abalone.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNYWWHdqrs0FFqqDYAMUHW8"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLxPSmrNL1kn",
        "outputId": "b9276d23-e552-4a8b-f6ae-98d5629028ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ucimlrepo\n",
            "  Downloading ucimlrepo-0.0.7-py3-none-any.whl.metadata (5.5 kB)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ucimlrepo) (2.1.4)\n",
            "Requirement already satisfied: certifi>=2020.12.5 in /usr/local/lib/python3.10/dist-packages (from ucimlrepo) (2024.8.30)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.0->ucimlrepo) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->ucimlrepo) (1.16.0)\n",
            "Downloading ucimlrepo-0.0.7-py3-none-any.whl (8.0 kB)\n",
            "Installing collected packages: ucimlrepo\n",
            "Successfully installed ucimlrepo-0.0.7\n"
          ]
        }
      ],
      "source": [
        "!pip install ucimlrepo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "VsOjajerP-wk"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "abalone = fetch_ucirepo(id=1)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = abalone.data.features\n",
        "y = abalone.data.targets"
      ],
      "metadata": {
        "id": "vPKLD71LQ2rH"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = X.reset_index()"
      ],
      "metadata": {
        "id": "UjV33db3eYIs"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# replace M, F and I with numerical values\n",
        "sex_list = []\n",
        "\n",
        "\n",
        "for index, row in X.iterrows():\n",
        "    sex = row['Sex']\n",
        "    if sex == 'M':\n",
        "        sex_list.append(2)\n",
        "    elif sex == 'F':\n",
        "        sex_list.append(1)\n",
        "    elif sex == 'I':\n",
        "        sex_list.append(0.5)"
      ],
      "metadata": {
        "id": "jVCku0T9b8hm"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# remove the male female classification\n",
        "X = X.drop('Sex', axis='columns')"
      ],
      "metadata": {
        "id": "f_fetOY4V3oV"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# add numerical values of sex\n",
        "X = X.assign(Sex=sex_list)"
      ],
      "metadata": {
        "id": "zBQJsS5HgyAz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "lzfWRIl9RG7x"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y,\n",
        "    test_size=0.8,\n",
        "    random_state=12,\n",
        ")"
      ],
      "metadata": {
        "id": "Wqq8nEyZTAvi"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cross Validation\n",
        "\n",
        "Cross validation is the process of selecting internal batches within the entire dataset, selecting one batch and using it is a validation set. This helps prevent overfitting, making sure the model picks the correct trends."
      ],
      "metadata": {
        "id": "sDL8frsnTk--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "bn2YaFSmUvNy"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "5lDKftIBU63j"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "itQmLt7FVBUY"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "pGnlKqkBVNxY"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "iIq795J-WkMg",
        "outputId": "e77449a0-57a2-416b-f9c7-6f3f5e2dcfa6",
        "collapsed": true
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\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-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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: 1ex;\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-5 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-5 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-5 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-5 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-5\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse = cross_val_score(\n",
        "    model, X_train, y_train,\n",
        "    scoring='neg_mean_squared_error',\n",
        "    cv=10,\n",
        ")"
      ],
      "metadata": {
        "id": "qxsEMmNfW8xz"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = np.sqrt(np.abs(mse))"
      ],
      "metadata": {
        "id": "17sj0b_RHn2e"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wavV_3kSXi3N",
        "outputId": "ced595b0-225a-4057-a39f-9c1b82d04510"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.20281388490066"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "PFRJK4huXk4g"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "eUwwQ65sXq8X"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(y_test - y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "Fw9gTQD6X911",
        "outputId": "9cca810d-d6f2-4ad2-ff37-10a9917c411b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7acf1d0ac2b0>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 588.25x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "DTEjjvZLYIy5"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcK2feu1Y2Ap",
        "outputId": "96ec650c-b0da-4a82-a87c-c92b58a6bf20"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5089685114567538"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    }
  ]
}