House_Rate_Predictor.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sineme01/House-Rate-Predictor/blob/main/House_Rate_Predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmFMtPduilA8"
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      "source": [
        "**1. Load Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Name**      : **Anand Kumar**\n",
        "\n",
        "**Roll        :   2001CB07**\n",
        "\n",
        "**Institute   :   Indian Institute Of Technology Patna.**\n",
        "\n",
        "**House Rate Prediction**\n",
        "\n",
        "**Data is taken from Kaggle.**\n",
        "\n",
        "**Description :** A prediction system is used in this project to estimate the house's selling price based on various features. Two files, train, and test are provided and the price of the test data is to be estimated. Two files, train, and test are provided and the price of the test data is to be estimated."
      ],
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      }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRY8CnryilA9"
      },
      "source": [
        "First lets import all the libraries that will be used to load train and test datasets and data manipulation."
      ]
    },
    {
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      "execution_count": 1,
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      "source": [
        "# Import libraries\n",
        "\n",
        "# Pandas\n",
        "import pandas as pd\n",
        "from pandas import Series,DataFrame\n",
        "\n",
        "# Numpy and Matplotlib\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "#sns.set_style('whitegrid')\n",
        "%matplotlib inline\n",
        "\n",
        "# Machine Learning\n",
        "from sklearn import preprocessing\n",
        "from sklearn.compose import ColumnTransformer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JG5okTq_zp-l",
        "outputId": "3b832e0f-fbea-4d9c-d092-70aa1478a6c3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCmWLdWCilA_"
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      "source": [
        "Loading train and test data"
      ]
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    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "id": "i4OSm43kilBA"
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      "outputs": [],
      "source": [
        "# Get Data in Dataframe\n",
        "X = pd.read_csv('/content/drive/MyDrive/CS_INNOVATION_LAB/house_train_.csv')\n",
        "y = pd.read_csv('/content/drive/MyDrive/CS_INNOVATION_LAB/house_test_.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hv9CEvBNilBA"
      },
      "source": [
        "Preview of train and test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        "id": "pTb7r13nilBB",
        "outputId": "9209754b-ea40-41b3-e166-c975cc664be2"
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          "output_type": "execute_result",
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            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
              "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
              "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
              "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
              "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
              "\n",
              "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
              "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
              "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
              "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
              "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
              "\n",
              "  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0   2008        WD         Normal     208500  \n",
              "1   2007        WD         Normal     181500  \n",
              "2   2008        WD         Normal     223500  \n",
              "3   2006        WD        Abnorml     140000  \n",
              "4   2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 81 columns]"
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# head() shows the first 5 rows of the data\n",
        "X.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAN_jlPKilBE"
      },
      "source": [
        "There are 1460 entries in the train data set and 1459 entries in test data set. The data contains some NaN values too."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id = pd.DataFrame()\n",
        "id['id']=y.Id\n",
        "print(id)"
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "cacbf095-b2e1-48e9-cb3d-4098c54f8fa4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        id\n",
            "0     1461\n",
            "1     1462\n",
            "2     1463\n",
            "3     1464\n",
            "4     1465\n",
            "...    ...\n",
            "1454  2915\n",
            "1455  2916\n",
            "1456  2917\n",
            "1457  2918\n",
            "1458  2919\n",
            "\n",
            "[1459 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvZPikfSilBE",
        "outputId": "b30241e5-bed0-450a-f3fb-38afd4536e2d"
      },
      "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     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"
          ]
        }
      ],
      "source": [
        "X.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "efZSTaCRilBF"
      },
      "source": [
        "**2. Data Manipulation and Visualization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYF95L6FilBF"
      },
      "source": [
        "Lets check for NaN (null) values in the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-FzNVb3ilBF",
        "outputId": "2924ee43-cf67-4c6d-95b1-acbb80fffdc5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "MSSubClass         0\n",
              "MSZoning           0\n",
              "LotFrontage      259\n",
              "LotArea            0\n",
              "                ... \n",
              "MoSold             0\n",
              "YrSold             0\n",
              "SaleType           0\n",
              "SaleCondition      0\n",
              "SalePrice          0\n",
              "Length: 81, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "X.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCOHuVkLilBF"
      },
      "source": [
        "Lets check for the mean, standard deviation for Sales price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eb3fKlHPilBG",
        "outputId": "af659cbc-2d60-486b-e6ef-0c3eace90ce7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      1460.000000\n",
              "mean     180921.195890\n",
              "std       79442.502883\n",
              "min       34900.000000\n",
              "25%      129975.000000\n",
              "50%      163000.000000\n",
              "75%      214000.000000\n",
              "max      755000.000000\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "X['SalePrice'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49aHDW6QilBI"
      },
      "source": [
        "Sales price is right skewed. So, we perform log transformation so that the skewness is nearly zero."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878
        },
        "id": "CIr8ml7iilBJ",
        "outputId": "27404ec1-77d0-4c04-86b8-c148bc4edba3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skew is: 1.8828757597682129\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skew is: 0.12133506220520406\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Determining the Skewness of data\n",
        "print (\"Skew is:\", X.SalePrice.skew())\n",
        "\n",
        "plt.hist(X.SalePrice)\n",
        "plt.show()\n",
        "\n",
        "# After log transformation of the data it looks much more center aligned\n",
        "X['Skewed_SP'] = np.log(X['SalePrice'])\n",
        "y['Skewed_SP']=X['Skewed_SP']\n",
        "print (\"Skew is:\", X['Skewed_SP'].skew())\n",
        "plt.hist(X['Skewed_SP'], color='blue')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11nMHx6KilBK"
      },
      "source": [
        "Lets analyze the numeric features using the numpy library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "scrolled": true,
        "id": "wvFLqCL4ilBL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "267edd9f-fcc1-4553-b039-2381c20d8ec9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 int64\n",
              "MSSubClass         int64\n",
              "LotFrontage      float64\n",
              "LotArea            int64\n",
              "OverallQual        int64\n",
              "OverallCond        int64\n",
              "YearBuilt          int64\n",
              "YearRemodAdd       int64\n",
              "MasVnrArea       float64\n",
              "BsmtFinSF1         int64\n",
              "BsmtFinSF2         int64\n",
              "BsmtUnfSF          int64\n",
              "TotalBsmtSF        int64\n",
              "1stFlrSF           int64\n",
              "2ndFlrSF           int64\n",
              "LowQualFinSF       int64\n",
              "GrLivArea          int64\n",
              "BsmtFullBath       int64\n",
              "BsmtHalfBath       int64\n",
              "FullBath           int64\n",
              "HalfBath           int64\n",
              "BedroomAbvGr       int64\n",
              "KitchenAbvGr       int64\n",
              "TotRmsAbvGrd       int64\n",
              "Fireplaces         int64\n",
              "GarageYrBlt      float64\n",
              "GarageCars         int64\n",
              "GarageArea         int64\n",
              "WoodDeckSF         int64\n",
              "OpenPorchSF        int64\n",
              "EnclosedPorch      int64\n",
              "3SsnPorch          int64\n",
              "ScreenPorch        int64\n",
              "PoolArea           int64\n",
              "MiscVal            int64\n",
              "MoSold             int64\n",
              "YrSold             int64\n",
              "SalePrice          int64\n",
              "Skewed_SP        float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "numerical_features = X.select_dtypes(include=[np.number])\n",
        "numerical_features.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "scrolled": true,
        "id": "CP4anNOCilBL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49298c5b-0866-46ec-bced-d5b8d1afe775"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SalePrice      1.000000\n",
            "Skewed_SP      0.948374\n",
            "OverallQual    0.790982\n",
            "GrLivArea      0.708624\n",
            "GarageCars     0.640409\n",
            "Name: SalePrice, dtype: float64 \n",
            "\n",
            "YrSold          -0.028923\n",
            "OverallCond     -0.077856\n",
            "MSSubClass      -0.084284\n",
            "EnclosedPorch   -0.128578\n",
            "KitchenAbvGr    -0.135907\n",
            "Name: SalePrice, dtype: float64\n",
            "SalePrice        1.000000\n",
            "Skewed_SP        0.948374\n",
            "OverallQual      0.790982\n",
            "GrLivArea        0.708624\n",
            "GarageCars       0.640409\n",
            "GarageArea       0.623431\n",
            "TotalBsmtSF      0.613581\n",
            "1stFlrSF         0.605852\n",
            "FullBath         0.560664\n",
            "TotRmsAbvGrd     0.533723\n",
            "YearBuilt        0.522897\n",
            "YearRemodAdd     0.507101\n",
            "GarageYrBlt      0.486362\n",
            "MasVnrArea       0.477493\n",
            "Fireplaces       0.466929\n",
            "BsmtFinSF1       0.386420\n",
            "LotFrontage      0.351799\n",
            "WoodDeckSF       0.324413\n",
            "2ndFlrSF         0.319334\n",
            "OpenPorchSF      0.315856\n",
            "HalfBath         0.284108\n",
            "LotArea          0.263843\n",
            "BsmtFullBath     0.227122\n",
            "BsmtUnfSF        0.214479\n",
            "BedroomAbvGr     0.168213\n",
            "ScreenPorch      0.111447\n",
            "PoolArea         0.092404\n",
            "MoSold           0.046432\n",
            "3SsnPorch        0.044584\n",
            "BsmtFinSF2      -0.011378\n",
            "BsmtHalfBath    -0.016844\n",
            "MiscVal         -0.021190\n",
            "Id              -0.021917\n",
            "LowQualFinSF    -0.025606\n",
            "YrSold          -0.028923\n",
            "OverallCond     -0.077856\n",
            "MSSubClass      -0.084284\n",
            "EnclosedPorch   -0.128578\n",
            "KitchenAbvGr    -0.135907\n",
            "Name: SalePrice, dtype: float64 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Then we will try to find the corretation between the feature and target\n",
        "corr = numerical_features.corr()\n",
        "print (corr['SalePrice'].sort_values(ascending=False)[:5], '\\n')\n",
        "print (corr['SalePrice'].sort_values(ascending=False)[-5:])\n",
        "print (corr['SalePrice'].sort_values(ascending=False)[:], '\\n')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "cC_i8gAEDXwC",
        "outputId": "9acc7e49-eae2-41a7-d9e6-b97586120b29"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  \\\n",
              "0        1          60         65.0     8450            7            5   \n",
              "1        2          20         80.0     9600            6            8   \n",
              "2        3          60         68.0    11250            7            5   \n",
              "3        4          70         60.0     9550            7            5   \n",
              "4        5          60         84.0    14260            8            5   \n",
              "...    ...         ...          ...      ...          ...          ...   \n",
              "1455  1456          60         62.0     7917            6            5   \n",
              "1456  1457          20         85.0    13175            6            6   \n",
              "1457  1458          70         66.0     9042            7            9   \n",
              "1458  1459          20         68.0     9717            5            6   \n",
              "1459  1460          20         75.0     9937            5            6   \n",
              "\n",
              "      YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  OpenPorchSF  \\\n",
              "0          2003          2003       196.0         706  ...           61   \n",
              "1          1976          1976         0.0         978  ...            0   \n",
              "2          2001          2002       162.0         486  ...           42   \n",
              "3          1915          1970         0.0         216  ...           35   \n",
              "4          2000          2000       350.0         655  ...           84   \n",
              "...         ...           ...         ...         ...  ...          ...   \n",
              "1455       1999          2000         0.0           0  ...           40   \n",
              "1456       1978          1988       119.0         790  ...            0   \n",
              "1457       1941          2006         0.0         275  ...           60   \n",
              "1458       1950          1996         0.0          49  ...            0   \n",
              "1459       1965          1965         0.0         830  ...           68   \n",
              "\n",
              "      EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  \\\n",
              "0                 0          0            0         0        0       2   \n",
              "1                 0          0            0         0        0       5   \n",
              "2                 0          0            0         0        0       9   \n",
              "3               272          0            0         0        0       2   \n",
              "4                 0          0            0         0        0      12   \n",
              "...             ...        ...          ...       ...      ...     ...   \n",
              "1455              0          0            0         0        0       8   \n",
              "1456              0          0            0         0        0       2   \n",
              "1457              0          0            0         0     2500       5   \n",
              "1458            112          0            0         0        0       4   \n",
              "1459              0          0            0         0        0       6   \n",
              "\n",
              "      YrSold  SalePrice  Skewed_SP  \n",
              "0       2008     208500  12.247694  \n",
              "1       2007     181500  12.109011  \n",
              "2       2008     223500  12.317167  \n",
              "3       2006     140000  11.849398  \n",
              "4       2008     250000  12.429216  \n",
              "...      ...        ...        ...  \n",
              "1455    2007     175000  12.072541  \n",
              "1456    2010     210000  12.254863  \n",
              "1457    2010     266500  12.493130  \n",
              "1458    2010     142125  11.864462  \n",
              "1459    2008     147500  11.901583  \n",
              "\n",
              "[1460 rows x 39 columns]"
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              "      <th>Id</th>\n",
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              "        document.querySelector('#df-3cbfdee9-306e-421d-9f50-6c3c29adbc71 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-3cbfdee9-306e-421d-9f50-6c3c29adbc71');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-336f2aff-4726-4136-8412-43ca437ed893\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-336f2aff-4726-4136-8412-43ca437ed893')\"\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-336f2aff-4726-4136-8412-43ca437ed893 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQoXfkb2ilBL"
      },
      "source": [
        "We will analyze the features in their descending of correlation with sales price"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.OverallQual.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYogwpBahPqG",
        "outputId": "1c1e8028-53c2-4eb3-c6f0-c3d6606306a6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7,  6,  8,  5,  9,  4, 10,  3,  1,  2])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a pivot table\n",
        "quality_pivot = X.pivot_table(index='OverallQual',values='SalePrice',aggfunc=np.median)\n",
        "quality_pivot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "zAqPAkcdhWkn",
        "outputId": "d32a0ed7-a302-4b72-d742-f8b0fb233774"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             SalePrice\n",
              "OverallQual           \n",
              "1                50150\n",
              "2                60000\n",
              "3                86250\n",
              "4               108000\n",
              "5               133000\n",
              "6               160000\n",
              "7               200141\n",
              "8               269750\n",
              "9               345000\n",
              "10              432390"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-69364b49-b54f-4593-8443-6b682eba28f9\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "      <th>SalePrice</th>\n",
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              "      <th>OverallQual</th>\n",
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              "      <th>1</th>\n",
              "      <td>50150</td>\n",
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              "      <td>60000</td>\n",
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              "      <td>86250</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>108000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>133000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>200141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>269750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>345000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>432390</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      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-69364b49-b54f-4593-8443-6b682eba28f9 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-69364b49-b54f-4593-8443-6b682eba28f9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a0068cb5-960a-479a-ac6f-6496a2965127\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0068cb5-960a-479a-ac6f-6496a2965127')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\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-a0068cb5-960a-479a-ac6f-6496a2965127 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quality_pivot.plot(kind='bar',color='blue')\n",
        "plt.xlabel('Overall Quality')\n",
        "plt.ylabel('Median')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "w0jO2nnIhfRM",
        "outputId": "0dbea257-f419-4dbf-986e-318b31da6f47"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The median Selling Price of house can be seen to be strictly increasing as the Overall Quality is increasing."
