realestate.ipynb

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        "<a href=\"https://colab.research.google.com/github/sneha30404/Real-Estate-Price-Prediction/blob/main/realestate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Importing necessary libraries."
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading data from csv file"
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      "source": [
        "df=pd.read_csv('Housing.csv')\n",
        "df.head(10)"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price   area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  13300000   7420         4          2        3      yes        no       no   \n",
              "1  12250000   8960         4          4        4      yes        no       no   \n",
              "2  12250000   9960         3          2        2      yes        no      yes   \n",
              "3  12215000   7500         4          2        2      yes        no      yes   \n",
              "4  11410000   7420         4          1        2      yes       yes      yes   \n",
              "5  10850000   7500         3          3        1      yes        no      yes   \n",
              "6  10150000   8580         4          3        4      yes        no       no   \n",
              "7  10150000  16200         5          3        2      yes        no       no   \n",
              "8   9870000   8100         4          1        2      yes       yes      yes   \n",
              "9   9800000   5750         3          2        4      yes       yes       no   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no             yes        2      yes        furnished  \n",
              "1              no             yes        3       no        furnished  \n",
              "2              no              no        2      yes   semi-furnished  \n",
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              "4              no             yes        2       no        furnished  \n",
              "5              no             yes        2      yes   semi-furnished  \n",
              "6              no             yes        2      yes   semi-furnished  \n",
              "7              no              no        0       no      unfurnished  \n",
              "8              no             yes        2      yes        furnished  \n",
              "9              no             yes        1      yes      unfurnished  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1870439,\n        \"min\": 1750000,\n        \"max\": 13300000,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          3773000,\n          5285000,\n          1820000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"furnished\",\n          \"semi-furnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking if missing values exist in dataset"
      ],
      "metadata": {
        "id": "eTV4VWYLxIIU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SD6esQKNpP1O",
        "outputId": "68e0af0c-baa7-425d-e77e-ed4e2b645def"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 545 entries, 0 to 544\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   price             545 non-null    int64 \n",
            " 1   area              545 non-null    int64 \n",
            " 2   bedrooms          545 non-null    int64 \n",
            " 3   bathrooms         545 non-null    int64 \n",
            " 4   stories           545 non-null    int64 \n",
            " 5   mainroad          545 non-null    object\n",
            " 6   guestroom         545 non-null    object\n",
            " 7   basement          545 non-null    object\n",
            " 8   hotwaterheating   545 non-null    object\n",
            " 9   airconditioning   545 non-null    object\n",
            " 10  parking           545 non-null    int64 \n",
            " 11  prefarea          545 non-null    object\n",
            " 12  furnishingstatus  545 non-null    object\n",
            "dtypes: int64(6), object(7)\n",
            "memory usage: 55.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hence, no missing data-values"
      ],
      "metadata": {
        "id": "xqctMSnRxN-X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PAIRPLOT AND DISTPLOT"
      ],
      "metadata": {
        "id": "vQ0hm_7WxRAD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['mainroad'] = df['mainroad'].map({'yes': 1, 'no': 0})\n",
        "df['price'] = df['price']/10000000\n",
