Support_Vector_Machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('iris.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(columns = ['Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-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>Iris-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>Iris-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>Iris-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>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   150 non-null    float64\n",
      " 4   Species        150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_var = data.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ['Species']\n",
    "x = [i for i in data_var if i not in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']\n",
      "['Species']\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data[x], data[y], test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah Training X 120\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah Training X\", X_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah Training Y 120\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah Training Y\", y_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah Test X 30\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah Test X\", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah Test Y 30\n"
     ]
    }
   ],
   "source": [
    "print(\"Jumlah Test Y\", y_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Training SVM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = svm.SVC(kernel = 'rbf', C=1, gamma = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, gamma=0.01)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_score = svm_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy is 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Accuracy is\", svm_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "support_vector = svm_model.support_vectors_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.7, 3.8, 1.7, 0.3],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [5.9, 3. , 5.1, 1.8],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6.7, 3.1, 5.6, 2.4]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "support_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearch digunakan untuk mencari model svm dengan score terbaik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model2 = svm.SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'C' : [0.1, 0.2, 0.5, 1], \n",
    "    'kernel' : ['linear', 'poly', 'rbf'], \n",
    "    'gamma' : [0.01, 0.1, 1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_grid = GridSearchCV(svm_model2, parameters, cv = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 0.2, 0.5, 1], 'gamma': [0.01, 0.1, 1],\n",
       "                         'kernel': ['linear', 'poly', 'rbf']})"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menentukan parameter terbaik untuk svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.5, 'gamma': 0.01, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melihat score terbaik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333334"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_grid.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Model Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: https://medium.com/swlh/visualizing-svm-with-python-4b4b238a7a92"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "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",
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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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</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>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "91             6.1           3.0            4.6           1.4\n",
       "135            7.7           3.0            6.1           2.3\n",
       "69             5.6           2.5            3.9           1.1\n",
       "128            6.4           2.8            5.6           2.1\n",
       "114            5.8           2.8            5.1           2.4"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Species\n",
       "91   Iris-versicolor\n",
       "135   Iris-virginica\n",
       "69   Iris-versicolor\n",
       "128   Iris-virginica\n",
       "114   Iris-virginica"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Take Two Features and Two Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = X_train.drop(['SepalLengthCm', 'SepalWidthCm'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PetalLengthCm  PetalWidthCm\n",
       "91             4.6           1.4\n",
       "135            6.1           2.3\n",
       "69             3.9           1.1\n",
       "128            5.6           2.1\n",
       "114            5.1           2.4"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['Species'] = y_train['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PetalLengthCm  PetalWidthCm          Species\n",
       "91             4.6           1.4  Iris-versicolor\n",
       "135            6.1           2.3   Iris-virginica\n",
       "69             3.9           1.1  Iris-versicolor\n",
       "128            5.6           2.1   Iris-virginica\n",
       "114            5.1           2.4   Iris-virginica"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's convert categorical values to numerical target\n",
    "data2 =data2.replace('Iris-setosa', 0)\n",
    "data2 =data2.replace('Iris-versicolor', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PetalLengthCm  PetalWidthCm         Species\n",
       "91             4.6           1.4               1\n",
       "135            6.1           2.3  Iris-virginica\n",
       "69             3.9           1.1               1\n",
       "128            5.6           2.1  Iris-virginica\n",
       "114            5.1           2.4  Iris-virginica"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PetalLengthCm  PetalWidthCm Species\n",
       "91            4.6           1.4       1\n",
       "69            3.9           1.1       1\n",
       "48            1.5           0.2       0\n",
       "53            4.0           1.3       1\n",
       "28            1.4           0.2       0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove 'Iris-virginica' class\n",
    "data2 = data2[(data2['Species'] != 'Iris-virginica')]\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split Data Again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20395f873d0>"
      ]
     },
     "execution_count": 102,
     "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": [
    "X2 = data2.iloc[:,0:2]\n",
    "y2 = data2['Species']\n",
    "plt.scatter(X2.iloc[:, 0], X2.iloc[:, 1], c=y2, s=50, cmap='autumn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train a SVM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model2 = svm.SVC(kernel = 'linear', C=0.5, gamma = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PetalLengthCm  PetalWidthCm\n",
       "91            4.6           1.4\n",
       "69            3.9           1.1\n",
       "48            1.5           0.2\n",
       "53            4.0           1.3\n",
       "28            1.4           0.2"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Species\n",
       "91       1\n",
       "69       1\n",
       "48       0\n",
       "53       1\n",
       "28       0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3 = y2.to_frame()\n",
    "y3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's convert numerical values to categorical target\n",
    "y3 = y3.replace(0, 'Iris-setosa')\n",
    "y3 = y3.replace(1, 'Iris-versicolor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Species\n",
       "91  Iris-versicolor\n",
       "69  Iris-versicolor\n",
       "48      Iris-setosa\n",
       "53  Iris-versicolor\n",
       "28      Iris-setosa"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.5, gamma=0.01, kernel='linear')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model2.fit(X2, y3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.7, 0.5],\n",
       "       [1.9, 0.2],\n",
       "       [3.3, 1. ],\n",
       "       [3.3, 1. ]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model2.support_vectors_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x203960286d0>"
      ]
     },
     "execution_count": 111,
     "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(X2.iloc[:, 0], X2.iloc[:, 1], c=y2, s=50, cmap='autumn')\n",
    "plt.scatter(svm_model2.support_vectors_[:,0],svm_model2.support_vectors_[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "plt.scatter(X2.iloc[:, 0], X2.iloc[:, 1], c=y2, s=50, cmap='autumn')\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "\n",
    "xx = np.linspace(xlim[0], xlim[1], 30)\n",
    "yy = np.linspace(ylim[0], ylim[1], 30)\n",
    "YY, XX = np.meshgrid(yy, xx)\n",
    "xy = np.vstack([XX.ravel(), YY.ravel()]).T\n",
    "Z = svm_model2.decision_function(xy).reshape(XX.shape)\n",
    "\n",
    "cs = ax.contour(XX, YY, Z, colors=['black', 'blue','black'], alpha=0.5, levels=[-0.94, 0.03, 1],\n",
    "                linestyles=['--', '-', '--'])\n",
    "plt.clabel(cs, levels = [0.03], fmt = 'Max. Margin', colors = 'blue')\n",
    "\n",
    "ax.scatter(svm_model2.support_vectors_[:, 0], svm_model2.support_vectors_[:, 1], s=100,\n",
    "           linewidth=1, facecolors='none', edgecolors='k')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}