      ],
      "metadata": {
        "id": "4yl_ypJdlimX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x='GrLivArea',y='Skewed_SP',data=X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "NJ3UT27YJwxZ",
        "outputId": "cfcbf57c-3048-4c69-d899-bb2332d40949"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GrLivArea', ylabel='Skewed_SP'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SalePrice increases as the GrLivArea increases. We will also get rid of the outliers which severely affect the prediction of the survival rate."
      ],
      "metadata": {
        "id": "pqhzRZD2J1tE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Removing outliers\n",
        "X = X[X['GrLivArea'] < 4000]\n",
        "sns.regplot(x='GrLivArea',y='Skewed_SP',data=X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "dLpIKCzqhf8O",
        "outputId": "f870ace3-830b-4401-963e-87ce365978f7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GrLivArea', ylabel='Skewed_SP'>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.regplot(x='GarageArea',y='Skewed_SP',data=X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "zKtIO8wKKB-U",
        "outputId": "05da4d19-fc04-46d5-b90a-7b5f2b2e8d3a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GarageArea', ylabel='Skewed_SP'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GarageArea and SalePrice are directly proportional.\n",
        "\n",
        "We will again get rid of the outliers."
      ],
      "metadata": {
        "id": "132RznwtKHhv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Removing outliers\n",
        "X = X[X['GarageArea'] < 1200]\n",
        "sns.regplot(x='GarageArea',y='Skewed_SP',data=X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "Rs5t4yzEKI0_",
        "outputId": "f3186523-7643-4e5a-a49a-2604eaaf260f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='GarageArea', ylabel='Skewed_SP'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Removing the null values\n",
        "nulls = pd.DataFrame(X.isnull().sum().sort_values(ascending=False)[:25])\n",
        "nulls.columns = ['Null Count']\n",
        "nulls.index.name = 'Feature'\n",
        "nulls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 864
        },
        "id": "TXscOHT8KNoK",
        "outputId": "f211e649-92c4-4610-8754-a9e8e728941e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Null Count\n",
              "Feature                 \n",
              "PoolQC              1447\n",
              "MiscFeature         1399\n",
              "Alley               1361\n",
              "Fence               1172\n",
              "FireplaceQu          689\n",
              "LotFrontage          258\n",
              "GarageType            81\n",
              "GarageYrBlt           81\n",
              "GarageCond            81\n",
              "GarageQual            81\n",
              "GarageFinish          81\n",
              "BsmtFinType2          38\n",
              "BsmtExposure          38\n",
              "BsmtQual              37\n",
              "BsmtCond              37\n",
              "BsmtFinType1          37\n",
              "MasVnrType             8\n",
              "MasVnrArea             8\n",
              "Electrical             1\n",
              "Id                     0\n",
              "Fireplaces             0\n",
              "Functional             0\n",
              "KitchenQual            0\n",
              "KitchenAbvGr           0\n",
              "BedroomAbvGr           0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-668a21bc-4dcf-4d80-ba86-e061979a0e91\" 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>Null Count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feature</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PoolQC</th>\n",
              "      <td>1447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MiscFeature</th>\n",
              "      <td>1399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alley</th>\n",
              "      <td>1361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fence</th>\n",
              "      <td>1172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FireplaceQu</th>\n",
              "      <td>689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LotFrontage</th>\n",
              "      <td>258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageType</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageYrBlt</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageCond</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageQual</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GarageFinish</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType2</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtExposure</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtQual</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtCond</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BsmtFinType1</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrType</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MasVnrArea</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electrical</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Id</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fireplaces</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Functional</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KitchenQual</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KitchenAbvGr</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BedroomAbvGr</th>\n",
              "      <td>0</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-668a21bc-4dcf-4d80-ba86-e061979a0e91')\"\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-668a21bc-4dcf-4d80-ba86-e061979a0e91 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-668a21bc-4dcf-4d80-ba86-e061979a0e91');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ee4ff74a-6fa7-4e45-b519-d674ae520d9f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee4ff74a-6fa7-4e45-b519-d674ae520d9f')\"\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-ee4ff74a-6fa7-4e45-b519-d674ae520d9f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pool null value refers to no pool area\n",
        "print (\"Unique values are:\", X.MiscFeature.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2DvP2JbK2H0",
        "outputId": "2faf5737-e1ea-4521-bf52-8f4ee788922b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values are: [nan 'Shed' 'Gar2' 'Othr' 'TenC']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Analysing the non numeric data\n",
        "categoricals = X.select_dtypes(exclude=[np.number])\n",
        "categoricals.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "OpU1MvUcK40b",
        "outputId": "8f49d736-f384-4cfe-d243-e3673dd10079"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       MSZoning Street Alley LotShape LandContour Utilities LotConfig  \\\n",
              "count      1452   1452    91     1452        1452      1452      1452   \n",
              "unique        5      2     2        4           4         2         5   \n",
              "top          RL   Pave  Grvl      Reg         Lvl    AllPub    Inside   \n",
              "freq       1144   1447    50      921        1307      1451      1047   \n",
              "\n",
              "       LandSlope Neighborhood Condition1  ... GarageType GarageFinish  \\\n",
              "count       1452         1452       1452  ...       1371         1371   \n",
              "unique         3           25          9  ...          6            3   \n",
              "top          Gtl        NAmes       Norm  ...     Attchd          Unf   \n",
              "freq        1375          225       1255  ...        865          605   \n",
              "\n",
              "       GarageQual GarageCond PavedDrive PoolQC  Fence MiscFeature SaleType  \\\n",
              "count        1371       1371       1452      5    280          53     1452   \n",
              "unique          5          5          3      3      4           4        9   \n",
              "top            TA         TA          Y     Fa  MnPrv        Shed       WD   \n",
              "freq         1303       1318       1332      2    156          48     1264   \n",
              "\n",
              "       SaleCondition  \n",
              "count           1452  \n",
              "unique             6  \n",
              "top           Normal  \n",
              "freq            1195  \n",
              "\n",
              "[4 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-872ec963-6833-4a01-a1de-5d21f37b8450\" 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>MSZoning</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</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>...</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>PoolQC</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "      <td>91</td>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "      <td>...</td>\n",
              "      <td>1371</td>\n",
              "      <td>1371</td>\n",
              "      <td>1371</td>\n",
              "      <td>1371</td>\n",
              "      <td>1452</td>\n",
              "      <td>5</td>\n",
              "      <td>280</td>\n",
              "      <td>53</td>\n",
              "      <td>1452</td>\n",
              "      <td>1452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>RL</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>...</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>Unf</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>Fa</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1144</td>\n",
              "      <td>1447</td>\n",
              "      <td>50</td>\n",
              "      <td>921</td>\n",
              "      <td>1307</td>\n",
              "      <td>1451</td>\n",
              "      <td>1047</td>\n",
              "      <td>1375</td>\n",
              "      <td>225</td>\n",
              "      <td>1255</td>\n",
              "      <td>...</td>\n",
              "      <td>865</td>\n",
              "      <td>605</td>\n",
              "      <td>1303</td>\n",
              "      <td>1318</td>\n",
              "      <td>1332</td>\n",
              "      <td>2</td>\n",
              "      <td>156</td>\n",
              "      <td>48</td>\n",
              "      <td>1264</td>\n",
