support_vector_machine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from sklearn.datasets import load_iris\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [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",
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       "        [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]]),\n",
       " '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]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " '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 ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'iris.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_module',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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",
       "      <th>flower_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\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",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\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",
       "..                 ...               ...                ...               ...   \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 flower_name  \n",
       "15        0      setosa  \n",
       "16        0      setosa  \n",
       "17        0      setosa  \n",
       "18        0      setosa  \n",
       "19        0      setosa  \n",
       "..      ...         ...  \n",
       "145       2   virginica  \n",
       "146       2   virginica  \n",
       "147       2   virginica  \n",
       "148       2   virginica  \n",
       "149       2   virginica  \n",
       "\n",
       "[135 rows x 6 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
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       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\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]"
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     "execution_count": 42,
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       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
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       "2                  4.7               3.2                1.3               0.2   \n",
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       "\n",
       "     target  \n",
       "0         0  \n",
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       "..      ...  \n",
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    "df"
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   "execution_count": 44,
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      "text/plain": [
       "sepal length (cm)  4.3  4.4  4.5  4.6  4.7  4.8  4.9  5.0  5.1  5.2  ...  6.8  \\\n",
       "target                                                               ...        \n",
       "0                    1    3    1    4    2    5    4    8    8    3  ...    0   \n",
       "1                    0    0    0    0    0    0    1    2    1    1  ...    1   \n",
       "2                    0    0    0    0    0    0    1    0    0    0  ...    2   \n",
       "\n",
       "sepal length (cm)  6.9  7.0  7.1  7.2  7.3  7.4  7.6  7.7  7.9  \n",
       "target                                                          \n",
       "0                    0    0    0    0    0    0    0    0    0  \n",
       "1                    1    1    0    0    0    0    0    0    0  \n",
       "2                    3    0    1    3    1    1    1    4    1  \n",
       "\n",
       "[3 rows x 35 columns]"
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     },
     "execution_count": 44,
     "metadata": {},
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   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "np.unique(iris.target)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
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       "    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\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",
       "\n",
       "    target  \n",
       "50       1  \n",
       "51       1  \n",
       "52       1  \n",
       "53       1  \n",
       "54       1  "
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     "execution_count": 47,
     "metadata": {},
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    "df[df.target==1].head()"
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   "execution_count": 48,
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   "outputs": [
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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",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\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",
       "      <th>101</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\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",
       "      <th>103</th>\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",
       "      <th>104</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\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",
       "\n",
       "     target  \n",
       "100       2  \n",
       "101       2  \n",
       "102       2  \n",
       "103       2  \n",
       "104       2  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==2].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['flower_name']=df.target.apply(lambda x: iris.target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
       "      <th>flower_name</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 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",
       "..                 ...               ...                ...               ...   \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 flower_name  \n",
       "0         0      setosa  \n",
       "1         0      setosa  \n",
       "2         0      setosa  \n",
       "3         0      setosa  \n",
       "4         0      setosa  \n",
       "..      ...         ...  \n",
       "145       2   virginica  \n",
       "146       2   virginica  \n",
       "147       2   virginica  \n",
       "148       2   virginica  \n",
       "149       2   virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0=df[df.target==0]\n",
    "df1=df[df.target==1]\n",
    "df2=df[df.target==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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",
       "      <th>flower_name</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</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",
       "      <td>0</td>\n",
       "      <td>setosa</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 flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25fc9676a90>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"sepal length (cm)\")\n",
    "plt.ylabel(\"sepal width (cm)\")\n",
    "plt.scatter(df0['sepal length (cm)'],df0['sepal width (cm)'],color='r',marker='*')\n",
    "plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'],color='b',marker='^')\n",
    "plt.scatter(df2['sepal length (cm)'],df2['sepal width (cm)'],color='g',marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25fc95bb190>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"petal length (cm)\")\n",
    "plt.ylabel(\"petal width (cm)\")\n",
    "plt.scatter(df0['petal length (cm)'],df0['petal width (cm)'],color='r',marker='*')\n",
    "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='b',marker='^')\n",
    "plt.scatter(df2['petal length (cm)'],df2['petal width (cm)'],color='g',marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "..                 ...               ...                ...               ...\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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.drop(['target','flower_name'],axis=1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "145    2\n",
       "146    2\n",
       "147    2\n",
       "148    2\n",
       "149    2\n",
       "Name: target, Length: 150, dtype: int32"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.target\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</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>61</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</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>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</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>93</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</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>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</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>43</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</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>62</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "15                 5.7               4.4                1.5               0.4\n",
       "65                 6.7               3.1                4.4               1.4\n",
       "128                6.4               2.8                5.6               2.1\n",
       "61                 5.9               3.0                4.2               1.5\n",
       "94                 5.6               2.7                4.2               1.3\n",
       "106                4.9               2.5                4.5               1.7\n",
       "133                6.3               2.8                5.1               1.5\n",
       "111                6.4               2.7                5.3               1.9\n",
       "30                 4.8               3.1                1.6               0.2\n",
       "49                 5.0               3.3                1.4               0.2\n",
       "120                6.9               3.2                5.7               2.3\n",
       "93                 5.0               2.3                3.3               1.0\n",
       "110                6.5               3.2                5.1               2.0\n",
       "95                 5.7               3.0                4.2               1.2\n",
       "113                5.7               2.5                5.0               2.0\n",
       "78                 6.0               2.9                4.5               1.5\n",
       "98                 5.1               2.5                3.0               1.1\n",
       "77                 6.7               3.0                5.0               1.7\n",
       "66                 5.6               3.0                4.5               1.5\n",
       "36                 5.5               3.5                1.3               0.2\n",
       "125                7.2               3.2                6.0               1.8\n",
       "117                7.7               3.8                6.7               2.2\n",
       "48                 5.3               3.7                1.5               0.2\n",
       "143                6.8               3.2                5.9               2.3\n",
       "43                 5.0               3.5                1.6               0.6\n",
       "58                 6.6               2.9                4.6               1.3\n",
       "121                5.6               2.8                4.9               2.0\n",
       "62                 6.0               2.2                4.0               1.0\n",
       "57                 4.9               2.4                3.3               1.0\n",
       "28                 5.2               3.4                1.4               0.2"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model=SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=x_test.values\n",
    "x_train=x_train.values\n",
    "y_test=y_test.values\n",
    "y_train=y_train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[5.7,4.4,1.5,0.4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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