Linear_Regression_&_IRIS_practice.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "eDdN5mXG5yjv"
      },
      "outputs": [],
      "source": [
        "# Import necessary Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "import matplotlib as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "hLTeFOI0_yuU"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "nJ1458HKBQMA"
      },
      "outputs": [],
      "source": [
        "X = np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1)\n",
        "y = [2, 3.5, 3.9, 4.6, 4.9, 5.7]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_F_w88l2LBI",
        "outputId": "0cd43a82-c66b-4462-abc8-20062b9afa61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "BVySmgfyyKOS",
        "outputId": "dd74a551-a1ae-41a8-8542-44439a5e9f16"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "model.fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0r-THHsyOEI",
        "outputId": "8783d9ca-83f7-42b4-c12a-29643de2b028"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.10285714])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "model.predict([[5]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Rw-vjwn2yQha",
        "outputId": "a9841b48-17ab-46b1-cf41-1d97fccb0db7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualize the model\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(X, y, marker = 'o')\n",
        "plt.plot(X, model.predict(X), color = 'red')\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('y')\n",
        "plt.title('Linear Regression model')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T79vDUl2_b6_",
        "outputId": "851ba108-93b2-45f1-f4f9-8683fd003a92"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9470079557246628"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "\n",
        "model.score(X, y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "EDJd289y15Fj"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "# Load the Iris dataset\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data[:, :2]\n",
        "y = (iris.target != 0).astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "x_b_G33V6fjw"
      },
      "outputs": [],
      "source": [
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "90M6pUa56fga"
      },
      "outputs": [],
      "source": [
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "1zkbtx1a6eJF",
        "outputId": "e037f5f9-825d-4fc9-a387-5119c73ca5bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "hqhoOO2l6eFk"
      },
      "outputs": [],
      "source": [
        "y_pred = model.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EuKMHC826l0N",
        "outputId": "a3201c02-169d-4b5a-845c-25a0efe0e978"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy 1.0\n",
            "conf_matrix [[10  0]\n",
            " [ 0 20]]\n",
            "classification_report_str               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00        20\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Evaluation\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "classification_report_str = classification_report(y_test, y_pred)\n",
        "print(\"accuracy\", accuracy)\n",
        "print(\"conf_matrix\", conf_matrix)\n",
        "print(\"classification_report_str\", classification_report_str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "KIEeda8P6lww"
      },
      "outputs": [],
      "source": [
        "# Plot the decision boundary\n",
        "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))\n",
        "\n",
        "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "z3cwQ42U3rbK",
        "outputId": "48d9e02e-c3d5-4676-84bf-0b48a7f8df2e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "plt.contourf(xx, yy, Z, alpha=0.3)\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o', s=50, linewidth=1)\n",
        "plt.title(\"Logistic Regression\")\n",
        "plt.xlabel(\"Feature 1\")\n",
        "plt.ylabel(\"Feature 2\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "tuCxkRzW3rWN",
        "outputId": "211be465-b4ac-4a47-ec2e-809e5e313da8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=True)\n",
        "\n",
        "classes = [\"Class 0\", \"Class 1\"]\n",
        "tick_marks = np.arange(len(classes))\n",
        "plt.xticks(tick_marks + 0.5, classes)\n",
        "plt.yticks(tick_marks + 0.5, classes)\n",
        "\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "\n",
        "for i in range(len(classes)):\n",
        "    for j in range(len(classes)):\n",
        "        plt.text(j + 0.5, i + 0.5, str(conf_matrix[i, j]), ha='center', va='center', color='w')\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}