Polynomial_Linear_Regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/Megha1230/ML/blob/main/Polynomial_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds=pd.read_csv(\"https://raw.githubusercontent.com/ezioauditore-tech/AI/main/datasets/Position_Salaries.csv\")\n",
        "ds"
      ],
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            "text/plain": [
              "            Position  Level   Salary\n",
              "0   Business Analyst      1    45000\n",
              "1  Junior Consultant      2    50000\n",
              "2  Senior Consultant      3    60000\n",
              "3            Manager      4    80000\n",
              "4    Country Manager      5   110000\n",
              "5     Region Manager      6   150000\n",
              "6            Partner      7   200000\n",
              "7     Senior Partner      8   300000\n",
              "8            C-level      9   500000\n",
              "9                CEO     10  1000000"
            ],
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              "      <td>Business Analyst</td>\n",
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              "      <td>C-level</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "x=ds.iloc[:,1:2].values\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8a47RCWiTN8J",
        "outputId": "1e8534b2-0496-45a8-c978-f7f3f374a1dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1],\n",
              "       [ 2],\n",
              "       [ 3],\n",
              "       [ 4],\n",
              "       [ 5],\n",
              "       [ 6],\n",
              "       [ 7],\n",
              "       [ 8],\n",
              "       [ 9],\n",
              "       [10]])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=ds.iloc[:,2].values\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kx1CXbPRTOwI",
        "outputId": "c58fafe8-2c88-4e90-de8e-e3f0d2eb9521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  45000,   50000,   60000,   80000,  110000,  150000,  200000,\n",
              "        300000,  500000, 1000000])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg=LinearRegression()\n",
        "lin_reg.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "dFOfvpQbTO80",
        "outputId": "172f361c-72b6-4f0f-b34c-4925513416bd"
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      "execution_count": null,
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        {
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            "text/plain": [
              "LinearRegression()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg2=PolynomialFeatures(degree=2)\n",
        "x_poly=poly_reg2.fit_transform(x)\n",
        "lin_reg_2=LinearRegression()\n",
        "lin_reg_2.fit(x_poly,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "8tCVX9aVTPG7",
        "outputId": "d229d5da-48f4-4ac2-98fc-11ace8806b79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg3=PolynomialFeatures(degree=3)\n",
        "x_poly3=poly_reg3.fit_transform(x)\n",
        "lin_reg_3=LinearRegression()\n",
        "lin_reg_3.fit(x_poly3,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "P5_wogv9TPPY",
        "outputId": "acaca130-0d30-4415-f64d-263239abf1f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x,y,color='r')\n",
        "plt.plot(x,lin_reg.predict(x),color='blue')\n",
        "plt.title('Linear Regression')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "jc2LlOKvTPV7",
        "outputId": "46c0be41-f993-461a-89d7-823e980a4299"
      },
      "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": [
        "plt.scatter(x,y,color='r')\n",
        "plt.plot(x,lin_reg_2.predict(poly_reg2.fit_transform(x)),color='blue')\n",
        "plt.plot(x,lin_reg_3.predict(poly_reg3.fit_transform(x)),color='green')\n",
        "plt.title('Polynomial Linear Regression')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "E6WfnMUYTPcD",
        "outputId": "9280327c-c37f-410d-8098-c870a09ad7d8"
      },
      "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": [],
      "metadata": {
        "id": "GjQ98SKqcPhk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x,y,color='r')\n",
        "plt.plot(x,lin_reg_2.predict(x_poly),color='blue')\n",
        "plt.plot(x,lin_reg_3.predict(x_poly3),color='green')\n",
        "plt.title('Polynomial Linear Regression')\n",
        "plt.xlabel('Position level')\n",
        "plt.ylabel('salary')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "w8WviiaJadDk",
        "outputId": "289282b6-532f-42fd-beb3-8e445745c930"
      },
      "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": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "eh4WLGHHcRbG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('https://raw.githubusercontent.com/ezioauditore-tech/AI/main/datasets/Real%20estate.csv')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "_5ev5RntdUWD",
        "outputId": "1d78a569-9d66-4496-ba6f-992f83b16c1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      No  X1 transaction date  X2 house age  \\\n",
              "0      1             2012.917          32.0   \n",
              "1      2             2012.917          19.5   \n",
              "2      3             2013.583          13.3   \n",
              "3      4             2013.500          13.3   \n",
              "4      5             2012.833           5.0   \n",
              "..   ...                  ...           ...   \n",
              "409  410             2013.000          13.7   \n",
              "410  411             2012.667           5.6   \n",
              "411  412             2013.250          18.8   \n",
              "412  413             2013.000           8.1   \n",
              "413  414             2013.500           6.5   \n",
              "\n",
              "     X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
              "0                                  84.87882                               10   \n",
