Task2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GRIP TASK 2\n",
    "\n",
    "\n",
    "# GRIPNOV20\n",
    "\n",
    "TASK:From the given ‘Iris’ dataset, predict the optimum number of clusters and\n",
    "represent it visually. \n",
    "\n",
    "ASHISH PRASAD\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn import datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_df = pd.read_csv('C:/Users/Puneeth Agarwal/Desktop/task2/Iris.csv')\n",
    "ir_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "ir_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ir_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Iris Scatter Plot')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= ir_df.iloc[:,[0,1,2,3]].values\n",
    "plt.scatter(x[:,0],x[:,1],x[:,2],x[:,3])\n",
    "plt.title('Iris Scatter Plot')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding number of optimum clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ir_df.iloc[:, [0, 1, 2, 3]].values\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\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",
   "metadata": {},
   "source": [
    "Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, 11), wcss,color='yellow')\n",
    "plt.title('The elbow method')\n",
    "plt.xlabel('k-clusters')\n",
    "plt.ylabel('Within Cluster sum of squares')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-Means Is the key!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LET'S VISUALIZE!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26554fa4f70>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xx6IL0lNcQd4q9HA4jIaGBvj9fng8Hvj9fjQ0NCDs8vK5ixYtwuuvv27oNS+//DLuu+++tH2slNYVBCC/HYsuSE9xB6mqdjndrJTPbWlpYa/Xy5qmMYCBpmkae71ebsnD8rnZLtFrJ1JtsTCorx9akTqxaZpeZtZtOFyE1VUgTbXFvFuhh8Nh1NbWoq+vD5GEZUQkEkFfXx9qa2str9TffPNNzJgxA1dffTUqKysBHFq979q1C9OmTUNVVRUqKiqwatWqQa/t7u7GuHHjcPDgQQBAX18fjj/+eEQiEVx33XUIhUIAgHHjxmHx4sU499xz8fzzz6OlpQWnnXYazj33XNx0002YNWsWAODXv/41brzxRgDAddddh5tuugnf+ta3cPLJJw+MtW3bNlRUVAAA+vv7cdttt6GyshKBQAC//OUvAQCLFy/GlClTUFFRgWAwqFT7RRg+ZLHune1IeopO3in0JUuWDFHkiUQiETxgQ3zSe++9h7vvvhsbN24cdDxWPnft2rVYt24dqqqqBp2PL58LYFD53ERKSkrwzjvv4PLLL8cNN9yA1tZWvPPOO0hXGmHXrl1455138Morr2DhwoVDzseX1u3s7MScOXMAADfeeCNWr16N9evXY9++fXjllVcM3xOhcMl3x6Kkp+ShQm9ublZS6E+5sHxuMmLHP/zwQ5x88skD1/ve976XUq7LL78cHo8HZ5xxBv761yGJu0lL6wLAG2+8gTPPPBOVlZVYuXIlNmzYkG76wjBD1WF48GD2o14yRd7Ezk+cCDzyiG5kueEGoKNjeKWn5J1C71VcHqj2S4fd5XPTXcOI+WPEiBEDfyd7HScprbt//340NDQgFArhgw8+wNy5c4eUyBWGNyqOxRjZjHrJFHmzeHF+RuY4Qd4p9HRRKGb6mcFs+dx0nHbaadi6dSu2bdsGAAOrezMkK60bU95HH300ent7B2zvghBDpT56jGxFvahE3txxR35G5jhB3in0urq6pLboeDRNwzUuLZ+bipEjR+KRRx7Bd77zHZx77rk45phjcPjhh5uSL1lp3VGjRmHu3LmorKzE5ZdfjilTppgaWyhc0jkWU+F0Or1KSn8mhkPKfwzl8rl2Y7Z8bjgcRiAQGLKPZzxerxednZ0ozzPDWW9vL3w+H5gZjY2NOOWUU3BrHmwXI+VzC4twWFeADz+s1t/v10spOYHfr7bvqco4TsmYbewqn+sKysvLEQqF4PV6h6zUNU2D1+tFKBTKO2UOAI8//jiqqqowfvx4dHd344Ybbsi1SEIBoZrSH6t7l+CGSYmTUS92je3WyBzbSRWg7nSzkljEzNzV1cWNjY3s9/vZ4/Gw3+/nxsZG7iqEzIE8QxKL3E9Li55Yk5g4pGn68WS5eGVl6RN1Ys3vd05uVRlyKWO2QSElFsUoLy/HsmXL0N3djf7+fnR3d2PZsmV5uTIXBCcxm9LvhnR6I5E3qRgWKf9R8lahC4Kghtla4SpRL5oGOOnmMRJ5kwqnZXQTotAFocAxm9LvhnR6FRnuuktS/mOIQheEAsdKSr8b0ukzybBoUe5ldA2pjOtON8tO0d1dXP9KPZfdU8Z0J3HZPWVc/0o9d+227hTdtWsXz549m08++WQ+/fTTuaamhjdv3mx4nCeeeII/+eQTw6+rqanhv/3tb0OO33HHHXz//fcbHs9p1q1bx/X19VxWVsZExGVlZVxfXy8OapfgBuemYB8oNKdo65ZWBJYH0NTRhJ4DPWAweg70oKmjCYHlAbRuMZ/ry8y44oorMH36dITDYWzcuBH33HNP0popmfj1r3+NnTt3Jj3X39+f8nUtLS0YNWqU4evlgu7ubuzatQtNTU3o6ekBM6OnpwdNTU0IBAJoHU551y7FDc5NITtkVOhE9E0iWhvX9hDRLQl9iIiWElEXEXUS0SSnBA5/EUbt87Xoi/QhcjChfO7BCPoifah9vhbhL8zl+r7xxhvQNA3z5s0bOFZVVYXzzjsP999/P6ZMmYJAIIA77rgDgF629vTTT8fcuXMxfvx4XHTRRdi3bx9CoRDWrFmDOXPmoKqqCvv27RtSLvfZZ59FZWUlKioqsGDBgoHrjRs3Dp9//jkA4O6778Y3v/lNfPvb38bmzZsH+ixduhRnnHEGAoEArrrqKlNztcr+/fsRDofBzI6WMhas4QbnppAdMip0Zt7MzFXMXAVgMoA+ACsSutUAOCXaggAetVvQGEv+uASR/gzVFvsjeOBP5nJ9169fj8mTJw85/tprr2HLli147733sHbtWrS3t+Ptt98GAGzZsgWNjY3YsGEDRo0ahRdeeAG1tbWorq7G008/jbVr12LkyJEADpXLnTZtGhYsWICVK1di7dq1WL16NV566aVB12xvb8dzzz2H999/Hy+++CJWr149cO6+++4bKI+7fPly5IK//vWvGYuK2VXKWDCPG5ybQnYwanK5EECYmbcnHL8MwJNRE8+fAIwiojG2SJhAc2fzkJV5IpGDETzVaW8V/tdeew2vvfYaJk6ciEmTJuHDDz/Eli1bAAAnnXTSQE30yZMnDxTYSkasrsvq1asxffp0jB49GsXFxZgzZ87AP4gYq1atwhVXXAGv1wu/349LL7104FwgEMCcOXPQ3Nw8UCY32+zevVtJodtRyliwhhucm4LzGFXoVwF4Nsnx4wB8HPd8R/TYIIgoSERriGhNug0c0tF7QLF8rmK/RMaPH4/29vYhx5kZt99++0BFxa6uLvzgBz8AMLicbVFR0UCVw2QYLZebWAY3xquvvorGxka0t7dj8uTJaa/pFLEdmTKxZ88eW/Z7FawRS+nv7gb6+/XHXNcKVy1HkGu5fD6gokJ/dJOciSgrdCI6DMClAJ5PdjrJsSEai5kfY+ZqZq4ePXq0upRx+A5TLJ+r2C+RCy64AF999RUef/zxgWOrV6+G3+/Hf/7nfw7UWf/kk0/w6aefph2rrKwMPSkqC5155pl466238Pnnn6O/vx/PPvsszj///EF9pk2bhhUrVmDfvn3o6enB7373OwC6Iv34448xY8YM/Md//Ae+/PJLW+q/G8XjUV8PiJNUSCRTnfNcfVSSybV3L7Bhg/7oFjmTYeS3eg2ADmZOFu6xA8Dxcc/HAkge3mGRukAdmjqa0ppdNI+GawLmXPZEhBUrVuCWW27Bfffdh5KSEowbNw4PPvggRo0ahbPPPhuAXm+9ubk5bZ3z6667DvPmzcPIkSPxxz/+cdC5MWPG4N5778WMGTPAzJg5cyYuu+yyQX0mTZqE2bNno6qqCieeeCLOO+88AHqETF1dHbq7u8HMuPXWW3MSFXPUUUcNOG8zEYlEEIlEUFtbm5eVMAV7iS9HkEgkorfaWt0clM2PSjq5EsmlnKlQLp9LRM8B+D0zP5Hk3CUAbgQwE8CZAJYy89R045kun/tFGIHlAfRF0pTP1bzonNeJ8iNdcIcLmP3792Pjxo349NNPUaNohNU0DcFgEMuWLXNYOsHNNDToK9x0Gayaptv4s/lRUZErkWzLabl8LhF5AfwDgBfjjs0jolhsXwuArQC6ADwOoMGSxGkoP7IcoStD8GpeaJ6E8rkeDV7Ni9CVIVHmWaCkpATl5eUgooybjsQQJ6kAmC9H4DQqciWSCzlToaTQmbmPmY9i5u64Y8uZeXn0b2bmRmYuZ+ZKZl6TejTr1JxSg855nQhODsI/wg8PeeAf4UdwchCd8zpRc4q47LPF4YcfjjFjxiAYDCq/Jhf2fsFdWClH4CRmr+eWj7TrMkVVTUDlR5Zj2cxl6F7Yjf5F/ehe2I1lM5fJyjzLMDM0TcOyZctQVlam9Bon93u1i3A4jIaGBvj9fng8Hvj9fonUScBKhIrqRyDbHxWz1zt40B2RL65S6CUlJUqxzYI7YGbs3r0bJSUlANyx36sdtLa2IhAISDmDNFiNUHFrOQIr9dfdEPniqj1FI5EIduzYMbBDveB+SkpKMHbsWGiaVhD7vRbCHJwmHNaVVrpIEK83feSHHWM4gYpcKjgpezqnqKuqLQr5T0tLC3u9XtY0jaHnIjAA1jSNvV4vtyTb68xF1NfXD5E9sWmaxo2NjbkWNWfU1w/dyi6xaRpzpltkZlu8bJBKLiNNZf5mQZpqi6LQBdvJ5/1ey8rK0irzWPMP41qzdpbj7erSFZ/fz+zx6I+NjfrxXJIol8/HXFGhP+Z6H1NR6EJB0tXVZXsddiJSUuhElHNZnaarS1+Nl5UxE+mP9fX63yoKzeNxToZs3bZk1zeyUndCVlHoQsHhlGlHdYUOQPka+WiGSmcOydYKNdcmGTtML0VF9ssqCl0oKLq6utjr9aZVtl6v19TqV8WGbuQaTsrqFF1duhIyq8RiSteKDVlFBq/XuZW6HffAKVnTKXRXhS0KggpLliwZsqFGImbrsM+fP99Q1mumazgpq1MsWWI8WzIRqxtmqMgQiQBO3TY77kE8Tsoaj6vCFgVBBb/fn7KKZWK/7u7ujP0SaW1txcyZM5VlSXcNp2V1Ar9fj6lWQdMGKz5N01soZK3GuqoMfr9eBthujNwDI2PaIavlWi6C4CZUSweYLTFQU1OTsg690Ws4LasTqIpC5NyGGbkuDeDEuNl4i0WhCznHaJq9aukAMyUGYrKo/nLNdA0nZXUKVVHKypzbMGOQDEeEgZkNwEI/cIdHf5zZABwRdqw0gBPjZuMtFoUu5BQzafZOlRiIl0UFlWvkYzkEN6TlD8jw961AfQCY3ASU9ADE+uPkJqA+gGk/cCbHXuUeEOlNhWyVMRAbupAzzKbZO5GerzKmmWvkYykBN6Tlh8NAxbQw9l8XAA5LLcjIIi8+aLB/7wOVe1BSoiv0ffsyj2fn/RIbuuBKzEaAlJeXIxQKwev1Dln9apoGr9eLUChkSEGqyGLmGk7I6jTl5bpT0+sdukrVNP14KORsjZXycuDC/70EKEr/nnzNETzwJ/vDR1TuwYsvAi+8oP+dauOyoqLs3K8BUsUzOt0kDl2wmmZvZ4kBIwlFZq6Rj+UQcp2WX3ZPGeNOZGz+e50rw6ByD2J9EssC+HzO3C+kiUMXk4vgOOFwGEuWLEFzczN6e3vh8/lQV1eH5cuXKzkfPR4P+vv7HZXR4/G4RpZsEQ7r8dbNzXoEhs+n247nz0++mnS6fyKeuzzgoXvND+1HHvQvct97YnX+qUhnchGFLjhKa2sramtrBzaJjqFpmrKJIxsx2vkYL26F1lZ9c+PYRscxUsWRO90/Gf57/eg5oPCejPCje6G73hM75p8KO/YUHUVEISL6kIg2EdHZCeenE1E3Ea2NtkXmRBUKiXA4jNraWvT19Q1R3kbs1dmIAMnHaBSzxO9sn/g2RCL68draQzvvON0/FXWBuiH7BieieTRcE3DXe2LX/M2g6hR9CMB/M/NpACYA2JSkzypmroq2xbZJKOQtRhyNqdA0DbdaySFXRCXlP1uyOI3RtHqn+6di/tnzoRVleE+KNNx6lrvek1yWLchociEiP4B1AE7mFJ2