code_kmean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>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>130</th>\n",
       "      <td>131</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "130  131            7.4           2.8            6.1           1.9   \n",
       "131  132            7.9           3.8            6.4           2.0   \n",
       "132  133            6.4           2.8            5.6           2.2   \n",
       "133  134            6.3           2.8            5.1           1.5   \n",
       "134  135            6.1           2.6            5.6           1.4   \n",
       "135  136            7.7           3.0            6.1           2.3   \n",
       "136  137            6.3           3.4            5.6           2.4   \n",
       "137  138            6.4           3.1            5.5           1.8   \n",
       "138  139            6.0           3.0            4.8           1.8   \n",
       "139  140            6.9           3.1            5.4           2.1   \n",
       "140  141            6.7           3.1            5.6           2.4   \n",
       "141  142            6.9           3.1            5.1           2.3   \n",
       "142  143            5.8           2.7            5.1           1.9   \n",
       "143  144            6.8           3.2            5.9           2.3   \n",
       "144  145            6.7           3.3            5.7           2.5   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "130  Iris-virginica  \n",
       "131  Iris-virginica  \n",
       "132  Iris-virginica  \n",
       "133  Iris-virginica  \n",
       "134  Iris-virginica  \n",
       "135  Iris-virginica  \n",
       "136  Iris-virginica  \n",
       "137  Iris-virginica  \n",
       "138  Iris-virginica  \n",
       "139  Iris-virginica  \n",
       "140  Iris-virginica  \n",
       "141  Iris-virginica  \n",
       "142  Iris-virginica  \n",
       "143  Iris-virginica  \n",
       "144  Iris-virginica  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = dataset.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: Species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = le.fit_transform(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train , X_test , Y_train , Y_test = train_test_split(X, Y, test_size = 0.3 , random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DEcisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X_train , Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,\n",