        "df['guestroom'] = df['guestroom'].map({'yes': 1, 'no': 0})\n",
        "df['airconditioning'] = df['airconditioning'].map({'yes': 1, 'no': 0})\n",
        "df['furnishingstatus'] = df['furnishingstatus'].map({'furnished': 1, 'semi-furnished': 0.5, 'unfurnished':0})\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "I57hHyllQucR",
        "outputId": "74f9a181-67d5-4952-93b1-efae0da01514"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    price  area  bedrooms  bathrooms  stories  mainroad  guestroom basement  \\\n",
              "0  1.3300  7420         4          2        3         1          0       no   \n",
              "1  1.2250  8960         4          4        4         1          0       no   \n",
              "2  1.2250  9960         3          2        2         1          0      yes   \n",
              "3  1.2215  7500         4          2        2         1          0      yes   \n",
              "4  1.1410  7420         4          1        2         1          1      yes   \n",
              "\n",
              "  hotwaterheating  airconditioning  parking prefarea  furnishingstatus  \n",
              "0              no                1        2      yes               1.0  \n",
              "1              no                1        3       no               1.0  \n",
              "2              no                0        2      yes               0.5  \n",
              "3              no                1        3      yes               1.0  \n",
              "4              no                1        2       no               1.0  "
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1870439615657392,\n        \"min\": 0.175,\n        \"max\": 1.33,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          0.3773,\n          0.5285,\n          0.182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38068633519841477,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "hCjnLjuGxsvm",
        "outputId": "b4977835-51fb-48de-9b9f-af4135dd0eef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2500 with 110 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['price'], color='orange')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "iM6FSwbV_mtc",
        "outputId": "8cde6a2a-1795-4c76-b78c-526514385318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-167-893d9c74734e>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df['price'], color='orange')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Identifying features and label"
      ],
      "metadata": {
        "id": "DhzFdOeCAg-R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.drop(['hotwaterheating','basement', 'prefarea','price'],axis=1)\n",
        "y=df['price']\n",
        "x.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "iq9VsAPlAj2S",
        "outputId": "395f3a7d-44b9-46fd-e861-63405779babf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    area  bedrooms  bathrooms  stories  mainroad  guestroom  airconditioning  \\\n",
              "0   7420         4          2        3         1          0                1   \n",
              "1   8960         4          4        4         1          0                1   \n",
              "2   9960         3          2        2         1          0                0   \n",
              "3   7500         4          2        2         1          0                1   \n",
              "4   7420         4          1        2         1          1                1   \n",
              "5   7500         3          3        1         1          0                1   \n",
              "6   8580         4          3        4         1          0                1   \n",
              "7  16200         5          3        2         1          0                0   \n",
              "8   8100         4          1        2         1          1                1   \n",
              "9   5750         3          2        4         1          1                1   \n",
              "\n",
              "   parking  furnishingstatus  \n",
              "0        2               1.0  \n",
              "1        3               1.0  \n",
              "2        2               0.5  \n",
              "3        3               1.0  \n",
              "4        2               1.0  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38068633519841477,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split dataset to train and test data"
