support_vector.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1960788e",
   "metadata": {},
   "source": [
    "Support vector machine is a popular classification algorithm. Here we will solve iris flower classification problem using svm and sklearn library. We also cover how to fine tune svm classifier using different parameters such as gamma and regularization. Basically the way support vector machine works is it draws a hyper plane in n dimension space such that it maximizes the margin between classification groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b040b68",
   "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": "a45db547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\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": "231f3796",
   "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": "66223f20",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <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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      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "379f9f9b",
   "metadata": {},
   "outputs": [
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       "      <th>petal length (cm)</th>\n",
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      "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  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target']=iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "071c5978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33cbeeeb",
   "metadata": {},
   "outputs": [
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       "      <th>petal length (cm)</th>\n",
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       "      <td>6.9</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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9635e9bf",
   "metadata": {},
   "outputs": [
    {
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       "      <th>petal length (cm)</th>\n",
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       "      <td>2</td>\n",
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      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.target==2].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2f73a3b",
   "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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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "      <td>setosa</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
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       "</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": 9,
     "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": 10,
   "id": "13ee5316",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8b9cdbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d977ee6",
   "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": 13,
   "id": "b0668e99",
   "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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       "      <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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e6abcea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'sepal width (cm)')"
      ]
     },
     "execution_count": 15,
     "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['sepal length (cm)'],df0['sepal width (cm)'],color='green',marker='+')\n",
    "plt.scatter(df1['sepal length (cm)'],df1['sepal width (cm)'],color='red',marker='.')\n",
    "plt.xlabel('sepal length (cm)')\n",
    "plt.ylabel('sepal width (cm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c29a7c65",
   "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='blue',marker='+')\n",
    "plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='orange',marker='.')\n",
    "plt.xlabel('petal length (cm)')\n",
    "plt.ylabel('petal width (cm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c4dd63da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28da915d",
   "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",
       "  </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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop(['target','flower_name'],axis='columns')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1272e645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: target, dtype: int32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.target\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "16d23cea",
   "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": 24,
   "id": "c4e15787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 4)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "467f5ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4be02ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf=SVC()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d846f890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "455b8fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "79f8dfbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf=SVC(C=10)   #C=regularization\n",
    "                # gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0dc9c209",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d5aeac13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1d63c4a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf=SVC(gamma=10)   #C=regularization\n",
    "                # gamma\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "41a6c5b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf=SVC(gamma=5)   #C=regularization\n",
    "                   # gamma, kernel=default ='rbf',change it to linear\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc425649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf=SVC(kernel='linear')   #C=regularization\n",
    "                   # gamma, kernel=default ='rbf',change it to linear\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0423da94",
   "metadata": {},
   "source": [
    "### Excercise Description\n",
    "Train SVM classifier using sklearn digits dataset (i.e. from sklearn.datasets import load_digits) and then,\n",
    "\n",
    "- Measure accuracy of your model using different kernels such as rbf and linear.\n",
    "- Tune your model further using regularization and gamma parameters and try to come up with highest accurancy score\n",
    "- Use 80% of samples as training data size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4136b43e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_digits\n",
    "digits=load_digits()\n",
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50f9d8a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=digits.data\n",
    "y=digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3aeb21d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1797"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b91d7e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5cd3344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1437"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69b00676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c1acaf66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd193752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2cefe407",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8eb7b33f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9916666666666667"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "045ac3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0037b423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[38,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 32,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 42,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 29,  0,  0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0, 40,  0,  0,  0,  0,  1],\n",
       "       [ 0,  0,  0,  0,  0, 42,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0, 29,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 34,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0, 35,  0],\n",
       "       [ 0,  0,  0,  0,  0,  1,  0,  0,  0, 36]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc3f6d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2ed3feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm,annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f52d9a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "for i in range(5):\n",
    "    plt.matshow(digits.images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6e09d7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3509b5da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(digits.data[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e8effc8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9, 0, 9, 5, 5, 6])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(digits.data[25:35])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b1201604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9, 0, 9, 5, 5, 6])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[25:35]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a6adf3",
   "metadata": {},
   "source": [
    "### Parameter tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "86e67a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9916666666666667"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC()\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fa6dfa46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9722222222222222"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC(kernel='linear')\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3f043fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9944444444444445"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC(C=10)\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f357a6d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9944444444444445"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC(C=5)\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "08a6e50b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08055555555555556"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC(gamma=1)\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f3c1af61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9861111111111112"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf=SVC(kernel='rbf')\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77a7b39d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}