       "       0, 2, 2, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 2, 1, 1, 0,\n",
       "       0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "accuracy_score(Y_pred , Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "plt.scatter(X_test[Y_test==0,0], X_test[Y_test== 0,1] , s= 100, c= 'Red', label= 'cluster_1')\n",
    "plt.scatter(X_test[Y_test==1,0], X_test[Y_test== 1,1] , s= 100, c= 'blue', label= 'cluster_2')\n",
    "plt.scatter(X_test[Y_test==2,0], X_test[Y_test== 2,1] , s= 100, c= 'green', label= 'cluster_2')\n",
    "\n",
    "plt.title('Clusters of Species')\n",
    "plt.xlabel('Id')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAACKoutkV17bofhaWqRYTGpuljo7Jffgb3NzsL0lj5cc3nbbbbrrrruG/bzjx4/rvvvuy0sMqVRKn/70p3XBBRdo/vz52rhxY16OK5GoAQAAoEgmT5ic13YormQyWOs6leq91rUUPE6lgv3FHlkbSaLm7jp16lTWfTfddJMOHTqk559/Xk899ZRa8pSNkqgBAACgKBpiDYpURQZtE6mKaE1sTYEiwmhs3tw/QesrnZa2jPCSw4cfflixWEyLFi3SmjW9/5u4/PLLtWfPHknS0aNHNXv2bEnSgQMHtGTJEtXW1ioWi6mtrU0bN25UMplUbW2tNmzYIEm68847dfHFFysWi+nWW2+VJL3++uuaN2+empqatHjxYh0+fLhfTNXV1briiiskSRMmTNDixYvV3t4+shfYB4kaAAAAimL9svWKjBsiURsX0bql6woUEUZjx47cErXtI7jk8MCBA9q0aZN2796tF154Qffcc09Oz9u2bZtuuOEG7d+/X3v27NGMGTN0xx13qKamRvv379edd96pXbt2qa2tTc8995z279+vvXv36sknn5Qkvfzyy7r22mv1/PPPa9asWYOe6/jx4/rud7+r5cuXD/8FZkGiBgAAgKKomVqjxKqEqiPV/UbWIlURVUeqlViVYNHrEtGV46WEubbraffu3YrH45o2bZokaerUqTk9b9myZfrqV7+qr33ta3rjjTc0adKkfm127dqlXbt26cILL9TixYt16NAhtbW1SZJmzZqlpUuXDnme999/X1dffbWuv/56zZkzZxivbGAkagAAACia+rn1al3bqsaLGhWdGFWVVSk6MarGixrVurZV9XPrix0icjQ5x0sJc23Xk7vLzAbcP378+DPXkL377rtntq9evVqPP/64Jk2apBUrVmj37t1Zj33zzTdr//792r9/v1555RVdd911kqSzzz47p/gaGxs1d+5cfeELXxjOyxoUiRoAAACKqmZqjbZ+aqtObDyh7j/t1omNJ7T1U1sZSSsxDQ1SZPCZrIpEpDUjuORw+fLlevTRR3Xs2DFJUkdHR6/9s2fP1t69eyVJiUTizPZXX31Vc+bM0fXXX6+VK1eqtbVVU6ZMUWdn55k2K1as0IMPPqiuzFDfm2++qXfeeSfn2L70pS/pxIkTuvvuu4f/wgZBogYAAABg1Navzy1RWzeCSw7nz5+vW265RZ/4xCe0aNEi3Xjjjb3233TTTbr//vt16aWX6ujRo2e2P/LII1qwYIFqa2t16NAhXXvttTr33HN12WWXacGCBdqwYYOuvPJKrV69WsuWLdPChQsVj8d7JXKDaW9v16ZNm/TSSy9p8eLFqq2tVXNz8/BfYBbm7nk