Sklearn_SVC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Support Vector Classsification using Scikit learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial is an introduction to SVC using Scikit learn and pandas. We will use scikit learn for the support vector machine algorithm and pandas to handle data as a dataframe. I prefer using pandas here since it helps me visualize data as dataframes which I am comfortable with since I am more comfortable handling R dataframes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall now import the dataset as a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=pd.DataFrame(datasets.load_iris().data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will explore the data to understand the columns in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <th>4</th>\n",
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       "      <th>6</th>\n",
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       "      <td>0.3</td>\n",
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       "      <th>7</th>\n",
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       "      <td>0.2</td>\n",
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       "      <th>9</th>\n",
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       "     0    1    2    3\n",
       "0  5.1  3.5  1.4  0.2\n",
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       "2  4.7  3.2  1.3  0.2\n",
       "3  4.6  3.1  1.5  0.2\n",
       "4  5.0  3.6  1.4  0.2\n",
       "5  5.4  3.9  1.7  0.4\n",
       "6  4.6  3.4  1.4  0.3\n",
       "7  5.0  3.4  1.5  0.2\n",
       "8  4.4  2.9  1.4  0.2\n",
       "9  4.9  3.1  1.5  0.1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "       0    1    2    3\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"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "iris.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
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    {
     "name": "stdout",
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      "       0    1    2    3\n",
      "0    5.1  3.5  1.4  0.2\n",
      "1    4.9  3.0  1.4  0.2\n",
      "2    4.7  3.2  1.3  0.2\n",
      "3    4.6  3.1  1.5  0.2\n",
      "4    5.0  3.6  1.4  0.2\n",
      "5    5.4  3.9  1.7  0.4\n",
      "6    4.6  3.4  1.4  0.3\n",
      "7    5.0  3.4  1.5  0.2\n",
      "8    4.4  2.9  1.4  0.2\n",
      "9    4.9  3.1  1.5  0.1\n",
      "10   5.4  3.7  1.5  0.2\n",
      "11   4.8  3.4  1.6  0.2\n",
      "12   4.8  3.0  1.4  0.1\n",
      "13   4.3  3.0  1.1  0.1\n",
      "14   5.8  4.0  1.2  0.2\n",
      "15   5.7  4.4  1.5  0.4\n",
      "16   5.4  3.9  1.3  0.4\n",
      "17   5.1  3.5  1.4  0.3\n",
      "18   5.7  3.8  1.7  0.3\n",
      "19   5.1  3.8  1.5  0.3\n",
      "20   5.4  3.4  1.7  0.2\n",
      "21   5.1  3.7  1.5  0.4\n",
      "22   4.6  3.6  1.0  0.2\n",
      "23   5.1  3.3  1.7  0.5\n",
      "24   4.8  3.4  1.9  0.2\n",
      "25   5.0  3.0  1.6  0.2\n",
      "26   5.0  3.4  1.6  0.4\n",
      "27   5.2  3.5  1.5  0.2\n",
      "28   5.2  3.4  1.4  0.2\n",
      "29   4.7  3.2  1.6  0.2\n",
      "..   ...  ...  ...  ...\n",
      "120  6.9  3.2  5.7  2.3\n",
      "121  5.6  2.8  4.9  2.0\n",
      "122  7.7  2.8  6.7  2.0\n",
      "123  6.3  2.7  4.9  1.8\n",
      "124  6.7  3.3  5.7  2.1\n",
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      "127  6.1  3.0  4.9  1.8\n",
      "128  6.4  2.8  5.6  2.1\n",
      "129  7.2  3.0  5.8  1.6\n",
      "130  7.4  2.8  6.1  1.9\n",
      "131  7.9  3.8  6.4  2.0\n",
      "132  6.4  2.8  5.6  2.2\n",
      "133  6.3  2.8  5.1  1.5\n",
      "134  6.1  2.6  5.6  1.4\n",
      "135  7.7  3.0  6.1  2.3\n",
      "136  6.3  3.4  5.6  2.4\n",
      "137  6.4  3.1  5.5  1.8\n",
      "138  6.0  3.0  4.8  1.8\n",
      "139  6.9  3.1  5.4  2.1\n",
      "140  6.7  3.1  5.6  2.4\n",
      "141  6.9  3.1  5.1  2.3\n",
      "142  5.8  2.7  5.1  1.9\n",
      "143  6.8  3.2  5.9  2.3\n",
      "144  6.7  3.3  5.7  2.5\n",
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      "149  5.9  3.0  5.1  1.8\n",
      "\n",
      "[150 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now from sklearn we will import the SVC as we are trying to do a classification problem here. We will be using support vector machine algorithm for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create an object clf (classifier) and assign SVC to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will assign the target variable from iris to a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=datasets.load_iris().target\n",
    "target_names=datasets.load_iris().target_names[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n",
      "['setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'\n",
      " 'setosa' 'setosa' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'\n",
      " 'versicolor' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'\n",
      " 'virginica' 'virginica' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(target)\n",
    "print(target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(target),type(target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use either of these arrays to fit the SVM/SVC model. If you desire the output as numeric values corresponding to the labels you can use target or if you want output as the labels as such you can use target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris,target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now try to predict the first 3 data points from the dataset using our classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['setosa', 'setosa', 'setosa']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(clf.predict(iris[:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below steps are alternates using the numeric values of the class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(iris,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(clf.predict(iris[:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will breakdown the dataframe into train and test sets and try classifying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = datasets.load_iris()\n",
    "df = pd.DataFrame(data['data'], columns=data['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']=data['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "5                  5.4               3.9                1.7               0.4   \n",
       "6                  4.6               3.4                1.4               0.3   \n",
