Classification - SVC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "       [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': '/home/aditya/miniconda3/envs/ML_Workshop/lib/python3.7/site-packages/sklearn/datasets/data/iris.csv'}\n"
     ]
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "print(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(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": [
    "print(iris.keys())"
   ]
  },
  {
   "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": 6,
   "metadata": {},
   "outputs": [
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       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>4.9</td>\n",
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       "      <td>4.6</td>\n",
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       "      <td>0.2</td>\n",
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       "      <th>5</th>\n",
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       "      <td>3.9</td>\n",
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       "      <td>0.4</td>\n",
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       "      <th>6</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
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       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
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       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
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       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\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",
       "..                 ...               ...                ...               ...\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",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.DataFrame(\n",
    "            data = iris.data,\n",
    "            columns = iris.feature_names)\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.. _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 ...'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe[\"Target\"] = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
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       "      <td>0</td>\n",
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       "      <td>5.4</td>\n",
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       "      <td>5.1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>...</td>\n",
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       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
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       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
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       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>125</th>\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",
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       "    <tr>\n",
       "      <th>126</th>\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",
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       "    <tr>\n",
       "      <th>127</th>\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",
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       "      <th>128</th>\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",
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       "    <tr>\n",
       "      <th>129</th>\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",
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       "    <tr>\n",
       "      <th>130</th>\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",
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       "    <tr>\n",
       "      <th>131</th>\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",
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       "      <td>2.2</td>\n",
       "      <td>2</td>\n",
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       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
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       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
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       "      <th>135</th>\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",
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       "    <tr>\n",
       "      <th>136</th>\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",
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       "      <td>6.4</td>\n",
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       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
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       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
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       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
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       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\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",
       "      <th>141</th>\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",
       "      <th>142</th>\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",
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       "    <tr>\n",
       "      <th>143</th>\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",
       "      <th>144</th>\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",
       "      <th>145</th>\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",
       "      <th>146</th>\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",
       "      <th>147</th>\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",
       "      <th>148</th>\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",
       "      <th>149</th>\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",
       "<p>150 rows × 5 columns</p>\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",
       "..                 ...               ...                ...               ...   \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",
       "..      ...  \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  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[\"Target\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7gcNbqJ8L7O/uOwNXAhOa1R/o7sOzPelIRERyK2fPpHb3l8ysfwv1r6W9fQPok6tYRERk3W0s5yDOAp5Me+/AM2Y21czGtzShmY03s3IzK6+srMxpkCIim5Oc7UG0lZkdSJAg9k0rHunuC8xsC+BZM/vQ3V+Kmt7dJxAeniorK/OcBywisplo1z0IM9sZuBUY4+5LG8rdfUH4ezHwMDCifSIUEdl8tVuCMLN+wEPAKe7+UVp5RzPr3PAaOBSIvBJKRERyJ2eHmMxsInAAUGJmFcDlQD6Au98EXAb0BG4wM4BEeMXSlsDDYVkc+Je7P5WrOEVEJFour2I6oZX6s4GzI8rnALtkTiEiIhvSxnIVk4iIbGSUIEREJJIShIiIRFKCEBGRSEoQIiISSQlCREQiKUGIiEgkJQgREYnUaoIwsw5mdomZ3RS+H2RmR+Q+NBERaU9t2YO4HTDW3m11AfD7nEUkIiIbhbYkiMHu/nugHsDdqwgShoiIbMLakiDqzKyI4CE+mNkAoC6nUYmISLtry836rgCeAvqY2V3A/