Iris Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "print(iris.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "       [4.8, 3. , 1.4, 0.1],\n",
      "       [4.3, 3. , 1.1, 0.1],\n",
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      "       [4.6, 3.6, 1. , 0.2],\n",
      "       [5.1, 3.3, 1.7, 0.5],\n",
      "       [4.8, 3.4, 1.9, 0.2],\n",
      "       [5. , 3. , 1.6, 0.2],\n",
      "       [5. , 3.4, 1.6, 0.4],\n",
      "       [5.2, 3.5, 1.5, 0.2],\n",
      "       [5.2, 3.4, 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.6, 0.2],\n",
      "       [4.8, 3.1, 1.6, 0.2],\n",
      "       [5.4, 3.4, 1.5, 0.4],\n",
      "       [5.2, 4.1, 1.5, 0.1],\n",
      "       [5.5, 4.2, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.2],\n",
      "       [5. , 3.2, 1.2, 0.2],\n",
      "       [5.5, 3.5, 1.3, 0.2],\n",
      "       [4.9, 3.6, 1.4, 0.1],\n",
      "       [4.4, 3. , 1.3, 0.2],\n",
      "       [5.1, 3.4, 1.5, 0.2],\n",
      "       [5. , 3.5, 1.3, 0.3],\n",
      "       [4.5, 2.3, 1.3, 0.3],\n",
      "       [4.4, 3.2, 1.3, 0.2],\n",
      "       [5. , 3.5, 1.6, 0.6],\n",
      "       [5.1, 3.8, 1.9, 0.4],\n",
      "       [4.8, 3. , 1.4, 0.3],\n",
      "       [5.1, 3.8, 1.6, 0.2],\n",
      "       [4.6, 3.2, 1.4, 0.2],\n",
      "       [5.3, 3.7, 1.5, 0.2],\n",
      "       [5. , 3.3, 1.4, 0.2],\n",
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      "       [6.9, 3.1, 4.9, 1.5],\n",
      "       [5.5, 2.3, 4. , 1.3],\n",
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      "       [6.3, 3.3, 4.7, 1.6],\n",
      "       [4.9, 2.4, 3.3, 1. ],\n",
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      "       [5.8, 2.7, 4.1, 1. ],\n",
      "       [6.2, 2.2, 4.5, 1.5],\n",
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      "       [7.2, 3.2, 6. , 1.8],\n",
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      "       [6.1, 2.6, 5.6, 1.4],\n",
      "       [7.7, 3. , 6.1, 2.3],\n",
      "       [6.3, 3.4, 5.6, 2.4],\n",
      "       [6.4, 3.1, 5.5, 1.8],\n",
      "       [6. , 3. , 4.8, 1.8],\n",
      "       [6.9, 3.1, 5.4, 2.1],\n",
      "       [6.7, 3.1, 5.6, 2.4],\n",
      "       [6.9, 3.1, 5.1, 2.3],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [6.8, 3.2, 5.9, 2.3],\n",
      "       [6.7, 3.3, 5.7, 2.5],\n",
      "       [6.7, 3. , 5.2, 2.3],\n",
      "       [6.3, 2.5, 5. , 1.9],\n",
      "       [6.5, 3. , 5.2, 2. ],\n",
      "       [6.2, 3.4, 5.4, 2.3],\n",
      "       [5.9, 3. , 5.1, 1.8]]), 'target': 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]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], 'filename': 'C:\\\\Users\\\\Upasana\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'}\n"
     ]
    }
   ],
   "source": [
    "print(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "print(iris[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df = pd.DataFrame(data = iris[\"data\"],columns = iris[\"feature_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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      "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(iris[\"feature_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "text": [
      "[[5.1 4.9 4.7 4.6 5.  5.4 4.6 5.  4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1\n",
      "  5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.  5.  5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.\n",
      "  5.5 4.9 4.4 5.1 5.  4.5 4.4 5.  5.1 4.8 5.1 4.6 5.3 5.  7.  6.4 6.9 5.5\n",
      "  6.5 5.7 6.3 4.9 6.6 5.2 5.  5.9 6.  6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1\n",
      "  6.3 6.1 6.4 6.6 6.8 6.7 6.  5.7 5.5 5.5 5.8 6.  5.4 6.  6.7 6.3 5.6 5.5\n",
      "  5.5 6.1 5.8 5.  5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3\n",
      "  6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.  6.9 5.6 7.7 6.3 6.7 7.2\n",
      "  6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.  6.9 6.7 6.9 5.8 6.8\n",
      "  6.7 6.7 6.3 6.5 6.2 5.9]\n",
      " [3.5 3.  3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 3.7 3.4 3.  3.  4.  4.4 3.9 3.5\n",
      "  3.8 3.8 3.4 3.7 3.6 3.3 3.4 3.  3.4 3.5 3.4 3.2 3.1 3.4 4.1 4.2 3.1 3.2\n",
      "  3.5 3.6 3.  3.4 3.5 2.3 3.2 3.5 3.8 3.  3.8 3.2 3.7 3.3 3.2 3.2 3.1 2.3\n",
      "  2.8 2.8 3.3 2.4 2.9 2.7 2.  3.  2.2 2.9 2.9 3.1 3.  2.7 2.2 2.5 3.2 2.8\n",
      "  2.5 2.8 2.9 3.  2.8 3.  2.9 2.6 2.4 2.4 2.7 2.7 3.  3.4 3.1 2.3 3.  2.5\n",
      "  2.6 3.  2.6 2.3 2.7 3.  2.9 2.9 2.5 2.8 3.3 2.7 3.  2.9 3.  3.  2.5 2.9\n",
      "  2.5 3.6 3.2 2.7 3.  2.5 2.8 3.2 3.  3.8 2.6 2.2 3.2 2.8 2.8 2.7 3.3 3.2\n",
      "  2.8 3.  2.8 3.  2.8 3.8 2.8 2.8 2.6 3.  3.4 3.1 3.  3.1 3.1 3.1 2.7 3.2\n",
      "  3.3 3.  2.5 3.  3.4 3. ]\n",
      " [1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5 1.6 1.4 1.1 1.2 1.5 1.3 1.4\n",
      "  1.7 1.5 1.7 1.5 1.  1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5 1.4 1.5 1.2\n",
      "  1.3 1.4 1.3 1.5 1.3 1.3 1.3 1.6 1.9 1.4 1.6 1.4 1.5 1.4 4.7 4.5 4.9 4.\n",
      "  4.6 4.5 4.7 3.3 4.6 3.9 3.5 4.2 4.  4.7 3.6 4.4 4.5 4.1 4.5 3.9 4.8 4.\n",
      "  4.9 4.7 4.3 4.4 4.8 5.  4.5 3.5 3.8 3.7 3.9 5.1 4.5 4.5 4.7 4.4 4.1 4.\n",
      "  4.4 4.6 4.  3.3 4.2 4.2 4.2 4.3 3.  4.1 6.  5.1 5.9 5.6 5.8 6.6 4.5 6.3\n",
      "  5.8 6.1 5.1 5.3 5.5 5.  5.1 5.3 5.5 6.7 6.9 5.  5.7 4.9 6.7 4.9 5.7 6.\n",
      "  4.8 4.9 5.6 5.8 6.1 6.4 5.6 5.1 5.6 6.1 5.6 5.5 4.8 5.4 5.6 5.1 5.1 5.9\n",
      "  5.7 5.2 5.  5.2 5.4 5.1]\n",
      " [0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 0.2 0.2 0.1 0.1 0.2 0.4 0.4 0.3\n",
      "  0.3 0.3 0.2 0.4 0.2 0.5 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.4 0.1 0.2 0.2 0.2\n",
      "  0.2 0.1 0.2 0.2 0.3 0.3 0.2 0.6 0.4 0.3 0.2 0.2 0.2 0.2 1.4 1.5 1.5 1.3\n",
      "  1.5 1.3 1.6 1.  1.3 1.4 1.  1.5 1.  1.4 1.3 1.4 1.5 1.  1.5 1.1 1.8 1.3\n",
      "  1.5 1.2 1.3 1.4 1.4 1.7 1.5 1.  1.1 1.  1.2 1.6 1.5 1.6 1.5 1.3 1.3 1.3\n",
      "  1.2 1.4 1.2 1.  1.3 1.2 1.3 1.3 1.1 1.3 2.5 1.9 2.1 1.8 2.2 2.1 1.7 1.8\n",
      "  1.8 2.5 2.  1.9 2.1 2.  2.4 2.3 1.8 2.2 2.3 1.5 2.3 2.  2.  1.8 2.1 1.8\n",
      "  1.8 1.8 2.1 1.6 1.9 2.  2.2 1.5 1.4 2.3 2.4 1.8 1.8 2.1 2.4 2.3 1.9 2.3\n",
      "  2.5 2.3 1.9 2.  2.3 1.8]]\n"
     ]
    }
   ],
   "source": [
    "features = iris.data.T # T is transpose, interchanging the rows and columns of the matrix\n",
    "print(features)        # So we would now have features as each row rather than having them as a column as above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1 4.9 4.7 4.6 5.  5.4 4.6 5.  4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1\n",