      ],
      "metadata": {
        "id": "hEHUVhz7M6fk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "x_train.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "wrhSP501M7Q1",
        "outputId": "30b7929a-d008-4611-bb2c-86c352198001"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     area  bedrooms  bathrooms  stories  mainroad  guestroom  airconditioning  \\\n",
              "46   6000         3          2        4         1          0                1   \n",
              "93   7200         3          2        1         1          0                1   \n",
              "335  3816         2          1        1         1          0                1   \n",
              "412  2610         3          1        2         1          0                0   \n",
              "471  3750         3          1        2         1          0                0   \n",
              "237  5010         3          1        2         1          0                0   \n",
              "544  3850         3          1        2         1          0                0   \n",
              "470  5076         3          1        1         0          0                0   \n",
              "511  3180         3          1        1         0          0                0   \n",
              "361  6020         3          1        1         1          0                0   \n",
              "\n",
              "     parking  furnishingstatus  \n",
              "46         1               1.0  \n",
              "93         3               0.5  \n",
              "335        2               1.0  \n",
              "412        0               0.0  \n",
              "471        0               0.0  \n",
              "237        0               0.5  \n",
              "544        0               0.0  \n",
              "470        0               0.0  \n",
              "511        0               0.0  \n",
              "361        0               0.5  "
            ],
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>335</th>\n",
              "      <td>3816</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>2610</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>471</th>\n",
              "      <td>3750</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>5010</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>3850</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>470</th>\n",
              "      <td>5076</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>511</th>\n",
              "      <td>3180</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>6020</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8352dd4-d25f-4f4b-af29-864b446e0875')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b8352dd4-d25f-4f4b-af29-864b446e0875 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b8352dd4-d25f-4f4b-af29-864b446e0875');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a93abfbe-5a08-4b51-8dc3-24ac07f0ea3d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a93abfbe-5a08-4b51-8dc3-24ac07f0ea3d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a93abfbe-5a08-4b51-8dc3-24ac07f0ea3d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 436,\n  \"fields\": [\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2204,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          2145,\n          3850,\n          3990\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3763478011159753,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model"
      ],
      "metadata": {
        "id": "v3yQFihJeXfQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg = LinearRegression()\n",
        "reg.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "GznQ67kjeYLG",
        "outputId": "f638a758-093e-46b7-a4d0-f7782c4cf38b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(reg.coef_, x.columns, columns=['coefficient'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "018mbNjgtdQh",
        "outputId": "6b41fd7b-a288-469e-d4df-0e45a70dc6c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  coefficient\n",
              "area                 0.000024\n",
              "bedrooms             0.014759\n",
              "bathrooms            0.111065\n",
              "stories              0.032051\n",
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          "data": {
            "text/plain": [
              "-0.01392813345870264"