gQP+BERkU1YqwnC3Z8ys6nAPgSHln4ePshHREQ2YW293ffewEiCw0xJ4LGcRSRf2fLqap77dA4pdw7sP5CS4uIm9YlUilumTeHDJUvYq09fjh+2I7FY06OMFStX8Mrnn9GxoICD+g+kY0FBk/raRILnP53LsuoqRvTuw6AePdc5zpW1NTw3dw61yST7b9OfrTp1blJfX1/PCQ8/yCfLlzK4ewn3jj2G/Pz8Jm3unDaVf0x9k3yLcc0hh/Otbfo3qf9i1QpOfeQhllStYf9t+vPXw0dnxPHP6e/w4Afvs1WnzvzuoIPpWdyxSX3lmtXcUP4WK2tqOHGnXdi9V+8m9YlEgp9NfpryhfMZ1L0HN40+mqJ40z+pZz/5mD+8+hKG8ev9D8qIsy6R4KapU/hk+TK+1a8/xw7bISPOhz6YwT3vvUP3og789sBR9OrStUn9lzXV/OOtN1haXc13tx/Gt/r1z+hj+qIveG/xInp17sy3+vUn3ux7/+zL5dw8dQoJd84cvhvblZQ2jTOZ5LLnJzNzSSUjevfh4n2+RV5eXpM281et5JXPPqVDfj4HDdiWThHLzgufzWVpVRV79OrD4J6Zy86HSyqZtnABW3TsyP7bDCC/2RiyYZl7y49xNrO/A8OA+8Ki44AP3f38Vjs3ux0YDSx29x0j6g34G3AkUAWc7u7TwrrTgP8Lm/7W3e9qbbyysjIvLy9vrdkm6bFZH3LR5KfJiwXXDyRTKX5zwCiO22EnAD5auoTRE+8hkUo1TtMxP58XTzubHmEi+cvrr3LLtCnEzIgFD2zi1u+MZc8+fYHgj/fEhx4gkUo19jN68FCuPvgwwgc8ter5T+dw3hOPETMj5U7KnQv33pezdysD4IW5czjzsYczprtrzDGNK9cdb/gbVYlEk/qhPXvy5EmnA3D1yy9y89uZy8FbZ59DSXExdckku0+4njX19U3qL913/8Y47nh7Kle+/EKT+j169eb+Y8cBMGfZUg7+550ZY9z87aM4ZNvBAOx/xy3MW7Uya5zvfrGQY/89kWTa32DXwiJeOv0sOhcWkUwm2fO2m1hWU9Okj/PK9uSn+wQXFT74wQwumtz0eVrb9Szh8RNOIRaLUZdMMv7xR5gyvwJ3yIvF6FpUyP3HjqN35y4A/Pal57n9nWlN+hgzdHv+etiRAJTPn89x/7mvSX3MjOdPPYu+XYNkdd2br3Fj+VuNy44Dt4w+mr379gOC5e/Ehx6gNpEk6cGyc8SgIfzxkMOJmZFMpbjgqf/y/KdzAIhbjKL8OPcdczwDu/fImM+y/pjZ1PBhbRnacpL6IOBgd7/F3W8BDg/L2uLOsH02RwCDw5/xwI1hwD0InkC3J8HzqC83s+5tHHOzU7lmDRdNfpraZIKq+nqq6uupTSa5/IXnqFi5AoCTwhV7ujX19Zzx6EMAvDW/gtveLqc2maQ6kWBNfT1r6usZ//gj1CYSuDvjH3uEL2tqWF1XR00iQU0iwX8/nsVjH33YpjhX1tZy3hOPNfZfnUhQm0zylzdeZeaSSgDOikgOAKc/+h8AfvLU4xnJAWDW0qW8Pu9zgMjkADDqrluDMSY9lJEcAH7/yoskk0lW1tRkJAeAKQvmc9e7bwMweuI9kWP84L+TALjr7WkZyaEhzic+Cp6we+ojDzZJDgAramv4/uNBHxdOfiojOQD8o/xNquvqqEsk+MXkzIctfrh0Cde++RoAt0ybwlsVFVQnEtQkE6ypr2PR6tX85KkngGDF3Tw5ADw6ayavhfPz5If/nVGfcufo+/8JwNSF87l56pQmy05VfT3jH3+UmkQ97s73H3+UZdXVrKlfu+w8NftjHv1wJgD3zXiPFz6d01i3ur6OpVVVnPvEpKjZLBtIWxLER0CftPdb08ZnRLv7S8CyFpqMAe72wBtANzPbGjgMeNbdl7n7cuBZWk40m7UnZ39E1AZ8ylP89+NZrKqtYWl1deS071cuAuCBGe9RE7HideD1inl8uHQJy2oy+6hOJPjXe9PbFOdzc+c07pmkq0smefTDDxrHi9JQ/vhHH2VpARc+8yQ3lr+ZtX5VmBRer5iXtc0d777NHe9mrjAb3BmuTGuSyaxxLly1kuumvJG1j9+98jwVK1ewqi76YsApCyoAeOrjj7P28cfXX+XBmTOyzq+J778HwP0z3qMm2fR7Tbrz7qIv+LKmmhumZJ9fN055k9V1ddSloj/r8jB5/XvG+5HLjgGvfv45s5ctY/GaNRn11Yl67n3/3SDe996lulkfDny+YgXzVqzIGqPkVlvOQXQFZprZGwTf2V7Aa2b2EIC7f/drjN8bSP9rrQjLspVnMLPxBHsf9OvX72uE8s1Vn0qRSmWuKpLu4S599sOIDTW1yUTWlU1dMkF9Moll+feX2mTmyiE6ziRRobh75AomimeNMuh/ZXXmFnfUeNmsqavLWKE2GSNLYkhXl0ySyLJSheD7qmthjIavMtXCZ62qr6MqYi+oQcP49clUZL1ZGEcLcdaG33trapPJrJHWJpPUp5LEshyBrA2/99os4xjW4ryS3GrLHsTvCLb0/wBcBRwNXANcH/58HVGLjbdQnlnoPsHdy9y9rLS0NKrJJu+gAQMjzwEU5OVxyMBt6VbUgeJmJ3kb9A1PeH5nyHYUxzPbJJIp9u7Tj2GlW5Cfl7m4FMXjjBm6fZvi3H+b/o3Hn5v2kc8Rg4a0qY/hW/XKWnfhXvvy0732yVpfEJ6YHdqjJGubs3Yr4+Sdhmetb/iseS2cc9mmW3e+u/2wrPVnD9+dgd17UpDlBOzQ8ORt2daR20QA/HivfThuWMZpvUaHhedBjhw8hPxY5jj9unSltLgjp+6c/bOesNMudO/QIXKvD6BDeEJ+9JChkctXfSrFvv36MbRnCYXxzG3R9GVnzNDtKIyYH12LCnUOoh216T+pgXJ3/1/Uz9ccvwLom/a+D8GtPLKVS4QB3brzg933oCgeJ0aQXTvE45y80y5sX7oFANcd/u2M6WJm3Dx6DAAHDxzEvv22afxDj8diFMXjXHngKDoXFhKPxbj2sG/TIR6nIFzhFOfnM6yklBN23LlNcW7RsRMXjdyPonicPLPGOEcPGcKI3sFRzB+N2Dty2gvDFf/EscdGbj10ys9n3E47k5+fzz59+ka0gInfPQ6Au44+JnKld+z2O9CpoIDeXbpwfMTKd4uOHfnJ3iMB+NthmVdFAfxoj70AuHz/URRFrPA65xcwvmwEAH86JPOoaTwW48ZvHwXAhO8cTTwizlHbDGCrTp3pUlTE+N0yzy12LSzksv0OBOCCEXvTu0vnxu+1KB6nU34Bfw5PQO/Vp1/k/Nq+pJSx2wVJ7vcHHRz5WW8eHcR5YP+BHLDNgMYNjLgZhXlxfnPAQXQpLCIvfdnJW7vsDO1Zwilhgjpz1zIGdu/RGGdhXh7F8XyuPezbbb4AQta/tlzFdBXwPeBN4HZ3n7xOA5j1Bx7PchXTt4HzCK5i2hO4zt1HhCeppwK7hU2nAbu7e0vnMzbrq5gA3l+8iEmzZpJyZ/SQ7Ri+1dZN6uet+JLfv/wis5cvY/hWW3PJyP0ar2CC4NDLK/M+Y/KcT+hcUMh3tx+WsfW2YNVK/jPzAxavWc2+/bZh1IBtMy6ZbM2spUt4ZOYH1CQTHDFoCHv06t1kJfDW/ArGT3qYVfV1dM4vYMJRYxsTCASXwZ7yyEOUL6wgZsbR2w/jmoObrmzve286V7z0HLXJJFt36sz9xxxP765rLw9dXVfHz555kjfnz6NLYRGX7XcgowZu26SPlz6by3VvvsGa+lrGDB3G+N3KmlwWPGfZUs587GEWrlpF98Iirjvi24zo0/Qw58+feZLHP56FAccM24ErDzykSf2c5Uv5w8sv8emKLxnRqw+/GPktuhQVNdbXJZNcNPkpXvx0LsX5BVw0cl/GDG26d/JWRQV/eeNVvqyp5vBBQzi3bAQFaVvstYkET87+iCkL5tO/W3e+u90weja7BHrSrJnc9vZUku6ctNMuGUl/ZuViLnzmSeavWsmgHj249rDRjVcwQbDsvFbxOZPnfELH/ILIZWfhqlX8Z+YMFq1ezch+23DwwKbLTn0yyTOfzOaNinls3bkzxw7bgS06dkJyq6WrmFpNEGEHMYIrjs4AdgEmEiSLT1uZbiJwAFACLCK4MikfwN1vCi9z/QfBCegq4Ax3Lw+nPRO4NOzqd+5+R2txbu4JQkRkXbWUINr0j3LunjKzT4FPgZ0IrmR61MyecPdLWpjuhFb6deCHWepuJ7iTrIiItIOsxwbMLB7+PtfM3iL4h7apwM7u/v+AXYHjN0iUIiKywbW0B/EWwTmAPsA4d5+TXhnuVRyVy+BERKT9tJQgDMDdL83WwN3b9A9zIiLyzdNSgig1s59mq3T3v+QgHhER2Ui0lCDygE7o6XEiIpullhLEQne/YoNFIiIiG5WW/sNJew4iIpuxlhLEqA0WhYiIbHSyJojWbmshIiKbtnW7iY6IiGw2lCBERCSSEoSIiERSghARkUhKECIiEkkJQkREIuU0QZjZ4WY2y8xmm9nFEfV/NbN3wp+PzOzLtLpkWt2kXMYpIiKZ2vTAoK/CzPKA64FDCJ4xPcXMJrn7Bw1t3P0nae3PJ3jGRINqd8/+RHUREcmpXO5BjABmu/scd68D7gPGtND+BIJHmYqIyEYglwmiNzAv7X1FWJbBzLYBBgDPpRUXmVm5mb1hZkdnG8TMxoftyisrK9dH3CIiQm4TRNTN/jxL23HAg+6eTCvrFz5I+0TgWjPbNmpCd5/g7mXuXlZaWvr1IhYRkUa5TBAVQN+0932ABVnajqPZ4SV3XxD+ngO8QNPzEyIikmO5TBBTgMFmNsDMCgiSQMbVSGY2FOgOvJ5W1t3MCsPXJcBI4IPm04qISO7k7Comd0+Y2XnA0wRPp7vd3WeY2RVAubs3JIsTgPvcPf3w0/bAzWaWIkhiV6Vf/SQiIrlnTdfL32xlZWVeXl7e3mGIiHxjmNnU8HxvBv0ntYiIRFKCEBGRSEoQIiISSQlCREQiKUGIiEgkJQgREYmkBCEiIpGUIEREJJIShIiIRFKCEBGRSEoQIiISSQlCREQiKUGIiEgkJQgREYmkBCEiIpGUIEREJFJOE4SZHW5ms8xstpldHFF/uplVmtk74c/ZaXWnmdnH4c9puYxTREQy5eyRo2aWB1wPHAJUAFPMbFLEo0Pvd/fzmk3bA7gcKAMcmBpOuzxX8YqISFO53IMYAcx29znuXgfcB4xp47SHAc+6+7IwKTwLHJ6jOEVEJEIuE0RvYF7a+4qwrLljzGy6mT1oZn3XcVrMbLyZlZtZeWVl5fqIW0REyG2CsIgyb/b+MaC/u+8MTAbuWodpg0L3Ce5e5u5lpaWlXzlYERFpKpcJogLom/a+D7AgvYG7L3X32vDtLcDubZ1WRERyK5cJYgow2MwGmFkBMA6YlN7AzLZOe3sUMDN8/TRwqJl1N7PuwKFhmYiIbCA5u4rJ3RNmdh7Bij0PuN3dZ5jZFUC5u08CLjCzo4AEsAw4PZx2mZldSZBkAK5w92W5ilVERDKZe9NiY+oAABJCSURBVOSh/W+ksrIyLy8vb+8wRES+McxsqruXRdXpP6lFRCSSEoSIiERSghARkUhKECIiEkkJQkREIilBiIhIJCUIERGJpAQhIiKRlCBERCSSEoSIiERSghARkUhKECIiEkkJQkREIilBiIhIJCUIERGJlNMEYWaHm9ksM5ttZhdH1P/UzD4ws+lm9j8z2yatLmlm74Q/k5pPKyIiuZWzJ8qZWR5wPXAIwTOmp5jZJHf/IK3Z20CZu1eZ2TnANcDxYV21uw/PVXwiItKyXO5BjABmu/scd68D7gPGpDdw9+fdvSp8+wbQJ4fxiIjIOshlgugNzEt7XxGWZXMW8GTa+yIzKzezN8zs6GwTmdn4sF15ZWXl14tYREQa5ewQE2ARZZEPwDazk4EyYP+04n7uvsDMBgLPmdl77v5JRofuE4AJEDyT+uuHLSIikNs9iAqgb9r7PsCC5o3M7GDgl8BR7l7bUO7uC8Lfc4AXgF1zGKuIiDSTywQxBRhsZgPMrAAYBzS5GsnMdgVuJkgOi9PKu5tZYfi6BBgJpJ/cFhGRHMvZISZ3T5jZecDTQB5wu7vPMLMrgHJ3nwT8EegE/NvMAD5396OA7YGbzSxFkMSuanb1k4iI5Ji5bzqH7cvKyry8vLy9wxAR+cYws6nuXhZVp/+kFhGRSEoQIiISSQlCREQiKUGIiEgkJQgREYmkBCEiIpGUIEREJJIShIiIRFKCEBGRSEoQIiISSQlCREQiKUGIiEgkJQgREYmkBCEiIpGUIEREJFIun0mNmR0O/I3ggUG3uvtVzeoLgbuB3YGlwPHu/mlYdwlwFpAELnD3p3MR49vPvcetF/+TeR8uYMttSjn9ynGMPHpEY/2st2bzi8OuZM2KKgC6lnbmutd+R69tt25s8+w9L3Lvb//DsoXL2XZ4f8b/8VS233NwY/3Eqx/mjl9OxFPBsze6lHTmrjnX06lTBwDqauq47OirmTb5PTzl9OzVnV/cfT67HrRTYx+/OPRKpk2e3vi+Z+/u3DdvQuP7D6fM5qf7XUZ9bT0A8YI8rnrmV+yy3w6Nbb7d6STqquoa33ffsisPLLy18f0fTvkbz937SpP5c+Pb1zBolwFBnHV1nFd2MXPfnxeOEecHfz6VMT88orH9x9PmMOGie5g1ZTbdSrsy7uKjOeKsUYQPhGLu+5/xg90uIpVIBRMYXHjrORx+xkGNfdxz5YPc94eHqKupp6BDASdeMpaT/u/YjO8um1QqxR