kernel_svm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "kernel_svm.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/antonio2505/SuperDataScience_Projects_ML/blob/main/kernel_svm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MRC0e0KhQ0S"
      },
      "source": [
        "# Kernel SVM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWd1UlMnhT2s"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvGPUQaHhXfL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1VMqkGvhc3-"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M52QDmyzhh9s"
      },
      "source": [
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, [2, 3]].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvxIPVyMhmKp"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVzJWAXIhxoC"
      },
      "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)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW3c7UYih0hT"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fQlDPKCh8sc"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the Kernel SVM model on the Training set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmePIEiiiKyw"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAWJhvF7mAMn",
        "outputId": "6c129dd5-a812-4e1f-b434-82cb6666d9aa"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWK4AcCgiNwF"
      },
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6bpZwUiiXic",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "242af765-4ae3-4db3-a25c-b3bcf0e23e66"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[64  4]\n",
            " [ 3 29]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.93"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NOjKvZRid5l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "390e985b-1263-4a14-9caf-d730d62e6209"
      },
      "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()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeTjz2vDilAC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "870e0df1-c724-463a-faf5-77fbd8db3a13"
      },
      "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()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}