              "      <td>1195</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 43 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-872ec963-6833-4a01-a1de-5d21f37b8450')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-872ec963-6833-4a01-a1de-5d21f37b8450 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-872ec963-6833-4a01-a1de-5d21f37b8450');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-af2547f5-b2e5-4f44-a2d7-d4b04a86a265\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-af2547f5-b2e5-4f44-a2d7-d4b04a86a265')\"\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-af2547f5-b2e5-4f44-a2d7-d4b04a86a265 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['Neighborhood'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "mrSmYa6qK9pO",
        "outputId": "06a95736-90cb-4b89-8b25-c4659d266d34"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['Condition1'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHtVTDkcLVkV",
        "outputId": "f4bae4fd-e925-4d90-dd31-5f295e3151da"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Norm      1255\n",
              "Feedr       80\n",
              "Artery      48\n",
              "RRAn        26\n",
              "PosN        17\n",
              "RRAe        11\n",
              "PosA         8\n",
              "RRNn         5\n",
              "RRNe         2\n",
              "Name: Condition1, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X['Condition2'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tjCTIlVLXT1",
        "outputId": "9fe8e015-310a-4893-cb0d-f104503be092"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Norm      1439\n",
              "Feedr        6\n",
              "Artery       2\n",
              "RRNn         2\n",
              "PosA         1\n",
              "RRAn         1\n",
              "RRAe         1\n",
              "Name: Condition2, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X['SaleCondition'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOutI6xGLeH4",
        "outputId": "289db28f-99c9-4aa5-e0af-5a61d6636547"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Normal     1195\n",
              "Partial     121\n",
              "Abnorml     100\n",
              "Family       20\n",
              "Alloca       12\n",
              "AdjLand       4\n",
              "Name: SaleCondition, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['SaleType'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcyN-_ltL6Hf",
        "outputId": "5d03e083-5464-483f-efa6-d84a9eea4edf"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WD       1264\n",
              "New       118\n",
              "COD        43\n",
              "ConLD       8\n",
              "ConLI       5\n",
              "ConLw       5\n",
              "CWD         4\n",
              "Oth         3\n",
              "Con         2\n",
              "Name: SaleType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lGV3e6yilBP",
        "outputId": "b2f99cd3-f938-46b6-c447-9f25b9a4367e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original: \n",
            "\n",
            "Series([], dtype: int64) \n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Data Trasformation\n",
        "print (\"Original: \\n\")\n",
        "print (X.value_counts(), \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E-w61pAjEfhq"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Turn into one hot encoding\n",
        "X['enc_street'] = pd.get_dummies(X.Street, drop_first=True)\n",
        "y['enc_street'] = pd.get_dummies(X.Street, drop_first=True)"
      ],
      "metadata": {
        "id": "rvCoOw0qSNwA"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoded\n",
        "print ('Encoded: \\n')\n",
        "print (X.enc_street.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kf-jptZ_SQUT",
        "outputId": "c6fd6fca-d5a5-48c9-b769-e11d10f071c9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoded: \n",
            "\n",
            "1    1447\n",
            "0       5\n",
            "Name: enc_street, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering\n",
        "condition_pivot = X.pivot_table(index='SaleCondition',\n",
        "                                    values='SalePrice', aggfunc=np.median)\n",
        "condition_pivot.plot(kind='bar', color='blue')\n",
        "plt.xlabel('Sale Condition')\n",
        "plt.ylabel('Median Sale Price')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ESsdgOP9STG3",
        "outputId": "74e555a7-f973-43df-bda5-0867b49ce249"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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uhzFnz57VwIEDVaZMGYWEhKhLly46dOiQw5j9+/erQ4cOCgoKUvny5TVs2DBlZ2c7jFm1apWaN28uf39/1axZU4mJifnqmT59uqpWraqAgADFxMTo+++/d7kWAABwdXJrqFq9erUGDhyo7777TsuWLVNWVpbat2+vU6dOWWOefPJJLVq0SAsWLNDq1at14MAB3XXXXdb2nJwcdejQQZmZmfr22281c+ZMJSYmauTIkdaYvXv3qkOHDrrpppu0efNmPfHEE+rdu7eWLl1qjfnggw80ePBgjRo1Shs3blSTJk0UHx+vw4cPO10LAAC4ipli5PDhw0aSWb16tTHGmOPHjxtfX1+zYMECa8zOnTuNJJOcnGyMMeaLL74w3t7eJjU11Rrz+uuvm7CwMJORkWGMMWb48OGmQYMGDs/VtWtXEx8fby23bt3aDBw40FrOyckxFSpUMOPGjXO6lotJS0szkkxaWppT4wEAKG6k4v+wm7O/v4vVPVVpaWmSpNKlS0uSUlJSlJWVpbi4OGtM3bp1VaVKFSUnJ0uSkpOT1ahRI0VERFhj4uPjlZ6eru3bt1tjzj9G3pi8Y2RmZiolJcVhjLe3t+Li4qwxztTydxkZGUpPT3d4AACAK1OxCVW5ubl64okndO2116phw4aSpNTUVPn5+alkyZIOYyMiIpSammqNOT9Q5W3P21bYmPT0dJ05c0Z//fWXcnJyChxz/jEuVsvfjRs3TuHh4dajcuXKTnYDAAB4mmITqgYOHKht27Zp3rx57i7FNgkJCUpLS7Mev/32m7tLAgAARcTH3QVI0mOPPabFixdrzZo1qlSpkrU+MjJSmZmZOn78uMMVokOHDikyMtIa8/dP6eV9Iu/8MX//lN6hQ4cUFhamwMBAlShRQiVKlChwzPnHuFgtf+fv7y9/f38XOgEAADyVW69UGWP02GOPaeHChVqxYoWqVavmsL1Fixby9fXV8uXLrXW7du3S/v37FRsbK0mKjY3V1q1bHT6lt2zZMoWFhal+/frWmPOPkTcm7xh+fn5q0aKFw5jc3FwtX77cGuNMLQAA4Cpm/z3yzuvfv78JDw83q1atMgcPHrQep0+ftsY8+uijpkqVKmbFihVmw4YNJjY21sTGxlrbs7OzTcOGDU379u3N5s2bzZIlS0y5cuVMQkKCNeaXX34xQUFBZtiwYWbnzp1m+vTppkSJEmbJkiXWmHnz5hl/f3+TmJhoduzYYfr27WtKlizp8KnCi9VyMXz6DwDg6dz9yb7i/Ok/t4YqSQU+ZsyYYY05c+aMGTBggClVqpQJCgoyd955pzl48KDDcfbt22duu+02ExgYaMqWLWuGDBlisrKyHMasXLnSNG3a1Pj5+Znq1as7PEeeadOmmSpVqhg/Pz/TunVr89133zlsd6aWwhCqAACezt2BqTiHKq9zDcLlkJ6ervDwcKWlpSksLMzd5QAA4DIvL3dXcHF2Jxtnf38Xm0//AQAAeDJCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYgFAFAABgA0IVAACADQhVAAAANiBUAQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYgFAFAABgA0IVAACADQhVAAAANiBUAQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYgFAFAABgA0IVAACADQhVAAAANiBUAQAA2OCSQ9WePXu0dOlSnTlzRpJkjLGtKAAAAE/jcqg6cuSI4uLiVLt2bd1+++06ePCgJKlXr14aMmSI7QUCAAB4ApdD1ZNPPikfHx/t379fQUFB1vquXbtqyZIlthYHAADgKXxc3eGrr77S0qVLValSJYf1tWrV0q+//mpbYQAAAJ7E5StVp06dcrhClefo0aPy9/e3pSgAAABP43Kouv766zVr1ixr2cvLS7m5uXrppZd000032VocAACAp3D57b+XXnpJ7dq104YNG5SZmanhw4dr+/btOnr0qJKSkoqiRgAAgGLP5StVDRs21O7du3XdddepU6dOOnXqlO666y5t2rRJNWrUKIoaAQAAij0vwwRTl016errCw8OVlpamsLAwd5cDAIDLvLzcXcHF2Z1snP397fKVqhkzZmjBggX51i9YsEAzZ8509XAAAABXBJdD1bhx41S2bNl868uXL68XX3zRlqIAAAA8jcuhav/+/apWrVq+9dHR0dq/f78tRQEAAHgal0NV+fLl9cMPP+Rbv2XLFpUpU8aWogAAADyNy6GqW7duevzxx7Vy5Url5OQoJydHK1as0KBBg3TfffcVRY0AAADFnsvzVI0dO1b79u1Tu3bt5ONzbvfc3Fx1796de6oAAMWSJ3xiTbL/U2u4vC55SoXdu3dry5YtCgwMVKNGjRQdHW13bVccplQAAPcgVNnHE3rprikVXL5Slad27dqqXbv2pe4OAABwRXEqVA0ePFhjx45VcHCwBg8eXOjYyZMn21IYAACAJ3EqVG3atElZWVmSpI0bN8rrAtf+LrQeAADgSsfX1FxG3FMFAO7hKf/n94TfyJ7QS4/4mpqsrCz5+Pho27Zt/7hAAACAK4lLocrX11dVqlRRTk5OUdUDAADgkVye/PPZZ5/VM888o6NHjxZFPQAAAB7J5SkVXnvtNe3Zs0cVKlRQdHS0goODHbZv3LjRtuIAAAA8hcuhqlOnTnzKDwAA4O+MG61evdp07NjRREVFGUlm4cKFDtt79OhhJDk84uPjHcYcOXLE3H///SY0NNSEh4ebRx55xJw4ccJhzJYtW8x1111n/P39TaVKlcyECRPy1TJ//nxTp04d4+/vbxo2bGg+//xzh+25ublmxIgRJjIy0gQEBJh27dqZ3bt3u/R609LSjCSTlpbm0n4AgH/m3OfBiv/DE7i7R+7oo7O/v52+p+rUqVPq37+/KlasqHLlyum+++7Tn3/++Y8C3alTp9SkSRNNnz79gmNuvfVWHTx40HrMnTvXYfsDDzyg7du3a9myZVq8eLHWrFmjvn37WtvT09PVvn17RUdHKyUlRS+//LKee+45vfXWW9aYb7/9Vt26dVOvXr20adMmde7cWZ07d3b4lONLL72kqVOn6o033tC6desUHBys+Ph4nT179h/1AAAAXCGcTWlPPvmkCQ4ONn379jWPP/64KVeunOncufM/Tn95dIErVZ06dbrgPjt27DCSzPr16611X375pfHy8jJ//PGHMcaY//73v6ZUqVImIyPDGvPUU0+ZOnXqWMv33nuv6dChg8OxY2JiTL9+/Ywx565SRUZGmpdfftnafvz4cePv72/mzp3r9GvkShUAuIe7r5xwpcqz+2j7laqFCxdqxowZevPNN/Xqq6/qyy+/1OLFi5WdnV1UeU+StGrVKpUvX1516tRR//79deTIEWtbcnKySpYsqZYtW1rr4uLi5O3trXXr1llj2rZtKz8/P2tMfHy8du3apWPHjllj4uLiHJ43Pj5eycnJkqS9e/cqNTXVYUx4eLhiYmKsMQXJyMhQenq6wwMAAFyZnA5Vv//+u6699lpruUWLFvL19dWBAweKpDDp3Ft/s2bN0vLlyzVhwgStXr1at912mzVPVmpqqsqXL++wj4+Pj0qXLq3U1FRrTEREhMOYvOWLjTl/+/n7FTSmIOPGjVN4eLj1qFy5skuvHwAAeA6nP/2Xm5srX19fx519fIp0ItD77rvP+nOjRo3UuHFj1ahRQ6tWrVK7du2K7HntkpCQ4PAF1Onp6QQrAACuUE6HKmOM2rVrJx+f/9/l9OnTuuOOOxzeWivKeaqqV6+usmXLas+ePWrXrp0iIyN1+PBhhzHZ2dk6evSoIiMjJUmRkZE6dOiQw5i85YuNOX973rqoqCiHMU2bNr1gvf7+/vL397+EVwoAADyN06Fq1KhR+dZ16tTJ1mIu5vfff9eRI0esYBMbG6vjx48rJSVFLVq0kCStWLFCubm5iomJscY8++yzysrKsq60LVu2THXq1FGpUqWsMcuXL9cTTzxhPdeyZcsUGxsrSapWrZoiIyO1fPlyK0Slp6dr3bp16t+//+V46YBH8YSp7IxxdwUArjj23yPvvBMnTphNmzaZTZs2GUlm8uTJZtOmTebXX381J06cMEOHDjXJyclm79695uuvvzbNmzc3tWrVMmfPnrWOceutt5pmzZqZdevWmbVr15patWqZbt26WduPHz9uIiIizEMPPWS2bdtm5s2bZ4KCgsybb75pjUlKSjI+Pj5m4sSJZufOnWbUqFHG19fXbN261Rozfvx4U7JkSfPpp5+aH374wXTq1MlUq1bNnDlzxunXy6f/cLVw9yd/rpRPWcE+7j7frqTz0t09ckcfnf397da/wpUrVxpJ+R49evQwp0+fNu3btzflypUzvr6+Jjo62vTp08ekpqY6HOPIkSOmW7duJiQkxISFhZmePXsWOvlnxYoVzfjx4/PVMn/+fFO7dm3j5+dnGjRocMHJPyMiIoy/v79p166d2bVrl0uvl1CFq4W7f6BeKb+8YB93n29X0nnp7h65o4/O/v72OtcgXA7p6ekKDw9XWlqawsLC3F0OUGR4+w/FjSeck5JnnJee0Eu7++js72+np1QAAADAhRGqAAAAbPCPQhXfewcAAHCOy6EqNzdXY8eOVcWKFRUSEqJffvlFkjRixAi9++67thcIAADgCVwOVc8//7wSExP10ksvOUz62bBhQ73zzju2FgcAAOApXA5Vs2bN0ltvvaUHHnhAJUqUsNY3adJEP/74o63FAQAAeAqXQ9Uff/yhmjVr5lufm5urrKwsW4oCAADwNC6Hqvr16+ubb77Jt/7DDz9Us2bNbCkKAHCOl1fxfwA4x+nv/sszcuRI9ejRQ3/88Ydyc3P18ccfa9euXZo1a5YWL15cFDUCAAAUey5fqerUqZMWLVqkr7/+WsHBwRo5cqR27typRYsW6ZZbbimKGgEAAIo9vqbmMuJranC18IS3hDzlJx+9tIcn9FGil3bha2oAAAA8mFP3VJUqVUpeTkbTo0eP/qOCAAAAPJFToeqVV14p4jIAAAA8m1OhqkePHkVdBwAAgEdzeUqF8509e1aZmZkO67gBGwAAXI1cvlH91KlTeuyxx1S+fHkFBwerVKlSDg8AAICrkcuhavjw4VqxYoVef/11+fv765133tHo0aNVoUIFzZo1qyhqBAAAKPZcfvtv0aJFmjVrlm688Ub17NlT119/vWrWrKno6GjNmTNHDzzwQFHUCQAAUKy5fKXq6NGjql69uqRz90/lTaFw3XXXac2aNfZWBwAA4CFcDlXVq1fX3r17JUl169bV/PnzJZ27glWyZElbiwMAAPAULoeqnj17asuWLZKkp59+WtOnT1dAQICefPJJDRs2zPYCAQAAPME//u6/ffv2aePGjapZs6YaN25sV11XpKL67r+r8XuYULxxTtqHXtrDE/oo0Uu7uOu7//7RPFWSVLVqVVWtWvWfHgYAAMCjOf32X3JyshYvXuywbtasWapWrZrKly+vvn37KiMjw/YCAQAAPIHToWrMmDHavn27tbx161b16tVLcXFxevrpp7Vo0SKNGzeuSIoEAAAo7pwOVZs3b1a7du2s5Xnz5ikmJkZvv/22Bg8erKlTp1qfBAQAALjaOB2qjh07poiICGt59erVuu2226zlVq1a6bfffrO3OgAAAA/hdKiKiIiw5qfKzMzUxo0b1aZNG2v7iRMn5Ovra3+FAAAAHsDpUHX77bfr6aef1jfffKOEhAQFBQXp+uuvt7b/8MMPqlGjRpEUCQAAUNw5PaXC2LFjddddd+mGG25QSEiIZs6cKT8/P2v7e++9p/bt2xdJkQAAAMWdy5N/pqWlKSQkRCVKlHBYf/ToUYWEhDgELThi8k9cLTgn7UMv7eEJfZTopV08ZvLP8PDwAteXLl3a1UMBAABcMVz+7j8AAADkR6gCAACwAaEKAADABoQqAAAAG7h8o7ok/fTTT1q5cqUOHz6s3Nxch20jR460pTAAAABP4nKoevvtt9W/f3+VLVtWkZGR8jrvs5VeXl6EKgAAcFVyOVQ9//zzeuGFF/TUU08VRT0AAAAeyeV7qo4dO6Z77rmnKGoBAADwWC6HqnvuuUdfffVVUdQCAADgsVx++69mzZoaMWKEvvvuOzVq1Ei+vr4O2x9//HHbigMAAPAULn/3X7Vq1S58MC8v/fLLL/+4qCsV3/2HqwXnpH3opT08oY8SvbSLx3z33969e/9RYQAAAFciJv8EAACwwSVN/vn777/rs88+0/79+5WZmemwbfLkybYUBgAA4ElcDlXLly/Xv/71L1WvXl0//vijGjZsqH379skYo+bNmxdFjQAAAMWey2//JSQkaOjQodq6dasCAgL00Ucf6bffftMNN9zA/FUAAOCq5XKo2rlzp7p37y5J8vHx0ZkzZxQSEqIxY8ZowoQJthcIAADgCVwOVcHBwdZ9VFFRUfr555+tbX/99Zd9lQEAAHgQl++patOmjdauXat69erp9ttv15AhQ7R161Z9/PHHatOmTVHUCAAAUOy5HKomT56skydPSpJGjx6tkydP6oMPPlCtWrX45B8AALhquTyjOi4dM6rjasE5aR96aQ9P6KNEL+3irhnVmfwTAADABk69/Ve6dGnt3r1bZcuWValSpeRVSEw9evSobcUBAAB4CqdC1ZQpUxQaGipJeuWVV4qyHgAAAI/EPVWXEfdU4WrBOWkfemkPT+ijRC/t4q57qpy6UpWenu70E9sZFgAAADyFU6GqZMmShd5Hdb6cnJx/VBDgLp7wvy/JM/4nCwBXI6dC1cqVK60/79u3T08//bQefvhhxcbGSpKSk5M1c+ZMjRs3rmiqBAAAKO6Mi26++Wbz/vvv51s/Z84cc8MNN7h0rNWrV5uOHTuaqKgoI8ksXLjQYXtubq4ZMWKEiYyMNAEBAaZdu3Zm9+7dDmOOHDli7r//fhMaGmrCw8PNI488Yk6cOOEwZsuWLea6664z/v7+plKlSmbChAn5apk/f76pU6eO8ff3Nw0bNjSff/65y7VcTFpampFk0tLSXNrvYs5duyjeD0/g7h7RS/pYEHf36Urppbt7RC89u4/O/v52eZ6q5ORktWzZMt/6li1b6vvvv3fpWKdOnVKTJk00ffr0Are/9NJLmjp1qt544w2tW7dOwcHBio+P19mzZ60xDzzwgLZv365ly5Zp8eLFWrNmjfr27WttT09PV/v27RUdHa2UlBS9/PLLeu655/TWW29ZY7799lt169ZNvXr10qZNm9S5c2d17txZ27Ztc6kWAABwFXM1rdWuXdsMGzYs3/phw4aZ2rVru3o4i+R4pSo3N9dERkaal19+2Vp3/Phx4+/vb+bOnWuMMWbHjh1Gklm/fr015ssvvzReXl7mjz/+MMYY89///teUKlXKZGRkWGOeeuopU6dOHWv53nvvNR06dHCoJyYmxvTr18/pWpzBlarizd09opf0sSDu7tOV0kt394heenYfi+xK1ZQpUzRt2jQ1atRIvXv3Vu/evdW4cWNNmzZNU6ZMsS3s7d27V6mpqYqLi7PWhYeHKyYmRsnJyZLOXTUrWbKkw5WzuLg4eXt7a926ddaYtm3bys/PzxoTHx+vXbt26dixY9aY858nb0ze8zhTS0EyMjKUnp7u8AAAAFcml0PV7bffrt27d+uOO+7Q0aNHdfToUd1xxx3avXu3br/9dtsKS01NlSRFREQ4rI+IiLC2paamqnz58g7bfXx8VLp0aYcxBR3j/Oe40Jjzt1+sloKMGzdO4eHh1qNy5coXedUAAMBTOfXpv7+rXLmyXnzxRbtrueIkJCRo8ODB1nJ6ejrBCgCAK9QlfaHyN998owcffFDXXHON/vjjD0nS7NmztXbtWtsKi4yMlCQdOnTIYf2hQ4esbZGRkTp8+LDD9uzsbB09etRhTEHHOP85LjTm/O0Xq6Ug/v7+CgsLc3gAAIArk8uh6qOPPlJ8fLwCAwO1ceNGZWRkSJLS0tJsvXpVrVo1RUZGavny5da69PR0rVu3zpofKzY2VsePH1dKSoo1ZsWKFcrNzVVMTIw1Zs2aNcrKyrLGLFu2THXq1FGpUqWsMec/T96YvOdxphYAAHCVc/UO+KZNm5qZM2caY4wJCQkxP//8szHGmI0bN5qIiAiXjnXixAmzadMms2nTJiPJTJ482WzatMn8+uuvxhhjxo8fb0qWLGk+/fRT88MPP5hOnTqZatWqmTNnzljHuPXWW02zZs3MunXrzNq1a02tWrVMt27drO3Hjx83ERER5qGHHjLbtm0z8+bNM0FBQebNN9+0xiQlJRkfHx8zceJEs3PnTjNq1Cjj6+trtm7dao1xppaL4dN/xZu7e0Qv6WNB3N2nK6WX7u4RvfTsPjr7+9vlpw4MDDR79+41xjiGqp9//tn4+/u7dKyVK1caSfkePXr0MMb8/4SbERERxt/f37Rr187s2rXL4RhHjhwx3bp1MyEhISYsLMz07Nmz0Mk/K1asaMaPH5+vlvnz55vatWsbPz8/06BBgwtO/llYLRdDqCre3N0jekkfC+LuPl0pvXR3j+ilZ/fR2d/fXuca5Lzq1avrrbfeUlxcnEJDQ7VlyxZVr15ds2bN0vjx47Vjxw57L6VdQZz9lmtXecJ31rl2lrmHJ/RRopd28YQ+SvTSLp7QR4le2sXuPjr7+9vle6r69OmjQYMGad26dfLy8tKBAwc0Z84cDR06VP379/9HRQMAAHgql6dUePrpp5Wbm6t27drp9OnTatu2rfz9/TV06FD9+9//LooaAQAAij2X3/7Lk5mZqT179ujkyZOqX7++QkJC7K7tisPbf8WbJ/RRopd28YQ+SvTSLp7QR4le2sVdb/9d0uSfkuTn56f69etf6u4AAABXFKdD1SOPPOLUuPfee++SiwEAAPBUToeqxMRERUdHq1mzZrrEdwwBAACuWE6Hqv79+2vu3Lnau3evevbsqQcffFClS5cuytoAAAA8htNTKkyfPl0HDx7U8OHDtWjRIlWuXFn33nuvli5dypUrAABw1XNpnip/f39169ZNy5Yt044dO9SgQQMNGDBAVatW1cmTJ4uqRgAAgGLP5ck/rR29veXl5SVjjHJycuysCQAAwOO4FKoyMjI0d+5c3XLLLapdu7a2bt2q1157Tfv372eeKgAAcFVz+kb1AQMGaN68eapcubIeeeQRzZ07V2XLli3K2gAAADyG0zOqe3t7q0qVKmrWrJm8CplO9eOPP7atuCsNM6oXb57QR4le2sUT+ijRS7t4Qh8lemmXYj+jevfu3QsNUwAAAFczlyb/BAAAQMEu+dN/AAAA+H+EKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxTrUPXcc8/Jy8vL4VG3bl1r+9mzZzVw4ECVKVNGISEh6tKliw4dOuRwjP3796tDhw4KCgpS+fLlNWzYMGVnZzuMWbVqlZo3by5/f3/VrFlTiYmJ+WqZPn26qlatqoCAAMXExOj7778vktcMAAA8U7EOVZLUoEEDHTx40HqsXbvW2vbkk09q0aJFWrBggVavXq0DBw7orrvusrbn5OSoQ4cOyszM1LfffquZM2cqMTFRI0eOtMbs3btXHTp00E033aTNmzfriSeeUO/evbV06VJrzAcffKDBgwdr1KhR2rhxo5o0aaL4+HgdPnz48jQBAAAUf6YYGzVqlGnSpEmB244fP258fX3NggULrHU7d+40kkxycrIxxpgvvvjCeHt7m9TUVGvM66+/bsLCwkxGRoYxxpjhw4ebBg0aOBy7a9euJj4+3lpu3bq1GThwoLWck5NjKlSoYMaNG+fS60lLSzOSTFpamkv7XYxU/B+ewN09opf0sSDu7tOV0kt394heenYfnf39XeyvVP3000+qUKGCqlevrgceeED79++XJKWkpCgrK0txcXHW2Lp166pKlSpKTk6WJCUnJ6tRo0aKiIiwxsTHxys9PV3bt2+3xpx/jLwxecfIzMxUSkqKwxhvb2/FxcVZYy4kIyND6enpDg8AAHBlKtahKiYmRomJiVqyZIlef/117d27V9dff71OnDih1NRU+fn5qWTJkg77REREKDU1VZKUmprqEKjytudtK2xMenq6zpw5o7/++ks5OTkFjsk7xoWMGzdO4eHh1qNy5cou9wAAAHgGH3cXUJjbbrvN+nPjxo0VExOj6OhozZ8/X4GBgW6szDkJCQkaPHiwtZyenk6wAgDgClWsr1T9XcmSJVW7dm3t2bNHkZGRyszM1PHjxx3GHDp0SJGRkZKkyMjIfJ8GzFu+2JiwsDAFBgaqbNmyKlGiRIFj8o5xIf7+/goLC3N4AACAK5NHhaqTJ0/q559/VlRUlFq0aCFfX18tX77c2r5r1y7t379fsbGxkqTY2Fht3brV4VN6y5YtU1hYmOrXr2+NOf8YeWPyjuHn56cWLVo4jMnNzdXy5cutMQAAAMX6swZDhgwxq1atMnv37jVJSUkmLi7OlC1b1hw+fNgYY8yjjz5qqlSpYlasWGE2bNhgYmNjTWxsrLV/dna2adiwoWnfvr3ZvHmzWbJkiSlXrpxJSEiwxvzyyy8mKCjIDBs2zOzcudNMnz7dlChRwixZssQaM2/ePOPv728SExPNjh07TN++fU3JkiUdPlXoDD79V7y5u0f0kj4WxN19ulJ66e4e0UvP7qOzv7+L9V9h165dTVRUlPHz8zMVK1Y0Xbt2NXv27LG2nzlzxgwYMMCUKlXKBAUFmTvvvNMcPHjQ4Rj79u0zt912mwkMDDRly5Y1Q4YMMVlZWQ5jVq5caZo2bWr8/PxM9erVzYwZM/LVMm3aNFOlShXj5+dnWrdubb777juXXw+hqnhzd4/oJX0siLv7dKX00t09opee3Udnf397nWsQLof09HSFh4crLS3N1vurvLxsO1SR8YSzzBP6KNFLu3hCHyV6aRdP6KNEL+1idx+d/f3tUfdUAQAAFFeEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKhdNnz5dVatWVUBAgGJiYvT999+7uyQAAFAMEKpc8MEHH2jw4MEaNWqUNm7cqCZNmig+Pl6HDx92d2kAAMDNCFUumDx5svr06aOePXuqfv36euONNxQUFKT33nvP3aUBAAA383F3AZ4iMzNTKSkpSkhIsNZ5e3srLi5OycnJBe6TkZGhjIwMazktLU2SlJ6eXrTFFkNX4UsuMvTSHvTRPvTSPvTSHnb3Me/3tjGm0HGEKif99ddfysnJUUREhMP6iIgI/fjjjwXuM27cOI0ePTrf+sqVKxdJjcVZeLi7K7hy0Et70Ef70Ev70Et7FFUfT5w4ofBCDk6oKkIJCQkaPHiwtZybm6ujR4+qTJky8vLycmNlF5aenq7KlSvrt99+U1hYmLvL8Wj00j700h700T700j6e0EtjjE6cOKEKFSoUOo5Q5aSyZcuqRIkSOnTokMP6Q4cOKTIyssB9/P395e/v77CuZMmSRVWircLCwortye1p6KV96KU96KN96KV9insvC7tClYcb1Z3k5+enFi1aaPny5da63NxcLV++XLGxsW6sDAAAFAdcqXLB4MGD1aNHD7Vs2VKtW7fWK6+8olOnTqlnz57uLg0AALgZocoFXbt21Z9//qmRI0cqNTVVTZs21ZIlS/LdvO7J/P39NWrUqHxvW8J19NI+9NIe9NE+9NI+V1IvvczFPh8IAACAi+KeKgAAABsQqgAAAGxAqAIAALABoeoKsGrVKnl5een48ePuLsVp+/btk5eXlzZv3uzuUpzy3HPPqWnTphdcdpfiUsel+Pt5m5iY6DHzuF2pqlatqldeecVa9vLy0ieffOK2eq5GnvjzvKhcyu+Jhx9+WJ07dy6ymi6GUOVBkpOTVaJECXXo0MHdpVwR/kk/hw4d6jBnmSeHm6LGeXvpHn74YXl5eeV77Nmzp0ieb/369erbt2+RHNsd8vo3fvx4h/WffPJJsf1WC09w/nnp5+enmjVrasyYMcrOzv5Hx/x7GKpcubIOHjyohg0b/sOKLx9ClQd599139e9//1tr1qzRgQMH3F2OJCkrK8vdJVyyf9LPkJAQlSlTpogqu7IUx/PWk9x66606ePCgw6NatWpF8lzlypVTUFBQkRzbXQICAjRhwgQdO3bMtmNmZmbadixPlXde/vTTTxoyZIiee+45vfzyyy4fJycnR7m5uQVuK1GihCIjI+Xj4zmzPxGqPMTJkyf1wQcfqH///urQoYMSExPzjUlKSlLjxo0VEBCgNm3aaNu2bda2vLdWli5dqnr16ikkJMT6R5EnNzdXY8aMUaVKleTv72/Nw5Un71LsBx98oBtuuEEBAQGaM2eO9T+MF198URERESpZsqT1v5Zhw4apdOnSqlSpkmbMmFGkPXLFxfo5fvx4RUREKDQ0VL169dLZs2cdtrt6ZWr27Nlq2bKlQkNDFRkZqfvvv1+HDx+2tudd8l++fLlatmypoKAgXXPNNdq1a5dLdRU3zpy3hXn99ddVo0YN+fn5qU6dOpo9e7bD9uPHj6tfv36KiIhQQECAGjZsqMWLF0uSjhw5om7duqlixYoKCgpSo0aNNHfuXLte2mXj7++vyMhIh8err76qRo0aKTg4WJUrV9aAAQN08uRJa5+8f++LFy9WnTp1FBQUpLvvvlunT5/WzJkzVbVqVZUqVUqPP/64cnJyrP3+/vbf+W6++WY99thjDuv+/PNP+fn5OVy1LW7i4uIUGRmpcePGXXDMRx99pAYNGsjf319Vq1bVpEmTHLZXrVpVY8eOVffu3RUWFqa+ffteco8v9rPAU+Sdl9HR0erfv7/i4uL02WefafLkyU6dm5999pnq168vf39/PfLII5o5c6Y+/fRT6wrYqlWr8r39l5OTo169eqlatWoKDAxUnTp19Oqrr7qpAwUjVHmI+fPnq27duqpTp44efPBBvffee/r7FGPDhg3TpEmTtH79epUrV0533HGHw5Wk06dPa+LEiZo9e7bWrFmj/fv3a+jQodb2V199VZMmTdLEiRP1ww8/KD4+Xv/617/0008/OTzP008/rUGDBmnnzp2Kj4+XJK1YsUIHDhzQmjVrNHnyZI0aNUodO3ZUqVKltG7dOj366KPq16+ffv/99yLskvMK6+f8+fP13HPP6cUXX9SGDRsUFRWl//73v//o+bKysjR27Fht2bJFn3zyifbt26eHH34437hnn31WkyZN0oYNG+Tj46NHHnnEoWa76ypqzpy3F7Jw4UINGjRIQ4YM0bZt29SvXz/17NlTK1eulHTuPwG33XabkpKS9L///U87duzQ+PHjVaJECUnS2bNn1aJFC33++efatm2b+vbtq4ceekjff/99kb3ey8Xb21tTp07V9u3bNXPmTK1YsULDhw93GHP69GlNnTpV8+bN05IlS7Rq1Srdeeed+uKLL/TFF19o9uzZevPNN/Xhhx869Zy9e/fW+++/r4yMDGvd//73P1WsWFE333yzra/PTiVKlNCLL76oadOmFfjzJyUlRffee6/uu+8+bd26Vc8995xGjBiR7z8AEydOVJMmTbRp0yaNGDFC0qX12NmfBZ4mMDBQmZmZTp+bEyZM0DvvvKPt27dr6tSpuvfeex2uyl5zzTX5niM3N1eVKlXSggULtGPHDo0cOVLPPPOM5s+ff7le5sUZeIRrrrnGvPLKK8YYY7KyskzZsmXNypUrjTHGrFy50kgy8+bNs8YfOXLEBAYGmg8++MAYY8yMGTOMJLNnzx5rzPTp001ERIS1XKFCBfPCCy84PG+rVq3MgAEDjDHG7N2710iy6sjTo0cPEx0dbXJycqx1derUMddff721nJ2dbYKDg83cuXMdjrVp06ZLbck/Ulg/Y2NjrdecJyYmxjRp0sRaHjVqVKHLF7N+/XojyZw4ccIY8/9/h19//bU15vPPPzeSzJkzZ5yuq7hx5rw9duyYMebcORoeHu6wb58+fRyOd88995jbb7/dGGPM0qVLjbe3t9m1a5fT9XTo0MEMGTLk0l/QZdajRw9TokQJExwcbD3uvvvufOMWLFhgypQpYy0X9O+9X79+JigoyDrnjDEmPj7e9OvXz1qOjo42U6ZMsZYlmYULFxpjjDlz5owpVaqU9TPFGGMaN25snnvuOTteapHo0aOH6dSpkzHGmDZt2phHHnnEGGPMwoULTd6vv/vvv9/ccsstDvsNGzbM1K9f31qOjo42nTt3dhhzqT3+uwv9LMj7d1Ecnd/X3Nxcs2zZMuPv72+GDh2ab+yFzs3Nmzdf8Jh5nPk9MXDgQNOlS5dCj3M5caXKA+zatUvff/+9unXrJkny8fFR165d9e677zqMO/+LnUuXLq06depo586d1rqgoCDVqFHDWo6KirIuO6enp+vAgQO69tprHY557bXXOhxDklq2bJmvxgYNGsjb+/9Pp4iICDVq1MhaLlGihMqUKVMsLnNfrJ87d+5UTEyMwz7/9EuzU1JSdMcdd6hKlSoKDQ3VDTfcIEnav3+/w7jGjRtbf46KipIkq2dFUVdRcva8vZCdO3cWej5u3rxZlSpVUu3atQvcPycnR2PHjlWjRo1UunRphYSEaOnSpfl6XtzddNNN2rx5s/WYOnWqvv76a7Vr104VK1ZUaGioHnroIR05ckSnT5+29vv7v/eIiAhVrVpVISEhDuuc/TcZEBCghx56SO+9954kaePGjdq2bZvHXGWZMGGCZs6cme/n2YXOs59++snhbbuCfu5dSo+d/VlQ3C1evFghISEKCAjQbbfdpq5du+q5555z6tz08/Nz+FnniunTp6tFixYqV66cQkJC9NZbbxWr3hGqPMC7776r7OxsVahQQT4+PvLx8dHrr7+ujz76SGlpaU4fx9fX12HZy8vL6bdizhccHOzUsQtad6EbEi8nu/rprFOnTik+Pl5hYWGaM2eO1q9fr4ULF0rKf8Pr+T3L+3RScejZpSjqPgcGBha6/eWXX9arr76qp556SitXrtTmzZsVHx/vcTcZBwcHq2bNmtYjIyNDHTt2VOPGjfXRRx8pJSVF06dPl+R4PhXFv8nevXtr2bJl+v333zVjxgzdfPPNio6O/gev7vJp27at4uPjlZCQcEn72/Fzz5WfBcVdXtj/6aefdObMGc2cOVN//vmnU+dmYGDgJX36ct68eRo6dKh69eqlr776Sps3b1bPnj2LVe8855b6q1R2drZmzZqlSZMmqX379g7bOnfurLlz56pu3bqSpO+++05VqlSRJB07dky7d+9WvXr1nHqesLAwVahQQUlJSdb/nKRzN7+3bt3aplfjfs70s169elq3bp26d+9ubfvuu+8u+Tl//PFHHTlyROPHj1flypUlSRs2bHD5OHbXVZRcOW8vpF69ekpKSlKPHj2sdUlJSapfv76kc1f1fv/9d+3evbvAq1VJSUnq1KmTHnzwQUnnwunu3but/T1VSkqKcnNzNWnSJOvq8OW6p6RRo0Zq2bKl3n77bb3//vt67bXXLsvz2mX8+PFq2rSp6tSpY63LO8/Ol5SUpNq1a1v359nFrp8FxUFe2D/fPzk3/fz8HK4MFiQpKUnXXHONBgwYYK37+eefXay8aBGqirnFixfr2LFj6tWrl8LDwx22denSRe+++671MdYxY8aoTJkyioiI0LPPPquyZcu6NAnasGHDNGrUKNWoUUNNmzbVjBkztHnzZs2ZM8fOl+RWzvRz6NChevjhh9WyZUtde+21mjNnjrZv367q1asXeuwzZ87km6QuNDRUVapUkZ+fn6ZNm6ZHH31U27Zt09ixY12ufdCgQZdUlzu4ct5eyLBhw3TvvfeqWbNmiouL06JFi/Txxx/r66+/liTdcMMNatu2rbp06aLJkyerZs2a+vHHH+Xl5aVbb71VtWrV0ocffqhvv/1WpUqV0uTJk3Xo0CGPD1U1a9ZUVlaWpk2bpjvuuENJSUl64403Ltvz9+7dW4899piCg4N15513XrbntUOjRo30wAMPaOrUqda6IUOGqFWrVho7dqy6du2q5ORkvfbaa0XyIRC7fhYUV//k3KxataqWLl2qXbt2qUyZMvl+bkhSrVq1NGvWLC1dulTVqlXT7NmztX79+iKbYuRS8PZfMffuu+8qLi6uwBOsS5cu2rBhg3744QdJ5/4XNmjQILVo0UKpqalatGiR/Pz8nH6uxx9/XIMHD9aQIUPUqFEjLVmyRJ999plq1apl2+txN2f6Wa9ePY0YMULDhw9XixYt9Ouvv6p///4XPfbu3bvVrFkzh0e/fv1Urlw5JSYmasGCBapfv77Gjx+viRMnulx7165dL6kud3DlvL2Qzp0769VXX9XEiRPVoEEDvfnmm5oxY4ZuvPFGa8xHH32kVq1aqVu3bqpfv76GDx9u/W/3P//5j5o3b674+HjdeOONioyMdOtMy3Zp0qSJJk+erAkTJqhhw4aaM2dOodMF2K1bt27y8fFRt27dFBAQcNme1y5jxoxxeMuzefPmmj9/vubNm6eGDRtq5MiRGjNmTJHcK2bXz4Li6p+cm3369FGdOnXUsmVLlStXLt/VQ0nq16+f7rrrLnXt2lUxMTE6cuSIw1Wr4sDLXMpNNcBVLiEhQd98843Wrl3r7lKAy2rfvn2qUaOG1q9fr+bNm7u7HKBY4UoV4AJjjH7++WctX75cDRo0cHc5wGWTlZWl1NRU/ec//1GbNm0IVEABCFWAC9LS0lS/fn35+fnpmWeecXc5wGWTlJSkqKgorV+//rLewwV4Et7+AwAAsAFXqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCcMVITExUyZIl3V3GP/Lwww87zPx+44036oknnih0nyvhdQNXAkIVgGLhzz//VP/+/VWlShX5+/srMjJS8fHxBX5dRVFLT0/Xs88+q7p16yogIECRkZGKi4vTxx9/rMs9C83HH3/s8P1wVatW1SuvvOIwpmvXrtq9e/dlrQtAfnyhMoBioUuXLsrMzNTMmTNVvXp1HTp0SMuXL9eRI0cuax3Hjx/Xddddp7S0ND3//PNq1aqVfHx8tHr1ag0fPlw333zzZb0qVLp06YuOCQwMVGBg4GWoBkBhuFIFwO2OHz+ub775RhMmTNBNN92k6OhotW7dWgkJCfrXv/5ljZs8ebIaNWqk4OBgVa5cWQMGDNDJkycLPfann36q5s2bKyAgQNWrV9fo0aOVnZ19wfHPPPOM9u3bp3Xr1qlHjx6qX7++ateurT59+mjz5s0KCQmRJB07dkzdu3dXqVKlFBQUpNtuu00//fSTdZy8t+SWLl2qevXqKSQkRLfeeqsOHjxojcnJydHgwYNVsmRJlSlTRsOHD893Jez8t/9uvPFG/frrr3ryySfl5eUlLy8vh+c63+uvv64aNWrIz89PderU0ezZsx22e3l56Z133tGdd96poKAg1apVS5999lmhvQRQOEIVALcLCQlRSEiIPvnkE2VkZFxwnLe3t6ZOnart27dr5syZWrFihYYPH37B8d988426d++uQYMGaceOHXrzzTeVmJioF154ocDxubm5mjdvnh544AFVqFChwDp9fM5d4H/44Ye1YcMGffbZZ0pOTpYxRrfffruysrKs8adPn9bEiRM1e/ZsrVmzRvv379fQoUOt7ZMmTVJiYqLee+89rV27VkePHtXChQsv+Ho+/vhjVapUSWPGjNHBgwcdAtr5Fi5cqEGDBmnIkCHatm2b+vXrp549e2rlypUO40aPHq17771XP/zwg26//XY98MADOnr06AWfH8BFGAAoBj788ENTqlQpExAQYK655hqTkJBgtmzZUug+CxYsMGXKlLGWZ8yYYcLDw63ldu3amRdffNFhn9mzZ5uoqKgCj3fo0CEjyUyePLnQ5929e7eRZJKSkqx1f/31lwkMDDTz58+3apFk9uzZY42ZPn26iYiIsJajoqLMSy+9ZC1nZWWZSpUqmU6dOlnrbrjhBjNo0CBrOTo62kyZMsWhnr+/7muuucb06dPHYcw999xjbr/9dmtZkvnPf/5jLZ88edJIMl9++WWhrx3AhXGlCkCx0KVLFx04cECfffaZbr31Vq1atUrNmzdXYmKiNebrr79Wu3btVLFiRYWGhuqhhx7SkSNHdPr06QKPuWXLFo0ZM8a6EhYSEqI+ffro4MGDBe5jnLwJfefOnfLx8VFMTIy1rkyZMqpTp4527txprQsKClKNGjWs5aioKB0+fFjSuS/nPnjwoMMxfHx81LJlS6dquFh91157rcO6a6+91qE2SWrcuLH15+DgYIWFhVn1AXAdoQpAsREQEKBbbrlFI0aM0LfffquHH35Yo0aNkiTt27dPHTt2VOPGjfXRRx8pJSVF06dPlyRlZmYWeLyTJ09q9OjR2rx5s/XYunWrfvrpJwUEBOQbX65cOZUsWVI//vijLa/H19fXYdnLy+uyf3qwMAXVl5ub66ZqAM9HqAJQbNWvX1+nTp2SJKWkpCg3N1eTJk1SmzZtVLt2bR04cKDQ/Zs3b65du3apZs2a+R7e3vl//Hl7e+u+++7TnDlzCjz2yZMnlZ2drXr16ik7O1vr1q2zth05ckS7du1S/fr1nXpt4eHhioqKcjhGdna2UlJSCt3Pz89POTk5hY6pV69evqkokpKSnK4NwKVhSgUAbnfkyBHdc889euSRR9S4cWOFhoZqw4YNeumll9SpUydJUs2aNZWVlaVp06bpjjvuUFJSkt54441Cjzty5Eh17NhRVapU0d133y1vb29t2bJF27Zt0/PPP1/gPi+88IJWrVqlmJgYvfDCC2rZsqV8fX31zTffaNy4cVq/fr1q1aqlTp06qU+fPnrzzTcVGhqqp59+WhUrVrTqdcagQYM0fvx41apVS3Xr1tXkyZN1/PjxQvepWrWq1qxZo/vuu0/+/v4qW7ZsvjHDhg3Tvffeq2bNmikuLk6LFi3Sxx9/rK+//trp2gC4jitVANwuJCREMTExmjJlitq2bauGDRtqxIgR6tOnj1577TVJUpMmTTR58mRNmDBBDRs21Jw5czRu3LhCjxsfH6/Fixfrq6++UqtWrdSmTRtNmTJF0dHRF9yndOnS+u677/Tggw/q+eefV7NmzXT99ddr7ty5evnllxUeHi5JmjFjhlq0aKGOHTsqNjZWxhh98cUX+d5SK8yQIUP00EMPqUePHoqNjVVoaKjuvPPOQvcZM2aM9u3bpxo1aqhcuXIFjuncubNeffVVTZw4UQ0aNNCbb76pGTNm6MYbb3S6NgCu8zLF6Q1+AAAAD8WVKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsMH/AZziRS1b4v8fAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def encode(x): return 1 if x == 'Partial' else 0\n",
        "X['enc_condition'] = X.SaleCondition.apply(encode)\n",
        "y['enc_condition'] = y.SaleCondition.apply(encode)\n",
        "condition_pivot = X.pivot_table(index='enc_condition', values='SalePrice', aggfunc=np.median)\n",
        "condition_pivot.plot(kind='bar', color='blue')\n",
        "plt.xlabel('Encoded Sale Condition')\n",
        "plt.ylabel('Median Sale Price')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "irsUChyNSZFd",
        "outputId": "53819598-b95f-477e-ef08-df8b6b39f5f1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "s7o-R1aY98ms",
        "outputId": "45badf54-824e-4f0f-a33c-3f9805a98caa"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
              "0     1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
              "1     1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
              "2     1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
              "3     1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
              "4     1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
              "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
              "1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
              "1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
              "1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
              "1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
              "1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
              "\n",
              "     LandContour Utilities  ...  Fence MiscFeature MiscVal MoSold YrSold  \\\n",
              "0            Lvl    AllPub  ...  MnPrv         NaN       0      6   2010   \n",
              "1            Lvl    AllPub  ...    NaN        Gar2   12500      6   2010   \n",
              "2            Lvl    AllPub  ...  MnPrv         NaN       0      3   2010   \n",
              "3            Lvl    AllPub  ...    NaN         NaN       0      6   2010   \n",
              "4            HLS    AllPub  ...    NaN         NaN       0      1   2010   \n",
              "...          ...       ...  ...    ...         ...     ...    ...    ...   \n",
              "1454         Lvl    AllPub  ...    NaN         NaN       0      6   2006   \n",
              "1455         Lvl    AllPub  ...    NaN         NaN       0      4   2006   \n",
              "1456         Lvl    AllPub  ...    NaN         NaN       0      9   2006   \n",
              "1457         Lvl    AllPub  ...  MnPrv        Shed     700      7   2006   \n",
              "1458         Lvl    AllPub  ...    NaN         NaN       0     11   2006   \n",
              "\n",
              "     SaleType SaleCondition  Skewed_SP  enc_street  enc_condition  \n",
              "0          WD        Normal  12.247694         1.0              0  \n",
              "1          WD        Normal  12.109011         1.0              0  \n",
              "2          WD        Normal  12.317167         1.0              0  \n",
              "3          WD        Normal  11.849398         1.0              0  \n",
              "4          WD        Normal  12.429216         1.0              0  \n",
              "...       ...           ...        ...         ...            ...  \n",
              "1454       WD        Normal  12.128111         1.0              0  \n",
              "1455       WD       Abnorml  12.072541         1.0              0  \n",
              "1456       WD       Abnorml  12.254863         1.0              0  \n",
              "1457       WD        Normal  12.493130         1.0              0  \n",
              "1458       WD        Normal  11.864462         1.0              0  \n",
              "\n",
              "[1459 rows x 83 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-19a34439-7a44-4fdd-a86c-abccd20e987b\" 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>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>Alley</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>...</th>\n",
              "      <th>Fence</th>\n",
              "      <th>MiscFeature</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>Skewed_SP</th>\n",
              "      <th>enc_street</th>\n",
              "      <th>enc_condition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "      <td>20</td>\n",
              "      <td>RH</td>\n",
              "      <td>80.0</td>\n",
              "      <td>11622</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>12.247694</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>81.0</td>\n",
              "      <td>14267</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Gar2</td>\n",
              "      <td>12500</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>12.109011</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>74.0</td>\n",
              "      <td>13830</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>12.317167</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>78.0</td>\n",
              "      <td>9978</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>11.849398</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>120</td>\n",
              "      <td>RL</td>\n",
              "      <td>43.0</td>\n",
              "      <td>5005</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IR1</td>\n",
              "      <td>HLS</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>12.429216</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1454</th>\n",
              "      <td>2915</td>\n",
              "      <td>160</td>\n",
              "      <td>RM</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1936</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>12.128111</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>2916</td>\n",
              "      <td>160</td>\n",
              "      <td>RM</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1894</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>4</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>12.072541</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>2917</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>160.0</td>\n",
              "      <td>20000</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>12.254863</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>2918</td>\n",
              "      <td>85</td>\n",
              "      <td>RL</td>\n",
              "      <td>62.0</td>\n",
              "      <td>10441</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>Shed</td>\n",
              "      <td>700</td>\n",
              "      <td>7</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>12.493130</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>2919</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>74.0</td>\n",
              "      <td>9627</td>\n",
              "      <td>Pave</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>11.864462</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1459 rows × 83 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "collapsed": true,
        "id": "cIP1JmcAilBR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "dce3c8ae-70df-4e21-c786-68847054db84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "0             20         80.0    11622            5            6       1961   \n",
