(재업로드)송원민_house_prices_advanced_regression_techniques.ipynb

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      "name": "송원민 house-prices-advanced-regression-techniques.ipynb",
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    {
      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/WonminSong/AI/blob/master/(%EC%9E%AC%EC%97%85%EB%A1%9C%EB%93%9C)%EC%86%A1%EC%9B%90%EB%AF%BC_house_prices_advanced_regression_techniques.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      },
      "source": [
        "# made by songwonmin\n",
        "# https://www.kaggle.com/c/house-prices-advanced-regression-techniques\n",
        "import numpy as np \n",
        "import pandas as pd \n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/AI/house-prices-advanced-regression-techniques\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "metadata": {
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        "outputId": "324f7b32-8e7c-4bcd-cbb4-cc9890bc7b26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
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        "trusted": true,
        "scrolled": true,
        "id": "6ix_DJiMWi9p"
      },
      "source": [
        "train = pd.read_csv(\"/content/drive/My Drive/AI/house-prices-advanced-regression-techniques/train.csv\")\n",
        "test = pd.read_csv(\"/content/drive/My Drive/AI/house-prices-advanced-regression-techniques/test.csv\")\n",
        "sample_submission = pd.read_csv(\"/content/drive/My Drive/AI/house-prices-advanced-regression-techniques/sample_submission.csv\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
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        "id": "YsFn8qnkWi9r",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
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      },
      "source": [
        "train.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
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              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>490</td>\n",
              "      <td>1145</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0   1          60       RL  ...        WD         Normal    208500\n",
              "1   2          20       RL  ...        WD         Normal    181500\n",
              "2   3          60       RL  ...        WD         Normal    223500\n",
              "3   4          70       RL  ...        WD        Abnorml    140000\n",
              "4   5          60       RL  ...        WD         Normal    250000\n",
              "\n",
              "[5 rows x 81 columns]"
            ]
          },
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            "tags": []
          },
          "execution_count": 5
        }
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    },
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        "train.columns"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
              "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
              "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
              "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
              "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
              "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
              "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
              "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
              "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
              "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
              "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
              "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
              "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
              "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
              "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
              "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
              "       'SaleCondition', 'SalePrice'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
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    {
      "cell_type": "code",
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        "id": "NfbZ4IXOWi9x",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
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      "source": [
        "sample_submission"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "  </tbody>\n",
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              "<p>1459 rows × 2 columns</p>\n",
              "</div>"
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              "        Id      SalePrice\n",
              "0     1461  169277.052498\n",
              "1     1462  187758.393989\n",
              "2     1463  183583.683570\n",
              "3     1464  179317.477511\n",
              "4     1465  150730.079977\n",
              "...    ...            ...\n",
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              "1458  2919  187741.866657\n",
              "\n",
              "[1459 rows x 2 columns]"
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      "source": [
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
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              "      <td>1460</td>\n",
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              "      <td>1460.000000</td>\n",
              "      <td>1460</td>\n",
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              "      <td>1460</td>\n",
              "      <td>1460</td>\n",
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              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460</td>\n",
              "      <td>1460</td>\n",
              "      <td>1460</td>\n",
              "      <td>1460</td>\n",
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              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
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              "      <td>1460.000000</td>\n",
              "      <td>1379</td>\n",
              "      <td>1379</td>\n",
              "      <td>1460</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>7</td>\n",
              "      <td>281</td>\n",
              "      <td>54</td>\n",
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              "      <td>1460.000000</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Grvl</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>Unf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GasA</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Typ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gd</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1151</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1454</td>\n",
              "      <td>50</td>\n",
              "      <td>925</td>\n",
              "      <td>1311</td>\n",
              "      <td>1459</td>\n",
              "      <td>1052</td>\n",
              "      <td>1382</td>\n",
              "      <td>225</td>\n",
              "      <td>1260</td>\n",
              "      <td>1445</td>\n",
              "      <td>1220</td>\n",
              "      <td>726</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1141</td>\n",
              "      <td>1434</td>\n",
              "      <td>515</td>\n",
              "      <td>504</td>\n",
              "      <td>864</td>\n",
              "      <td>NaN</td>\n",
              "      <td>906</td>\n",
              "      <td>1282</td>\n",
              "      <td>647</td>\n",
              "      <td>649</td>\n",
              "      <td>1311</td>\n",
              "      <td>953</td>\n",
              "      <td>430</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1256</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1428</td>\n",
              "      <td>...</td>\n",
              "      <td>1365</td>\n",
              "      <td>1334</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>735</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1360</td>\n",
              "      <td>NaN</td>\n",
              "      <td>380</td>\n",
              "      <td>870</td>\n",
              "      <td>NaN</td>\n",
              "      <td>605</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1311</td>\n",
              "      <td>1326</td>\n",
              "      <td>1340</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>157</td>\n",
              "      <td>49</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1267</td>\n",
              "      <td>1198</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>56.897260</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.049958</td>\n",
              "      <td>10516.828082</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.099315</td>\n",
              "      <td>5.575342</td>\n",
              "      <td>1971.267808</td>\n",
              "      <td>1984.865753</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>103.685262</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>443.639726</td>\n",
              "      <td>NaN</td>\n",
              "      <td>46.549315</td>\n",
              "      <td>567.240411</td>\n",
              "      <td>1057.429452</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1162.626712</td>\n",
              "      <td>346.992466</td>\n",
              "      <td>5.844521</td>\n",
              "      <td>1515.463699</td>\n",
              "      <td>0.425342</td>\n",
              "      <td>0.057534</td>\n",
              "      <td>1.565068</td>\n",
              "      <td>0.382877</td>\n",
              "      <td>2.866438</td>\n",
              "      <td>1.046575</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.517808</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.613014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1978.506164</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.767123</td>\n",
              "      <td>472.980137</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94.244521</td>\n",
              "      <td>46.660274</td>\n",
              "      <td>21.954110</td>\n",
              "      <td>3.409589</td>\n",
              "      <td>15.060959</td>\n",
              "      <td>2.758904</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43.489041</td>\n",
              "      <td>6.321918</td>\n",
              "      <td>2007.815753</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>180921.195890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>421.610009</td>\n",
              "      <td>42.300571</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.284752</td>\n",
              "      <td>9981.264932</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.382997</td>\n",
              "      <td>1.112799</td>\n",
              "      <td>30.202904</td>\n",
              "      <td>20.645407</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>181.066207</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>456.098091</td>\n",
              "      <td>NaN</td>\n",
              "      <td>161.319273</td>\n",
              "      <td>441.866955</td>\n",
              "      <td>438.705324</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>386.587738</td>\n",
              "      <td>436.528436</td>\n",
              "      <td>48.623081</td>\n",
              "      <td>525.480383</td>\n",
              "      <td>0.518911</td>\n",
              "      <td>0.238753</td>\n",
              "      <td>0.550916</td>\n",
              "      <td>0.502885</td>\n",
              "      <td>0.815778</td>\n",
              "      <td>0.220338</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.625393</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.644666</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.689725</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.747315</td>\n",
              "      <td>213.804841</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>125.338794</td>\n",
              "      <td>66.256028</td>\n",
              "      <td>61.119149</td>\n",
              "      <td>29.317331</td>\n",
              "      <td>55.757415</td>\n",
              "      <td>40.177307</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>496.123024</td>\n",
              "      <td>2.703626</td>\n",
              "      <td>1.328095</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>79442.502883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1300.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1872.000000</td>\n",
              "      <td>1950.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>334.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2006.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34900.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>365.750000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>7553.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1954.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>795.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>882.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1129.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1961.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>334.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2007.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>129975.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>9478.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1973.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>383.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>477.500000</td>\n",
              "      <td>991.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1087.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1464.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1980.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2008.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>163000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1095.250000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>11601.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>712.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>808.000000</td>\n",
              "      <td>1298.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1391.250000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1776.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2002.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>576.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>214000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1460.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>313.000000</td>\n",
              "      <td>215245.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5644.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1474.000000</td>\n",
              "      <td>2336.000000</td>\n",
              "      <td>6110.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4692.000000</td>\n",
              "      <td>2065.000000</td>\n",
              "      <td>572.000000</td>\n",
              "      <td>5642.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1418.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>857.000000</td>\n",
              "      <td>547.000000</td>\n",
              "      <td>552.000000</td>\n",
              "      <td>508.000000</td>\n",
              "      <td>480.000000</td>\n",
              "      <td>738.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15500.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>755000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 81 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Id   MSSubClass  ... SaleCondition      SalePrice\n",
              "count   1460.000000  1460.000000  ...          1460    1460.000000\n",
              "unique          NaN          NaN  ...             6            NaN\n",
              "top             NaN          NaN  ...        Normal            NaN\n",
              "freq            NaN          NaN  ...          1198            NaN\n",
              "mean     730.500000    56.897260  ...           NaN  180921.195890\n",
              "std      421.610009    42.300571  ...           NaN   79442.502883\n",
              "min        1.000000    20.000000  ...           NaN   34900.000000\n",
              "25%      365.750000    20.000000  ...           NaN  129975.000000\n",
              "50%      730.500000    50.000000  ...           NaN  163000.000000\n",
              "75%     1095.250000    70.000000  ...           NaN  214000.000000\n",
              "max     1460.000000   190.000000  ...           NaN  755000.000000\n",
              "\n",
              "[11 rows x 81 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "6ue4D_bjWi92",
        "outputId": "7da81648-463c-4e2a-b215-37d9ec9fc75c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 81 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             1460 non-null   int64  \n",
            " 1   MSSubClass     1460 non-null   int64  \n",
            " 2   MSZoning       1460 non-null   object \n",
            " 3   LotFrontage    1201 non-null   float64\n",
            " 4   LotArea        1460 non-null   int64  \n",
            " 5   Street         1460 non-null   object \n",
            " 6   Alley          91 non-null     object \n",
            " 7   LotShape       1460 non-null   object \n",
            " 8   LandContour    1460 non-null   object \n",
            " 9   Utilities      1460 non-null   object \n",
            " 10  LotConfig      1460 non-null   object \n",
            " 11  LandSlope      1460 non-null   object \n",
