Support-Vector-Machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sapan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\sapan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\Users\\sapan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[66,  2],\n",
       "       [ 8, 24]], dtype=int64)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classification template\n",
    "\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "Y = dataset.iloc[:, 4].values\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\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 = 0.25, random_state = 0)\n",
    "\n",
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "# Fitting classifier to the Training set\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X_train,Y_train)\n",
    "\n",
    "\n",
    "# Predicting the Test set results\n",
    "Y_pred = classifier.predict(X_test)\n",
    "\n",
    "#Making a Confusion Matrix \n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(Y_test, Y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Training Set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, Y_set = X_train, Y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set == j, 0], X_set[Y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('SVM Classifier (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Test Set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "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": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, Y_set = X_test, Y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set == j, 0], X_set[Y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('SVM Classifier (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}