              "1                                 306.59470                                9   \n",
              "2                                 561.98450                                5   \n",
              "3                                 561.98450                                5   \n",
              "4                                 390.56840                                5   \n",
              "..                                      ...                              ...   \n",
              "409                              4082.01500                                0   \n",
              "410                                90.45606                                9   \n",
              "411                               390.96960                                7   \n",
              "412                               104.81010                                5   \n",
              "413                                90.45606                                9   \n",
              "\n",
              "     X5 latitude  X6 longitude  Y house price of unit area  \n",
              "0       24.98298     121.54024                        37.9  \n",
              "1       24.98034     121.53951                        42.2  \n",
              "2       24.98746     121.54391                        47.3  \n",
              "3       24.98746     121.54391                        54.8  \n",
              "4       24.97937     121.54245                        43.1  \n",
              "..           ...           ...                         ...  \n",
              "409     24.94155     121.50381                        15.4  \n",
              "410     24.97433     121.54310                        50.0  \n",
              "411     24.97923     121.53986                        40.6  \n",
              "412     24.96674     121.54067                        52.5  \n",
              "413     24.97433     121.54310                        63.9  \n",
              "\n",
              "[414 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a3f2e8fb-09a6-499a-9635-3dcce1bd8b88\" 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>No</th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <th>X5 latitude</th>\n",
              "      <th>X6 longitude</th>\n",
              "      <th>Y house price of unit area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>32.0</td>\n",
              "      <td>84.87882</td>\n",
              "      <td>10</td>\n",
              "      <td>24.98298</td>\n",
              "      <td>121.54024</td>\n",
              "      <td>37.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>19.5</td>\n",
              "      <td>306.59470</td>\n",
              "      <td>9</td>\n",
              "      <td>24.98034</td>\n",
              "      <td>121.53951</td>\n",
              "      <td>42.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2013.583</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>54.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2012.833</td>\n",
              "      <td>5.0</td>\n",
              "      <td>390.56840</td>\n",
              "      <td>5</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "      <td>43.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>409</th>\n",
              "      <td>410</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4082.01500</td>\n",
              "      <td>0</td>\n",
              "      <td>24.94155</td>\n",
              "      <td>121.50381</td>\n",
              "      <td>15.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>411</td>\n",
              "      <td>2012.667</td>\n",
              "      <td>5.6</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>412</td>\n",
              "      <td>2013.250</td>\n",
              "      <td>18.8</td>\n",
              "      <td>390.96960</td>\n",
              "      <td>7</td>\n",
              "      <td>24.97923</td>\n",
              "      <td>121.53986</td>\n",
              "      <td>40.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>413</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>8.1</td>\n",
              "      <td>104.81010</td>\n",
              "      <td>5</td>\n",
              "      <td>24.96674</td>\n",
              "      <td>121.54067</td>\n",
              "      <td>52.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>414</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>6.5</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "      <td>63.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>414 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      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",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a3f2e8fb-09a6-499a-9635-3dcce1bd8b88 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-a3f2e8fb-09a6-499a-9635-3dcce1bd8b88');\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-f1a6f19e-cb9f-429b-8f23-d62146dd1aec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f1a6f19e-cb9f-429b-8f23-d62146dd1aec')\"\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",
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              "    <g>\n",
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              "    </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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    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-f1a6f19e-cb9f-429b-8f23-d62146dd1aec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "VT4pyavodbN7",
        "outputId": "ca0e2edf-be5c-4ef8-84a9-f0b2c1cd39c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   No  X1 transaction date  X2 house age  \\\n",
              "0   1             2012.917          32.0   \n",
              "1   2             2012.917          19.5   \n",
              "2   3             2013.583          13.3   \n",
              "3   4             2013.500          13.3   \n",
              "4   5             2012.833           5.0   \n",
              "\n",
              "   X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