            " 12  Neighborhood   1460 non-null   object \n",
            " 13  Condition1     1460 non-null   object \n",
            " 14  Condition2     1460 non-null   object \n",
            " 15  BldgType       1460 non-null   object \n",
            " 16  HouseStyle     1460 non-null   object \n",
            " 17  OverallQual    1460 non-null   int64  \n",
            " 18  OverallCond    1460 non-null   int64  \n",
            " 19  YearBuilt      1460 non-null   int64  \n",
            " 20  YearRemodAdd   1460 non-null   int64  \n",
            " 21  RoofStyle      1460 non-null   object \n",
            " 22  RoofMatl       1460 non-null   object \n",
            " 23  Exterior1st    1460 non-null   object \n",
            " 24  Exterior2nd    1460 non-null   object \n",
            " 25  MasVnrType     1452 non-null   object \n",
            " 26  MasVnrArea     1452 non-null   float64\n",
            " 27  ExterQual      1460 non-null   object \n",
            " 28  ExterCond      1460 non-null   object \n",
            " 29  Foundation     1460 non-null   object \n",
            " 30  BsmtQual       1423 non-null   object \n",
            " 31  BsmtCond       1423 non-null   object \n",
            " 32  BsmtExposure   1422 non-null   object \n",
            " 33  BsmtFinType1   1423 non-null   object \n",
            " 34  BsmtFinSF1     1460 non-null   int64  \n",
            " 35  BsmtFinType2   1422 non-null   object \n",
            " 36  BsmtFinSF2     1460 non-null   int64  \n",
            " 37  BsmtUnfSF      1460 non-null   int64  \n",
            " 38  TotalBsmtSF    1460 non-null   int64  \n",
            " 39  Heating        1460 non-null   object \n",
            " 40  HeatingQC      1460 non-null   object \n",
            " 41  CentralAir     1460 non-null   object \n",
            " 42  Electrical     1459 non-null   object \n",
            " 43  1stFlrSF       1460 non-null   int64  \n",
            " 44  2ndFlrSF       1460 non-null   int64  \n",
            " 45  LowQualFinSF   1460 non-null   int64  \n",
            " 46  GrLivArea      1460 non-null   int64  \n",
            " 47  BsmtFullBath   1460 non-null   int64  \n",
            " 48  BsmtHalfBath   1460 non-null   int64  \n",
            " 49  FullBath       1460 non-null   int64  \n",
            " 50  HalfBath       1460 non-null   int64  \n",
            " 51  BedroomAbvGr   1460 non-null   int64  \n",
            " 52  KitchenAbvGr   1460 non-null   int64  \n",
            " 53  KitchenQual    1460 non-null   object \n",
            " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
            " 55  Functional     1460 non-null   object \n",
            " 56  Fireplaces     1460 non-null   int64  \n",
            " 57  FireplaceQu    770 non-null    object \n",
            " 58  GarageType     1379 non-null   object \n",
            " 59  GarageYrBlt    1379 non-null   float64\n",
            " 60  GarageFinish   1379 non-null   object \n",
            " 61  GarageCars     1460 non-null   int64  \n",
            " 62  GarageArea     1460 non-null   int64  \n",
            " 63  GarageQual     1379 non-null   object \n",
            " 64  GarageCond     1379 non-null   object \n",
            " 65  PavedDrive     1460 non-null   object \n",
            " 66  WoodDeckSF     1460 non-null   int64  \n",
            " 67  OpenPorchSF    1460 non-null   int64  \n",
            " 68  EnclosedPorch  1460 non-null   int64  \n",
            " 69  3SsnPorch      1460 non-null   int64  \n",
            " 70  ScreenPorch    1460 non-null   int64  \n",
            " 71  PoolArea       1460 non-null   int64  \n",
            " 72  PoolQC         7 non-null      object \n",
            " 73  Fence          281 non-null    object \n",
            " 74  MiscFeature    54 non-null     object \n",
            " 75  MiscVal        1460 non-null   int64  \n",
            " 76  MoSold         1460 non-null   int64  \n",
            " 77  YrSold         1460 non-null   int64  \n",
            " 78  SaleType       1460 non-null   object \n",
            " 79  SaleCondition  1460 non-null   object \n",
            " 80  SalePrice      1460 non-null   int64  \n",
            "dtypes: float64(3), int64(35), object(43)\n",
            "memory usage: 924.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "iO8cPCnLWi95",
        "outputId": "ed7d411a-ba63-4c1a-8341-1e02629a5da6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "train.drop(['Alley','PoolQC','Fence','MiscFeature',\"FireplaceQu\"],axis=1)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>HouseStyle</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>RoofStyle</th>\n",
              "      <th>RoofMatl</th>\n",
              "      <th>Exterior1st</th>\n",
              "      <th>Exterior2nd</th>\n",
              "      <th>MasVnrType</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>ExterQual</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>Foundation</th>\n",
              "      <th>BsmtQual</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>BsmtExposure</th>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <th>BsmtFinSF2</th>\n",
              "      <th>BsmtUnfSF</th>\n",
              "      <th>TotalBsmtSF</th>\n",
              "      <th>Heating</th>\n",
              "      <th>HeatingQC</th>\n",
              "      <th>CentralAir</th>\n",
              "      <th>Electrical</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>LowQualFinSF</th>\n",
              "      <th>GrLivArea</th>\n",
              "      <th>BsmtFullBath</th>\n",
              "      <th>BsmtHalfBath</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>Functional</th>\n",
              "      <th>Fireplaces</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageCars</th>\n",
              "      <th>GarageArea</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2003</td>\n",
              "      <td>2003</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>196.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>706</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>856</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>0</td>\n",
              "      <td>1710</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>8</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>548</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>1976</td>\n",
              "      <td>1976</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>978</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>284</td>\n",
              "      <td>1262</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>460</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2001</td>\n",
              "      <td>2002</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>162.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>486</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>434</td>\n",
              "      <td>920</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>0</td>\n",
              "      <td>1786</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>608</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1915</td>\n",
              "      <td>1970</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Wd Sdng</td>\n",
              "      <td>Wd Shng</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>BrkTil</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>216</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>540</td>\n",
              "      <td>756</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>0</td>\n",
              "      <td>1717</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>3</td>\n",
              "      <td>642</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2000</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>BrkFace</td>\n",
              "      <td>350.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>Av</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>655</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>490</td>\n",
              "      <td>1145</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>2198</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>3</td>\n",
              "      <td>836</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>1456</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>62.0</td>\n",
              "      <td>7917</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Gilbert</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1999</td>\n",
              "      <td>2000</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>953</td>\n",
              "      <td>953</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>953</td>\n",
              "      <td>694</td>\n",
              "      <td>0</td>\n",
              "      <td>1647</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>7</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>2</td>\n",
              "      <td>460</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>175000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>1457</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>85.0</td>\n",
              "      <td>13175</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NWAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1978</td>\n",
              "      <td>1988</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>Plywood</td>\n",
              "      <td>Plywood</td>\n",
              "      <td>Stone</td>\n",
              "      <td>119.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>ALQ</td>\n",
              "      <td>790</td>\n",
              "      <td>Rec</td>\n",
              "      <td>163</td>\n",
              "      <td>589</td>\n",
              "      <td>1542</td>\n",
              "      <td>GasA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>2073</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2073</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>7</td>\n",
              "      <td>Min1</td>\n",
              "      <td>2</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1978.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>2</td>\n",
              "      <td>500</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>349</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>210000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>1458</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>66.0</td>\n",
              "      <td>9042</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1941</td>\n",
              "      <td>2006</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>CemntBd</td>\n",
              "      <td>CmentBd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Stone</td>\n",
              "      <td>TA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>275</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0</td>\n",
              "      <td>877</td>\n",
              "      <td>1152</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Ex</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1188</td>\n",
              "      <td>1152</td>\n",
              "      <td>0</td>\n",
              "      <td>2340</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>9</td>\n",
              "      <td>Typ</td>\n",
              "      <td>2</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1941.0</td>\n",
              "      <td>RFn</td>\n",
              "      <td>1</td>\n",
              "      <td>252</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2500</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>266500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>1459</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>9717</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NAmes</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1950</td>\n",
              "      <td>1996</td>\n",
              "      <td>Hip</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>MetalSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Mn</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>49</td>\n",
              "      <td>Rec</td>\n",
              "      <td>1029</td>\n",
              "      <td>0</td>\n",
              "      <td>1078</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Y</td>\n",
              "      <td>FuseA</td>\n",
              "      <td>1078</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1078</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Gd</td>\n",
              "      <td>5</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>1</td>\n",
              "      <td>240</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>366</td>\n",
              "      <td>0</td>\n",
              "      <td>112</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>142125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>1460</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9937</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Edwards</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>1Story</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1965</td>\n",
              "      <td>1965</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>HdBoard</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>CBlock</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>BLQ</td>\n",
              "      <td>830</td>\n",
              "      <td>LwQ</td>\n",
              "      <td>290</td>\n",
              "      <td>136</td>\n",
              "      <td>1256</td>\n",
              "      <td>GasA</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>1256</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1256</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>0</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1965.0</td>\n",
              "      <td>Fin</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>736</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>147500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1460 rows × 76 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
              "0        1          60       RL  ...        WD         Normal    208500\n",
              "1        2          20       RL  ...        WD         Normal    181500\n",
              "2        3          60       RL  ...        WD         Normal    223500\n",
              "3        4          70       RL  ...        WD        Abnorml    140000\n",
              "4        5          60       RL  ...        WD         Normal    250000\n",
              "...    ...         ...      ...  ...       ...            ...       ...\n",
              "1455  1456          60       RL  ...        WD         Normal    175000\n",
              "1456  1457          20       RL  ...        WD         Normal    210000\n",
              "1457  1458          70       RL  ...        WD         Normal    266500\n",
              "1458  1459          20       RL  ...        WD         Normal    142125\n",
              "1459  1460          20       RL  ...        WD         Normal    147500\n",
              "\n",
              "[1460 rows x 76 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "jQTsaHacWi9-"
      },
      "source": [
        "feature_names = [\"Id\",\"MSSubClass\",\"MSZoning\",\"LotArea\",\"Street\",\"Condition1\",\"BldgType\",\"OverallQual\",\"OverallCond\",\"YearBuilt\",\"YearRemodAdd\",\"ExterCond\",\"BsmtCond\",\"TotalBsmtSF\",\"1stFlrSF\",\"FullBath\",\"BedroomAbvGr\",\"TotRmsAbvGrd\",\"GarageYrBlt\",\"GarageArea\",\"MiscVal\",\"YrSold\",\"SaleType\",\"SaleCondition\"]\n",
        "X = train[feature_names]\n",
        "test = test[feature_names]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "j6qjDsC9Wi-B"
      },
      "source": [
        "object_columns = X.select_dtypes(include=['object'])\n",
        "numerical_columns =X.select_dtypes(exclude=['object'])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "l7es6yXsWi-E",
        "outputId": "85e2fb58-87c6-44d3-c238-880d7fcab7f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "object_columns.dtypes"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MSZoning         object\n",
              "Street           object\n",
              "Condition1       object\n",
              "BldgType         object\n",
              "ExterCond        object\n",
              "BsmtCond         object\n",
              "SaleType         object\n",
              "SaleCondition    object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "aYeiluMDWi-H",
        "outputId": "6e03d32f-d2c0-40f6-bbd2-d719af258cea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "numerical_columns.dtypes"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                int64\n",
              "MSSubClass        int64\n",
              "LotArea           int64\n",
              "OverallQual       int64\n",
              "OverallCond       int64\n",
              "YearBuilt         int64\n",
              "YearRemodAdd      int64\n",
              "TotalBsmtSF       int64\n",
              "1stFlrSF          int64\n",
              "FullBath          int64\n",
              "BedroomAbvGr      int64\n",
              "TotRmsAbvGrd      int64\n",
              "GarageYrBlt     float64\n",
              "GarageArea        int64\n",
              "MiscVal           int64\n",
              "YrSold            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "yzZexN-BWi-K",
        "outputId": "d78d3545-cab0-43fe-a85b-41a4968b0a27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"MSZoning\",y=\"SalePrice\",data=train)\n",
        "# MSZoning: 일반 구역 구분\n",
        "# 물위에 있는 집이 가장 비싸고 주거용으로 저밀도인 지역이 가격이 높다."
