logistic_regression_using_sklearn.ipynb

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      "metadata": {
        "id": "2tCDfEJ-mMId"
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      "source": [
        "import numpy as np\n",
        "\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = make_classification(\n",
        "    n_samples=1000,\n",
        "    n_features=2,\n",
        "    n_informative=2,\n",
        "    n_redundant=0,\n",
        "    n_clusters_per_class=1,\n",
        "    flip_y=0.01,\n",
        "    class_sep=1.0,\n",
        "    random_state=42\n",
        ")"
      ],
      "metadata": {
        "id": "WjoW-Yvpmi8u"
      },
      "execution_count": 3,
      "outputs": []
    },
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        "X"
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              "       [ 1.35447877, -0.94852841],\n",
              "       ...,\n",
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              "       [-0.02587554,  1.6192578 ],\n",
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        "y"
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      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)"
      ],
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        "id": "WZSa2fpCABMB"
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      "execution_count": 7,
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      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
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              "LogisticRegression()"
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance on training data\n",
        "y_pred_train = model.predict(X_train)\n",
        "print(accuracy_score(y_train, y_pred_train))\n",
        "print(confusion_matrix(y_train, y_pred_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMj1nxUGAah8",
        "outputId": "256aaaa9-52f0-42c5-eece-a70b9878b3fa"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.90625\n",
            "[[371  30]\n",
            " [ 45 354]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance on test data\n",
        "y_pred_test = model.predict(X_test)\n",
        "print(accuracy_score(y_test, y_pred_test))\n",
        "print(confusion_matrix(y_test, y_pred_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfaKYnnmAsuw",
        "outputId": "1c774694-29da-442f-e0da-21c9149add4b"
      },
      "execution_count": 12,
      "outputs": [
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          "name": "stdout",
          "text": [
            "0.9\n",
            "[[93  7]\n",
            " [13 87]]\n"
          ]
        }
      ]
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