      " 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.  5.  5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.\n",
      " 5.5 4.9 4.4 5.1 5.  4.5 4.4 5.  5.1 4.8 5.1 4.6 5.3 5.  7.  6.4 6.9 5.5\n",
      " 6.5 5.7 6.3 4.9 6.6 5.2 5.  5.9 6.  6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1\n",
      " 6.3 6.1 6.4 6.6 6.8 6.7 6.  5.7 5.5 5.5 5.8 6.  5.4 6.  6.7 6.3 5.6 5.5\n",
      " 5.5 6.1 5.8 5.  5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3\n",
      " 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.  6.9 5.6 7.7 6.3 6.7 7.2\n",
      " 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.  6.9 6.7 6.9 5.8 6.8\n",
      " 6.7 6.7 6.3 6.5 6.2 5.9]\n"
     ]
    }
   ],
   "source": [
    "sepal_length = features[0]\n",
    "sepal_width = features[1]\n",
    "petal_length = features[2]\n",
    "petal_width = features[3]\n",
    "print(sepal_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(sepal_length.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sepal_length_label = iris.feature_names[0]\n",
    "sepal_width_label = iris.feature_names[1]\n",
    "petal_length_label = iris.feature_names[2]\n",
    "petal_width_label = iris.feature_names[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2EwqGo/p9RX42LNscNzsUilojtAGEBaIeZPgguLI2LDoiiOWO4DkhxH1CiMFCiGMOvKrdsnpEYmoCdqd54bYWneIXN9+mWwvTdqlL0pul0qpLc6y26Ph9Z4KDdj3bxM0OhaLWsLY3FLqicICta42bc6QQiyPoCVwDPMrBx0JPVqdR9Q2L1cK4SZGx9WDMEVz1UOxC1YfisvvH4HCVyQFwOzj3lpE4XA76jOhJ49YZEc5As2i4Ep0MH6dCNxX1H2HrBrbuQOnfgQDhQLjH1pZZ9Z5YHMG5QHsp5fFSyuHFrxOr27D6xoW3n8X4Ry4irUkKCONOYNLHt9L35F5x20b3IUfxwFd30KZbSxCQ3CiRS+45nyv/azgbTdN4evaDHHfBEGwOKxarRv/TevPC/EfURLHiiEGkvQ6uczHSmjSwD0Q0+kRNFFeBWKKGPgFullKaF5CpYepq1FBppJTVLo5yqG0c+F6VSIviSKYmfmtHClXVI2gC/C2EWAiUhMXUVvhofaAmDszytuEp9PLQmGdY8stydCnpOqATd396GxnND14tTf3fj7x9z2Q8+R6SGiUx4fFLOOXy4SX9BbmFfPzIFGZ/Nh+Hy86o60/hrBtONRWfOVzC4TDfvDyNb/73E35vgGGjB3LRpPNITk+K2zYURz7KCcSHWO4Ijjdrl1LOOsR6TmA24MBwOJ9LKe8rs8wVwBPAjuKmF6WUb1Q0bn24I6gtdF3ngqbj2b+3IKLd5rAxJectnG4nHz78Be/cPTlq3Zuev4qzbxqJz+Pn2l63k7Vtb0kYqsPtYNCovtw9+R9xs/Xhi55l3tSFJeWqbXYrGS0b8dryp3CWmWtRKBRVp6p5BFuBBVLKWcUn/z+ALTGs5wdOlFL2AnoDpwkhzIRxP5FS9i5+VegEFBUz7d2ZUU4AIOgP8vbdnwDwwQOfma77+v99AMCMj+ewb3duRC6C3+Nn/jeL2LJ6e1zs3LZmB3O/+iNCsyAYCJG7J49fP5gdl20oFIrYicURfAaUjtcKF7dViDQoLP5oK37VL4HkekZFJS2WzVxBwBcwzTMASk7Ky2atxFfkj+oXmmDNH+vjYueahRtMy1T7ivwsm6ViwRWKmiYWR2CVUpZcuhW/t1ewfAlCCIsQYilG5dKfpZQLTBYbLYRYLoT4XAjRqpxxJgghFgkhFmVnZ8ey6QZJs/ZNyu1r0jYTq738KSFRXDK6WfsmpsIzQhNktGxUdSOBzJaNwOTRrs1upXkF+6BQKKqHWBxBthCiZGJYCHE2sDeWwaWUYSllb6AlMEAI0aPMIt8AbaWURwO/AO+WM85rUsp+Usp+mZmZsWy6QXLx3aNLTuhlGf/IxWiaRu/h3U37h54zAIDTx58UdbWuWTRSM1PKXbey9DyuK2mNU9AskYefxWbh9GtOiss2FApF7MTiCK4DJgkhtgohtgL/BiZUZiNSyjxgJnBamfYcKeWB5xCvA30rM64iEneii8em3RMhPGOxWfjXOzfR6igjK/mRn+6mc7/2Eev1PK4r93x6G2BcrT/yw100aZuJ3WXH5rDSZWAnnpr5AJoWH0E7TdN4csYDdB3UCZvDisNlp0mbTB7+/i4at1aOXqGoaQ4ZNVSyoBCJxctHz0aaL58JBKWUeUIIFzANeExK+W2pZZpJKXcVvz8X+LeU0mxCuQQVNRQb65duIugPclT/jqYn8Ly9+9m0fAsderc1DdmUUpK1dS92p420JqnVZmduVj4Bb4DGrTNUKKBCUY0cVh6BEOIS4CMpjcIepSZ+D/R3AJpJKeeUM0Qz4F0hhAXjzuNTKeW3QogHgUVSyqnALcWPnULAPuCKyu1a3WLbmh1MefY7tv69gx5Du3DOzSMrfRL97vWf+ejhL/Hkezj6+G5MfPka0pumxby+ruu8c+9kfnhzOnoozHHnD+b6Z67A7jx4l7Br0x6mPPsdG5dvoXO/Dpw38QzjuX0xwUCQXz/4jRmT5+BKcnHGhJPpf2rvSu1H1tZsnr3uNVbNX0tSWiKXP3AhJ10SHYmc1jilUuPWBgu2b+P9v5aS6/VyWodOXNC9B06reW0pM6TUwT8N6ZkCSITrHHCORIiDDlqGNiOL3oHQerD3QbgvQ1jU3ZGiZqhIoWwicBWGTOViIBsjp7sjcDzGPMH/SSnX1YypBnX1jmDZrJXcPeoRAr4geljH5rDiTHDy0sJHadYutgnQRy99nl8//C2izWqz8P6m/0UkhFXE+J7/YMvKyDDPlIxkJu98FavVytrFG/jn8PsJ+YOEgmGsdgt2p53n5j5E2+6tCIfC3H7i/az/c1NJ9JAzwcE5N43k6kcujsmGHRt2cVWXW9HDkcXBzr7pNG56/uqYxqgrvL54Ic8umIc3ZITTOq1W2qWm8cWF42J2Bnre7eD7GSjWeRJusB+HSH0OIQQysAi572qM0sphwA7CiWg0BWFtXR27pWiAHK5C2XPAMcDHQCYwovjzDuBSKeXomnYCdRUpJc9c8wq+In/JyS/oD1GUV8Rbk2LTI8jNyotyAgChYJhnrn01pjF+/3ZxlBMAyN+7n8+f/AaA5294HV+hrySMNBQI4y3w8vI/3gFg7ld/sH7p5ogQUl+RnynPfRez0tkTl78U5QQAvn7xR3weX0xj1AXyfT6enj+3xAkA+EIhNufl8uXqVTGNIYN/RToBAOkB/ywILjU+5t9d3H8gtDcAshBZ8ERc9kOhOBQVzv4VR/38LKW8X0p5rZTyVinlqzUtWF/XKcgtZM/W6EAqXZcsmrYspjFmViAc89es2E46v7xffrL3rM9+JxwOs3bRxqg+KWH5bGMb879djK8w+mStWS0snbEiJjvWLt5Qbt/vX9e9u7nyWLxrJ3aTshreUIgfN8R4DeT/nUgRlZIOCMxF6oUQNvs56RCYWxlzFYrDJj5hIA2csqWhS5OQ4o5pjIwW5T/6cSbGVnIhNTO53L6kRolomoatHN0Ed5JRnTQlM9k02UvTBElpiTHZUZ42A0BGq/jkItQEKU4HusmjUwFkuBNiG0RLxjztxg4iBYQd06QKABHb/1uhqCrKEcQBh8vBsHMHRCViOdx2zr15ZExjDD1nQLkJX+f/48yYxrj47tHl9l3xwBiEEJx25fCoE7XDZeeMa08GYOTVI0zFbaw2K31POTomO0Zdd6ppuzPBQc9h9Uc8pE/T5qQ6XVGnaafVyiU9Yywv7jwNyouGcp2BEHZwjiTaWTjBfWklLVYoDg/lCOLEra9eS7fBR+Fw2UlIcWNz2Bg+dhjn3HJ6TOtrmsbjv9wbdRIeeMYxXPivs2MaI71pGre+OiHqvDNu0rl0G3wUABOeuJQ+I3pid9pISHFjd9oYOKovl913IQCtu7Tgtjeuw5ngwJ3swpXkJL1ZKo/9fA82e2yTo+MfuZgex3aJaLM5bDzz239iWr+uoAnBu+eMpkVyMgk2G0l2O06rlTuHHU+fZs1jGkNoqYjUV0AkG1f4ItEQWk97uaR+vki+H+x9ASeIJMBwDiLhqmrbN4WiNLFUH3UAo4G2lAo3lVI+WK2WlUNdjRo6wLY1O9i9OZt2PVqR0aLyj0F0XWf6R7+RtS2HERcNo0mbxpUeI+AL8MMbvxLwBxl59YkkpkY/Yti5YTc71u2iddeWNGkTHabo8/hZNW8NDreDroM6HVYy2bY1O5j9xXxadmzGsecPiltCWk0jpWT5nt3sD/jp3aQZSY7KV0eVMgjBP40JGXsf406g7DKhTRDeDtZOCEvTeJiuUJRQUdRQLI7gRyAfI4S0pGKZlPKpeBoZK3XdEdQFNq/cxvSP5xAKhDh29CC6DuwU0e8p8DL9ozlsXrmVjn3ac8KYIar0cy2z35vFqm1vQWgjFkcvjm59BQ5bjPMQcUT3TYfC14AguC5ESxhT4zYoqoeqOoIVUsqyNYJqDeUIKubzZ77lnbs/JhgIIXWJ3WXntKuGl8Tv79q0h1sGTcLn8eMr8uNMcJCQ4ubFPx6NOVdBEV+25iwmqegKbFoYtzVEUdBGftCNu/GXpCe2rDE79NybwD8tstHSERp9W2/v5hQHqaoewTwhRM8426SoBrK35/D2XR/h9wbQwzpSSvwePz++NYPVC4xwx+eue439OQUleQK+Ij+5e/JL8ggUNY8n53aSrH7cViNfIcEWJMNRwLqtd9WYDXpgZbQTAAivB2+0kJHiyKJcRyCE+EsIsRwYBiwRQqwpLhd9oF1Rx1jw3RKEyZVbwBtgzpT5hMNh/py+Al2PvAvUwzrzv11cU2YqSuEJ5NMucSdlvza7Radz4pIaNOS98vu8n9acHYpaoSLN4lE1ZoUiLlhtFtNIRc0isNqtCCGMMtUm2jQWq7r1rw00oWHkEUQ/og3LGvxOREURYbFImyvqMxWVmNgipdwC/PfA+9JtNWeiIlYGn9UPPRx9QrHYrJw4bhiaphn5CmVCVK12K8PHDqspMxWlcNqS+Ht/B4J6pAf3hSys8xxbc4YkXFtBX/2qD6WoPLFcckSokRRXE1W6AXWQlIxk/u/9m3G47DgTHDjcduxOG1c/PI423Qzxt1teGk/zjk1xJTmxO224Ep207d6KCU+o5KXaonmLF8n2JVMYtOELW/CErGwpakbvDg/XmA2atRW4TGRG7MPRXLElRSrqLxVVH70TmAS4AM+BZozCKa9JKe+sEQvLoKKGDs3+nALmTV1EKBBi0KhjovIZdF1nyS9/sX3NTtr2aEWvE7orLYBaJhwOsmLHF3h9m0hO7E2XpqfWSqSOHtoCha8AAXBfjmaPLZtcUfepavjoI7V10jejuhyBlJLVC9axYelmmrVvQp8RPbCYFByriHA4zJJf/mL3piw69mlHlwEdo06wy2au5Me3ppOUnsBFd59Pakb59YEOl4LcQuZ/u5hwMEz/kX1o1Cx2PYOGyMbcfSzYsZ0Uh5MR7drjsNbNZ+I7c1exfe8PaJYkurUcg9seqeUgpR/8M0HPBXt/hLVD3G2QUkJgAYQ3gbUj2PpFHeMyvBv8v4FwgONEhBaZ0BgMh5m1ZRNZRUX0adacrhmV112QeqGxr9IDjmEIS2yZ3g2Zw3IEQohjKhpUSlmDIQ0HqQ5H4Pf6mXT6w6xdtAGpSzSrRlrjFJ757T8xi8Lk7MrltuPuITcrHz2kIzTBUf078vD3k0pEYW4adCdr/lgfsd7ElycwqrjOTzyY+9UfPHLxc2gWDSklelhnwhOXcvaN6va+LFJK7p35K5+vWokmjJISVk3j/XMvoEfj2DQkaorfV99Kr+Sf0KVAl8aJd7v2BN1aGCVMZPBv5L7LgCDIMCDBdSYi+aG43e1JPR+57xIIbwOpg9DA0haR/j5CM1Tu9MLXofB5jKfOxiS4SHsB4TgOgE15uYz9/BO8wSAhQ/OK4W3b8/xpZ2CJ8Q5I+ucj864zxpc6oEPiBLTEm+Oyn0cqh5tH8FTx6yVgAfAahq7wAuD5eBtZm3z43y/4e8E6fEV+/N4A3gIfe7Zk8+SV/4t5jCeueJE9W7LxFvjwewP4ivysnr+Wjx76AoDJj34Z5QQAnrvhtbjV6N+fU8AjFz9n7EOhD1+Rn4AvyOt3fMDWv3fEZRtHEtM2rmfK6lX4wyG8oRBFwSD5fj/jv/nStOpobbFy+1R6Jk3DaTESzhJtQRJtQZqH/00g5EVKicy9DmQeyCLAB/jB+x34vo+bHXL/fyG00bgKx2f8Da1HFjxq9AdXQeELxrbxYjxR9iLzbjau4IHrv/uavZ4iCoMBfKEQvlCImZs3MnnlX7HZIH3IvBuMbcui4u34ofANZKBWrk2PCCqKGhoupRwObAGOkVL2k1L2BfoA0We0esyPb88g4AtGtIVDOkum/4XP4y9nrYN4C70sm7mScChSjCXgC/LDWzMAmPryT+YrS5j6Ujl9lWTe1EUIS/RXGgqGmf5RtOhNQ2fyiuV4Q8Go9qJAgL/27K4Fi8zx7J+M0xKKateEzt87v4PQasMJROFFej6Jiw1SymKnUvb/FQCfIUMuvV9irr2ggX8W2/fnszU/PypQ1hsK8dFfsel24C9Pt8OH9H4R2xiKKGK5F+sipSxx11LKFUDlBGzrOOGgSWB9MWZKW2bLlHf9GAoaP+BQIPqHfIBYnHEV9l8AACAASURBVE0sBP1BpB5trx7WoxydwlAbM0MIgT9c/jFR02j40cp5uqNLH8gg5WoaEJ9jq3hr5s2y+P8oA+UsI4EAgXC4XCsDMf+/g5jlXIAEWX/U7+oasTiC1UKIN4QQJwghjhdCvA6srm7DapIh5/THUia2Xghof3Qb3EmuQ66fkJJA+57R2rJWm4VjzxsIwAljhpS7/pnXn1JJi80ZeHqfqKxhALvLzrBzB8RlG0cS53Tphqsc3eHeTZvVsDXlI1yj8ISiJ7AtQtKx6Rlg64Z50pcTnGfFxwYhwD6E6FOGBsXP/4XzVBAmvxcZBvtxtEtNI8XpjOp2WCyc1blL9Hpm2AcfdDwRBroRzjNiG0MRRSyO4EpgJTARuBVYVdx2xHD1wxeR0TwdZ4JRgdPhtuNOcfOvt2+MeYx/vX0jCSnuErUyZ4KDRs3TufK/4wAY//ilpDaOjhA6+6bTSGucGoe9gMatM7n8gTE4XHY0i4YQAqfbwcmXHV+iR6A4yHldutGrSVPcNsMZ2DQLTquVp08ZaSpRWVv0bnMZaws6UxSyoksIhjW8ISur/DeR6EhDCBsi9SnACRQ7NuEGWzeE+4K42SGS7zdU1ThwsneBloZIvsf4aB8MjgPOQAAWw6akfyMsjRBC8OypZ+C22XAU/3/dNhvt09K5+hjTOcxoG7RkSL7fGLfE+bnBfiw4TojLfjZEDhk+WteorvBRv9fPzE/m8fcf62jZuTknX3Y8yelJlRpj/74Cpr07kx3rdtFlQCdOGDMEh+tgeedQKMQnj3/NrMlzcae4uez+CzlmRPzjtDcs28z0j34jGAhx3PmD6T7kKJUnUA5hXWfm5k3M2rKZdJeL87t1p2VyyqFXrGF0PczK7V9SWPgTUiTRqsnltEqPVEmT4Z1IzxTQ9yIcQ43QTRFfhyb1QqT3awitAWtXhOvMiPBQI7z0D6TvZ9CcCOfZCFtkGfSsokI+X7WSXQUFDGrZilM6dMRWSccrQxuR3q9AL0I4R4B9sDrGD8Hhho9+KqW8UAjxFyYP5aSUtZJpUtcTynZu2M3uTVm07dEq5tDT0kgp2bh8C/tzCujcrwMJydGax9vW7GDe1wtpd3RrBpxWYZSv4ghCSi8EloGWCNbqSQIMhUPMWPcVobCfYR3OJMlZ+TwXf2AfO7PeQ1gTaZ15OZolNmU7RfVyuI6gmZRylxCijVl/cc2hGqeuOgJvoZf7Rz/Jijl/Y7NbCfiCnHrFCdz80viYM0SztmYz6fSH2bMlG4vVQigQ4qqHx3HeRKP+n67r3DzoTtYu2liyjjvJxStLn6BZu7oV966IL7pnChQ8gPG4RQetESLtdYS1fdy2MX3dVLpZ78VtDXLgtDB93/Wc2/ummMfYsOX/aGufctBuBLu1+2nVZFzc7FQcHoeVRyCl3FX8dgRgNyk8pyjFs9e9xl+zVxPwBijK9xD0B/n5/dl89cIPMa0vpeSuMx5h25qd+Ir8FOV78HsDvHXXZJbOWGFs49rXIpwAGGpjNw+aFPf9UdQdZHAV7L8fpBdkoRFDH96O3HcFUh46qi0W8ry59HFMorHLQ6ItSJLdeJ2S8RLLd8ZWonxXzk+0tU9BCEpeFiFppt+HP2AW3qqoK8RyqdoWeFUIsUEI8akQ4mYhxBEVPlpVAr4Av30xn6A/MkTT7/Hz5XPfxTTG5pXb2L05Kypc1e/x8+XzRlLQLx/ONl03P3s/OzbsMu1T1H+k52Oi4/MlyAIIxufu+JfVb2IRJpVrheTvba/HNEZw/zOm7QLYvvvJqpinqGYO6QiklPdKKU8EegBzgH9h6BcrivF5/JR3YVaY7zHvKEPBvkI0k2QwgLysfKDifIecHfti2o6iHqLvxTw+X4AenyvtcDgXTURvw27RcWj5MY1hFwWmehhA8T4o6iqHdARCiLuFED8A04COwO1AzQmp1gOS0hJp3LpRVLumCfqMiE3ls9Mx7aIyk8HIARhydn8Amndsarqu0ATdhqjw0CMV4RhRTnx+EGzxqQjfrvGppieDoqAV3R6bVkWROJbyghATk8ccvnGKaieWR0PnAY2AX4ApwNRS8wcKjGSbf7x2HQ63o+Sq3ma34k5xc82jl8Q0hivRxYQnLsXhtpdcVdlddjKapzPqOiPh7M73bzaNFBl353lY62jFTEUccJ0JlrYYsfMljZB4HcISfQFyOAxocxy/7OpFUfDgceQJWVmZ15Qzuo2PaYw2Le7FG3ZEOAMpYbe/FU3Sh8fFTkX1EFMegRAiCUO7eBhwIbBHSlkrklZ1NWoIYMuqbXz+9LdsW7OD7kO6cO7E08lonl6pMVbMWc2Xz3/Pvt15DD6rP6OuPTkiu3nL6u08e+2rbFi2hdTMZK5++CKOv7D8rGXFkYGUPqTnc6Pej5aCcF9i5ArEET0c5qvlL5KpfYdNC7HFfzxn9rwdtyMh5jECwf1s3/FPMi0LCEsLOeJM2jW/D60OJeg1VKqqR9ADOBY4HugHbAN+k1LeG29DY6EqjqAgtxBfkZ+MFummV9bBQJB9u/JIyUzG6XaYjFAzZG3NJmdXLp36tje90g+HwuzdsY/kRom4Es1LYORl5xMO6bWqRRDWdXYXFZJsd5DkOLz/Z4Hfx9qcHDqkp5PqNN/XrKJCLEKjkTs65yIWdD1MVsEG3PZUkl2NTZeRer4RraM1NT92Qj5yCreQ4m6Gyx5/jYlY0QNrIbwF7MejWexR/SFdZ09RIakOJwn26H6AfYXbCetBMpPbmfZL6Tee+WsZCFE9vxMpw6DvAZEcpWdQsoyeC9IPWpNaSyaTUi+2M8HIejZb5hDHTk1RkSOI5XnCY8AsjNLTC6WUMVUvE0I4gdmAo3g7n0sp7yuzjAN4D0P6MgcYI6XcHMv4lSEvO59HL3me5bNWISwaKRlJ/OvtG+lz4sHn9188+y3v3f8pekhHl5LTx4/guqcux2KtuSuZrK3Z3DxoEvt2GxOAQhOMu/M8rvzP2JJlfnjzV1771/sEAyGkrjP8omHc8tI12B1G0s6uTXt4+KLn2PDnJhCCZu0bc+cHE+nYx/xHXV18s/ZvHpg5HU8oiC4lp7TvyKMnnVpSzuFQ6LrO5V9/wdxtW0vaejVpymcXjMNanJexKjuLW3/6jq35+SCha2Ymz512Bq1TYi/ZsXTLxzSTj5Ns9SGEZMn+o+jY9vUShyD1XGTe7RCYD2igpUHKIxFX4/PXPEQ398ckCh1ZJJmfP5S+nV/EZo2uq1Nd6IENsO9sSkcX6Y5T0dJeKPn8+aoVPPTbTPzhMLqUnNm5C/8dflKJEM/uvDXk7rmedok7kQg2Z6cQTHycTk2MWkJS6sjCZ6Ho3ZIadzJhPCLhprie4HTvj1BwP+geQEc6TkSkPILQjDsTGc5G5v8TAosBDSyZkPIYwt4/bjbEgvTPRuZPAn0/EEbahyJSn0BoRma61POQef+CwDzAAloKpDyMcNSgFnWMVFuJCWEcGQlSykIhhA0j4miilHJ+qWVuAI6WUl4nhBgLnCulrHBWqbJ3BFJKru97B1tWbiNUKurG4Xbw6tInaNGxGb9++BvPXPsq/lJVQB1uO2defyrXPnFZzNuqKuekX05RXnSU0aQPJzJ83DAWfL+E/1z4dKSdLjsnjB3K7W/eQCgY4pJ2N5C7Oy+i+Jw72cX7G14iuVHlSmYcLgt3bufyr76IqO7psFg4rk1bXh11TkxjTPzhW75ZtyaqfVCLlnw0egz5Ph/HvfMGBYGD/wtNCDJcbmZfeU1MtYI2Zs2lSeAaXNaDdvrDGhsKW9PjqGkA6HtHG2WeKV3ozIXI+BJhbc/ijW9wlP1p3KXG8IasLN9/HIO7vRLTvsYDfXdXwCSqLOFmtKSbmb1lM9d/9zXeiO/EyshOnXj6lNMJhQNkbx1MI0chVu3gsVMYtBFK+4n0xJbFojMvYmgAHMAFSbehJVwel/2QgaXFAjulK4nawT4ELf01Q3th7+nGXU/p70S4EBnfIywt4mLHIe0MrkXmnF/GThvYeqI1mgyAnnMBBFcRWbrbiciYgrB2rBE7S3O4wjRVQhoUFn+0Fb/Kep2zgXeL338OjBBxvndat2QjO9btinACYJSH/vrFHwH48KEvIk6uAH5PgG9enlZSRrq6+XP6X6ZOAODNuz4CDAGdKDu9AaZ/PAdPgZc/vv8TT4E3qgJpOBguNwehOnhl0R9RJZ794TCzt2wmu6gopjG+W7/WtH3+ju3ous5Xf68iqEd+p7qUFAUDzNi80XTdsuzd+z9sWuQYDotOu4RtbNu3DBlcDaH1RDoBgACy6H0A0vS3I5wAgMsa4ujk2fiDsYUOVxXd8wOmTgCgyMgBeGnh/AgnAOAPh/h+3Vr2+32s2P4FCVZfhBMAsAidNTveKDWWl0i8UPRa1XeiGFn0KtGlswMQ+N2QwAwuAX0XUd+JDCE9k+Nmx6GQnneIzu0IQnAVMrQeGVwHwTVE6zcEkUXv1ISJlaJa1bGFEBYhxFIgC/hZSrmgzCItMOYckFKGgHyMCKWy40wQQiwSQizKzs6ulA1ZW/eaxueHg2F2rDOCn3J25pquGw6F8RbWTI3zjcvLT9bO31sAGPtihsWikZ+9n6yte01zDfzeALs3ZcXH0BjYtt887txmsbCnqNC0rywVKYT5QiG27d9vqicQDOvsLCiIaRtJlt1RJz6AkLSQ79kM4V1gWrQtDOHNAKTZ95uOrQlJkb+GcjuCf1bQaZxUdxSY22nVNPZ6PPgC27Ca5BG4rGFEeLtRTM5U/AbQ47if4W2Y6g0IG4T3GN+JqapBEEI1WPAgtAXT3A5hg/Bu0Hca76MI16ydMVKtjkBKGZZS9sbIOxhQPPFcGrNv1KzA3WvFCmn9MjMrJ3TduW97giaiMA6XnV7De5QsY0ZyeiKJqbFHTFSFQWeUHw/etnsrALoN6YwwUSixWC1ktmpE5/4dTJ2eK9FJ9xosQ92/eUusJjd2IV2nXWpsk9eucsJhLULgtts5pllz0/kGq0WjVxPzfIuy5Ok98Yej/192LUyLtH5GnX9pprjlBLuhM7HV0woTCQiKQnZS3TWkaeCq4HGbMKLW+jZrgWbynQgELZKSSU8agG7ycywK2rA6iwXqLeXUNYrnYw7bAEynLmUIrB3A1sNcjwAX2GtQc8M+EDCZbJd+sHYBa1fjfRQOcAysbusqTbmOQAjxjRBianmvymxESpkHzAROK9O1HWhVvD0rkALE9TKqcetMRlx0LI5SUUAWq4WE1AROHz8CgPGPXYLD7YjIinS47Vz79OU1NsvfolMzOveL/qEJAf94dQIAlz8wBqfbEWGTw+3gqocvwmqz0nVgJ7oNPqpEEwHA5rDSuHUGQ86puYm0G/oNxGWzRZx4XFYrN/YfWG6kSlnuGHqcafv1/Ywf+8ntO9AyOSViLsBptdKrSVP6xCgq07nV7XhCdkL6QTs9IStL84eTltACYWkKrvM4WH8fjEm/RITbmMBPSLsTX9hKaWE4b8jKxtA1aFrNBBpo9m4gzKOdSHkcgIkDB+OyWqO+k38OHorDaqVz0xNYX9Aeb+igzf6wxr5AEj1bGfNkIvkuInMZAJyIpPjVuhKJ1xQnz5U6NQkXJF6D0BIR1rbgPJnI78QGWiqiIocYZ0TCJSASMYoAlrLTPQZhyUBYGoPrgjJ2WouPnYtrzM5Yqaj66PEVrSilnFXhwEJkAkEpZZ4QwoWRmfyYlPLbUsvcCPQsNVl8npTyworGPZzwUV3X+fbVn/nqhe/xFPgYfGZfLr33gogy0ev/3MTb90xm/Z8badquMZfeeyH9TulVwajxR9d1nr7mFaZ/9BuhYJhm7Zvw73dvihCV2bJqG+/c8wmr5q8lo0U6F981uiTzGCDgD/L509/w45vTCQXDDB87lIvuOs+0nHV1siUvj6fnz2XB9m00cru5rt8AzoxVhaqYT1f+xaNzZpPv95FotzNx4BCu6nPwzqnA7+flRQuYuuZvLJrGhd16MP6YfiVRMLGwO38tW3b8h7buFRSFXOzlQvq1v6WkYqyUuqH763nPKPjmOAGReLPxQy9mY9Zc8nMeo4VrGzn+NIKu6zi6dYWHcdzRw2HIuxyCfxS3uCHlUTTXwWuvDftyeOr3uSzZtZPGiYnc2H8gp3Y4qBUQCHlZvP5hWtp/wirCbPYOpnv7+0l2ZpQsIwMLkYXPGyL21o6IxIkIe3xLocvQVmMbgd9BS0ckXAPOM0sugKQMIz0fgOdDQ57ScTIi6UaEVrmcnSrbGd6NLHwB/LNAJEPCFQjXBaXslEjPZ+B5B+T+4mPnJuMCoxaoUh5BFTZ6NMZEsAXDvX8qpXxQCPEgsEhKObU4xPR9oA/GncBYKWWFM311OaFMoVAo6ipVyiMQQnQCHgG6Ueq+UEpZYSF0KeVyjBN82fZ7S733AfHT0qsCC75fwqu3v8uOdbtJa5rKJXeP5owJJyvVo1rkx3VreWzeb2zbn0+ThERuGzyU0V27l/TvLizg/pnTmbF5I5rQOKNTZ+45bripLu7h4g+FePL3OXyy4i+8oSD9m7fg/hNG0LnRwavkWZs38dCcmWzMzSXD5eamAYO4uGevkmMnx+PhwdkzmLZhHRI4qV0H7jvhRDLdxXHxUiK9nxqhmXo2WNogkv4P4TxYlmHDvhzumzmdP3Zux2GxcEG3Htwx9Fic5Wgum7Fg+zYenD2DNTl7SXU6mXBMf8Yf08907qC+I32/IAseMyaftcaQOBHNPbq2zaqzxJJZPAe4D3gGOBNDr1iUTQ6rKarjjmDxz8u475zH8XsPTgw63A6u+M9Yzv/HqLhuSxEb0zas49afvo+IDHJZrdx73ImM6dETbzDIie+9SbbHUxJhZNM02qam8cPFl8ft5Db+my+Zu3UL/vDBaKxEu51pl1xB08Qk5m3byvhvvoyy85aBg7m27wBCus7J77/NjoL9hIonEqxC0CQxiV8vuwq7xYJe9C4UPm3oDZTgRKS9hHAcS3ZRESe9/zaFAX9JJIXDYmFAi5a8e875Me3Hsj27GffFJ1F2XtarD/8uZz6mviJ9M5B5E4mM8XdB0v+hJTRcgZyq5hG4pJS/Ypz8t0gp7wdOjKeBtc1bd30c4QTA0AH44D+fEQ6XX/pZUX08Pm9OVHioNxTiqflzAPhu3RoKAoGIMNOgrrOzYD9zt8YnPG9zXi5zt26NcAIAgXCYd5cZIZtP/W5u50sLFxDSdaZv2sBeT1GJEwAISUmez8vPG9YbJQoKXyzjBAB8yIKnAPjgr6UEwqGIcDp/OMzCnTtYl5MT0748v2AefhM73132J55gTMUC6g2y8EkinQCAFwqfo75ptNcUsTgCnxBCA9YJIW4SQpwLlBOiUD/ZvnanabvfY6iNKWqe7eXkIuR4PATCYf7em216AgvqOuv2xXZyPBQb9u3DZhKOGwiHWZG1B4CNueZBbsFwmDyfj/X79uE1SUosCgYNOw8ojpkRNhzayqysKGcEYBUa63Nj29c1e/eaRedjEYLdhbHlXdQbQtvM22U+0Q5CAbE5glsBN3ALRk2gS4H45JPXEVp0NA83dLjsJKTUbLSNwqBlknkBr3SXG5umcVRGpmkegU3T6Jgen9LM7dPTI67kD27DQvdMQyO6vLwIm2Yh1emkQ3o6Llv0VFyCzUbH9HQQCeZaAwCW1gB0y2xsWjIjJHU6pMUWKdOpkfn/JCwlTRNrpvRIjWEpRy5FJBMd/qqA2BTKFhaXitgP3CKlPK90vaAjgSsfGofDHRnf7nA7uOiu0VhU+dxa4fYhx+IsEwbqslr5x6AhCCEY1ekoEmz2iLkAm6bRLDGJYa3bxMWGdqlpDG7ZCkeZY8BusXB5LyMO4rbBw0ztvL7fAKyaxolt25PucpcUygPjKjzZ4eTk9h0RwgKJNxAZbw5GfP5tAFxydC8cFktEupfDYuGYps0jJq0rYuLAIaZ2Xtqzd8yFAOsLIumfRJ/wXZBoruehiE2hrJ8Q4i9gOfCXEGKZECI+skh1hP6n9mbSR7caCmAC0pqkcM1jF3PBP8+sbdMaLKd17MTjJ51acmfQJCGBe487kYt6GrkdLpuNL8dcxAlt2mERAptm4bSOnfnk/LFxjYL53+lnMbb70bisNgTQv3kLPr1gLM2SjKvoYa3b8MLIUSV3Bo1cbm4fPIzrihPfbBYLn184jpPbd8SmaVg1jRHtOvDlmItK8h2E+0pI+hdoxSd1S2tIeQrhMFJ5Gick8tkF4xjQoiWaELisVs7v1oPXz4w9gap302a8cea5dC6+W0p1Ormh/0D+PezImigGEM4RRiLdgTsDLROS7qyTiVx1hViihpYDN0opfyv+PAz4n5Ty6BqwL4rqziOQUqqrhjrGob6TA8dwdX9vsdhRVTuruo1YaEjHeEPa10NRVT2CggNOAEBKOUcIcYTNLh1EHTR1Bxn8C1nwAoRWI63tjazMUjXns4sKufH7b/hz9y6EEAxt1ZoXRp5JYqkyFtI/E1n4MoR3gq0PImkiwtrhsOwp79j4Yf1aHpg1nb0eD26bjev7DeD6fgfryUjpRxa9Cd4pxmfn2YjE8YhScwPS/zuy8CUIb0HauhsZu7auJf16aAPk/QNCa5FYwXEqpDyGplmLtyGZuvZvXl+yiFyvl2Gt2zBx4GCam8y1VOUYX7xrB8/Nn8f6ffvo3CiDWwcNoXeMJT1qg/L2VeoeZNHr4JsKaOAajUi4CiFiK4ESC0Z+yBQjs1g/kFl8Y0RWel0hljuCZzAmiz/GKAg3BsgFvgCQUi6pZhsjUJnFDQMZWIzcdyWRUR5ORNrzCMcJ+EIh+rz6YlQ0TarDyaJrrkfTNHTPp7D/IQ6WTtZAOBGNPo9bPfhv1/7NLT9+F9V+ac9ePDD8JONksG8cBFdysLyyA2xHIdI/RQjNEGLJv6PUvgpjXxt9gLD1RA/thr0nEFXt0tIWLdPQTXhm/lzeWLIYb8iIpLIIQZLDwY8XX07jBHOFr8oyZ+sWJnz7VUS4rNNq5a2zzmNQy1Zx2UZNIGUYmTMaQhs4+J04wdYbkf5u3C4G9f0PgedTDh5/VqMmUsb3CC128aR4UdU8gt5AZ4yksvuBrsAQ4CngyTjZqFBEIAseITrUz4fc/1/AOPGZhVTm+X18tmoFUoag4HEi6+frIH3IgufiZud9M381bf/gr2VGxFFgPoT+JrLGvt/QOQjMMR4XFTxE5L5KwIssMArGsf9BTEsehzejBxaz3+/jtcULS5wAGNFARYEAb/25uGo7WIoHZ0+PypnwhUL8d/aMuG2jRvDPLC4jXvo78UFoOQTj8/+S4WzwfEzk8RcCvQDp+Sgu24gnh3w0JKUcfqhlFIq4E/zbvD28DSmD/F5KwrIsM7Zs4sKujYgWBQHQD1G/v3Lk+czj0iWwOTeXDo7lRmG0qAU8EFwGtj6gl5MLEFxh/A1VYK/vF9YUNsFusUQ5xqCu8/v2cmLqK4mUkvX7zHMm1uSY62TUVWTwT/PcDRkwvhO76UVz5QitBuEwKWPuB//vxZFidYdYooaaCCHeFEL8UPy5mxDi6uo3TdGgKa+SpEgArLQoJ88AoE1KKohUkCZX0QCWJlW3rxhbBaWmmyQmgKUpCLPYdTdoTYtzCMoJ39SKtTe0Cuy1dqBpQhIBk7sjAbRKTil/3UoghCDVYR6Dn+YsJw+ijiIszYkO18U4cWtxmu/Qmpajm2ABa+v4bCOOxPJo6B3gJ6B58ee1GElmCkX1kXAt0T9WF7ivQAjB/5UT9iiAmwcMNoTOXaMwiycXCfG7GhvX0zx4rkujDJIcTnCegiFgUua5s7CCcyRCWME9LtpO4YKE6433SXeUs3U7OM+jVUoKfZu1wF7GKTmtVq45Jg5Xt8Vcc0y/KMEgl9XKhL41KxpfZZyjTJTnBOAA50lx2YSwdQZrJ6IfutgQ7rqXjxuLI8iQUn5K8UPKYklJVYBHUa0I90VwQKREuAEnuMciEm8EoE1qGs+cMjIiUctpsfLeOeeXRA2J5AfAeTrGidgFIgmS7jDizOPEfcefyEntIgvxdkhL5/MLjOJmQrgQjT4Ca2fAYdhi6YhI/xChGZO4Iul2cI02+oXbeCXcWCK0ojmGQOIdRIq1JEGjKSW6Cf874yyObdMWu8WCy2ol3eXiqZNH0iuOET3X9hvAZb364LRacdtsuKxWrurTl6v71K+0IqElI9I/KFZcK/5OrF0QjT6Oa9SQSH8d7IOM8XGClmkEO9g6x20b8SKWqKGZwGgMzeFjhBCDMARmKhSuqS5U1FDDQkq/oVWrZSC06HIfuq6zbM9uHFYL3TLNH6FIvdDQ1bU0Q5jqyFadAr+PldlZtEtNp0mieZSODO8BZLnCJFIvMuYLLE1NT0i6rhvi7ZZ0NKt5Ffg8n5cCf4DmSUlYtFiu8yqPNxgkq6iIJokJlSqDXReR4d2AVq0hnVLPBb0QLC0wyrbVDlUSphFCHAO8APQAVgCZwPnFegM1jnIE1U8gHOarv1fxzdo1uG02Lu7Zi+PatK1xO3YVFPDOsiUs27ObLo0yuKpPX1qnVC7sbsmm90jXXyLVVsQ2T3NSMh+ldaPKKWot2L6N95cvJdfn5bQOnbige4+4nwBlaDOy6G0jpNHeB+G+DGE5qM8tpR/pmQL+H0EkI9wXIxyD4mqD4simygplxXrCR2E8SFsjpay1urXKEVQvIV3noimfsjIrqyQc0WW1cXmv3uXqCFcH6/flcN6nH+EPhQnqYayaht1i4cPzLoxZnH7e6n8zMPVLwNB+ltKI5tkk3qRT02NjGuP1xQt5dsE8vMVhk06rlXapaXxx4bi4OQMZWITcdzUQwHjqai/Od5iCsLZGygAyZ4whD1kSjuiCxOvREq+Liw2KI58q5REIIS7A0CRYCZwDfFJ8l6A4Apm2YT2rsrMiYtK9oSBvL13CzoL9NWbHf2bPs7uDUgAAG/RJREFUoCgQIKgb01EhXccTDHL39J9jWj8UCtA/5UuEMJwAGH8FYCv8V0xj5Pt8PD1/bokTACNufnNeLlNWr6rU/lSEzL8b4wR/YOotALLwYB6B9xsIl3YCGO8LX0Lq5iGdCkVliOWB1T1SyoLiGkOnYugQv1y9Zilqi+mbNpjW+bdoGgu2b68xO/7Ysd20fv6q7CyCMYgFrc+ehWaSICoEtHDnxmTD4l07Tcs/e0MhftqwLqYxDoXUCyFslhOhQ2CesYz/FxPhGkDYIKDujhVVJxZHcOBXdwbwspTya4xpcMURSLrLhdUkxV4TgmSno8bscNvMDzG7xRLTJGiSs3m5fSEZWwmBFKcjQgHtAALIKNYbrjLCJLS0pK940llrhPlPVYKIT56AomETiyPYIYR4FbgQ+F4I4YhxPUU9ZEz3nlhNroKtmsaxrdvWmB2XHN0rqn6+w2JhdNfuMZWZbpHWnVy/i7LncSlheX5sTzb7NG1OqtMVdZp2Wq1cUlwOu6oIYQfnSKKvrZzgvtRYxj3WpF8YyXXxyIJVNHhiOaFfiJFQdpqUMg9IB2J7yKqod3RIb8SjI07BbbWRaLeTYLPT2J3AB+deYPqYpLq4qf8gTmrXAYfFQpLdjsNiZXDL1tx17Akxj+FLfh9PyIaUoEvDCWwqzKR/5/diWl8TgnfPGU2L5GQSbDaS7HacVit3DjuePs3Kv+OoLCL5frD3BZxGfgB2cI1EJFxl9Nt6QPI9xf2JhgPQ/r+9O4+Sqj4TPv59aunu6qZZGpFdoEFZdFQWQULEBXCiMe6jjm8mY0yOrx41IWY5M5lsJ5tJxuPEODPxNZo3Oho1IgTigkZF0BiMyNLsm2wNCM3W9N5VdZ/5496G7uqq7mqorbuezzl96L516/ZTP6Ceuvf+fs8zGCn7ndvYxpjTlNSsoVxis4YyoyEcZuUn+ygKBJg4aEhKm710xd6a42w7fJiRffsxou+pVWxctfMZGhvXM7DsOsrP7PqUS1Wl4sAnHG9uYuKgIW3KXKeSRnZAtBICZ8dda6BOHYRXu8kgeL6VTDddcrr9CEweCgWDzBiempaPiXx89AgLN2+kPhxmTvkYLhoytN2b29DS3h3WFVr9yX4Wb9tCwOfnc2PHMTamdePRhgZWHR3PnuNDmOzry7D+0S6f2YhIhyt0VZuh8XW0eTUERiKhaxFf16/dS2AUBEYlftxXAoUzunxckz0a3Ys2LATnKFI4EwpmZHVRWSJ2RmCy4g/r1/KDpW8TcRyijkNRIMiVo8fw8JVXJf1J90fLlvD8ugoaIxF8IgT9fuZO+9SJ2jfrDh7g9vl/IOI4NEYilASDDO5Vyku33E5pYWpufKtzDD18MziHvIqWRSBBpOy5nCwlYDJHG99Ejz2AO98m7JYOCU5B+j3m1pjKsNPtR2BMSh1rbOD777xFYyRCxHHc6vuRMH/evo13d+9K6hgVBz7h+XUVNEQiKG79/cZIhP