50HDV1dX56cosAAAAAMLr4MGDmjdv3pDtWlqCEvzpdO/CIpFIcEskpPoKnc2a7T00s73uXpetPSNqAAAAAPKivl5qbZUaG6VoVKqqCv42NgbbKzVJG4nxxQ4AAAAAQPmoqZG2bg1u5eSSSy7Re++912vb9u3btXDhwjE535CJmpl9XNIjPTbNkfSn7n53jzaXS/p7Sa9lNn3b3b+SxzgBAAAAoGieffbZgp5vyETN3V+WVCtJZjZO0puSHsvS9Ifu/pn8hgcAAAAAlWe416gtl5R09zfGIhgAAAAAwPATtaskfWuAfcvM7AUzazGz+aOMCwAAAAAqVs6JmplNkLRS0s4su/dJmuXuiyT9uaTvDHCMRjPbY2Z7jhw5MpJ4AQAAAIRYsiOppieaFL09qqovVyl6e1RNTzQp2ZEsdmglZTgjavWS9rn7T/vucPefu3tX5v73JUXMbFqWdg+4e527102fPn3EQQMAAAAIn5a2FsW2xdS8r1mdJzvlcnWe7FTzvmbFtsXU0taSt3Pddtttuuuuu4b9vOPHj+u+++7LSwypVEqf/vSndcEFF2j+/PnauHFjXo4rDS9Ru1oDTHs0sw+bmWXuL8kc99jowwMAAABQCpIdScV3xpVKp5Q+le61L30qrVQ6pfjOeNFH1kaSqLm7Tp06lXXfTTfdpEOHDun555/XU089pZaW/CSjOSVqZlYt6bckfbvHtrVmtjbzMC7pRTN7QdK9kq5yd89LhAAAAABCb/PTm5XuTg/aJt2d1pZntozo+A8//LBisZgWLVqkNWvW9Np3+eWXa8+ePZKko0ePavbs2ZKkAwcOaMmSJaqtrVUsFlNbW5s2btyoZDKp2tpabdiwQZJ055136uKLL1YsFtOtt94qSXr99dc1b948NTU1afHixTp8+HC/mKqrq3XFFVdIkiZMmKDFixervb19RK+vr5wWvHb3lKRz+2zb1uP+VklltqQdAAAAgFztaN3RbyStr/SptLa3btfWTw0vdThw4IA2bdqkp556StOmTVNHR4fuvffeIZ+3bds23XDDDbrmmmt08uRJdXd364477tCLL76o/fv3S5J27dqltrY2Pffcc3J3rVy5Uk8++aRmzpypl19+WQ899FBOI3DHjx/Xd7/7Xd1www3Dem0DySlRAwAAAIDBdJ3symu7nnbv3q14PK5p04IyGFOnTs3pecuWLdOmTZvU3t6u3/3d39XcuXP7tdm1a5d27dqlCy+8MIivq0ttbW2aOXOmZs2apaVLlw55nvfff19XX321rr/+es2ZM2cYr2xgwy3PDwAAAAD9TJ4wOa/tenJ3ZUpiZDV+/Pgz15C9++67Z7avXr1ajz/+uCZNmqQVK1Zo9+7dWY998803a//+/dq/f79eeeUVXXfddZKks88+O6f4GhsbNXfuXH3hC18YzssaFIkaAAAAgFFriDUoUhUZtE2kKqI1sTWDtslm+fLlevTRR3XsWFCvsKOjo9f+2bNna+/evZKkRCJxZvurr76qOXPm6Prrr9fKlSvV2tqqKVOmqLOz80ybFStW6MEHH1RXVzDS9+abb+qdd97JObYvfelLOnHihO6+++5hv67BkKgBAAAAGLX1y9YrMm6IRG1cROuWrhv2sefPn69bbrlFn/jEJ7Ro0SLdeOONvfbfdNNNuv/++3XppZfq6NGjZ7Y/8sgjWrBggWpra3Xo0CFde+21Ovfcc3XZZZdpwYIF2rBhg6688kqtXr1ay5Yt08KFCxWPx3slcoNpb2/Xpk2b9NJLL2nx4sWqra1Vc3PzsF9fNlas4ox1dXV+ujILAAAAgPA6ePCg5s2bN2S7lrYWxXfGle5O9yosEqmKKDIuosSqhOrn1o9lqKGV7T00s73uXpetPSNqAAAAAPKifm69Wte2qvGiRkUnRlVlVYpOjKrxoka1rm2t2CRtJKj6CAAAACBvaqbWaOuntg67BH/YXXLJJXrvvfd6bdu+fbsWLlw4JucjUQMAAACAITz77LMFPR9THwEAAAAMqVi1LcrBSN47EjUAAAAAgzrrrLN07NgxkrURcHcdO3ZMZ5111rCex9RHAAAAAIOaMWOG2tvbdeTIkWKHUpLOOusszZgxY1jPIVEDAAAAMKhIJKLzzz+/2GFUFKY+AgAAAEDIkKgBAAAAQMiQqAEAAABAyJCoAQAAAEDIkKgBAAAAQMiQqAEAAABAyJCoAQAAAEDIkKgBAAAAQMiQqAEAAABAyJCoAQAAAEDIkKgBAAAAQMiQqAEAAABAyJCoAQAAAEDIkKgBAAAAQMgMmaiZ2cfNbH+P28/N7At92piZ3Wtmr5hZq5ktHruQAQAAAKC8jR+qgbu/LKlWksxsnKQ3JT3Wp1m9pLmZ2yWS7s/8BQAAAAAM03CnPi6XlHT3N/ps/6ykhz3wjKRzzOwjeYkQAAAAACrMcBO1qyR9K8v2j0k63ONxe2YbAAAAAGCYck7UzGyCpJWSdmbbnWWbZzlGo5ntMbM9R44cyT1KAAAAAKggwxlRq5e0z91/mmVfu6TzejyeIemtvo3c/QF3r3P3uunTpw8vUgAAAACoEMNJ1K5W9mmPkvS4pGsz1R+XSjrh7m+POjoAAAAAZSPZkVTTE02K3h5V1ZerFL09qqYnmpTsSBY7tNAx934zFPs3MqtWcA3aHHc/kdm2VpLcfZuZmaStkj4pKSXpc+6+Z7Bj1tXV+Z49gzYBAAAAUCZa2loU3xlXujut9Kn0me2Rqogi4yJKrEqofm59ESMsPDPb6+51WfflkqiNBRI1AAAAoDIkO5KKbYsplU4N2KY6Uq3Wta2qmVpTwMiKa7BEbbhVHwEAAABgWDY/vVnp7vSgbdLdaW15ZkuBIgo/EjUAAAAAY2pH645e0x2zSZ9Ka3vr9gJFFH4kagAAAADGVNfJrry2qwQkagAAAADG1OQJk/ParhKQqAEAKl4yKTU1SdGoVFUV/G1qCrYDAEavIdagSFVk0DaRqojWxNaceVzppfyp+ggAqGgtLVI8LqXTwe20SCS4JRJSfWVViwaAvBtu1cdKKeVP1UcAALJIJoMkLZXqnaRJweNUKtjPyBoAjE7N1BolViVUHanuN7IWqYqoOlKtxKqEaqbWKNmRVHxnXKl0ql8BkvSptFLplOI742U/skaiBgCoWJs390/Q+kqnpS1UiwaAUaufW6/Wta1qvKhR0YlRVVmVohOjaryoUa1rW8+MkFHKP8DURwBAxYpGpc7O3NqdODH28QAApOjtUXWeHPrLOToxqhMbS/vLmamPAABk0ZVjFehc2wEARo9S/gESNQBAxZqcYxXoXNsBAEaPUv4BEjUAQFaVULK+oSGo7DiYSERas2bwNmFVCX1YiSq9ZDnKH6X8A1yjBgDop1JK1ieTUiwWVHccSHW11Noq1dQULq58qJQ+rDSVUrIcla2SSvlzjRoAIGeVVLK+piZIWKqr+4+sRSLB9kSi9JK0SurDSkLJclQKSvkHSNQAAL1UWsn6+vpgxKyxsfcUwcbGYHspjjpVWh9WCkqWo5JQyp+pjwCAPihZX/row/JUSSXLgVyV+ueCqY8AgJxRsr700YfliZLlQH/l/LkgUQMA9ELJ+tJHH5YnSpYD/ZXz54JEDQDQS7mXrK8E9GF5GknJcqDclfPngkQNANDL+vW5/chft64w8WD46MPytH7ZekXGDfGDdFxE65bSsagc5fy5IFEDAPRSriXrKwl9WJ6GU7IcqBTl/LkgUQMA9FOOJesrDX1YnnItWQ5UknL9XFCeHwAAAACKgPL8AAAAAFBCSNQAAAAAIGRI1AAAAMZAsiOppieaFL09qqovVyl6e1RNTzQp2ZGsiPMDGJ2crlEzs3MkNUtaIMkl/YG7P91j/+WS/l7Sa5lN33b3rwx2TK5RAwAA5aqlrUXxnXGlu9NKn0qf2R6piigyLqLEqsSYFjgo9vkB5CYf16jdI+kf3P0CSYskHczS5ofuXpu5DZqkAQAAlKtkR1LxnXGl0qleSZIkpU+llUqnFN8ZH7ORrWKfH0B+DJmomVlU0q9L+oYkuftJdz8+1oEBAACUos1Pb1a6Oz1om3R3Wlue2VKW5weQH7mMqM2RdETSQ2b2vJk1m9nZWdotM7MXzKzFzObnN0wAAIDSsKN1R7+RrL7Sp9La3rq9LM8PID9ySdTGS1os6X53v1DSLyRt7NNmn6RZ7r5I0p9L+k62A5lZo5ntMbM9R44cGUXYAAAA4dR1siuv7Urt/ADyI5dErV1Su7s/m3mcUJC4neHuP3f3rsz970uKmNm0vgdy9wfcvc7d66ZPnz7K0AEAAMJn8oTJeW1XaucHkB9DJmru/hNJh83s45lNyyW91LONmX3YzCxzf0nmuMfyHCsAjFoyKTU1SdGoVFUV/G1qCrYDQD40xBoUqYoM2iZSFdGa2JqyPD8+MBZLJLDsQuXItTx/rYLy/BMkvSrpc5L+uyS5+zYz+2NJfyTpfUm/lHSju/9osGNSnh9AobW0SPG4lE4Ht9MikeCWSEj1VKsGMErJjqRi22JKpVMDtqmOVKt1batqptaU3fkRGIslElh2ofwMVp4/p0RtLJCoASikZFKKxaTUwL9bVF0ttbZKNfxuATBKxf5BXezzV7qxSJZJwMtTPtZRA4CStnlz71G0bNJpaQvVqgHkQf3cerWubVXjRY2KToyqyqoUnRhV40WNal3bOuZJUrHPX+nGYokEll2oPIyoAagI0ajU2ZlbuxMnxj4eAED5it4eVefJof+nE+6QyfMAABgkSURBVJ0Y1YmNuf1PZyyOieJjRA1AxevKsQp1ru0AABjIWCyRwLILlYdEDUBFmJxjFepc2wEAMJCxWCKBZRcqD4kagIrQ0BBUdhxMJCKtyWO1apYCAIDKNBZLJLDswvCV+lIGXKMGoCIUuuojSwEAQOWi6mPxlUrlU65RA1DxamqC5Ki6uv/IWiQSbE8k8pOkJZNBkpZK9a80mU4H2+NxRtYAoFzVTK1RYlVC1ZHqfqNgkaqIqiPVSqxKDCuhGotjlqtkR1LxnXGl0qleSZokpU+llUqnFN8ZD/3IGokagIpRXx+MmDU29p6O2NgYbM/XCBdLAQAAxmKJBJZdyE25LGXA1EcAyDOWAgAAoHhKaSkDpj4CQAGxFAAAAMVTLksZkKgBQJ6xFAAAAMVTLksZkKgBQJ4VYymAMGJ5AgBAMZTLUgYkagCQZ+vX55aorVtXmHiKoaUlWA6huTm4Xs89+NvcHGxvaSl2hACAcrV+2XpFxg2RqI2LaN3ScP+PmEQNAPKskEsBhBHLEwAAiqlcljIgUQOAMVCopQDCiOUJAADFVg5LGVCeHwCQVyxPAABAbijPDwAoGJYnAABg9EjUAAB5xfIEAACMHokaACCvirE8AUsBACimZEdSTU80KXp7VFVfrlL09qianmhSsoMvIYwc16gBAPIqmQxK8KdSA7eprg6KquSj8mVLS1BFMp3uXcQkEgluiUR5F28BUFwtbS2K74wr3Z1W+tQHX0KRqogi4yJKrEqUROEKFAfXqAEACqaQyxOwFACAYkp2JBXfGVcqneqVpElS+lRaqXRK8Z1xRtYwIiRqAIC8K9TyBCwFAKCYNj+9Wenuwb+E0t1pbXmGLyEMH1MfAQAli6UAABRT9PaoOk8O/SUUnRjViY18CaE/pj4CAMoSSwEAKKauk7l9ueTaDuiJRA0AULJYCgBAMU2ekNuXS67tgJ5I1AAAJYulAMIbE1AJGmINilQN/iUUqYpoTSyPX0KoGFyjBgAoWSwFEM6YgEqR7Egqti2mVHrgL6HqSLVa17aqZmoevoRQdkZ9jZqZnWNmCTM7ZGYHzWxZn/1mZvea2Stm1mpmi/MROAAAg6n0pQDCGBNQSWqm1iixKqHqSHW/kbVIVUTVkWolViVI0jAiuU59vEfSP7j7BZIWSTrYZ3+9pLmZW6Ok+/MWIQAAg6jkpQDCGBNQaern1qt1basaL2pUdGJUVVal6MSoGi9qVOvaVha7xogNOfXRzKKSXpA0xwdobGZ/Iemf3P1bmccvS7rc3d8e6LhMfQQAlJIwLgUQxpgAALkb7dTHOZKOSHrIzJ43s2YzO7tPm49JOtzjcXtmW99AGs1sj5ntOXLkSI7hAwBQfGFcCiCMMQEA8iOXRG28pMWS7nf3CyX9QtLGPm0sy/P6jb65+wPuXufuddOnTx92sAAAFEsYlwIIY0wAgPzIJVFrl9Tu7s9mHicUJG5925zX4/EMSW+NPjwAAMKhGEsBDCWMMQEA8mPIRM3dfyLpsJl9PLNpuaSX+jR7XNK1meqPSyWdGOz6NAAASs369bklRevWFSYeKZwxAQDyI9eqj/9L0jfNrFVSraSvmtlaM1ub2f99Sa9KekXSX0pqynukAAAUUSGXAijlmAAA+cGC1wAADEMyGZS73749KNIxeXIwtXDduuIlRGGMCQAwtMGqPpKoAQAAAEARjLY8PwAAAACggEjUAAAAACBkSNQAAAAAIGRI1AAAKLJkUmpqkqJRqaoq+NvUFGwHgEqQ7Eiq6YkmRW+PqurLVYreHlXTE01KdlTuFyHFRAAAKKKWFikel9Lp4HZaJBLcEgmpvr548QHAWGtpa1F8Z1zp7rTSpz74IoxURRQZF1FiVUL1c8vzi5BiIgAAhFAyGSRpqVTvJE0KHqdSwX5G1gCUq2RHUvGdcaXSqV5JmiSlT6WVSqcU3xmvyJE1EjUAAIpk8+b+CVpf6XSwRhoAlKPNT29WunvwL8J0d1pbnqm8L0ISNQAAimTHjtwSte3bCxMPABTajtYd/UbS+kqfSmt7a+V9EZKoAQBQJF1d+W0HAKWm62RuX3C5tisnJGoAABTJ5Mn5bQcApWbyhNy+4HJtV05I1CrNaGtAU0MayIqPBkaioSGo7DiYSERas6Yw8QBAoTXEGhSpGvyLMFIV0ZpY5X0RUp6/koy2BjQ1pIGs+GhgpJJJKRYLqjsOpLpaam2VamoKFxcAFEqyI6nYtphS6YG/CKsj1Wpd26qaqeX3RUh5foy+BjQ1pIGs+GhgNGpqgkS+urr/yFokEmxPJEjSAJSvmqk1SqxKqDpS3W9kLVIVUXWkWolVibJM0oZColYpRlsDmhrSQFZ8NDBa9fXBiFljY++ps42NwXZGYwGUu/q59Wpd26rGixoVnRhVlVUpOjGqxosa1bq2tWwXux4KUx8rRTQqdXbm1u7Eifw/HyhTfDQAAMBIMfURo68BTQ1pICs+GgAAYCyQqFWK0daApoY0kBUfDQAAMBZI1CrFaGtAU0MayKpcPhqFXF6ApQwAABga16hVitHWgKaGNJBVOXw0Crm8AEsZAADwAa5Rw+hrQFNDGsiq1D8ahVxegKUMAADIHYlaJRltDWhqSANZlfJHo5DLC7CUAQAAuWPqIwBUsEIuL8BSBgAA9MbURwBAVoVcXoClDAAAyB2JGgBUsEIuL8BSBgAA5I5EDSg11DZHHhVyeYFyWcoAAIBCyOkaNTN7XVKnpG5J7/edR2lml0v6e0mvZTZ9292/MtgxuUYNGAFqmyPPCrm8QDksZQAAQD7l6xq1K9y9dqADSfphZn/tUEkagBGgtjnGQCGXFyj1pQwAACgkpj4CpYLa5hgjhVxeoJSXMgAAoJBynfr4mqSfSXJJf+HuD/TZf7mkv5PULuktSTe5+4HBjsnUR2CYqG0OAABQVgab+jg+x2Nc5u5vmdmHJP3AzA65+5M99u+TNMvdu8zsU5K+I2lulkAaJTVK0syZM4f1IoCKR21zAACAipHT1Ed3fyvz9x1Jj0la0mf/z929K3P/+5IiZjYty3EecPc6d6+bPn36qIMHKgq1zQEAACrGkImamZ1tZlNO35d0paQX+7T5sJlZ5v6SzHGP5T/cMhWGcuthiKGUFeL9o7Y5AABAxRjyGjUzm6NgFE0Kpkr+jbtvMrO1kuTu28zsjyX9kaT3Jf1S0o3u/qPBjss1ahlhKLcehhhKWaHeP2qbAwAAlJXBrlHLqZjIWCBRUzh+eIchhlJW6PePpBoAAKBs5GsdNeRbGMqthyGGUlbo94/a5gAAABWBEbViCkO59TDEUMp4/wAAADBCjKiFVRjKrYchhlLG+wcAAIAxQKJWTGEotx6GGEoZ7x8AAADGAIlaMYWh3HoYYihlvH8AAAAYAyRqxbR+fW4/8tetK+8YShnvHwAAAMYAiVox1dQE5dSrq/v/2I9Egu2JxNiWxQ9DDKWM9w8AAABjgESt2MJQbj0MMZQy3j8AAADkGeX5AQAAAKAIKM8PAAAAACWERA0AAAAAQoZEDZUlmZSamnpfS9bUFGwv5XMBAACgrHCNGipHS4sUj0vpdHA7LRIJbolE/gp/FPJcAAAAKElcowYkk0HilEr1Tpyk4HEqFezPx2hXIc8FAACAskSihsqweXP/pKmvdFrasqW0zgUAAICyxNRHVIZoVOrszK3diROlcy4AAACULKY+Al1d+W0XlnMBAACgLJGooTJMnpzfdmE5FwAAAMoSiRryI+yl6BsagmqLg4lEpDVrSutcAAAAKEtco4bRK4VS9MmkFIsFFRcHUl0ttbZKNTWlcy4AAACULK5Rw9gplVL0NTVBwlhd3X+0KxIJticS+UmcCnkuAAAAlCUSNYxOKZWir68PRrEaG3tP0WxsDLbnc9SvkOcCAABA2WHqI0aHUvQAAADAiDD1EWOHUvQAAABA3pGoYXQoRQ8AAADkHYmaFP7S8mEW5lL09CsAAABKVE7XqJnZ65I6JXVLer/vPEozM0n3SPqUpJSk33f3fYMdMzTXqJVCafkwC2spevoVAAAAIZeva9SucPfaAQ5UL2lu5tYo6f7hh1kEpVJaPszCWIqefgUAAECJy9fUx89KetgDz0g6x8w+kqdjj51SKi0fZmErRU+/AgAAoMTlOvXxNUk/k+SS/sLdH+iz/3uS7nD3f8k8/kdJf+LuA85tDMXUR0rLlyf6FQAAACVgsKmP43M8xmXu/paZfUjSD8zskLs/2fMcWZ7TLwM0s0YFUyM1c+bMHE89higtX57oVwAAAJS4nKY+uvtbmb/vSHpM0pI+Tdolndfj8QxJb2U5zgPuXufuddOnTx9ZxPlEafnyRL8CAACgxA2ZqJnZ2WY25fR9SVdKerFPs8clXWuBpZJOuPvbeY8234pRWp6S8WMvzEsGAAAAADkY8ho1M5ujYBRNCqZK/o27bzKztZLk7tsy5fm3SvqkgvL8nxvs+jQpJNeoFbq0PCXjCyOsSwYAAAAAPQx2jVpOxUTGQigSNalwyRPJQ2GRFAMAACDk8rWOWnkqVGl5SsYXVtiWDAAAAACGgRG1QqFkPAAAAIAeGFELA0rGAwAAAMgRiVqhUDIeAAAAQI5I1AplJCXjs5Xyb2iQrrmG8v7FxBILo8P7BwAAMCSuUSuU4VZ9HKhqYTZUMiwcqkmODu8fAADAGVyjFgY1NcGP0Orq/iNrkUiwPZEI2iWTwY/ZVGroJE0K2qRSwXMYlRg7g/ULfTA03j8AAICckagVUq4l43Mp5Z8N5f3HFkssjA7vHwAAQM6Y+hhGuZbyH+i5lPcfGyyxMDq8fwAAAL0w9bHUjKZEP+X9xw5LLIwO7x8AAEDOSNTCaDQl+invP3ZYYmF0eP8AAAByRqIWRrmU8s+mb3l/jE7fMvLvviuZDf4c+mBgI1miAgAAoEKRqIXR+vUjT9TWrct/PJWopSVYTqG5Obiuyj0odDHUNZ30wcBy+e+a9w8AAEASiVo4DVbKP5u+5f0xOsNdHkGiD3IxnCUqAAAAKhyJWlgNVMr/mmuCKWSDlffH6ORSRt5MmjCBPhiuXJeoAAAAqHCU5wf6oow8AAAACoDy/MBwUEYeAAAARUaiBvRFGXkAAAAUGYkawqFvKfxoNHicTBY+FsrIAwAAoMhI1FB82Urhd3YGj2OxYH8hUUYeAAAARUaihuIarBR+Oh1sj8cLO7JGGXkAAAAUGYkaiiuXUvjptLRlS2HiOY0y8gAAACgiyvOjuCiFDwAAgApFeX6EF6XwAQAAgH5I1FBclMIHAAAA+iFRQ3FRCh8AAADoh0QNxUUpfAAAAKCfnBM1MxtnZs+b2fey7LvczE6Y2f7M7U/zGybKFqXwAQAAgH6GM6J2g6SDg+z/obvXZm5fGWVcqCSUwgcAAAB6ySlRM7MZkj4tqXlsw0HFqqmRtm4NSvB3dwd/t25lJA0AAAAVKdcRtbslfVHSqUHaLDOzF8ysxczmZ2tgZo1mtsfM9hw5cmS4sQIAAABARRgyUTOzz0h6x933DtJsn6RZ7r5I0p9L+k62Ru7+gLvXuXvd9OnTRxQwAAAAAJS7XEbULpO00sxel/S3kn7DzHb0bODuP3f3rsz970uKmNm0fAcLIASSSampqff1hE1NwXYAAADkxZCJmrvf7O4z3H22pKsk7Xb3hp5tzOzDZmaZ+0syxz02BvECKKaWFikWk5qbpc5OyT3429wcbG9pKXaEAAAAZWH8SJ9oZmslyd23SYpL+iMze1/SLyVd5e6enxABhEIyKcXjUirVf186Hdzi8aBSJ0VgAAAARmVYiZq7/5Okf8rc39Zj+1ZJW/MZGICQ2bw5SMYGk05LW7YEFTsBAAAwYsNZRw1AJduxI7dEbfv2wsQDAABQxkjUAOSmqyu/7QAAADAgEjUAuZk8Ob/tAAAAMCASNQC5aWiQIpHB20Qi0po1hYkHAACgjJGoAcjN+vW5JWrr1hUmHgAAgDJGogYgNzU1UiIhVVf3T9gikWB7IkFpfgAAgDwgUQOQu/r6YJ20xkYpGpWqqoK/jY3B9vr6YkcIAABQFqxY61LX1dX5nj17inJuAAAAACg2M9vr7nXZ9jGiBgAAAAAhQ6IGAAAAACFDogYAAAAAIUOiBgAAAAAhQ6IGAAAAACFDogYAAAAAIVO08vxmdkTSGzk2nybp6BiGg/yiv0oHfVU66KvSQV+VDvqqdNBXpYX+yt0sd5+ebUfRErXhMLM9A60vgPChv0oHfVU66KvSQV+VDvqqdNBXpYX+yg+mPgIAAABAyJCoAQAAAEDIlEqi9kCxA8Cw0F+lg74qHfRV6aCvSgd9VTroq9JCf+VBSVyjBgAAAACVpFRG1AAAAACgYoQ+UTOzT5rZy2b2ipltLHY8+ICZnWdm/5+ZHTSzA2Z2Q2b7VDP7gZm1Zf7+arFjRcDMxpnZ82b2vcxj+iqEzOwcM0uY2aHM52sZfRVOZrYu8/33opl9y8zOoq/Cw8weNLN3zOzFHtsG7B8zuznze+NlM1tRnKgr0wB9dWfme7DVzB4zs3N67