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7038553748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "NQSfPMENWi-M",
        "outputId": "14a5d4a7-735b-4533-e210-b1290fe662d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        }
      },
      "source": [
        "X.MSZoning.sample(10)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "670     RL\n",
              "473     RL\n",
              "127     RM\n",
              "637     RM\n",
              "287     RL\n",
              "990     RL\n",
              "405     RL\n",
              "849     RL\n",
              "1117    RL\n",
              "833     RL\n",
              "Name: MSZoning, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "TuX-CO_tWi-P"
      },
      "source": [
        "MSZoning_group_mapping={\"RL\":0,\"RM\":1,\"C (all)\":2,\"FV\":3,\"RH\":4}\n",
        "X[\"MSZoning\"]=X[\"MSZoning\"].map(MSZoning_group_mapping)\n",
        "test[\"MSZoning\"]=test[\"MSZoning\"].map(MSZoning_group_mapping)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "jSbluSypWi-R",
        "outputId": "2835f5b8-57ed-4f7f-b087-3d1a6965ab95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"Street\",y=\"SalePrice\",data=train)\n",
        "# Street: 도로 접근 유형 -> 비포장 도로 보단, 포장도로일수록 가격이 높다는 것을 알 수 있다"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f703847da20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "WiWf6WpAWi-T"
      },
      "source": [
        "Street_group_mapping={\"Pave\":0,\"Grvl\":1}\n",
        "X[\"Street\"]=X[\"Street\"].map(Street_group_mapping)\n",
        "test[\"Street\"]=test[\"Street\"].map(Street_group_mapping)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "OUp21mlyWi-W",
        "outputId": "121d9f4b-cec8-4ce7-c5d9-71aabad83d7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "sns.barplot(x=\"Condition1\",y=\"SalePrice\",data=train)\n",
        "# Condition1: 간선도로 또는 철도와의 근접성\n",
        "# 공원이 근처에 있고 철도가 바로 옆에 있는 것 보다 200호이내 있을 때 가격이 높다."
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7037f59358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "MXeEfdsqWi-Z"
      },
      "source": [
        "Condition1_group_mapping={\"Norm\":0,\"Feedr\":1,\"PosN\":2,\"Artery\":3,\"RRAe\":4,\"RRNn\":5,\"RRAn\":6,\"PosA\":7,\"RRNe\":8}\n",
        "X[\"Condition1\"]=X[\"Condition1\"].map(Condition1_group_mapping)\n",
        "test[\"Condition1\"]=test[\"Condition1\"].map(Condition1_group_mapping)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "IWXcxNSDWi-b",
        "outputId": "14482672-dfa6-433b-f1d5-7d4792e1d2a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "source": [
        "X.isnull().sum()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                0\n",
              "MSSubClass        0\n",
              "MSZoning          0\n",
              "LotArea           0\n",
              "Street            0\n",
              "Condition1        0\n",
              "BldgType          0\n",
              "OverallQual       0\n",
              "OverallCond       0\n",
              "YearBuilt         0\n",
              "YearRemodAdd      0\n",
              "ExterCond         0\n",
              "BsmtCond         37\n",
              "TotalBsmtSF       0\n",
              "1stFlrSF          0\n",
              "FullBath          0\n",
              "BedroomAbvGr      0\n",
              "TotRmsAbvGrd      0\n",
              "GarageYrBlt      81\n",
              "GarageArea        0\n",
              "MiscVal           0\n",
              "YrSold            0\n",
              "SaleType          0\n",
              "SaleCondition     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "TBF1_cPvWi-e",
        "outputId": "181d74d1-ac3d-4abb-cb47-b94922dafd63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "sns.barplot(x=\"BldgType\",y=\"SalePrice\",data=train)\n",
        "# BldgType: 주거 유형 -> 1인 가구와 Townhouse End Unit의 가격이 비싸고 나머지는 비슷하다."
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7038444da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "TYaLx4WOWi-g",
        "outputId": "080ded42-c9bc-4d15-b065-0184f7d683eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        }
      },
      "source": [
        "X.BldgType.sample(10)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1100      1Fam\n",
              "985     2fmCon\n",
              "1236     Twnhs\n",
              "649      Twnhs\n",
              "323       1Fam\n",
              "1046      1Fam\n",
              "396       1Fam\n",
              "1419      1Fam\n",
              "980       1Fam\n",
              "605       1Fam\n",
              "Name: BldgType, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "OgGm4yoYWi-i"
      },
      "source": [
        "BldgType_group_mapping={\"1Fam\":0,\"2fmCon\":1,\"Duplex\":2,\"TwnhsE\":3,\"Twnhs\":4}\n",
        "X[\"BldgType\"]=X[\"BldgType\"].map(BldgType_group_mapping)\n",
        "test[\"BldgType\"]=test[\"BldgType\"].map(BldgType_group_mapping)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "Wpp4UJgTWi-l",
        "outputId": "bf78cb6a-67e7-423b-e1bc-168ed9eb42e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"ExterCond\",y=\"SalePrice\",data=train)\n",
        "# ExterCond: 외관 -> 평균 이상이면 가격 변동은 크지 않지만, 평균 미만일 경우 가격 하락폭이 크다."
