUlnSYWCu3/d3c8BPgdsJTjHsL3/Z0lfRg4tZdLwCn0bm4ikpWSHj/7MzH4GvAtcDkwHLkplYdmsob5Ws6aKSFpKdvioFPgBMNndz3f3v3H33x3qRs1sqJk9ZWa/N7MPzexMM6sKp9NYGv4edqivn+4aJsXo6HJmL9sSdSkiIvtIdvjoHnef4+4D9dH2XuCF8GT1ScCHwLeBl9x9IsEX+Xx7gLaVduKtqUt0XkFE0kuyRwoDxsyOILgi+kEAd28P51W6DHgkXO0RgmGqrFRUkMdZE2qYFbamioiki0EPBWA80Ag8ZGbvmNkDZlYODHf3DQDh79oIahs0DfW1ak0VkbQTRSgUAKcCP3b3U4AWDmKoyMxuNbN5ZjavsTFzh18a6mOAWlNFJL1EEQprgbXuPie8/xRBSGwysxEA4e8+ezbd/T53n+LuU2Kx2KAUnApHha2pmjVVRNLJoIeCu28E1phZfbjofOAD4JfA9eGy64FnBru2wXb+McN5c/lWfr1wQ9SliIgAyU+dPdC+CjxmZkXAcuAGgoB60sxuAlYDX4qotkFz27QJzF3RxFefeIcf5+fx6WP1vUUiEi3L5O6XKVOm+Lx586Iu47Ds3NPBtQ/O5YP1O7jv2ilMm5zV59dFJA2Y2Xx3n9LXY1GcU5AER5QUMuOG06k/spI/enQ+r+jaBRGJkEIhDQwpK+QnN57B+Jpybpkxj9d1pbOIREShkCaGlRfx2M1nMKaqjJsemcfcFU1RlyQiOUihkEaqK4p57JYzGDG0hBsemsv8VduiLklEcoxCIc3UVpbwxC1TiVUW85Xpc3lvjWYoF5HBo1BIQ8OPKOHxW6YytLyQax+cw6J1O6IuSURyhEIhTR01tJTHb55KZUkh1zw4hw837Iy6JBHJAQqFNDa6qozHbzmDkoJ8rn5gDks27Yq6JBHJcgqFNDe2upzHbzmDgjzjqvvn8FGjZlUVkdRRKGSA8bEKHr/lDMC56v43WbmlJeqSRCRLKRQyxITaSh67eSrtnd1cdf+brGlqjbokEclCCoUMUn9kJY/efAYt7V1cef+brNu+O+qSRCTLKBQyzHFHDeHRm85gx+4Orrr/TTbu2BN1SSKSRRQKGeiEUUOYcePpbG1u56r732TzTgWDiAwMhUKGOmXMMB6+4TQ27tzDVQ/MYUtzW9QliUgWUChksCl1VUz/ymms3dbKNQ/MoamlPeqSRCTDKRQy3NTx1Tx4/Wms2NLCNQ/MYXurgkFEDp1CIQucNaGG/7r2Eyzb3Mx10+eyc09H1CWJSIZSKGSJhvpafnzNqXy4YSfXT59Lc1tn1CWJSAZSKGSR848Zzo+uOpWFa3dww0NzaVEwiMhBUihkmc8cdyT3XnEK81dt46ZH3mJ3e1fUJYlIBlEoZKGLTxzBv3z5ZOasaOKWGfPY06FgEJHkKBSy1GUnj+SeL57E7I+28MePzqetU8EgIgemUMhiX/zEKP7xcycwc3Ejtz32Nu2d3VGXJCJpLrJQMLN8M3vHzJ4N748zszlmttTMfmZmRVHVlk2uOH0Mf3f58fy/Dzdz+xPv0NGlYBCR/YvySOFrwIcJ9+8G/sXdJwLbgJsiqSoLXTt1LH99ybG88P5G/vzJ9+hUMIjIfkQSCmY2CrgYeCC8b8B5wFPhKo8Al0dRW7a68exxfOcPJvOr99bzl08toKvboy5JRNJQQUTb/SHwTaAyvF8NbHf3nsb6tcDIvp5oZrcCtwKMGTMmxWVml1vPOZqOLuee3yymIM+4+wsnkpdnUZclImlk0I8UzOwSYLO7z09c3MeqfX6Udff73H2Ku0+JxWIpqTGb3TZtAl87fyI/n7+Wv/qfRbjriEFE9oriSOEs4FIz+wOgBDiC4MhhqJkVhEcLo4D1EdSWE+64YCIdXd38x8yPKMo3vnfpcQQjeCKS6wb9SMHd73T3Ue5eB1wB/M7drwZeBr4YrnY98Mxg15YrzIy//Ew9t54znkfeWMXfP/ehjhhEBIjunEJfvgX81Mz+HngHeDDierKamXHnRZNp7+zmwddW0Lynk9svmMjIoaVRlyYiEYo0FNx9JjAzvL0cOD3KenKNmfHdzx5LYb4xffZKnnp7LRcedyQ3nl3HqWOGaUhJJAdZJg8bTJkyxefNmxd1GVlh3fbdzHhjJU/MWc3OPZ2cNGoIN549jouOH0FRgS58F8kmZjbf3af0+ZhCQRK1tnfyi7fX8dDsFSxvbGH4EcVcd2YdV54+hqpyXWQukg0UCnLQurudWUsbmf7aCl5duoXigjw+f+pIbjhrHJOGVx74BUQkbfUXCul0olnSSF6eMa2+lmn1tSzZtIuHZq/k6bfX8sTcNXxqYg03njWOcyfFdPGbSJbRkYIkbVtLO4/PXc2MN1ayaWcb42vK+cpZdXzh1FGUF+vzhUim0PCRDKiOrm6eX7iB6bNX8t6a7VSWFHDl6WO47syxjBpWFnV5InIACgVJmbdXb2P6ayv49aKNuDsXHn8kN541jk+MVUurSLrSOQVJmVPHDOPUq4axfvtuZryxiifmrub5hRs5cdQQbjxrHH9wglpaRTKJjhRkQLW2d/L02+uYHra01lYWc92ZY7nqjLFqaRVJExo+kkHX3e28srSR6bNX8sqSRooL8vjcKUFLa/2RamkViZKGj2TQ5eUZDfW1NNTXsnTTLh56PWhp/elbazh7Qg03nl1Hw6RatbSKpBkdKcig2dbSzhNvrWbG66vYuHMP42rKuUEtrSKDTsNHklY6urr59aKNTH9tBe+GLa1XnDaa686sY3SVWlpFUk2hIGmrd0vrOZNiXHLiUXz62OEMKS2MujyRrKRQkLS3fvtuHn1zFc+8u55123dTlJ/HOZNquPjEEVxwzHAqSxQQIgNFoSAZw915d812nluwgecWbmDDjj0UFeTRMCnGxSeO4PxjhlOh8w8ih0WhIBmpu9t5Z802nl2wgecXbmDTzjaKC/I4b3ItF584gvMm11JWpIAQOVgKBcl43d3O/NXbePa99Ty/aCONu9ooLcznvGNqueSEETTU11JalB91mSIZQaEgWaWr23lrZRPPLljPC4s2sqW5nbKifM4/ZjiXnDiCcyfFKClUQIjsj0JBslZnVzdzVzTx7MINvLBoI00t7VQUF3DBMbVccuJRfGpSDcUFCgiRRAoFyQmdXd28sXwrzy3YwAvvb2R7aweVxQV8+rjgCOLsCTFNzieCQkFyUEdXN7OXbeG5BRv4zfsb2bmnkyNKCvjMcUdy8YkjOGtCDYX5CgjJTQoFyWntnd28tqyRZxds4MX3N7GrrZOhZYVcGAbEmeOrKVBASA5RKIiE2jq7eGXJFp5bsJ4XP9hES3sXVeVFXHj8kVxywgjOGF9NvibpkyyXVqFgZqOBGcCRQDdwn7vfa2ZVwM+AOmAl8Ifuvq2/11IoyOHY09HFrCXBEcRLH26itb2LmoogIKaMrWJCbQVHxyrU6ipZJ91CYQQwwt3fNrNKYD5wOfAVoMnd7zKzbwPD3P1b/b2WQkEGyu72LmYu3syzC4OA2NPRDYAZjB5WxqThFUyorWRibQUThwdhoZldJVOl1fcpuPsGYEN4e5eZfQiMBC4DGsLVHgFmAv2GgshAKS3K56ITRnDRCSNo7+xm5dYWlm5qZunmXSzd3MyyTc3MWtJIR9feD1Ejh5YycXhFGBRBYEyordA8TZLRIv2oY2Z1wCnAHGB4GBi4+wYzq93Pc24FbgUYM2bM4BQqOaWoII9JwyuZNLwSGBFf3tHVzeqm1iAsNgVhsXRzM69/tJX2zu74eiOGlDChtoKJtZVMHF4RHGXEKhlSprCQ9BfZiWYzqwBmAf/g7k+b2XZ3H5rw+DZ3H9bfa2j4SNJBV7ezpqk1DIldLNsUhMWyzc3s7uiKr1dbWRweWVSGoREcYei7q2WwpdXwEYCZFQK/AB5z96fDxZvMbER4lDAC2BxFbSIHKz/PqKspp66mnE8fOzy+vLvbWbd9dzAEFQbF0s3N/HzeGlra94ZFTUXRPkcWPbdrKoowUyeUDK5BDwUL/i9/EPjQ3X+Q8NAvgeuBu8Lfzwx2bSIDKS/PGF1VxuiqMs6bvDcs3J0NO/YEIbFpF8s2N7Nk0y7+59117NrTGV9vaFkhddXl1FWXMSb8Pba6nLHVZVSXKzAkNaLoPjobeBVYSNCSCvAdgvMKTwJjgNXAl9y9qb/X0vCRZBN3Z/Outn1OcK/e2srKrS2s376b7oR/qhXFBYytLqOuupwx1WX7BMbwyhLydK2F9COtWlIHkkJBckV7Zzdrt7WyKgyJVVtbWRX+XrOtdZ+uqOKCPMb2hERVGWNrwqOMqnKOGlqiq7cl/c4piMjBKSrIY3ysgvGxio891tXtrN++OyEwekKjlVeXNsavuQAoCIe0xlaXBYFRXU5dTRljqsoZXVWqGWVFoSCS6fITzl2cPbFmn8d6hqRWbmnZGxpNwVHG/JXb2NW29xyGGRw1pDQeEj1DUqOrShl+RAlVZUUalsoBCgWRLGZmDD+ihOFHlHDG+Op9HnN3mlra4yGxcksrq5uC4PjN+8F3UyTKzzOqy4uIVRYTqyymNvwdqygmVlmyz3Jd7Z259F9OJEeZGdUVxVRXFHPqmI9fErRzTwert7aypqmVxuY2GncFP5vD37/fsIstzW10dn/8vGRZUX5CYPQKkcpiYhVBiFRXFGkK8zSjUBCRPh1RUsjxI4dw/Mgh+12nu9vZvruDzbv2xEMj/tPcxuadbfGrvnfs7vjY882gqqwoISwSgiMxTCpKOKK0QG24g0ChICKHLC/PqCovoqq8iMlH9r9uW2cXW5rbg6ONnXv2OfroCZHljS00NrftM21Ij6L8vPi2qiuKqC4vorqimKryImoqiqgqT7xdREWxQuRQKBREZFAUF+QzcmgpI4eW9rueu7NzT2evI449bGlup6mlja3N7WxtaWfl1haamtv3uTo8UVFBXhgcQWDUhIFSVVFETRggQbgEw1hlRfkKERQKIpJmzIwhpYUMKS1kQu3HW3B729PRxdaWdrY2t7G1pZ2m5na2trSFy9ppCh9b3tjM1ub2feajSlRckEdNeOTR79FIWRFDSgupLCnIym4shYKIZLSSwuSOQHq0tnfuDYvwyCO43R4ehbTR1NLOss3NbGluo62PoSyAPIMjwvAaWlrIkDAshpYWMrSsMB5sQ3uWlwWPHVFaSElh+l4PolAQkZxSVlRAWVUBo6vKDriuu9Pa3kVTSztbmoOw2NbawfbWdnbu7mD77g62t3awI7y9emsLO3YH9/toyoorKcxjaGkQFkPKCj8eJmVFQdDEw2Twjk4UCiIi+2FmlBcXUF6cXIj06O52drV1BsHR2sH23e1BcIQBEtzeu2xNUyuLwtv7G94K6iF+BHLt1LHc/KnxA7Gb+1AoiIgMsLy8vedFRlcd3HPbOruC4GgNjj56ficenezY3UGssjgltSsURETSSHFBPrWV+dRWlkSyfV1KKCIicQoFERGJUyiIiEicQkFEROIUCiIiEqdQEBGROIWCiIjEKRRERCTO3PuZoCPNmVkjsCrqOg5TDbAl6iLSiN6Pfen92Evvxb4O5/0Y6+6xvh7I6FDIBmY2z92nRF1HutD7sS+