SVM_Social-Network-Ads.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('/home/sambhavgarrg/Avik-ML100/Mine/datasets/Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sambhavgarrg/anaconda3/lib/python3.7/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",
      "/home/sambhavgarrg/anaconda3/lib/python3.7/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",
      "/home/sambhavgarrg/anaconda3/lib/python3.7/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",
      "/home/sambhavgarrg/anaconda3/lib/python3.7/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"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=0,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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_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 (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 (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
}