/PuJ7nJ75KMpGkU7eOXHD92Rx4wr6NbT77YB4TLrqH917+kM49OnLsT0Yz5rwjiMW0zSTSIGcPDDKzPOAj4BCC51NPAU5IfzKcmZ0L7OzuPzCzccBYdz/ezIYBE4ERQC9gMjDE3ZMtjbmuDwyaNnk6l425mtrqtSvNwuICfjLhB4w68VusWLaKY0vOjPhs8GTdfeTl5fHgXx/nzl/dR21VbZM+/vzCFQwt25bJ/3qZq0++LqOPvHgeT9XdB8DJA89l0aeVzQaBm9/+IwN37s8vR/+Bt56YltFHcdcOPLr8bmpqavhO8SmRn/Hfi2+hW0k3Dss/jlQy87tuSBIP/OlRbrnon5F9PJv6NwDHlJ7JyqWrMuovnfgjDjx+X+a+/zkX7H0pNWvS50Uhx180hlMu+x719fUcWXhi5Bg3TLuGwcMHcPul/2LiVQ9n1J9w6VjO/G30tM397KBf8+4LMzLKf/3wRYwcswcL5y7i+8N/Ts3qahoW/8LiQo48exTnXntGm8YQ2VS01wODRgCz3X2Ou9cB9wFjmrUZA9wVvn4QGGXBpuYY4D53r3X3ucDssL/1asJF9zRJDgC1VXXcctE9uHvkih3AHa479xYS9Qnu+c0DTZJDQx93/ipY+V/7/Zsj+0gmkkz+18t88PqszOQA4HDducHWfVRyAKhaUc2KJSu47Khrsn7GXx7xB2pqaiKTA8DyRSsAsiYHgAsPvpxZb82OTA4A151zCwD3XPHviPlZy/3XPEpNVS2/HvunrGNccuhvAXjgz5Mi6x/4Y3R5c18uWRmZHAD+cf5tQV/XPEpddR3p20a1VbX8d8KzrFwW/RlFNke5TBC9gXlp7yvCssg27p4AVgA92zjt1zbvw/mR5csXraC+tp7Z73yaddoZr81i2RdfkkxE79R88s5cAGrX1EbWA/zv7hcpf+bdrPWfzazIWtfg1Ufe4pMW4pz30QJeeWhKq/205MPXZ/Pig69nrV+zohqAj8vnNB5GSxfLMxZ/voSZb8zK2sfKZSsBSNZHz89s5c3NfD37GMu/WB60eePjyO8tvzCfio8Wtmkckc1BLhOERZQ1X3tka9OWaYMOzMabWbmZlVdWRmyJt6CkT8/I8uIuHcgvzGer/ltknbbf0N50LemcJSrYeuCWQHAuIJvdDtmZoXsMylpf2jc6vnTDD9yRLfqVZK3vuXUPyo7cKWt9W2zVv5Tho7L3UdChAIDeQ7aOrE/UJ+m5dTd6DYquh+AQD4DFor767OXNbbtL/6x1Hbt2BKDf9r0j+6urqW9xXopsbnKZICqAvmnv+wALsrUxszjQFVjWxmkBcPcJ7l7m7mWlpaXrFOBpvz6uccXUoLC4kHEXj8XM+PmdP8w67c/v/CGFHQoZ/YNDKSwuaNZHAadcfhwAp/zqe9EdGHzvwqPY88jd6NitOLLJOX85HYD+O/aNrM+Lx+i17dZc+fglWeP8zaMX0a1bt6z1DSv3w844IGub66dexYjDhpNflB9Zf+rlwWc8+f+OiZwXh5yyPx27duSa536VdYxf3H0+APsft3dk/YEnjMw6bbot+pXSZ2ivyLrTrjgegOMuGkNBs89S0KGAvb6zOyW9erRpHJHNQS4TxBRgsJkNMLMCYBzQ/EDyJOC08PWxwHMenDWfBIwzs0IzGwAMBt5a3wEedOK3OOevp9G1pDPx/Dw6di3m5F8dw3E/OwqAvkN6ceHt5xLLW7u1mZefx+8ev5gO4RVI/++akxl7wZF06FREXn4ePXt158LbzqXs0F0AOPGXx7DfsXs1GTcvHuOW6X9ufH/r+39lqwFr91byC+P86MbxjVcx3TL9L5T07t6kj3hBHvdX3g5AyVbd+ekt38/4fOP/eAr9hgZH5h6ruiej3vKM/665F4Cf3fbDyBXr+defSVFREQB3ffz3jGR2xFkHcdzPg1NLO+67PZfe+2O26FdCXn4ehcWFjP7+IZz/j7MAKC4u5md3nJsxxhFnjWLfo/cE4JJ//oh9jt5j7T6kwcixI/jFXednTJfNjVOvZtCu/Rvfx/JinHDpWI465zAABg0fwBWP/IJeg7YiL55HQVE+B5+yHxff3fYxRDYHObuKCcDMjgSuJbjM9XZ3/52ZXQGUu/skMysC7gF2JdhzGOfuc8JpfwmcCSSAH7v7k62Nt65XMTVIpVJUr6qmqFMReXnRh4QWfV5JfkGcHlt1j6xPJpLUrKmhuEtx4yWdzX00bQ49enWnJEsfVaurqVpZnXUrtr6+nvdfmcXA3QbQNTxc0tz82QtJJlONiaG5xV8sZ8pj5Yz83h5Z9yxefPA1tuhXwvYjhkTWr16xmuWLVtJ3SPSWurtTtaqaouJC8uLR8/PzWfOpXlnF0D0GR9YnEgmWLVhOj17dice/2tXYdTV1rFiyip69ukdevtoQZ2GHAuL5Ob3iW2Sj1dJVTDlNEBvaV00QIiKbq/a6zFVERL7BlCBERCSSEoSIiERSghARkUhKECIiEkkJQkREIm1Sl7maWSXwWTuGUAIsacfx20pxrn/flFgV5/q1KcS5jbtH3oZik0oQ7c3MyrNdT7wxUZzr3zclVsW5fm3qceoQk4iIRFKCEBGRSEoQ69eE1ptsFBTn+vdNiVVxrl+bdJw6ByEiIpG0ByEiIpGUIEREJJISxFdkZnlm9raZPR5Rd7qZVZrZO+HP2e0U46dm9l4YQ8Z90C1wnZnNNrPpZrbbRhrnAWa2Im1+XtZOcXYzswfN7EMzm2lmezer31jmZ2txbizzc2haDO+Y2Uoz+3GzNu0+T9sY58YyT39iZjPM7H0zmxg+cye9vtDM7g/n55tm1r+l/vSUlK/uR8BMoEuW+vvd/bwNGE82B7p7tn+QOYLgaX2DgT2BG8Pf7aGlOAFedvfRGyyaaH8DnnL3Y8OnJDZ/VuzGMj9bixM2gvnp7rOA4RBscAHzgYebNWv3edrGOKGd56mZ9QYuAIa5e7WZPUDwJM8705qdBSx390FmNg64Gjg+W5/ag/gKzKwP8G3g1vaO5WsaA9ztgTeAbma2dXsHtTEysy7AfsBtAO5e5+5fNmvW7vOzjXFujEYBn7h78zshtPs8bSZbnBuLONDBzOIEGwYLmtWPAe4KXz8IjLJsj8BECeKruha4CEi10OaYcJf4QTPru4Hias6BZ8xsqpmNj6jvDcxLe18Rlm1orcUJsLeZvWtmT5rZDhsyuNBAoBK4Izy0eKuZNX/u68YwP9sSJ7T//GxuHDAxonxjmKfpssUJ7TxP3X0+8Cfgc2AhsMLdn2nWrHF+unsCWAH0zNanEsQ6MrPRwGJ3n9pCs8eA/u6+MzCZtRl7Qxvp7rsR7Kb/0Mz2a1YfteXQHtc9txbnNIL7xewC/B14ZEMHSLBlthtwo7vvCqwBLm7WZmOYn22Jc2OYn43Cw