9H3vpvdhXqt4PDR+JiEicQkFEROIUCtG7L+oC0ozej33p/dhL78W+UvJ+6JyCiIjE6UhBRETiFAoiIhKnUIiImY02s5fN7EMze9/MvhZ1TVEzs3wze8fMno26lqiZ2VAze8rMfn3Yu1YAAAXOSURBVB/+P3Jm1DVFycy+Hv47WWRmT5hZNN9AExEzm25mm81sUcKyKjN70cyWhr+HDcS2FArR6QT+wt2PAaYCt5nZsRHXFLWvAR9GXUSauBd4wd0nAyeRw++LmY0EbgemuPvxQD5wRbRVDbqHgQt7Lfs28JK7TwReCu8fNoVCRNx9g7u/Hd7eRfCPfmS0VUXHzEYBFwMPRF1L1MzsCOAc4EEAd2939+3RVhW5AqDUzAqAMmB9xPUMKnd/BWjqtfgy4JHw9iPA5QOxLYVCGjCzOuAUYE60lUTqh8A3ge6oC0kD44FG4KFwOO0BMyuPuqiouPs64PvAamADsMPdfxttVWlhuLtvgOBDJlA7EC+qUIiYmVUAvwDucPedUdcTBTO7BNjs7vOjriVNFACnAj9291OAFgZoaCAThWPllwHjgKOAcjO7JtqqspdCIUJmVkgQCI+5+9NR1xOhs4BLzWwl8FPgPDN7NNqSIrUWWOvuPUeOTxGERK66AFjh7o3u3gE8DXwy4prSwSYzGwEQ/t48EC+qUIiImRnBmPGH7v6DqOuJkrvf6e6j3L2O4ATi79w9Zz8JuvtGYI2Z1YeLzgc+iLCkqK0GpppZWfjv5nxy+MR7gl8C14e3rweeGYgXLRiIF5FDchZwLbDQzN4Nl33H3Z+PsCZJH18FHjOzImA5cEPE9UTG3eeY2VPA2wRde++QY1NemNkTQANQY2Zrge8CdwFPmtlNBMH5pQHZlqa5EBGRHho+EhGROIWCiIjEKRRERCROoSAiInEKBRERiVMoSFozMzezf064/w0z+94AvfbDZvbFgXitA2znS+FMpy+nsi4zqzOzqw6+QpG9FAqS7tqAz5tZTdSFJDKz/INY/SbgT919WqrqCdUBBxUKB7kfkgMUCpLuOgkuVPp67wd6f6I2s+bwd4OZzTKzJ81siZndZWZXm9lcM1toZkcnvMwFZvZquN4l4fPzzeweM3vLzBaY2R8lvO7LZvY4sLCPeq4MX3+Rmd0dLvtr4GzgP83snj6e883wOe+Z2V19PL6yJxDNbIqZzQxvn2tm74Y/75hZJcHFTJ8Kl3092f0ws3Izey6sYZGZfTmZ/zCSnXRFs2SCfwcWmNk/HcRzTgKOIZhueDnwgLufHn6Z0VeBO8L16oBzgaOBl81sAnAdwUycp5lZMTDbzHpm5TwdON7dVyRuzMyOAu4GPgFsA35rZpe7+9+a2XnAN9x9Xq/nXEQw3fEZ7t5qZlUHsX/fAG5z99nhpIp7CCbN+4a794Tbrcnsh5l9AVjv7heHzxtyEHVIltGRgqS9cPbYGQRftJKst8LvrGgDPgJ6/hguJAiCHk+6e7e7LyUIj8nA/wKuC6cfmQNUAxPD9ef2DoTQacDMcNK2TuAxgu9E6M8FwEPu3hruZ+/58vszG/iBmd0ODA232Vuy+7GQ4IjpbjP7lLvvOIg6JMsoFCRT/JBgbD7xewU6Cf8fDidKK0p4rC3hdnfC/W72PULuPc+LAwZ81d1PDn/GJczf37Kf+izZHen1nAPNMxPfRyD+FZTufhdwM1AKvGlmk/fz+gfcD3dfQnCEsxD4x3DIS3KUQkEyQvgp+kmCYOixkuCPGQTz7Rcewkt/yczywvMM44HFwG+APwmnNsfMJiXxJTdzgHPNrCY8eXslMOsAz/ktcKOZlYXb6Wv4aCV79/ELPQvN7Gh3X+judwPzCI5wdgGVCc9Naj/Coa9Wd3+U