              "0                                84.87882                               10   \n",
              "1                               306.59470                                9   \n",
              "2                               561.98450                                5   \n",
              "3                               561.98450                                5   \n",
              "4                               390.56840                                5   \n",
              "\n",
              "   X5 latitude  X6 longitude  Y house price of unit area  \n",
              "0     24.98298     121.54024                        37.9  \n",
              "1     24.98034     121.53951                        42.2  \n",
              "2     24.98746     121.54391                        47.3  \n",
              "3     24.98746     121.54391                        54.8  \n",
              "4     24.97937     121.54245                        43.1  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>No</th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>32.0</td>\n",
              "      <td>84.87882</td>\n",
              "      <td>10</td>\n",
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              "      <td>121.54024</td>\n",
              "      <td>37.9</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>121.53951</td>\n",
              "      <td>42.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2013.583</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>47.3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
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              "      <td>121.54391</td>\n",
              "      <td>54.8</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>390.56840</td>\n",
              "      <td>5</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "      <td>43.1</td>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "    .colab-df-convert {\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-502e03f2-dc21-46b2-a2f1-ad5cd60d55f5 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-502e03f2-dc21-46b2-a2f1-ad5cd60d55f5');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9336c62e-c566-476c-925c-1aac84664acc')\"\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-9336c62e-c566-476c-925c-1aac84664acc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKEk4v9vdbgz",
        "outputId": "38018c52-808e-44b2-ad88-5b1e4758b48d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(414, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yc1YQqidd-gj",
        "outputId": "814ee4a9-ab51-410c-d2c3-0b9ebee59abf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 414 entries, 0 to 413\n",
            "Data columns (total 8 columns):\n",
            " #   Column                                  Non-Null Count  Dtype  \n",
            "---  ------                                  --------------  -----  \n",
            " 0   No                                      414 non-null    int64  \n",
            " 1   X1 transaction date                     414 non-null    float64\n",
            " 2   X2 house age                            414 non-null    float64\n",
            " 3   X3 distance to the nearest MRT station  414 non-null    float64\n",
            " 4   X4 number of convenience stores         414 non-null    int64  \n",
            " 5   X5 latitude                             414 non-null    float64\n",
            " 6   X6 longitude                            414 non-null    float64\n",
            " 7   Y house price of unit area              414 non-null    float64\n",
            "dtypes: float64(6), int64(2)\n",
            "memory usage: 26.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "bVJ0uYmneBSD",
        "outputId": "4d4db5ca-c155-4d81-c702-6a8a4d41082b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              No  X1 transaction date  \\\n",
              "No                                      1.000000            -0.048658   \n",
              "X1 transaction date                    -0.048658             1.000000   \n",
              "X2 house age                           -0.032808             0.017549   \n",
              "X3 distance to the nearest MRT station -0.013573             0.060880   \n",
              "X4 number of convenience stores        -0.012699             0.009635   \n",
              "X5 latitude                            -0.010110             0.035058   \n",
              "X6 longitude                           -0.011059            -0.041082   \n",
              "Y house price of unit area             -0.028587             0.087491   \n",
              "\n",
              "                                        X2 house age  \\\n",
              "No                                         -0.032808   \n",
              "X1 transaction date                         0.017549   \n",
              "X2 house age                                1.000000   \n",
              "X3 distance to the nearest MRT station      0.025622   \n",
              "X4 number of convenience stores             0.049593   \n",
              "X5 latitude                                 0.054420   \n",
              "X6 longitude                               -0.048520   \n",
              "Y house price of unit area                 -0.210567   \n",
              "\n",
              "                                        X3 distance to the nearest MRT station  \\\n",
              "No                                                                   -0.013573   \n",
              "X1 transaction date                                                   0.060880   \n",
              "X2 house age                                                          0.025622   \n",
              "X3 distance to the nearest MRT station                                1.000000   \n",
              "X4 number of convenience stores                                      -0.602519   \n",
              "X5 latitude                                                          -0.591067   \n",