9Y/pcT6x3mvv4Ktc3NJ2ro14XD7K6u5r8+/CBlr0VrHoHo/lZljRtBa9DqRIXiTD5QbUKrv4nbZ8Kbjq310PwhNL6azdDiskRgMu693bsIxCnfXB8J8/KWBH0IYizetpWmSPsyvyLCWzs+5kBtLXuPt18A1+xEeXlrcr8jKU2Lidv3ILIFdapT93tM99L8EfGnBTe4l4pyjCUCk3EBn594V38ECCZ5/b7A74t7A1uAAp8Pv88Xd0Ga+/tT+c++o3htRk/e6qi4YQornKaKJQKTcTNHjIy7UKsoEOCm8ecmdYzPnTMu7lmFAnNGj+GM4mLGnzGgXbIo8ge47dz4PQROSUv56Db8UDD5RJlpk4eCE4nfcCiEhG7JdDSdskRgMq44GOS/r76WkFfXvigQoNDv565JFzEpyfn5o8v6860Zl1Do1d8vDgQp9Ad4aM5nKAu55ap/9ZlrGFBcQkkw6O0XZMqQodyZwvr50useCJ7v9Uwo8Ob4D0T6/HvKfofpfkQCSL/HTq77oND9Ct0EhZdlObr2bNaQyZrjTU289fF26iNhLh0xkmGn0Fbxk9oaluzcQcDnY/ao0fQLte1qFo5GWbprB/tqarhg4KCUNmppoapuH+TwBvAPhcJLsjIrxOQedeqh6W3QaiiYjiToI5EJto6gh9HweghXuH1RT+FNx1FleeUedhw7ytll/ePO388Enwg+nxAQiXu9X1VZsX8vWw4fZlTfflw8bHi7/QaGGrmtfIvberBwOLHtLYN+P7PLx6TzZbhjVzDJ/TpFjhNlfeUC6pt20KfXhZwzcNaJ7mOZpM5xaFriNl0vvDStDVvygfiKvZapuS1tiUBEhgNPA4Nw21w+rqqPxOxzGbAQ2OFtmq+qP0xXTN2dahg9dh80LcctOOZ3SxKU/R4JDEvqGMcaG7jtpT9QebyaqOPg9/kY3a+MZ2+8JW0rZuN5b/cu7n5lIYKbmBxV7r1oGvdNnQ5AbXMz/7TgRbYeOYzjKH6fMLi0Ny/cdOuJT/1O3bNQ8zMQH6gA30f7PIQvdGXGXkcqHKrZRf3BWxhZUIsv5KBRYePWoYwun09RsDRjcWjj2+ixr+HWMVI4/kO09Ov4Su7IWAwmO9L5kSMCfF1VxwMXA/eKyIQ4+72rqhd6X5YEOqB1T0PTX3Hr0jeC1oFzEK3+WtLH+N6St9hx9Aj14TBN0Sj14TCbDx3iZ+8tTVvcsRrCYe55ZSH14TB14TANkQhN0Si/XvE3Vn+yH4Bf/GUZG6uqqA+HaYxGqAuH2XXsKN9d8ibglWOo+TnQ5JVorgcaofobbmvAbqSy8j4Gho5REgwTCkQpDkQY1auSldv+LWMxqHMcPTYX999WvTemTVDzMBrekrE4THakLRGo6n5VXel9XwNsBIam6/flhYYXcBeotOZAeCMaPdTp0x1VXt++lbDjtNne7ERZtDmFc+s7sWz3TiTOHOumaJR5G9YDsHDzRpqdtn0Hwo7DGx9vw1FFG/6E+1kjhgg0vpmOsNOiMVzD+D5bCfra3qsr8kc5p3hZ5gJpepv4bwdhtHFR5uIwWZGRi5AiMhKYCMRb0jldRNaIyGsiEnfuoIjcJSIrRGRFVVVVGiPNcQk7hApxFzXFPl2VaILJAbHJIZ3C0SgaZ5a/o0pT1H0dkQTxtFxGOtnWMcaJx7qHqBNFEqx4CEjnDXhSRpshbhwOaFPm4jBZkfZEICK9gJeAuaoau9RzJTBCVS8AHgX+GO8Yqvq4qk5R1SkDBgyIt0t+CF1N3J5A/kHujeNO+H0+Lh7a/oarT4TLRyYudpZqM4aPiPtGXxwI8tmzxwFw+ahy/HHinDpkGAGfDymcAxTFObrm5PS8REoK+/Jx7WBihyMc9bG59sLMBVI4E/dWXqwipKh73XMxXZfWRCAiQdwk8Kyqzo99XFWPq2qt9/2rQFBEzojdz7ik5G7wD/fmrAMUgpQgfR5KetbPT66YQ5/CIkJe05dQIEj/UDHfnXl5mqJur18oxPcuvYKiQICAuBeJigNBrigv59IRIwH4ziWX0b+4mGKvQXwoEKB3YSE/mTUHACm4wJ2TLSHcMyIfUAS97kf83esKZFHZQ9RGCmmIuH8ndeEgh5tLGHPWgxmLQfyDoPQB3OTqxx3TkDvjJWjNb3q6tK0jEPed6SngiKrOTbDPIOCAqqqITAXm4Z4hJAwq39cRnCx5vAL8w5DQjYi/f5eOcbypiYWbNrDp8CHOG3Am144dT0kGZwy12H7kMAs2baQu3Myc8jFMHza8TUKrD4dZtHkjaw8eYGz/M7h+3AR6t6oa2jJ/XxsXAwEkdA0SjDcfIfdV1x9kQ+X/QyI78RWcz/kj7szojKEWGt7s3hPQZvdMIDjF+h70EB2tI0hnIvg08C6wlpPnnN8GzgJQ1cdE5D7gHty7fg3AA6r6fkfHzfdEAHCkoZ4NVVUMKS2lvF9ZtsM5ZRHHYcHG9dQ0N3Pj+An0LQp1/iRjzCnJSiJIl3xOBKrKg+8t5emK1RT6/YQdh3MHnMkTn7uBPkXxrpfnrte2beH+115uU3PoC+dfyA8um5XFqIzpuawfQQ8xf9MGnl27huZolBqvzn7FgU/42uu5V9+8I/XNzdz36p/aFZ57umI1S3Zsz1JUxuQvSwTdyJMrV9AQU4M/7Di8v2c3xxobshRV1z2x6qOEJaIfXt7hlUFjTBpYIuhGjjXFLiZz+XxCbXP3mTtfVV+X8LHqxviv0RiTPpYIupFLR4wiEGcGR2lBAUM6aPCea24cl3hmz6zy0RmMxBgDlgi6la9Om06foiIKvS5ePhGKAgF+esWVcat35qqJg4dw4cD2C+CKg0G+Of3TWYjImPxms4a6mcP19Ty1ZhV/rdzNWX368uWJkxk/oPuVCnYch0c/XM4zFWtojka4fGQ5P7p8FqWF3Wv2kzHdhU0fTaGjB44B0G9g36zFkIzD9fVE1eHMktxul3i0oYGmaISBJb3yfuFSQzjMkYYGBpSUUJBk72ZjkmWNaVJg18ZKHrz9EXZvqgTgrHHD+PZzczlrXG6VM9hTXc1XFr/MxqoqEBjeuw+//PurOffMgdkOrY0DtbXMff0VVu7fh0+Egb168dCcq5gyJLfGMxMijsOD7y3lubUViIBPfHx12nS+PMlKO5jMsDOCJDTUNfL5kfdQc6SWluESgdKyXjy76zGKimObl2dHOBpl5u9+Q1V9fZs5+r0KClj6z19u18YxWxxVZv/Pb9lTXd2mGmpxMMifP/9FBpdmvrRCNv3svWU8XbGKxlZTg0OBAD++fDY3jI9bkNeYLrMFZafp3XnLaW6K0DpnqkK4KcK7Ly3PXmAx3tm5g9rmcLuFWpGowx83b8hSVO19uLeSqrq6diWxI1GH59ZVZCmq7Ig4Ds9UrG6TBAAaIhH+88N4VduNST1LBEk4uPsQjXXt57c31jdxcHfnDWEyZV/tcSJO+xr2jdEIldWxFcCzZ19NTdwFZc1OlF3VxzIeTzbVh5vbNeBpcbAu8XoLY1LJEkESxl40mlBJ+9ksRcWFjL0ovY3Ru+L8Mwfhk/Z/pcXBIJMGD8lCRPGdP3AgUad9KggFgkwbmlzv5Z6itKCQsgSX7M47s/vNBjPdkyWCJEy+8gLOGj+UgqLgiW0FRUFGTBjGpNl/l8XI2rpw0GAmDR5MUeDkHIACn5+hpb2ZMzp3Etbosv7MKi8/0RMBIOjzURYKcX0Hi816IhHhO5dc3ubvTHDvEfzLjJnZC8zkFbtZnKSGukZe+MVC3nx6KQjM+cKl3Pqt63PmRnGLpkiEJ1at4MX164g4DtecM457L5pGaWFuxRlxHJ5as5Lfr62gIRLm70efzf1TL6YsVNz5k3ugd3fv5Fcf/JU91dVMGHAmD0yfwXk5NtPLdG+2jsAYY/KcrSMw5hQ1hetYufUbnFu6jCJ/hC01I+jV/8eMPGNq0sc4XF/PD5ct4fXtWwGYPWo037/sCgYUl6QrbGO6xO4RGNOBDdtu54Le79ArGCbgU8b13klZw50cPJ5c34SI43Dzi8/x2rYtNEejNEejvLF9Kze+8Huao/FnCxmTaZYIjEmg8kgFY0u3UBQ4+YbtEwj6omzb+0hSx3h7x3YO1dcRcZwT2yKqHGts4M/bt6U8ZmNOhSUCYxI4VLOWsLav+VPoj9JLtiZ1jG1HjtAQjrTbXhcOs/XI4dOO0ZhUsERgTAL9S88jKO0v3zRFfdTq2UkdY3RZGaFg+1txJcEgY8rKTjtGY1LBEoExCQwvu4AtNWNojJw8K3AUwo6f0UO+ktQxrhhZTlmomIDv5H81vwi9C4uYU547aztMfrNEYEwHxo95njXHZ1IfCeAobD4+gkNFTzKwT3Jv4kG/n5duuZ0ry8cQ9PkI+HzMLh/NgltvpzBgk/ZMbrB1BMYkyXEcfL5T/+zU8n8t3/sumOywdQTGpMDpJAGwBGByl10aMsaYPGeJwBhj8pwlAmOMyXOWCIwxJs9ZIjDGmDxnicAYY/KcJQJjjMlzto6gh1FV3tqxnefXrSXsRLlh3ASuOWdcmxIHxhjTWtoSgYgMB54GBgEO8LiqPhKzjwCPAFcD9cAdqroyXTHlg+8teYsFmzZQHwkDsGLfPhZu3sST196AzxY0GWPiSOfHxAjwdVUdD1wM3CsisZ3JrwLO9r7uAn6dxnh6vG1HDvPSpvUnkgBAQyTMh/sq+cvuXVmMzBiTy9KWCFR1f8une1WtATYCQ2N2uw54Wl3Lgb4iMjhdMfV07+/ZTbzaUfXhMEt37chCRMaY7iAjF45FZCQwEfgg5qGhwJ5WP1fSPlkgIneJyAoRWVFVVZWuMLu9PkVFce8FBH0++hWFshCRMaY7SHsiEJFewEvAXFU9HvtwnKe0+0irqo+r6hRVnTJgwIB0hNkjzB41Om5hM7/Px/XjY6/KGWOMK62JQESCuEngWVWdH2eXSmB4q5+HAfvSGVNPVlJQwFPX3URZKESvggL3K1jAo5+5hqGlvbMdnjEmR6Vz1pAATwIbVfXhBLstAu4TkeeBaUC1qu5PV0z5YOLgISz/0t2s+mQf4ajD5MFDrAGKMaZD6XyHmAH8E7BWRFZ7274NnAWgqo8Br+JOHd2GO330i2mMJ28EfD4uGjIs22EYY7qJtCUCVX2P+PcAWu+jwL3pisEYY0znbLmpMcbkOUsExhiT5ywRGGNMnrNEYIwxec4SgTHG5DmJV5sml4lIFZDtCmpnAIeyHEMyLM7UsjhTy+JMrc7iHKGqcUszdLtEkAtEZIWqTsl2HJ2xOFPL4kwtizO1TidOuzRkjDF5zhKBMcbkOUsEp+bxbAeQJIsztSzO1LI4U+uU47R7BMYYk+fsjMAYY/KcJQJjjMlzlgg6ICJ+EVklIi/HeewOEakSkdXe15ezEaMXy04RWevFsSLO4yIivxKRbSJSISKTcjTOy0SkutWYfi9LcfYVkXkisklENorI9JjHc2U8O4sz6+MpImNb/f7VInJcRObG7JP18UwyzqyPpxfH10RkvYisE5HnRKQo5vFCEXnBG88PvFbBHbKOJR37KrARSNTe6wVVvS+D8XTkclVNtJjkKuBs72sa8Gvvz2zoKE6Ad1X1moxFE98jwGJVvVlECoDimMdzZTw7ixOyPJ6quhm4ENwPVsBeYEHMblkfzyTjhCyPp4gMBb4CTFDVBhH5A3Ab8LtWu30JOKqqY0TkNuDnwK0dHdfOCBIQkWHAZ4Ensh1LClwHPK2u5UBfERmc7aBykYj0BmbidtdDVZtV9VjMblkfzyTjzDWzgO2qGlsZIOvjGSNRnLkiAIREJICb/GPb+14HPOV9Pw+YJfGambdiiSCxXwLfApwO9rnJO5WdJyLDO9gv3RR4Q0Q+EpG74jw+FNjT6udKb1umdRYnwHQRWSMir4nIuZkMzlMOVAH/37ss+ISIlMTskwvjmUyckP3xbO024Lk423NhPFtLFCdkeTxVdS/wELAb2I/b3veNmN1OjKeqRoBqoH9Hx7VEEIeIXAMcVNWPOtjtT8BIVT0feJOTGTgbZqjqJNxT7HtFZGbM4/E+DWRj3nBnca7ErYdyAfAo8MdMB4j7aWsS8GtVnQjUAf8Ss08ujGcycebCeALgXbq6Fngx3sNxtmVlXnsncWZ9PEWkH+4n/lHAEKBERD4fu1ucp3Y4npYI4psBXCsiO4HngStE5JnWO6jqYVVt8n78DTA5syG2iWWf9+dB3OuaU2N2qQRan7EMo/3pZNp1FqeqHlfVWu/7V4GgiJyR4TArgUpV/cD7eR7uG27sPtkez07jzJHxbHEVsFJVD8R5LBfGs0XCOHNkPGcDO1S1SlXDwHzgUzH7nBhP7/JRH+BIRwe1RBCHqv6rqg5T1ZG4p4lvq2qbrBtzDfNa3JvKGSciJSJS2vI9cCWwLma3RcAXvNkZF+OeTu7PtThFZFDLtUwRmYr77/NwJuNU1U+APSIy1ts0C9gQs1vWxzOZOHNhPFv5RxJfbsn6eLaSMM4cGc/dwMUiUuzFMov27z2LgH/2vr8Z9/2rwzMCmzXUBSLyQ2CFqi4CviIi1wIR3Gx7R5bCGggs8P59BoDfq+piEbkbQFUfA14Frga2AfXAF3M0zpuBe0QkAjQAt3X2DzhN7