Support Vector Machine.ipynb

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   "source": [
    "## Support Vector Machine\n",
    "\n",
    "- This method is used in Classification problems. \n",
    "   - The algorithm draws a hyperplane in a N-dimensional space for the categories in such a way that the distance between the plane & the classification groups is MAXIMUM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "318dbb86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ba771a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing iris data set\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5271a63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = load_iris()\n",
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2b712b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9c6f8d94",
   "metadata": {},
   "outputs": [
    {
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       "<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",
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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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(iris.data, columns= iris.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f85c697d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a9fbd629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding taget to the df\n",
    "df['target'] = iris.target\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1731a19f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ed914e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>flower_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </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",
       "\n",
       "   target flower_name  \n",
       "0       0      setosa  \n",
       "1       0      setosa  \n",
       "2       0      setosa  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding target names to the df:\n",
    "df['flower_name'] = df['target'].apply(lambda x : iris.target_names[x])\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f14148e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets plot some graphs:\n",
    "df_setosa = df[df['target']==0]\n",
    "df_versicolor = df[df['target']==1]\n",
    "df_virginica = df[df['target']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5008844c",
   "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": [
    "# plotting scatter plots for sepal length\n",
    "plt.scatter(df_setosa['sepal length (cm)'],df_setosa['sepal width (cm)'], marker='o',color ='red')\n",
    "plt.scatter(df_versicolor['sepal length (cm)'],df_versicolor['sepal width (cm)'], marker='+',color ='green')\n",
    "plt.scatter(df_virginica['sepal length (cm)'],df_virginica['sepal width (cm)'], marker='*',color ='blue')\n",
    "plt.xlabel(\"sepal length (cm)\")\n",
    "plt.ylabel(\"sepal width (cm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "3b11dc52",
   "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": [
    "# plotting scatter plots for petal length\n",
    "plt.scatter(df_setosa['petal length (cm)'],df_setosa['petal width (cm)'], marker='o',color ='red')\n",
    "plt.scatter(df_versicolor['petal length (cm)'],df_versicolor['petal width (cm)'], marker='+',color ='green')\n",
    "plt.scatter(df_virginica['petal length (cm)'],df_virginica['petal width (cm)'], marker='*',color ='blue')\n",
    "plt.xlabel(\"petal length (cm)\")\n",
    "plt.ylabel(\"petal width (cm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "4a0a2fd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets create SVM models:\n",
    "# First split the dataset, but before that we'll define X & y:\n",
    "X = df.drop(['target','flower_name'],axis='columns')\n",
    "y = df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "6803ae6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_test_split\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "97d89237",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data in train & test\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "a91c8e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "05657e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Support Vector Classifier from SVM model\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f32ccd30",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b8c2bbf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model:\n",
    "svm_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c6375301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "1aa03a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regularization\n",
    "svm_model.C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83263747",
   "metadata": {},
   "source": [
    "#### Using load_digits dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "2f51e0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d05c6442",
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "74e2b211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3acf8c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#digits.feature_names\n",
    "digits.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "27e674b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>pixel_0_0</th>\n",
       "      <th>pixel_0_1</th>\n",
       "      <th>pixel_0_2</th>\n",
       "      <th>pixel_0_3</th>\n",
       "      <th>pixel_0_4</th>\n",
       "      <th>pixel_0_5</th>\n",
       "      <th>pixel_0_6</th>\n",
       "      <th>pixel_0_7</th>\n",
       "      <th>pixel_1_0</th>\n",
       "      <th>pixel_1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel_6_6</th>\n",
       "      <th>pixel_6_7</th>\n",
       "      <th>pixel_7_0</th>\n",
       "      <th>pixel_7_1</th>\n",
       "      <th>pixel_7_2</th>\n",
       "      <th>pixel_7_3</th>\n",
       "      <th>pixel_7_4</th>\n",
       "      <th>pixel_7_5</th>\n",
       "      <th>pixel_7_6</th>\n",
       "      <th>pixel_7_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  pixel_0_5  \\\n",
       "0        0.0        0.0        5.0       13.0        9.0        1.0   \n",
       "1        0.0        0.0        0.0       12.0       13.0        5.0   \n",
       "\n",
       "   pixel_0_6  pixel_0_7  pixel_1_0  pixel_1_1  ...  pixel_6_6  pixel_6_7  \\\n",
       "0        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "1        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "\n",
       "   pixel_7_0  pixel_7_1  pixel_7_2  pixel_7_3  pixel_7_4  pixel_7_5  \\\n",
       "0        0.0        0.0        6.0       13.0       10.0        0.0   \n",
       "1        0.0        0.0        0.0       11.0       16.0       10.0   \n",
       "\n",
       "   pixel_7_6  pixel_7_7  \n",
       "0        0.0        0.0  \n",
       "1        0.0        0.0  \n",
       "\n",
       "[2 rows x 64 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe:\n",
    "digitdf = pd.DataFrame(digits.data,columns= digits.feature_names)\n",
    "digitdf.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "85d91d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel_0_0</th>\n",
       "      <th>pixel_0_1</th>\n",
       "      <th>pixel_0_2</th>\n",
       "      <th>pixel_0_3</th>\n",
       "      <th>pixel_0_4</th>\n",
       "      <th>pixel_0_5</th>\n",
       "      <th>pixel_0_6</th>\n",
       "      <th>pixel_0_7</th>\n",
       "      <th>pixel_1_0</th>\n",
       "      <th>pixel_1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel_6_7</th>\n",
       "      <th>pixel_7_0</th>\n",
       "      <th>pixel_7_1</th>\n",
       "      <th>pixel_7_2</th>\n",
       "      <th>pixel_7_3</th>\n",
       "      <th>pixel_7_4</th>\n",
       "      <th>pixel_7_5</th>\n",
       "      <th>pixel_7_6</th>\n",
       "      <th>pixel_7_7</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
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       "<p>2 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  pixel_0_5  \\\n",
       "0        0.0        0.0        5.0       13.0        9.0        1.0   \n",
       "1        0.0        0.0        0.0       12.0       13.0        5.0   \n",
       "\n",
       "   pixel_0_6  pixel_0_7  pixel_1_0  pixel_1_1  ...  pixel_6_7  pixel_7_0  \\\n",
       "0        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "1        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "\n",
       "   pixel_7_1  pixel_7_2  pixel_7_3  pixel_7_4  pixel_7_5  pixel_7_6  \\\n",
       "0        0.0        6.0       13.0       10.0        0.0        0.0   \n",
       "1        0.0        0.0       11.0       16.0       10.0        0.0   \n",
       "\n",
       "   pixel_7_7  target  \n",
       "0        0.0       0  \n",
       "1        0.0       1  \n",
       "\n",
       "[2 rows x 65 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add target as well\n",
    "digitdf['target'] = digits.target\n",
    "digitdf.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "dd9540d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the X & y\n",
    "X = digitdf.drop('target',axis='columns')\n",
    "y = digitdf.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "22d98932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split train & test data:\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train,y_test = train_test_split(X,y,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "45f9faa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import SVC from svm\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "630c434d",
   "metadata": {},
   "outputs": [],
   "source": [
    "digitmodel = SVC()   # uses rbf kernel\n",
    "        # here we can tune by supplying other parameters [regularization, kernels, & gamma paramters]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "1e34b6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digitmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "895c06b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digitmodel.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e7eed5",
   "metadata": {},
   "source": [
    "##### using linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ccf77eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "digitmodel = SVC(kernel ='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "6e7ce7ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digitmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "388abdb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digitmodel.score(X_test,y_test)"
   ]
  }
 ],
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