KOviiRbX/XYd5OZuZlN67GNvhqhUCdqZjZO0tcl1Uv6NUlXm9mvFTcq9PC+pPXuPk/SUkmfz/TPRkn/6O5zJf1j5jHC4QZJB3s8pq/C6R5J/+DuF0hapKDP6KuQMbOPSbpeUp27L5A0TtJVoq/C5K8kfbLPtqz9k/n/11WS5meec1/mdwgK46/Uv69+IGmBu8ck/bukmyX6KgT+Sv37SmZ2nqTfkvTjHtvoq1EIdaImaYmkV9z9VXc/KelvJX22yDEhw93fdvd9mfudCn5MfkxBH/11ptlfS/qd4kSInsxshqRPS2rusZm+Chkzi0r6dUnfkCR3P+nux0VfhdV4SZPMbLykaklvib4KDXd/UlJHn80D9c9nJf2tu7/n7q9JekXB7xAUQLa+cvdd7v5+5uEzkmZk7tNXRTTA50qStkj6oqSeBTDoq1EIe6L2MUmHezxuz2xDyJjZbEkXSnpW0n9w97elIJmT9KHiRYYe7lbwBXqqxzb6KnzmSDoi6aHMNNVmMztb9FXouPubku5S8K/Hb0s64e67RF+F3UD9w2+OcPsDSS2Z+/RVyJjZSklvuvsLfXbRV6MQ9kTNsmyjTGXImNlkSX8n6Qvu/vNix4P+zOwzkt5x973FjgVDGi9psaT73f1CSb8QU+dCKXNt02clnS/po5LONrOG4kaFUeA3R0iZ2S0KLrf45ulNWZrRV0ViZtWSbpH0p9l2Z9lGX+Uo7Ilau6TzejyeoWBaCULCzCIKkrRvuvu3M5t/amYfyez/iKR3ihUfzrhM0koze13BFOLfMLMdoq/CqF1Su7s/m3mcUJC40Vfh85uSXnP3I+6elvRtSZeKvgq7gfqH3xwhZGa/J+kzkq7xD9aUoq/CpUbBP1i9kPmdMUPSPjP7sOirUQl7ovavkuaa2flmNkHBxYiPFzkmZJiZKbiO5qC7/1mPXY9L+r3M/d+T9PeFjg29ufvN7j7D3Wcr+BztdvcG0Veh4+4/kXTYzD6e2bRc0kuir8Lox5KWmll15vtwuYJrdemrcBuofx6XdJWZTTSz8yXNlfRcEeJDhpl9UtKfSFrp7qkeu+irEHH3f3P3D7n77MzvjHZJizP/P6OvRmF8sQMYjLu/b2Z/LOn/KKim9aC7HyhyWPjAZZLWSPo3M9uf2fa/Jd0h6VEzu07BD5lVRYoPQ6Ovwul/Sfpm5h+oXpX0OQX/sEZfhYi7P2tmCUn7FEzLel7SA5Imi74KBTP7lqTLJU0zs3ZJt2qA7z13P2Bmjyr4h5H3JX3e3buLEngFGqCvbpY0UdIPgn8L0TPuvpa+Kq5sfeXu38jWlr4aHftgFBkAAAAAEAZhn/oIAAAAABWHRA0AAAAAQoZEDQAAAABChkQNAAAAAEKGRA0AAAAAQoZEDQBQMcysa4Dtf2Vm8ULHAwDAQEjUAAAAACBkQr3gNQAAY8GC1XP/XNJvSHpNkhU3IgAAemNEDQBQif6LpI9LWijpDyVdWtxwAADojUQNAFCJfl3St9y9293fkrS72AEBANATiRoAoFJ5sQMAAGAgJGoAgEr0pKSrzGycmX1E0hXFDggAgJ4oJgIAqESPKSgk8m+S/l3SPxc3HAAAejN3Zn4AAAAAQJgw9REAAAAAQoZEDQAAAABChkQNAAAAAEKGRA0AAAAAQoZEDQAAAABChkQNAAAAAEKGRA0AAAAAQoZEDQAAAABC5v8HXOYNHasFNCkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "plt.scatter(X_test[Y_pred==0,0], X_test[Y_pred== 0,1] , s= 100, c= 'Red', label= 'cluster_1')\n",
    "plt.scatter(X_test[Y_pred==1,0], X_test[Y_pred== 1,1] , s= 100, c= 'blue', label= 'cluster_2')\n",
    "plt.scatter(X_test[Y_pred==2,0], X_test[Y_pred== 2,1] , s= 100, c= 'green', label= 'cluster_2')\n",
    "\n",
    "plt.title('Clusters of Species')\n",
    "plt.xlabel('Id')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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
}