240916_과제_캐글(회귀).ipynb

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      "authorship_tag": "ABX9TyO73DmGa36iI8s1JRYDxXbO",
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ggoddll99/24-2ESAA/blob/main/240916_%EA%B3%BC%EC%A0%9C_%EC%BA%90%EA%B8%80(%ED%9A%8C%EA%B7%80).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8gTqENTUSYA",
        "outputId": "ca60434e-55f4-4f4b-e51c-c3106cd020f7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "92EopTkFTp9x"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "color=sns.color_palette()\n",
        "sns.set_style('darkgrid')\n",
        "import warnings\n",
        "def ignore_warn(*args, **kwargs):\n",
        "    pass\n",
        "warnings.warn=ignore_warn\n",
        "\n",
        "from scipy import stats\n",
        "from scipy.stats import norm, skew\n",
        "\n",
        "pd.set_option('display.float_format', lambda x : '{:.3f}'.format(x))\n",
        "\n",
        "#from subprocess import check_output\n",
        "#print(check_output(['ls', '../input']).decode('utf8'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train=pd.read_csv('/content/drive/MyDrive/24-1/ESAA/과제/house-prices-advanced-regression-techniques/train.csv')\n",
        "test=pd.read_csv('/content/drive/MyDrive/24-1/ESAA/과제/house-prices-advanced-regression-techniques/test.csv')\n",
        "train.head(5)"
      ],
      "metadata": {
        "colab": {
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          "height": 255
        },
        "id": "A0ccylAvT97-",
        "outputId": "bb01ae7e-f051-4860-ec2e-94bdcdbe6336"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL       65.000     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL       80.000     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL       68.000    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL       60.000     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL       84.000    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
              "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
              "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
              "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
              "\n",
              "  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0   2008        WD         Normal     208500  \n",
              "1   2007        WD         Normal     181500  \n",
              "2   2008        WD         Normal     223500  \n",
              "3   2006        WD        Abnorml     140000  \n",
              "4   2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 81 columns]"
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              "      <td>NaN</td>\n",
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              "      <td>2</td>\n",
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              "      <td>68.000</td>\n",
              "      <td>11250</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.000</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
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              "      <td>2</td>\n",
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              "      <td>14260</td>\n",
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              "      <td>IR1</td>\n",
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              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
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              "<p>5 rows × 81 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "test.head(5)"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0  1461          20       RH       80.000    11622   Pave   NaN      Reg   \n",
              "1  1462          20       RL       81.000    14267   Pave   NaN      IR1   \n",
              "2  1463          60       RL       74.000    13830   Pave   NaN      IR1   \n",
              "3  1464          60       RL       78.000     9978   Pave   NaN      IR1   \n",
              "4  1465         120       RL       43.000     5005   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
              "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
              "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
              "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
              "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
              "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
              "\n",
              "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
              "0       0      6    2010        WD         Normal  \n",
              "1   12500      6    2010        WD         Normal  \n",
              "2       0      3    2010        WD         Normal  \n",
              "3       0      6    2010        WD         Normal  \n",
              "4       0      1    2010        WD         Normal  \n",
              "\n",
              "[5 rows x 80 columns]"
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
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              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>...</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "      <td>20</td>\n",
              "      <td>RH</td>\n",
              "      <td>80.000</td>\n",
              "      <td>11622</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>81.000</td>\n",
              "      <td>14267</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gar2</td>\n",
              "      <td>12500</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>74.000</td>\n",
              "      <td>13830</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>78.000</td>\n",
              "      <td>9978</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>120</td>\n",
              "      <td>RL</td>\n",
              "      <td>43.000</td>\n",
              "      <td>5005</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>HLS</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>144</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 80 columns</p>\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('The train data size before dropping Id feature is : {}'.format(train.shape))\n",
        "print('The test data size before dropping Id feature is : {}'.format(test.shape))\n",
        "\n",
        "train_ID=train['Id']\n",
        "test_ID=test['Id']\n",
        "\n",
        "train.drop('Id', axis=1, inplace=True)\n",
        "test.drop('Id', axis=1, inplace=True)\n",
        "\n",
        "print('\\nThe train data size after dropping Id feature is : {}'.format(train.shape))\n",
        "print('The test data size after dropping Id feature is : {}'.format(test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kcef2vIwUCTu",
        "outputId": "f9edc82a-049e-4c43-fd0f-4168ab7b7fa5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The train data size before dropping Id feature is : (1460, 81)\n",
            "The test data size before dropping Id feature is : (1459, 80)\n",
            "\n",
            "The train data size after dropping Id feature is : (1460, 80)\n",
            "The test data size after dropping Id feature is : (1459, 79)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax=plt.subplots()\n",
        "ax.scatter(x=train['GrLivArea'], y=train['SalePrice'])\n",
        "plt.ylabel('SalePrice', fontsize=13)\n",
        "plt.xlabel('GrLivArea', fontsize=13)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "xazDxJO1UDhe",
        "outputId": "7a972b05-23fa-4aba-e659-91d0af79c849"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* the bottom right two with extremely large GrLivArea that are of a low price --> huge outliers --> delete"
      ],
      "metadata": {
        "id": "PKu9qnQvUu3m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train=train.drop(train[(train['GrLivArea']>4000)&(train['SalePrice']<300000)].index)\n",
        "\n",
        "fig, ax=plt.subplots()\n",
        "ax.scatter(train['GrLivArea'], train['SalePrice'])\n",
        "plt.ylabel('SalePrice', fontsize=13)\n",
        "plt.xlabel('GrLivArea', fontsize=13)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "RoZVCuovUluN",
        "outputId": "fb7a5901-46f6-41a9-8ad6-60b18d97ddba"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "instead of removing them all, we will just manage to make some of our models robust on them."
      ],
      "metadata": {
        "id": "edx5knTPVUdu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Target Variable\n",
        "SalePrice is the variable we need to predict. So let's do some analysis on this variable first."
      ],
      "metadata": {
        "id": "iM381a4CVVXO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(train['SalePrice'], fit=norm);\n",
        "\n",
        "(mu, sigma)=norm.fit(train['SalePrice'])\n",
        "print('\\n mu={:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f})'.format(mu, sigma)], loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "\n",
        "fig=plt.figure()\n",
        "res=stats.probplot(train['SalePrice'], plot=plt)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "id": "WEXu1PebVU_e",
        "outputId": "89c4371e-b031-4548-a298-19ba03d05804"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " mu=180932.92 and sigma = 79467.79\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The target variable is right skewed. As (linear) models love normally distributed data , we need to transform this variable and make it more normally distributed."
      ],
      "metadata": {
        "id": "fOVhKCvZV_3W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Log-transformation of the target variable"
      ],
      "metadata": {
        "id": "LaPHSIkjWctO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train['SalePrice']=np.log1p(train['SalePrice'])\n",
        "\n",
        "sns.distplot(train['SalePrice'], fit=norm);\n",
        "\n",
        "(mu, sigma)=norm.fit(train['SalePrice'])\n",
        "print('\\n mu={:.2f} and sigma={:.2f}\\n'.format(mu, sigma))\n",
        "\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f})'.format(mu, sigma)], loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('SalePrice distribution')\n",
        "\n",
        "fig=plt.figure()\n",
        "res=stats.probplot(train['SalePrice'], plot=plt)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "id": "f5Ne9eL8V_HG",
        "outputId": "1a846e48-801e-46e0-fcb1-a31abcaff0aa"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " mu=2.57 and sigma=0.03\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The skew seems now corrected and the data appears more normally distributed."
