Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "healthy-material",
   "metadata": {},
   "source": [
    "# NAME- Sanya Sachdeva\n",
    "# \n",
    "# GRIP- THE SPARKS FOUNDATION\n",
    "# Data Science & Business Analytics Internship\n",
    "# K- Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intelligent-being",
   "metadata": {},
   "source": [
    "# TASK-2 : Prediction using Unsupervised ML\n",
    "## From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "satisfactory-depth",
   "metadata": {},
   "source": [
    "### *Import the required libraries*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "functioning-suffering",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-choir",
   "metadata": {},
   "source": [
    "### *Loading the iris dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "convenient-theorem",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "iris_df = pd.DataFrame(iris.data, columns = iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "arabic-award",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "transsexual-canal",
   "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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "endangered-yemen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-hearing",
   "metadata": {},
   "source": [
    "### *Checking for null values*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cheap-router",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "painted-boxing",
   "metadata": {},
   "source": [
    "### *Data Visualization*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "intense-weight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'sepal length (cm)'}>,\n",
       "        <AxesSubplot:title={'center':'sepal width (cm)'}>],\n",
       "       [<AxesSubplot:title={'center':'petal length (cm)'}>,\n",
       "        <AxesSubplot:title={'center':'petal width (cm)'}>]], dtype=object)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UTk1i6mudr8l7Ggi/t6nRSxfKBmD/wvI84Ce9hWNWa67zViu9JPDvAAdK+lVJTwfeCnytP2GZ1ZLrvNVK110oEbFN0ruAfwemARdGxOq+RTYYtejKaeGYyqk8pgHU+crf0wD5vU0BRTylC8/MzBrAV2KamTWUE7iZWUPtNAlc0jpJd0q6XdKtVccDIGmWpMskfV/SGkmvqDCWg/JnM/Z4XNIZVcVTiOtPJK2WdJekSyTtXnVMvZC0v6Tr8u97taTTq46pXyTtLukWSd/N7+0vq46p3yRNk3SbpCurjgV2oj5wSeuABRFRm4sLJK0AvhkRn8lnNewREY9WHNbYJeM/Bo6IiHsrjGM/4EbgkIh4QtKlwL9GxEVVxdQrSXOBuRGxStKewErgxIj4XsWh9UySgBkRsUXSrqTf3ekR8e2KQ+sbSX8KLAD2iojjq45np2mB142kvYBXARcARMQv6pC8s6OBH1SZvAt2AaZL2gXYg4afdx0RGyNiVX6+GVhDusKz8SLZkhd3zY+haSFKmgccB3ym6ljG7EwJPICrJa3MlzpX7bnAT4HP5q9kn5E0o+qgsrcCl1QdRET8GPg4cB+wEXgsIq6uNqr+kTQCHAbcXHEofZO7GG4HNgHXRMTQvDfg74D3Av9TcRy/tDMl8KMi4qWkmeTeKelVFcezC/BS4BMRcRiwFah8etLclfNG4Ms1iOWZpMmifhXYF5gh6W3VRtUfkmYClwNnRMTjVcfTLxHxZEQcSrpK9XBJL6o4pL6QdDywKSJWVh1L0U6TwCPiJ/nnJuArpJnlJk3SRZI+3GHbYkk3lixqA7Ch0EK5jJTQuzJeXOPs8x+SDmtZ/TpgVUQ80GUcL5b0rW72beMY4EcR8dOI+G/gCuDX+1R2ZXL/8OXAxRFxRdXxlDWZOpa7A0eBY1vKeL+kjl0Q+WSDY8bZPirptHIRg6TdJH1P0q+U3aeDo4A35rG0LwKL8vNK7RQJXNKMPGBE7qZ4LXBXXh63wgxKRNwPrJd0UF51NFBqIGuS/yg6lfEGYHNE3Nay6SR66D6JiDuAR3P5vboPOFLSHnmA7GhSn3Fj5fdxAbAmIs6pOJa+1n1J+0ialZ9PJ/0D/n7xNRHx0YgolYAlnSXpCz2GtQS4If+9dS0i3hcR8yJihNTFeA1ppskX9xhfT3aKBA7MAW6U9F3gFuCqiPh/FccE8G7gYkl3AIcCH53CY78D+HxxhaQ9gEWklm4vLgb+oMcyyN9OLgNWAXeS6mttLmPu0lHAKcBrCqdsvr7qoPpkLnBdrs/fIfWBV3263R/QUs/76BK2TxtcjYho/ANYB7yP1IJ9BPgssHth+/HA7cCjwLeAF+f1nycNSDwBbAHem9d/GbgfeAy4AXhhoayLgA93iGMxcGNh+ddI/6kfBu4G3tJSznnAVcBm0kDW8wrbX5v3eQz4R+B64DTgYOBnwJM55kfLlNcS59Pze55XWDcNeD/wg7z/SmD/vC2APwLW5m0fAp4H3AQ8DlwKPL1Q1n65/N2qrhvD/qhR3b8XeFl+/rZcZw7Jy6cBX83PzwK+UNjvlLzvQ8AH8vs5htT18gvgv3N8382vH8317z9yXbwamN0hpufk97dLYd104Ox8zMdIpzpOB0ZyzL9LmvP9EVIj5+XAHfnzO7el/KNIXXzV/f6rroB9rMR3kab63Dv/cj+ct72UNCJ+RE5Sp+bX71bY95iW8n4P2BPYjTTyfHvJSryYnMCBGbki/C7bBywfHPuDyOU8TOqL34XUav1i3jablBh/O287PVfk01qP0xJX2/LaxPlCYGvLuj8jtXIPIs17/RLgWXlbkGbd2yvv+3PgWtKZNM8gJY9TW8p7nJws/Ngp6v7ngKX5+fmkhsAfFrb9SX5+FjmBk25Lt4V0Ou1uwDnAtrGYaEn2ed1oLvsFpMQ7CizvENNxwOqWdeflffbLn8mv52OP5Hr+SWB3UgPqZ8BXgWfn128CXl0oa++8z15V/f6HqQvl3IhYHxEPAx8h9eUC/D7wqYi4OdII+QpSAjqyU0ERcWFEbI6In5Mq0UskPWOS8RwPrIuIz0bEtkjn/l4OvKnwmisi4paI2EZKuIfm9a8nVbwr8rZ/ILWKJtKpvFazSK2XotOAD0bE3ZF8NyIeKmz/WEQ8Hmn2vbuAqyPdWuwx4N9Ip8MVbc7HscGrQ92/Hnh1fv4bwF8Vll+dt7d6E3BlRNyQj/fnlDtF77MR8Z8R8QTp29+hHV43i0I9l/Q00j+o0yPix/kz+VY+9pgPRcTPIp2uuhW4JCI2RTql9ZvsWM/Hyp5VIuaBGKYEXrzV1b2k084g3YpoqaRHxx6k1sq+tJHPY10u6QeSHie1UiC1iifjAOCIluOeDBRHw4tJ+b+Amfn5vsX3E+nf/YYSx+xUXqtHSK2sov1JLZtOimelPNFmufVYe5K+dtrg1aHuXw/8Rj7bYxrwJeCofK77M0jdOK1a6/lWUlfKRLqt57NJret+1fOxsh8dp7yBGqYEXrxTynPYfsXeeuAjETGr8NgjIsbOtGi9Uux3SOceH0OqeCN5fbvbaY1nPXB9y3FnRsQflth3I+k82nTgdObCvML2Xq9uW5uLLV4BuJ7Ur90zSfuS+tkru//pTqbyuh8R95CS6R+TzvrYTEq0S0jdfe1a1huLsedB9GcVi53ouBO4A3huvooXUhfmz+hTPSeNR62LCs/jH6YE/k5J8yTtTRqM+1Je/2ngHZKOUDJD0nFjpxWS/sM+t1DOnqSvmQ+RLt3u9syQK4EXSDpF0q758XJJB5fY9ypgvqQTc+V7Jzu23B8A5uWLbiYt0jnVX2f7V1xIlwd/SNKB+XN6saRntS9hQguBb7R8NbXBqUvdvx54F9u7S0ZblltdBhwv6ZW5Lv8fdsxJDwAjuetj0iJiA6mxcnhe/h/gQuAcSfvmbxyvkLRbN+WT/n7+rct9+2KYEvg/kUakf5gfHwaIiFtJfYHnkr5S3UMaBBzzV8AH81fM95AGXO4lTeb0PaCriXhyC+S1pHNGf0JqjXyMNGAy0b4PAm8G/pr0x3QIcCvpjwvgG8Bq4H5J3U7O9SnSGQBjziH1J15NGoC8gDRI1I2TSYNBNjXqUvevJ/0TuKHD8g7yeMo7c/wbc4zFrsKxq4EfkrRqkrGMaa3n7yEN1n+HNOj/MbrPgyfl8iszFLMR5iuiTouIr1cdyyDkFsgG4OSIuK6P5d4IvDueejFPL2XOB86PiMqmxt2ZDHvd71VuXd8GHB0RG/tY7huAUyLiLf0qsxtd3xPTBkvSb5HO5X6CdIqf6PLbQCcR8cp+lpfLvBNw8rZayN14hwyg3H8B/qXf5U7WMHWhDJtXkEbLHwTeQJoz+olqQzKzOhmKLhQzs52RW+BmZg01pX3gs2fPjpGRkak8JFu3bmXGjLrcJ6Ecxzy+lStXPhgR+0zJwXo0e/bs2GeffRr3+yyriXV1Mury/jrV+SlN4CMjI9x669TeT3h0dJSFCxdO6TF75ZjHJ6kOt3orZWRkhI9//OON+32W1cS6Ohl1eX+d6ry7UMzMGsoJ3MysoZzAzTrIl1rfJunKvLy3pGskrc0/n1l1jLZzq82FPCPLrpr0PuuWHzeASMx+6XTSLdz2ysvLgGsjYrmkZXn5zG4Ld523XrkFbtaGpHmkGwIUb8B7ArAiP18BnDjFYZntoDYtcLOa+Tvgvew4n/Scsfk0ImKjpGe321HSEvK9EufMmcOWLVsYHR19yuuWzt826aDalVOlTu9tWNT9/TmBm7WQdDywKSJWSlo42f0j4nzyzZcXLFgQM2fObHsq2uJuulBOnnQ4A1WX0+wGpe7vzwnc7KmOAt6Y7xa/O7CXpC8AD0iam1vfc0n3SDSrjPvAzVpExPsiYl5EjJDmc/9GRLyNdGPnU/PLTgX+uaIQzQAncLPJWA4skrQWWJSXzSrjLhSzcUTEKOnWYETEQ8DRVcZjVuQWuJlZQzmBm5k1lBO4mVlDOYGbmTWUE7iZWUM5gZuZNdSECVzS7pJukfRdSasl/WVe76k1zcwqVKYF/nPgNRHxEuBQ4FhJR7J9as0DgWvzspmZTZEJE3gkW/LirvkReGpNM7NKlboSU9I0YCXwfOC8iLhZUldTa3aamnFQU2vWfTrIdhyz7Ux8Y4vulUrgEfEkcKikWcBXJL2o7AFap9bsNDXjoKbWrPt0kO04ZjMrY1JzoUTEo5JGgWPx1Jq14RaM2c6pzFko++SWN5KmA8cA38dTa5qZVapMC3wusCL3gz8NuDQirpR0E3CppLcD9wFvHmCcZmbWYsIEHhF3AIe1We+pNc3MKuQrMc3MGsoJ3MysoZzAzcwaygnczKyhfE/MmhlZdhVL52/r6sImM9u5uAVuZtZQTuBmZg3lBG5m1lBO4GZmDeUEbmbWUE7gZmYN5QRuZtZQTuBmZg3lBG5m1lBO4GZmDVXmjjz7S7pO0hpJqyWdntfvLekaSWvzz2cOPlwzMxtTZi6UbcDSiFglaU9gpaRrgMXAtRGxXNIyYBlw5uBCNTPrXjf3jl06fxsL+x9K30zYAo+IjRGxKj/fDKwB9gNOAFbkl60AThxQjGZm1sakZiOUNEK6vdrNwJyI2AgpyUt6dod9lgBLAObMmcPo6GjbspfO3zaZUAA6llW0ZcuWUq+ri6XztzFnenefx2T0+zNp2udsNgxKJ3BJM4HLgTMi4nFJpfaLiPOB8wEWLFgQCxcubPu6bqZPXXdy+7KKRkdH6XTMOlqcp5M9+87BzvRb5rObjKZ9zmbDoNRZKJJ2JSXviyPiirz6AUlz8/a5wKbBhGg2tTxwb01R5iwUARcAayLinMKmrwGn5uenAv/c//DMKjE2cH8wcCTwTkmHkAbqr42IA4Fr87JZZcq0wI8CTgFeI+n2/Hg9sBxYJGktsCgvmzWeB+6tKSbsaI2IG4FOHd5H9zccmyrdnFK1bvlxA4ik3voxcN9pgHdQA/dTqR+D11P1OXRznDnT6/eZF/memGYd9GvgfubMmW0HeAc1cD+V+jF4PVWfQzfHWTp/G2+p8eC8L6U3a8MD99YETuBmLTxwb03hLhSzpxobuL9T0u153ftJA/WXSno7cB/w5mrCM0ucwM1aeODemsJdKGZmDeUEbmbWUE7gZmYN5QRuZtZQTuBmZg3lBG5m1lCNPo2wzHweS+dv2+ES2p1xPg8zG06NTuBmZnU0VZPFuQvFzKyhnMDNzBqqzB15LpS0SdJdhXW+tZSZWcXK9IFfBJwLfK6wbuzWUsslLcvLZ/Y/PKuT8fr1WgeLizxwbDYYE7bAI+IG4OGW1b61lJlZxbo9C6XUraXgqbeX6nR7om5ud1TGnOk7ll3n2yNBirU15iYYL+a6f+ZmTTXw0whbby/V6fZL3dzuqIyl87dx9p3b32bdbknVavGyq54ScxOMF3PdP3Ozpur2LBTfWsrMrGLdJnDfWsrMrGJlTiO8BLgJOEjShnw7qeXAIklrgUV52czMptCEHa0RcVKHTb611Di6uZTWzOqnzn/LvhLTzKyhmnWqg9lObqomSepWnVurw8gtcDOzhnIL3Mza6ma+fZtaboGbmTWUE7iZWUO5C8XMGseDpYkTuA1c3c+cMGsqd6GYmTWUE7iZWUM5gZuZNZT7wEvwgImZ1ZFb4GZmDeUEbmbWUE7gZmYN5QRuZtZQPSVwScdKulvSPZKW9Ssos7pynbc66fosFEnTgPNIt1TbAHxH0tci4nv9Cs6sTppa530W1fDqpQV+OHBPRPwwIn4BfBE4oT9hmdWS67zViiKiux2lNwHHRsRpefkU4IiIeFfL65YAS/LiQcDd3YfbldnAg1N8zF455vEdEBH7TNGxfqmHOv8Qzft9ltXEujoZdXl/bet8LxfyqM26p/w3iIjzgfN7OE5PJN0aEQuqOn43HHNtdVXnh/mzGeb3BvV/f710oWwA9i8szwN+0ls4ZrXmOm+10ksC/w5woKRflfR04K3A1/oTllktuc5brXTdhRIR2yS9C/h3YBpwYUSs7ltk/VNZ900PHHMN9VDnh/mzGeb3BjV/f10PYpqZWbV8JaaZWUM5gZuZNdTQJnBJ+0u6TtIaSaslnV51TGVImibpNklXVh1LGZJmSbpM0vfzZ/2KqmOqk2G99F7ShZI2Sbqr6lj6rUm5Y2j7wCXNBeZGxCpJewIrgRPrftmzpD8FFgB7RcTxVcczEUkrgG9GxGfymRl7RMSjFYdVC/nS+/+kcOk9cFLd62AZkl4FbAE+FxEvqjqefmpS7hjaFnhEbIyIVfn5ZmANsF+1UY1P0jzgOOAzVcdShqS9gFcBFwBExC+cvHcwtJfeR8QNwMNVxzEITcodQ5vAiySNAIcBN1ccykT+Dngv8D8Vx1HWc4GfAp/N3T6fkTSj6qBqZD9gfWF5AzVNBNZe3XPH0CdwSTOBy4EzIuLxquPpRNLxwKaIWFl1LJOwC/BS4BMRcRiwFRiaft4+KHXpvdVTE3LHUCdwSbuSfgEXR8QVVcczgaOAN0paR/qq/RpJX6g2pAltADZExFjr5DJSQrfEl943VFNyx9AmcEki9c2uiYhzqo5nIhHxvoiYFxEjpEu0vxERb6s4rHFFxP3AekkH5VVHA7Ub6KmQL71voCbljqFN4KQW7Smkluzt+fH6qoMaQu8GLpZ0B3Ao8NFqw6mPiNgGjF16vwa4tKbTTUyapEuAm4CDJG2Q9PaqY+qjxuSOoT2N0Mxs2A1zC9zMbKg5gZuZNZQTuJlZQzmBm5k1lBO4mVlDOYGbmTWUE7iZWUP9f3ykV/surQdsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_df.hist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selective-field",
   "metadata": {},
   "source": [
    "### *Finding the optimum number of clusters for k-means classification*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "useful-sullivan",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris_df.iloc[:, [0, 1, 2, 3]].values\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', \n",
    "                    max_iter = 300, n_init = 10, random_state = 0)\n",
    "kmeans.fit(x)\n",
    "wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "purple-budget",
   "metadata": {},
   "source": [
    "### *Plotting the results onto a line graph*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "transsexual-trinity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 7))\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The elbow method')\n",
    "plt.xlabel('Number of clusters')\n",
    "# Within cluster sum of squares\n",
    "plt.ylabel('WCSS') \n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unable-framing",
   "metadata": {},
   "source": [
    "### *Applying kmeans to the dataset / Creating the kmeans classifier*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "false-result",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 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 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2\n",
      " 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 2 2 2 2 0 2 2 2 0 2 2 2 0 2\n",
      " 2 0]\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++',\n",
    "                max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(x)\n",
    "print(y_kmeans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-swiss",
   "metadata": {},
   "source": [
    "### *Visualising the clusters - On the first two columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "altered-ratio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q7r//frz55puDtp8bodjR0YHXX38dpaWlg+4fOEIRwKARiolGjx6Nl156CTfeeCPuuusuBINBvPTSSzhx4sSweR0/fhwvvfQSnn32Wdx3331J9w8crxgOh3HbbbcBAO655x7s2bMH+/fvx6lTp/Dss89q3SVZYzMmIrKDT1PPMu7XdSbrh8r1CMWhnNv+1ltv4atf/Wr/4916663D5nXjjTfC4XDgkksuwX//938n3T/UeEUA2LVrFy6//HLMmjULbW1tOHDgQKrydcFmTERkB2NTD4noVzwy+4fK8QjFVI+h5SnjUaNG9X8+1PcNNV7x9OnTqK6uRiAQwBtvvIE777wzaUxiPrAZExHZwZKLgKI0v9KLHPE4nWQ6QjGViy++GO+++y7ee+89AOg/q87EUOMVzzXeSZMmoaury7AXe/HV1EREdnD3HGDrW6n/bqw4gOWluqXQ3t6OH/7wh1AUBcXFxXj88ceHjLv55puxZMkStLe3p11zzJgxePjhh3Httddi0qRJmDt3bsb5ffe738U777wDj8cDRVFw55134p577sGdd96JWbNmYdq0abjssssyXj8bHKGogt3Ht9m1vsjJCBpebkBLuAXrp61H7Xu1qPJUoWZ+DUomlhidXk7Z9RieY/f6gNyMUMTOo/G3L8V6BzflIke8ET/mBa6empuENcpmhGJXVxeKi4shpcSKFSswY8YMrF6t31zmTHGEIlGC4KEgPFs8aNrXhOiZKAAgeiaKpn1N8GzxIHgoaHCGRDq4emr8fcTLZgLjRgIC8Y/LZsa3G9SIs/Xoo4+itLQUM2fORGdnJ+666y6jU8o5Pk1NthM5GYFvmw/dse6k+2K98aHrvm0+hJeHbXeGTITp44GHFsRvNrF69WpTngnnEs+MyXYaXm5ArCf12zxiPTFsfGVjnjIiIkqNzZhspyXcglhvmmbcG0NzuDlPGRERpcZmTLbTdaYrp3FERHpjMybbKR45/HSZTOKIrCQCoBqAC/Ff8K6+ryNGJkVpsRmT7VR5qqA4Ul+NSHEoWOpZmqeMiPIjCMADoAlAFPEJitG+rz1992fqv/7rv3DLLbegpKQEl1xyCRYvXox33nlH8zq/+MUv8P7772v+vsWLF+Pjjz9O2v6DH/wA9fX1mtczGzZjsp2a+TVQRqRpxiMUrJ5n71dnUmGJAPAB6AaQ+IqJWN92HzI7Q5ZS4qabbkJFRQUikQjefPNNPPDAA0Ne/zmdVM24p6dn2O/bsWMHJkyYoPnxrILNmGynZGIJAksCcCrOpDNkxaHAqTgRWBLg25rIVhqQ3IQTxQBk8h6CXbt2QVEULF++vH9baWkprrzySvzwhz/EZZddBo/Hg/Xr1wOIjy382te+hjvvvBMzZ87ENddcg1OnTiEQCGDv3r247bbbUFpailOnTiWNS3zqqacwa9YsuN1urF27tv/xpk2bhg8//BAAcP/99+Oiiy7C1Vdfjbff/nwK1ebNm3HJJZfA4/HglltuyaBS47AZky15Z3gRXh6Gv8wP1ygXAMA1ygV/mR/h5WF4Z3gNzpAot1qgrhln8h6C/fv3o6ysLGn7888/j0OHDuHVV19FR0cHQqEQXnjhBQDAoUOHsGLFChw4cAATJkzAb37zG/h8PpSXl+OJJ55AR0cHxowZA+DzcYlXXXUV1q5di7a2NnR0dGDPnj145plnBj1mKBTC1q1b8dprr+HXv/419uzZ03/fgw8+2D8eccuWLRlUahw2Y7KtkoklaFzciM77OlF2QRk67+tE4+JGnhGTLal9b0Au30Pw/PPP4/nnn8ecOXNw6aWX4q233sKhQ4cAANOnT++fZ1xWVoY//vGPw65zblzinj17UFFRgcmTJ6OoqAi33XZbf3M/58UXX8RNN90Ep9MJl8uF66+/vv8+j8eD2267DS0tLf1jEq2CzZiIyAbUvjcgk/cQzJw5E6FQKGm7lBLf//730dHRgY6ODhw+fBh/8zd/A2DwOMMRI0b0T0oaitZxiYljEM/Zvn07VqxYgVAohLKyspSPaTZsxkRENlAFIN1EYwVAJu8hqKysxGeffYZHH320f9uePXvgcrnw2GOPoasrfr79n//5n/jggw9SrjVu3DhEo9Eh77v88suxe/dufPjhh+jp6cFTTz2FBQsGX9bzqquuwtNPP41Tp04hGo3i3//93wEAvb29+NOf/oSFCxfin/7pn/Dxxx/352UF1jqPJyKiIdUA+Fek/ruxAiCT9xAIIfD000/je9/7Hh588EGMHj0a06ZNw49+9CNMmDAB8+fPBwAUFxejpaUl5Yzi22+/HcuXL8eYMWPw8ssvD7rvggsuwIYNG7Bw4UJIKbF48WLccMMNg2IuvfRS3HzzzSgtLcXUqVNx5ZVXAoi/EruqqgqdnZ2QUmL16tWWevU1RyiqYPfxbXavD7B/jazP+nIxQjGI+NuXYhjclJW+WwCAUS9dzGaEolVwhCKZSuRkBNXbq+Ha4IKj1gHXBheqt1cjcpLXACLSkxdAGIAfg6/A5e/bzvcQmBebMeVU4hxhCck5wkR5VAKgEUAngJ6+j41928m82IwpZwbOEU6cmhTrjaE71g3fNh/PkIkyYNSfFCkzWo8XmzHlDOcIE+lj9OjR+Oijj9iQLUJKiY8++gijR49W/T18NTXljJY5wo2LG/OUFZH1TZkyBceOHcOJEyeMTiVjp0+f1tScrGhgjaNHj8aUKVNUfy+bMeUM5wgT6UNRFEyfPt3oNLLS3t6OOXPmGJ2GrrKpkU9TU85wjjARUWbYjClnOEeYiCgzbMaUM5wjTESUGTZjyhnOESYiygybMeVU4hxhh3BwjjARURp8NTXl3Lk5wnz7EhGROjwzJiIiMhibMRERkcHYjImIiAzGZkxERGQwNmMiIiKDsRkT6SxyMoLq7dVwbXDBUeuAa4ML1durOUqSiPqxGRPpKHgoCM8WD5r2NSF6JgoJieiZKJr2NcGzxYPgoaDRKRKRCahuxkKIEUKI14QQzw5xnxBCbBZCHBZChIUQl+Y2TSLriZyMwLfNh+5Yd9JoyVhvDN2xbvi2+XiGTESazoxXATg4zH1eADP6bn4Aj2SZF5HlNbzcgFhPmvnOPTFsfGVjnjIiIrNS1YyFEFMA/BWApmFCbgDwuIx7BcAEIcQFOcqRyJJawi1JZ8SJYr0xNIeb85QREZmVkFKmDxIiAGADgHEA1kgpr0u4/1kAD0opX+r7uhXAWinl3oQ4P+Jnzjj//PPLtm7dmpMi9NbV1YXiYvvO4LV7fYAxNYaOh1THll1QltVj2f0Y2r0+wP412r0+QF2NCxcuDEkpyxO3p702tRDiOgAfSClDQoiK4cKG2JbU5aWUPwPwMwAoLy+XFRXDLWcu7e3tsEqumbB7fYAxNV6/4XpEz0TTxrlGudB5a2dWj2X3Y2j3+gD712j3+oDsalTzNPUVAK4XQrwHYCuASiFES0LMMQBfGfD1FADvZ5QRkU1UeaqSRkkmUhwKlnqW5ikjIjKrtM1YSvl9KeUUKeU0ALcAaJNSViWE/RbAsr5XVc8D0CmlPJ77dImso2Z+DZQRaZrxCAWr563OU0ZEZFYZv89YCLFcCLG878sdAN4FcBjAowCqc5AbkaWVTCxBYEkATsWZdIasOBQ4FScCSwIomVhiUIZEZBaa5hlLKdsBtPd9vmXAdglgRS4TI7ID7wwvwsvD2PjKRjSHm9F1pgvFI4ux1LMUq+etZiMmIgAamzERaVcysQSNixvRuLjR6FSIyKR4OUwiIiKDsRkTEREZjM2YiIjIYGzGREREBmMzJstoO9IG98NuiFrRf3M/7EbbkTajUyMiygqbMVlC3e46LHp8EQ6cODBo+4ETB7Do8UWo211nUGZERNljMybTazvShvXt61PGrG9fzzNkIrIsNmMyvZXBlariVj23SudMiIj0wWZMppf41PRw9n+wX+dMiIj0wWZMRERkMDZjIiIig7EZk+nNnDxTVZz7PLfOmRAR6YPNmExvs3ezqrhN127SORMiIn2wGZPpVU6vRG1FbcqY2opaVE6vzFNGRES5xWZMlrBuwTq0LmtNeirafZ4brctasW7BOoMyIyLKHucZk2VUTq/EG3e/YXQaREQ5xzNjIiIig7EZExERGYzNmIiIyGBsxkRERAZjMyYiIjIYX01NqrSEW7AyuBJ/Pv3n/m1fGP0FbPZuRpWnysDM8i9yMoKGlxvQEm5B15kuFI8sRpWnCjXza1AyscTo9IgKw5FO4JHXgG1vA5/GgLEKsOQi4O45wPTx5ls3DZ4ZU1pLf70US59eOqgRA8CfT/8ZS59eiqW/XmpQZvkXPBSEZ4sHTfuaED0ThYRE9EwUTfua4NniQfBQ0OgUiexv51FgwVNA85tAVwyQiH9sfjO+fedRc62rApsxpdQSbkHLGy2pY95oQUs4dYwdRE5G4NvmQ3esG7He2KD7Yr0xdMe64dvmQ+RkxKAMiQrAkU7gjiBw6ixwtnfwfWd749vvCMbjzLCuSmzGlNLK4EpVcauCq3TOxHgNLzcg1hNLGRPriWHjKxvzlBFRAXrkNSDWmzom1gts6TDHuiqxGVNKiU9ND+fk6ZM6Z2K8lnBL0hlxolhvDM3h5jxlRFSAtr2dfOaa6GxvPM4M66rEZkykUteZrpzGEVEGPk39H+J+XWfMsa5KbMZEKhWPLM5pHBFlYKyiLq54pDnWVYnNmFL6wugvqIqbOHqizpkYr8pTBcWR+h+s4lCw1FM4ry4nyrslFwFFaVpXkSMeZ4Z1VWIzppQ2ezeritvk3aRzJsarmV8DZUSaZjxCwep5q/OUEVEBunsOoKRpXYoDWF5qjnVVYjOmlKo8VaialfqiHlWzqgriwh8lE0sQWBKAU3EmnSErDgVOxYnAkgAv/EGkp+njgce8wJii5DPZIkd8+2Ne7Rfo0GtdldiMKa3mbzWj+abmpKeiJ46eiOabmtH8rcJ59bB3hhfh5WH4y/xwjXLBIRxwjXLBX+ZHeHkY3hleo1Mksr+rpwK7bwWWzQTGjQQE4h+XzYxvv3qqudZVgZfDJFWqPIVx9qtGycQSNC5uROPiRqNTISpc08cDDy2I36ywbho8MyYiIjIYmzEREZHB2IyJiIgMxmZMRERkMDZjUiVyMoLq7dVwbXDBUeuAa4ML1durs55QpGVdvXIgIjIamzGlpdcMXy3rco4wEdkZmzGlpNcMXy3rco4wEdkdmzGlpNcMXy3rco4wEdkdmzGlpNcMXy3rco4wEdkdr8BFKek1w1fLulJKXXIgIjILnhlTSnrN8NWyLucIE5HdsRlTSnrN8NWyLucIE5HdsRlTSnrN8NWyLucIE5HdsRlTSnrN8NWyLucIE5HdsRlTWnrN8NWyLucIE5Gd8dXUpIpeM3y1rMs5wkRkVzwzJiIiMhibMRERkcHYjImIiAzGZkxERGQwNuMcMMuc3UxnA4eOhzgbmMhOjnQC97YD038KnNcY/3hve3w7mRKbcZbMMmc3m9nAADgbmMgudh4FFjwFNL8JdMUAifjH5jfj23ceNTpDGgKbcRbMMmeXs4GJCED8zPeOIHDqLHC2d/B9Z3vj2+8I8gzZhNiMs2CWObucDUxEAIBHXgNivaljYr3Alo68pEPqsRlnwSxzdjkbmIgAANveTj4jTnS2Nx5HpsIrcGVBr1m/eubB2cBENvZp6v9o9+s6o28epBnPjLNgljm7nA1MRACAsamnm/UrHqlvHqQZm3EWzDJnl7OBiQgAsOQioCjNr/UiRzyOTIXNOAtmmbPL2cBEBAC4ew6gpPm1rjiA5aV5SYfUYzPOglnm7HI2MBEBAKaPBx7zAmOKks+Qixzx7Y9543FkKmzGWTLLnN1sZgMD4GxgIru4eiqw+1Zg2Uxg3EhAIP5x2cz49qunGp0hDYGvps4Bs8zZzXQ2cHt7Ozpv5UUAiGxj+njgoQXxG1kCz4yJiIgMxmZMRERkMDZjIiIig7EZExERGYzNmIiIyGBsxgWq7Ugb3A+7IWoFQsdDELUC7ofdaDvSljb+3G24+MjJCKq3V8O1wQVHrQOuDS5Ub6/O+1jGgXmEjocMy4OIKB024wJUt7sOix5fhAMnDgzafuDEASx6fBHqdtdlHB88FIRniwdN+5oQPROFhET0TBRN+5rg2eJB8FBQv8IGSMwDgCF5EBGpkbYZCyFGCyFeFUK8LoQ4IISoHSKmQgjRKYTo6Lut0yddylbbkTasb1+fMmZ9+/r+M14t8ZGTEfi2+dAd604a0xjrjaE71g3fNp/uZ6ZmyYOISC01Z8afAaiUUs4GUArgWiHEvCHiXpRSlvbd6oa4n0xgZXClqrhVz63SHN/wcgNiPWlmJffEsPGVjarWzJRZ8iAiUittM5Zx54bbKn03dUNxyXQSn2oezv4P9muObwm3JJ2JJor1xtAcbla1ZqbMkgcRkVpCzbB5IcQIACEAfwngJ1LKtQn3VwD4FYBjAN4HsEZKmfRbXAjhB+AHgPPPP79s69atWaafH11dXSgutsd839DxUNK2KaOm4Nhnx5K2l11QNmR8LpRdUKbLuoD2Gu3ATj+jQ7F7fYD9a7R7fYC6GhcuXBiSUpYnblfVjPuDhZgA4GkAfyul3D9guwtAr5SySwixGMAmKeWMVGuVl5fLvXv3qn5sI7W3t6OiosLoNHJC1IqkbfUX1mPNO2uStsv1csj44YwbOa7/xVKpuEa50HmfftfCdm1wJeUxVI1655FPdvoZHYrd6wPsX6Pd6wPU1SiEGLIZa3o1tZTyYwDtAK5N2P7JuaeypZQ7AChCiEla1qb8mDl5pqo493luzfFVnqqksYyJFIeCpZ6lqtbMlFnyICJSS82rqSf3nRFDCDEGwNUA3kqI+aIQQvR9Prdv3Y9yni1lbbN3s6q4Tddu0hxfM78Gyog0TXCEgtXzVqtaM1NmyYOISC01Z8YXANglhAgD2APgd1LKZ4UQy4UQy/tifAD2CyFeB7AZwC1Sy/PflDeV0ytRW5H07rRBaitqUTm9UnN8ycQSBJYE4FScSWemikOBU3EisCSAkokl2RWRhlnyICJSS82rqcNSyjlSSo+U0n3ubUtSyi1Syi19nzdKKWdKKWdLKedJKX+vd+KUuXUL1qF1WWv/U9HnuM9zo3VZK9YtWJdxvHeGF+HlYfjL/HCNcsEhHHCNcsFf5kd4eRjeGV79ChsgMQ8AhuRBRKRGkdEJkDEqp1fijbvfABB/0YG8NfUTGQPj0ymZWILGxY1oXNyYdZ7ZGJhHe3s7Om+1x4u1iMh+eDnMAhYBUA3gNcR/EFx9X/O6VERE+cVmXKCCADwAmgD0In4Vl2jf156++4mIKD/YjAtQBPFX3HUDSLxOVaxvuw88QyYiyhc24wLUgOQmnCgGgFduJiLKDzbjHNBzfq+WOcJqtUBdMx545WYtNeq1P/TYF1YWiURQXV0Nl8uFUCgEl8uF6upqRCI5eE7jSCdwbzsw/afAeY3xj/e2x7dnE0tEQ2IzzpKe83u1zh1WK6ryLeDnpoNoqVGv/aHXvrCqYDAIj8eDpqYmRKN985qjUTQ1NcHj8SAYzOKv/juPAgueAprfBLpi8RcUdMXiXy94Kn5/JrFENCw24yzoOTdX69xhLevizCeqYouhrUa99ode+8KqIpEIfD4furu7EYsl7OdYDN3d3fD5fJmdIR/pBO4IAqfOAmd7B993tje+/Y5gPE5LLBGlxGacBT3n5mqdO6xp3XAL0HMmdWBPDEuhrUa99ode+8KqGhoakppwolgsho0bM/ir/yOvAbHe1DGxXmBLh7ZYIkqJzTgLes7N1Tp3WNO6L/8zkKZpoucMVkNbjXrtD732hVW1tLSoasbNzRnMa972dvJZbqKzvfE4LbFElBKbcRa6znSlD9IQlzd/fhfY5gPOfJp8htxzJr59mw8l0FajZfeHxXR1qdzPKuMG+TTdS/vOLX5GWywRpcRmnIXikeoGZauNy6vDzwFbPEDoZ4DsAXp7gNOd8a+3eOL3Q1uNlt4fFqJ2QHtGg9zHpp529fniI7XFElFKbMZZ0HNurta5wxmt++d3geDfAv/VAfx/RcBDE+Jf//nd/nW11KjX/tBrX1hVVVUVFCXNflYULF2awbzmJRcBRWl+LRQ54nFaYokoJTbjLOg5N1fr3GG91tVSo177Q699YVU1NTWqmvHq1RnMa757DqCk+bWgOIDlpdpiiSglNuMs6Dk3V+vcYb3W1VKjXvtDr31hVSUlJQgEAnA6nUlNWVEUOJ1OBAIBlJRkMK95+njgMS8wpij5rLfIEd/+mDcepyWWiFJiM86SnvN7tc4d1mtdLTXqtT/02hdW5fV6EQ6H4ff74XL1zWt2ueD3+xEOh+H1ZjGv+eqpwO5bgWUzgXEjAYH4x2Uz49uvnppZLBENS0iVV2PKtfLycrl3715DHlur9vZ2VFRUGJ2GbuxeH2D/Glmf9dm9RrvXB6irUQgRklKWJ27nmTEREZHB2IyJiIgMxmZMRERkMDZjIiIig7EZExERGYzNuEBFTkZQvb0arg0uhI6H4NrgQvX26mHHGw6Md9Q60sZT4Ti6O4zd8+rwyaR69E76MT6ZVI/d8+pwdHfY6NSG9+Ix4MongcmNn9+ufDK+ncgAbMYFKHgoCM8WD5r2NSF6pm8w/ZkomvY1wbPFg+ChYMp4CZkyngrHngcDmPTXOzH/sAsuMRoOIeASozH/sAuT/non9jwYMDrFZPWvAt96Bnjr5ODtb52Mb69/1YisqMCxGReYyMkIfNt86I51J407jPXG0B3rhm+br/+MV2s8FY6ju8O4pP6PGCtGYqQoGnTfSFGEsWIkLqn/o7nOkF88BjyUptk+9CrPkCnv2IwLTMPLDYilmWUc64lh4ysbM4qnwvHe2megpPkVosCB9+77TZ4yUuHvX8htHFGOsBkXmJZwS9IZbqJYbwzN4eaM4qlwzDnsTDojTjRSFGHOoTF5ykiFxKems40jyhE24wLTdUblYPq+OK3xVDiKMUpV3FhwnjFROmzGBaZ4pMrB9H1xWuOpcHThM1Vxn+KMzpkQWR+bcYGp8lQljTdMpDgULPUszSieCsdrf9mNM/Jsypgz8ixem3EqTxmpcPHE3MYR5QibcYGpmV8DZUSa5jpCwep5qzOKp8Ix7aEbEUNvypgYejHtwRvylJEKD1yV2ziiHGEzLjAlE0sQWBKAU3EmnfEqDgVOxYnAkgBKJpZkFE+FY+oCD95c8xf4VJ5JOkM+I8/iU3kGb675C0xd4DEowyFcOQVYOzd1zNq58TiiPGIzLkDeGV6El4fhL/PDNapvMP0oF/xlfoSXh+Gd4U0Z7xCOlPFUOC67z4cPf3U1Xp4RxSfyNHpkLz6Rp/HyjCg+/NXVuOw+n9EpJlszF/j1jclPRV88Mb59TZpmTaSD1O9LINsqmViCxsWNaFzciPb2dnTe2qk6nmigqQs8mPry52e/LgALjEtHnSunAC9+2+gsiPrxzJiIiMhgbMZEREQGYzMmIiIyGJsxERGRwQqqGes1k1frum1H2uB+2A1RK/pv7ofdaDvSllUemeasZp4x5UckEkF1dTVcLhccDgdcLheqq6sRidjjuGiafXykE7i3HZj+U+D1E/GP97bHt+fTwDzOa0yfh4b4gcc7FArl7nhrzZkMVzDNWK+ZvFrXrdtdh0WPL8KBEwcGbT9w4gAWPb4IdbvrMq4x05yB1POMKT+CwSA8Hg+ampoQjUYhpUQ0GkVTUxM8Hg+CQWsfF02zj3ceBRY8BTS/CXT1DSrpisW/XvBU/P58SMxDpslDQ3zi8QaQm+OtNWcyhYJoxnrN5NW6btuRNqxvX59yzfXt63U9Q+Z8YnOKRCLw+Xzo7u5GLJZwXGIxdHd3w+fzWfYMWdPs4yOdwB1B4NRZ4GzCFb7O9sa33xHU/yxPax4a4nU73mbZd6RZQTRjvWbyal13ZXClqnVXPbdKUx5acD6xOTU0NCT9Uk4Ui8WwcaM1j4um2cePvAbEUl9mE7FeYEtH7hIcitY8NMTrdrzNsu9Is4JoxnrN5NW6buJT08PZ/8F+TXlowfnE5tTS0qLql3NzszWPi6bZx9veTj6rS3S2Nx6nJ615aIjX7XibZd+RZgXRjPWayWvFWb9WzLkQdHWpPC4q48xG0+zjT1M3qX5dOo9m1JqHhnjdjrdZ9h1pVhDNWK+ZvFac9WvFnAtBcbHK46Iyzmw0zT4em3pKWL/ikVlkpILWPDTE63a8zbLvSLOCaMZ6zeTVuu7MyTNVres+z60pDy04n9icqqqqoChpjouiYOlSax4XTbOPl1wEFKX51VTkiMfpSWseGuJ1O95m2XekWUE0Y71m8mpdd7N3s6p1N127SVMeWnA+sTnV1NSo+uW8erU1j4um2cd3zwGUNL+aFAewvDR3CQ5Fax4a4nU73mbZd6RZQTRjvWbyal23cnolaitqU65ZW1GLyumVmvLQM2fKj5KSEgQCATidzqRf0oqiwOl0IhAIoKTEmsdF0+zj6eOBx7zAmKLks7wiR3z7Y954nJ605qEhXrfjbZZ9R5oVRDMG9JvJq3XddQvWoXVZa9JT0e7z3Ghd1op1C9ZlXGOmOQOp5xlTfni9XoTDYfj9/kFX4PL7/QiHw/B6rX1cNM0+vnoqsPtWYNlMYFzf3zfHjYx/vfvW+P35kJiHSJOHhvjE4w0gN8dba85kCkJKacgDl5eXy7179xry2Fq1t7ejoqLC6DR0Y/f6APvXyPqsz+412r0+QF2NQoiQlLI8cXvBnBkTERGZFZsxERGRwdiMiYiIDMZmTEREZDA2YyIiIoOlvnI7qRI5GUHDyw1oCbeg60wXikcWo8pThZr5NVm/X1fL2m1H2rAyuHLQQIqZk2dis3ezru9dJlLtSGd8stC2t+PXUR6rxK8GdfecvL739eUnn8N79z2DxZ+WYJwYjag8jR1jI5j24I2Y/+1rs1t829vA378AfDzgEqATRgEPXMUrX9GweGacpeChIDxbPGja14TomSgkJKJnomja1wTPFg+ChzIfCK9l7brddVj0+KKkyVAHThzAoscXoW53XcZ5EOWESYbeP3nHBnhWHsBfd1+M8Y4xcAiB8Y4x+Ovui+FZeQBP3rEh88Xvfh6o/t3gRgzEv67+Xfx+oiGwGWchcjIC3zYfumPdSWMJY70xdMe64dvmQ+Sk9oHwWtZuO9KG9e3rU663vn092o60