       "7                  5.0               3.4                1.5               0.2   \n",
       "8                  4.4               2.9                1.4               0.2   \n",
       "9                  4.9               3.1                1.5               0.1   \n",
       "10                 5.4               3.7                1.5               0.2   \n",
       "11                 4.8               3.4                1.6               0.2   \n",
       "12                 4.8               3.0                1.4               0.1   \n",
       "13                 4.3               3.0                1.1               0.1   \n",
       "14                 5.8               4.0                1.2               0.2   \n",
       "15                 5.7               4.4                1.5               0.4   \n",
       "16                 5.4               3.9                1.3               0.4   \n",
       "17                 5.1               3.5                1.4               0.3   \n",
       "18                 5.7               3.8                1.7               0.3   \n",
       "19                 5.1               3.8                1.5               0.3   \n",
       "20                 5.4               3.4                1.7               0.2   \n",
       "21                 5.1               3.7                1.5               0.4   \n",
       "22                 4.6               3.6                1.0               0.2   \n",
       "23                 5.1               3.3                1.7               0.5   \n",
       "24                 4.8               3.4                1.9               0.2   \n",
       "25                 5.0               3.0                1.6               0.2   \n",
       "26                 5.0               3.4                1.6               0.4   \n",
       "27                 5.2               3.5                1.5               0.2   \n",
       "28                 5.2               3.4                1.4               0.2   \n",
       "29                 4.7               3.2                1.6               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "120                6.9               3.2                5.7               2.3   \n",
       "121                5.6               2.8                4.9               2.0   \n",
       "122                7.7               2.8                6.7               2.0   \n",
       "123                6.3               2.7                4.9               1.8   \n",
       "124                6.7               3.3                5.7               2.1   \n",
       "125                7.2               3.2                6.0               1.8   \n",
       "126                6.2               2.8                4.8               1.8   \n",
       "127                6.1               3.0                4.9               1.8   \n",
       "128                6.4               2.8                5.6               2.1   \n",
       "129                7.2               3.0                5.8               1.6   \n",
       "130                7.4               2.8                6.1               1.9   \n",
       "131                7.9               3.8                6.4               2.0   \n",
       "132                6.4               2.8                5.6               2.2   \n",
       "133                6.3               2.8                5.1               1.5   \n",
       "134                6.1               2.6                5.6               1.4   \n",
       "135                7.7               3.0                6.1               2.3   \n",
       "136                6.3               3.4                5.6               2.4   \n",
       "137                6.4               3.1                5.5               1.8   \n",
       "138                6.0               3.0                4.8               1.8   \n",
       "139                6.9               3.1                5.4               2.1   \n",
       "140                6.7               3.1                5.6               2.4   \n",
       "141                6.9               3.1                5.1               2.3   \n",
       "142                5.8               2.7                5.1               1.9   \n",
       "143                6.8               3.2                5.9               2.3   \n",
       "144                6.7               3.3                5.7               2.5   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         0  \n",
       "7         0  \n",
       "8         0  \n",
       "9         0  \n",
       "10        0  \n",
       "11        0  \n",
       "12        0  \n",
       "13        0  \n",
       "14        0  \n",
       "15        0  \n",
       "16        0  \n",
       "17        0  \n",
       "18        0  \n",
       "19        0  \n",
       "20        0  \n",
       "21        0  \n",
       "22        0  \n",
       "23        0  \n",
       "24        0  \n",
       "25        0  \n",
       "26        0  \n",
       "27        0  \n",
       "28        0  \n",
       "29        0  \n",
       "..      ...  \n",
       "120       2  \n",
       "121       2  \n",
       "122       2  \n",
       "123       2  \n",
       "124       2  \n",
       "125       2  \n",
       "126       2  \n",
       "127       2  \n",
       "128       2  \n",
       "129       2  \n",
       "130       2  \n",
       "131       2  \n",
       "132       2  \n",
       "133       2  \n",
       "134       2  \n",
       "135       2  \n",
       "136       2  \n",
       "137       2  \n",
       "138       2  \n",
       "139       2  \n",
       "140       2  \n",
       "141       2  \n",
       "142       2  \n",
       "143       2  \n",
       "144       2  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.054000           3.758667   \n",
       "std             0.828066          0.433594           1.764420   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)      target  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.198667    1.000000  \n",
       "std            0.763161    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now split this into train and test using sklearn.model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(df, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After spliting we get two dataframes of size 120 and 30 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    120\n",
       "sepal width (cm)     120\n",
       "petal length (cm)    120\n",
       "petal width (cm)     120\n",
       "target               120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    30\n",
