House_Predictions_task1_.ipynb

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      "source": [
        "# Importing libraries\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
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      "id": "3a9a902e",
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      "source": [
        "# Importing Dataset\n",
        "house_data = pd.read_csv('/content/train.csv')\n",
        "test_data = pd.read_csv('/content/test.csv')\n"
      ]
    },
    {
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          "data": {
            "text/plain": [
              "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
              "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
              "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
              "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
              "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
              "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
              "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
              "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
              "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
              "\n",
              "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
              "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000  ...   \n",
              "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726  ...   \n",
              "std       1.112799    30.202904     20.645407   181.066207   456.098091  ...   \n",
              "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
              "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000  ...   \n",
              "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000  ...   \n",
              "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000  ...   \n",
              "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
              "\n",
              "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
              "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
              "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
              "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
              "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
              "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
              "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
              "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
              "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
              "\n",
              "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
              "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
              "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
              "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
              "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
              "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
              "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
              "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
              "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
              "\n",
              "[8 rows x 38 columns]"
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
              "      <th>OverallCond</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>YearRemodAdd</th>\n",
              "      <th>MasVnrArea</th>\n",
              "      <th>BsmtFinSF1</th>\n",
              "      <th>...</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>SalePrice</th>\n",
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>1460.000000</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>1452.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>...</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>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>1460.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>56.897260</td>\n",