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          "metadata": {},
          "execution_count": 172
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              "price\n",
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              "       Test  Prediction\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6db1c80b-7f1d-4b39-a40c-aa24c7da7db1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6db1c80b-7f1d-4b39-a40c-aa24c7da7db1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5f8c6b4e-0929-4ee4-81e2-2dd17394e10f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f8c6b4e-0929-4ee4-81e2-2dd17394e10f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5f8c6b4e-0929-4ee4-81e2-2dd17394e10f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14245866613005878,\n        \"min\": 0.245,\n        \"max\": 0.665,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.245,\n          0.665,\n          0.49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14020243549336925,\n        \"min\": 0.29055250762836576,\n        \"max\": 0.6877037496392318,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.3125313009106902,\n          0.6877037496392318,\n          0.3880200092335695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evalution of the model"
      ],
      "metadata": {
        "id": "LwUCrq4Dqn4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "MAE = metrics.mean_absolute_error(y_predict, y_test)\n",
        "MSE = metrics.mean_squared_error(y_predict, y_test)\n",
        "RMSE = np.sqrt(MSE)\n",
        "pd.DataFrame([MAE, MSE, RMSE], index=['MAE', 'MSE', 'RMSE'], columns=['Metrics'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "rHHPIJJJqo0_",
        "outputId": "e57eda25-3840-4ccc-c392-330ac7b3f1bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Metrics\n",
              "MAE   0.101554\n",
              "MSE   0.019413\n",
              "RMSE  0.139331"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e218b33d-b39f-4184-8866-f2b1f6fead3c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Metrics</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MAE</th>\n",
              "      <td>0.101554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSE</th>\n",
              "      <td>0.019413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.139331</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e218b33d-b39f-4184-8866-f2b1f6fead3c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e218b33d-b39f-4184-8866-f2b1f6fead3c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e218b33d-b39f-4184-8866-f2b1f6fead3c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-489591aa-41d3-4c57-b606-f13c348db54a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-489591aa-41d3-4c57-b606-f13c348db54a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-489591aa-41d3-4c57-b606-f13c348db54a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Metrics\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.061311422726643736,\n        \"min\": 0.01941318972389191,\n        \"max\": 0.13933122307613577,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.10155374439883649,\n          0.01941318972389191,\n          0.13933122307613577\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['price'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rnRim65q7s0",
        "outputId": "b1ba300b-4b66-4886-dc6c-3571159bdb8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4766729247706422"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing the residuals in the model"
      ],
      "metadata": {
        "id": "by1VWTNGrU8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "residuals = y_test-y_predict\n",
        "sns.scatterplot(x=y_test, y=y_predict)\n",
        "plt.xlabel('y_test')\n",
        "plt.ylabel('y_predict')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "GLexLvTPrZdo",
        "outputId": "3f3f5784-a755-4b94-98c3-ef8b281f5157"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "sns.distplot(residuals)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "-sSw47NXUHeA",
        "outputId": "aa8a581e-75a0-4aff-b1f5-4732676a35b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-178-449001059fb5>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(residuals)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='price', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 178
        },
        {
          "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": [