2BHAf+Oqo4oa5dr81uJs93nqZl1J9hDGAD0Ajqa2cnNm0VMmnV+KkGsu5HAUWb2KXAfcJCZ/TO9gbsvdffa8O0twO4bNsTGOBaEvxcTHDMd0axJBZC+d9OHzF3SnGstTndf6e6rw9dPAPlmVrKBw6wAKtz9zfD9gwQr4uZt2nt+thrnRjI/0x0BTHP3RRF1G8M8bZA1zo1knh4MzHX3SnevBx4C9mnWpnF+hoehugLLsnWoBLGO3P0Sd+/j7v0Jdjefc/cmWbrZMdKjCE5mb1Bm1tHMOje8Bg4F3m/WbBJwanilyF4Eu6QLN7Y4zWyrhuOkZjaCYLlduiHjdPcvgHlmNjQsGgV80KxZu8/PtsS5MczPZk4g+2Gbdp+nabLGuZHM08+BvcysOIxlFJnrnknAaeHrYwnWX1n3IHQV03piZlcA5e4+CbjAzI4CEgTZ+fR2CGlL4OFwmY0D/3L3p8zsBwDufhPwBHAkMBuoAs7YSOM8FjjHzBJANTCupYU6h84H7g0PNcwBztgI52db4txY5idmVgwcAnw/rWyjm6dtiLPd56m7v2lmDxIc7koAbwMTmq2bbgPuMbPZBOumcS31qVttiIhIJB1iEhGRSEoQIiISSQlCREQiKUGIiEgkJQgREYmkBCGbBTP7ZXiXy+nh3TbX6w3fLLibZ9SdfSPL1/PYl6a97m9mzf/fReQrUYKQTZ4Ft7seDewW3v7kYJre3+eb7tLWm4isOyUI2RxsDSxpuP2Juy9puL2Hme1uZi+GNwp8uuG/4M3sBTO71sxes+De+iPC8hFh2dvh76FZR21BK+NebWZvmdlHZvatsLzYzB4I94Dut+Be/mVmdhXB3TvfMbN7w+7zzOyWcI/pGTPr8LXmnmy2lCBkc/AM0Ddc4d5gZvsDmFk+wY3VjnX33YHbgd+lTdfR3fcBzg3rAD4E9gtvhHcZ8Pt1DaYN48bdfQTwY+DysOxcgvv47wxcSXh/L3e/GKh29+HuflLYdjBwvbvvAHwJHLOuMYqAbrUhmwF3X21muwPfAg4E7jezi4FyYEfg2fBWH3kEt0luMDGc/iUz62Jm3YDOwF1mNpjgLpj5XyGkoa2M+1D4eyrQP3y9L8GDgHD3981segv9z3X3dyL6EFknShCyWXD3JPAC8IKZvUdww7KpwAx33zvbZBHvrwSed/exFjyu8YWvEI61Mm7DnYCTrP0bzfpQlxamb+hDh5jkK9EhJtnkWfBM4cFpRcOBz4BZQGl4Ehszy7emD3o5Pizfl+AuoisIbo88P6w//SuG1Nq4UV4BjgvbDwN2SqurDw9biaxX2oOQzUEn4O/hIaIEwZ1Bx7t7nZkdC1xnZl0J/h6uBWaE0y03s9cInjt+Zlh2DcEhpp8Cz7Vx/FFmVpH2/nsEd//MNm6UG8JxpxPcpXM6wdPAACYA081sGvDLNsYk0irdzVUkgpm9APzM3cvbOxYAM8sD8t29xsy2Bf4HDHH3unYOTTZh2oMQ+WYoBp4PDyUZcI6Sg+Sa9iBERCSSTlKLiEgkJQgREYmkBCEiIpGUIEREJJIShIiIRPr/KbdfMO4PNnoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = dataframe[\"Target\"]    # Takes the targets and gets the unique values\n",
    "\n",
    "plt.scatter(dataframe[\"sepal length (cm)\"], dataframe[\"Target\"], c = label)  # c plots the different labels with different colours\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Type\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = dataframe[\"Target\"]\n",
    "plt.scatter(dataframe[\"sepal width (cm)\"], dataframe[\"Target\"], c = label)\n",
    "plt.xlabel(\"Sepal Width\")\n",
    "plt.ylabel(\"Type\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = dataframe[\"Target\"]\n",
    "plt.scatter(dataframe[\"petal length (cm)\"], dataframe[\"Target\"], c=label)\n",
    "plt.xlabel(\"Petal Length\")\n",
    "plt.ylabel(\"Type\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = dataframe[\"Target\"]    \n",
    "plt.scatter(dataframe[\"petal width (cm)\"], dataframe[\"Target\"], c=label)\n",
    "plt.xlabel(\"Petal Width\")\n",
    "plt.ylabel(\"Type\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    50\n",
       "1    50\n",
       "0    50\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[\"Target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f44c3612f28>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe[\"Target\"].value_counts().plot(kind=\"pie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f44c35dc438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c35874e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c3538a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c34f5080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c34a7630>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f44c34d9be0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c34971d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c