       "sepal width (cm)     30\n",
       "petal length (cm)    30\n",
       "petal width (cm)     30\n",
       "target               30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define our Y and X variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=train.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train.iloc[:,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "62                 6.0               2.2                4.0               1.0\n",
       "140                6.7               3.1                5.6               2.4\n",
       "95                 5.7               3.0                4.2               1.2\n",
       "39                 5.1               3.4                1.5               0.2\n",
       "67                 5.8               2.7                4.1               1.0\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "102                7.1               3.0                5.9               2.1\n",
       "115                6.4               3.2                5.3               2.3\n",
       "88                 5.6               3.0                4.1               1.3\n",
       "9                  4.9               3.1                1.5               0.1\n",
       "26                 5.0               3.4                1.6               0.4\n",
       "12                 4.8               3.0                1.4               0.1\n",
       "47                 4.6               3.2                1.4               0.2\n",
       "63                 6.1               2.9                4.7               1.4\n",
       "134                6.1               2.6                5.6               1.4\n",
       "24                 4.8               3.4                1.9               0.2\n",
       "68                 6.2               2.2                4.5               1.5\n",
       "49                 5.0               3.3                1.4               0.2\n",
       "15                 5.7               4.4                1.5               0.4\n",
       "98                 5.1               2.5                3.0               1.1\n",
       "69                 5.6               2.5                3.9               1.1\n",
       "77                 6.7               3.0                5.0               1.7\n",
       "44                 5.1               3.8                1.9               0.4\n",
       "131                7.9               3.8                6.4               2.0\n",
       "139                6.9               3.1                5.4               2.1\n",
       "121                5.6               2.8                4.9               2.0\n",
       "91                 6.1               3.0                4.6               1.4\n",
       "106                4.9               2.5                4.5               1.7\n",
       "10                 5.4               3.7                1.5               0.2\n",
       "110                6.5               3.2                5.1               2.0\n",
       "..                 ...               ...                ...               ...\n",
       "32                 5.2               4.1                1.5               0.1\n",
       "122                7.7               2.8                6.7               2.0\n",
       "82                 5.8               2.7                3.9               1.2\n",
       "133                6.3               2.8                5.1               1.5\n",
       "147                6.5               3.0                5.2               2.0\n",
       "87                 6.3               2.3                4.4               1.3\n",
       "17                 5.1               3.5                1.4               0.3\n",
       "29                 4.7               3.2                1.6               0.2\n",
       "46                 5.1               3.8                1.6               0.2\n",
       "93                 5.0               2.3                3.3               1.0\n",
       "54                 6.5               2.8                4.6               1.5\n",
       "65                 6.7               3.1                4.4               1.4\n",
       "75                 6.6               3.0                4.4               1.4\n",
       "119                6.0               2.2                5.0               1.5\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "103                6.3               2.9                5.6               1.8\n",
       "149                5.9               3.0                5.1               1.8\n",
       "64                 5.6               2.9                3.6               1.3\n",
       "70                 5.9               3.2                4.8               1.8\n",
       "59                 5.2               2.7                3.9               1.4\n",
       "74                 6.4               2.9                4.3               1.3\n",
       "129                7.2               3.0                5.8               1.6\n",
       "33                 5.5               4.2                1.4               0.2\n",
       "36                 5.5               3.5                1.3               0.2\n",
       "72                 6.3               2.5                4.9               1.5\n",
       "42                 4.4               3.2                1.3               0.2\n",
       "27                 5.2               3.5                1.5               0.2\n",
       "145                6.7               3.0                5.2               2.3\n",
       "57                 4.9               2.4                3.3               1.0\n",
       "31                 5.4               3.4                1.5               0.4\n",
       "\n",
       "[120 rows x 4 columns]>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a similar process as earlier we will fit the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and try predicting the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X=test.iloc[:,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=clf.predict(test_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a confusion matrix to understand the error rate of the classifier model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(test.target, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 9  0  0]\n",
      " [ 0  8  0]\n",
      " [ 0  1 12]]\n"
     ]
    }
   ],
   "source": [
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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