Support Vector Machine (SVM).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8adcadde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "413e1434",
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23a06332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6850b935",
   "metadata": {},
   "outputs": [
    {
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       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
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       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
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       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [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",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1336f2e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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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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       "   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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2cd31279",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
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       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d42a7c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9c27d44",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
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       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
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       "      <td>6.4</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>54</th>\n",
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       "      <td>4.6</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "    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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "869d2e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==2].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "434252e6",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flower_name'] = df.target.apply(lambda x : iris.target_names[x])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da0f586e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5737d83d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4556794c",
   "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": 14,
   "id": "e3ab167a",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7a112fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    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 flower_name  \n",
       "50       1  versicolor  \n",
       "51       1  versicolor  \n",
       "52       1  versicolor  \n",
       "53       1  versicolor  \n",
       "54       1  versicolor  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "31914de4",
   "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>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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</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",
       "      <td>virginica</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 flower_name  \n",
       "100       2   virginica  \n",
       "101       2   virginica  \n",
       "102       2   virginica  \n",
       "103       2   virginica  \n",
       "104       2   virginica  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69c6fecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'sepal width (cm)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEJCAYAAACZjSCSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcV0lEQVR4nO3dfZRkVXnv8e8vMyOQ8DI3MMsAA44R44oxCEMHHUGnARMQWKAZCCTRZNDcIS55u0aNeBNwESIQk/gCCaQFBaNXkRlkjYjIBBjAMCI9AwxvgujlNdzQvA2gMDjDc/84p6C66K6u01276pw6v89avbrOqV2nnn1qdT+1z977bEUEZmZWX7/S7wDMzKy/nAjMzGrOicDMrOacCMzMas6JwMys5pwIzMxqLnkikDRL0i2SLp/guaWSxiTdmv/8Rep4zMxsvNk9eI8TgbuBbSd5/uKIOK4HcZiZ2QSSJgJJ84FDgL8HPtqNY+6www6xYMGCbhzKzKw21q5d+3hEzJvoudQtgs8DnwC2aVNmiaR3AfcC/ysiHmp3wAULFjA6Otq9CM3MakDSA5M9l6yPQNKhwGMRsbZNse8ACyJid2AVcNEkx1omaVTS6NjYWIJozczqK2Vn8T7AYZLuB74J7C/pa80FIuKJiNiYb54P7DXRgSJiJCKGImJo3rwJWzZmZjZNyRJBRJwcEfMjYgFwNHBNRLy/uYykHZs2DyPrVDYzsx7qxaihcSSdBoxGxErgBEmHAZuAJ4GlvY7HzKzuVLXbUA8NDYU7i83MipG0NiKGJnrOM4vNEhq+cJjhC4f7HYZZW04EZmY11/M+ArM6aLQCrnvgunHbq5eu7k9AZm24RWBmVnNuEZgl0Pjm75aAVYFbBGZmNecWgVlCbglYFbhFYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGCGF5CxenMiMDOrOd90zmrNC8iYuUVgZlZ7bhFYrXkBGTO3CMzMas8tAjPcErB6c4vAzKzmnAisrzx+36z/nAjMzGrOfQTWFx6/b1YebhGYmdWcWwTWFx6/b1YebhGYmdWcWwTWV24JmPVf8haBpFmSbpF0+QTPbSHpYkn3SbpJ0oLU8ZiZ2Xi9uDR0InD3JM99CHgqInYDPgec1YN4zErL8yqsH5ImAknzgUOA8ycpcjhwUf54OXCAJKWMyczMxkvdR/B54BPANpM8vzPwEEBEbJK0AdgeeDxxXGal4nkV1k/JWgSSDgUei4i1XTjWMkmjkkbHxsa6EJ2ZmTWkbBHsAxwm6WBgS2BbSV+LiPc3lXkE2AV4WNJsYDvgidYDRcQIMAIwNDQUCWM26wvPq7B+StYiiIiTI2J+RCwAjgauaUkCACuBP88fH5GX8T96M7Me6vk8AkmnAaMRsRK4APh3SfcBT5IlDLPackvA+qEniSAiVgOr88enNO1/ATiyFzGYmdnEfIsJG0hzz5zL3DPn9jsMs0pwIjAzqznfa8gGSqMVsGHjhnHbT3/y6f4