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7037ef0898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEGCAYAAACpXNjrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZvElEQVR4nO3dfbRddX3n8feHYDSKCkgETMBQjVa0ihKBli4fYMTgrBmwgwp1JMuhxlZw6YwOoqtr8IkZZKpMrRaLJRVcVkStkrqwGMGqdclDUOSxljsokgyBQHgQUR7Cd/44v2sOl5t7L2Sfc26S92uts87e3/3be//uWcGPe+/f3jtVhSRJXdph1B2QJG17DBdJUucMF0lS5wwXSVLnDBdJUud2HHUHZovddtutFi1aNOpuSNJW5Yorrri9quZPrBsuzaJFi1i9evWouyFJW5UkN01W97SYJKlzhoskqXOGiySpc4aLJKlzhoskqXOGiySpc4aLJKlzhoskqXPeRClJW5kTTzyRdevWsccee3DaaaeNujuTMlwkaSuzbt061q5dO+puTMnTYpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTODSxckjwpyWVJfpLk2iQfavV9klyaZCzJl5LMbfUntvmxtnxR37be3+o/TfLavvrSVhtLclJffdJ9SJKGY5BHLvcDh1TVS4D9gKVJDgI+BpxeVc8F7gSOa+2PA+5s9dNbO5LsCxwNvBBYCvx1kjlJ5gCfBg4H9gWOaW2ZYh+SpCEYWLhUz71t9gntU8AhwFda/WzgyDZ9RJunLT80SVr93Kq6v6p+BowBB7TPWFXdWFUPAOcCR7R1NrcPSdIQDPSaSzvCuBK4DVgF/F/grqp6qDVZAyxo0wuAmwHa8ruBZ/TXJ6yzufozptiHJGkIBhouVbWxqvYDFtI70vjdQe7vsUqyPMnqJKvXr18/6u5I0jZjKKPFquou4DvA7wM7Jxl/SdlCYPyNN2uBvQDa8qcDd/TXJ6yzufodU+xjYr/OrKolVbVk/vz5W/Q3SpI2GeRosflJdm7T84DXANfTC5mjWrNlwPltemWbpy2/uKqq1Y9uo8n2ARYDlwGXA4vbyLC59C76r2zrbG4fkqQhGORrjvcEzm6junYAzquqbyS5Djg3yUeBHwNntfZnAZ9PMgZsoBcWVNW1Sc4DrgMeAo6vqo0ASU4ALgTmACuq6tq2rfdtZh+SpCEYWLhU1VXASyep30jv+svE+m+AN2xmW6cAp0xSvwC4YKb7kCQNh3foS5I6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjo3sHBJsleS7yS5Lsm1Sd7V6h9MsjbJle3zur513p9kLMlPk7y2r7601caSnNRX3yfJpa3+pSRzW/2JbX6sLV80qL9TkvRogzxyeQh4T1XtCxwEHJ9k37bs9Krar30uAGjLjgZeCCwF/jrJnCRzgE8DhwP7Asf0bedjbVvPBe4Ejmv144A7W/301k6SNCQDC5equqWqftSmfwlcDyyYYpUjgHOr6v6q+hkwBhzQPmNVdWNVPQCcCxyRJMAhwFfa+mcDR/Zt6+w2/RXg0NZekjQEQ7nm0k5LvRS4tJVOSHJVkhVJdmm1BcDNfautabXN1Z8B3FVVD02oP2Jbbfndrb0kaQgGHi5JdgK+Cry7qu4BzgCeA+wH3AJ8fNB9mKJvy5OsTrJ6/fr1o+qGJG1zBhouSZ5AL1i+UFX/AFBVt1bVxqp6GPgsvdNeAGuBvfpWX9hqm6vfAeycZMcJ9Udsqy1/emv/CFV1ZlUtqaol8+fP39I/V5LUDHK0WICzgOur6hN99T37mr0euKZNrwSObiO99gEWA5cBlwOL28iwufQu+q+sqgK+AxzV1l8GnN+3rWVt+ijg4tZekjQEO07f5HE7GHgLcHWSK1vtA/RGe+0HFPBz4O0AVXVtkvOA6+iNNDu+qjYCJDkBuBCYA6yoqmvb9t4HnJvko8CP6YUZ7fvzScaADfQCSZI0JAMLl6r6F2CyEVoXTLHOKcApk9QvmGy9qrqRTafV+uu/Ad7wWPorSeqOd+hLkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOme4SJI6Z7hIkjpnuEiSOjewcEmyV5LvJLkuybVJ3tXquyZZleSG9r1LqyfJJ5OMJbkqycv6trWstb8hybK++v5Jrm7rfDJJptqHJGk4Bnnk8hDwnqraFzgIOD7JvsBJwEVVtRi4qM0DHA4sbp/lwBnQCwrgZOBA4ADg5L6wOAN4W996S1t9c/uQJA3BjMMlybOT/Ls2PS/JU6dqX1W3VNWP2vQvgeuBBcARwNmt2dnAkW36COCc6rkE2DnJnsBrgVVVtaGq7gRWAUvbsqdV1SVVVcA5E7Y12T4kSUOw40waJXkbvaOJXYHnAAuBzwCHznD9RcBLgUuB3avqlrZoHbB7m14A3Ny32ppWm6q+ZpI6U+xjYr+Wt7+LvffeeyZ/iiRt1qfe849D2c9dt//qt9/D2OcJH/8Pj3mdmR65HA8cDNwDUFU3AM+cyYpJdgK+Cry7qu7pX9aOOGrGvX0cptpHVZ1ZVUuqasn8+fMH2Q1J2q7MNFzur6oHxmeS7MgMQiHJE+gFyxeq6h9a+dZ2Sov2fVurrwX26lt9YatNVV84SX2qfUiShmCm4fLdJB8A5iV5DfBlYMpjsTZy6yzg+qr6RN+ilcD4iK9lwPl99WPbqLGDgLvbqa0LgcOS7NIu5B8GXNiW3ZPkoLavYydsa7J9SJKGYEbXXOiNtjoOuBp4O3AB8LfTrHMw8Bbg6iRXttoHgFOB85IcB9wEvLEtuwB4HTAG3Ae8FaCqNiT5CHB5a/fhqtrQpt8BfA6YB3yzfZhiH5KkIZhpuMwDVlTVZwGSzGm1+za3QlX9C5DNLH7UQIB2beT4zWxrBbBikvpq4EWT1O+YbB+SpOGY6Wmxi+iFybh5wLe7744kaVsw0yOXJ1XVveMzVXVvkicPqE/bpRNPPJF169axxx57cNppp426O5K0RWYaLr9K8rLxmyKT7A/8enDd2v6sW7eOtWvXTt9QkrYCMw2XdwNfTvL/6F1H2QN408B6JUnaqs0oXKrq8iS/Czy/lX5aVQ8OrluSpK3ZlOGS5JCqujjJH01Y9Lwk9N0YuU3b/7+fM/B9PPX2XzIH+MXtvxzK/q7438cOfB+Pl9efpK3fdEcurwQuBiZ7sEwB20W4aLi8/iRt/aYMl6o6OckOwDer6rwh9UmStJWb9j6XqnoYOHEIfdmuPTz3KWx84tN4eO5TRt0VSdpiMx0t9u0k7wW+BPxqvNj3GBZtoV8tPmzUXZCkzsw0XMaHHfc/nqWA3+m2O5KkbcFMhyLvM+iOSJK2HVNec0lyYJKfJLk3yQ+TvGBYHZMkbb2mu6D/aeC9wDOATwD/Z+A9kiRt9aY7LbZDVa1q019O8v5Bd0iz2y8+/HsD38dDG3YFduShDTcNZX97/4+rB74PaXszXbjsPOHu/EfMby936EuSHpvpwuW7PPLu/P5579CXJE1qujv03zqsjkiSth0zehNlkt2TnJXkm21+3/Z+ekmSHmWmrzn+HHAh8Kw2/2/03vEiSdKjzDRcdmsPrnwYoKoeAjYOrFeSpK3aTMPlV0meQe8iPkkOAu4eWK8kSVu1mT5b7L8BK4HnJPkBMB84amC90nZttyc9DDzUviVtjWZ05FJVP6L34rA/AN4OvLCqrppqnSQrktyW5Jq+2geTrE1yZfu8rm/Z+5OMJflpktf21Ze22liSk/rq+yS5tNW/lGRuqz+xzY+15Ytm9lNotnjvi+/i1AM28N4X3zXqrkh6nKZ7zfHE1xuPm8lrjj8HfAqY+M7e06vqLybsZ1/gaOCF9AYNfDvJ89riTwOvAdYAlydZWVXXAR9r2zo3yWeA44Az2vedVfXcJEe3dm9CkjQ0050Wm+z1xuOmvImyqr73GI4ajgDOrar7gZ8lGQMOaMvGqupGgCTnAkckuR44BPjj1uZs4IP0wuWINg3wFeBTSVJVNcO+SJK20ChuojwhybHAauA9VXUnsAC4pK/NmlYDuHlC/UB6D9K8q41am9h+wfg6VfVQkrtb+9sndiTJcmA5wN57773lf5kkCZj5BX2S/Ht6p62eNF6rqg8/xv2dAXyE3lHPR4CPA//lMW6jM1V1JnAmwJIlSzyykaSOzPQO/c/Qu27xTiDAG4BnP9adVdWtVbWxqh4GPsumU19rgb36mi5stc3V76D3EM0dJ9Qfsa22/OmtvSRpSGZ6n8sfVNWx9C6Ufwj4feB506zzKEn27Jt9PTA+kmwlcHQb6bUPsBi4DLgcWNxGhs2ld9F/Zbt+8h02DYdeBpzft61lbfoo4GKvt0jScM30tNiv2/d9SZ4FbAD2nKI9Sb4IvArYLcka4GTgVUn2o3da7Of0hjVTVdcmOQ+4DngIOL6qNrbtnEDv0TNzgBVVdW3bxfuAc5N8FPgxcFarnwV8vg0K2EAvkCRJQzTTcPlGkp2B04ArWu1vp1qhqo6ZpHzWJLXx9qcAp0xSvwC4YJL6jWw6rdZf/w2903aSpBGZ7j6XlwM3V9VH2vxOwNXAvwKnD757kqSt0XTXXP4GeAAgySuAU1vtbtooK0mSJprutNicqtrQpt8EnFlVXwW+muTKwXZNkrS1mu7IZU7fcN9DgYv7ls34HhlJ0vZluoD4IvDdJLfTGzH2fYAkz8VH7kuSNmO6x7+ckuQiesOOv9V3v8gO9G6olCTpUaY9tVVVl0xS+7fBdEeStC2Y6R36kiTNmOEiSeqc4SJJ6pzhIknqnOEiSeqc4SJJ6pzhIknqnI9wkWaxE088kXXr1rHHHntw2mmnjbo70owZLtIstm7dOtauXTt9Q2mWMVwkaSvzlLlPe8T3bGS4SNJW5uDn/NGouzAtL+hLkjpnuEiSOme4SJI6Z7hIkjpnuEiSOjewcEmyIsltSa7pq+2aZFWSG9r3Lq2eJJ9MMpbkqiQv61tnWWt/Q5JlffX9k1zd1vlkkky1D0nS8AxyKPLngE8B5/TVTgIuqqpTk5zU5t8HHA4sbp8DgTOAA5PsCpwMLAEKuCLJyqq6s7V5G3ApcAGwFPjmFPuQOnXwXx088H3MvWsuO7ADN99181D294N3/mDg+9D2YWBHLlX1PWDDhPIRwNlt+mzgyL76OdVzCbBzkj2B1wKrqmpDC5RVwNK27GlVdUlVFb0AO3KafUiShmTY11x2r6pb2vQ6YPc2vQC4ua/dmlabqr5mkvpU+3iUJMuTrE6yev369Y/jz5EkTWZkF/TbEUeNch9VdWZVLamqJfPnzx9