34477b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c34477f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c33ba2e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f44c3368898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c339ae48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c335b438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c33089e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c32bdf98>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f44c327a588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c322bb38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c31e9128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c321b6d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c31cdc88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f44c318b278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c313c828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c30eddd8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c30aa3c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f44c30db978>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(dataframe, c=dataframe[\"Target\"], figsize=(15, 15), marker=\"o\", hist_kwds={\"bins\": 60})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(probability=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris.data     # dataframe.iloc[:, 0:3]\n",
    "y = iris.target   # dataframe.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTrain, XTest, YTrain, YTest = train_test_split(x, y, test_size=0.3, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aditya/miniconda3/envs/ML_Workshop/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=True, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(XTrain, YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 1, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
       "       2, 0, 1, 0, 1, 1, 1, 2, 1, 1, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 1, 0,\n",
       "       1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict(XTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 1, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
       "       2, 0, 1, 0, 1, 1, 1, 2, 1, 1, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 1, 0,\n",
       "       1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.9809523809523809)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(svc.predict(XTest), YTest), accuracy_score(svc.predict(XTrain), YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict([[5.2, 3.5, 0.5, 0.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc.probability = True   # Stores the probabilities of of x being of a part of a class/target. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01100175, 0.95643015, 0.0325681 ],\n",
       "       [0.01201132, 0.04337485, 0.94461383],\n",
       "       [0.95701602, 0.0228574 , 0.02012658],\n",
       "       [0.01034921, 0.91502777, 0.07462302],\n",
       "       [0.95868793, 0.02160393, 0.01970814],\n",
       "       [0.01609419, 0.92754073, 0.05636508],\n",
       "       [0.01425052, 0.590661  , 0.39508848],\n",
       "       [0.00891601, 0.98272046, 0.00836353],\n",
       "       [0.95807087, 0.0226011 , 0.01932803],\n",
       "       [0.00925946, 0.90295776, 0.08778279],\n",
       "       [0.01099735, 0.80640414, 0.18259851],\n",
       "       [0.01093705, 0.04146234, 0.94760061],\n",
       "       [0.0079025 , 0.94983864, 0.04225886],\n",