              "1             20         81.0    14267            6            6       1958   \n",
              "2             60         74.0    13830            5            5       1997   \n",
              "3             60         78.0     9978            6            6       1998   \n",
              "4            120         43.0     5005            8            5       1992   \n",
              "...          ...          ...      ...          ...          ...        ...   \n",
              "1454         160         21.0     1936            4            7       1970   \n",
              "1455         160         21.0     1894            4            5       1970   \n",
              "1456          20        160.0    20000            5            7       1960   \n",
              "1457          85         62.0    10441            5            5       1992   \n",
              "1458          60         74.0     9627            7            5       1993   \n",
              "\n",
              "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  OpenPorchSF  \\\n",
              "0             1961         0.0       468.0       144.0  ...            0   \n",
              "1             1958       108.0       923.0         0.0  ...           36   \n",
              "2             1998         0.0       791.0         0.0  ...           34   \n",
              "3             1998        20.0       602.0         0.0  ...           36   \n",
              "4             1992         0.0       263.0         0.0  ...           82   \n",
              "...            ...         ...         ...         ...  ...          ...   \n",
              "1454          1970         0.0         0.0         0.0  ...            0   \n",
              "1455          1970         0.0       252.0         0.0  ...           24   \n",
              "1456          1996         0.0      1224.0         0.0  ...            0   \n",
              "1457          1992         0.0       337.0         0.0  ...           32   \n",
              "1458          1994        94.0       758.0         0.0  ...           48   \n",
              "\n",
              "      EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  \\\n",
              "0                 0          0          120         0        0       6   \n",
              "1                 0          0            0         0    12500       6   \n",
              "2                 0          0            0         0        0       3   \n",
              "3                 0          0            0         0        0       6   \n",
              "4                 0          0          144         0        0       1   \n",
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              "1454              0          0            0         0        0       6   \n",
              "1455              0          0            0         0        0       4   \n",
              "1456              0          0            0         0        0       9   \n",
              "1457              0          0            0         0      700       7   \n",
              "1458              0          0            0         0        0      11   \n",
              "\n",
              "      YrSold  enc_street  enc_condition  \n",
              "0       2010         1.0              0  \n",
              "1       2010         1.0              0  \n",
              "2       2010         1.0              0  \n",
              "3       2010         1.0              0  \n",
              "4       2010         1.0              0  \n",
              "...      ...         ...            ...  \n",
              "1454    2006         1.0              0  \n",
              "1455    2006         1.0              0  \n",
              "1456    2006         1.0              0  \n",
              "1457    2006         1.0              0  \n",
              "1458    2006         1.0              0  \n",
              "\n",
              "[1459 rows x 38 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12500</td>\n",
              "      <td>6</td>\n",
              "      <td>2010</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>74.0</td>\n",
              "      <td>13830</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1997</td>\n",
              "      <td>1998</td>\n",
              "      <td>0.0</td>\n",
              "      <td>791.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60</td>\n",
              "      <td>78.0</td>\n",
              "      <td>9978</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>1998</td>\n",
              "      <td>1998</td>\n",
              "      <td>20.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>36</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>2010</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>120</td>\n",
              "      <td>43.0</td>\n",
              "      <td>5005</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>1992</td>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1454</th>\n",
              "      <td>160</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1936</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1970</td>\n",
              "      <td>1970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>6</td>\n",
              "      <td>2006</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>160</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1894</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1970</td>\n",
              "      <td>1970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>252.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2006</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>20</td>\n",
              "      <td>160.0</td>\n",
              "      <td>20000</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>1960</td>\n",
              "      <td>1996</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1224.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</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>9</td>\n",
              "      <td>2006</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>85</td>\n",
              "      <td>62.0</td>\n",
              "      <td>10441</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1992</td>\n",
              "      <td>1992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>700</td>\n",
              "      <td>7</td>\n",
              "      <td>2006</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>60</td>\n",
              "      <td>74.0</td>\n",
              "      <td>9627</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1993</td>\n",
              "      <td>1994</td>\n",
              "      <td>94.0</td>\n",
              "      <td>758.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>2006</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1459 rows × 38 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3829a8b-55d7-49b0-a312-9e3bfe942597')\"\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c3829a8b-55d7-49b0-a312-9e3bfe942597 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-c3829a8b-55d7-49b0-a312-9e3bfe942597');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8771507c-58b3-454d-a445-a515ce52f873\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8771507c-58b3-454d-a445-a515ce52f873')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\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-8771507c-58b3-454d-a445-a515ce52f873 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "feats = y.select_dtypes(\n",
        "        include=[np.number]).drop(['Id','Skewed_SP'], axis=1).interpolate()\n",
        "feats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "O_OZRd7HilBQ"
      },
      "outputs": [],
      "source": [
        "#Interpolation of data\n",
        "data = X.select_dtypes(include=[np.number]).interpolate().dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CA1S--T8ilBQ",
        "outputId": "158c8ad9-d75e-4be5-f884-18a8142b2120"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "sum(data.isnull().sum() != 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "collapsed": true,
        "id": "0MeLUmh0ilBQ"
      },
      "outputs": [],
      "source": [
        "# Linear Model for the  train and test\n",
        "target = np.log(X.SalePrice)\n",
        "train= data.drop(['SalePrice','Skewed_SP', 'Id'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "wOGUXlDt-njF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "collapsed": true,
        "id": "lLPXiaHmilBQ"
      },
      "outputs": [],
      "source": [
        "#Train Test Split.\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "                                    train, target, random_state=42, test_size=.33)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "L_IUeFIGAjSG",
        "outputId": "46cd6941-077a-4461-e4aa-c98bbb4197de"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "497           50    60.000000     9120            7            6       1925   \n",
              "308           30    98.666667    12342            4            5       1940   \n",
              "461           70    60.000000     7200            7            9       1936   \n",
              "681           50    55.000000     4500            5            5       1932   \n",
              "781           60    65.000000     7153            6            5       1992   \n",
              "...          ...          ...      ...          ...          ...        ...   \n",
              "1100          30    60.000000     8400            2            5       1920   \n",
              "1135          30    60.000000     6180            6            5       1926   \n",
              "1302          60    92.000000    10120            8            5       1994   \n",
              "864           20    72.000000     8640            7            5       2007   \n",
              "1131          20    63.000000    10712            5            5       1991   \n",
              "\n",
              "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  OpenPorchSF  \\\n",
              "497           1950         0.0         329           0  ...          100   \n",
              "308           1950         0.0         262           0  ...            0   \n",
              "461           2007         0.0         350         210  ...            0   \n",
              "681           2000         0.0         182           0  ...            0   \n",
