Logistic_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Logistic Regression",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyP8hE+5l3ImGQBmLPGeGxCE"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyphVmXORlki"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vOrAjAaHiAA",
        "outputId": "48d71b36-6642-4028-cf05-aa8a67682190",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "from IPython.display import Image\n",
        "\n",
        "Image(filename =r'lr.jpeg')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVHHR4qNRqKc"
      },
      "source": [
        "## lib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIy0AWXHQ-sk"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "662T5BiYRr1Z"
      },
      "source": [
        "## data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FY6l1F_BRPU3"
      },
      "source": [
        "df = pd.read_csv('Social_Network_Ads.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQZg7DVcRUs-"
      },
      "source": [
        "x = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B_Edrj6vRz_Y"
      },
      "source": [
        "## split training and test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11NLcZOXRzCA"
      },
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vg4XWtnSSMj"
      },
      "source": [
        "## feature scaling\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGMrxqLBST0l"
      },
      "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": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9zZNNk8TGzu"
      },
      "source": [
        "## train logistic model on train set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpS4ZEdPS2Ie",
        "outputId": "453d07db-a788-46f6-d940-a0bd64aeefbb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(x_train, y_train)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mnf6qonRTrhf"
      },
      "source": [
        "## predict new class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOYTOo1eTpuO",
        "outputId": "20cac83a-7e6e-4ba9-ad2b-58229f237b3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(classifier.predict(sc.transform([[30,87000]])))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Uha_WKfo0D5"
      },
      "source": [
        "## predict test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06C2gMJGolZu",
        "outputId": "a3cd8d69-7e4e-4177-b6a0-93cf0d223337",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "y_pred = classifier.predict(x_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [1 0]\n",
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            " [1 1]\n",
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            " [0 0]\n",
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            " [0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " [1 0]\n",
            " [0 0]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 1]\n",
            " [1 0]\n",
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            " [0 0]\n",
            " [1 1]\n",
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            " [0 0]\n",
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            " [0 0]\n",
            " [0 0]\n",
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            " [0 1]\n",
            " [0 0]\n",
            " [0 1]\n",
            " [1 1]\n",
            " [1 1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHgD5ZtQpcnC"
      },
      "source": [
        "## making confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtsr1MIFppGT",
        "outputId": "c2913206-8b12-4321-9b29-a2dce9da6c0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[65  3]\n",
            " [ 8 24]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.89"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H8Fix7X_pgCq"
      },
      "source": [
        "## visualizing training test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeQ5lw--TnlN",
        "outputId": "28a1ee9c-71a8-4c11-aa85-6a49838360b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(x_train), y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 1),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 1))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('K-NN (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "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": "fmCSSV-lpkgI"
      },
      "source": [
        "## visualize test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4N3i-oAFpWKL",
        "outputId": "9e98bf52-8c51-4d69-fd81-1e8e07069f43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = sc.inverse_transform(x_test), y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 1),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 1))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(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], c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('K-NN (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "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"
          }
        }
      ]
    }
  ]
}