        "sns.scatterplot(x=y_test, y=residuals)\n",
        "plt.axhline(y=0, color='r', ls='--')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "p4xRgNo_URj5",
        "outputId": "4073e797-fff1-4390-b27b-38afeaa0890e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7b61c27bb250>"
            ]
          },
          "metadata": {},
          "execution_count": 179
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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s1KaMn+yr1qPv7ul0PE6o5wCAWIsqCK1cuVKrV6/Wo48+qrS0tPbbx48frz179sSscwDsIZJ9bMJNfW3Zf1KpKdKUkgFhnwMAYi2qIHTo0CGNHTu22+2ZmZmqr6/vc6cA2Esk+9j0NH32Z2+jbp88VFPZCwdAAkVVIzR06FDt2rVLgwcP7nT7+vXrNWLEiJh0DEBsJGpfnp72selp+iw9NUX3ranQf9w7WakpKeyFAyAhogpC8+fP17333qvGxkYZhqHt27drzZo1Wrp0qV566aVY9xFAlBK9L0+4fWzCbco4uWSAKo7UqMHfqtSUFE5KB5AwUe8j9MYbb+jxxx/XgQMHJElFRUVavHix5s2bF9MOmo19hGBXVtyX52jN6W6bMk4uGaC5k4fq/jUVGj+4P/sFAYiJSN+/+7yhYkNDg+rq6lRYWNiXp7EsghDs6sDxOl25/OOQ92+YP92UkRdvg19Vvkb976nTkqSKIzV6ZfMhjR/cX0/dNFrnsYM0gBiI64aKhw4dUktLi0pLS5WTk6OcnBxJ0r59+5SRkaEhQ4ZE1WkAsWPVE+MD02eD3FmqrvNrQK5LN5adTy0QAFNEtWrs9ttv19atW7vd/umnn+r222/va5+AhPM2+HXgeJ0qKk/pwIk6eRvCb+5nB1Y/MT4/x6XhhXkqu6C/hhfmEYIAmCKqEaGKigpNnjy52+2XXHKJysvL+9wpIJGS9aDPSE+MBwAni2pEKCUlRbW1td1u93q97btMA3YQarfjTfuqteCd3QkbGYrHiFQke/sAgNNFNSI0bdo0LV26VGvWrGnfWbq1tVVLly7VlClTYtpBIJ6scNBnPEeketrbB/GXqH2cAEQnqiD01FNPadq0abrwwgs1depUSdInn3win8+njRs3xrSDQDyZXVDc04hULJaSh9vbB/GVrNOuQDKJamps5MiR2r17t26++WYdP35ctbW1mj17tr788kuNGjUq1n0E4sbsguJIRqRgT1aZdgUQXlQjQtKZDRSffPLJWPYFSDizC4rNHpFC/Fhh2hVAzyIOQrt379aoUaOUmpqq3bt3h207evToPncMSIRAQXHX3Y4TVVBs9oiUVSRjHQ0hF7CHiINQWVmZqqqqVFhYqLKyMqWkpCjYptQpKSmsHIOtmFlQbPaIlBUkax0NIRewh4iD0KFDh3Tuuee2/xtIJmYVFJs9ImW2RBSLm4WQC9hDr88aa25u1j/90z/pscce09ChQ+PVL8vgrDH0xTFfo07V++VrbJE7O139c1wa6M7q1i4wNeS0Je5WPQ8tVoIdMhsIuZypBsRX3M4ay8jI0DvvvKPHHnusTx0Ekl3lyXo9vG6Ptuw/2X7blJIBevLGi3XBgNxObbuOSAU2WEymmplgkr2OJpJp12SsjwLsJKpVYzfccIPeffddff/73491f4CkcMzX2C0ESdLm/Sf1yLo9+tnNZUFHhqTkrZkJxgl1NOGmXZ30vY4UwRCJFlUQKi0t1RNPPKEtW7Zo3Lhxys3t/Nft/fffH5POAXZ1qt7fLQQFbN5/Uqfq/SGnyJK1ZiYYJ9fROO17HQmCIcwQVRB6+eWXVVBQoB07dmjHjh2d7ktJSSEIwfF8jS1R3d/XvWfs9te0k4vF2WeoM4IhzBJVEOq4aixQa52SkhKbHgFJwJ0V/lcr1P19qZmx61/TTj0PLdnro3qLYAizRHXEhnRmVGjUqFHKyspSVlaWRo0apZdeeimWfQNsq3+uS1NKBgS9b0rJAPXPDf6CHm3NjN2Pc8jPcWl4YZ7KLuiv4YV5jnjDc0J9VG8QDGGWqILQwoUL9cADD+i6667T22+/rbffflvXXXedvv/972vhwoWx7iNgOwPdWXryxou7haHAqrFQhdKBmplgwtXMcGaZ/UT7vU5WBEOYJaqpseeff16rV6/WrFmz2m+7/vrrNXr0aN1333164oknYtZBwK4uGJCrn91cdnYfoax09c8Nvo9QQLQ1M/w1bT9Oro8KxsmF8zBXVEGoublZ48eP73b7uHHj1NISvkgUcJKB7qywwSeYaGpm+GvanpxaHxUMwRBmiSoI3XbbbXr++ee1fPnyTre/+OKLuvXWW2PSMcDJenvkB39N25dZx7tYEcEQZuj1ERuSdN999+nf//3fVVxcrEsuuUSS9Omnn6qyslKzZ89WRsbZvz67hiW74YgN2AXHOQDAWZG+f0cVhC6//PKI2qWkpGjjxo29fXpLIQjBTuxwZpnd9joCYE9xO2tMkj788MOoOwYgfqw+zWLXvY66IswBySOqIAQAvZUsOwcnS5gDcEbUGyoCQG8kw15Hdt+4EkB3BCEACZEMex0lQ5gD0BlBCEBCJMNeR8kQ5gB0Ro0QEGfxLKy1U9FuMux1lAxhDkBnBCEgjuJZWBur505UmEqGnYOTIcwB6CyqfYSchH2EEE6oEOFt8Ot4bZMq/9KglJQU7aw8pVc2H1KDv1XSmTfNvqyS8jb4Vb6mImi9Sm+e24wVUHbY6ygcNq4E7CGu+wgBCB4irhpRqMeuHalH3/280+2TSwbomVljdf+aCjX4W9sLa6MNAJEU7fb03GYtZ7f6Xkc94RgIILkQhIAohAoRF57n1sPr9mjL/pOdbg/8/44pQ/Xsxv2S+lZYG4ui3ViEKaeye5gDcBarxpBQ3ga/DhyvU0XlKR04UWfbfVdChYixxQXdQlDAlv0nNba4oP3/fSmsjUXRLiugAIARISRQMu3IGypENLW0hX1c4P6+FtbGomjXCSug7LSqDoA5GBFCQiTbjryhQkRmevhfqcz01JiskgqswJpW6ul0e2+eOxCmgon3CqhEjAwerTmt8jUVunL5x7rxua268mcf6741FTpaczrmnwuAfTEihIToaz2K1f6yDzUiU3GkRlNKBmhzkOmxqaUelZybF7Mi5L4W7Zq1nD0RI4PJcq4ZgPgjCCEh+lKPYsUptVAhYu+ffXryxov1o3c/T8jy6r4W7SZ6BVSiAgqF4AAiRRBCQkRbj2Llv+zDhQg7La9O5AqoRAUUCsEBRIoghITw5Ll01YhCXXieW2OLC9TU0qasjDTtrDylvX/2haxHiccbZyyn2UKFCKsurzZ7ijFRAcUJheAAYoMghITIz3HpsWtH6uF1e9r30ZGkKSUD9OSNF4d8M471G6cVp9kSxQpfe6ICCkdhAIgUq8aQEN4Gvx599/Nue+xs3n9SP3r385CrhmL5xplsK9eCCbUayypfe6JWqsViVR0AZ2BECAkR7RRXLP+yT7aVa12FG/FpbG61RPFwIleqcRQGgEgQhJAQ0U5xxfKNM9lWrnXU04jPj64dGfbxiSweTmRAsWqtFgDrIAghIfoyxRWrN85kXLkW0NNoV1ubEfbxiS4eJqAAsApqhJAQ4WpDrhpRqLys9LA7DefnuDS8ME9lF/TX8MK8qN5Eo61PiWRKzWw9jXY1+FtN20UaAKyMIISECFW8etWIQj127Ug9+Pb/i/tRCNEW0NphT5qeRrvyszMoHgaAIJgaQ8IUFWTrp38/Rqfq/fI1tsidnS53VkZCp52imWazw540kRSV222jRwBIBIIQEiZYwfGbd05M+Gqm3tan2GFPmkiLyqnNAYDOCELok0iXlIcqOK45bf1pJ7MOJ+0tlosDQO/ZLgitWrVKP/3pT1VVVaUxY8Zo5cqVmjBhQsj2b7/9th577DEdPnxYpaWleuqpp3T11VcnsMfJqzdLykMVHGemhy9Ts8K0k2SfkBHPER+r76MEANGwVbH0W2+9pfnz52vRokXauXOnxowZo5kzZ+r48eNB22/dulWzZs3SvHnzVFFRoRtuuEE33HCDPv/88wT3PPn0dqfiUAXHFUdqNLlkQND7rDLtFBCLlWuxEGr36Hg6WnNa5Wsq4l7QDgCJlmIYRvgNRixk4sSJ+sY3vqFnn31WktTW1qbi4mLdd999WrBgQbf2t9xyi+rr6/X++++333bJJZeorKxML7zwQkSf0+fzKT8/X96jR+V2u7s3SEuTsrLO/r++PvSTpaZK2dnRtW1okEJ9q1JSpJyc6NqePi21tYXuR25u0LYHj9fpmpWbOzU97Tp7HTbeM1HDBpztf9f2gbY5rjQ9+39G6M2thzodvzG5ZIB+/O1RGlSQfaa/KSln7mhqklpaQve3N22zs89cZ0ny+6XmMNNwvWmblXXm50KSt6ZOJ0/Vq7bpzCjSgK6jKB3aqrn5zHOHkpmpo3XNeuid3dr2ZZUyWs98bZNLBmjJt0fpvI6jcJmZUvpfB3xbWs5ci1BcLikjI2Rbb4Nf//L2/9OW/SfVnJaulrQzz5va1qorhrj1s78fEzwUZmSceW5Jam2VGhtD96Fj27a2Mz9rsWibnn7mWkhnficaGmLTtje/9w59jeixbWPjmZ+LWLS18WtEr9pG8BrR/nvfm7Z9fI0I2bY3v/dxeI1of//2eoO/fwcYNtHU1GSkpaUZ69at63T77Nmzjeuvvz7oY4qLi41/+7d/63TbwoULjdGjR4f8PI2NjYbX623/OHLkiCHJ8J552ej+cfXVnZ8gJyd4O8kwpk/v3NbjCd12/PjObQcPDt125MjObUeODN128ODObcePD93W4+ncdvr0kG3rMzKNwQ+93/5Rc/lVoZ9X6tR2x4Qrw7Y16urO9mHOnPBtjx8/2/aee8K3PXTobNsHHwzb9ve/3mLsP15r1NQ3GcaiReGfd/t2wzAM46tTDcab3ykP3/bDD8/24dlnw7ate2ed8Y8v/dYY/ND7xr9c/b3wz/uLX5x93l/8InzbV1892/b998O2/dFV/9z+fbtl1pPhn/cnPzn7vNu3h2+7aNHZtp9/Hr7tgw+ebXvoUPi299xztu3x4+Hbzplztm1dXfi23/lO59+NcG15jTjzkZPTue3VV4e/bh195zvh21rgNcL4/POzbSN8jTAM48zvSbi2vXiNMN5//2zbV18N3zZOrxHGs8+ebfvhh+Hbxvk1wuv1GpIMr9drhGObqbHq6mq1trZq4MCBnW4fOHCgqqqqgj6mqqqqV+0laenSpcrPz2//KC4u7nvnHSg9LSWidleNKNRFg/qFbXOwwzSQvyXMX5txdP+aXe3TQZEUcAemDg9Vh/mLvpd8p1tCrrADAETHNlNjR48e1fnnn6+tW7dq0qRJ7bf/8Ic/1Mcff6xPP/2022NcLpdef/11zZo1q/225557TosXL9axY8eCfp6mpiY1dRj28/l8Ki4uZmqsS9uO0yXtd/91umtaqUcrbxyh/My0bk/nbfDrZJ1fvjSX+mVlKC8rXQ+/uV3b/niiU7scV5qe/8e/1YsfH9RvKuvah7KvGOLWv143ovM0UKcHhh/2Dnz+2qZm5fV3y9Mv68y0Tpfh6a5fX2OGS0bKmb8bLh+arxX/5+uha4SysnTg5GldufxjZbQ2Kz3IkP5/3TdFwwrzejXsXXGsQTe+uF2SlN7a0j41FvDW3RM1urj/mf/EcNi747Rm16mxzJbms19LV0yN9b5tEr1G9NiWqbHet2Vq7My/Yzw1ZptVYx6PR2lpad0CzLFjxzRo0KCgjxk0aFCv2ktSZmamMgMvhh3l5nb+xQwlkjbRtO34whTLttkhAkUPbfNzc/XEP0wMvaQ8RFDJz81V/rln/3/geJ02Hq6VOtQXSdK8K0r0f397VFuO1J990ZK08bBPD60/ENlGi5mZZ9/Y9NdVbu/9MfQqN9fZ56uuN/Sbyvpu/ZKkDw95VW1kKD/M9y9QHN6clqHmtO4r33xpru7f/4yMsy8gQbjzzr5xtXQIJAG55xQE/5lKTz/7gteTIG0HDMzQN77+tW77KLWlpukbXx+oAQP7Sz19L9LSIv95T02NT9uUlPi0lazR1mKvET3K6v67FZO2XX7vY9bW5er0GmFK2x5eI6Ju28fXiJB683sfr9eICNgmCLlcLo0bN04bNmzQDTfcIOlMsfSGDRtUXl4e9DGTJk3Shg0b9L3vfa/9tl//+tedRpQQvVgsKfc1NivHlaY7pgzV2OICNbW0KSsjTZ48l17ZfCjoY3raaDHYMm9JvdrBuq/HasRjN2qzNna0yz5KABAN2wQhSZo/f77mzJmj8ePHa8KECVqxYoXq6+s1d+5cSdLs2bN1/vnna+nSpZKkBx54QNOnT9fPfvYzXXPNNVq7dq0+++wzvfjii2Z+GUmlr/vW5Gdn6JlZY/XqlkN6duP+9tunlgzQM7PG6v41FWrwdx8SDxVEQu1t9Og1I8LuYH28tqlTeDonx6UcV1rQzy31HGTiEVrMDCSx3EeJ/YgAWImtgtAtt9yiEydOaOHChaqqqlJZWZnWr1/fXhBdWVmp1NSz9d+XXnqp3nzzTf3oRz/SI488otLSUr377rsaNWqUWV8CusjNTNerWzovnZekHZU1+ruLG/Xvd0zQyXq/ivKz1dTSqhN1TcpMT1P/XuxevWlfteacCr/fTeVfGjTv9c/a/z+t1KNXbv+G7njtd93CUCRBJl6hxcyNHfsSegPh51SDX82tbdpy4KRe2XxIDf7WkJtwAkAi2KZY2iwR70OAqBw4Xqcrl3/c6bYcV1r7KFFFZU37vzuGpWBvnsGeK+DlOeM7BZ1I7p9a6tHVF5+nh3+1p9Pnfeqm0aGLtbsIBAAr70Ydb8FG6SaXDNDcyUPbR/ymlnr0bIwP2AXgbElXLI346etURV8eH6wW544pQ9uDT/kVJUFHjILV9oSr66k4UqOppZ6g02OTSwao4khNt9s/2VethdeO1Ib506MOMk4/5DTUKF3g+3nHlKF6duN+ffLX6UknXysA5iAIOVxvzguLx+ODFRWPLS5orxfq+O+uuhZNhytQfmXzIX1w/1Qt/I/PO01VTS31aM6lQ3T/moqgj6tvalHZBf17/DrMZOWam1BnzElnwtAdk4e2/9/bwwG8ABAPBCEH6+m8sJ6WqPf18VLwouKmDpsmNvWwgWLHoulwBcrjB/dX/5yMbvU16akp+rtnPom6KLqv+hpijtac1kO/3K1P9kcXROOtp9V3Hb+/Oa7u+04BQLzZZmdpxF64v9YDoy3RPv6zP51STUNzj4eDBoqKp5V62m/reCJ9b06nD/ZcUucC5a4HpxbkZGj84OAjPvE+9LWvB5l6G/zdQpB05nv3UJCDb83Q0zYCge/v5JIBynXxdxmAxOOVx8H6uldOqMcHip1/9O4efdJDgbPUfSVU/5yzIzuB0+m71ggFnq9rUOntqiqzlqTHYjTteG1TtxAUYJWam3CjdIHarMklA3TfFaUqyInv6BsABEMQcrC+bvoX6vEdi507Cvcm37WoOBBOXtl8SM/MGitJ3VaNhQoqvS1QNmNJeiSjcT19/poeamqsUHMTKmhOLfHoR9eO0J9rGnV+QbaGnJNjemgD4EwEIQfz5LlCrqSaGsG0UKi/9ntT4BxKx3BS39SsJ2+4WP7WNtU3tcQlqCR6dVdfR+MkKbeHmhqr1Nx0DZq5melypaXKe9qv4nNyNPaCgoivvZULwwHYE0HI4e69vERthtFptGVyyQDde3lJj48N9dd+TyJ5kw88f7K+ycXiCI5cV3rIaUOr1dwE/1727qygvq5QBIBgrPNKiYSrrvPrjtd+pzumDNUdk4eqqaVNmempqjhSozte+53+s3xKr0ZuAtNKbT3s0RnvlVi9YdYIQyyO4CjIydB9V5RKUrcgm2w1N7GoqQKAYAhCDhZseialw0nv0Y7ceBv8phwO2ltmjjDEokg7P8elwefk6NrRRZ2C7PHapqSruYlFTRUABMMRGz1I5iM2Dp6o08Hq+m6FzYHjD4Z5cjXs3LyonvtozemQb/KRHk8RT94Gv8rXVAR9c51W6knYCEMsjuBwwjEeFZWndONzW0Pe/+49l1p+40sAicURG+hRqANPt+w/qRRJP7u5LOrnLirI1k//foxO1fvla2yROztd7qwMNTa3qqLylOmFrlYZYYhFHVQy11IFxKKmCgCCIQg5WF1jS9BCW0navP+k6hpbNDDKQbBg005TSgbo9g4HbZpZ6BqLVVtInFjUVAFAMOws7QDeBn/QHZ7jFQZCFbZu3n9Sr245pDumnDlfKlDoasYOyIwwmCfUz2M4kewaDgDRYEQoyYUrCM7Pjk8Y6M1Bm2YVujLCYI6+FKibsfElgOTHiFAS62nJcW5mere/sAP6EgZ6c9CmZM40FCMMidfTz2OkI0Mdz4rj+wSgrxgRSmI9FQTXNbbE5ZytSA/aDDBrGirWIwzsehyeVQrUAaAjglASi6QGaHhhXsynGyI5aDPA7GmoWK24YtfjnlGgDsCKmBpLYnmZ4XNu7l/vj/V0Q6hppyl/3Z/olc2HJCXPNFQspnycgAJ1AFbEiFASc6Wlhj2LypUWvxwcbNopLytd9U0tevPOiUlV6Gr1KR+rTNlRoA7AighCSazmtF9z/7pCK9jO0d7TfvX24MvecMJGf5K1p3ysNGUXi2NFACDWCEJJLC8zQ7NWfxr0UNX711ToP8unmN3FpGDVKR8rHlTKEngAVkMQSmKePJfGD+6vZzfu73ZfvKYirDINk0jxmPKJxXW02pRd169pqCc36X82AFgfQSiJJXoqIhHTMB3fTPMy0+VKS1XNab/ysswLXbG+zrG6jlaasrPSFB0AdMTp8z1IhtPnE3E6eSJOcw/2Zhqod7p/TYXGD+5v6htrrE6Sj9V1PHC8Tlcu/zjk/RvmT9fwwrxe9S8aifjZAICuOH0e7aIpWu7t1Ey8p2FC1bsEisDvmDJUz27c3632JZFTdbEoDo/ldbTKKi2rTdEBQEcEIXTTceQlx5WmO6YM1aXDBsiVnqr+ua6gYSLe0zCRnl/W8Y3VjtMxsbyOVlmlZaUpOgDoiiCETjqOvOS40vTMrLF6dcuhTgXXwcJEvFdO9eb8strGZkuumIpErK+jFVZpWXVVHQBI7CyNLjqOvNwxZahe3XKo24aMwXZMDkzDBDOt1KP01BRVVJ7SgRN1Ue203Jvzy/plZUQ0HWNFPV3HaKazzD6oNB5fEwDECkEInXQceRlbXBB0V2qpc5gI1OHcf2Wp1tw1UeVXlCjHlSZJmlrq0T2Xl+jvnvlENz63VVf+7GPdt6ZCR2tO96pf4d5MO55fFnhjtet0TKjjSey86WAyfk0AkgdTYzaSiMLfjiMvHaebOgrUDflbW3XgRJ0e/4/P9UmHwDS11KP/vG+KDMPQ7w6f0h2v/U4N/tb2+6OZngpV79Jx1VjHN1Z3VvARn0DfszLSVFF5ypJ7HVlhOivWkvFrApAcCEI2kajC344rjTpONwV0rBuSpIrKU91GjT7ZV63F732hJd8epYd/tSfo54lmtVDXN9Pcv+4j5D3t13+WT+n0xhpsxVRvap7MlozHkyTj1wTA/thHqAeJ3EfomK9Rp+r98jW2yJ2drv45Lg10ZyV8H5ajNae14J3dGl1c0C3olF9RoorKU6qorNHauy/RidomNbW0KSsjTTsrT+mVzYfaR38+uH+Krn5mc8jP8+49l6rsgv4x63eoryMQhgJ9Dzbdx342AJBc2EfIZipP1uvhdXs6vUlPKRmgJ2+8WC1tRkL3YQmMvJys9+vGsefr8fe+aP/8Y4sL9MrmQ3pm1lj9dP2XnabEJpcM0DOzxur+NRVq8Ld2mg4LJtrVQpFOEXYdQcrKSAt63IjEfjYA4FQEIQs45mvsFoIkafP+k3pk3R4tvHZk2MdHWvjbmxqjjtMYz3YIE81tRsjVZF03N8zPzoj5hn69nSLs+HVUVJ4K+9xWLaAGAMQPq8Ys4FS9P+TqrM37T6q1h8nLSEZWjtacVvmaCl25/ONer97quPz6nBxX2NVkW/af1NjiAk0r9aiwX2ZMVwv1tDdQT8vy2c8GANAVI0Im6DoyU9vUErZ9XVNLn0ZWYrm5oCfPpcMn63tsFwg6+TmK2Wqhvh7VYJUjJwAA1kEQSrBgUztv3jkx7GP6ZaX36aiEWJ71lJ/j0tf6h19ddcE5OTqvwzRVrFYL9XVvIKscOQEAsA6CUAKFGpnZevCkppQM0OYg001TSga0rx4LN7ISrv4n1psLDnJnhR1ZKeyX2avni1QsprbYzwYA0BFBKIFCjcwEVmGlKEWf7D97f2DV2EB3lqTQIys9FRDHujbGrJGVWE1tsZ8NACCAIJRAoUZmGvytun9Nhd69Z7LaDOPMPkJZ6eqf62oPQaFEUv8Ty9qYwMhTXVOzltwwSv6WNtU3tSRkZIWpLQBArBGEEijcyEyDv1VpqSn6m8J+vXrOSOp/hhfmxSRAJGp363CY2gIAxBJBKIHisWop0vqfvgaIWK486yumtgAAscI+QgkUj1O4e1P/03E/oOGFeb36fJGMPAEAYDeMCCVYrKd2ErU3TqxXngEAYAWMCJmgLyMzwZ4r1qNMwbArMwAgGTEilAQSUUDMrswAgGTEiFCSiOUoU6jnT8TIEwAAicSIkMl6cyK82YoKsvXTvx+jU/X+M3sdZae373oNAIAdEYRMZIV9eXrDbv2NJTsFVgBA5FIMwzDM7oSV+Xw+5efny+v1yu12x+x5vQ1+la+pCLokfVqpJ6H78kTCbv2NJScHQACwq0jfvxkRMkmsToRP1EhFLE+wt5PARpI7/nRK5VeUaGxxgZpa2pSVkaaP/3hCV48alJRfNwA4BUHIJLHYlyeRIxVO3Ueous6vHX86pWdmjdWrWw7p2Y372++bXDJAk4YNIAgBgI2xaswkfd2Xp6cjL7wNsd3p2an7CPkam3XHlKF6dcshbdl/stN9W/af1GP/8XnMrzUAIHEIQiYJ7MsTTCT78iT6yIu+9teu3FkZGltc0C0EBXzC8SIAYGsEIZP0dV+eRE9VOXUfoUgCXrJOCwKAE1AjZKK+7AhtxlRVInawtpr8HJe+1j98vVWyTgsCgBMQhEyWnxNdkDDryIto+2tng9xZHC8SIfZbAmA37CPUg3jtIxQLR2tOa8E7uzu9QQemqs5jf5uY4lr3jP2WAFhJpO/fBKEeWDkISWf/AnfKVJWZuNahOXnDTQDWxIaKDuHEqSqzcK1Dc+qGmwDsj1VjAPrMqRtuArA/ghCAPnPqhpsA7I8gBMfxNvh14HidKipP6cCJOnaGjgGnbrgJwP6oEYKjsLIpPgIbboZaWUd9EACrYtVYD6y+agyRY2VT/LGyDoBVsGoM6IKVTfHHyjoAdkONEByDlU0AgK5sE4T+8pe/6NZbb5Xb7VZBQYHmzZunurq6sO3vu+8+XXjhhcrOztYFF1yg+++/X16vN4G9hpWwsgkA0JVtgtCtt96qL774Qr/+9a/1/vvva9OmTbr77rtDtj969KiOHj2qp59+Wp9//rlee+01rV+/XvPmzUtgr2ElrGwCAHRli2LpP/zhDxo5cqR+97vfafz48ZKk9evX6+qrr9b//u//qqioKKLnefvtt/WP//iPqq+vV3p68PKopqYmNTU1tf/f5/OpuLiYYukkwZlhAOAMSVUsvW3bNhUUFLSHIEmaMWOGUlNT9emnn+rGG2+M6HkCFyNUCJKkpUuXavHixX3uM6ypqCBbK2eNZWUTAECSTabGqqqqVFhY2Om29PR0nXPOOaqqqoroOaqrq7VkyZKw02mS9PDDD8vr9bZ/HDlyJOp+w5ryc1waXpinsgv6a3hhHiEIABzM1CC0YMECpaSkhP348ssv+/x5fD6frrnmGo0cOVKPP/542LaZmZlyu92dPgAAQHIydWrsX/7lX3T77beHbTNs2DANGjRIx48f73R7S0uL/vKXv2jQoEFhH19bW6tvfetb6tevn9atW6eMDFYGAQCAM0wNQueee67OPffcHttNmjRJNTU12rFjh8aNGydJ2rhxo9ra2jRx4sSQj/P5fJo5c6YyMzP13nvvKSsrK2Z9BwAA9meLGqERI0boW9/6lu666y5t375dW7ZsUXl5ub773e+2rxj76quvdNFFF2n79u2SzoSgb37zm6qvr9fLL78sn8+nqqoqVVVVqbW11cwvBwAAWIQtVo1J0htvvKHy8nJdeeWVSk1N1U033aRnnnmm/f7m5mbt3btXDQ0NkqSdO3fq008/lSSVlJR0eq5Dhw5pyJAhCes7AACwJlvsI2QmDl0FAMB+In3/tsXUGAAAQDwQhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGMRhAAAgGOlm90BxIe3wa/qOr98jc1yZ2fIk+tSfo7L7G4BAGApBKEkdLTmtB56Z7c+2Vfdftu0Uo+W3TRaRQXZJvYMAABrYWosyXgb/N1CkCRt2letBe/slrfBb1LPAACwHoJQkqmu83cLQQGb9lWruo4gBABAAEEoyfgam8PeX9vD/QAAOAlBKMm4szLC3t+vh/sBAHASglCS8eS5NK3UE/S+aaUeefJYOQYAQABBKMnk57i07KbR3cLQtFKPnrppNEvoAQDogOXzSaioIFsrZ41VdZ1ftY3N6peVIU8e+wgBANAVQShJ5ecQfAAA6AlTYwAAwLEIQgAAwLEIQgAAwLEIQgAAwLEIQgAAwLFYNQZ5G/yqrvPL19gsd3aGPLmsOAMAOANByOGO1pzudlr9tFKPlt00WkUF2Sb2DACA+GNqzMG8Df5uIUg6c0r9gnd2y9vASfUAgORGEHKw6jp/txAUsGlftarrCEIAgORGEHIwX2Nz2Ptre7gfAAC7Iwg5mDsrI+z9/Xq4HwAAuyMIOZgnz9XtlPqAaaUeefJYOQYASG4EIQfLz3Fp2U2ju4WhaaUePXXTaJbQAwCSHsvnHa6oIFsrZ41VdZ1ftY3N6peVIU8e+wgBAJyBIATl5xB8AADOxNQYAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLIIQAABwLI7Y6IFhGJIkn89nck8AAECkAu/bgffxUAhCPaitrZUkFRcXm9wTAADQW7W1tcrPzw