EZFYBbhGYmdWcWwQ2UBrf/N0SMOucWwRmZjXnFoENJLcEzDrnFoGZWc05EZiZ1ZwTgXXd7NNmM/s0X3UELzRj1eBEYGZWc/7aZl3TaAVsjs3jtjedsqlvMfWLF5qxKnGLwMys5twisK5pfPOvc0ugwQvNWJW4RWBmVnNuEVjX1bkl0MotAasCtwjMzGrOicC6LtXY+SLH9fh9s845EZiZ1Zz7CKxrUo2dL3Jcj983K84tAjOzmlNEtC8gDQHvBHYCngfuAFZFxFPpw3u1oaGhGB0d7cdbW4dSfQsvcly3BMzGk7Q2IoYmem7SFoGkYyStA04GtgLuAR4D9gX+Q9JFknZNEbCZmfXOpC0CSR8BvhwRz0/y/B7A9hFxdbrwXs0tAjOz4tq1CCbtLI6If2l30Ii4dYZxmZlZCUw5akjS64HjgQXN5SPisHRhDa6yXLv29XYza+hk+OhlwAXAd4CXkkZjZmY910kieCEivpg8kgFXlvHtHpNvZq06SQRfkHQqcBWwsbEzItYli8rMzHqmk3kEZwAfAH7KK5eGIiL2TxzbhKo+aqgs36rdR2BWL9MaNdTkSOA3I+LF7oZlZmZl0EmL4DJgWUQ81pOIplD1FoGZWT/MtEUwF/ixpJsZ30fg4aNmZgOgk0Rw6nQOLGlL4Hpgi/x9lkfEqS1llgKfBR7Jd50TEedP5/0snblnzgXg6U8+3fXyZel/KEscZv3QSSJ4EHg0Il4AkLQV8NoOXrcR2D8inpM0B/iBpO9FxA9byl0cEccVitrMzLqmk0RwCfCOpu3N+b7fa/eiyDofnss35+Q/7TskrFQa3+w3bNwwbnuyb/pFypdljkJZ4jDrp07WI5jdPGIof/yaTg4uaZakW8nuWroqIm6aoNgSSeslLZe0yyTHWSZpVNLo2NhYJ29tZmYd6mTU0Crg7IhYmW8fDpwQEQd0/CbSXODbwPERcUfT/u2B5yJio6RjgaOmmp/gUUO95z4Cs+qb1noETf4S+JSkByU9CPw1sKxIABHxNHAtcFDL/iciojES6XxgryLHNTOzmZuyRfByQWlrgIh4bqqyefl5wC8j4um8g/kq4KyIuLypzI4R8Wj++H3AX0fE29sd1y0CM7PiprtC2fslvfx8RDzXnAQkvUHSvm3ed0fgWknrgZvJ+ggul3SapMYchBMk3SnpNuAEYGnn1TIzs25oN2poe+AWSWuBtcAYsCWwG7AYeBz45GQvjoj1wJ4T7D+l6fHJZEthmplZn7RboewLks4B9gf2AXYnW7z+buADEfFgb0IcLCk7JYt26qY6bhluaJfqXJgNorbzCCJiM7Aq/zGzilqzBlavhuFhWLSo39FY2XTcWVwWVe0sbp24tPh1i4HufBNunci13RbbATP/Nlz0uEXqmOp8pDoXVbZmDRxwALz4IrzmNXD11U4GdTTT4aNmVmGrV2dJYPPm7Pfq1f2OyMrGLYIecx/B9MsW4T6CV7hFYDDD21BL2gJYAixoLh8Rp3UrQDNLZ9Gi7J+/+whsMp3cYuJKYAPZENLNjf0R8U9pQ5tY1VsEZmb9MNOFaeZHxEFTFzMzsyrqpLP4Rkm/mzwSK63hC4dfvpaforyZ9dekLQJJt5OtHzAbOEbSz8gWmxHZcgO79yZEMxsUns9QTu0uDR3asyislIou2uJFXqwdj14qr0kvDUXEAxHxAHB643Hzvt6FaGaDwPMZyquTzuLfad6QNAuvG1ALjW/ynX6zL1re6mV4OGsJNFoEw8P9jsga2vURnAx8CthK0jON3cCLwEgPYjOzAeL5DOXVyTyCM/LbRZeC5xGYmRU3rXkEkhbmDy9pevyyiFjXpfjMzKyP2vURNGYObwkMAbeRXRraHRgFBrphl+o6d9HjluGeOb7mbzbY2o0a2i8i9gMeBRZGxFBE7EW26tgjvQrQzCa2Zg2ccUb2exANev2KSnk+Ohk19KaIuL2xERF3SPrt7odSDqnGwhc9but99fvRMvC8gPIa9DH5g16/olKfj05uMbFe0vmShvOfLwHruxeCmRU16GPyB71+RaU+H520CI4BPgycmG9fD5zb3TDKI9VY+KLHbXzz72cfgecFlNegj8kf9PoVlfp8TJkIIuIF4HP5j5mVwKCPyR/0+hWV+nxMOo9A0rci4o+abj43Tr9uOud5BGZmxU13PYLGpSDffM7MbIBNmggi4tH84buB6yPiJ70JabClvN5e5NhlmJ9gZuXQyaihXYF/k/QzSZdIOl7SHonjMjMrhZEROPDA7He3lWWuRCedxacCSNoK+J/Ax4HPA7OSRjZgUo7JL3LsMsxPMKuKkRE49tjs8VVXZb+XLevOscs0V2LKFoGkv5H0PeAqYDfgY8D81IGZmfXbihXtt2eiTHMlOplH8IfAJuC7wHXAmojYmDSqAZRyTH6RY5dhfoJZVSxZ8kpLoLHdLWWaK9HJpaGFkrYF9gF+HxiR9FhE7Js8OjOzPmpcBlqxIksC3bosBOWaK9HJegRvAd4JLCa7C+lDwA0RcUr68F7N8wjMzIqb7jyChjPJbivxReDmiPhlN4MzM7P+6uTS0LQmlEnakiyBbJG/z/LGCKSmMlsAXyVbA/kJ4KiIuH8672dmZtPTyTyC6doI7B8RbwX2AA6S9PaWMh8CnoqI3cjuZXRWqmCGLxx+uTM1RfkymHvm3Jc7gadSpH5VPBdm1rlkiSAyz+Wbc/Kf1g6Jw4GL8sfLgQMkKVVMVh8pJ+oUmWCUMo5Uxy7LJKeUitSxDuejkz6CaZM0C1hLNv/gXyLippYiO5N1PhMRmyRtALYHHu9WDEUnclVxMZYik8SK1K+K5wLSTtQpMsEoZRypjl2mSU6pFKljHc4HtGkRSPqOpJWT/XRy8IjYHBF7kE1A2zsfgVSYpGWSRiWNjo2NTecQViMpJ+oUmWCUMo5Uxy7TJKdUitSxDucD2rcI/rFbbxIRT0u6FjgIuKPpqUeAXYCHJc0GtiPrNG59/QgwAtnw0SLvXXQiVxUXYykySaxI/ap4LiDtRJ0iE4xSxpHq2GWa5JRKkTrW4XxA+7uPXjeTA0uaB/wyTwJbkU1Ga+0MXgn8ObAGOAK4Jqaa2GA2hZQTdYpMMEoZR6pjl2mSUypF6liH8wGdTSh7I3AG8GZgy8b+iPjNKV63O1lH8CyyS1DfiojTJJ0GjEbEynyI6b8DewJPAkdHxM/aHdcTyszMipvphLKvAKeSDe/cj2wN4ylHG0XEerJ/8K37T2l6/AJwZAcxmJlZIp0MH90qIq4maz08EBGfBg5JG1b/eey8mdVFJ4lgo6RfAX4i6ThJ7wO2ThyXWWlVcQx6ypirOJ+hLJ9LaURE2x/g98j+8c8nu0x0KfD2qV6X6mevvfaKlBZ/ZXEs/sri4NMEn+blbbOIiBtvjNhqq4hZs7LfN97YnbIppYw5VR1TnruyfC69RtY3O+H/1U6u9d8c2QzhZ4ATIuIPI+KH6VKTWXlVcQx6ypirOJ+hLJ9LmUzZWSxpiKwlsE2+vQH4YESsTRxbX1R17Lz1RhXHoKeMuYrzGcryuZRJJ8NH1wMfiYgb8u19gX+NiN17EN+r9Gr4qBOBTWbNms7HlRcpm1LKmFPVMeW5K8vn0kvtho92kghuiYg9W/ati4iFXYyxY55HYGZW3EznEVwn6d+Ab5DdPfQoYLWkhQARsa5rkZqZWc91kgjemv8+tWX/nmSJYf+uRmRmZj3VyQpl+/UiELNBNDKSZuHzlKoYc1mu+ZcljqI6GTX0WuAzwE4R8R5JbwYWRcQFyaMzq7AiaxeURRVjLsuaAWWJYzo6mVl8IfB9YKd8+17gpETxmA2MImsXlEUVYy7LvICyxDEdnSSCHSLiW8BLkK0kBmxOGpXZAGhdq6Dd2gVlUcWYG/MCZs0qx3yNfscxHZ10Fv9c0vbk6w3nC9BvSBqV2QAosnZBWVQx5rKsGVCWOKajk3kEC4GzgbeQrS42DzgisttM95znEZiZFTejeQQRsU7SYuBNgIB7IuKXXY7RzMz6ZMo+AklHkq1JcCfwXuDixmQyMzOrvk46i/82Ip7N7zF0AHABcG7asKwuqnhf+JQxj4zAgQdmv7upiufZeqeTzuLGCKFDgC9FxHclnZ4wJquJKo67ThlzqjH8VTzP1ludtAgeye81dBRwhaQtOnydWVtVHHedMuZUY/ireJ6ttzr5h/5HZBPKDoyIp4FfBz6eMiirhyqOu04Zc6ox/FU8z9ZbUw4fLRsPHx0sVbw3S8qYU93np4rn2bprRusRlI0TgZlZce0Sga/1m5nVnBOBmVnNORGYkW6cfdHjery/9UMn8wjMBlqqcfZFj+vx/tYvbhFY7aUaZ1/0uB7vb/3iRGC1l2qcfdHjery/9YsvDVntpbqPfNHjVvl+9lZtnkdgZlYDnkdgZmaTSpYIJO0i6VpJd0m6U9KJE5QZlrRB0q35zymp4jEzs4ml7CPYBPxVvsLZNsBaSasi4q6WcjdExKEJ4zAzszaStQgi4tGIWJc/fha4G9g51ftZeVRxUlSRmKtYv7LwuSunnowakrQA2BO4aYKnF0m6Dfgv4GP5kphWUVWcFFUk5irWryx87soreWexpK2BFcBJEfFMy9PrgNdFxFuBs4HLJjnGMkmjkkbHxsaSxmszU8VJUUVirmL9ysLnrrySJgJJc8iSwNcj4tLW5yPimYh4Ln98BTBH0g4TlBuJiKGIGJo3b17KkG2GqjgpqkjMVaxfWfjclVeyeQSSBFwEPBkRJ01S5jeA/46IkLQ3sJyshTBpUJ5HUH5VXASlSMxVrF9Z+Nz1T18WppG0L3ADcDvwUr77U8CuABFxnqTjgA+TjTB6HvhoRNzY7rhOBGZmxbVLBMk6iyPiB4CmKHMOcE6qGMzMbGqeWWxmVnNOBDXl8dzjjYzAgQdmv83qxncfrSGP5x5vZASOPTZ7fNVV2e9ly/oXj1mvuUVQQx7PPd6KFe23zQadE0ENeTz3eEuWtN82G3S+NFRDXgBlvMZloBUrsiTgy0JWN16YxsysBrwwjZmZTcqJoAuGLxxm+MLhfodhZjYtTgQ2pTrMOahDHcvA57mc3Fk8A41WwHUPXDdue/XS1f0JKIE6zDmoQx3LwOe5vNwisLbqMOegDnUsA5/n8nKLYAYa3/wHsSXQ0Jhz0PgWN4hzDupQxzLweS4vJwJrqw5zDupQxzLweS4vzyMwM6sBzyMwM7NJORGYmdWcE4FZDaQcv++5AdXnzmKzAZdy/L7nBgwGtwjMBlzK8fueGzAYnAjMBlzK9Se8tsVg8KUhswGXcvy+5wYMBs8jMDOrAc8jMDOzSTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNJUsEknaRdK2kuyTdKenECcpI0hcl3SdpvaSFqeIxM7OJpbzX0CbgryJinaRtgLWSVkXEXU1l3gO8Mf95G3Bu/tvMzHokWYsgIh6NiHX542eBu4GdW4odDnw1Mj8E5kraMVVMNj1eeMRssPXk7qOSFgB7Aje1PLUz8FDT9sP5vkd7EZdNzQuPmA2+5J3FkrYGVgAnRcQz0zzGMkmjkkbHxsa6G6C15YVHzAZf0kQgaQ5ZEvh6RFw6QZFHgF2atufn+8aJiJGIGIqIoXnz5qUJ1ibkhUfMBl+yS0OSBFwA3B0R/zxJsZXAcZK+SdZJvCEifFmoRLzwiNngS9lHsA/wAeB2Sbfm+z4F7AoQEecBVwAHA/cBvwCOSRiPTdOiRU4AZoMsWSKIiB8AmqJMAB9JFYOZmU3NM4vNzGrOicDMrOacCMzMas6JwMys5pwIzMxqTtnAneqQNAY80O84JrAD8Hi/g0ho0OsHg19H16/6ZlLH10XEhDNyK5cIykrSaEQM9TuOVAa9fjD4dXT9qi9VHX1pyMys5pwIzMxqzomge0b6HUBig14/GPw6un7Vl6SO7iMwM6s5twjMzGrOiaAgSbMk3SLp8gmeWyppTNKt+c9f9CPGmZB0v6Tb8/hHJ3hekr4o6T5J6yUt7EecM9FBHYclbWj6HE/pR5zTJWmupOWSfizpbkmLWp6v9GfYQf2q/vm9qSn2WyU9I+mkljJd/Qx7slTlgDmRbP3lbSd5/uKIOK6H8aSwX0RMNlb5PcAb85+3Aefmv6umXR0BboiIQ3sWTXd9AbgyIo6Q9BrgV1uer/pnOFX9oMKfX0TcA+wB2RdPssW6vt1SrKufoVsEBUiaDxwCnN/vWProcOCrkfkhMFfSjv0OyjKStgPeRbYoFBHxYkQ83VKssp9hh/UbJAcAP42I1km0Xf0MnQiK+TzwCeClNmWW5E215ZJ2aVOurAK4StJaScsmeH5n4KGm7YfzfVUyVR0BFkm6TdL3JP1OL4ObodcDY8BX8kuY50v6tZYyVf4MO6kfVPfza3U08I0J9nf1M3Qi6JCkQ4HHImJtm2LfARZExO7AKuCingTXXftGxEKypudHJL2r3wElMFUd15FNx38rcDZwWY/jm4nZwELg3IjYE/g58Mn+htRVndSvyp/fy/LLXocBl6R+LyeCzu0DHCbpfuCbwP6SvtZcICKeiIiN+eb5wF69DXHmIuKR/PdjZNcl924p8gjQ3NKZn++rjKnqGBHPRMRz+eMrgDmSduh5oNPzMPBwRNyUby8n+8fZrMqf4ZT1q/jn1+w9wLqI+O8JnuvqZ+hE0KGIODki5kfEArLm2jUR8f7mMi3X6A4j61SuDEm/JmmbxmPgD4A7WoqtBP4sH7XwdmBDRDza41CnrZM6SvoNScof7032d/JEr2Odjoj4f8BDkt6U7zoAuKulWGU/w07qV+XPr8UfM/FlIejyZ+hRQzMk6TRgNCJWAidIOgzYBDwJLO1nbNPwWuDb+d/QbOD/RMSVkv4SICLOA64ADgbuA34BHNOnWKerkzoeAXxY0ibgeeDoqNbMy+OBr+eXFn4GHDNgn+FU9av659f4kvL7wLFN+5J9hp5ZbGZWc740ZGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBFZr+Z0qJ7qT7IT7u/B+75X05qbt1ZKmXINW0o7diEfSPElXzvQ4NlicCMx6673Am6cqNIGPAl+a6ZtHxBjwqKR9ZnosGxxOBFZq+Uzg7+Y3ELtD0lH5/r0kXZffOO77jVnd+TfsL+T3cb8jn1mKpL0lrclvVHZj08zUTmP4sqQf5a8/PN+/VNKlkq6U9BNJ/9D0mg9Jujd/zZcknSPpHWQzzj+bx/eGvPiRebl7Jb1zkjCWAFfmx54l6R/z+q2XdHy+/35JZ+THHpW0MD83P21MRspdBvxpp/W3weeZxVZ2BwH/FRGHQHYbYklzyG4mdnhEjOXJ4e+BD+av+dWI2CO/mdyXgbcAPwbeGRGbJL0b+AzZP9dO/G+yW4p8UNJc4EeS/iN/bg9gT2AjcI+ks4