      ],
      "metadata": {
        "id": "TrKAZLjNXE1a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Features engineering"
      ],
      "metadata": {
        "id": "Qx5h85PaXFgx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ntrain=train.shape[0]\n",
        "ntest=test.shape[0]\n",
        "y_train=train.SalePrice.values\n",
        "all_data=pd.concat((train, test)).reset_index(drop=True)\n",
        "all_data.drop(['SalePrice'], axis=1, inplace=True)\n",
        "print('all_data size is : {}'.format(all_data.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gjvl94g8XIGK",
        "outputId": "3c48f67d-9620-45c6-9bd8-7950a91537d8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "all_data size is : (2917, 79)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Missing Data"
      ],
      "metadata": {
        "id": "JUQ8Fl8_XbkO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_data_na=(all_data.isnull().sum()/len(all_data))*100\n",
        "all_data_na=all_data_na.drop(all_data_na[all_data_na==0].index).sort_values(ascending=False)[:30]\n",
        "missing_data=pd.DataFrame({'Missing Ratio' : all_data_na})\n",
        "missing_data.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "XQ2I9Dj0XeUF",
        "outputId": "839c195b-ed61-432f-e0d5-909101fbc791"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Missing Ratio\n",
              "PoolQC               99.691\n",
              "MiscFeature          96.400\n",
              "Alley                93.212\n",
              "Fence                80.425\n",
              "MasVnrType           60.542\n",
              "FireplaceQu          48.680\n",
              "LotFrontage          16.661\n",
              "GarageFinish          5.451\n",
              "GarageQual            5.451\n",
              "GarageCond            5.451\n",
              "GarageYrBlt           5.451\n",
              "GarageType            5.382\n",
              "BsmtExposure          2.811\n",
              "BsmtCond              2.811\n",
              "BsmtQual              2.777\n",
              "BsmtFinType2          2.743\n",
              "BsmtFinType1          2.708\n",
              "MasVnrArea            0.788\n",
              "MSZoning              0.137\n",
              "BsmtFullBath          0.069"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-364a622e-8f8b-4795-9570-887f233bfab2\" 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>Missing Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PoolQC</th>\n",
              "      <td>99.691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MiscFeature</th>\n",
              "      <td>96.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alley</th>\n",
              "      <td>93.212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fence</th>\n",
              "      <td>80.425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrType</th>\n",
              "      <td>60.542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FireplaceQu</th>\n",
              "      <td>48.680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LotFrontage</th>\n",
              "      <td>16.661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageFinish</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageQual</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageCond</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <td>5.451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageType</th>\n",
              "      <td>5.382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtExposure</th>\n",
              "      <td>2.811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtCond</th>\n",
              "      <td>2.811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtQual</th>\n",
              "      <td>2.777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <td>2.743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <td>2.708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrArea</th>\n",
              "      <td>0.788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSZoning</th>\n",
              "      <td>0.137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <td>0.069</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-364a622e-8f8b-4795-9570-887f233bfab2')\"\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-364a622e-8f8b-4795-9570-887f233bfab2 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-364a622e-8f8b-4795-9570-887f233bfab2');\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-e1af3135-ac79-4ebb-9f7a-f348634e6f29\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1af3135-ac79-4ebb-9f7a-f348634e6f29')\"\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-e1af3135-ac79-4ebb-9f7a-f348634e6f29 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "missing_data",
              "summary": "{\n  \"name\": \"missing_data\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"Missing Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.81944878692539,\n        \"min\": 0.03428179636612959,\n        \"max\": 99.69146383270483,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          99.69146383270483,\n          96.4004113815564,\n          48.680150839904016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax = plt.subplots(figsize=(15, 12))\n",
        "plt.xticks(rotation=90)\n",
        "sns.barplot(x=all_data_na.index, y=all_data_na)\n",