              "      <td>70.049958</td>\n",
              "      <td>10516.828082</td>\n",
              "      <td>6.099315</td>\n",
              "      <td>5.575342</td>\n",
              "      <td>1971.267808</td>\n",
              "      <td>1984.865753</td>\n",
              "      <td>103.685262</td>\n",
              "      <td>443.639726</td>\n",
              "      <td>...</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>43.489041</td>\n",
              "      <td>6.321918</td>\n",
              "      <td>2007.815753</td>\n",
              "      <td>180921.195890</td>\n",
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              "      <th>std</th>\n",
              "      <td>421.610009</td>\n",
              "      <td>42.300571</td>\n",
              "      <td>24.284752</td>\n",
              "      <td>9981.264932</td>\n",
              "      <td>1.382997</td>\n",
              "      <td>1.112799</td>\n",
              "      <td>30.202904</td>\n",
              "      <td>20.645407</td>\n",
              "      <td>181.066207</td>\n",
              "      <td>456.098091</td>\n",
              "      <td>...</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>496.123024</td>\n",
              "      <td>2.703626</td>\n",
              "      <td>1.328095</td>\n",
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              "      <th>min</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>2006.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>365.750000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>7553.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1954.000000</td>\n",
              "      <td>1967.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>730.500000</td>\n",
              "      <td>50.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-64c84d92-901d-44e3-b1f9-f168c1691e30 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Summary  of our dataset\n",
        "house_data.head()\n",
        "house_data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "1c05b149",
      "metadata": {
        "id": "1c05b149",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "357ff3ad-f6f3-4847-f311-34166b37d74c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 81)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Size of dataset\n",
        "house_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8a3deeb8",
      "metadata": {
        "id": "8a3deeb8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4769cc3-e2ca-4418-8010-c9573ea2bb09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features in the dataset:\n",
            "Id\n",
            "MSSubClass\n",
            "MSZoning\n",
            "LotFrontage\n",
            "LotArea\n",
            "Street\n",
            "Alley\n",
            "LotShape\n",
            "LandContour\n",
            "Utilities\n",
            "LotConfig\n",
            "LandSlope\n",
            "Neighborhood\n",
            "Condition1\n",
            "Condition2\n",
            "BldgType\n",
            "HouseStyle\n",
            "OverallQual\n",
            "OverallCond\n",
            "YearBuilt\n",
            "YearRemodAdd\n",
            "RoofStyle\n",
            "RoofMatl\n",
            "Exterior1st\n",
            "Exterior2nd\n",
            "MasVnrType\n",
            "MasVnrArea\n",
            "ExterQual\n",
            "ExterCond\n",
            "Foundation\n",
            "BsmtQual\n",
            "BsmtCond\n",
            "BsmtExposure\n",
            "BsmtFinType1\n",
            "BsmtFinSF1\n",
            "BsmtFinType2\n",
            "BsmtFinSF2\n",
            "BsmtUnfSF\n",
            "TotalBsmtSF\n",
            "Heating\n",
            "HeatingQC\n",
            "CentralAir\n",
            "Electrical\n",
            "1stFlrSF\n",
            "2ndFlrSF\n",
            "LowQualFinSF\n",
