k_fold_cross_validation_for_social_network_ads_classification_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "taLo6Q7NxKRE"
   },
   "source": [
    "# k-Fold Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xQ1Zs9MKxXJ1"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D3x14CwtxcV5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tunde\\Anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1L1wq9evxg83"
   },
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TMeZ6FKbxkU6"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i3FBTRwzxzl7"
   },
   "source": [
    "## Splitting the dataset into the Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V5TZ-0qSx5mX"
   },
   "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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WPt8zMSIxrJ7"
   },
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c60y-wPDxvY-"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "29ry3GSGx9P_"
   },
   "source": [
    "## Training the Kernel SVM model on the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "id": "BUM0MSyGyCjF",
    "outputId": "87e8308a-4b5f-40ff-826a-106ecbcfb840"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tunde\\Anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "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='rbf', max_iter=-1, probability=False, random_state=0,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fBbur5RQyTcL"
   },
   "source": [
    "## Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "_1llVHNPyWkZ",
    "outputId": "14747434-1b3e-45d7-f27a-879d9bf0e2b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64  4]\n",
      " [ 3 29]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.93"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "y_pred = classifier.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U-eGh0nuyabx"
   },
   "source": [
    "## Applying k-Fold Cross Validation\n",
    "### We have to call the model_selection on the training set and not on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "N5puHSW9ydyi",
    "outputId": "810d6306-fdd0-4f48-d4d3-c8ed5cdc4d03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90.05 %\n",
      "Standard Deviation: 6.39 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "C:\\Users\\tunde\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10) # 10-fold cross validation\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M6HUu8z8ygbT"
   },
   "source": [
    "## Visualising the Training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "id": "wkjkHN6Qykw3",
    "outputId": "ac964bb4-4d67-4b90-df8d-535875397b10"
   },
   "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('Kernel SVM (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9rLgiI19yn8m"
   },
   "source": [
    "## Visualising the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 349
    },
    "colab_type": "code",
    "id": "PSuas5JgyrpC",
    "outputId": "c4bee11b-8ae3-4f79-c0b7-4708ab054f8c"
   },
   "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('Kernel SVM (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
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