        "plt.xlabel('Features', fontsize=15)\n",
        "plt.ylabel('Percent of missing values', fontsize=15)\n",
        "plt.title('Percent missing data by feature', fontsize=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "UPcUGc2-Xwc2",
        "outputId": "35ad4307-dd4d-4092-e517-3e28c5aac0da"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Percent missing data by feature')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_cols = train.select_dtypes(include=['float64', 'int64'])\n",
        "\n",
        "# 상관행렬 계산\n",
        "corrmat = numeric_cols.corr()\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.heatmap(corrmat, vmax=0.9, square=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "8PKS2OQ7X1Ps",
        "outputId": "70c2b170-65ac-49ad-850a-918c6022532b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"PoolQC\"] = all_data[\"PoolQC\"].fillna(\"None\")"
      ],
      "metadata": {
        "id": "Sq92E5HWX7Tk"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"MiscFeature\"] = all_data[\"MiscFeature\"].fillna(\"None\")"
      ],
      "metadata": {
        "id": "M-wr0Dw3X8Bs"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"Alley\"] = all_data[\"Alley\"].fillna(\"None\")"
      ],
      "metadata": {
        "id": "BpUftIYaX88d"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"Fence\"] = all_data[\"Fence\"].fillna(\"None\")"
      ],
      "metadata": {
        "id": "Dtobiex3YeM-"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"FireplaceQu\"] = all_data[\"FireplaceQu\"].fillna(\"None\")"
      ],
      "metadata": {
        "id": "H3BrI5KgYgwV"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"LotFrontage\"] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(\n",
        "    lambda x: x.fillna(x.median()))"
      ],
      "metadata": {
        "id": "GnuTgihPYiQm"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):\n",
        "    all_data[col] = all_data[col].fillna('None')"
      ],
      "metadata": {
        "id": "Zp4sjF1DYjxG"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
        "    all_data[col] = all_data[col].fillna(0)"
      ],
      "metadata": {
        "id": "JV-sKvQJYlNe"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):\n",
        "    all_data[col] = all_data[col].fillna(0)"
      ],
      "metadata": {
        "id": "D9-bpVNCYmbd"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
        "    all_data[col] = all_data[col].fillna('None')"
      ],
      "metadata": {
        "id": "jttlNykHYnmm"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\n",
        "all_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)"
      ],
      "metadata": {
        "id": "mmzDR4AYYqE-"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])"
      ],
      "metadata": {
        "id": "fRR13pxdYrTm"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = all_data.drop(['Utilities'], axis=1)"
      ],
      "metadata": {
        "id": "hI_d73GDYsmO"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")"
      ],
      "metadata": {
        "id": "GUqrI8hlYtve"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])"
      ],
      "metadata": {
        "id": "9e-M7VdOYwGe"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])"
      ],
      "metadata": {
        "id": "ZGZJHBfqYxDY"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\n",
        "all_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])"
      ],
      "metadata": {
        "id": "pU5vR1rVYyr-"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])"
      ],
      "metadata": {
        "id": "LIedjGRxYzs2"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_data['MSSubClass'] = all_data['MSSubClass'].fillna(\"None\")"
      ],
      "metadata": {
        "id": "PxkNlK_xY0-m"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check remaining missing values if any\n",
        "all_data_na = (all_data.isnull().sum() / len(all_data)) * 100\n",
        "all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)\n",
        "missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n",
        "missing_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "Py5ULEGJY2SW",
        "outputId": "8be0ac5d-181f-4da8-8638-185049db041d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Missing Ratio]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
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              "    <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>Missing Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d5620ded-d28a-4ea6-b91f-9d915f2b4ff8');\n",
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              "          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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "missing_data",
              "summary": "{\n  \"name\": \"missing_data\",\n  \"rows\": 0,\n  \"fields\": [\n    {\n      \"column\": \"Missing Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MSSubClass=The building class\n",
        "all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)\n",
        "\n",
        "\n",
        "#Changing OverallCond into a categorical variable\n",
        "all_data['OverallCond'] = all_data['OverallCond'].astype(str)\n",
        "\n",
        "\n",
        "#Year and month sold are transformed into categorical features.\n",
        "all_data['YrSold'] = all_data['YrSold'].astype(str)\n",
