{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Dx2aLKrsLp4r"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import RidgeCV\n",
        "from sklearn.metrics import r2_score\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = fetch_california_housing()\n",
        "X = data.data\n",
        "y = data.target\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "bxp4PnoHLqxA"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "Qmm_b08OLqzt"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ridge_cv = RidgeCV(alphas=[0.1, 1.0, 10.0], cv=5)\n",
        "ridge_cv.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "sfIYVghlLq17",
        "outputId": "5171fea4-85e0-437c-cd56-3dbd6928cb25"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RidgeCV(alphas=[0.1, 1.0, 10.0], cv=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeCV(alphas=[0.1, 1.0, 10.0], cv=5)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RidgeCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.RidgeCV.html\">?<span>Documentation for RidgeCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RidgeCV(alphas=[0.1, 1.0, 10.0], cv=5)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ridge_cv.predict(X_test_scaled)\n",
        "\n",
        "print(\"Best alpha selected:\", ridge_cv.alpha_)\n",
        "print(\"Model score (R^2):\", r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33chghuALq4c",
        "outputId": "6073fd2a-030d-41d1-b08d-d164a214f4d6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best alpha selected: 10.0\n",
            "Model score (R^2): 0.595944060491304\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.scatter(y_test, y_pred, color='blue', alpha=0.5, label='Predicted vs Actual')\n",
        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], color='yellow', linewidth=2, label='Best Fit Line')\n",
        "plt.xlabel(\"Actual Values\")\n",
        "plt.ylabel(\"Predicted Values\")\n",
        "plt.title(\"Ridge Regression: Actual vs Predicted\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "vuLxPga-Lq9q",
        "outputId": "ae1aa622-9d60-446e-d65c-31d97795004e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zvuZ0pdoMFA-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}