4Mtscnma7pyncwqSSf4Z+LOE+/cDz5jZXILvqN3fp/j+LCb44z0c+GN332NmDxAMMb0dHoE0coCvOnT3DWZ2J/AywSf0592936mM3f0FMzsZmGdm7cDzwHd6rfY3wINm9h32/Wa+O8xsGtBFMK32rwmOgjrN7D2C7/S9N8n9OAG4x8y6gQ7gT/qrW7KbZkkVEZE4DR+JiEicQkFEROIUCiIiEqdQEBGROIWCiIjEKRRERCROoSAiInH/H3dNXESowT8wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "for i in range(1,11):\n",
    "    kmean = KMeans(n_clusters=i,init='k-means++',random_state=0)\n",
    "    kmean.fit(x)\n",
    "    wcss.append(kmean.inertia_)\n",
    "\n",
    "#plotting the line graph to find number of cluster using the Elbow Method\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2\n",
      " 1 1 1 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 1 1 1\n",
      " 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 2 1 1 1 2 1 1 1 2 1 1 1 2 1\n",
      " 1 2]\n"
     ]
    }
   ],
   "source": [
    "#From the above graph come to know that number of clusters can be 3\n",
    "\n",
    "#Applying K-Mean to the dataset \n",
    "kmean = KMeans(n_clusters=3,init='k-means++',random_state=0)\n",
    "y_kmean = kmean.fit_predict(x)\n",
    "print(y_kmean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualising the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b9ab755208>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x[y_kmean==0,0],x[y_kmean==0,1],s=100,c='red',label='cluster 1')\n",
    "plt.scatter(x[y_kmean==1,0],x[y_kmean==1,1],s=100,c='blue',label='cluster 2')\n",
    "plt.scatter(x[y_kmean==2,0],x[y_kmean==2,1],s=100,c='yellow',label='cluster 3')\n",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('sepal width')\n",
    "\n",
    "#plotting the centroid of the clusters\n",
    "plt.scatter(kmean.cluster_centers_[:,0],kmean.cluster_centers_[:,1],s=200,c='black',label='centroid')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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