        "all_data['MoSold'] = all_data['MoSold'].astype(str)"
      ],
      "metadata": {
        "id": "pqV6-eR1Y4_G"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "cols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond',\n",
        "        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1',\n",
        "        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n",
        "        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond',\n",
        "        'YrSold', 'MoSold')\n",
        "# process columns, apply LabelEncoder to categorical features\n",
        "for c in cols:\n",
        "    lbl = LabelEncoder()\n",
        "    lbl.fit(list(all_data[c].values))\n",
        "    all_data[c] = lbl.transform(list(all_data[c].values))\n",
        "\n",
        "# shape\n",
        "print('Shape all_data: {}'.format(all_data.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dw4QTUtlY7hG",
        "outputId": "caccb89e-8700-40a9-c821-157efb7c1344"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape all_data: (2917, 78)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding total sqfootage feature\n",
        "all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']"
      ],
      "metadata": {
        "id": "m9Wil75CY89-"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n",
        "\n",
        "# Check the skew of all numerical features\n",
        "skewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
        "print(\"\\nSkew in numerical features: \\n\")\n",
        "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
        "skewness.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "SS9Gav7-Y-NG",
        "outputId": "d86ec4eb-94fb-49d3-bf15-a68435b6dd94"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Skew in numerical features: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Skew\n",
              "MiscVal       21.940\n",
              "PoolArea      17.689\n",
              "LotArea       13.109\n",
              "LowQualFinSF  12.085\n",
              "3SsnPorch     11.372\n",
              "LandSlope      4.973\n",
              "KitchenAbvGr   4.301\n",
              "BsmtFinSF2     4.145\n",
              "EnclosedPorch  4.002\n",
              "ScreenPorch    3.945"
            ],
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              "      <td>21.940</td>\n",
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              "      <td>17.689</td>\n",
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              "      <td>11.372</td>\n",
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              "      <td>4.973</td>\n",
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              "      <td>4.301</td>\n",
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              "      <td>4.145</td>\n",
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              "      <td>4.002</td>\n",
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              "      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-69a42621-4266-47b2-ba11-8979c2ba81b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "skewness",
              "summary": "{\n  \"name\": \"skewness\",\n  \"rows\": 59,\n  \"fields\": [\n    {\n      \"column\": \"Skew\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.079862376715606,\n        \"min\": -21.217599903984524,\n        \"max\": 21.939672170380096,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          21.939672170380096,\n          4.973253614489942,\n          0.1319957613452532\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skewness = skewness[abs(skewness) > 0.75]\n",
        "print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n",
        "\n",
        "from scipy.special import boxcox1p\n",
        "skewed_features = skewness.index\n",
        "lam = 0.15\n",
        "for feat in skewed_features:\n",
        "    #all_data[feat] += 1\n",
        "    all_data[feat] = boxcox1p(all_data[feat], lam)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2HKXP3v1ZAj2",
        "outputId": "b86fc904-1566-4112-b5cf-4d3f7c2add88"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 59 skewed numerical features to Box Cox transform\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_data = pd.get_dummies(all_data)\n",
        "print(all_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHdALiOQZBtG",
        "outputId": "92bccb30-57d6-45fb-891a-9ea11e71be5d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2917, 220)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = all_data[:ntrain]\n",
        "test = all_data[ntrain:]"
      ],
      "metadata": {
        "id": "nVm6qV0_ZCym"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb"
      ],
      "metadata": {
        "id": "v0c_VEsLZEoO"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Validation function\n",
        "n_folds = 5\n",
        "\n",
        "def rmsle_cv(model):\n",
        "    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n",
        "    rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n",
        "    return(rmse)"
      ],
      "metadata": {
        "id": "z0q5ha_gZGQf"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))"
      ],
      "metadata": {
        "id": "fVEsy4TKZHrO"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))"
      ],
      "metadata": {
        "id": "s-1vDNmJZI02"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)"