JaDqA25h5luqFxeRS+KiaMYChJhhN06BpGkKhEGqsph0qks48lG1ZnMRoWr3T/dPRuqUVtc/XItIfQeRg3Hvi0aAVaQhdGXLdpvBOly2wanI5GcBnAJ4goveJqImISpP0O5uI1hFRKxGNTyFIkIjWENGazz77TH0GQl6ims5ORAgGg4MyRYPBIDo7O7OqQGtqatDZ2ekKWZzEaFq90/3TUXNKDTrndSI4OQj/CD885IF/hB/ByUF0zut0nTIHcly2IFX4S6wBqAbwNYAzo88fAvDvCX38AHzRv2cC2JJpXAlbLDwSN3GAYhigG3b/yccNKMxidMch1f4+n96/tNTY+FbItAGG6gYZdm6kYeeOTsmAlXroAI4FsC3u+XkAXs3wmm0Ajk7XRxR6YZFqE4dMzQ37c+bjBhRWMLonqEp/gLm4mPmuu/RNHTL1tWPPzUwbYNx1l9oGGXZvpGHXnqupsKTQ9ddjFYBvRv++E8D9CeePxSF7/FQAf4k9T9VEoRcOKps4pGq53twhHzegsIrRzSPs3uwhcXyn5qAiQ1ub/RtpOL05RzqFrhrl8i8AniaiTgBVAO4honlENC96vhbAeiJaB2ApgGzv8XcAAB9cSURBVKuiFxaGAWaiWdyS9p6PG1BYxWhqf6x/qvR2oxQXW0+Ft2MDikgEuPlm+yNSclo6IZWmd7rJCr1wMJI277a0d6vlB/IZo6n9qrbxTC1ma7eC0c2arTYzb79TpRMgW9AJTqIazRJLm+/o6MDBgwcxceJEpfrndhNff101rNKuDSiM1n63di2goWHwBhQNDcDKlfrjxInAI4/oKuuGG4COjvT1zA0UokyLyjipZI/dpmzvB2LkejHZjd5fW0il6Z1uskIvHIyscnPtgDTrvLVjhZ7Nuady9MUclomOSxUHoF2r4ky3UsVJ6dYVut0O1mTAqlPUiSYKvXCor6/PqCA1TeO6urqcOiDNOm/tiMTJpvPVisMwnbNONdolXcsU3aHqUJwzx7osdslsVHYxuQiuRjVtnplz6oA0W4rAjpT/bDpfrTgM0zkA58/PvItPJjQNSHcrVdPmiazLokommWPkMuV/gFSa3ukmK/TCQsWckGsHpBHnbaLs2bq2HXO3ao5IJ4Jdsd9WZff71WSxY4Wu+vY7nVAUA2JyEbJBpk0cVLNHPR6PI/IZzV61MxInm3MnsqbAMomQKXrDSnSHquwxGTNdy+q9ILL/vlt9i0WhC64gW6vUVGn8paWlWbl+Mpyc+9CSC2UM1DPQZUqJ5TJC08ivC5UUfSd/rVi5ltmyAsyi0AWXoOo8teKATGf6KSoq4qKiIkevnwqn5p46akdjwMtAiyEFZkdKvhXMOF7TmXOsOHKN3gsj17IS9SIKXXAFTkd6WClBYMf1rcpm9Npq8/WykZW6HVEYVrA7QsepiB+7ZDdzv9MpdIlyEbJGeXk5QqEQvF7vkKgYO0oBqESSFBUVobi42JHrp8OJuatF7UQAHAqriKXvJ6bxO56Srki6tPlMJIsgSTee3ffCjOy2R72k0vRON1mhD18yOU/Nomqn9vl8jlxfBTvnrh614x/kMGxrcyYl3U4SnZ2qK95UNu9UzlMn7kXsWlZlTgXE5CIUAplqlhuJYom9/uqrr+Y5c+a4pg66kbrsuY4ayiaqESSZolLsrHtul8xG3x5R6ELeY2ece6aWqzroRksD5DquP5sYiSBJ9bZlIy3fjMyyQheGFaoOxTlz5hiu0ZJpzGyt1M04TbMRNeQWjESQmHWO2u0Qdmqji3QKXZyigutRTZsnoowlCIyQzTroZkoDqJZcsFq2wA0YKTuQzNGYi7R8FZlVywook0rTO91khS6oYkc1R7MtW+YKs+aTXFevzCYtLepml8S3LVtp+clkttvMA1mhC/mMai3y3t5e1NTUoLOzE8FgcKDmeDaubRUjc4wn2Xz9fj+CwSA6OztRU1PjhLg5oaZGL8qlQuLtVH0b7X67a2qAzk4gGBxc2z0Y1I/b/vak0vRON1mhC6pYTdm34iyNjakafZLYr7S0lMePH8+lpaVpXzecHJzM5qNNzK60VV/n82UvCsYssGGT6FEAQgA+BLAJwNkJ5wn6XqJdADoBTMo0pih0QYWWlpaM6fpAeuefivMw3ZiqZg1Vc08yc8hwcnBaMUOYdTSqvK6oiLm4OHtRMGaxQ6H/BsD10b8PAzAq4fxMAK1RxX4WgHczjSkKXciEkVT+dBEpZksCeL1ebmtrU4o+UemXTuZsboCRS6xGm5h9vZUSACpyZZN0Cj2jgZGI/ACmAfgVADDzAWb+MqHbZQCejF7vTwBGEdGYTGMLQjpUN6QoLi5OmzafLu1+EEdAX5rcDuAOgBcybvr9TThQeiDt9SORCG6++WbDm2fER604XRbBLViNNkmXXp8uZT/T64qKhpYAMCKXa0il6WMNQBWA9wD8GsD7AJoAlCb0eQXAuXHP2wBUpxtXVuhCJoyk8quQLO2+rq6O58yZwyMDIxn/BsZPwLgzrv0E+vG/N2eDz9QSbeJOlUVwC3ZFm5ituZ7qdaWl9siVDZBmhU76+dQQUTWAPwE4h5nfJaKHAOxh5p/E9XkVwL3M/E70eRuAHzFze8JYQQBBADjhhBMmb9++PdP/G2EY4/F4kOnzGevX39+f8nw4HMaSJUvQ3NyM3t5e+Hw+1NXVYf78+SgvL0f4izACywPoi6TZjv4AgEcB/M34PKzIXmh4PLpqVOmXzduSDbnCYf0XSnOzHk3j8wF1dXq8upEfXkTUzszVSeVTeP0OADuY+d3o8xCASUn6HB/3fCyAnYkDMfNjzFzNzNWjR49WuLQwXGltbVVS5gDg8/nSjhMIBNDU1ISenh4wM3p6etDU1IRAIIDW1lYs+eMSRPoz2AGKAJxtYAKKpJO9EFGdbrZvi9NytbYCgQDQ1AT09Oj/PHp69OeBgH7eDjIqdGb+fwA+JqJvRg9dCGBjQreXAXyfdM4C0M3Mu+wRURhuhMNh1NbWKvXVNA3XXHNN2nH6+vqG2LcjkQj6+vpQW1uLJ9c9ichBBYUeSC1DRUWF4SzVdLIXKnV1atmT2b4tTsoVDgO1tUBf31D/QSSiH6+t1ftZRTXr4l8APE1EndBt6vcQ0Twimhc93wJgK/SwxccBNFgXTRiuqDpDgfSp7arp9HsP7FUT7LDUMjz00EOmFHohpOUbISfp8Ao4KVc2yw5ktKE7RXV1Na9ZsyYn1xbcjd/vR09Pj1LflpaWlNmQyuPcDmCEwsX2A7jv0FNN06BpGkKhEGpqatDa2ora2lpEIpG0/0gSXzfcaG3VV6SRyGBFp2l6C4UcyKDMoVx+v25eUenX3Z25n1UbuiBkFdU0eCJKqxCV0/Y7Ac2TfnlWTMWo4Iq06fXJ0vB9Ph8qKirg8/kKOi3fCFlPh8+xXFktO5Aq/MXplhdhi9mshi8MYDYNPjHtXmUMAOw73sfeu72DwxUTmvduL3ftTv2+G9mYQgU7x8vWx3g4fF3MzNHuwmCQeugmyHY1fGEAM2nwZqssDqT3/7mFvXd7WVusDVLk2mKNvXd7ueXPqd9vuyse2jletj7Gw+HrYnaOdtdFF4VulFxUwxcGMJoGbza1f8g4u7u48dVG9t/rZ89dHvbf6+fGVxszrsztTNm3c7xsfYyHw9fFyhztvj+i0I3i1FYjgjJGVqlmim/ZVS/c7qJado6XrY/xcPi6WJ2jnb9gRKEbJVfV8IVBqKbBGymPa3c6vd1lb+0cL1sf4+HwdbFjjmbLFSQiCt0oqtt1A4XvBbIRux2HMVQdoB6j26srzMHIPxI75xK/Wtc0bdD9bGtr4/r6egbKGKDoYz0DXUk/wlZvi1O727sJN81RFLpRjGwxXlQ0+HkheYFsxMmt0rK1OYSV7e3sXqGnarHa8UNryGsMeBloGfIRtnJbrGwLl0+46VdIOoUucejJUMkDjpFYqcfuXN4CQDUFP2zyftXV1SltlmwlzT7dHDJh5Noqc0lHrNDX0IJfEQB9AGoBHLrPVtLsYyntKuQind9O3FqyYAipNL3TzdUrdDuq4ee7F8hGnN6NJxubQ5jd9cjota1E7Kg1jYHGgY+plegTFUehHddxA26K5IGYXExg5LdkIf7GtJFsmEScNOkYmYMd17Zi2lFrflssg0Ysk4VggXRLrL0odLMYcY4ma/nsBbKRbDktndwcwqgD1Oq1E+dir0L3mIquGHpP1L4GRNau4ybsilSxgih0sxhZgiRrskJnZvtX6ImRJqWlpTx+/HguLS1NGu1hR1SN03PIJFtpaaltCt2qc/jQPbHvazAcygbYhSh0sxgxEiY2saEPYKcNXdUckSraw6wZJBtzSCVbS0tLkqgVc82Kr2LoPbEnocgtpox8QRS6Waw4R/PdC2Qjdjkt7XQYGnWSZnMOdpU1sGPe6edi3VHoJmdjvpBOoUvYYjrSbRUe2yI8cavwdFuPD1Ps2tHeyMYXmYhEInjAwI4C2ZxDvGxG51wU/TwWJXwujcioSrqvh+rXIJubPwwLUml6p1terNBjpPKEtLXl3kOSR1h1WlpNuklsZmzJ2ZpDTDYjc47J0tbW5phzOPk9Mf81cFPCTr4AMbkUIKm8SG1trvEu2Z3qbzQtPlOzElUTm1uis7K0tDTtHI3Mob6+PqtlDXKBm1Lq8wVR6IVGKi9SrAyBC8oROBEX7oYVevzcUjkqi4qKUs7RyByMOELtilzJNrJCN046ha5kQyeibUT0ARGtJaIhG4ES0XQi6o6eX0tEi1TGFUyQbgvxWLp3jssROJXqbzUtPh6zpQDi5zY0vV6nv78/5RyNzCHV+IlYLWuQS/ImpT5fSKXp4xuAbQCOTnN+OoBXVMaKNVmhmyQPQimdSvXPZZSLkbmlm6MTqf12Rq5kG4lyMQ6smlxEobuIPEh2cjLVP1dx6Ebnlm6OLS0tphS3U2UNco3EoRvDDoX+EYAOAO0AgknOTwewG8A6AK0AxqcYJwhgDYA1J5xwQvbuQCGRB+UInHbkJUaa+Hw+rqioYJ/PNyiqw4loD6OO2VRzNDoOEWU1ciXbuCGlPl+wQ6H/XfTxG1GlPS3hvB+AL/r3TABbMo3p6ApdNY/YaL5xpv5O5i/HxraizAtghZ5rzDhmk0X42LHSF4YnlhX6oBcAdwK4LUOfbUhjomEnFbrq7zejv/My9b/rLud+N6a6ttGW5zZ0N2CljG68icSqLV4YvlhS6ABKAZTF/f0HAN9J6HMsAIr+PRXAX2LPUzVHFLqqh6WtzZgnxo766GY9O3Zc26oMhkV2vj55rrDDqen1ermtrU15nHy9V4IzpFPoKmGLxwB4h4jWAXgPwKvM/N9ENI+I5kX71AJYH+2zFMBV0QtnF9U84ptvNpZvrDJuJszmLxu5tkvKEdiVJu9G4ueWmF6vSiQSwYsvvphxnKKiory+V0IOSKXpnW6OrNCtRoAktpjd0q5xzdhBjVzbZeUInKxPnmtic/P5fKZW6TGbeKpxfD5fwdwrwV6QZoUeM5Nknerqal6zZkiOkjU8Hl212Tlef79948bGM/oalWubGdsM4bD+q6G5GejtBXw+PTtk/nwpRgbA4/FA5Tvl8XiUE4cEIR4iamfm6mTnCqvaos/nzHh2jWtmHNXX2D33ZLS2AoEA0NQE9PTo/2h6evTngYB+fpjjU3wfVPsJghEKS6Gr5hFXVBjLN1YZNxNm85fdkhudruRAlksLuBmV1P58TtUX3E1hKfT589WU30MPqfW79Vb1cTMRP54RVOdkZmwjSOFqJebPn6+k0G91+v0ShiWFpdBVK+5fcIGxyvwq4951l7VK/1bn5LT9urlZTaE/9ZSzcricQo7wEfKAVN5Sp5vjmaIqkR5G840z9beSvxzLBC0tHRy5Ulp6qM55YyOzz5f8vNPREFkoXG13/fRcUsgRPkJuwbCJcslXWlt1+/NXXyWPVCkqAkaMABYsAH72M30lHL9a1jS9hUJATY0zMvr9ugNUpV93t+HhW1tbUVtbi0gkMqjkrqZp0DQNoVAINU7NTRDyiOET5ZKPxDsbU4Wx9ffr5++4I3dOSQeds07VTxeE4YYo9FxjRxZqDCedkg46Z41unCwIQnLE5JJrVE0ZRsYzYfJQImYastnk4/f70aNwD/x+P7qdmpsg5AlicnEzvb3uHi+emhqgsxMIBvV/HB6P/hgM6sdN2rh7FWVW7ScIw5X8UejhMNDQMFiRNDTkJpElURafT09W8vnUnsfL7lR2ayaZU92/THObOBE4eBDo6NBt+93dwLJllsImJbvSHsLhMBoaGuD3++HxeOD3+9HQ0CC+h+FEqvAXp5uhsEU37VFlZ21yr5f5kkusjxU/ZrK62VZrxKeS3ab7Xsj107NFqq35CmWbOuEQsHODC7uaskJ30y6ydtYmj7WSEuaRI+0ZK9l9sLNGvEP3vZDrp2cDuX/Di3QK3f0mFzelnNsZkRKjvx+48EI94zNVfe2iIvPZqHbWiE/2Ohvuu2RXWkOihIQBUml6p5vyCl21Hng29ly0u956vOyxLNPETFCfz1o2qlMyO3DfJbvSHIW8h6swFOS1ycVIynmqTZrb2uzZvFlVljSt6whw/Uxw2UIw3aE/1s8Ed40utiZbqrnbIHPGZvOm2IVUAsAqKveCiJQUusdCWQbBPeS3QlddYY4cmdyhV1Q0+DHWzDj2LK52W/4e7P03sPYTMO481LSf6Mdb/t6kbOmcnk4r88Rm0WEqzr1DqN4LWaEPL9IpdCUbOhFtI6IPiGgtEQ3JBiKdpUTURUSdRDTJmOEnDSop58XFh9LfE22JsXT6xLR6M+nyFuqih48Aav8J6DsMiCSYyiNF+vHaf9L7GZItU53ybGOhDIGUADiEkXshNdiFGEacojOYuYqTZyjVADgl2oIAHrVDOABqKefM5sc34tizUBd9ydlDFfkQUYqAB842KJsTjlo7MOEwFefeIYzcC6nBLsRQSv0nom0Aqpn58xTn/w+AN5n52ejzzQCmM/OuVGMaSv3PlHLODOzbpzZWMoyky6eSJdMlFgI9JQr99gPd9xmQze7SAXZisAyBlAA4hNF7IdUqhw92pP4zgNeIqJ2IgknOHwfg47jnO6LHEgUJEtEaIlrz2WefKV4amVPO9+9XHysZRlLKk8mikCnaO0JRlMMMypbNdPjYfVfFoGxSAuAQRu9FTU0NOjs7EQwGB2WKBoNBdHZ2ijIfJqgq9HOYeRJ000ojEU1LOE9JXjNk6c/MjzFzNTNXjx492pik5eV6inlHB3DDDfqq/JFH9FT04mJjYyVy8KCxMgIxWbq7ddt8Tw/wwQf6Y4rnvhFlSkP7DiQeyJDu7vWqyWwVv/9Qqn+Z2lwyyZ6Yqq7ya1EftvBLAJgph1BeXo5ly5ahu7sb/f396O7uxrJly5Tj991UXUMwh5JCZ+ad0cdPAawAMDWhyw4Ax8c9Hwtgpx0CDiLVrvNff219bId3rq8L1EHjZP/3DqH1A9d0xh/IUF+8tdX6rxMVEuWwoTZ6a2srAoEAmpqa0NPTo6zMh4tzL9uOzlRfLYe/FoLdpAp/iTUApQDK4v7+A4DvJPS5BEAr9JX6WQDeyzSu4S3onEi7T9YcKiPQtbuLvf8+clC4YmLz/psep64kS7buRzI5LJZjUElVT9WGSwp7NtP53VRdQ8gMLIYtHgPgHSJaB+A9AK8y838T0Twimhft0wJgK4AuAI8DaDDxvyU9qtEclLAKjqXTp0qrT8ShMgLlR5YjNPsFeD0joCVEUGr9gPcAEPq/QPnfoLb5s53RLanuUSo5LG5crRLBkchwKwGQzXIIbqquIVgklaZ3uhleoasm9Wja0LT42AbLqitSBxMwunZ3ceMzc9i/6DD23AH2LwQ3ziLu+oamZ1uqbixtR0p/sntkZINrk5tiqybCABj2JQCyUQ7BTdU1hMwgrzNFY9ix67zRNPjSUltS2QdIlZ6fSWk6kdKfKQ3cjKyKSKq6u7DjqyVkj8JQ6HYsI8ysaouK7Kn9baamu5Mp/enuk8P15yVV3V3ICj2/SKfQ3V8+N4Ydu86bSd3v7zedyj5ApvT8ZOM7mdKf7j6ZkdUgkqruLuz4agnuIH8Uuh27zltI3bfkFTLjdXIypT/dfcqCh0xS1d2FHV8twSWkWro73QybXJjtMQVY2ULO7G9OM79pjZiH7KwwmaXf31JV0V24aZdHIT0oCBt6DJORFUnHMKrQzXqFzHidVF9DlPx+mIlaMSurSWRDC3dhx1dLcJ50Cl2pOJcTGCrOlYpwWDcRNDfrdUN8Pt0gOH++2i70RgtbGSw2Zfg68eP7fMDevc7JlG48o7IKgpA17CjO5T7syFU24iS14hUy6nVSTel3wlMlHjJByFvyc4UeDutKu68vdR+vV6+KmG6lrjKOkfHskBfIjkypsOveCoLgCIW3QrcrEiM+hT1VaYCiosxp+JkwkiqvGt1SXGxNJjtkFQTBXaQyrjvdTDtFme2PxIh5g3y+wa/3+ez1Cql4nVTn5vPZI5MVWQVByDooiMSieFQ3ONizR63Ac6y+ecwWH2s9Pfpxs6vRxALTEyfqtdc7Og7VFk8cX3VuKiYZKyTWfO/o0GWfOFGKZQuCS8lPG7qR6JSiosEbRMe2rQuF9N2HnCLTtnmpru/GKBOzcxEEwXYKz4ZuJDqlP6FWrU3p62mxkj7vtiiTLJQCEATBHvJToVtJ4Y/hZIFnK05bt+VhS7FsQcgb8tPkAuhmgJkzrQnhlNnCqtnETSYON5qABGEYU3gmF0BXaIm7ExnFqd3jVcdN1a+mRo/zDgYHO3SDQf14Nu3VVuciCELWyF+FDmTcVd7x11sdN12/xCiTZBExVlDd4t2OuQiCkBXyW6GbqW8ew0nHotscm4kYKZvg9rkIgjCAskInoiIiep+IXklybjoRdRPR2mhbZK+YKbDiHHXSseg2x2Y8RqNW3DwXQRAGYWSFfjOATWnOr2LmqmhbbFEuNdKlqRvdyT5bcuU6fd5o1Iqb5yIIwiCUFDoRjQVwCYAmZ8UxQSoH4rx5QFub/pgLx6KbHJvxNDerKfSnnjr03K1zEQRhEEphi0QUAnAvgDIAtzHzrITz0wG8AGAHgJ3RPhuSjBMEEASAE044YfL27dutym8Mq/XT85HEOauGqRLpqf6CILgKS2GLRDQLwKfM3J6mWweAE5l5AoBfAngpWSdmfoyZq5m5evTo0Qqi24gd9dPzjWRzVoW5MO+JIBQwKiaXcwBcSkTbADwH4AIiao7vwMx7mLk3+ncLAI2IjrZbWNMMx/T1dHNWpdDuiSAUOBkVOjPfzsxjmXkcgKsArGTmuvg+RHQskZ7lQ0RTo+PudkBecwzH9HXVuurpKLR7IggFjuk4dCKaR0Tzok9rAawnonUAlgK4inNVUyAZZhyB+Y7KnDNRaPdEEAqc/K3lYgSPR81+7PEMrc6Yr6jOWWWcQrknglAAFGYtFyMYTV9XTYt3M3al4ktKvyDkDcNDoRtJXy+UaBgrZRFiSEq/IOQVw0Ohq6avf/e7hRMNY0fNeEnpF4S8YngodNX09VCocKJhVOZ8112S0i8IBcTwUOiAWvp6oUXDZJrzokWS0i8IBcTwiHJRZThGwwiCkFdIlIsqspmDIAh5jCj0eGQzB0EQ8hhR6PHIZg6CIOQxotDjkc0cBEHIY0ShJyKbOQiCkKdIlIsgCEIeIVEugiAIwwBR6IIgCAWCKHRBEIQCIWc2dCL6DIDRXaKPBvC5A+LYichoDyKjPYiM1nGbfCcyc9JNmXOm0M1ARGtSOQPcgshoDyKjPYiM1nG7fPGIyUUQBKFAEIUuCIJQIOSbQn8s1wIoIDLag8hoDyKjddwu3wB5ZUMXBEEQUpNvK3RBEAQhBaLQBUEQCoS8UehE9B0i2kxEXUS0MNfyAAARHU9EbxDRJiLaQEQ3R48fSUT/Q0Rboo9H5FjOIiJ6n4hecal8o4goREQfRu/l2S6U8dboe7yeiJ4lopJcy0hE/0lEnxLR+rhjKWUiotuj35/NRHRxDmW8P/pedxLRCiIa5TYZ487dRkRMREfnUkZV8kKhE1ERgIcB1AA4A8D3iOiM3EoFAPgawHxmPh3AWQAao3ItBNDGzKcAaIs+zyU3A9gU99xt8j0E4L+Z+TQAE6DL6hoZieg4ADcBqGbmCgBFAK5ygYy/BvCdhGNJZYp+Lq8CMD76mkei36tcyPg/ACqYOQDgzwBud6GMIKLjAfwDgL/EHcuVjErkhUIHMBVAFzNvZeYDAJ4DcFmOZQIz72LmjujfPdAV0XHQZftNtNtvAFyeGwkBIhoL4BIATXGH3SSfH8A0AL8CAGY+wMxfwkUyRikGMJKIigF4AexEjmVk5rcBfJFwOJVMlwF4jpm/YuaPAHRB/15lXUZmfo2Zv44+/ROAsW6TMcoDAH4EID5yJCcyqpIvCv04AB/HPd8RPeYaiGgcgIkA3gVwDDPvAnSlD+AbuZMMD0L/UB6MO+Ym+U4G8BmAJ6JmoSYiKnWTjMz8CYCfQ1+p7QLQzcyvuUnGOFLJ5Nbv0P8C0Br92zUyEtGlAD5h5nUJp1wjYzLyRaFTkmOuibckIh+AFwDcwsx7ci1PDCKaBeBTZm7PtSxpKAYwCcCjzDwRwF7k3gQ0iKgd+jIAJwH4OwClRFSXW6kM47rvEBH9GLrZ8unYoSTdsi4jEXkB/BjAomSnkxxzjS7KF4W+A8Dxcc/HQv/Jm3OISIOuzJ9m5hejh/9KRGOi58cA+DRH4p0D4FIi2gbdTHUBETW7SD5Af293MPO70ech6AreTTJ+G8BHzPwZM0cAvAjgWy6TMUYqmVz1HSKiawHMAjCHDyXDuEXGcuj/vNdFvztjAXQQ0bFwj4xJyReFvhrAKUR0EhEdBt0p8XKOZQIREXTb7yZm/kXcqZcBXBv9+1oA/5Vt2QCAmW9n5rHMPA76PVvJzHVukQ8AmPn/AfiYiL4ZPXQhgI1wkYzQTS1nEZE3+p5fCN1f4iYZY6SS6WUAVxHRCCI6CcApAN7LgXwgou8AWADgUmbuizvlChmZ+QNm/gYzj4t+d3YAmBT9rLpCxpQwc140ADOhe8TDAH6ca3miMp0L/edWJ4C10TYTwFHQIwy2RB+PdIGs0wG8Ev3bVfIBqAKwJnofXwJwhAtlvAvAhwDWA3gKwIhcywjgWeg2/Qh0pfODdDJBNyOEAWwGUJNDGbug26Fj35nlbpMx4fw2AEfnUkbVJqn/giAIBUK+mFwEQRCEDIhCFwRBKBBEoQuCIBQIotAFQRAKBFHogiAIBYIodEEQhAJBFLogCEKB8P8BPWxYrQYhBAgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \n",
    "            s = 100, c = 'blue', label = 'Iris-setosa')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \n",
    "            s = 100, c = 'red', label = 'Iris-versicolour')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\n",
    "            s = 100, c = 'black', label = 'Iris-virginica')\n",
    "\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \n",
    "            s = 100, c = 'green', label = 'Centroids')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}