gee9S4TfAx8MQfHM5k4c2I8RaQYmAP831bbcm48k4gz6+Opqh+IyDzcy1QRYBXweMx705PA/4jINtz3pts6O66VmDDGmDxnl4aMMSbPWSIwxpg8Z4nAGGPynCUCY4zJc5YIjDEmz1kiMHnNqygZr7ps3O0p+H3Xi8iEVj+/IyKdNhwXkcGpiEdEBojI4tM9julZLBEYk1nXAxM63au9B3BXsJ8WVa0C9ovIjNM9luk5LBGYnOatSH7FK/S1TkRu9bZPFpGlXgG711tWenufsH8pIu97+0/1tk/1tq3y/hzb0e+NE8NvReRD7/nXedvvEJH5IrJYRLaKyC9aPedLIrLFi+c3IvKfIvIp3FXo/y5uPfvR3u7/ICJ/8/a/JEEYNwGLvWP7ReQhcXs7VIjI/d72nSLyUxH5q4isEJFJ3thsb1kY5fkj8H+Sff2m57OVxSbXfQbYp6qfBRCRPiISxC36dZ2qVnnJ4SfAnd5zSlT1U+IWtfstcB6wCZipqhERmQ38FPfNNRn/hrtM/04R6Qv8TUTe9B67EJgINAGbReRRIAp8F7f2Tw3wNrBGVd8XkUXAy6o6z3s9AAFVnSoiVwPfx60nc4KIjMKtL99S2+ou3KJjE73XU9Zq9z2qOl1E/gO3Rv0MoAhYDzzm7bMC+HGSr93kAUsEJtetBR4SkZ/jvoG+KyLn4b65/9l7I/XjluRt8RyAqi4Tkd7em3cp8JSInI1biTHYhRiuxC1C+A3v5yLgLO/7t1S1GkBENgAjgDOApap6xNv+InBOB8ef7/35ETAyzuODcUtOt5gNPOaVGKbl93gWeX+uBXqpag1QIyKNItLX61lwELdypTGAJQKT41R1i4hMxq1F86CIvIFbuXS9qk5P9LQ4P/8IWKKqN4jbuu+dLoQhwE1eF6uTG0Wm4Z4JtIji/p/qsAlIHC3HaHl+rAbc5NM6nkS1YVqO5cTE5rQ6dpF3TGMAu0dgcpyIDAHqVfUZ3IYck4DNwADxevSKSFDaNglpuY/wadxKltW4pXj3eo/f0cUwXgfu96o9IiITO9n/b8ClItJP3DLArS9B1eCenXTFFtqeKbwB3O0dm5hLQ8k4h/YVak0es0Rgct3f4V6TX417rf7HqtqMWwny5yKyBlhN25rsR0Xkfdxr4l/ytv0C94ziL7iXkrriR7iXkipEZJ33c0JeF6mfAh/gNi3agNslCtz+Ft/0bjqPTnCI2OPVAdtFZIy36QnccsQV3uu/vYuv53LglS4+x/RgVn3U9Cgi8g7wDVVdkeU4eqlqrfepfQHwW1WN1ww92ePdAExW1e+kILZluDfaj57usUzPYGcExqTHD7yzmHXADk6zraGXRHaeblAiMgB42JKAac3OCIwxJs/ZGYExxuQ5SwTGGJPnLBEYY0yes0RgjDF5zhKBMcbkuf8FP8fTbIx03jgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sepal_length,sepal_width,c=iris.target)\n",
    "plt.xlabel(sepal_length_label)\n",
    "plt.ylabel(sepal_width_label)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(iris[\"data\"],iris[\"target\"],random_state=0) #X here is all of the training data and y is the target data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_demo = np.array([[7.4,2.8,6.0,1.4]])\n",
    "knn.predict(X_demo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_df[\"target\"] = pd.Series(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </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   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>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   \n",
       "5                  5.4               3.9                1.7               0.4   \n",
       "6                  4.6               3.4                1.4               0.3   \n",
       "7                  5.0               3.4                1.5               0.2   \n",
       "8                  4.4               2.9                1.4               0.2   \n",
       "9                  4.9               3.1                1.5               0.1   \n",
       "10                 5.4               3.7                1.5               0.2   \n",
       "11                 4.8               3.4                1.6               0.2   \n",
       "12                 4.8               3.0                1.4               0.1   \n",
       "13                 4.3               3.0                1.1               0.1   \n",
       "14                 5.8               4.0                1.2               0.2   \n",
       "15                 5.7               4.4                1.5               0.4   \n",
       "16                 5.4               3.9                1.3               0.4   \n",
       "17                 5.1               3.5                1.4               0.3   \n",
       "18                 5.7               3.8                1.7               0.3   \n",
       "19                 5.1               3.8                1.5               0.3   \n",
       "20                 5.4               3.4                1.7               0.2   \n",
       "21                 5.1               3.7                1.5               0.4   \n",
       "22                 4.6               3.6                1.0               0.2   \n",
       "23                 5.1               3.3                1.7               0.5   \n",
       "24                 4.8               3.4                1.9               0.2   \n",
       "25                 5.0               3.0                1.6               0.2   \n",
       "26                 5.0               3.4                1.6               0.4   \n",
       "27                 5.2               3.5                1.5               0.2   \n",
       "28                 5.2               3.4                1.4               0.2   \n",
       "29                 4.7               3.2                1.6               0.2   \n",
       "30                 4.8               3.1                1.6               0.2   \n",
       "31                 5.4               3.4                1.5               0.4   \n",
       "32                 5.2               4.1                1.5               0.1   \n",
       "33                 5.5               4.2                1.4               0.2   \n",
       "34                 4.9               3.1                1.5               0.2   \n",
       "35                 5.0               3.2                1.2               0.2   \n",
       "36                 5.5               3.5                1.3               0.2   \n",
       "37                 4.9               3.6                1.4               0.1   \n",
       "38                 4.4               3.0                1.3               0.2   \n",
       "39                 5.1               3.4                1.5               0.2   \n",
       "40                 5.0               3.5                1.3               0.3   \n",
       "41                 4.5               2.3                1.3               0.3   \n",
       "42                 4.4               3.2                1.3               0.2   \n",
       "43                 5.0               3.5                1.6               0.6   \n",
       "44                 5.1               3.8                1.9               0.4   \n",
       "45                 4.8               3.0                1.4               0.3   \n",
       "46                 5.1               3.8                1.6               0.2   \n",
       "47                 4.6               3.2                1.4               0.2   \n",
       "48                 5.3               3.7                1.5               0.2   \n",
       "49                 5.0               3.3                1.4               0.2   \n",
       "50                 7.0               3.2                4.7               1.4   \n",
       "51                 6.4               3.2                4.5               1.5   \n",
       "52                 6.9               3.1                4.9               1.5   \n",
