9. Logistic_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Logistic Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNyaS8L8JpfxzU3Ci7HzlCm",
      "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/MainakRepositor/ML-Algorithms/blob/master/Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hMMUfsG4L4R"
      },
      "source": [
        "# Logistic Regression\n",
        "\n",
        "<hr>\n",
        "\n",
        "## Logistic regression is also known as Log regression because it used the logarithmic formula to predict the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OwnFlTtm4iWf"
      },
      "source": [
        "## 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7B3GBHu4Dzn",
        "outputId": "69d411a3-6a6c-4afa-b8cf-61f446fc785a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "print(\"Basic libraries included successfully!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic libraries included successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PFNh7YDn40Uu"
      },
      "source": [
        "## 2. Importing  the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gaL_Yl6m4wEC",
        "outputId": "dcec5b75-7fbc-4259-ca02-4316bfc76689",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Social_Network_Ads.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>27</td>\n",
              "      <td>58000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>27</td>\n",
              "      <td>84000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>32</td>\n",
              "      <td>150000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>25</td>\n",
              "      <td>33000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>35</td>\n",
              "      <td>65000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  EstimatedSalary  Purchased\n",
              "0   19            19000          0\n",
              "1   35            20000          0\n",
              "2   26            43000          0\n",
              "3   27            57000          0\n",
              "4   19            76000          0\n",
              "5   27            58000          0\n",
              "6   27            84000          0\n",
              "7   32           150000          1\n",
              "8   25            33000          0\n",
              "9   35            65000          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2Fh-EfwYJOp"
      },
      "source": [
        "## 3. Splitting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wheTBSsNYHys"
      },
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJFSvHf9YQI0"
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "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)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmvX3rs4YZXf"
      },
      "source": [
        "## 4. Feature scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZcEHAL6YVv2"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SDobk8IYin7"
      },
      "source": [
        "## 5. Training the Logistic Regression model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjQI7Z4vYcEH",
        "outputId": "46330485-b31a-43d3-f31e-bc178f03526f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXEtWnb4YkWt",
        "outputId": "399c8e2f-5707-4377-a348-5a1238e01315",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Predicting a new result\n",
        "print(classifier.predict(sc.transform([[45,250000]])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLuZoT9nY3Jo",
        "outputId": "569b4568-8a7c-4289-b931-c8d674bb445c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            " [1 1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6-0C1oHY9QG"
      },
      "source": [
        "## 6. Making the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PljnPt89Y6Vh",
        "outputId": "5afe1052-2665-4bd8-961d-fa48329e77ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix :\")\n",
        "print(cm)\n",
        "print(\"The accuracy of the model is :\",accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix :\n",
            "[[65  3]\n",
            " [ 8 24]]\n",
            "The accuracy of the model is : 0.89\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8hSx2AxZaGH"
      },
      "source": [
        "## 7. Visualizing results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQeHaf6qYrsw",
        "outputId": "4345fb26-f4a6-4ac4-c0d1-5b9245597c41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Training set results\n",
        "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 = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\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('Logistic Regression (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "code",
      "metadata": {
        "id": "qsWKA8TPYyA4",
        "outputId": "0f7f2449-68e4-457b-8e6c-11347554937f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Visualising the Test set results\n",
        "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 = 0.25),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\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('Logistic Regression (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "code",
      "metadata": {
        "id": "Zr704YOrZhad"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}