              "X6 longitude                                                         -0.806317   \n",
              "Y house price of unit area                                           -0.673613   \n",
              "\n",
              "                                        X4 number of convenience stores  \\\n",
              "No                                                            -0.012699   \n",
              "X1 transaction date                                            0.009635   \n",
              "X2 house age                                                   0.049593   \n",
              "X3 distance to the nearest MRT station                        -0.602519   \n",
              "X4 number of convenience stores                                1.000000   \n",
              "X5 latitude                                                    0.444143   \n",
              "X6 longitude                                                   0.449099   \n",
              "Y house price of unit area                                     0.571005   \n",
              "\n",
              "                                        X5 latitude  X6 longitude  \\\n",
              "No                                        -0.010110     -0.011059   \n",
              "X1 transaction date                        0.035058     -0.041082   \n",
              "X2 house age                               0.054420     -0.048520   \n",
              "X3 distance to the nearest MRT station    -0.591067     -0.806317   \n",
              "X4 number of convenience stores            0.444143      0.449099   \n",
              "X5 latitude                                1.000000      0.412924   \n",
              "X6 longitude                               0.412924      1.000000   \n",
              "Y house price of unit area                 0.546307      0.523287   \n",
              "\n",
              "                                        Y house price of unit area  \n",
              "No                                                       -0.028587  \n",
              "X1 transaction date                                       0.087491  \n",
              "X2 house age                                             -0.210567  \n",
              "X3 distance to the nearest MRT station                   -0.673613  \n",
              "X4 number of convenience stores                           0.571005  \n",
              "X5 latitude                                               0.546307  \n",
              "X6 longitude                                              0.523287  \n",
              "Y house price of unit area                                1.000000  "
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>No</th>\n",
              "      <th>X1 transaction date</th>\n",
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              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
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              "      <td>-0.032808</td>\n",
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              "      <td>-0.012699</td>\n",
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              "      <td>-0.011059</td>\n",
              "      <td>-0.028587</td>\n",
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              "      <th>X1 transaction date</th>\n",
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              "      <th>X2 house age</th>\n",
              "      <td>-0.032808</td>\n",
              "      <td>0.017549</td>\n",
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              "      <td>0.025622</td>\n",
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              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <td>-0.013573</td>\n",
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              "      <td>0.025622</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.602519</td>\n",
              "      <td>-0.591067</td>\n",
              "      <td>-0.806317</td>\n",
              "      <td>-0.673613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <td>-0.012699</td>\n",
              "      <td>0.009635</td>\n",
              "      <td>0.049593</td>\n",
              "      <td>-0.602519</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.444143</td>\n",
              "      <td>0.449099</td>\n",
              "      <td>0.571005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X5 latitude</th>\n",
              "      <td>-0.010110</td>\n",
              "      <td>0.035058</td>\n",
              "      <td>0.054420</td>\n",
              "      <td>-0.591067</td>\n",
              "      <td>0.444143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.412924</td>\n",
              "      <td>0.546307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>X6 longitude</th>\n",
              "      <td>-0.011059</td>\n",
              "      <td>-0.041082</td>\n",
              "      <td>-0.048520</td>\n",
              "      <td>-0.806317</td>\n",
              "      <td>0.449099</td>\n",
              "      <td>0.412924</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.523287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y house price of unit area</th>\n",
              "      <td>-0.028587</td>\n",
              "      <td>0.087491</td>\n",
              "      <td>-0.210567</td>\n",
              "      <td>-0.673613</td>\n",
              "      <td>0.571005</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      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",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-c0600862-3805-4db1-9c06-c2f834de4248 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-c0600862-3805-4db1-9c06-c2f834de4248');\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",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
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              "  }\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-6a658343-dd10-4e48-8532-43420ce18f39 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(12,6))\n",