       "53                 5.5               2.3                4.0               1.3   \n",
       "54                 6.5               2.8                4.6               1.5   \n",
       "55                 5.7               2.8                4.5               1.3   \n",
       "56                 6.3               3.3                4.7               1.6   \n",
       "57                 4.9               2.4                3.3               1.0   \n",
       "58                 6.6               2.9                4.6               1.3   \n",
       "59                 5.2               2.7                3.9               1.4   \n",
       "60                 5.0               2.0                3.5               1.0   \n",
       "61                 5.9               3.0                4.2               1.5   \n",
       "62                 6.0               2.2                4.0               1.0   \n",
       "63                 6.1               2.9                4.7               1.4   \n",
       "64                 5.6               2.9                3.6               1.3   \n",
       "65                 6.7               3.1                4.4               1.4   \n",
       "66                 5.6               3.0                4.5               1.5   \n",
       "67                 5.8               2.7                4.1               1.0   \n",
       "68                 6.2               2.2                4.5               1.5   \n",
       "69                 5.6               2.5                3.9               1.1   \n",
       "70                 5.9               3.2                4.8               1.8   \n",
       "71                 6.1               2.8                4.0               1.3   \n",
       "72                 6.3               2.5                4.9               1.5   \n",
       "73                 6.1               2.8                4.7               1.2   \n",
       "74                 6.4               2.9                4.3               1.3   \n",
       "75                 6.6               3.0                4.4               1.4   \n",
       "76                 6.8               2.8                4.8               1.4   \n",
       "77                 6.7               3.0                5.0               1.7   \n",
       "78                 6.0               2.9                4.5               1.5   \n",
       "79                 5.7               2.6                3.5               1.0   \n",
       "80                 5.5               2.4                3.8               1.1   \n",
       "81                 5.5               2.4                3.7               1.0   \n",
       "82                 5.8               2.7                3.9               1.2   \n",
       "83                 6.0               2.7                5.1               1.6   \n",
       "84                 5.4               3.0                4.5               1.5   \n",
       "85                 6.0               3.4                4.5               1.6   \n",
       "86                 6.7               3.1                4.7               1.5   \n",
       "87                 6.3               2.3                4.4               1.3   \n",
       "88                 5.6               3.0                4.1               1.3   \n",
       "89                 5.5               2.5                4.0               1.3   \n",
       "90                 5.5               2.6                4.4               1.2   \n",
       "91                 6.1               3.0                4.6               1.4   \n",
       "92                 5.8               2.6                4.0               1.2   \n",
       "93                 5.0               2.3                3.3               1.0   \n",
       "94                 5.6               2.7                4.2               1.3   \n",
       "95                 5.7               3.0                4.2               1.2   \n",
       "96                 5.7               2.9                4.2               1.3   \n",
       "97                 6.2               2.9                4.3               1.3   \n",
       "98                 5.1               2.5                3.0               1.1   \n",
       "99                 5.7               2.8                4.1               1.3   \n",
       "100                6.3               3.3                6.0               2.5   \n",
       "101                5.8               2.7                5.1               1.9   \n",
       "102                7.1               3.0                5.9               2.1   \n",
       "103                6.3               2.9                5.6               1.8   \n",
       "104                6.5               3.0                5.8               2.2   \n",
       "105                7.6               3.0                6.6               2.1   \n",
       "106                4.9               2.5                4.5               1.7   \n",
       "107                7.3               2.9                6.3               1.8   \n",
       "108                6.7               2.5                5.8               1.8   \n",
       "109                7.2               3.6                6.1               2.5   \n",
       "110                6.5               3.2                5.1               2.0   \n",
       "111                6.4               2.7                5.3               1.9   \n",
       "112                6.8               3.0                5.5               2.1   \n",
       "113                5.7               2.5                5.0               2.0   \n",
       "114                5.8               2.8                5.1               2.4   \n",
       "115                6.4               3.2                5.3               2.3   \n",
       "116                6.5               3.0                5.5               1.8   \n",
       "117                7.7               3.8                6.7               2.2   \n",
       "118                7.7               2.6                6.9               2.3   \n",
       "119                6.0               2.2                5.0               1.5   \n",
       "120                6.9               3.2                5.7               2.3   \n",
       "121                5.6               2.8                4.9               2.0   \n",
       "122                7.7               2.8                6.7               2.0   \n",
       "123                6.3               2.7                4.9               1.8   \n",
