Regression.ipynb

{
  "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": null,
      "metadata": {
        "id": "hNfDbTCcxthd"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the data\n",
        "data = pd.read_csv('Salary_Data.csv')\n",
        "# input variable\n",
        "x = data[['YearsExperience']]\n",
        "# output variable\n",
        "y = data['Salary']"
      ],
      "metadata": {
        "id": "11_rhB9wyPoG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.scatter(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "2qRJLsGNyW8R",
        "outputId": "02665109-8927-4035-fb8c-194e6e475aac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x79561bef1650>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import the class\n",
        "from sklearn.linear_model import LinearRegression\n",
        "# create the object\n",
        "reg = LinearRegression()\n",
        "# train\n",
        "reg.fit(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "3qNuZycnzmyH",
        "outputId": "3a9aceee-a27f-4348-9d0e-ad2d48244fb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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>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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</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.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = reg.coef_[0]\n",
        "c = reg.intercept_"
      ],
      "metadata": {
        "id": "NgYrtHhZ1q10"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "12 * m + c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTiv7JE92WOL",
        "outputId": "0732eb84-1d54-4e06-aaa0-ecab962e94ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "139191.7480561296"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[12]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_PPmhWv2uql",
        "outputId": "e338aec7-418e-4044-ada8-0c5c7232efa1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([139191.74805613])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = reg.predict(x)\n",
        "plt.xlabel('Experience')\n",
        "plt.ylabel('Salary')\n",
        "plt.scatter(x, y, color = 'red')\n",
        "plt.plot(x, pred, color = 'green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "hLCYJob32_3o",
        "outputId": "09ff52fc-aaec-4b13-bdd3-284527eda864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x79560f547350>]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.score(x, y) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkeNJRM237F2",
        "outputId": "075a3819-a731-4b2b-d98e-5173706b3941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95.69566641435085"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gjEOtZc04K6-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}