HNwN+S3QPnWeAa4LaIuFHSSuDyiFie1wdgdkTsLelg4FTg3c1vLun1wFNN97FaBiwA9sjr8+tNxR/M6/454EKye2RtSXYbjfPyMqPA6R3W3WrAicDK7nbgnySdRfYP9AZJbyH7574q/0c6C2i+z8o3ACLieknb5v+8twEukvRGsttQzykQwx+Q3XDwY/n2lsCu+eOrI2IDgKS7gNcBOwDXRcST+f5LgN9qc/xL899ryf7Bt9qR7NbLDe8GzouITXk9n2x6bmX++3Zg64h4FnhW0kZJc/N79z8G7NS2xlYrTgRWahFxr7Jl+A4GTpd0NdkdQ++MiEWTvWyC7b8Dro2I90laAKwuEIaAJfnKUa/slN5G1hJo2Mz0/qYax5js9c+TJZ8ix3qpJbaXmo69ZX5MM8B9BFZyknYCfhERXwM+S3a55R5gnvK1aiXN0fjFRxr9CPuS3ZVxA7Adr9ymd2nBML4PHC+9fEfLPacofzOwWNL/kDSb8ZegniVrnRRxL+NbCquAY/Nj03JpqBO/xavvKms15kRgZfe7ZNfkbyW7fn56RLxIdofJsyTdBtwKvKPpNS9IuoXsmviH8n3/AJyR7y/6rf3vyC4lrZd0Z749qXy9g88APwL+E7gf2JA//U3g43mn8xsmPsKrjvdz4KeSdst3nQ88mMdzG/AnxarDfsB3C77GBpjvPmoDRdJq4GMRMdrnOLaOiOfyb+3fBr4cEa0LkBc53vuAvSLib7oQ2/VkHe1PzfRYNhjcIjBL49N5K+YO4P8yw+US8yRy/0yDkjQP+GcnAWvmFoGZWc25RWBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjX3/wGnx6l8nvp/YQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'], color = 'green', marker = '+')\n",
    "plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'], color = 'blue', marker = '.')\n",
    "plt.xlabel('sepal length (cm)')\n",
    "plt.ylabel('sepal width (cm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c32b0365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'petal width (cm)')"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'], color = 'green', marker = '+')\n",
    "plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'], color = 'blue', marker = '.')\n",
    "plt.xlabel('petal length (cm)')\n",
    "plt.ylabel('petal width (cm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e5e53a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0213bbff",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['target', 'flower_name'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "36190b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6aafa237",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8e6aee0",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9741ad7c",
   "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": 25,
   "id": "66c4668f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27e19893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "581c497d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "107349fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c8994a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760d0779",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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