       "124                6.7               3.3                5.7               2.1   \n",
       "125                7.2               3.2                6.0               1.8   \n",
       "126                6.2               2.8                4.8               1.8   \n",
       "127                6.1               3.0                4.9               1.8   \n",
       "128                6.4               2.8                5.6               2.1   \n",
       "129                7.2               3.0                5.8               1.6   \n",
       "130                7.4               2.8                6.1               1.9   \n",
       "131                7.9               3.8                6.4               2.0   \n",
       "132                6.4               2.8                5.6               2.2   \n",
       "133                6.3               2.8                5.1               1.5   \n",
       "134                6.1               2.6                5.6               1.4   \n",
       "135                7.7               3.0                6.1               2.3   \n",
       "136                6.3               3.4                5.6               2.4   \n",
       "137                6.4               3.1                5.5               1.8   \n",
       "138                6.0               3.0                4.8               1.8   \n",
       "139                6.9               3.1                5.4               2.1   \n",
       "140                6.7               3.1                5.6               2.4   \n",
       "141                6.9               3.1                5.1               2.3   \n",
       "142                5.8               2.7                5.1               1.9   \n",
       "143                6.8               3.2                5.9               2.3   \n",
       "144                6.7               3.3                5.7               2.5   \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   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         0  \n",
       "7         0  \n",
       "8         0  \n",
       "9         0  \n",
       "10        0  \n",
       "11        0  \n",
       "12        0  \n",
       "13        0  \n",
       "14        0  \n",
       "15        0  \n",
       "16        0  \n",
       "17        0  \n",
       "18        0  \n",
       "19        0  \n",
       "20        0  \n",
       "21        0  \n",
       "22        0  \n",
       "23        0  \n",
       "24        0  \n",
       "25        0  \n",
       "26        0  \n",
       "27        0  \n",
       "28        0  \n",
       "29        0  \n",
       "30        0  \n",
       "31        0  \n",
       "32        0  \n",
       "33        0  \n",
       "34        0  \n",
       "35        0  \n",
       "36        0  \n",
       "37        0  \n",
       "38        0  \n",
       "39        0  \n",
       "40        0  \n",
       "41        0  \n",
       "42        0  \n",
       "43        0  \n",
       "44        0  \n",
       "45        0  \n",
       "46        0  \n",
       "47        0  \n",
       "48        0  \n",
       "49        0  \n",
       "50        1  \n",
       "51        1  \n",
       "52        1  \n",
       "53        1  \n",
       "54        1  \n",
       "55        1  \n",
       "56        1  \n",
       "57        1  \n",
       "58        1  \n",
       "59        1  \n",
       "60        1  \n",
       "61        1  \n",
       "62        1  \n",
       "63        1  \n",
       "64        1  \n",
       "65        1  \n",
       "66        1  \n",
       "67        1  \n",
       "68        1  \n",
       "69        1  \n",
       "70        1  \n",
       "71        1  \n",
       "72        1  \n",
       "73        1  \n",
       "74        1  \n",
       "75        1  \n",
       "76        1  \n",
       "77        1  \n",
       "78        1  \n",
       "79        1  \n",
       "80        1  \n",
       "81        1  \n",
       "82        1  \n",
       "83        1  \n",
       "84        1  \n",
       "85        1  \n",
       "86        1  \n",
       "87        1  \n",
       "88        1  \n",
       "89        1  \n",
       "90        1  \n",
       "91        1  \n",
       "92        1  \n",
       "93        1  \n",
       "94        1  \n",
       "95        1  \n",
       "96        1  \n",
       "97        1  \n",
       "98        1  \n",
       "99        1  \n",
       "100       2  \n",
       "101       2  \n",
       "102       2  \n",
       "103       2  \n",
       "104       2  \n",
       "105       2  \n",
       "106       2  \n",
       "107       2  \n",
       "108       2  \n",
       "109       2  \n",
       "110       2  \n",
       "111       2  \n",
       "112       2  \n",
       "113       2  \n",
       "114       2  \n",
       "115       2  \n",
       "116       2  \n",
       "117       2  \n",
       "118       2  \n",
       "119       2  \n",
       "120       2  \n",
       "121       2  \n",
       "122       2  \n",
       "123       2  \n",
       "124       2  \n",
       "125       2  \n",
       "126       2  \n",
       "127       2  \n",
       "128       2  \n",
       "129       2  \n",
       "130       2  \n",
       "131       2  \n",
       "132       2  \n",
       "133       2  \n",
       "134       2  \n",
       "135       2  \n",
       "136       2  \n",
       "137       2  \n",
       "138       2  \n",
       "139       2  \n",
       "140       2  \n",
       "141       2  \n",
       "142       2  \n",
       "143       2  \n",
       "144       2  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_rows\",200)\n",
    "display(iris_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
       "       'petal width (cm)', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min sepal length (cm) : 4.3 \t Max sepal length (cm) : 7.9\n",
      "Min sepal width (cm) : 2.0 \t Max sepal width (cm) : 4.4\n",
      "Min petal length (cm) : 1.0 \t Max petal length (cm) : 6.9\n",
      "Min petal width (cm) : 0.1 \t Max petal width (cm) : 2.5\n"
     ]
    }
   ],
   "source": [
    "#iris_df['sepal length (cm)'].max(axis=0)\n",
    "\n",
    "for index in iris_df.columns:\n",
    "    if index not in ['target']:\n",
    "        print(\"Min \"+index + \" : \"+str(iris_df[index].min(axis = 0))+\" \\t Max \"+index + \" : \"+str(iris_df[index].max(axis = 0)))\n",
    "    #iris_df[index].max(axis = 0)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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