kVyRpuzLscLm1ndKifd/W6muBvfraLWy1qeoLJ6lPtQ9J0pAMO1xWAuMjvpYB5/fVj22jxg4C7m6nti4EDkuySxv1dRhwYVt2T5KD2iixYydsa7J9SJKGZGCjxZJ8EXgVsFuSNfRGfZ0KnJfkOOAm4I2t+QXA64Ax4D7grQBVtSHJR4DLW7sPV9X4IIF30BuRNo/eKLFvtvrm9iFJGpKBhUtVHbOZRYdO0raA4zeznRXAiknqq4EXTVK/Y7J9SJKGxzv0JUmdM1wkSZ0zXCRJnfNNlNIsVk8uHuZh6skDvSVM6pzhIs1iDx784Ki7ID0unhaTJHXOcJEkdc5wkSR1znCRJHXOC/qStth3X/HKUXehc6/83ndH3YWtmkcukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOjSRckvw8ydVJrkyyutV2TbIqyQ3te5dWT5JPJhlLclWSl/VtZ1lrf0OSZX31/dv2x9q6Gf5fKUnbr1Eeuby6qvarqiVt/iTgoqpaDFzU5gEOBxa3z3LgDOiFEXAycCBwAHDyeCC1Nm/rW2/p4P8cSdK42XRa7Ajg7DZ9NnBkX/2c6rkE2DnJnsBrgVVVtaGq7gRWAUvbsqdV1SVVVcA5fduSJA3BqMKlgG8luSLJ8lbbvapuadPrgN3b9ALg5r5117TaVPU1k9QfJcnyJKuTrF6/fv2W/D2SpD6jehPlH1bV2iTPBFYl+df+hVVVSWrQnaiqM4EzAZYsWTLw/UnS9mIkRy5VtbZ93wZ8jd41k1vbKS3a922t+Vpgr77VF7baVPWFk9QlSUMy9HBJ8pQkTx2fBg4DrgFWAuMjvpYB57fplcCxbdTYQcDd7fTZhcBhSXZpF/IPAy5sy+5JclAbJXZs37YkSUMwitNiuwNfa6ODdwT+vqr+KcnlwHlJjgNuAt7Y2l8AvA4YA+4D3gpQVRuSfAS4vLX7cFVtaNPvAD4HzAO+2T6SpCEZerhU1Y3ASyap3wEcOkm9gOM3s60VwIpJ6quBF21xZyVJj8tsGoosSdpGGC6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM4ZLpKkzhkukqTOGS6SpM5ts+GSZGmSnyYZS3LSqPsjSduTbTJckswBPg0cDuwLHJNk39H2SpK2H9tkuAAHAGNVdWNVPQCcCxwx4j5J0nYjVTXqPnQuyVHA0qr6kzb/FuDAqjphQrvlwPI2+3zgp0Pt6KPtBtw+4j7MFv4Wm/hbbOJvscls+S2eXVXzJxZ3HEVPZouqOhM4c9T9GJdkdVUtGXU/ZgN/i038LTbxt9hktv8W2+ppsbXAXn3zC1tNkjQE22q4XA4sTrJPkrnA0cDKEfdJkrYb2+Rpsap6KMkJwIXAHGBFVV074m7NxKw5RTcL+Fts4m+xib/FJrP6t9gmL+hLkkZrWz0tJkkaIcNFktQ5w2UEkjwjyZXtsy7J2r75ZyZ5MMmfjrqfw5Jk9yR/n+TGJFck+WGS10/S7p+TzNqhl11JsrHv38OVSRaNuk+j0Pc7XJPky0mePOo+jcok/yZm/SOttskL+rNdVd0B7AeQ5IPAvVX1F23+z4BLgGOAz4yqj8OSJMDXgbOr6o9b7dnAfxxpx0br11W136g7MQv89ndI8gXgT4FPjLZLI7PV/ZvwyGX2OQZ4D7AgycJRd2YIDgEeqKrfBmlV3VRVf5VkXpJzk1yf5GvAvNF1c3SS7JTkoiQ/SnJ1ku3xUUbfB56bZNckX09yVZJLkrx41B0blSRPbw/nfX6b/2KSt426X+MMl1kkyV7AnlV1GXAe8KYRd2kYXgj8aDPL/gy4r6peAJwM7D+0Xo3WvL7TH18DfgO8vqpeBrwa+Hg74tsuJNmR3kNorwY+BPy4ql4MfAA4Z5R9G6L+fxNXJnlTVd0NnAB8LsnRwC5V9dkR9/O3PC02u7yJXqhA72GbK4CPj647w5fk08AfAg/Qe6rCJwGq6qokV42yb0P0iFMgSZ4A/M8krwAeBhYAuwPrRtS/YZmX5Mo2/X3gLOBS4D8BVNXF7frl06rqnlF1ckgmPS1WVauSvIHeU+BfMvxubZ7hMrscA+yR5M1t/llJFlfVDaPs1IBdS/sfC4CqOj7JbsBqfGTPuDcD84H9q+rBJD8HnjTaLg3Fo/4HdTs6YJuRJDsALwDuA3YB1oy2R5t4WmyWSPI8YKeqWlBVi6pqEfC/6AXOtuxi4EltIMO48VFB3wPGL/K/CNhez68/HbitBcurgWePukMj9H16YUuSVwG3bwdHLVP5r8D19P47+bt2lDsreIf+iI2PFgOeAsyrqpP6lr0Y+FK75rDNSrIncDpwILAe+BW9kXIrgb+jd7h/Pb3TQcdX1eoRdXUoktxbVTv1ze8G/COwE70juoOAw6vq56Pp4XBM/B1abVd6p4t/h97/W19eVdv86dIkG+ldcxr3T/T+2/g6cEBV/TLJJ4BfVtXJo+jjRIaLJKlznhaTJHXOcJEkdc5wkSR1znCRJHXOcJEkdc5wkbbAY31abZIPPM79PCHJqUluaM8Y+2GSwx9frx+17Xu72I7Uz6HI0haY7F6MLtu3deYApwB70ruv4/4kuwOvrKrzpl57MH2SpuORi9SxzT2tNsmpbHoA4Rfasv+c5LJW+5sWJCS5N8nHk/wEOBh4G/DOqrofoKpuHQ+WJMe0pyVfk+Rjff24N8kpSX7SniC8e6vv0458rk7y0WH+Ntp+GC7Slpnx02rb0xd+XVX7VdWbk7yA3sNKD27P0NpIe7QJvSc2XFpVLwHuAn4x2WNOkjwL+Bi9VxfsB7w8yZF927ikbeN79AIK4C+BM6rq94BbOv9FJHxwpbSltuRptYfSe43A5e2BjPOA29qyjcBXZ7D/lwP/XFXr4bcv1XoFvceCPAB8o7W7AnhNmz6YTQ8L/Ty9cJI6ZbhIAzDDp9WG3hs43z/Jst9U1cY2PQbs/TgeLf9gbbqoupFH/vfuxVYNlKfFpMHY3NNqH+ybvgg4KskzofdQxvaK50eoqvvovcvkL5PMbW3ntyOjy4BXJtmtXa85BvjuNH37AXB0m37zVA2lx8twkbbMxGsup7YL+X8CvKeqvk/veseft/ZnAlcl+UJVXdfq32ovQltFb0TYZP6c3hOjr0tyDb3TXfdU1S3AScB3gJ8AV1TV+dP0+V3A8UmupvekaalzDkWWJHXOIxdJUucMF0lS5wwXSVLnDBdJUucMF0lS5wwXSVLnDBdJUuf+PyITIqfQF3jcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "XwZZ9Qv0Wi-n"
      },
      "source": [
        "ExterCond_group_mapping={\"TA\":0,\"Gd\":1,\"Fa\":2,\"Po\":3,\"Ex\":4}\n",
        "X[\"ExterCond\"]=X[\"ExterCond\"].map(ExterCond_group_mapping)\n",
        "test[\"ExterCond\"]=test[\"ExterCond\"].map(ExterCond_group_mapping)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "ItsLmDtEWi-p",
        "outputId": "d43c7a5d-a26d-4e64-e152-b7a1c3521674",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"BsmtCond\",y=\"SalePrice\",data=train)\n",
        "# BsmtCond : 지하실의 일반상태\n",
        "# 지하실의 상태가 좋을수록 가격이 높다."
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7037dd4a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "cTDHJVbtWi-s"
      },
      "source": [
        "BsmtCond_group_mapping={\"TA\":0,\"Gd\":1,\"Fa\":2,\"Po\":3}\n",
        "X[\"BsmtCond\"]=X[\"BsmtCond\"].map(BsmtCond_group_mapping)\n",
        "test[\"BsmtCond\"]=test[\"BsmtCond\"].map(BsmtCond_group_mapping)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "zByen4jpWi-u",
        "outputId": "7f34aa1c-aeb1-4199-bc5f-ee2f6f839bf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"SaleType\",y=\"SalePrice\",data=train)\n",
        "#SaleType: 판매 유형\n",
        "#새로 지은 집일 때 가격이 비쌌고 저금리나 현금으로 구매 할 때도 가격이 비쌌다."
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7037da9710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "5GmLKVHGWi-w"
      },
      "source": [
        "SaleType_group_mapping={\"WD\":0,\"New\":1,\"COD\":2,\"ConLD\":3,\"ConLi\":4,\"CWD\":5,\"ConLw\":6,\"Con\":7,\"Oth\":8}\n",
        "X[\"SaleType\"]=X[\"SaleType\"].map(SaleType_group_mapping)\n",
        "test[\"SaleType\"]=test[\"SaleType\"].map(SaleType_group_mapping)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "QtPPJVMVWi-y",
        "outputId": "6aca3f55-a053-4e1d-87e4-781badc4066f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "sns.barplot(x=\"SaleCondition\",y=\"SalePrice\",data=train)\n",
        "# SaleCondition: 판매조건\n",
        "# 새로 지은 집이 가장 비싸다."
