K-Fold Cross Validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Fold Cross Validation\n",
    "- It allows you to measure in the most relevant way the performance of each of your future machinery models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numpy allows us to work with array.\n",
    "import numpy as np\n",
    "\n",
    "# Maptplotlib which allows us to plot some chart.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pandas allows us to not only import the datasets but also create the matrix of features(independent) and \n",
    "# dependent variable.\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing the dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The independent variable usally in the first columns of dataset and dependent variable usally in the last columns of the data sets.\n",
    "- X is Independent Variable.\n",
    "- Y is Dependent Variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "x = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 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 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 1 0 0 0 1 0 1\n",
      " 1 1 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 1 0 0 1\n",
      " 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0\n",
      " 1 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0 0 1 1 0 1 0\n",
      " 0 1 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1\n",
      " 1 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Splitting the dataset into the training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [    32 100000]\n",
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      " [    34 112000]\n",
      " [    38  80000]\n",
      " [    42  75000]\n",
      " [    47  47000]\n",
      " [    39  75000]\n",
      " [    19  25000]\n",
      " [    37  80000]\n",
      " [    36  60000]\n",
      " [    41  52000]\n",
      " [    36 125000]\n",
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      " [    46  22000]\n",
      " [    35  55000]\n",
      " [    54 104000]\n",
      " [    48 119000]\n",
      " [    35  53000]\n",
      " [    37 144000]\n",
      " [    23  66000]\n",
      " [    37 137000]\n",
      " [    31  58000]\n",
      " [    33  41000]\n",
      " [    45  22000]\n",
      " [    30  15000]\n",
      " [    19  19000]\n",
      " [    49  74000]\n",
      " [    39 122000]\n",
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      " [    30 135000]\n",
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      " [    56 133000]\n",
      " [    29  61000]\n",
      " [    30  89000]\n",
      " [    26  16000]\n",
      " [    33  31000]\n",
      " [    41  72000]\n",
      " [    36  33000]\n",
      " [    55 125000]\n",
      " [    48 131000]\n",
      " [    41  71000]\n",
      " [    30  62000]\n",
      " [    37  72000]\n",
      " [    41  63000]\n",
      " [    58  47000]\n",
      " [    30 116000]\n",
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      " [    37  74000]\n",
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      " [    28  79000]\n",
      " [    53  82000]\n",
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      " [    60  34000]\n",
      " [    35 108000]\n",
      " [    21  72000]\n",
      " [    38  71000]\n",
      " [    39 106000]\n",
      " [    37  57000]\n",
      " [    26  72000]\n",
      " [    35  23000]\n",
      " [    54 108000]\n",
      " [    30  17000]\n",
      " [    39 134000]\n",
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      " [    33  43000]\n",
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      " [    39 134000]\n",
      " [    27 137000]\n",
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      " [    31  66000]\n",
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      " [    42 108000]\n",
      " [    20  23000]\n",
      " [    40  65000]\n",
      " [    47  20000]\n",
      " [    18  86000]\n",
      " [    35  79000]\n",
      " [    57  33000]\n",
      " [    34  72000]\n",
      " [    49  39000]\n",
      " [    27  31000]\n",
      " [    19  70000]\n",
      " [    39  79000]\n",
      " [    26  81000]\n",
      " [    25  80000]\n",
      " [    28  85000]\n",
      " [    55  39000]\n",
      " [    50  88000]\n",
      " [    49  88000]\n",
      " [    52 150000]\n",
      " [    35  65000]\n",
      " [    42  54000]\n",
      " [    34  43000]\n",
      " [    37  52000]\n",
      " [    48  30000]\n",
      " [    29  43000]\n",
      " [    36  52000]\n",
      " [    27  54000]\n",
      " [    26 118000]]\n"
     ]
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Dividing training and test set.\n",
    "# The best ratio is 80 - 20 for trainging and testing respectively.\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1\n",
      " 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1\n",
      " 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0\n",
      " 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0\n",
      " 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0\n",
      " 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0\n",
      " 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0\n",
      " 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1\n",
      " 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Package\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "# Fitting and Transforming\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training the Kernal SVM model on the Training Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "\n",
    "# Fitting\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making the Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64  4]\n",
      " [ 3 29]]\n",
      "Accuracy Score of Cross Validation is 0.93\n"
     ]
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "# Predicting\n",
    "y_pred = classifier.predict(x_test)\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "\n",
    "# Accuracy Score\n",
    "print(\"Accuracy Score of Cross Validation is\",accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying K-Fold Cross Validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90.33 %\n",
      "Standard Deviation: 6.57 %\n"
     ]
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "accuracies = cross_val_score(estimator = classifier, X = x_train, y = y_train, cv =10)\n",
    "\n",
    "print('Accuracy: {:.2f} %'.format(accuracies.mean() * 100))\n",
    "print('Standard Deviation: {:.2f} %'.format(accuracies.std() * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Training Set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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": [
    "# Importing Package\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x_set, y_set = x_train, y_train\n",
    "\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",
    "\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', 'blue')))\n",
    "\n",
    "plt.xlim(x1.min(), x1.max())\n",
    "plt.ylim(x2.min(), x2.max())\n",
    "\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', 'blue'))(i), label = j)\n",
    "plt.title('Logistic Regression (Training set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Test Set results**"
   ]
  },
  {
   "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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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": [
    "# Importing Package\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "x_set, y_set = x_test, y_test\n",
    "\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",
    "\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', 'blue')))\n",
    "\n",
    "plt.xlim(x1.min(), x1.max())\n",
    "plt.ylim(x2.min(), x2.max())\n",
    "\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', 'blue'))(i), label = j)\n",
    "\n",
    "plt.title('Logistic Regression (Test set)')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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