support_vector_machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading necessary data for training\n",
    "\n",
    "The MNIST dataset consist 42000 hand written digit examples from 0 to 9. Each digit is made up of 28 X 28 pixels varrying from 0 to 255.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"Data\\\\MNIST\\\\train.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Digit 1')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "one = train_data.iloc[2, 1:]\n",
    "one = one.values.reshape(28,28)\n",
    "plt.imshow(one)\n",
    "plt.title(\"Digit 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising the data on the basis of number of digit present in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\adity\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_data['label'],palette = 'icefire')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we are scalling our dataset by dividing it by 255. to make it between 0-1 as 255 is the maximum pixel value and also to convert the pixels into floating point number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train_data.drop(\"label\" , axis = 1)\n",
    "y = train_data[\"label\"]\n",
    "x = x/255.                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of training set is 33600\n",
      "Length of test set is 8400\n"
     ]
    }
   ],
   "source": [
    "x_train , x_test , y_train , y_test = train_test_split(x , y , test_size=0.2)\n",
    "print(\"Length of training set is\",x_train.shape[0])\n",
    "print(\"Length of test set is\",x_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Let's first try with linear kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9285714285714286"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear model\n",
    "model_linear = SVC(kernel='linear')\n",
    "model_linear.fit(x_train, y_train)\n",
    "\n",
    "model_linear.score(x_test , y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With linear kernel we are getting an accuracy of 92.85%\n",
    "\n",
    "2. Now let's try with non-linear kernels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. with rbf kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9755952380952381"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rbf = SVC(kernel=\"rbf\")\n",
    "model_rbf.fit(x_train , y_train)\n",
    "\n",
    "model_rbf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. with poly kernal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9701190476190477"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_poly = SVC(kernel=\"poly\")\n",
    "model_poly.fit(x_train , y_train)\n",
    "\n",
    "model_poly.score(x_test , y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### From the above result we can conclude that the model is giving high accuracy (i.e 97.55%) when kernel is set to rbf however this can vary from system to system"
   ]
  }
 ],
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   "language": "python",
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   "file_extension": ".py",
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