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7038554898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0/LKkN1GM2XVgc5pkZu0yZcoUuru7GTZsGNOmTWt3c6yXdhuw0xbP7dRocvmppMHANGBJil3dnCaZWbt0d3fT1dXVc0HbLr1jv73a3YRNerrO5V3A0xFxcZrfE1gO/Jri/vRmZmZv0NOx0/8FXgOQ9F7g0hRbR7rJlpmZWaWeTosNiIi1afp0YEZE3AzcLGlpc5tmZpbXHrvsvcWzNU+PyaXsVsLHk+433+C6ZmbblWMOrTWileXWU4K4HrhL0hqKHmO/BJB0GB5y38zMauhp+JdLJC2k6Hb887J70e8EfLbZjTMzs76px1NbEXFfldjjzWmOmZn1B+2/0sbMzPodJxczM8vOycXMzLJzcjEzs+ycXMzMLDsnFzMzy87JxczMsnNyMTOz7Jo2PpikmcBfAasi4vAU2xe4ARgJPAV8JCKelyTgn4GTgZeBj0fEA2mdicDXU7XfiojZKT4WmAUMAuYBn4+IqLWNZu2nWbvc9d5js9f5ysABIPHKypVNqf/Yu+/KXqdtn5p55DILGF8RuwBYGBGjgIVpHuAkYFR6TAKmw6ZkNBU4EhgHTJU0JK0zHfhE2Xrje9iGmZm1SNOSS0TcTXE75HITgNlpejZwSln82ijcBwyWdCBwIrAgItamo48FwPi0bO+IuC+Nd3ZtRV3VtmFmZi3S6t9cDoiIZ9J0N3BAmj4IeLqs3MoUqxdfWSVebxtvIGmSpMWSFq9evXordsfMzKpp2w/66YgjeizYxG1ExIyI6IiIjqFDhzazKWZmO5RWJ5dn0ykt0vOqFO8CRpSVG55i9eLDq8TrbcPMzFqk1cllLjAxTU8Ebi2Ln63CUcC6dGprPnCCpCHph/wTgPlp2YuSjko9zc6uqKvaNszMrEWa2RX5euB9wP6SVlL0+roUuFHSucBvgY+k4vMouiF3UnRFPgcgItZKuhhYlMpdFBGlTgKfYXNX5NvSgzrbMDOzFmlacomIM2ssOr5K2QAm16hnJjCzSnwxcHiV+HPVtmFmZq3jK/TNzCw7JxczM8vOycXMzLJzcjEzs+ycXMzMLDsnFzMzy87JxczMsnNyMTOz7JxczMwsu6ZdoW9mfc/giC2ezbaWk4uZbfI3Gza2uwnWT/i0mJmZZefkYmZm2Tm5mJlZdk4uZmaWnZOLmZll5+RiZmbZObmYmVl2Ti5mZpadk4uZmWXn5GJmZtk5uZiZWXZOLmZmlp2Ti5mZZefkYmZm2Tm5mJlZdk4uZmaWXVuSi6SnJC2XtFTS4hTbV9ICSU+k5yEpLklXSOqUtEzSO8vqmZjKPyFpYll8bKq/M62r1u+lmdmOq51HLsdFxJiI6EjzFwALI2IUsDDNA5wEjEqPScB0KJIRMBU4EhgHTC0lpFTmE2XrjW/+7piZWcn2dFpsAjA7Tc8GTimLXxuF+4DBkg4ETgQWRMTaiHgeWACMT8v2joj7IiKAa8vqMjOzFmhXcgng55KWSJqUYgdExDNpuhs4IE0fBDxdtu7KFKsXX1kl/gaSJklaLGnx6tWrt2V/zMyszMA2bfc9EdEl6U+ABZJ+Xb4wIkJSNLsRETEDmAHQ0dHR9O2Zme0o2nLkEhFd6XkVcAvFbybPplNapOdVqXgXMKJs9eEpVi8+vErczMxapOXJRdIekvYqTQMnAA8Dc4FSj6+JwK1pei5wduo1dhSwLp0+mw+cIGlI+iH/BGB+WvaipKNSL7Gzy+oyM7MWaMdpsQOAW1Lv4IHAjyLi3yQtAm6UdC7wW+Ajqfw84GSgE3gZOAcgItZKuhhYlMpdFBFr0/RngFnAIOC29DAzsxZpeXKJiCeBv6gSfw44vko8gMk16poJzKwSXwwcvs2NNTOzrbI9dUU2M7N+wsnFzMyyc3IxM7PsnFzMzCw7JxczM8vOycXMzLJzcjEzs+ycXMzMLDsnFzMzy87JxczMsnNyMTOz7JxczMwsOycXMzPLzsnFzMyyc3IxM7PsnFzMzCw7JxczM8vOycXMzLJzcjEzs+ycXMzMLDsnFzMzy87JxczMsnNyMTOz7JxczMwsOycXMzPLzsnFzMyyc3IxM7Ps+m1ykTRe0mOSOiVd0O72mJntSPplcpE0ALgSOAkYDZwpaXR7W2VmtuPol8kFGAd0RsSTEfEaMAeY0OY2mZntMBQR7W5DdpJOA8ZHxP9K82cBR0bEeRXlJgGT0uxbgMda2Mz9gTUt3F6r9ef968/7Bt6/vq7V+/fmiBhaGRzYwgZsdyJiBjCjHduWtDgiOtqx7Vboz/vXn/cNvH993fayf/31tFgXMKJsfniKmZlZC/TX5LIIGCXpEEm7AGcAc9vcJjOzHUa/PC0WEeslnQfMBwYAMyNiRZubVaktp+NaqD/vX3/eN/D+9XXbxf71yx/0zcysvfrraTEzM2sjJxczM8vOyWUrSApJl5XNny/pwha34ReSsnY3lHRK2re3pvn3Sfppzm1sRZuekrR/k+reIGmppIcl/VjS7r1Yd4ykk8vm/3tPwwxJ+rik725Lm3uof4vXr8ryTe8ZSfMkDU7TLzWhLRdKOj93vVW2U/meHSnp4TTd9vfv1ip7b5YeI7exvk3vz1a9Nk4uW+dV4MNb+09P0vbakeJM4J703DTb0f6/EhFjIuJw4DXgU42slNo/BtiUXCJibkRc2pxmNqzh1y8iTo6IF5rfpKZryXu2DUrvzdLjqW2prB3vTyeXrbOeokfG31YuSN+c7pC0TNJCSQen+CxJV0m6H5iW5qdLuk/Sk+lb1kxJj0qaVVbfdEmLJa2Q9M1m7ZCkPYH3AOdSdN0u2VvSz9IgoFdJ2imVf0nSJZIeSvtwQDP2v4V+CRwm6UOS7pf0oKTby/brQknXSboXuA64CDg9fas8vfyopFYdzVTt9ZM0SNKc9De9BRhUVr7uEWEPf4eZ6SjoSUmfK1vna5Iel3QPxYgXTVXnPVut7L6S/l96X94n6YhSHZJ+IGl5WvY/Urwln7tGpXYulPRAauuEFB8p6dfp8/S4pB9K+oCkeyU9IWlcKveGo2ZJh0p6oGx+VPn8tnJy2XpXAh+TtE9F/DvA7Ig4AvghcEXZsuHAuyPii2l+CHA0RZKaC1wOvA14u6QxqczX0tW2RwDHlj4UTTAB+LeIeBx4TtLYFB8HfJZiANBDgQ+n+B7AfRHxF8DdwCdSPPf+N106EjkJWE7xLfioiHgHxZh0U8qKjgY+EBFnAt8AbkjfKm+oqLJeHc1S7fX7NPByRPw5MBUYW6+CCvX24a3AiRTvjamSdk7bO4PNR3Tv2tYdakCt92w13wQeTO/LrwLXpvjfA+si4u1p2R0p3qrPXS2DtPmU2C3AH4FTI+KdwHHAZZKUyh4GXEbxurwV+ChF0j2fYl+rioj/BNaVfdbOAX6QawecXLZSRLxI8Qb9XMWio4EfpenrKF7kkh9HxIay+X+Noi/4cuDZiFgeERuBFcDIVOYj6dvEgxT/eJs1uvOZFP9ESM+l0wy/SgOAbgCuZ/P+vAaUzmcvKWtv7v1vpkGSlgKLgd8B11AkwPmSlgN/R/E3L5kbEa80UG+9Opql2uv3XuBfACJiGbCsF/XV24efRcSrEbEGWAUcAPwlcEtEvJw+G624aLnWe7aa91C8H4mIO4D9JO0NfIDiiyJp2fNpslWfu1rKT4udCgj4B0nLgNuBgyj+7gC/qfjsLCz7XI3sYTtXA+eoGEn+dDZ/drfZ9nLuu6/6J+ABGs/2f6iYfzU9byybLs0PlHQIxbePd0XE8+l00W5b39zqJO0LvJ/iiCEoLjwN4GfpuVxp/vXYfJHUBhp7L/Vq/xtr/TZ5JSK2OEKS9B3g2xExV9L7gAvLFle2v5Z6dWRX5/V7cBuqrbcP5a9Vo699VnX2+cq6KzZWd0s+d730MWAoMDYiXpf0FJvbVPnZKf9c9fTa3ExxVHsHsCQinsvVYB+5bIOIWAvcSHHOt+Tf2Xz+92MU5/K31t4U/9DWpXPeJ21DXfWcBlwXEW+OiJERMQL4DcW30XEqhtHZieKbzT091JVz/9thHzaPQzexTrnfA3ttYx251Hr9llCcIkHS4RSneBrV2324Gzgl/c6zF/ChXmxra9Ta5xE1yv+S4v1ISpZr0hHWAmByqZCkIbTuc9cb+wCrUmI5Dnhzjkoj4o8UI5lMJ+MpMXByyeEyiiGuSz5LcZi5DDgL+PzWVhwRD1F8+/w1xeHqvdvQznrOBG6piN2c4ouA7wKPUnx4K8tVyrb/bXIh8GNJS6g/bPmdwOjSD/pbWUcutV6/Q4A9JT1K0QFhSUWZ0pHn7pJWlj2+SC/3ISIeAG4AHgJuo3jfNFOtff5KjfIXAmPT+/JSNifMbwFDVHRHfwg4roWfu974IdCRTlOeTdG2nHVvBH6esU4P/2K2o0nn11cBwyLi9Xa3x9pLxTUv+0TE3+es17+5mO14VgBXO7FY6ol2KMXvV3nr9pGLmZnl5t9czMwsOycXMzPLzsnFzMyyc3IxqyONl7UijTu1VNKRdcrOknRaA3Wen8aDWippkaSzM7V103hhkv49PY+U9NGyMh2SrqhVh1ku7i1mVoOko4G/At4ZEa+mf9y7bGOdnwI+CIyLiBfTECSnbntrtxQR706TIykupPxRii+mGO7GrKl85GJW24EUV3K/ChARayLivyR9Ix1xPCxpRtkAgptIGivpLklLJM2XdGBa9FXg0+nqcCLixYiYndY5XsUoxMtVjDy8a4o/Jemb2jwibuneJftJ+nk6srqaYvyp0vZL92i5FPjLdJT0tyq7x4lqjxRcc+Rjs0Y5uZjV9nNghIqhzL8n6dgU/25EvCvdB2YQxdHNJpJ2phib67SIGAvMBC5JRyl7RcSTlRuStBswCzg9It5OcVbh02VF1qQRcadTjHsFxZhQ90TE2yiuVj+4yj5cAPwyDYB4ecWyWiMFQ5WRj2v8jcyqcnIxqyEiXqIYpn4SsBq4QdLHgeNU3OtkOcXFZ5WjHr8FOBxYoGLU5a9TjDJcz1soRrd9PM3PphjVuOQn6bl8BOryUY9/BjxP79QaKRiqj3xs1jD/5mJWR7pFwC+AX6Rk8kmKASA7IuJpFbe3rhwxV8CKiDi6sj4VN1n702pHLz0ojXTbqlGI2z7ysfVtPnIxq0HSWySNKguNAR5L02tU3AmxWu+wx4ChqUMAKm6mVTq6+d/AlaUjBBV3GDw7rTNS0mGp3FnAXT008W42j3p8EsXN1yrVG7251kjBZtvM30bMatsT+I6kwRS3tu6kOEX2AvAw0E2V0X8j4rXUJfkKFXcqHUhx758VFL+Z7AkskvQ68DpwWUT8UdI5FCMRD0z1XtVD+74JXC9pBcWtDn5XpcwyYEMa8XcWW97j5UJgZhop+GVac3sA20F4bDEzM8vOp8XMzCw7JxczM8vOycXMzLJzcjEzs+ycXMzMLDsnFzMzy87JxczMsvv/DKZg/kb5LtIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "ohWCObWJWi-1"
      },
      "source": [
        "SaleCondition_group_mapping={\"Normal\":0,\"Abnorml\":1,\"Partial\":2,\"AdjLand\":3,\"Alloca\":4,\"Family\":5}\n",
        "X[\"SaleCondition\"]=X[\"SaleCondition\"].map(SaleCondition_group_mapping)\n",
        "test[\"SaleCondition\"]=test[\"SaleCondition\"].map(SaleCondition_group_mapping)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "lihcsfNMWi-3",
        "outputId": "10409224-54fe-423a-a33d-e9ee7bdd7e5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"MSSubClass\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7037c52160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "Y7iHJ4CfWi-5",
        "outputId": "94ec02ab-619a-42c7-c8d7-938e198462b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"LotArea\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7037804860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "GRdDTGkzWi-7",
        "outputId": "371d20b6-3590-4276-8f32-c8b34c71a0a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"OverallQual\",y=\"SalePrice\",data=train)\n",
        "#OverallQual: 전체재료 및 마감 품질\n",
        "#마감 품질이 높을수록 가격이 높다는 것을 알 수 있다."