95f4rRU1RyuLa2Nh09elT9+vVTSkpKwj+/z+dTcXGxjhw5IrfbnfDPb1dct+hw3aLDdYsO1y06XLfIGIah2tpaFRUVKTU1dCUQI0I9SE1N1de+9jWzuyG3280PfBS4btHhukWH6xYdrlt0uG49CzcSFECxNAAAcCyCEAAAcCyCkMVlZmZq0aJFyszMNLsrtsJ1iw7XLTpct+hw3aLDdYstiqUBAIBjMSIEAAAciyAEAAAciyAEAAAciyAEAAAciyBkAatWrdKQIUOUlZWliRMnavv27SHbrl69WlOnTlX//v3Vv39/zZgxI2z7ZNab69bR2rVrlZKSohtuuCG+HbSo3l63mpoa3XvvvTrvvPOUmZmpv/mbv9EHH3yQoN5aR2+v24oVK3ThhRcqOztbxcXF+v73v6/GxsYE9dYaNm3apOuuu05FRUVKSUnRu+++2+NjPvroI/3t3/6tMjMzVVJSotdeey3u/bSa3l63X/3qV7rqqqt07rnnyu12a9KkSfrv//7vxHQ2CRCETPbWW29p/vz5WrRokXbu3KkxY8Zo5syZOn78eND2H330kWbNmqUPP/xQ27ZtU3Fxsb75zW/qq6++SnDPzdXb6xZw+PBhPfjgg5o6dWqCemotvb1ufr9fV111lQ4fPqxf/vKX2rt3r1avXq3zzz8/wT03V2+v25tvvqkFCxZo0aJF+sMf/qCXX35Zb731lh555JEE99xc9fX1GjNmjFatWhVR+0OHDumaa67R5Zdfrl27dul73/ue7rzzTse9qff2um3atElXXXWVPvjgA+3YsUOXX365rrvuOlVUVMS5p0nCgKkmTJhg3Hvvve3/b21tNYqKioylS5dG9PiWlhajX79+xuuvvx6vLlpSNNetpaXFuPTSS42XXnrJmDNnjvHtb387AT21lt5et+eff94YNmyY4ff7E9VFS+rtdbv33nuNK664otNt8+fPNyZPnhzXflqZJGPdunVh2/zwhz80vv71r3e67ZZbbjFmzpwZx55ZWyTXLZiRI0caixcvjn2HkhAjQiby+/3asWOHZsyY0X5bamqqZsyYoW3btkX0HA0NDWpubtY555wTr25aTrTX7YknnlBhYaHmzZuXiG5aTjTX7b333tOkSZN07733auDAgRo1apSefPJJtba2Jqrbpovmul166aXasWNH+/TZwYMH9cEHH+jqq69OSJ/tatu2bZ2usyTNnDkz4tdDnNHW1qba2lpHvS/0BYeumqi6ulqtra0aOHBgp9sHDhyoL7/8MqLneOihh1RUVNTtxSOZRXPdNm/erJdfflm7du1KQA+tKZrrdvDgQW3cuFG33nqrPvjgA+3fv1/33HOPmpubtWjRokR023TRXLd/+Id/UHV1taZMmSLDMNTS0qJ//ud/dtzUWG9VVVUFvc4+n0+nT59Wdna2ST2zl6efflp1dXW6+eabze6KLTAiZGPLli3T2rVrtW7dOmVlZZndHcuqra3VbbfdptWrV8vj8ZjdHVtpa2tTYWGhXnzxRY0bN0633HKLHn30Ub3wwgtmd83SPvroIz355JN67rnntHPnTv3qV7/Sf/3Xf2nJkiVmdw1J7s0339TixYv1i1/8QoWFhWZ3xxYYETKRx+NRWlqajh071un2Y8eOadCgQWEf+/TTT2vZsmX6zW9+o9GjR8ezm5bT2+t24MABHT58WNddd137bW1tbZKk9PR07d27V8OHD49vpy0gmp+38847TxkZGUpLS2u/bcSIEaqqqpLf75fL5Yprn60gmuv22GOP6bbbbtOdd94pSbr44otVX1+vu+++W48++qhSU/kbNJhBgwYFvc5ut5vRoAisXbtWd955p95++21HzRL0Fb+NJnK5XBo3bpw2bNjQfltbW5s2bNigSZMmhXzcT37yEy1ZskTr16/X+PHjE9FVS+ntdbvooou0Z88e7dq1q/3j+uuvb1+ZUlxcnMjumyaan7fJkydr//797cFRkv74xz/qvPPOc0QIkqK7bg0NDd3CTiBMGhzvGNKkSZM6XWdJ+vWvfx329RBnrFmzRnPnztWaNWt0zTXXmN0dezG7Wtvp1q5da2RmZhqvvfaa8fvf/964++67jYKCAqOqqsowDMO47bbbjAULFrS3X7ZsmeFyuYxf/vKXxp///Of2j9raWrO+BFP09rp15dRVY729bpWVlUa/fv2M8vJyY+/evcb7779vFBYWGj/+8Y/N+hJM0dvrtmjRIqNfv37GmjVrjIMHDxr/8z//YwwfPty4+eabzfoSTFFbW2tUVFQYFRUVhiRj+fLlRkVFhfGnP/3JMAzDWLBggXHbbbe1tz948KCRk5Nj/OAHPzD+8Ic/GKtWrTLS0tKM9evXm/UlmKK31+2NN94w0tPTjVWrVnV6X6ipqTHrS7AVgpAFrFy50rjgggsMl8tlTJgwwfjtb3/bft/06dONOXPmtP9/8ODBhqRuH4sWLUp8x03Wm+vWlVODkGH0/rpt3brVmDhxopGZmWkMGzbM+Nd//VejpaUlwb02X2+uW3Nzs/H4448bw4cPN7Kysozi4mLjnnvuMU6dOpX4jpvoww8/DPp6FbhWc+bMMaZPn97tMWVlZYbL5TKGDRtmvPrqqwnvt9l6e92mT58etj3CSzEMxmkBAIAzUSMEAAAciyAEAAAciyAEAAAciyAEAAAciyAEAAAciyAEAAAciyAEAAAciyAEAAAciyAEwHEOHz6slJQU7dq1y+yuADAZO0sDcJzW1ladOHFCHo9H6enpZncHgIkIQgAcxe/3y+Vymd0NABbB1BgAW7vssstUXl6u8vJy5efny+Px6LHHHlPgb7whQ4ZoyZIlmj17ttxut+6+++6gU2NffPGFrr32WrndbvXr109Tp07VgQMH2u9/6aWXNGLECGVlZemiiy7Sc889l+gvFUAcMCYMwPZef/11zZs3T9u3b9dnn32mu+++WxdccIHuuusuSdLTTz+thQsXatGiRUEf/9VXX2natGm67LLLtHHjRrndbm3ZskUtLS2SpDfeeEMLFy7Us88+q7Fjx6qiokJ33XWXcnNzNWfOnIR9nQBij6kxALZ22WWX6fjx4/riiy+UkpIiSVqwYIHee+89/f73v9eQIUM0duxYrVu3rv0xhw8f1tChQ1VRUaGysjI98sgjWrt2rfbu3auMjIxun6OkpERLlizRrFmz2m/78Y9/rA8++EBbt26N/xcJIG6YGgNge5dcckl7CJKkSZMmad++fWptbZUkjR8/Puzjd+3apalTpwYNQfX19Tpw4IDmzZunvLy89o8f//jHnabOANgTU2MAkl5ubm7Y+7Ozs0PeV1dXJ0lavXq1Jk6c2Om+tLS0vncOgKkIQgBs79NPP+30/9/+9rcqLS2NOKiMHj1ar7/+upqbm7uNCg0cOFBFRUU6ePCgbr311pj1GYA1MDUGwPYqKys1f/587d27V2vWrNHKlSv1wAMPRPz48vJy+Xw+ffe739Vnn32mffv26ec//7n27t0rSVq8eLGWLl2qZ555Rn/84x+1Z88evfrqq1q+fHm8viQACcKIEADbmz17tk6fPq0JEyYoLS1NDzzwgO6+++6IHz9gwABt3LhRP/jBDzR9+nSlpaWprKxMkydPliTdeeedysnJ0U9/+lP94Ac/UG5uri6++GJ973vfi9NXBCBRWDUGwNYuu+wylZWVacWKFWZ3BYANMTUGAAAciyAEAAAci6kxAADgWIwIAQAAxyIIAQAAxyIIAQAAxyIIAQAAxyIIAQAAxyIIAQAAxyIIAQAAxyIIAQAAx/r/Cb6IqjfqGgEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(reg.predict(x_train), reg.predict(x_train) - y_train, color = \"green\", s = 35, edgecolor='black', label = 'Train data')\n",
        "plt.scatter(reg.predict(x_test), reg.predict(x_test) - y_test, color = \"red\", s = 35, edgecolor='black', label = 'Test data')\n",
        "plt.hlines(y = 0, xmin = 0, xmax = 2, colors='#5e03fc', linewidth = 2)\n",
        "plt.legend(loc = 'upper right')\n",
        "plt.title(\"Residual errors\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "hUPj6zqXUrOU",
        "outputId": "97da22ae-50e5-4d6b-f9c5-544fa52a7694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}