LogisticRegression.ipynb

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        "<a href=\"https://colab.research.google.com/github/AjayKadoula/Mtech_Lab_1/blob/main/LogisticRegression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **Logistic Regression**\r\n",
        "\r\n",
        "Logistic regression, or logit regression, or logit model is a regression model where the dependent variable is categorical. Logistic Regression gives the probability associated with each category or each individual outcome. The probability function is joined with the linear equation using probability distribution. In Logistic Regression we use binomial distribution where we work on two category problems.\r\n",
        "\r\n",
        "Logistic Regression is a statistical analysis method used to predict a data value based on prior observations of a data set.\r\n",
        "\r\n",
        "Logistic regression is generally used where the dependent variable is Binary or Dichotomous. That means the dependent variable can take only two possible values such as “Yes or No”, “Default or No Default”, “Living or Dead”, “Responder or Non Responder”, “Yes or No” etc. Independent factors or variables can be categorical or numerical variables.\r\n",
        "\r\n",
        "Logistic regression uses an equation as the representation, very much like linear regression. It’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits.\r\n",
        "\r\n",
        "Below is an example logistic regression equation:\r\n",
        "\r\n",
        "z = (b0 + b1*x)\r\n",
        "\r\n",
        "y = e^(z) / (1 + e^(z))\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIdd4CNzP140"
      },
      "source": [
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import pandas as pd"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "-TU5twlZWKBx",
        "outputId": "2eca389b-d179-4afe-85c3-6d8ce2ee5ea3"
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      "source": [
        "dataset = pd.read_csv('S_N_Ads.csv')\r\n",
        "X = dataset.iloc[:, [2, 3]].values\r\n",
        "Y = dataset.iloc[:, 4].values\r\n",
        "print(X)\r\n",
        "print(Y)\r\n",
        "\r\n",
        "\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.25, random_state = 0)\r\n"
      ],
      "execution_count": 12,
      "outputs": [
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NiIw4WuRXGfu"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\r\n",
        "sc_X = StandardScaler()\r\n",
        "X_Train = sc_X.fit_transform(X_Train)\r\n",
        "X_Test = sc_X.transform(X_Test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqKJAC9_W_1B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f209300-dac8-4ed1-a748-c3f31d2b571f"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\r\n",
        "classifier = LogisticRegression(random_state = 0)\r\n",
        "classifier.fit(X_Train, Y_Train)"
      ],
      "execution_count": 14,
      "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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8o2OXv21XRKp"
      },
      "source": [
        "Y_Pred = classifier.predict(X_Test)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daIxXlcNXRNe"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\r\n",
        "cm = confusion_matrix(Y_Test, Y_Pred)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOF_nvMAXRmM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "1bdea858-bbfe-49d1-b7c8-1bad264af5b5"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\r\n",
        "X_Set, Y_Set = X_Train, Y_Train\r\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_Set[:,0].min() -1, stop = X_Set[:, 0].max() +1, step = 0.01),\r\n",
        "                     np.arange(start = X_Set[:,1].min() -1, stop = X_Set[:, 1].max() +1, step = 0.01))\r\n",
        "\r\n",
        "plt.contourf(X1,X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\r\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'white')))\r\n",
        "\r\n",
        "plt.xlim(X1.min(), X2.max())\r\n",
        "plt.ylim(X2.min(), X2.max())\r\n",
        "for i, j in enumerate(np.unique(Y_Set)):\r\n",
        "    plt.scatter(X_Set[Y_Set == j, 0], X_Set[Y_Set == j,1],\r\n",
        "                c = ListedColormap(('blue', 'green'))(i), label = j)\r\n",
        "plt.title('Logistic Regression ( Training set)')\r\n",
        "plt.xlabel('Age')\r\n",
        "plt.ylabel('Estimated Salary')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "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"
          }
        }
      ]
    }
  ]
}