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7037d8ca20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "n4lSSaCwWi-9",
        "outputId": "2e93a8b9-e6cc-492f-b06a-c17d0c7eb04e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"OverallCond\",y=\"SalePrice\",data=train)\n",
        "# OverallCond: 전반적인 상태 등급 -> 2, 5, 9등급의 집값이 주변 등급 대비 높으며, 6~8등급의 집값은 비슷하다."
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f70356a1a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "VIqW8ytFWi-_",
        "outputId": "4229a512-a720-45ee-87c7-22e3ef519cce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"YearBuilt\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f703564b128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "LL-442OrWi_B",
        "outputId": "4e3e93f9-17c1-45e6-8fae-774c1e7df39b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "X[\"YearBuilt\"].describe()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1460.000000\n",
              "mean     1971.267808\n",
              "std        30.202904\n",
              "min      1872.000000\n",
              "25%      1954.000000\n",
              "50%      1973.000000\n",
              "75%      2000.000000\n",
              "max      2010.000000\n",
              "Name: YearBuilt, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "Hl22CUjKWi_D",
        "outputId": "f0b32072-34de-4ddc-cb19-cbbde7554fb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "X[\"YearBuilt\"]"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       2003\n",
              "1       1976\n",
              "2       2001\n",
              "3       1915\n",
              "4       2000\n",
              "        ... \n",
              "1455    1999\n",
              "1456    1978\n",
              "1457    1941\n",
              "1458    1950\n",
              "1459    1965\n",
              "Name: YearBuilt, Length: 1460, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "3MrFiv07Wi_F"
      },
      "source": [
        "YearBuilt_slice=np.linspace(1870,2010,15)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "jCYtHVdmWi_I",
        "outputId": "c501b705-2231-4e4b-b0d4-0c06769c8e91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "YearBuilt_slice"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1870., 1880., 1890., 1900., 1910., 1920., 1930., 1940., 1950.,\n",
              "       1960., 1970., 1980., 1990., 2000., 2010.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "XBzCuAWmWi_L"
      },
      "source": [
        "YearBuilt_labels=np.arange(0,14)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "3AHjwlr-Wi_N",
        "outputId": "6f51dc05-80bd-4836-efac-ef8bd52ca06d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "YearBuilt_labels"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "WsuSMW-EWi_V"
      },
      "source": [
        "X[\"YearBuiltGroup\"]=pd.cut(X[\"YearBuilt\"],YearBuilt_slice,labels=YearBuilt_labels)\n",
        "test[\"YearBuiltGroup\"]=pd.cut(test[\"YearBuilt\"],YearBuilt_slice,labels=YearBuilt_labels)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "e0sIXY_5Wi_X",
        "outputId": "76acdf8d-3f14-4198-c393-ea75a62addad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "X[\"YearBuiltGroup\"]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       13\n",
              "1       10\n",
              "2       13\n",
              "3        4\n",
              "4       12\n",
              "        ..\n",
              "1455    12\n",
              "1456    10\n",
              "1457     7\n",
              "1458     7\n",
              "1459     9\n",
              "Name: YearBuiltGroup, Length: 1460, dtype: category\n",
              "Categories (14, int64): [0 < 1 < 2 < 3 ... 10 < 11 < 12 < 13]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "mVOJp826Wi_Z",
        "outputId": "fd1fbdc8-2f09-4631-8bca-e6a14ad5763e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"YearRemodAdd\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f703532b0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "j2CFJ8gtWi_b",
        "outputId": "3c40dfcb-b68f-49af-80d9-899cf3528ef4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "X[\"YearRemodAdd\"].describe()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1460.000000\n",
              "mean     1984.865753\n",
              "std        20.645407\n",
              "min      1950.000000\n",
              "25%      1967.000000\n",
              "50%      1994.000000\n",
              "75%      2004.000000\n",
              "max      2010.000000\n",
              "Name: YearRemodAdd, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "bSOEYLjEWi_e"
      },
      "source": [
        "YearRemodAdd_slice=np.linspace(1950,2010,7)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "6-CuM25RWi_g"
      },
      "source": [
        "YearRemodAdd_labels=np.arange(0,6)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "KzrLp21-Wi_h"
      },
      "source": [
        "X[\"YearRemodAddGroup\"]=pd.cut(X[\"YearRemodAdd\"],YearRemodAdd_slice,labels=YearRemodAdd_labels)\n",
        "test[\"YearRemodAddGroup\"]=pd.cut(test[\"YearRemodAdd\"],YearRemodAdd_slice,labels=YearRemodAdd_labels)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "XU4Ww3GTWi_j",
        "outputId": "a77ffae7-b93a-4f86-d578-76d6d089d666",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "X[\"YearRemodAddGroup\"]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       5\n",
              "1       2\n",
              "2       5\n",
              "3       1\n",
              "4       4\n",
              "       ..\n",
              "1455    4\n",
              "1456    3\n",
              "1457    5\n",
              "1458    4\n",
              "1459    1\n",
              "Name: YearRemodAddGroup, Length: 1460, dtype: category\n",
              "Categories (6, int64): [0 < 1 < 2 < 3 < 4 < 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "f3GHb6SNWi_n",
        "outputId": "ba05fd1c-8f61-42d3-f879-8ae0495c1537",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"TotalBsmtSF\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7034f4e9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "FygsK_YEWi_s",
        "outputId": "b612952d-41a4-46db-c92f-77b6074d8a67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"1stFlrSF\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f703376bb38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "F15U7h28Wi_t",
        "outputId": "3c39adc9-8644-4ff8-f3f9-b078130a6d3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "X[\"1stFlrSF\"].describe()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1460.000000\n",
              "mean     1162.626712\n",
              "std       386.587738\n",
              "min       334.000000\n",
              "25%       882.000000\n",
              "50%      1087.000000\n",
              "75%      1391.250000\n",
              "max      4692.000000\n",
              "Name: 1stFlrSF, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "TwPxC5p9Wi_v",
        "outputId": "f7bcecb9-1deb-4c48-9387-7064733188a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"FullBath\",y=\"SalePrice\",data=train)\n",
        "# FullBath: 등급 이상의 전체 욕실\n",
        "# 욕실의 개수가 많을수록 가격이 높다."
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f70321497b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "lVDjTq5ZWi_y",
        "outputId": "0eb23618-d2b5-4d98-abe4-c2520761df17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"BedroomAbvGr\",y=\"SalePrice\",data=train)\n",
        "# Bedroom: 지하층 이상 침실 수\n",
        "# 침실개수는 4개일 때와 8개일 때 가격이 높은 걸로 보아 가족 구성원수에 맞게 침실이 있을 때 가격이 높은 것 같다."
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7031ef27f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "AVbUQOuFWi_0",
        "outputId": "260d7f82-e646-4fad-8053-a5acca81a3d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.barplot(x=\"TotRmsAbvGrd\",y=\"SalePrice\",data=train)\n",
        "# TotRmsAbvGrd: 총 객실 등급 이상(화장실 포함 안 함) -> 방의 수가 11개까지는 가격이 증가하고 이후로는 감소한다."
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7031e558d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "JdrNCTnnWi_1",
        "outputId": "c165d45b-ae77-47cd-c536-fdc30d942c80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"GarageYrBlt\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7031e28c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "jwZqwmh1Wi_4",
        "outputId": "15d1e92a-3d4f-4d4d-92fb-355345b1b120",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"GarageArea\",y=\"SalePrice\",data=train)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f703195a160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "OliDMWKOWi_6",
        "outputId": "2f4b0480-e912-4727-8a72-f17b7694b1c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"MiscVal\",y=\"SalePrice\",data=train)\n",
        "# MiscVal: 기타 기능의 $값  -> 기타 기능의 값과 집값의 연관성은 크게 없다"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7030c19b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "MMnSwh9wWi_8",
        "outputId": "7c5911ac-b1d9-4100-ef0d-38e8e9c8e09f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x=\"YrSold\",y=\"SalePrice\",data=train)\n",
        "# YrSold: 판매 연도 -> 판매 연도와 짒갑의 연관성은 없다."