      ],
      "metadata": {
        "id": "2qVjk4fTZJom"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n",
        "                                   max_depth=4, max_features='sqrt',\n",
        "                                   min_samples_leaf=15, min_samples_split=10,\n",
        "                                   loss='huber', random_state =5)"
      ],
      "metadata": {
        "id": "cMru7FwxZLhW"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_xgb = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468,\n",
        "                             learning_rate=0.05, max_depth=3,\n",
        "                             min_child_weight=1.7817, n_estimators=2200,\n",
        "                             reg_alpha=0.4640, reg_lambda=0.8571,\n",
        "                             subsample=0.5213, silent=1,\n",
        "                             random_state =7, nthread = -1)"
      ],
      "metadata": {
        "id": "cuZbnqgFZNEW"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n",
        "                              learning_rate=0.05, n_estimators=720,\n",
        "                              max_bin = 55, bagging_fraction = 0.8,\n",
        "                              bagging_freq = 5, feature_fraction = 0.2319,\n",
        "                              feature_fraction_seed=9, bagging_seed=9,\n",
        "                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)"
      ],
      "metadata": {
        "id": "7uZsrKQsZOUG"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(lasso)\n",
        "print(\"\\nLasso score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BazVxWFZPYm",
        "outputId": "c6e2642e-e545-4d1a-808f-824c159b0730"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Lasso score: 0.0098 (0.0006)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(ENet)\n",
        "print(\"ElasticNet score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WamscERQZQyW",
        "outputId": "9726940e-505e-47c3-c44e-1690e249c19d"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ElasticNet score: 0.0097 (0.0006)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(KRR)\n",
        "print(\"Kernel Ridge score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgW2nStZZSAG",
        "outputId": "22bf8767-8a0c-45bb-c392-7afc8863415b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel Ridge score: 0.0098 (0.0007)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(GBoost)\n",
        "print(\"Gradient Boosting score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEhRkfENZTQm",
        "outputId": "feaf6424-9eff-4c76-d407-d2fb36b353f0"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting score: 0.0091 (0.0007)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = rmsle_cv(model_xgb)\n",
        "print(\"Xgboost score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lS-z6WUiZUQp",
        "outputId": "6f24e3cf-96f8-475f-b542-9e0ac7e12815"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Xgboost score: 0.0193 (0.0011)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "import lightgbm as lgb\n",
        "\n",
        "# Suppress warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Define model with cleaned-up parameters\n",
        "params = {\n",
        "    'min_sum_hessian_in_leaf': 11,\n",
        "    'min_data_in_leaf': 6,\n",
        "    'feature_fraction': 0.2319,\n",
        "    'bagging_fraction': 0.8,\n",
        "    'bagging_freq': 5,\n",
        "    'objective': 'regression',\n",
        "    'learning_rate': 0.01,\n",
        "    'num_leaves': 31,\n",
        "    'verbosity': -1  # LightGBM 로그 메시지 억제\n",
        "}\n",
        "\n",
        "# Create a LightGBM model instance\n",
        "model_lgb = lgb.LGBMRegressor(**params)\n",
        "\n",
        "# Perform cross-validation\n",
        "score = rmsle_cv(model_lgb)\n",
        "\n",
        "# Print the result\n",
        "print(\"LGBM score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Wy-uew2ZVb-",
        "outputId": "463dd5be-319f-4aa9-8b3d-b8d480b42cce"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LGBM score: 0.0164 (0.0008)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, models):\n",
        "        self.models = models\n",
        "\n",
        "    # we define clones of the original models to fit the data in\n",
        "    def fit(self, X, y):\n",
        "        self.models_ = [clone(x) for x in self.models]\n",
        "\n",
        "        # Train cloned base models\n",
        "        for model in self.models_:\n",
        "            model.fit(X, y)\n",
        "\n",
        "        return self\n",
        "\n",
        "    #Now we do the predictions for cloned models and average them\n",
        "    def predict(self, X):\n",
        "        predictions = np.column_stack([\n",
        "            model.predict(X) for model in self.models_\n",
        "        ])\n",
        "        return np.mean(predictions, axis=1)"
      ],
      "metadata": {
        "id": "YFq8hlWkZYIX"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "averaged_models = AveragingModels(models = (ENet, GBoost, KRR, lasso))\n",
        "\n",
        "score = rmsle_cv(averaged_models)\n",
        "print(\"Averaged base models score: {:.4f} ({:.4f})\\n\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mwj8lgIZZcu",