        "sns.heatmap(data.corr(),annot=True,cmap='Blues',)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "-7leHEX4eKym",
        "outputId": "7c029903-00d3-4a31-8b37-b93b1bec4708"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(data=data,x='Y house price of unit area',kde=True,color='black')"
      ],
      "metadata": {
        "id": "n7GM-fGZeLCW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "b852e13f-8e4c-431e-b609-baa9b5e22d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7d05b1806950>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop('Y house price of unit area',axis=1)\n",
        "x"
      ],
      "metadata": {
        "id": "vj0ytW68eLQ0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "c6bf63df-9b07-4e7d-b820-1d7c64c13ba0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      No  X1 transaction date  X2 house age  \\\n",
              "0      1             2012.917          32.0   \n",
              "1      2             2012.917          19.5   \n",
              "2      3             2013.583          13.3   \n",
              "3      4             2013.500          13.3   \n",
              "4      5             2012.833           5.0   \n",
              "..   ...                  ...           ...   \n",
              "409  410             2013.000          13.7   \n",
              "410  411             2012.667           5.6   \n",
              "411  412             2013.250          18.8   \n",
              "412  413             2013.000           8.1   \n",
              "413  414             2013.500           6.5   \n",
              "\n",
              "     X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
              "0                                  84.87882                               10   \n",
              "1                                 306.59470                                9   \n",
              "2                                 561.98450                                5   \n",
              "3                                 561.98450                                5   \n",
              "4                                 390.56840                                5   \n",
              "..                                      ...                              ...   \n",
              "409                              4082.01500                                0   \n",
              "410                                90.45606                                9   \n",
              "411                               390.96960                                7   \n",
              "412                               104.81010                                5   \n",
              "413                                90.45606                                9   \n",
              "\n",
              "     X5 latitude  X6 longitude  \n",
              "0       24.98298     121.54024  \n",
              "1       24.98034     121.53951  \n",
              "2       24.98746     121.54391  \n",
              "3       24.98746     121.54391  \n",
              "4       24.97937     121.54245  \n",
              "..           ...           ...  \n",
              "409     24.94155     121.50381  \n",
              "410     24.97433     121.54310  \n",
              "411     24.97923     121.53986  \n",
              "412     24.96674     121.54067  \n",
              "413     24.97433     121.54310  \n",
              "\n",
              "[414 rows x 7 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>No</th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <th>X5 latitude</th>\n",
              "      <th>X6 longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>32.0</td>\n",
              "      <td>84.87882</td>\n",
              "      <td>10</td>\n",
              "      <td>24.98298</td>\n",
              "      <td>121.54024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2012.917</td>\n",
              "      <td>19.5</td>\n",
              "      <td>306.59470</td>\n",
              "      <td>9</td>\n",
              "      <td>24.98034</td>\n",
              "      <td>121.53951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2013.583</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2012.833</td>\n",
              "      <td>5.0</td>\n",
              "      <td>390.56840</td>\n",
              "      <td>5</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>409</th>\n",
              "      <td>410</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>13.7</td>\n",
              "      <td>4082.01500</td>\n",
              "      <td>0</td>\n",
              "      <td>24.94155</td>\n",
              "      <td>121.50381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>411</td>\n",
              "      <td>2012.667</td>\n",
              "      <td>5.6</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>412</td>\n",
              "      <td>2013.250</td>\n",
              "      <td>18.8</td>\n",
              "      <td>390.96960</td>\n",
              "      <td>7</td>\n",
              "      <td>24.97923</td>\n",
              "      <td>121.53986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>413</td>\n",
              "      <td>2013.000</td>\n",
              "      <td>8.1</td>\n",
              "      <td>104.81010</td>\n",
              "      <td>5</td>\n",
              "      <td>24.96674</td>\n",
              "      <td>121.54067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>414</td>\n",
              "      <td>2013.500</td>\n",
              "      <td>6.5</td>\n",
              "      <td>90.45606</td>\n",
              "      <td>9</td>\n",
              "      <td>24.97433</td>\n",
