LinearRegression.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Linear Regression\n",
        "\n",
        "HousePrice_2col dataset"
      ],
      "metadata": {
        "id": "iPBwTOm2beFT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "lFSt8U5dgArP"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(r\"/content/HousePrice_2col.csv\")\n",
        "X = data.iloc[:,:-1]\n",
        "y = data.iloc[:,-1]"
      ],
      "metadata": {
        "id": "YZNSz0jBgMDz"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
      ],
      "metadata": {
        "id": "zyYGZB9Lgeup"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "linear = LinearRegression()\n",
        "linear.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "T8e_yGbpggkw",
        "outputId": "e24ec4fe-ed91-4bc2-9b33-27bd99b5232e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\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-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = linear.predict(X_test)"
      ],
      "metadata": {
        "id": "nd8ShTkigkia"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rq = linear.score(X_test, y_test)\n",
        "print(\"Coeff of determination: \", rq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "co3cvSSyg9YD",
        "outputId": "3e307552-8792-437a-dab6-30fa2cd81aca"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coeff of determination:  0.8773564150358519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Intercept: \", linear.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMUZN8abhFfD",
        "outputId": "1095c4cb-27ba-43b3-c06d-90dc73c4aeea"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept:  50.437572928821396\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Slop\", linear.coef_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCezvve7hPCg",
        "outputId": "91a14497-c83b-4181-a774-7ef960ceaa78"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Slop [0.22666278]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.scatter(X_train, y_train, color = 'red')\n",
        "plt.plot(X_train,linear.predict(X_train), color = 'blue')\n",
        "plt.title(\"Training set\")\n",
        "plt.xlabel(\"Area\")\n",
        "plt.ylabel(\"Price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "Z9zg_gHNhUip",
        "outputId": "783c7309-0d8d-4200-d2df-595f5d4d5b61"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7d1d91652b30>]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "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": [
        "plt.scatter(X_test, y_test, color = 'red')\n",
        "plt.plot(X_train, linear.predict(X_train), color = 'blue')\n",
        "plt.title(\"Testing Set\")\n",
        "plt.xlabel(\"Area\")\n",
        "plt.ylabel(\"Price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "K7wSZW4uiXS1",
        "outputId": "ea0837e5-5b0b-41a2-8617-c79bd7c8fd8e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Price')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}