        "outputId": "0d5f1101-ee12-4c59-ec39-8b1563b48f95"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Averaged base models score: 0.0088 (0.0006)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):\n",
        "    def __init__(self, base_models, meta_model, n_folds=5):\n",
        "        self.base_models = base_models\n",
        "        self.meta_model = meta_model\n",
        "        self.n_folds = n_folds\n",
        "\n",
        "    # We again fit the data on clones of the original models\n",
        "    def fit(self, X, y):\n",
        "        self.base_models_ = [list() for x in self.base_models]\n",
        "        self.meta_model_ = clone(self.meta_model)\n",
        "        kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)\n",
        "\n",
        "        # Train cloned base models then create out-of-fold predictions\n",
        "        # that are needed to train the cloned meta-model\n",
        "        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))\n",
        "        for i, model in enumerate(self.base_models):\n",
        "            for train_index, holdout_index in kfold.split(X, y):\n",
        "                instance = clone(model)\n",
        "                self.base_models_[i].append(instance)\n",
        "                instance.fit(X[train_index], y[train_index])\n",
        "                y_pred = instance.predict(X[holdout_index])\n",
        "                out_of_fold_predictions[holdout_index, i] = y_pred\n",
        "\n",
        "        # Now train the cloned  meta-model using the out-of-fold predictions as new feature\n",
        "        self.meta_model_.fit(out_of_fold_predictions, y)\n",
        "        return self\n",
        "\n",
        "    #Do the predictions of all base models on the test data and use the averaged predictions as\n",
        "    #meta-features for the final prediction which is done by the meta-model\n",
        "    def predict(self, X):\n",
        "        meta_features = np.column_stack([\n",
        "            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)\n",
        "            for base_models in self.base_models_ ])\n",
        "        return self.meta_model_.predict(meta_features)"
      ],
      "metadata": {
        "id": "AO-I3JTdZcVu"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_averaged_models = StackingAveragedModels(base_models = (ENet, GBoost, KRR),\n",
        "                                                 meta_model = lasso)\n",
        "\n",
        "score = rmsle_cv(stacked_averaged_models)\n",
        "print(\"Stacking Averaged models score: {:.4f} ({:.4f})\".format(score.mean(), score.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYLk7lAAZeag",
        "outputId": "a0649820-f097-4ef9-a02e-f5e7a7871973"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stacking Averaged models score: 0.0087 (0.0005)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def rmsle(y, y_pred):\n",
        "    return np.sqrt(mean_squared_error(y, y_pred))"
      ],
      "metadata": {
        "id": "jab_Bc2ZZfoe"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stacked_averaged_models.fit(train.values, y_train)\n",
        "stacked_train_pred = stacked_averaged_models.predict(train.values)\n",
        "stacked_pred = np.expm1(stacked_averaged_models.predict(test.values))\n",
        "print(rmsle(y_train, stacked_train_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JU7WY3tQZgie",
        "outputId": "378d8b94-501b-4eee-c293-a3112b605b68"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.005804760334216999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_xgb.fit(train, y_train)\n",
        "xgb_train_pred = model_xgb.predict(train)\n",
        "xgb_pred = np.expm1(model_xgb.predict(test))\n",
        "print(rmsle(y_train, xgb_train_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ra9X3Q8gZhef",
        "outputId": "b5b929f6-f9d0-4ad3-af04-243abd97ef1e"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.01815959738056761\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_lgb.fit(train, y_train)\n",
        "lgb_train_pred = model_lgb.predict(train)\n",
        "lgb_pred = np.expm1(model_lgb.predict(test.values))\n",
        "print(rmsle(y_train, lgb_train_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-i1ejwmbZisv",
        "outputId": "3688ae66-9bea-4bf8-8cb1-72bc8b80b4fe"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.015295039956451397\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('RMSLE score on train data:')\n",
        "print(rmsle(y_train,stacked_train_pred*0.70 +\n",
        "               xgb_train_pred*0.15 + lgb_train_pred*0.15 ))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuffydjaZkVu",
        "outputId": "d68494c6-306d-48b3-a222-0c15929e7346"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSLE score on train data:\n",
            "0.007963205453275377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble = stacked_pred*0.70 + xgb_pred*0.15 + lgb_pred*0.15"
      ],
      "metadata": {
        "id": "Y9F-N5rmZmq-"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sub = pd.DataFrame()\n",
        "sub['Id'] = test_ID\n",
        "sub['SalePrice'] = ensemble\n",
        "sub.to_csv('submission.csv',index=False)"
      ],
      "metadata": {
        "id": "JgGUkk8HZn-9"
      },
      "execution_count": 74,
      "outputs": []
    }
  ]
}