ac6DKCdMMvT+5Sefww2/HYmxYlTSSMeRoghjxSjc8NuRePnJ57Qvvu1tIPBO6pjAOxxfSENiM85Cw8sNiPWkmUnaE8PGV7QPhNey9srgSlVrrnpuleY8iHLCJEPv37vvGSgYkTJGwQi8d98z2hf/+xdyG0cFhc04Cy3hlqSz1kSx3hiaw9oHwmtZO/Gp6eHs/2C/5jyIcsIkQ+8Xf1qSdEacaKQowuJP07/WIwKgGoAL8V+krn3fQfVDCxCZ5kr9jYlPYROBzTgrXWdUDghXGZevtYnyziRD78eJ0ariisWolPcHAXgANAGIIv7n7+i4kWiqugSe3bciuOgvssyUCg2bcRaKR6ocEK4yLl9rE+WdSYbeR+VpVXFdcviz1wgAH4BuAIn/xYiNHIFupwLfY970Z8hEA7AZZ6HKU5U0hjCR4lCw1KN9ILyWtWdOnqlqzcRJUUR5Y5Kh9zvGRpJGOCY6I89ix9jhX3TZgOQmnChW5MDGu0qHvnNC6rNuKkxsxlmomV8DZUSahjlCwep52gfCa1l7s3ezqjU3XbtJcx5EOWGSoffTHrwRMfSkjImhB9MevHHY+1ugohmPHIHm/2eY/1g8cFWa76ZCxGachZKJJQgsCcCpOJPOYhWHAqfiRGBJIKMLf2hZu3J6JWoralOuV1tRywt/kHFMMvR+/revxW+uP4NP5WdJZ8hn5Fl8Kj/Db64/k/LCH2pfpdE11FPuvgt54Q8aEptxlrwzvAgvD8Nf5odrlAsO4YBrlAv+Mj/Cy8Pwzsh8ILyWtdctWIfWZa1JT0W7z3OjdVkr1i1Yl3EeRDlhkqH3337s+whvnolfOd9CZ+8p9MhedPaewq+cbyG8eSa+/dj3U36/2ldpFA98MdqEUcDD/xN45JrMEydb4+Uwc6BkYgkaFzeicXGjoWtXTq/EG3e/kfMciHJm+njgoQXxm4Hmf/vaQWe/4wHcqvJ7qxB/FXWqp6oVAEtdo4AT92ScIxUWnhkTEWlQg3izTUUBoP2VIlTI2IyJiDQoARAA4ERyU1b6tgf64ojUYjMmItLICyAMwI8BV+Dq+zrcdz+RFvybMRFRBkoANPbdiLJl6TPjyMkIqrdXw7XBBUetA64NLlRvrx52SpLWeDNoO9IG98NuiFrRf3M/7B5yApOW+gbGho6HLLEvIpEIqqur4XK54HA44HK5UF1djUgk+5zb2trgdrshhOi/ud1utLUNsZ9NmEcoFEqZh5Z1TWPb28CMR4HJjcDrJ+IfZzw67PWrNR2XF48BVz4ZX/Pc7con49sTHekE7m0Hpv8UOK8x/vHedt0nTGWVh1ly1kJrzlasMQUhpTTkgcvLy+XevXsz/v7goSB823yI9cQGDVRQHAqUEQoCSwKD3vqjNX6g9vZ2VFRUZJxrpup216UcjVhbUdv/liUt9SXG1l9YjzXvrFG1L4wSDAbh8/kQi8UQiw2oT1GgKAoCgQC83uFzTnUM6+rqsH59iv1cW4t169blJI9Ussmjvr4ea9asGTIPLeuaxt3PDxpH2P7/no+Kf/zvz+/3XTjobUKajkv9q8BDrw7/2GvnAmvmxj/feTQ+2jHWO3jQRZEjfpGSx7w5e0tWyt8zWvLIY85a5Ky+TOLzRE2vEEKEpJTlSdvTNWMhxGgALwAYhfjT2gEp5fqEGAFgE4DFiF+y9XYp5b5U62bTjCMnI/Bs8aA71j1sjFNxIrw8jJKJJZrjExnRjNuOtGHR44vSxrUua8XU8VNV1wcgKfZcM06MzeRiJXqIRCLweDzo7k5Rn9OJcDiMkpKhcx7uGLa1tWHRIhX7ubUVU6dOzTqP4WSbx7lmnJjH0aNHVa9bWWmSi8Jsexuo/t2gTUnNGIi/b3fJRdp+Pt4fBXzrmfQ5/PpGYMo4YMFT8VnLwxlTFH+PdA4uVjLs75kjnerzAPKasxY5qW/6eO3xeZRNM1bzNPVnACqllLMBlAK4VggxLyHGC2BG380P4BEV62ZM6xxhPecO60XLjGIt9VlxXzQ0NAw62xlKLBbDxo3ac165UuV+XrXKknloWdc0NM4F1nRctKxtkhnMmvIwS85aaM3ZijWqkLYZy7hzV4BT+m6Jp9M3AHi8L/YVABOEEBfkNtXPaZ0jrOfcYb1omVGspT4r7ouWlhZVv2ybm7XnfOCAyv28f78l89CyrmmonffbF6fpuLx1Ut3ab500zQxmTXmYJWcttOZsxRpVUPU3YyHECAAhAH8J4CdSyrUJ9z8L4EEp5Ut9X7cCWCul3JsQ50f8zBnnn39+2datWzNKOnQ8pDq27IIyzfGJurq6UFyc31GFWnLO1pRRU3Dss+QXrgy1L4wQCmk4fmVD5zzcMdSythbD5TGcbPOYMmUKjh0b4sVHGmjNWTevn0ja1HVBEYqPD/G05OzJ2n4+inSaMzx7ctZLDPt7Zoj9kRM5yFmLnNQ3e7L2+DxS0ysWLlyY2d+MBwULMQHA0wD+Vkq5f8D27QA2JDTje6WUw/4ryeZvxq4NLkTPRNPHjXKh875OzfGJjPibsagVqmPHjRynuj4pZVJs4t+Mz8UOtS+M4HK5EI2qqM/lQmfn0DkPdwzjL3dQZ9y4cVnnMZxs80j8m/G5PD755BPV6xr1Ys4kk5PfLDTk34wB4MQ92n4+Rt6vPo9iBehKN58J8etrv+tXv+4whv09M/2n6vOQMq85a5GT+t71a4/PI73/ZtxPSvkxgHYAiSNNjgH4yoCvpwB4X8vaWmidI6zn3GG9aJlRrKU+K+6LqqoqKEqanBUFS5dqz3nmTJX72e22ZB5a1jUNtfN+++I0HZeLJ6pb++KJppnBrCkPs+SshdacrVijCmmbsRBict8ZMYQQYwBcDeCthLDfAlgm4uYB6JRSHs91sudonSOs59xhvWiZUaylPivui5qaGlW/bFev1p7z5s0q9/OmTZbMQ8u6pqF23m9fnKbjomVtk8xg1pSHWXLWQmvOVqxRBTVnxhcA2CWECAPYA+B3UspnhRDLhRDL+2J2AHgXwGEAjwKo1iXbPlrnCOs5d1gvWmYUa6nPivuipKQEgUAATqcz6ZeuoihwOp0IBAKa304EAJWVlaitTbOfa2tRWVlpyTy0rGsaSy6Kv484lQFzgTUdlyunxN9HnMraufE4k8xg1pSHWXLWQmvOVqxRDSmlIbeysjKZrcMfHZYrtq+Qrg0u6ah1SNcGl1yxfYU8/NHhnMSfs2vXrqxzzVTru63S/bBb4gfov7kfdsvWd1uTYrXUNzC2/sl61fvCSIcPH5YrVqyQLpdLOhwO6XK55IoVK+Thw+lzTncMW1tbpdvtloi/U0ACkG63W7a2DrGfs8gjnUzzqK+vT5mHlnVN49/ekvIvfyblpB/LXT/6Nykn/Tj+9b+9NWS4puPywp+k/MYT8TXP3b7xRHx7onc/lvLedimn/1TKyT+Of7y3Pb49h9L+ntGSR55y1iKn9WUSnwdqegWAvXKInmjZK3Dlk1FX4MoXu9cH2L9G1md9dq/R7vUBeXwBFxEREeUemzEREZHB2IyJiIgMxmZMRERkMDZjIiIigxVUM46cjKB6ezVcG1xw1Drg2uBC9fZqRE5mPxSerE3LYHpNQ+zzlHMoFMppzpriNQx513PfmeW4WJKGY0g6Ger9Tvm45eJ9xlrseGeHdN7vlEqdMug9u0qdIp33O+WOd3YM+71Gvs84H+xen5Spa9yxY4d0Op1SUZRB78NVFEU6nU65Y8eOjGL1lJhHfX19znLWFP+796T8yiNSfvEng9+z+8WfxLf/7r2M8xgo3c+oWY5LNgz7d6jhGGaj0H/PnINh3mdcEGfGkZMR+Lb50B3rThofGOuNoTvWDd82H8+QC1AkEoHP50N3d3fSGL5YLIbu7m74fD5EIhFNsVbNWVP8kU7gjmB8yHviSLuzvfHtdwSBI5267juzHBdL0nAMSV8F0YwbXm5ArCfNvNOeGDa+on0oPFmblsH0mobY60jPnDXFaxjyrue+M8txsSQNx5D0VRDNuCXcknRGnCjWG0NzWPtQeLI2LYPpNQ2x15GeOWuK1zDkXc99Z5bjYkkajiHpq8joBPKh60xXTuPIPrq6VP5sdHWpnverds1M6ZmzlrUxSsVMWQDoOqNtXY30XNv2PlV/DElfBXFmXDyyOKdxZB/FxSp/NoqLNcXqSc+cNcWPTT228PPgkbruO7McF0vScAxJXwXRjKs8VUnjAhMpDgVLPdqHwpO1aRlMr2mIvY70zFlTvIYh73ruO7McF0vScAxJXwXRjGvm10AZkeYf6wgFq+dpHwpP1qZlML2mIfY60jNnTfEahrzrue/MclwsScMxJH0VRDMumViCwJIAnIoz6QxZcShwKk4ElgRQMlH7UHiyNi2D6TUNsbdozpriNQx513PfmeW4WJKGY0j6KohmDADeGV6El4fhL/PDNcoFh3DANcoFf5kf4eVheGd4jU6RDOL1ehEOh+H3+wddvcnv9yMcDsPr9WYUm8+cAeQsZ03xV08Fdt8KLJsJjBsJCMQ/LpsZ33711IzzyGZ/GHVcLEnDMST9CLWvtsy18vJyuXfvXkMeWyu7D8W2e32A/WtkfdZn9xrtXh+grkYhREhKWZ64vWDOjImIiMyKzZiIiMhgbMZEREQGYzMmIiIyGJsxEbTNwm1ra4Pb7YYQov/mdrvR1taW9dp60TNnTfW9eAy48klgcuPntyufjG9PsW66ec16MsPxMxWzzD42Sx65MtRcxXzc8j3POBt2n8Np9/qkzN0849ra2kExibfa2tqM19arPj1z1lTfD/8weF5u4u2Hfxh23VTzmvWUzznJlvh3mMXs45zWl6cZzFpxnjFRhrTMwm1ra8P69etTrrd+/fr+s00zzNnVM2dN9b14DHjo1dTJPvQq8OIxU+w3wBzHz1TMMvvYLHnkGJsxFTQts3BXrlypas1Vq1ZpXlsveuasqb6/f0Fdwn//gin2G2CO42cqZpl9bJY8cozNmAqallm4Bw4cULXm/v37Na+tFz1z1lTfWyfVJfzWSVPsN8Acx89UzDL72Cx55FhBzDMmGg7n7A6m26zkifrkoCez5GEaZpl9bJY8coxnxlTQOGd3MDPMdzbLfjNLHqZhltnHZskjx9iMqaBpmYU7c+ZMVWu63W7Na+tFz5w11XexylPjiyeaYr8B5jh+pmKW2cdmySPH2IypoGmZhbt582ZVa27atEnz2nrRM2dN9T1wlbqEH7jKFPsNMMfxMxWzzD42Sx45xmZMBU3LLNzKykrU1tamXK+2thaVlZWa1z4nAqAagAvxf5yuvq8zffOMnjlrqu/KKcDauamTXTsXuHKKaeYTmyUP0zDL7GOz5JFrQ735OB83XvTDPOxen5Tpazx8+LBcsWKFdLlc0uFwSJfLJVesWCEPHz6cFNva2irdbvegi0C43W7Z2tqa1do7pJROKaUiB/9jUfq2p7q8RLr69MpZa6x84U9SfuOJwRdq+MYT8e0p1q2vr0+9ro401ZcFy/w7fPdjKe9tl3L6T6Wc/OP4x3vb49tTyHl9Geahp2wu+sF5xirYfQ6n3esDzF9jBIAHQHeKGCeAMIChzsPMXl+27F4fYP8a7V4fwHnGRJbXACDdGzZiAArk8hJEBYfNmMgEWqCuGRfI5SWICg6bMZEJqL1sRIFcXoKo4LAZE5mA2stGFMjlJYgKDpsxkQlUAUh3XSEFQIFcXoKo4LAZU85ZcRi7njk3NDRg9OjREEL030aPHo2Ghob+mBqoa8YDLy8xMOdQKGTYfta07+w2EJ4oV4Z6v1M+bnyfsXnksr58DmPXIlWNeua8cOHCQWsm3hYuXPh5HlL9+4wTc66vrzdkP2vad2YZTG9Sdq/R7vVJmd37jHlmTDljxWHseubc0NCAXbt2pYzZtWtX/xmyF/H3Efsx+Apc/r7t3jzkrIWmPGw6EJ4oV9iMKWesOIxdz5z/4R/+QXNcCYBGAJ0Aevo+NmLwhT7Msp815WHTgfBEucJmTDljxWHseub82Wef5TTuHLPsZ0152HQgPFGuFBmdANmHFYexM+c85THKngPhiXKFZ8aUM1Ycxs6c85SHTQfCE+UKmzHljBWHseuZ86hRo3Iad45Z9rOmPGw6EJ4oV9iMKWesOIxdz5zvv//+nMadY5b9rCkPmw6EJ8oVNmPKGSsOY9cz55qaGixcuDBlzMKFC1FTU2OanHXLw64D4YlyhM2Ycsrr9SIcDsPv9w+6IpPf70c4HIbX602/SJ7pmXNbWxvq6+uTnooeNWoU6uvr0dbWlpOcARiynzXtu6unArtvBZbNBMaNBATiH5fNjG+/empeciYyIxG/IEj+lZeXy7179xry2FrZfSi23esD7F8j67M+u9do9/oAdTUKIUJSyvLE7TwzJiIiMhibMRERkcHYjImIiAzGZkxERGQwNmOyLb3m/WqdfWyK+c6cI0x2Y7OfaTZjsqVgMAiPx4OmpiZEo1EAQDQaRVNTEzweD4LBYE7WlVKmXFdrvC52HgUWPAU0vwl0xeITh7ti8a8XPBW/n8hKbPgzzWZMtqPXvF+t65pi7jDnCJPd2PRnms2YbEeveb9a1zXF3GHOESa7senPNJsx2Y5e8361rmuKucOcI0x2Y9OfaTZjsh295v1qXdcUc4c/5Rxhshmb/kyzGZPt6DXvV+u6ppg7zDnCZDc2/ZlmMybb0Wver9Z1TTF3mHOEyW5s+jPNZky2o9e8X63rmmLuMOcIk93Y9GeazZhsR695v1rXNcXcYc4RJrux6c80mzHZkl7zfrXOPjbFfGfOESa7seHPdJHRCRDppaSkBI2NjWhsbER7ezs6O3NzEYCB6+oRr4vp44GHFsRvRHZgs59pnhkTEREZjM2YiIjIYGzGREREBmMzJiIiMhibMRERkcHYjG1EzyH2eq6tl4E5h0KhlDlrqc+K+0JPbW1tcLvdEEL039xuN9ra2oxObXg2G0xPNiClNORWVlYmrWLXrl1Gp5DWjh07pNPplIqiSMRHbUsAUlEU6XQ65Y4dO4b93nT1ZbO2URJzrq+vHzZnLfWZdV8Y9TNaW1s7aD8k3mpra3PyODmt73fvSfmVR6T84k+knPTjz29f/El8++/ey91jaWCF3zPZsHt9UqqrEcBeOURPTHtmLIT4ihBilxDioBDigBBi1RAxFUKITiFER99tXbb/SSD19Bxir+faetGSs16xhaCtrQ3r169PGbN+/XpznSHbdDA9WZ+ap6nPAqiRUn4NwDwAK4QQlwwR96KUsrTvVpfTLCklPYfY67m2XrTkrFdsIVi5cqWquFWrkv7/bhybDqYn60vbjKWUx6WU+/o+jwI4CODLeidG6uk5xF7PtfWiJWe9YgvBgQMHVMXt379f50w0sOlgerI+EX8KW2WwENMAvADALaX8ZMD2CgC/AnAMwPsA1kgpk/6lCiH8APwAcP7555dt3bo1i9Tzp6urS9+Zs1kKhUKqY8vKypK2paov27WNMFTOU6ZMwbFjx/KWQ773hRE/o/n82chZfa+fUB87e3L2j6eB2X/PZMvu9QHqaly4cGFISlmeuF11MxZCFAPYDeB+KeWvE+5zAeiVUnYJIRYD2CSlnJFqvfLycrl3715Vj2209vZ2VFRUGJ3GsFwuF6LRqKq4oa7PnKq+bNc2wlA519fXY82aNUlxUkrV9WmJzfe+MOJnVAihOlbLf/qHkrP6pv8U6Er97AaA+NCBd/3ZP54GZv89ky271weoq1EIMWQzVvXWJiGEgviZ7xOJjRgApJSfSCm7+j7fAUARQkxSszZlT88h9nqurRctOesVWwhmzpypKs7tduuciQY2HUxP1qfm1dQCwL8AOCil/OdhYr7YFwchxNy+dT/KZaI0PD2H2Ou5tl605KxXbCHYvHmzqrhNmzbpnIkGNh1MT9an5sz4CgBLAVQOeOvSYiHEciHE8r4YH4D9QojXAWwGcIvM9nkpUk3PIfZ6rq0XLTnrFVsIKisrUVtbmzKmtrYWlZWVecpIBZsOpicbGOrNx/m48aIfuXf48GG5YsUK6XK5pMPhkC6XS65YsUIePnw45fepqS/TtY00MOf6+vqUOWupz4z7wsif0dbWVul2uwdd7MPtdsvW1tacPUbO63v3YynvbZdy+k+lnPzj+Md72+PbDWKV3zOZsnt9UmZ30Q9Nr6bOJb6AyzzsXh9g/xpZn/XZvUa71wfk4QVcREREpB82YyIiIoOxGRMRERmMzZiIiMhgbMY2wjm7gw2csxsKhawxZ5eIChKbsU0Eg0F4PB40NTUhGo32X7qxqakJHo8HwWDQ6BTzqq6uDosWLUoaZnDgwAEsWrQIdXUcLEZE5sFmbAOcszuYJefsElFBYzO2Ac7ZHcySc3aJqKCxGdsA5+wOZsk5u0RU0NiMbaCrqyuncURElF9sxjagdmC33Qd7ExFZFZuxDXDO7mCWnLNLRAWNzdgGOGd3MEvO2SWigsZmbAOcszuYJefsElFBYzO2Ca/Xi3A4DL/fP+gKXH6/H+FwGF6v1+gU82rdunVobW1Neira7XajtbUV69atMygzIqJkRUYnQLlTUlKCxsZGNDY2Gp2KKVRWVuKNN94AEJ8zatTsbiKidHhmTEREZDA2YyIiIoOxGRMRERmMzZiIiMhgbMZEREQGYzMeRuRkBNXbq+Ha4ELoeAiuDS5Ub69G5KQ9xhBGIhFUV1fD5XIhFArB5XKhurraVmMWC6FGMxi4n8+9pY77mUgbNuMhBA8F4dniQdO+JkTPRAEA0TNRNO1rgmeLB8FDQYMzzE4wGITH40FTUxOi0b76olE0NTXB4/EgGLR2fUBh1GgGiftZSsn9TJQBNuMEkZMR+Lb50B3rRqx38FjCWG8M3bFu+Lb5LHuGHIlE4PP50N3dnTR2MRaLobu7Gz6fz9JnNYVQoxlwPxPlDptxgoaXGxDrSTMbuCeGja9szFNGudXQ0KBq9vHGjdasDyiMGs2A+5kod9iME7SEW5LOiBPFemNoDjfnKaPcamlpUfULtLnZmvUBhVGjGXA/E+UOm3GCrjNdOY0zm64ulfWpjDOjQqjRDLifiXKHzThB8cjinMaZTXGxyvpUxplRIdRoBtzPRLnDZpygylMFxZFmNrBDwVLP0jxllFtVVVWqZh8vXWrN+oDCqNEMuJ+JcofNOEHN/BooI9L8ghmhYPW81XnKKLdqampU/QJdvdqa9QGFUaMZcD8T5Q6bcYKSiSUILAnAqTiTzpAVhwKn4kRgSQAlE0sMyjA7JSUlCAQCcDqdSb9IFUWB0+lEIBBASYk16wMKo0Yz4H4myh024yF4Z3gRXh6Gv8wP1ygXAMA1ygV/mR/h5WF4Z3gNzjA7Xq8X4XAYfr8fLldffS4X/H4/wuEwvF5r1wcURo1mkLifz12Bi/uZSBth1MD18vJyuXfvXkMeW6v29nZUVFQYnYZu7F4fYP8aWZ/12b1Gu9cHqKtRCBGSUpYnbueZMRERkcHYjImIiAzGZkxERGQwNmMiIiKDsRkTWVRbWxvcbjeEEAiFQhBCwO12o62tLeu1OaOYKL/YjIksqK6uDosWLcKBAwcGbT9w4AAWLVqEurq6jNfmjGKi/GMzJrKYtrY2rF+/PmXM+vXrMzpD5oxiImOwGRNZzMqVK1XFrVq1SvPanFFMZAw2YyKLSXxqejj79+/XvDZnFBMZg82YiPpxRjGRMdiMiagfZxQTGYPNmMhiZs6cqSrO7XZrXpsziomMwWZMZDGbN29WFbdp0ybNa3NGMZEx2IyJLKayshK1tbUpY2pra1FZWal5bc4oJjIGmzGRBa1btw6tra1JT0W73W60trZi3bp1Ga/NGcVE+VdkdAJElJnKykq88cYbAOJzVHM5m7ykpASNjY1obGzM2ZpENDyeGRMRERmMzZiIiMhgbMZEREQGYzMmIiIyGJsxERGRwdiMiYiIDMZmTEREZDA2YyIiIoOxGRMRERmMzZiIiMhgbMZEREQGYzMmIiIyGJsxERGRwdiMiYiIDMZmTEREZDA2YyIiIoOxGRMRERmMzZiIiMhgbMZEREQGYzMmIiIyGJsxERGRwdiMC1QkEkF1dTVcLhdCoRBcLheqq6sRiUSMTo2IqOCwGRegYDAIj8eDpqYmRKNRAEA0GkVTUxM8Hg+CwaDBGRIRFZa0zVgI8RUhxC4hxEEhxAEhxKohYoQQYrMQ4rAQIiyEuFSfdClbkUgEPp8P3d3diMVig+6LxWLo7u6Gz+fjGTIRUR6pOTM+C6BGSvk1APMArBBCXJIQ4wUwo+/mB/BITrOknGloaEhqwolisRg2btyYp4yIiChtM5ZSHpdS7uv7PArgIIAvJ4TdAOBxGfcKgAlCiAtyni1lraWlRVUzbm5uzlNGREQkpJTqg4WYBuAFAG4p5ScDtj8L4EEp5Ut9X7cCWCul3Jvw/X7Ez5xx/vnnl23dujXrAvKhq6sLxcXFRqeRE6FQKGnblClTcOzYsaTtZWVl+UgpL+x0DIfC+qzP7jXavT5AXY0LFy4MSSnLk+6QUqq6ASgGEALwrSHu2w7gGwO+bgVQlmq9srIyaRW7du0yOoWcGTdunAQw6FZfX5+0zeVyGZ1qTtnpGA6F9Vmf3Wu0e31SqqsRwF45RE9U9WpqIYQC4FcAnpBS/nqIkGMAvjLg6ykA3lezNuVXVVUVFEVJGaMoCpYuXZqnjIiISM2rqQWAfwFwUEr5z8OE/RbAsr5XVc8D0CmlPJ7DPClHampqVDXj1atX5ykjIiJSc2Z8BYClACqFEB19t8VCiOVCiOV9MTsAvAvgMIBHAVTrky5lq6SkBIFAAE6nM6kpK4oCp9OJQCCAkpISgzIkIio8RekCZPxFWSJNjASwIldJkb68Xi/C4TA2btzY/6ppl8uFpUuXYvXq1WzERER5lrYZkz2VlJSgsbERjY2NaG9vR2dnp9EpEREVLF4Ok4iIyGBsxkRERAZjMyYiIjIYmzEREZHB2IyJiIgMxmZMRERkMDZjIiIig7EZExERGYzNmIiIyGBsxkRERAZjMyYiIjIYmzEREZHB2IyJiIgMJuLTDw14YCFOADhqyINrNwnAh0YnoSO71wfYv0bWZ312r9Hu9QHqapwqpZycuNGwZmwlQoi9Uspyo/PQi93rA+xfI+uzPrvXaPf6gOxq5NPUREREBmMzJiIiMhibsTo/MzoBndm9PsD+NbI+67N7jXavD8iiRv7NmIiIyGA8MyYiIjIYmzEREZHB2IwHEEKMEEK8JoR4doj7KoQQnUKIjr7bOiNyzIYQ4j0hxBt9+e8d4n4hhNgshDgshAgLIS41Is9sqKjR0sdRCDFBCBEQQrwlhDgohJifcL+lj6GK+qx+/C4akHuHEOITIcT3EmIsewxV1mf1Y7haCHFACLFfCPGUEGJ0wv0ZHb8ifdK1rFUADgJwDXP/i1LK6/KYjx4WSimHe1O6F8CMvtvlAB7p+2g1qWoErH0cNwF4TkrpE0KMBOBMuN/qxzBdfYCFj5+U8m0ApUD8P/8A/hPA0wlhlj2GKusDLHoMhRBfBrASwCVSylNCiH8DcAuAXwwIy+j48cy4jxBiCoC/AtBkdC4GugHA4zLuFQAThBAXGJ0UxQkhXACuAvAvACClPCOl/DghzLLHUGV9drIIQERKmXglQssewwTD1Wd1RQDGCCGKEP/P4vsJ92d0/NiMP/cjAPcC6E0RM18I8boQIiiEmJmftHJKAnheCBESQviHuP/LAP404OtjfdusJF2NgHWP41cBnADw874/pzQJIcYmxFj5GKqpD7Du8Ut0C4Cnhthu5WM40HD1ARY9hlLK/wRQD+CPAI4D6JRSPp8QltHxYzMGIIS4DsAHUspQirB9iF9TdDaAHwN4Jh+55dgVUspLEX8aZYUQ4qqE+8UQ32O1976lq9HKx7EIwKUAHpFSzgHwKYD7EmKsfAzV1Gfl49ev7yn46wFsG+ruIbZZ5RgCSFufZY+hEOILiJ/5TgfwJQBjhRBViWFDfGva48dmHHcFgOuFEO8B2AqgUgjRMjBASvmJlLKr7/MdABQhxKS8Z5oFKeX7fR8/QPzvOHMTQo4B+MqAr6cg+SkYU0tXo8WP4zEAx6SUf+j7OoB480qMseoxTFufxY/fQF4A+6SU/z3EfVY+hucMW5/Fj+HVAI5IKU9IKWMAfg3g6wkxGR0/NmMAUsrvSymnSCmnIf7USpuUctD/doQQXxRCiL7P5yK+7z7Ke7IZEkKMFUKMO/c5gGsA7E8I+y2AZX2vBpyH+FMwx/OcasbU1Gjl4yil/C8AfxJCXNS3aRGANxPCLHsM1dRn5eOX4FYM/xSuZY/hAMPWZ/Fj+EcA84QQzr4aFiH+ot+BMjp+fDV1CkKI5QAgpdwCwAfgbiHEWQCnANwirXX5svMBPN33b6AIwJNSyucSatwBYDGAwwC6Afxvg3LNlJoarX4c/xbAE31PA74L4H/b7Bimq8/qxw9CCCeA/wngrgHbbHMMVdRn2WMopfyDECKA+FPtZwG8BuBnuTh+vBwmERGRwfg0NRERkcHYjImIiAzGZkxERGQwNmMiIiKDsRkTEREZjM2YiIjIYGzGREREBvv/AZmx/g2d8KzjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \n",
    "            s = 100, c = 'black', label = 'Iris-setosa')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \n",
    "            s = 100, c = 'green', label = 'Iris-versicolour')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\n",
    "            s = 100, c = 'deeppink', label = 'Iris-virginica')\n",
    "\n",
    "# Plotting the centroids of the clusters\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \n",
    "            s = 100, c = 'cyan', label = 'Centroids')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}