            "GrLivArea\n",
            "BsmtFullBath\n",
            "BsmtHalfBath\n",
            "FullBath\n",
            "HalfBath\n",
            "BedroomAbvGr\n",
            "KitchenAbvGr\n",
            "KitchenQual\n",
            "TotRmsAbvGrd\n",
            "Functional\n",
            "Fireplaces\n",
            "FireplaceQu\n",
            "GarageType\n",
            "GarageYrBlt\n",
            "GarageFinish\n",
            "GarageCars\n",
            "GarageArea\n",
            "GarageQual\n",
            "GarageCond\n",
            "PavedDrive\n",
            "WoodDeckSF\n",
            "OpenPorchSF\n",
            "EnclosedPorch\n",
            "3SsnPorch\n",
            "ScreenPorch\n",
            "PoolArea\n",
            "PoolQC\n",
            "Fence\n",
            "MiscFeature\n",
            "MiscVal\n",
            "MoSold\n",
            "YrSold\n",
            "SaleType\n",
            "SaleCondition\n",
            "SalePrice\n"
          ]
        }
      ],
      "source": [
        "# Printing out all features\n",
        "features = house_data.columns\n",
        "print(\"Features in the dataset:\")\n",
        "for feature in features:\n",
        "    print(feature)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "d36e1df8",
      "metadata": {
        "id": "d36e1df8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb0f4b04-ff2f-4a93-9dc8-385ecf211a41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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     588 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"
          ]
        }
      ],
      "source": [
        "house_data.info()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "b68d63cd",
      "metadata": {
        "id": "b68d63cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65eacb49-d0e0-4bac-cd6b-fee6acae38cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LotFrontage      259\n",
            "Alley           1369\n",
            "MasVnrType       872\n",
            "MasVnrArea         8\n",
            "BsmtQual          37\n",
            "BsmtCond          37\n",
            "BsmtExposure      38\n",
            "BsmtFinType1      37\n",
            "BsmtFinType2      38\n",
            "Electrical         1\n",
            "FireplaceQu      690\n",
            "GarageType        81\n",
            "GarageYrBlt       81\n",
            "GarageFinish      81\n",
            "GarageQual        81\n",
            "GarageCond        81\n",
            "PoolQC          1453\n",
            "Fence           1179\n",
            "MiscFeature     1406\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Check for train missing values\n",
        "missing_value= (house_data.isnull().sum())\n",
        "print(missing_value[missing_value > 0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "47d1da7d",
      "metadata": {
        "id": "47d1da7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0eaba4e5-558d-42b6-8199-070e317c83bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSZoning           4\n",
            "LotFrontage      227\n",
            "Alley           1352\n",
            "Utilities          2\n",
            "Exterior1st        1\n",
            "Exterior2nd        1\n",
            "MasVnrType       894\n",
            "MasVnrArea        15\n",
            "BsmtQual          44\n",
            "BsmtCond          45\n",
            "BsmtExposure      44\n",
            "BsmtFinType1      42\n",
            "BsmtFinSF1         1\n",
            "BsmtFinType2      42\n",
            "BsmtFinSF2         1\n",
            "BsmtUnfSF          1\n",
            "TotalBsmtSF        1\n",
            "BsmtFullBath       2\n",
            "BsmtHalfBath       2\n",
            "KitchenQual        1\n",
            "Functional         2\n",
            "FireplaceQu      730\n",
            "GarageType        76\n",
            "GarageYrBlt       78\n",