              "      <td>121.54310</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>414 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fc6c76e-572c-4f7e-a6d5-1e61748e2312')\"\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-2fc6c76e-572c-4f7e-a6d5-1e61748e2312 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-2fc6c76e-572c-4f7e-a6d5-1e61748e2312');\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-52292cd9-1c2f-4e8c-ab80-9b7849fd8915\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-52292cd9-1c2f-4e8c-ab80-9b7849fd8915')\"\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-52292cd9-1c2f-4e8c-ab80-9b7849fd8915 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=data['Y house price of unit area']\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OV93pE5q0QCD",
        "outputId": "41df4ee2-717c-42a9-e3ba-74f914f7cdeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      37.9\n",
              "1      42.2\n",
              "2      47.3\n",
              "3      54.8\n",
              "4      43.1\n",
              "       ... \n",
              "409    15.4\n",
              "410    50.0\n",
              "411    40.6\n",
              "412    52.5\n",
              "413    63.9\n",
              "Name: Y house price of unit area, Length: 414, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "polynomial_reg=PolynomialFeatures(degree=2,include_bias=False)"
      ],
      "metadata": {
        "id": "0QW1BV5S0QOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "poly_feat=polynomial_reg.fit_transform(x)\n",
        "poly_feat.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Eks-Wlx06_5",
        "outputId": "ededfa5f-a6b7-420a-8ea7-19db5ed43f01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(414, 35)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(poly_feat,y,test_size=0.3,random_state=101)"
      ],
      "metadata": {
        "id": "7RDu689C060v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "polymodel=LinearRegression()"
      ],
      "metadata": {
        "id": "RLoV-s3V06n2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "polymodel.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "obAROZcb2lzL",
        "outputId": "fa0e667f-9295-4c33-add1-a65a7fee5599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=polymodel.predict(x_test)"
      ],
      "metadata": {
        "id": "6qYb1GCI3tc6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame({'Y_test':y_test,'Y_pred':y_pred,'Residuals':y_test-y_pred})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jHWv_Oab35Dz",
        "outputId": "1240e098-b726-4126-8a1f-f8e0265834fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Y_test     Y_pred  Residuals\n",
              "176    19.2  16.382290   2.817710\n",
              "347    11.2   3.929467   7.270533\n",
              "307    24.7  17.438173   7.261827\n",
              "299    46.1  47.146074  -1.046074\n",
              "391    31.3  27.340929   3.959071\n",
              "..      ...        ...        ...\n",
              "16     70.1  62.313409   7.786591\n",
              "31     25.0  37.389346 -12.389346\n",
              "36     22.9  22.208545   0.691455\n",
              "41     18.2  20.202241  -2.002241\n",
              "24     38.8  39.007776  -0.207776\n",
              "\n",
              "[125 rows x 3 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Y_test</th>\n",
              "      <th>Y_pred</th>\n",
              "      <th>Residuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>19.2</td>\n",
              "      <td>16.382290</td>\n",
              "      <td>2.817710</td>\n",
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              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>11.2</td>\n",
              "      <td>3.929467</td>\n",
              "      <td>7.270533</td>\n",
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              "      <th>307</th>\n",
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              "      <td>17.438173</td>\n",
              "      <td>7.261827</td>\n",
              "    </tr>\n",
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              "      <th>299</th>\n",
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              "      <td>47.146074</td>\n",
              "      <td>-1.046074</td>\n",
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              "    <tr>\n",
              "      <th>391</th>\n",
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              "      <td>37.389346</td>\n",
              "      <td>-12.389346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>22.9</td>\n",
              "      <td>22.208545</td>\n",
              "      <td>0.691455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>18.2</td>\n",
              "      <td>20.202241</td>\n",
              "      <td>-2.002241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>38.8</td>\n",
              "      <td>39.007776</td>\n",
              "      <td>-0.207776</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>125 rows × 3 columns</p>\n",
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              "        document.querySelector('#df-e5be9bd7-b03e-443a-8d27-9f26a9f76272 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-e5be9bd7-b03e-443a-8d27-9f26a9f76272');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "\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",
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              "\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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "</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-414f32e9-425a-4bec-8238-008f560e60b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poly_MAE=metrics.mean_absolute_error(y_test,y_pred)\n",
        "poly_MSE=metrics.mean_squared_error(y_test,y_pred)\n",
        "poly_RMSE=np.sqrt(poly_MSE)"
      ],
      "metadata": {
        "id": "RVfDJfvV4XuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2fTGtnAg4Xql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tpMvv6Pw4XpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3DLRNkNP4XnJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FLylLCO74Xkp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}