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7030bb45f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "_gqH0kYoWi_-"
      },
      "source": [
        "blacklist = [\"LotArea\",\"TotalBsmtSF\",\"1stFlrSF\",\"GarageYrBlt\",\"GarageArea\",\"YearBuilt\",\"YearRemodAdd\"]"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "eoLCFjctWjAA"
      },
      "source": [
        "X = X.drop(blacklist,axis=1)\n",
        "test = test.drop(blacklist,axis=1)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "YwYRFgKNWjAC",
        "outputId": "3d9c0824-d958-4ec4-fafd-b1d3f821e7eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        }
      },
      "source": [
        "X.info()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1460 entries, 0 to 1459\n",
            "Data columns (total 19 columns):\n",
            " #   Column             Non-Null Count  Dtype   \n",
            "---  ------             --------------  -----   \n",
            " 0   Id                 1460 non-null   int64   \n",
            " 1   MSSubClass         1460 non-null   int64   \n",
            " 2   MSZoning           1460 non-null   int64   \n",
            " 3   Street             1460 non-null   int64   \n",
            " 4   Condition1         1460 non-null   int64   \n",
            " 5   BldgType           1460 non-null   int64   \n",
            " 6   OverallQual        1460 non-null   int64   \n",
            " 7   OverallCond        1460 non-null   int64   \n",
            " 8   ExterCond          1460 non-null   int64   \n",
            " 9   BsmtCond           1423 non-null   float64 \n",
            " 10  FullBath           1460 non-null   int64   \n",
            " 11  BedroomAbvGr       1460 non-null   int64   \n",
            " 12  TotRmsAbvGrd       1460 non-null   int64   \n",
            " 13  MiscVal            1460 non-null   int64   \n",
            " 14  YrSold             1460 non-null   int64   \n",
            " 15  SaleType           1455 non-null   float64 \n",
            " 16  SaleCondition      1460 non-null   int64   \n",
            " 17  YearBuiltGroup     1460 non-null   category\n",
            " 18  YearRemodAddGroup  1282 non-null   category\n",
            "dtypes: category(2), float64(2), int64(15)\n",
            "memory usage: 197.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "oCzot-C5WjAD"
      },
      "source": [
        "y = train[\"SalePrice\"]"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "ZLmR-10cWjAF",
        "outputId": "c2214ce7-9580-4dda-bb58-9dfdfc1d629c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "X.isnull().sum()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                     0\n",
              "MSSubClass             0\n",
              "MSZoning               0\n",
              "Street                 0\n",
              "Condition1             0\n",
              "BldgType               0\n",
              "OverallQual            0\n",
              "OverallCond            0\n",
              "ExterCond              0\n",
              "BsmtCond              37\n",
              "FullBath               0\n",
              "BedroomAbvGr           0\n",
              "TotRmsAbvGrd           0\n",
              "MiscVal                0\n",
              "YrSold                 0\n",
              "SaleType               5\n",
              "SaleCondition          0\n",
              "YearBuiltGroup         0\n",
              "YearRemodAddGroup    178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "7RTKdhpIWjAH"
      },
      "source": [
        "X[\"SaleType\"]=X[\"SaleType\"].notnull().astype(\"int64\")\n",
        "X[\"YearBuiltGroup\"]=X[\"YearBuiltGroup\"].notnull().astype(\"int64\")\n",
        "X[\"YearRemodAddGroup\"]=X[\"YearRemodAddGroup\"].notnull().astype(\"int64\")\n",
        "test[\"SaleType\"]=test[\"SaleType\"].notnull().astype(\"int64\")\n",
        "test[\"YearBuiltGroup\"]=test[\"YearBuiltGroup\"].notnull().astype(\"int64\")\n",
        "test[\"YearRemodAddGroup\"]=test[\"YearRemodAddGroup\"].notnull().astype(\"int64\")"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "FYkOwczfWjAI"
      },
      "source": [
        "X[\"BsmtCond\"]=X[\"BsmtCond\"].fillna(\"0\")\n",
        "X[\"YearRemodAddGroup\"]=X[\"YearRemodAddGroup\"].fillna(\"0\")\n",
        "\n",
        "test[\"MSZoning\"]=test[\"MSZoning\"].fillna(\"0\")\n",
        "test[\"BsmtCond\"]=test[\"BsmtCond\"].fillna(\"0\")\n",
        "test[\"YearRemodAddGroup\"]=test[\"YearRemodAddGroup\"].fillna(\"0\")"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "KqlgaPkVWjAK"
      },
      "source": [
        "numerical_columns2 = X.select_dtypes(exclude=['object'])\n",
        "numerical_columnstest = test.select_dtypes(exclude=['object'])"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "QGfJ4g-3WjAL",
        "outputId": "5e5f5ce4-bddf-47c3-c918-b10c8e0b305d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "numerical_columns2.dtypes"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                   int64\n",
              "MSSubClass           int64\n",
              "MSZoning             int64\n",
              "Street               int64\n",
              "Condition1           int64\n",
              "BldgType             int64\n",
              "OverallQual          int64\n",
              "OverallCond          int64\n",
              "ExterCond            int64\n",
              "FullBath             int64\n",
              "BedroomAbvGr         int64\n",
              "TotRmsAbvGrd         int64\n",
              "MiscVal              int64\n",
              "YrSold               int64\n",
              "SaleType             int64\n",
              "SaleCondition        int64\n",
              "YearBuiltGroup       int64\n",
              "YearRemodAddGroup    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "YBI-vvIHWjAP",
        "outputId": "0e08e96e-5002-43a7-c64a-7bc16dbbd0b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "numerical_columnstest.dtypes"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                   int64\n",
              "MSSubClass           int64\n",
              "Street               int64\n",
              "Condition1           int64\n",
              "BldgType             int64\n",
              "OverallQual          int64\n",
              "OverallCond          int64\n",
              "ExterCond            int64\n",
              "FullBath             int64\n",
              "BedroomAbvGr         int64\n",
              "TotRmsAbvGrd         int64\n",
              "MiscVal              int64\n",
              "YrSold               int64\n",
              "SaleType             int64\n",
              "SaleCondition        int64\n",
              "YearBuiltGroup       int64\n",
              "YearRemodAddGroup    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "TtIPHNy3WjAQ"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "SmI_yvsEWjAS"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "def rmse(y,preds):\n",
        "    return sqrt(mean_squared_error(preds, y))"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "NJnzZuacWjAU"
      },
      "source": [
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "a9z5J5wHWjAV",
        "outputId": "407518f1-1d84-4161-b37b-6d1b0d254c01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ferrari = DecisionTreeRegressor(random_state=1)\n",
        "ferrari.fit(train_X, train_y)\n",
        "val_predictions = ferrari.predict(val_X)\n",
        "val_mae = rmse(val_predictions, val_y)\n",
        "print(\"score: {:.0f}\".format(val_mae))"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "score: 42478\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "mJe2uzZSWjAW",
        "outputId": "aa94f89e-a851-4fc0-cb27-35ea28f5eb6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "porsche = DecisionTreeRegressor(max_leaf_nodes=100, random_state=1)\n",
        "porsche.fit(train_X, train_y)\n",
        "val_predictions = porsche.predict(val_X)\n",
        "val_mae = rmse(val_y, val_predictions)\n",
        "print(\"score: {:.0f}\".format(val_mae))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "score: 39224\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "H_YuKetFWjAb",
        "outputId": "d3415d7b-0343-4b35-8215-44d24abec45d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "pagani = RandomForestRegressor(random_state=1)\n",
        "pagani.fit(train_X, train_y)\n",
        "rf_val_predictions = pagani.predict(val_X)\n",
        "rf_val_mae = rmse(rf_val_predictions, val_y)\n",
        "\n",
        "print(\"score: {:.0f}\".format(rf_val_mae))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "score: 35548\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "EAsm3h__WjAe",
        "outputId": "56161caf-b264-4ca7-c823-068464605675",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "lamborghini = RandomForestRegressor(random_state=1)\n",
        "lamborghini.fit(X, y)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=1, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "c8rDUBKCWjAh",
        "outputId": "734f4895-f535-4323-af77-6a8e0f05071f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "sample_submission"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>SalePrice</th>\n",
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              "      <td>2915</td>\n",
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              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>2916</td>\n",
              "      <td>164788.778231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>2917</td>\n",
              "      <td>219222.423400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2918</td>\n",
              "      <td>184924.279659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>187741.866657</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1459 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id      SalePrice\n",
              "0     1461  169277.052498\n",
              "1     1462  187758.393989\n",
              "2     1463  183583.683570\n",
              "3     1464  179317.477511\n",
              "4     1465  150730.079977\n",
              "...    ...            ...\n",
              "1454  2915  167081.220949\n",
              "1455  2916  164788.778231\n",
              "1456  2917  219222.423400\n",
              "1457  2918  184924.279659\n",
              "1458  2919  187741.866657\n",
              "\n",
              "[1459 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "4d7RkuxGWjAk",
        "outputId": "fda1e7e2-909e-4555-ac93-3ddb6c989113",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "test"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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",
              "      <td>...</td>\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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1454</th>\n",
              "      <td>2915</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>2916</td>\n",
              "      <td>160</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>2917</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>2918</td>\n",
              "      <td>85</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>700</td>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>2919</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1459 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id  MSSubClass  ... YearBuiltGroup  YearRemodAddGroup\n",
              "0     1461          20  ...              1                  1\n",
              "1     1462          20  ...              1                  1\n",
              "2     1463          60  ...              1                  1\n",
              "3     1464          60  ...              1                  1\n",
              "4     1465         120  ...              1                  1\n",
              "...    ...         ...  ...            ...                ...\n",
              "1454  2915         160  ...              1                  1\n",
              "1455  2916         160  ...              1                  1\n",
              "1456  2917          20  ...              1                  1\n",
              "1457  2918          85  ...              1                  1\n",
              "1458  2919          60  ...              1                  1\n",
              "\n",
              "[1459 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "kUJJYwMbWjAm",
        "outputId": "a447ec5f-65cd-45e5-a9b2-898db764f5fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "X"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>Street</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>BldgType</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>ExterCond</th>\n",
              "      <th>BsmtCond</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
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              "      <td>6</td>\n",
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              "      <td>2007</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>1456</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2007</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>1457</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>1458</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>2500</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>1459</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>1460</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1460 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id  MSSubClass  ...  YearBuiltGroup  YearRemodAddGroup\n",
              "0        1          60  ...               1                  1\n",
              "1        2          20  ...               1                  1\n",
              "2        3          60  ...               1                  1\n",
              "3        4          70  ...               1                  1\n",
              "4        5          60  ...               1                  1\n",
              "...    ...         ...  ...             ...                ...\n",
              "1455  1456          60  ...               1                  1\n",
              "1456  1457          20  ...               1                  1\n",
              "1457  1458          70  ...               1                  1\n",
              "1458  1459          20  ...               1                  1\n",
              "1459  1460          20  ...               1                  1\n",
              "\n",
              "[1460 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "scrolled": true,
        "id": "-qdOBzOeWjAo"
      },
      "source": [
        "test_preds = lamborghini.predict(test)\n",
        "output = pd.DataFrame({'Id': test[\"Id\"],\n",
        "                       'SalePrice': test_preds})\n",
        "output.to_csv('lamborghini1.csv', index=False)"
      ],
      "execution_count": 83,
      "outputs": []
    }
  ]
}