            "GarageFinish      78\n",
            "GarageCars         1\n",
            "GarageArea         1\n",
            "GarageQual        78\n",
            "GarageCond        78\n",
            "PoolQC          1456\n",
            "Fence           1169\n",
            "MiscFeature     1408\n",
            "SaleType           1\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Check for test missing values\n",
        "missing_value = (test_data.isnull().sum())\n",
        "print(missing_value[missing_value > 0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "9407a06c",
      "metadata": {
        "id": "9407a06c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "cd80028a-649b-4f0c-f787-9a75b2c6b11a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='SalePrice', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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kZCgiIsKzJCQk1EjPAAAg8FTryM7ChQu1ePFi3XHHHTXdj0dhYaEkKSYmxmt7TEyMZ6ywsFDR0dFe48HBwYqKivLUVCQ9PV1paWmedZfLReABAMBS1Qo7x44d0xVXXFHTvfiM0+mU0+n0dxsAAMAHqnUa66677tLSpUtruhcvsbGxkqQDBw54bT9w4IBnLDY2VgcPHvQaP378uA4fPuypAQAAdVu1juwcPXpUzz33nFavXq2kpCTVr1/fa3zOnDnn3FjLli0VGxurNWvWqEuXLpJ+Od20ZcsWjR07VpLUq1cvFRUVKTs7W927d5ckrV27Vm63W8nJyefcAwAAqP2qFXZyc3M9ASQvL89rzOFwVHk/JSUl2rlzp2d99+7dysnJUVRUlJo3b677779ff/vb33TxxRerZcuWmjJliuLj4z0PN2zfvr0GDBig0aNHa+HChSovL9f48eN12223nfZOLAAAULdUK+ysW7euRt5869at6tOnj2f95EXDw4cP1+LFi/Xggw+qtLRUY8aMUVFRka688kqtXLlSDRo08LxmyZIlGj9+vPr27augoCANGTJETz/9dI30BwAAar9qP2enJvTu3VvGmNOOOxwOzZw5UzNnzjxtTVRU1Hm/fggAANRe1Qo7ffr0qfR01dq1a6vdEAAAQE2qVtg5eb3OSeXl5crJyVFeXt4pXxAKAADgT9UKO0899VSF26dPn86XcAIAgIBSrefsnM7tt99e5e/FAgAA8IUaDTtZWVled0oBAAD4W7VOY918881e68YYFRQUaOvWrZoyZUqNNAYAAFATqhV2IiIivNaDgoLUtm1bzZw5U/369auRxgAAAGpCtcLOokWLaroPAACA8+KcHiqYnZ2tHTt2SJI6dOigrl271khTAAAANaVaYefgwYO67bbbtH79ekVGRkqSioqK1KdPHy1btkzNmjWryR4BAACqrVp3Y917770qLi7W9u3bdfjwYR0+fFh5eXlyuVy67777arpHAACAaqvWkZ2VK1dq9erVat++vWfbpZdeqvnz53OBMgAACCjVOrLjdrtVv379U7bXr19fbrf7nJsCAACoKdUKO9dee60mTJig/fv3e7bt27dPEydOVN++fWusOQAAgHNVrbDzzDPPyOVyqUWLFmrdurVat26tli1byuVyad68eTXdIwAAQLVV65qdhIQEffbZZ1q9erW++uorSVL79u2VkpJSo80BAACcq7M6srN27Vpdeumlcrlccjgcuu6663Tvvffq3nvv1WWXXaYOHTpo06ZN56tXAACAs3ZWYWfu3LkaPXq0wsPDTxmLiIjQ3XffrTlz5tRYcwAAAOfqrMLO559/rgEDBpx2vF+/fsrOzj7npgAAAGrKWYWdAwcOVHjL+UnBwcH64YcfzrkpAACAmnJWYefCCy9UXl7eacdzc3MVFxd3zk0BAADUlLMKOwMHDtSUKVN09OjRU8Z+/vlnTZs2TTfccEONNQcAAHCuzurW84cfflivv/66LrnkEo0fP15t27aVJH311VeaP3++Tpw4ocmTJ5+XRgEAAKrjrMJOTEyMPv74Y40dO1bp6ekyxkiSHA6H+vfvr/nz5ysmJua8NAoAAFAdZ/1QwcTERL377rv68ccftXPnThljdPHFF6tx48bnoz8AAIBzUq0nKEtS48aNddlll9VkLwAAADWuWt+NBQAAUFsQdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAq1X7CcpAbeMqLlFUs8q/uy0uLk7bc3N80xAAwCcIO6gzjNutfrMyK635YPJgn/QCAPCdgD+N1aJFCzkcjlOWcePGSZJ69+59ytg999zj564BAECgCPgjO59++qlOnDjhWc/Ly9N1112nW265xbNt9OjRmjlzpme9YcOGPu0RAAAEroAPO82aNfNanz17tlq3bq1rrrnGs61hw4aKjY31dWsAAKAWCPjTWL927NgxvfLKKxo5cqQcDodn+5IlS9S0aVN17NhR6enp+umnnyrdT1lZmVwul9cCAADsFPBHdn4tMzNTRUVFGjFihGfbH//4RyUmJio+Pl65ubmaNGmS8vPz9frrr592PxkZGZoxY4YPOgYAAP5Wq8LOiy++qNTUVMXHx3u2jRkzxvPvTp06KS4uTn379tWuXbvUunXrCveTnp6utLQ0z7rL5VJCQsL5axwAAPhNrQk73333nVavXl3pERtJSk5OliTt3LnztGHH6XTK6XTWeI8AACDw1JprdhYtWqTo6Ghdf/31ldbl5ORI+uXhcAAAALXiyI7b7daiRYs0fPhwBQf/v5Z37dqlpUuXauDAgWrSpIlyc3M1ceJEXX311UpKSvJjxwAAIFDUirCzevVq7d27VyNHjvTaHhISotWrV2vu3LkqLS1VQkKChgwZoocffthPnQIAgEBTK8JOv379ZIw5ZXtCQoI2bNjgh44AAEBtUWuu2QEAAKgOwg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1YL93QBQ23RI6qKCgoJKa+Li4rQ9N8c3DQEAKkXYAc5SQUGB+s3KrLTmg8mDfdILAODMOI0FAACsRtgBAABWI+wAAACrcc2ORbhw9ty5iksU1SzmDDXFPuoGAFATCDsW4cLZc2fc7jP+DJePv9Y3zQAAagSnsQAAgNUIOwAAwGqEHQAAYDXCDgAAsBoXKNcx3G0EAKhrCDt1DHcbAQDqGk5jAQAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVAjrsTJ8+XQ6Hw2tp166dZ/zo0aMaN26cmjRpokaNGmnIkCE6cOCAHzsGAACBJqDDjiR16NBBBQUFnuXDDz/0jE2cOFFvv/22li9frg0bNmj//v26+eab/dgtAAAINAH/RaDBwcGKjY09ZfuRI0f04osvaunSpbr22l++uHLRokVq3769Nm/erMsvv9zXrQIAgAAU8Ed2vvnmG8XHx6tVq1YaNmyY9u7dK0nKzs5WeXm5UlJSPLXt2rVT8+bNlZWVVek+y8rK5HK5vBYAAGCngA47ycnJWrx4sVauXKkFCxZo9+7duuqqq1RcXKzCwkKFhIQoMjLS6zUxMTEqLCysdL8ZGRmKiIjwLAkJCedxFgAAwJ8C+jRWamqq599JSUlKTk5WYmKiXnvtNYWGhlZ7v+np6UpLS/Osu1wuAg8AAJYK6LDzW5GRkbrkkku0c+dOXXfddTp27JiKioq8ju4cOHCgwmt8fs3pdMrpdJ7nblGXuYpLFNUsptKauLg4bc/N8U1DAFCH1aqwU1JSol27dumOO+5Q9+7dVb9+fa1Zs0ZDhgyRJOXn52vv3r3q1auXnztFXWfcbvWblVlpzQeTB/ukFwCo6wI67Pz3f/+3Bg0apMTERO3fv1/Tpk1TvXr1NHToUEVERGjUqFFKS0tTVFSUwsPDde+996pXr17ciQUAADwCOuz8+9//1tChQ3Xo0CE1a9ZMV155pTZv3qxmzZpJkp566ikFBQVpyJAhKisrU//+/fXss8/6uWsAABBIAjrsLFu2rNLxBg0aaP78+Zo/f76POgIAALVNQN96DgAAcK4IOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALBasL8bAOoqV3GJoprFVFrz089H1TC0wRn3FRcXp+25OTXUGQDYhbAD+Ilxu9VvVmalNcvHX6t+c1aecV8fTB5cM00BgIU4jQUAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYLaDDTkZGhi677DKFhYUpOjpagwcPVn5+vldN79695XA4vJZ77rnHTx0DAIBAE9BhZ8OGDRo3bpw2b96sVatWqby8XP369VNpaalX3ejRo1VQUOBZHnvsMT91DAAAAk2wvxuozMqVK73WFy9erOjoaGVnZ+vqq6/2bG/YsKFiY2N93R4AAKgFAvrIzm8dOXJEkhQVFeW1fcmSJWratKk6duyo9PR0/fTTT5Xup6ysTC6Xy2sBAAB2CugjO7/mdrt1//3363e/+506duzo2f7HP/5RiYmJio+PV25uriZNmqT8/Hy9/vrrp91XRkaGZsyY4Yu2AZ9wFZcoqllMpTVxcXHanptTaU2HpC4qKCg45/0AQCCpNWFn3LhxysvL04cffui1fcyYMZ5/d+rUSXFxcerbt6927dql1q1bV7iv9PR0paWledZdLpcSEhLOT+OADxi3W/1mZVZa88HkwWfcT0FBQY3sBwACSa0IO+PHj9eKFSu0ceNGXXTRRZXWJicnS5J27tx52rDjdDrldDprvE8AABB4AjrsGGN077336o033tD69evVsmXLM74mJydH0i+H2gEAAAI67IwbN05Lly7Vm2++qbCwMBUWFkqSIiIiFBoaql27dmnp0qUaOHCgmjRpotzcXE2cOFFXX321kpKS/Nw9AAAIBAEddhYsWCDplwcH/tqiRYs0YsQIhYSEaPXq1Zo7d65KS0uVkJCgIUOG6OGHH/ZDtwAAIBAFdNgxxlQ6npCQoA0bNvioGwAAUBvVqufsAAAAnC3CDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwWkB/XQSAwOMqLlFUs5hKa+Li4rQ9N8c3DQHAGRB2AJwV43ar36zMSms+mDzYJ70AQFVwGgsAAFiNsAMAAKxG2AEAAFYj7AAAAKtxgXIt0SGpiwoKCiqtcRUX+6gb1EZVuYuKvyEANiLs1BIFBQVnvANm+fhrfdMMaqWq3EXF3xAAG3EaCwAAWI2wAwAArMZprADA9TiwTU09Zbkq/23wtGYAZ0LYCQBcjwPb1NRTlqvy3wZPawZwJpzGAgAAViPsAAAAq3EaC0CtxrewAzgTwg6AWo1vYQdwJpzGAgAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGreen2d87xXgfzyLB6jbCDvnGd97Bfgfz+IB6jbCDgC/qMrRFl8e9axKPz/9fFQNQxuccw1HkQDfIuwA8IuqHG3x5VHPqvbTb87Kc67hKBLgW1ygDAAArEbYAQAAVrPmNNb8+fP1+OOPq7CwUJ07d9a8efPUs2dPf7cFAKeoy3eHVeUOVV/OPdD6qY1qw8/QirDzz3/+U2lpaVq4cKGSk5M1d+5c9e/fX/n5+YqOjvZ3ewDgpS7fHVaVO1R9OfdA66c2qg0/QyvCzpw5czR69Gj96U9/kiQtXLhQ77zzjl566SU99NBDfu4OAM5eoN0dFmj/7z3Q+kFgq/Vh59ixY8rOzlZ6erpnW1BQkFJSUpSVlVXha8rKylRWVuZZP3LkiCTJ5XLVeH/G7Vb5z6WV1xhDDTXVrgnEnqg59xr3iRPq8/CSSmve+O8bdP2s/++ca9bOHHrGz7/9+/bp2qmvnvN+qvSZ6HbXun7qMn/+DE/u0xhTeaGp5fbt22ckmY8//thr+wMPPGB69uxZ4WumTZtmJLGwsLCwsLBYsHz//feVZoVaf2SnOtLT05WWluZZLyoqUmJiovbu3auIiAg/duZ7LpdLCQkJ+v777xUeHu7vdnyqrs69rs5bqrtzr6vzlpi77XM3xqi4uFjx8fGV1tX6sNO0aVPVq1dPBw4c8Np+4MABxcbGVvgap9Mpp9N5yvaIiAhr/yDOJDw8nLnXMXV13lLdnXtdnbfE3G2ee1UOUtT65+yEhISoe/fuWrNmjWeb2+3WmjVr1KtXLz92BgAAAkGtP7IjSWlpaRo+fLh69Oihnj17au7cuSotLfXcnQUAAOouK8LOrbfeqh9++EFTp05VYWGhunTpopUrVyompvLbNk9yOp2aNm1ahae2bMfc697c6+q8pbo797o6b4m519W5/5bDmDPdrwUAAFB71fprdgAAACpD2AEAAFYj7AAAAKsRdgAAgNUIO5Lmz5+vFi1aqEGDBkpOTtYnn3zi75Y8Nm7cqEGDBik+Pl4Oh0OZmZle48YYTZ06VXFxcQoNDVVKSoq++eYbr5rDhw9r2LBhCg8PV2RkpEaNGqWSkhKvmtzcXF111VVq0KCBEhIS9Nhjj53Sy/Lly9WuXTs1aNBAnTp10rvvvnvWvVRVRkaGLrvsMoWFhSk6OlqDBw9Wfn6+V83Ro0c1btw4NWnSRI0aNdKQIUNOebjk3r17df3116thw4aKjo7WAw88oOPHj3vVrF+/Xt26dZPT6VSbNm20ePHiU/o5099IVXqpqgULFigpKcnzILBevXrpvffes37evzV79mw5HA7df//91s99+vTpcjgcXku7du2sn/dJ+/bt0+23364mTZooNDRUnTp10tatWz3jtn7OtWjR4pTfu8Ph0Lhx4yTZ/3v3qXP/dqrabdmyZSYkJMS89NJLZvv27Wb06NEmMjLSHDhwwN+tGWOMeffdd83kyZPN66+/biSZN954w2t89uzZJiIiwmRmZprPP//c3HjjjaZly5bm559/9tQMGDDAdO7c2WzevNls2rTJtGnTxgwdOtQzfuTIERMTE2OGDRtm8vLyzKuvvmpCQ0PN3//+d0/NRx99ZOrVq2cee+wx8+WXX5qHH37Y1K9f33zxxRdn1UtV9e/f3yxatMjk5eWZnJwcM3DgQNO8eXNTUlLiqbnnnntMQkKCWbNmjdm6dau5/PLLzRVXXOEZP378uOnYsaNJSUkx27ZtM++++65p2rSpSU9P99R8++23pmHDhiYtLc18+eWXZt68eaZevXpm5cqVnpqq/I2cqZez8dZbb5l33nnHfP311yY/P9/8z//8j6lfv77Jy8uzet6/9sknn5gWLVqYpKQkM2HChCq/X22d+7Rp00yHDh1MQUGBZ/nhhx+sn7cxxhw+fNgkJiaaESNGmC1btphvv/3WvP/++2bnzp2eGls/5w4ePOj1O1+1apWRZNatW2eMsfv37mt1Puz07NnTjBs3zrN+4sQJEx8fbzIyMvzYVcV+G3bcbreJjY01jz/+uGdbUVGRcTqd5tVXXzXGGPPll18aSebTTz/11Lz33nvG4XCYffv2GWOMefbZZ03jxo1NWVmZp2bSpEmmbdu2nvU//OEP5vrrr/fqJzk52dx9991V7uVcHDx40EgyGzZs8Oy7fv36Zvny5Z6aHTt2GEkmKyvLGPNLUAwKCjKFhYWemgULFpjw8HDPXB988EHToUMHr/e69dZbTf/+/T3rZ/obqUov56px48bmhRdeqBPzLi4uNhdffLFZtWqVueaaazxhx+a5T5s2zXTu3LnCMZvnbcwvnzVXXnnlacfr0ufchAkTTOvWrY3b7bb+9+5rdfo01rFjx5Sdna2UlBTPtqCgIKWkpCgrK8uPnVXN7t27VVhY6NV/RESEkpOTPf1nZWUpMjJSPXr08NSkpKQoKChIW7Zs8dRcffXVCgkJ8dT0799f+fn5+vHHHz01v36fkzUn36cqvZyLI0eOSJKioqIkSdnZ2SovL/d6v3bt2ql58+Zec+/UqZPXwyX79+8vl8ul7du3V2leVfkbqUov1XXixAktW7ZMpaWl6tWrV52Y97hx43T99def0p/tc//mm28UHx+vVq1aadiwYdq7d2+dmPdbb72lHj166JZbblF0dLS6du2q559/3jNeVz7njh07pldeeUUjR46Uw+Gw/vfua3U67PznP//RiRMnTnnSckxMjAoLC/3UVdWd7LGy/gsLCxUdHe01HhwcrKioKK+aivbx6/c4Xc2vx8/US3W53W7df//9+t3vfqeOHTt63i8kJESRkZGV9lTdeblcLv38889V+hupSi9n64svvlCjRo3kdDp1zz336I033tCll15q/byXLVumzz77TBkZGaeM2Tz35ORkLV68WCtXrtSCBQu0e/duXXXVVSouLrZ63pL07bffasGCBbr44ov1/vvva+zYsbrvvvv08ssve/Vv++dcZmamioqKNGLECM972fx79zUrvi4Cdhs3bpzy8vL04Ycf+rsVn2nbtq1ycnJ05MgR/etf/9Lw4cO1YcMGf7d1Xn3//feaMGGCVq1apQYNGvi7HZ9KTU31/DspKUnJyclKTEzUa6+9ptDQUD92dv653W716NFDjzzyiCSpa9euysvL08KFCzV8+HA/d+c7L774olJTUxUfH+/vVqxUp4/sNG3aVPXq1TvlivIDBw4oNjbWT11V3ckeK+s/NjZWBw8e9Bo/fvy4Dh8+7FVT0T5+/R6nq/n1+Jl6qY7x48drxYoVWrdunS666CLP9tjYWB07dkxFRUWV9lTdeYWHhys0NLRKfyNV6eVshYSEqE2bNurevbsyMjLUuXNn/e///q/V887OztbBgwfVrVs3BQcHKzg4WBs2bNDTTz+t4OBgxcTEWDv334qMjNQll1yinTt3Wv07l6S4uDhdeumlXtvat2/vOY1XFz7nvvvuO61evVp33XWXZ5vtv3dfq9NhJyQkRN27d9eaNWs829xut9asWaNevXr5sbOqadmypWJjY736d7lc2rJli6f/Xr16qaioSNnZ2Z6atWvXyu12Kzk52VOzceNGlZeXe2pWrVqltm3bqnHjxp6aX7/PyZqT71OVXs6GMUbjx4/XG2+8obVr16ply5Ze4927d1f9+vW93i8/P1979+71mvsXX3zh9SG4atUqhYeHez5czzSvqvyNVKWXc+V2u1VWVmb1vPv27asvvvhCOTk5nqVHjx4aNmyY59+2zv23SkpKtGvXLsXFxVn9O5ek3/3ud6c8VuLrr79WYmKiJLs/505atGiRoqOjdf3113u22f579zl/XyHtb8uWLTNOp9MsXrzYfPnll2bMmDEmMjLS6+p2fyouLjbbtm0z27ZtM5LMnDlzzLZt28x3331njPnlNsjIyEjz5ptvmtzcXHPTTTdVeEtm165dzZYtW8yHH35oLr74Yq9bMouKikxMTIy54447TF5enlm2bJlp2LDhKbdkBgcHmyeeeMLs2LHDTJs2rcJbMs/US1WNHTvWREREmPXr13vdmvnTTz95au655x7TvHlzs3btWrN161bTq1cv06tXL8/4ydsy+/XrZ3JycszKlStNs2bNKrwt84EHHjA7duww8+fPr/C2zDP9jZypl7Px0EMPmQ0bNpjdu3eb3Nxc89BDDxmHw2E++OADq+ddkV/fjWXz3P/yl7+Y9evXm927d5uPPvrIpKSkmKZNm5qDBw9aPW9jfnnMQHBwsJk1a5b55ptvzJIlS0zDhg3NK6+84qmx9XPOmF/ufGrevLmZNGnSKWM2/959rc6HHWOMmTdvnmnevLkJCQkxPXv2NJs3b/Z3Sx7r1q0zkk5Zhg8fboz55VbIKVOmmJiYGON0Ok3fvn1Nfn6+1z4OHTpkhg4daho1amTCw8PNn/70J1NcXOxV8/nnn5srr7zSOJ1Oc+GFF5rZs2ef0strr71mLrnkEhMSEmI6dOhg3nnnHa/xqvRSVRXNWZJZtGiRp+bnn382f/7zn03jxo1Nw4YNzX/913+ZgoICr/3s2bPHpKammtDQUNO0aVPzl7/8xZSXl3vVrFu3znTp0sWEhISYVq1aeb3HSWf6G6lKL1U1cuRIk5iYaEJCQkyzZs1M3759PUHH5nlX5Ldhx9a533rrrSYuLs6EhISYCy+80Nx6661ez5mxdd4nvf3226Zjx47G6XSadu3ameeee85r3NbPOWOMef/9942kCvdh++/dlxzGGOOXQ0oAAAA+UKev2QEAAPYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAVBrLF68WJGRkef9ffbs2SOHw6GcnJzz/l4Azj/CDgCf+eGHHzR27Fg1b95cTqdTsbGx6t+/vz766KPz9p4tWrSQw+GQw+HQBRdcoG7dumn58uWVviYhIUEFBQXq2LHjeesLgO8QdgD4zJAhQ7Rt2za9/PLL+vrrr/XWW2+pd+/eOnTo0Hl935kzZ6qgoEDbtm3TZZddpltvvVUff/xxhbXHjh1TvXr1FBsbq+Dg4PPaFwDfIOwA8ImioiJt2rRJjz76qPr06aPExET17NlT6enpuvHGGyVJc+bMUadOnXTBBRcoISFBf/7zn1VSUlLpft98801169ZNDRo0UKtWrTRjxgwdP37cqyYsLEyxsbG65JJLNH/+fIWGhurtt9+W9MuRn7/+9a+68847FR4erjFjxlR4Gmv79u264YYbFB4errCwMF111VXatWuXZ/yFF15Q+/bt1aBBA7Vr107PPvtsDf3kAJwrwg4An2jUqJEaNWqkzMxMlZWVVVgTFBSkp59+Wtu3b9fLL7+stWvX6sEHHzztPjdt2qQ777xTEyZM0Jdffqm///3vWrx4sWbNmnXa1wQHB6t+/fo6duyYZ9sTTzyhzp07a9u2bZoyZcopr9m3b5+uvvpqOZ1OrV27VtnZ2Ro5cqQnVC1ZskRTp07VrFmztGPHDj3yyCOaMmWKXn755ar+eACcT/7+2nUAdce//vUv07hxY9OgQQNzxRVXmPT0dPP555+ftn758uWmSZMmnvVFixaZiIgIz3rfvn3NI4884vWa//u//zNxcXGe9cTERPPUU08ZY4wpKyszjzzyiJFkVqxY4RkfPHiw1z52795tJJlt27YZY4xJT083LVu2NMeOHauwz9atW5ulS5d6bfvrX/9qevXqddq5AfAdhzHG+DtwAag7jh49qk2bNmnz5s1677339Mknn+iFF17QiBEjtHr1amVkZOirr76Sy+XS8ePHdfToUZWWlqphw4ZavHix7r//fhUVFUmSmjVrppKSEtWrV8+z/xMnTni9pkWLFiooKFD9+vV19OhRNWrUSOnp6Zo0aZKkX05jjR49WpMnT/bsY8+ePWrZsqW2bdumLl26aODAgWrWrFmFR2pKS0vVqFEjhYaGKijo/x0sP378uCIiInTgwIHz9JMEUFVcfQfApxo0aKDrrrtO1113naZMmaK77rpL06ZNU+/evXXDDTdo7NixmjVrlqKiovThhx9q1KhROnbsmBo2bHjKvkpKSjRjxgzdfPPNFb7PSQ888IBGjBihRo0aKSYmRg6Hw6v2ggsuqLTn0NDQ046dvKbo+eefV3JystfYr0MYAP8h7ADwq0svvVSZmZnKzs6W2+3Wk08+6TlC8tprr1X62m7duik/P19t2rSptK5p06ZnrKlMUlKSXn75ZZWXl6t+/fpeYzExMYqPj9e3336rYcOGVfs9AJw/hB0APnHo0CHdcsstGjlypJKSkhQWFqatW7fqscce00033aQ2bdqovLxc8+bN06BBg/TRRx9p4cKFle5z6tSpuuGGG9S8eXP9/ve/V1BQkD7//HPl5eXpb3/7W431Pn78eM2bN0+33Xab0tPTFRERoc2bN6tnz55q27atZsyYofvuu08REREaMGCAysrKtHXrVv34449KS0ursT4AVA93YwHwiUaNGik5OVlPPfWUrr76anXs2FFTpkzR6NGj9cwzz6hz586aM2eOHn30UXXs2FFLlixRRkZGpfvs37+/VqxYoQ8++ECXXXaZLr/8cj311FNKTEys0d6bNGmitWvXqqSkRNdcc426d++u559/3nOU56677tILL7ygRYsWqVOnTrrmmmu0ePFitWzZskb7AFA9XKAMAACsxpEdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFjt/wfB0VZyqHLADQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Exploratory Data Analysis (EDA)\n",
        "sns.histplot(house_data.SalePrice, bins=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "a1f80c60",
      "metadata": {
        "id": "a1f80c60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8b5f00d9-9349-4de9-acf1-b618ac034183"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   LotArea  YearBuilt  1stFlrSF  2ndFlrSF  FullBath  HalfBath  BedroomAbvGr  \\\n",
              "0     8450       2003       856       854         2         1             3   \n",
              "1     9600       1976      1262         0         2         0             3   \n",
              "2    11250       2001       920       866         2         1             3   \n",
              "3     9550       1915       961       756         1         0             3   \n",
              "4    14260       2000      1145      1053         2         1             4   \n",
              "\n",
              "   TotRmsAbvGrd  \n",
              "0             8  \n",
              "1             6  \n",
              "2             6  \n",
              "3             7  \n",
              "4             9  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6c4c4e50-6732-42b6-9d99-9830f37e29b8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LotArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>1stFlrSF</th>\n",
              "      <th>2ndFlrSF</th>\n",
              "      <th>FullBath</th>\n",
              "      <th>HalfBath</th>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <th>TotRmsAbvGrd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8450</td>\n",
              "      <td>2003</td>\n",
              "      <td>856</td>\n",
              "      <td>854</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9600</td>\n",
              "      <td>1976</td>\n",
              "      <td>1262</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11250</td>\n",
              "      <td>2001</td>\n",
              "      <td>920</td>\n",
              "      <td>866</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9550</td>\n",
              "      <td>1915</td>\n",
              "      <td>961</td>\n",
              "      <td>756</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14260</td>\n",
              "      <td>2000</td>\n",
              "      <td>1145</td>\n",
              "      <td>1053</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c4c4e50-6732-42b6-9d99-9830f37e29b8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6c4c4e50-6732-42b6-9d99-9830f37e29b8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6c4c4e50-6732-42b6-9d99-9830f37e29b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ef40fa4d-690c-4370-ae20-665f0543da79\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef40fa4d-690c-4370-ae20-665f0543da79')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef40fa4d-690c-4370-ae20-665f0543da79 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 1460,\n  \"fields\": [\n    {\n      \"column\": \"LotArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9981,\n        \"min\": 1300,\n        \"max\": 215245,\n        \"num_unique_values\": 1073,\n        \"samples\": [\n          10186,\n          8163,\n          8854\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 1872,\n        \"max\": 2010,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          1999,\n          1936,\n          2000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"1stFlrSF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 386,\n        \"min\": 334,\n        \"max\": 4692,\n        \"num_unique_values\": 753,\n        \"samples\": [\n          1274,\n          1530,\n          1131\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2ndFlrSF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 436,\n        \"min\": 0,\n        \"max\": 2065,\n        \"num_unique_values\": 417,\n        \"samples\": [\n          406,\n          1392,\n          1104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FullBath\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HalfBath\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BedroomAbvGr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotRmsAbvGrd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 14,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2,\n          3,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Creating target and relevant feature variables\n",
        "features = ['LotArea', 'YearBuilt','1stFlrSF', '2ndFlrSF',\n",
        "            'FullBath','HalfBath','BedroomAbvGr', 'TotRmsAbvGrd']\n",
        "y = house_data.SalePrice\n",
        "X = house_data[features]\n",
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "09adcaed",
      "metadata": {
        "id": "09adcaed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9d96aa6-e292-4ed3-fce9-19bef04f1000"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7393349966640457"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Data splitting into train and test\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Model Fitting\n",
        "model = LinearRegression()\n",
        "\n",
        "# Fit the model on the training data\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on testing data\n",
        "y_pred = model.predict(X_test)\n",
        "model.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "5b3b2b32",
      "metadata": {
        "id": "5b3b2b32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "165aadb3-0ec9-41bd-d127-37a8e56138a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (MSE): 1999385606.38\n",
            "R-squared (R2): 0.74\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "\n",
        "# Display the evaluation metrics\n",
        "print(f'Mean Squared Error (MSE): {mse:.2f}')\n",
        "print(f'R-squared (R2): {r2:.2f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "ecfc21d6",
      "metadata": {
        "id": "ecfc21d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "9eb170fd-2b81-4ae4-b427-d29fb155b3d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot actual vs. predicted values\n",
        "plt.scatter(y_test, y_pred, c = \"blue\")\n",
        "plt.xlabel('Actual Prices')\n",
        "plt.ylabel('Predicted Prices')\n",
        "plt.title('Actual vs. P)redicted Prices (Linear Regression)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "913eb2b1",
      "metadata": {
        "id": "913eb2b1"
      },
      "outputs": [],
      "source": [
        "# Prediction of new houses on test data\n",
        "\n",
        "test_data = pd.read_csv('test.csv')\n",
        "test_data.head()\n",
        "\n",
        "features = ['LotArea', 'YearBuilt','1stFlrSF', '2ndFlrSF',\n",
        "            'FullBath','HalfBath','BedroomAbvGr', 'TotRmsAbvGrd']\n",
        "\n",
        "X = test_data[features]\n",
        "prediction_new = model.predict(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "7a0ecfca",
      "metadata": {
        "id": "7a0ecfca"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}