       "       [0.94575074, 0.02944091, 0.02480835],\n",
       "       [0.96148066, 0.02016558, 0.01835376],\n",
       "       [0.01227309, 0.02406431, 0.9636626 ],\n",
       "       [0.0141012 , 0.87652411, 0.10937469],\n",
       "       [0.93904392, 0.03170342, 0.02925266],\n",
       "       [0.96042663, 0.0211587 , 0.01841467],\n",
       "       [0.95743217, 0.02095356, 0.02161427],\n",
       "       [0.01173991, 0.01390492, 0.97435517],\n",
       "       [0.01210796, 0.0062823 , 0.98160974],\n",
       "       [0.01344248, 0.41867365, 0.56788387],\n",
       "       [0.93013406, 0.0434817 , 0.02638424],\n",
       "       [0.00938065, 0.96074771, 0.02987164],\n",
       "       [0.95591291, 0.02240241, 0.02168468],\n",
       "       [0.00968045, 0.972383  , 0.01793655],\n",
       "       [0.00894348, 0.90911209, 0.08194443],\n",
       "       [0.01450276, 0.90470695, 0.08079029],\n",
       "       [0.02115917, 0.02975298, 0.94908785],\n",
       "       [0.01214136, 0.96225144, 0.0256072 ],\n",
       "       [0.00938006, 0.97795301, 0.01266693],\n",
       "       [0.01543741, 0.07413877, 0.91042383],\n",
       "       [0.01372503, 0.01052379, 0.97575119],\n",
       "       [0.01398631, 0.13530698, 0.85070671],\n",
       "       [0.95207026, 0.02514902, 0.02278071],\n",
       "       [0.01206758, 0.01291737, 0.97501504],\n",
       "       [0.01261388, 0.01127019, 0.97611593],\n",
       "       [0.02761131, 0.03199784, 0.94039084],\n",
       "       [0.01768777, 0.0830496 , 0.89926263],\n",
       "       [0.95222943, 0.02748962, 0.02028094],\n",
       "       [0.94779167, 0.03035735, 0.02185098],\n",
       "       [0.00951758, 0.93393609, 0.05654633],\n",
       "       [0.85117169, 0.08451489, 0.06431341],\n",
       "       [0.01373604, 0.82448995, 0.161774  ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict_proba(XTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(probability=True, kernel=\"poly\", degree=10, gamma=200, C=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = iris.data     # dataframe.iloc[:, 0:3]\n",
    "y = iris.target   # dataframe.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTrain, XTest, YTrain, YTest = train_test_split(x, y, test_size=0.3, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=10, gamma=200, kernel='poly',\n",
       "    max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(XTrain, YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 2, 2, 2, 2,\n",
       "       2])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict(XTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 1, 0, 1, 1, 1, 0, 1, 1, 2, 1, 0, 0, 2, 1, 0, 0, 0, 2, 2,\n",
       "       2, 0, 1, 0, 1, 1, 1, 2, 1, 1, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 1, 0,\n",
       "       1])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict([[5.2, 3.5, 0.5, 0.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00],\n",
       "       [2.44186112e-14, 2.93023329e-14, 1.00000000e+00]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.predict_proba(XTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3111111111111111, 0.34285714285714286)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(svc.predict(XTest), YTest), accuracy_score(svc.predict(XTrain), YTrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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