Day_38_Support_Vector_Machine.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyO8rTzFElR/iT04A/QUkdkN"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Machine is a supervised machine learning algorithm which is used for both classification or regression problems. In this technique we start by plotting each data item as point in n-dimensional space with the value of a particular coordinate as being the value of the feature/independent variable. Then, we find the hyper-plane that differentiates the two classes by a boundary as shown in the image below.\n",
        "\n",
        "<div style=\"text-align:center\"><img alt=\"Classification\" src=\"https://github.com/thunderstroke325/60-Days-of-Data-Science-and-ML/blob/main/assets/svm.png?raw=true\"/></div>"
      ],
      "metadata": {
        "id": "8gQQmY4pBsy1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "srKd5KC3As6n"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from matplotlib.colors import ListedColormap"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load the Data"
      ],
      "metadata": {
        "id": "FGodojR3CHRv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the dataset\n",
        "dataset = pd.read_csv('https://raw.githubusercontent.com/thunderstroke325/60-Days-of-Data-Science-and-ML/main/datasets/data38.csv')\n",
        "X = dataset.iloc[:, [2, 3]].values\n",
        "Y = dataset.iloc[:, 4].values"
      ],
      "metadata": {
        "id": "PoUhL77cCKwC"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "8YkE97bOCmQH"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Scaling for normalization"
      ],
      "metadata": {
        "id": "9uYnpz-ECoLV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "umO5dVujCrFe"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Fitting and Predictions"
      ],
      "metadata": {
        "id": "9AFTgdF4Cvdp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svmclassifier = SVC(kernel = 'linear' , random_state = 0)\n",
        "svmclassifier.fit (X_train, Y_train)\n",
        "Y_pred = svmclassifier.predict(X_test)"
      ],
      "metadata": {
        "id": "7BOFUlFVCzrs"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Confusion Matrix"
      ],
      "metadata": {
        "id": "GNlNIPj1C1eV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "id": "yiE7stndC41G"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualize the training set results "
      ],
      "metadata": {
        "id": "p8Y1BcW6DFuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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, svmclassifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'blue')))\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', 'blue'))(i), label = j)\n",
        "plt.title('SVM (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salaries')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "cyIoIlY_DJ3-",
        "outputId": "684e31da-9b1f-4d45-e7bd-f2c696aeccc7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.axes._axes:*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",
            "WARNING:matplotlib.axes._axes:*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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualize the testing set results"
      ],
      "metadata": {
        "id": "NBVvPkbVDNw2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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, svmclassifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'blue')))\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', 'blue'))(i), label = j)\n",
        "plt.title('SVM (Testing set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salaries')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "2hrpdwwvDRsZ",
        "outputId": "2a3a1ca7-94af-425e-c117-66b22242dc5f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.axes._axes:*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",
            "WARNING:matplotlib.axes._axes:*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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}