              "781           1992         0.0         387           0  ...          124   \n",
              "...            ...         ...         ...         ...  ...          ...   \n",
              "1100          1950         0.0         290           0  ...            0   \n",
              "1135          1950         0.0           0           0  ...          128   \n",
              "1302          1994       391.0         740           0  ...           78   \n",
              "864           2008         0.0           0           0  ...          140   \n",
              "1131          1992         0.0         212           0  ...           28   \n",
              "\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "SqT2KJt9AlT2",
        "outputId": "932f413a-d73c-4174-847a-5be3507294b7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "1040          20         88.0    13125            5            4       1957   \n",
              "1129          90         60.0     7200            5            5       1980   \n",
              "1001          30         60.0     5400            5            6       1920   \n",
              "1324          20         75.0     9986            8            5       2006   \n",
              "530           80         85.0    10200            6            5       1988   \n",
              "...          ...          ...      ...          ...          ...        ...   \n",
              "1256          20         91.0    14303            8            5       1994   \n",
              "478           20         79.0    10637            8            5       2007   \n",
              "758          160         24.0     2280            7            5       1999   \n",
              "553           20         67.0     8777            4            5       1949   \n",
              "1336          90         87.0     9246            5            5       1973   \n",
              "\n",
              "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  OpenPorchSF  \\\n",
              "1040          2000        67.0         168         682  ...            0   \n",
              "1129          1980       180.0         936           0  ...            0   \n",
              "1001          1950         0.0           0           0  ...           20   \n",
              "1324          2007       428.0           0           0  ...           49   \n",
              "530           1989       219.0         783           0  ...            0   \n",
              "...            ...         ...         ...         ...  ...          ...   \n",
              "1256          1994       554.0        1314           0  ...           36   \n",
              "478           2008       336.0        1288           0  ...           44   \n",
              "758           1999       360.0         549           0  ...            0   \n",
              "553           2003         0.0           0           0  ...           96   \n",
              "1336          1973       564.0           0           0  ...          211   \n",
              "\n",
              "      EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  \\\n",
              "1040              0          0            0         0        0       1   \n",
              "1129              0          0            0         0        0       8   \n",
              "1001             94          0            0         0        0       1   \n",
              "1324              0          0            0         0        0       2   \n",
              "530               0          0            0         0        0       8   \n",
              "...             ...        ...          ...       ...      ...     ...   \n",
              "1256              0          0            0         0        0       8   \n",
              "478               0          0            0         0        0       9   \n",
              "758               0          0            0         0        0       8   \n",
              "553               0          0            0         0        0       5   \n",
              "1336              0          0            0         0        0      11   \n",
              "\n",
              "      YrSold  enc_street  enc_condition  \n",
              "1040    2006           1              0  \n",
              "1129    2007           1              0  \n",
              "1001    2007           1              0  \n",
              "1324    2007           1              1  \n",
              "530     2008           1              0  \n",
              "...      ...         ...            ...  \n",
              "1256    2008           1              0  \n",
              "478     2009           1              0  \n",
              "758     2008           1              0  \n",
              "553     2009           1              0  \n",
              "1336    2008           1              0  \n",
              "\n",
              "[480 rows x 38 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>OverallQual</th>\n",
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              "      <th>1040</th>\n",
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              "      <td>1957</td>\n",
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              "    <tr>\n",
              "      <th>1129</th>\n",
              "      <td>90</td>\n",
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              "      <td>7200</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1980</td>\n",
              "      <td>1980</td>\n",
              "      <td>180.0</td>\n",
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              "    <tr>\n",
              "      <th>1001</th>\n",
              "      <td>30</td>\n",
              "      <td>60.0</td>\n",
              "      <td>5400</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1920</td>\n",
              "      <td>1950</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>20</td>\n",
              "      <td>94</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1324</th>\n",
              "      <td>20</td>\n",
              "      <td>75.0</td>\n",
              "      <td>9986</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2006</td>\n",
              "      <td>2007</td>\n",
              "      <td>428.0</td>\n",
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              "      <td>49</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>80</td>\n",
              "      <td>85.0</td>\n",
              "      <td>10200</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>1988</td>\n",
              "      <td>1989</td>\n",
              "      <td>219.0</td>\n",
              "      <td>783</td>\n",
              "      <td>0</td>\n",
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              "      <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>2008</td>\n",
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              "      <td>91.0</td>\n",
              "      <td>14303</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>1994</td>\n",
              "      <td>1994</td>\n",
              "      <td>554.0</td>\n",
              "      <td>1314</td>\n",
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              "      <td>36</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>20</td>\n",
              "      <td>79.0</td>\n",
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              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>2008</td>\n",
              "      <td>336.0</td>\n",
              "      <td>1288</td>\n",
              "      <td>0</td>\n",
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              "      <td>44</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2009</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>758</th>\n",
              "      <td>160</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2280</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>1999</td>\n",
              "      <td>1999</td>\n",
              "      <td>360.0</td>\n",
              "      <td>549</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>20</td>\n",
              "      <td>67.0</td>\n",
              "      <td>8777</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1949</td>\n",
              "      <td>2003</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>96</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2009</td>\n",
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              "      <th>1336</th>\n",
              "      <td>90</td>\n",
              "      <td>87.0</td>\n",
              "      <td>9246</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1973</td>\n",
              "      <td>1973</td>\n",
              "      <td>564.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>211</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>2008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "<p>480 rows × 38 columns</p>\n",
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              "    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-f28adffb-868d-4afa-92ff-0defe2e2a05a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "collapsed": true,
        "id": "awgdL0AfilBQ"
      },
      "outputs": [],
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn import ensemble\n",
        "lr = ensemble.GradientBoostingRegressor()\n",
        "lr2 =  ensemble.RandomForestRegressor(n_estimators = 100, oob_score = True, n_jobs = -1,random_state =50,max_features = \"sqrt\", min_samples_leaf = 50)\n",
        "lr3 = linear_model.LinearRegression()\n",
        "lr4 = linear_model.TheilSenRegressor()\n",
        "lr5 = linear_model.RANSACRegressor(random_state=50)\n",
        "lr6 = linear_model.Lasso()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "collapsed": true,
        "id": "ljy7WKHgilBQ"
      },
      "outputs": [],
      "source": [
        "model = lr.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92XV7t9NilBQ",
        "outputId": "f18426b3-bdd5-4424-e794-80ee904edb96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2 is: \n",
            " 0.8912793943119743\n"
          ]
        }
      ],
      "source": [
        "print (\"R^2 is: \\n\", model.score(X_test, y_test))\n",
        "rsq1 = model.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "collapsed": true,
        "id": "elWYfFE9ilBR"
      },
      "outputs": [],
      "source": [
        "predictions = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-m96_1milBR",
        "outputId": "cf53b03f-c296-4ab3-9f3a-b3ec2e5ab346"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE is: \n",
            " 0.016800916379699125\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "print ('RMSE is: \\n', mean_squared_error(y_test, predictions))\n",
        "rmse1 = mean_squared_error(y_test, predictions)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "print(\"MAE is: \\n\", mean_absolute_error(y_test,predictions))\n",
        "mae1 = mean_absolute_error(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uarKXDBUMDAu",
        "outputId": "a02f4512-5ab6-4536-9bd0-b910049a36c8"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE is: \n",
            " 0.08636464481495196\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "hByyP8_JilBR",
        "outputId": "85d8eb5b-50d6-4062-a8df-468832b00275"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Gradient Boosting Regression Model')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ef/55PPLII3j66adxxRVXNPq4LXWf+vTp4xIyf/zjH2EymXD77bdj7NixOPnkkwHI7/mMM87Arbfe6vMYffv2bdS5fX1unU4n0tLS8N///tfnPkpsqNo43377Lf73v//ho48+wuWXX46HHnoI3377LeLj45GWlob8/Hx89NFH+OCDD/DBBx/gpZdewvTp0/Hyyy83as7e+Ps9BUt6ejrGjBmDhx56CF999VWLZn6pv+NXXnnFlTjgTmuWaSChg79FEtZMmjQJzz//vM+aIMGyZcsW/Pbbb3j55Zcxffp013pvd4CqJ+TLhbNt27Z6z5GamoqEhAQ4HA7XQzQUhKrSbG1tLQDdqqau1dd1/frrr0hJSUFcXBwsFgs6dOjgd5zRaPTIxEtOTsasWbMwa9Ys2O12nHbaaViwYIFLADVH5dysrCw4nU7s3LnT41u+e0ZdY/j73/+O5557Dv/4xz9c1pJevXrBbrc3+DvOysrCZ599hoqKCg8rUDBz6tWrFz755BOMHDnSZ7CuN6eeeipOPfVU3HPPPXj11Vdx6aWXYtmyZa57bzabMXnyZEyePBlOpxPXXnstnnnmGfzzn//0aZly/4yogGTFtm3bmrX+1iWXXIIrrrgCSUlJOPvss32OSU1NDfizmZWVFdDfda9evQAAaWlpIf07JuEFXWAkrLn11lvRoUMHXH755Th06FCd7cF8+1ffTN330TTNIwUYkG+eubm5ePnll11uMUCE0s8//9zgOc4//3wsX77cp+WqqKgo4Pm6ExcX5zGXxvK///0PAJCTkwPA81pLSkpc47Zu3YqPP/7Y9dAxmUw488wz8c4773hY3Q4dOoRXX30Vo0aNQmJiIgBJ+3YnPj4evXv39nDFqNo27udsKhMmTACAOrV6Hn/88SYdNykpCVdffTU++ugj5OfnA5CMtG+++QYfffRRnfElJSUuoTlhwgTU1NTgueeec213Op0+Y9b8ceGFF8LhcODuu++us622ttZ1D48dO1bn7yE3NxcAXPfe+3djNBpdFkn33487J598MtLS0vD00097jPnggw/wyy+/NDnLrj4uuOACzJ8/H08++aTfwozBfDbPPvtsfPvttx6FQIuKiupY1yZMmIDExETce++9PuOpGvt3TMILWoBIWNOnTx+8+uqrmDZtGvr16+eqBK1pGnbu3IlXX30VRqMxoPT07Oxs9OrVC7fccgv27duHxMRELF++3GdswqJFizBp0iSMGjUKl19+OY4ePYrHH38cAwcO9BmT5M59992Hzz77DMOHD8eVV16JAQMG4OjRo9i4cSM++eSTRrWiGDp0KF5//XXMmzcPp5xyCuLj4zF58uR699m3b58rRqq6uhqbN2/GM888g5SUFA/317///W9MnDgRI0aMwOzZs11p8Far1aN2zr/+9S+sXr0ao0aNwrXXXouoqCg888wzqKqqwgMPPOAaN2DAAIwZMwZDhw5FcnIyNmzYgLfeegtz5szxuB4AuP766zFhwgSYTCZcfPHFQd8X73t0/vnnY/HixThy5IgrDf63334D0DSr0w033IDFixfjvvvuw7Jly/DXv/4V7777Lv74xz9i5syZGDp0KMrLy7Flyxa89dZb2LVrF1JSUjB16lQMGzYMN998MwoKCpCdnY13333X9RkIZE6jR4/G1VdfjUWLFiE/Px9nnnkmoqOjsX37drz55pt49NFHccEFF+Dll1/Gk08+iXPPPRe9evVCWVkZnnvuOSQmJrqE7BVXXIGjR4/i9NNPR7du3VBYWIjHH38cubm56N+/v8/zR0dH4/7778esWbMwevRoTJs2zZUG36NHD9x0002Nvq8N4f0Z9Eegn81bb70Vr7zyCs466yzccMMNrjT4rKwsjzi1xMREPPXUU7jssstw0kkn4eKLL0Zqaip2796N9957DyNHjsQTTzzRHJdMWpLWSj8jJBgKCgq0a665Ruvdu7dmsVi02NhYLTs7W/vLX/6i5efne4ydMWOGFhcX5/M4P//8szZ+/HgtPj5eS0lJ0a688kpt8+bNdVKSNU1SmPv376/FxMRoAwYM0FasWKHNmDGjwTR4TdO0Q4cOadddd52WmZmpRUdHa126dNHGjRunPfvss64xvtJ5NU1Px3afj91u1y655BItKSnJZyq+N95p8EajUUtLS9OmTZumFRQU1Bn/ySefaCNHjtRiY2O1xMREbfLkydrPP/9cZ9zGjRu1CRMmaPHx8VqHDh20sWPHal9//bXHmH/961/asGHDtKSkJNfv6Z577tGqq6tdY2pra7W5c+dqqampmsFg8Egj9r6fKs24qKjI4zwvvfSSR+qypmlaeXm5dt1112nJyclafHy8NnXqVG3btm0aAO2+++6r956p+/7vf//b5/aZM2dqJpPJdf/Kysq0O+64Q+vdu7dmNpu1lJQU7Q9/+IP24IMPelxrUVGRdskll2gJCQma1WrVZs6cqX311VcaAG3ZsmWucfV9bjVN05599llt6NChWmxsrJaQkKANHjxYu/XWW7X9+/drmia/m2nTpmndu3fXYmJitLS0NO2Pf/yjtmHDBtcx3nrrLe3MM8/U0tLSNLPZrHXv3l27+uqrtQMHDrjGeKfBK15//XXtxBNP1GJiYrTk5GTt0ksv1fbu3esxxt81BJoq7p4G7w9/fzeBfDY1TdN+/PFHbfTo0ZrFYtG6du2q3X333doLL7xQ57OkzjVhwgTNarVqFotF69WrlzZz5kyPe8o0+LaLQdOaIdKSEELChPz8fJx44on4z3/+UyfdubVYuXIlzj33XHz55ZcYOXJka0+HkIiEMUCEkHaDr5YmixcvhtFoxGmnndYKM6o7J4fDgccffxyJiYk46aSTWmVOhBDGABFC2hEPPPAAfvjhB4wdOxZRUVGuVO+rrrrKI0utJZk7dy6OHz+OESNGoKqqCitWrMDXX3+Ne++9N6CsLkJI80AXGCGk3bB69WosXLgQP//8M+x2O7p3747LLrsMf//731utdsurr76Khx56CAUFBaisrETv3r1xzTXXeASFE0JaHgogQgghhEQcjAEihBBCSMRBAUQIIYSQiINB0D5wOp3Yv38/EhISmqVkPyGEEEJCj6ZpKCsrQ0ZGRp2+et5QAPlg//79rZYxQgghhJCmsWfPngY7BFAA+SAhIQGA3EDVQ4YQQggh4U1paSkyMzNdz/H6oADygXJ7JSYmUgARQgghbYxAwlcYBE0IIYSQiIMCiBBCCCERBwUQIYQQQiIOCiBCCCGERBwUQIQQQgiJOCiACCGEEBJxUAARQgghJOKgACKEEEJIxEEBRAghhJCIg5WgCSGEENJiOJ3Ajz8CR44AnToBQ4YADfQtbRYogAghhBDSIqxbBzz0EPDrr0BVFRATA2RnAzffDOTltexc6AIjhBBCSLOzbh0wdy6Qnw8kJgKZmfK6ebOsX7euZedDAUQIIYSQZsXpFMtPSQnQowcQFweYTPKalQXYbMDDD8u4loICiBBCCCHNyo8/itsrLQ3wbtRuMACpqcAvv8i4loICiBBCCCHNypEjEvNjsfjebrEA1dUyrqWgACKEEEJIs9KpkwQ8V1b63l5ZCZjNMq6loAAihBBCSLMyZIhkexUVAZrmuU3TZH3//jKupaAAIoQQQkizYjRKqrvVChQWAuXlgMMhr4WFsn7evJatB0QBRAghhJBmJy8PePxxICcHKC0F9u6V19xcWd/SdYBYCJEQQgghLUJeHjByJCtBE0IIIaQNEYo2FkajWH1aGwogQgghhDRIOLWxCAWMASKEEEJIvYRbG4tQQAFECCGEEL+EYxuLUEABRAghhBC/hGMbi1BAAUQIIYQQv4RjG4tQQAFECCGEEL+EYxuLUEABRAghhBC/hGMbi1BAAUQIIYQQv4RjG4tQ0MamSwghhJCWJtzaWIQCFkIkhBBCSIOEUxuLUEABRAghhJCACJc2FqGgjeo2QgghhJDGQwFECCGEkIiDAogQQgghEQcFECGEEEIiDgogQgghhEQcFECEEEIIiTgogAghhBAScVAAEUIIISTioAAihBBCSMRBAUQIIYSQiIOtMAghhBDiwulsP/2+6oMCiBBCCCEAgHXrgIceAn79FaiqAmJigOxs4Oab22bH9/poh5qOEEIIIcGybh0wdy6Qnw8kJgKZmfK6ebOsX7eutWcYWiiACCGEkAjH6RTLT0kJ0KMHEBcHmEzympUF2GzAww/LuPYCBRAhhBAS4fz4o7i90tIAg8Fzm8EApKYCv/wi4+rD6RQL0po18hrOgokxQIQQQkiEc+SIxPxYLL63WyxAcbGM80dbix+iBYgQQgiJcDp1EsFSWamv0zSgrAw4dgw4ehSIjpZxvmiL8UOtKoDWrl2LyZMnIyMjAwaDAStXrvTYvmDBAmRnZyMuLg4dO3bE+PHj8d133zV43CVLlqBHjx6wWCwYPnw4vv/++2a6AkIIIaTtM2SIWGuKikT4lJQAW7cCP/8MbNsmS2mpxAJ501bjh1pVAJWXlyMnJwdLlizxub1v37544oknsGXLFnz55Zfo0aMHzjzzTBQVFfk95uuvv4558+Zh/vz52LhxI3JycjBhwgQcPny4uS6DEEIIaXFCGW9jNIqrymrVBY/dLvE/mibWHwC44Ya61pxQxQ+1NAZN07TWngQAGAwGvP3225g6darfMaWlpbBarfjkk08wbtw4n2OGDx+OU045BU888QQAwOl0IjMzE3PnzsXtt98e0FzUeWw2GxITE4O+FkIIIaQ5aa54my++AC6+WOJ9TCYRRnFxukursBDIzQWWL9eLI65ZA1xxhYwxmeoe0+EA9u4FnnsO8PPoDhnBPL/bTAxQdXU1nn32WVitVuTk5Pgd88MPP2D8+PGudUajEePHj8c333zTUlMlhBBCmo3mjLexWuVY/fqJoBo4EBg0SNb7s+b4ih9yp7ISMJv9xw+1FmEvgFatWoX4+HhYLBY88sgjWL16NVJSUnyOLS4uhsPhQOfOnT3Wd+7cGQcPHvR7jqqqKpSWlnoshBBCSLjR3PE2R44A1dVAcjLQsSMQH++53WKR7e7ZYN7xQ+5omqzv31/GhRNhL4DGjh2L/Px8fP311zjrrLNw4YUXhjyeZ9GiRbBara4lMzMzpMcnhBBCQkFzx9s0xprjHj9UWAiUl4vbq7xc3lutwLx54ddPLMymU5e4uDj07t0bp556Kl544QVERUXhhRde8Dk2JSUFJpMJhw4d8lh/6NAhdOnSxe857rjjDthsNteyZ8+ekF4DIYQQEgoCqdfjbaEJhsZac/LygMcfB3JyJFts7155zc2V9eFYB6jNFUJ0Op2oqqryuc1sNmPo0KFYs2aNK5ja6XRizZo1mDNnjt9jxsTEICYmpjmmSwghhIQMdwtNXFzd7U2Nt1HWnLlzxXqTmiqiqrJSxE991py8PGDkyLbTSb5VBZDdbkdBQYHr/c6dO5Gfn4/k5GR06tQJ99xzD6ZMmYL09HQUFxdjyZIl2LdvH/70pz+59hk3bhzOPfdcl8CZN28eZsyYgZNPPhnDhg3D4sWLUV5ejlmzZrX49RFCCCGhRFloNm8GOnTwdIMpC01ubtPibZQ1R2WZFReLqMrNFfFTnzXHaJRxbYFWFUAbNmzA2LFjXe/nzZsHAJgxYwaefvpp/Prrr3j55ZdRXFyMTp064ZRTTsG6deswcOBA1z47duxAcXGx6/1FF12EoqIi3HnnnTh48CByc3Px4Ycf1gmMJoQQQtoaTbHQBENbs+Y0hrCpAxROsA4QIYSQcMa9DlB1tVho+vdv2ELT3gnm+d3mYoAIIYSQ9oDT2XgLSyRYaJobCiBCCCGkhQlFJee2FG8TjlArEkIIIS1IW+yc3h6hACKEEEJaiLbaOb09QgFECCGEtBBttXN6e4QCiBBCCGkhmruSMwkcCiBCCCGkhWirndPbIxRAhBBCSAsR7p3TnU4Jzl6zRl7bcywS0+AJIYSQFqI5Kjk3pZ6QO6FIzW9LsBK0D1gJmhBCSKjwJVC++io0lZxDJVpUan5JiQRoe4uycO3o7k0wz28KIB9QABFCCAkF9QmUQCs5+7PwhEq0OJ3AeeeJy6tHj7oNVgsLpeDi8uXhX2marTAIIYSQVsafQFEFDwMRKP4E1E03Sb2gw4fFjeZ0ijiJi5Mu8YWFsn3kyIZFSzCp+e2p8jQFECGEEBJivAseKmERjECpT0BddhlQXCwWmmPH5PgdOkhV6aSk4ERLIKn5xcXtLzU/zI1ZhBBCSNujqQUP66sYnZQEHDgAHD8u62JigKgowG4Htm+XfYKpJxSpqfkUQIQQQkiIaWrBQ28BZbeLpaesDNi7V8YYDGIBMhhECFksQG0tsGePiKNARUu4p+Y3F3SBEUIIISHG3aoSF1d3e0NWFSWgqquBnTuB8nIRI5omIsdsFitRTQ0QHS0iyGCQ9RUVwL59wPDhgYmW5kjNbwu0s8shhBBCWp+mWlU6dQIcDuC338TqExUlgspoFOFTWSlWH5NJhJLDoQukmhogNjY40ZKXJ0HZOTlAaalYmUpLJX6oraTABwstQIQQQkiI8WVVqa0V60xpqbi26hMogwaJkKmuFguSGmcyyatyffXrJ9YedwuRxQL8/e/Bi5a8vMBT89sDFECEEEJIM6CsKn/7m9TYqarSBUqvXvXvu3WruLbMZtnPbNaFiHtQdXS0iCW7XcRSUREwbBgwc2bj5mw0tq9U9/pop7qOEEIICQ9KS4GEBOCEE4DBg8U1tnevWIfWrfO9z5EjYu3p0weIjxfrkXJ1JSSIO8zhEPeYwyGiSFmWbr65/VptQgktQIQQQkiQBNJ/S6Wy22xA376elpu4uPprAakg6pgY3cJTWyuxQPHxwKFDIqIqK+XVbBbLTbCtNCIZCiBCCCHtnlA1DAUC77/VlArLKoh682YgK0usPgpNE+Ezbhxw552SHt/e43WaAwogQggh7ZpQdjkPpr1FUyosB5KafvPNwEknBTd/okOtSAghpN2iBEt+PpCYKK0iEhN1weIvBscX9VVnzsoSV9fDD8s4ILAKy9HRIoLWrJE5qn0Bz9R0mw3YsUMqQPfoATz6KF1dTYUCiBBCSLskWMHSEMG2t2ioFtCePRK4fOedwBVXABdfLF3Z3UVZXp40Ps3I0GsA7d8PPPJIcOKN1IUCiBBCSLskGMHidIoFxpclRhFsewvlxrJaxY1VXi4ZW+XlwLZtIsw0Tbb7s0ytWwfccINUg05Pl/R5q7VxFiziCQUQIYSQdkl9gsVuFxdUWRnw6adiebn4Yv+WGKBxTUN9VVi22WSb1SoWIn+Wqdra0FqwiCcMgiaEENIu8dWPy2YT15Oyxjid4oKKjwe6d68/qNk9M6tDB0+rkmpvkZtbt72Fd4Xl4mI5p9Vav2Vq+fLGZ5GRhqEFiBBCSLvEOwbHZtN7a5lMuqg4flwsQjU19VtY6nNpFRZ6Ng31dqkBIlLGjQNSUsRV1pArbffupnWUJ/VDCxAhhJB2iRIsc+aI8CktFcEQEyNix2jUW1PU1IhlSFll3C0s+fkyVtUQevRRCUL+9Vex5ngXIWwo7T7QTvHduzetozypHwogQggh7ZrERGD7drH0AGKdiY8XS8y+fbo1qKJCLEGq6KDForesUPFESszcdJOIJffCigDw4ovAPffIubp2FfeVu0vt0Ufl+J06SWBznz6exQvdXWnnnw+89lrwLjdFKIs/tkcogAghhLRL3IsWZmaK4DCZRBhUV0uQsdMp1p+oKBEVtbX6/kVFwNGj8nO3bp7xQTfcIPFB48bp53roIXF5HT8ulpnqajlvUpIImG3bJMA6MVHccEePAuvXi7stNdWzyOG8eTKnhooh+usoH8rij+0VakFCCCEhJ5C08uY+v3sGVUKCCIroaFmqqsS6U1srgqW8XPaJ+j+zgKaJ6DAaxUpTXwaWElrffy/HjY4Wa43dLpankhIZb7OJyyw6GujdG+jZU861cydQUCAuutxcz8BrX1lkvsa5E8rij+0ZWoAIIYSElHCwPnjXAIqPFytMaakELmuaLDExepd1TROrTXm5CA2nU0SKt4XFOz7ooYeAgwfFhVZbK4uKI3I6JbZI0+Rnk0lElskEdOkCdO4s8UknnAA89pgIG+/zeWeR1efO8hZ+ym0WFyfXX18D1kgjwi+fEEJIKAkX64N3DSCDQdxYTqcsShgYDGKRMRrl5x07xFJzwglAx44idLxRrjKbDVixAvjhBxFWVVWyXR3L6ZRxpaUiqkwm2RYdrR9Lzau4WLb5EyVGo55F5kskKYKtVh3J0AJECCEkJIST9cFXplVUlKeLS805MVFESE2NiJq77hK31yWX1M3AKinR6wjV1gLPPy/ixWCQcZom1iSDQa7R4ZBx6rwJCWKNcqe+pqjB0pQGrJEGLUCEEEJCQiDWh59/Bt58s/ljg3z14VKuqbg4ESQJCcCgQbIkJQHJyWKdSUkRK4v3/iUlEtNjt8u8rVYRMw6HvsTEyFi1j0q1dzjk58zMunM9flyOV1DQ9HvSmGrVkQoFECGEkJDQkPVBBR7fckv9LSdCga+ihcrqpLqw9+qlp7yr9UoceO9vt4vlp6ZGxkZHS52e+HgRVZom+5tMYu1S2WZKzHToIPdFCSLFsWPATz/Jvbvvvqbfk4YasBYVAf37+0+djyQogAghhISE+qwPynpSXV1/889Q4p1BdeyY7gbr00fmofAlDtz3LyqSYxgMIpr69pX9o6NFNBkMYuVR1aQtFj3jzGwWq1N5uVzv5s2SAn/woFjEamvFBReKexJMtepIx6Bp3hqRlJaWwmq1wmazITExsbWnQwghbQKnU6wXmzdLqrhyg2kasHWriKCkJGDwYH0flW6emyu9r5rjwexeELCwUIoRlpb6rqvjK7Xc6QSeew5YsADIyBCR4s7WrRI75HCIuFJB0NHRkhkWHS1utupq3ZpkMOhxQf36yX0J5T1xz8SrrhYR1r+/Xq26vRLM85tB0IQQQkKCsj54F+47elQEgmrv4E5LNPVUGVSKPn1EHPzyi1SCVrV+5s/3LQ6MRmD4cBE+VVW6JUm5vzIzxcKiaWLJ6dBBRNPOnbK9Xz+xAMXFSWZZWRmwa5eIo4ED6wZFh+KeBJM6H6lQABFCCAkZym2krA/FxeIWMpvrup0ULZ2ZlJcnAmXBAgk8djhECD3yiAgEXyLIZhOrUXGxiB+DQYROZqZck7quqCi9qKLJJJYwd+sOIC60lBRJuXc4fM8xFPfEW/gRTyiACCGEhBRv60NxMXDnnSKCfBFsZlJTe1ytWyetLEpKgPR0zxYXc+fWdYOp8Zom7iynU85nt0t7C6tVChqqPl/r14v4e+MN33WEAL23V0WFb1HIbK3mhwKIEEJIyHG3PjidTWvq6U6wVaa9xdKgQcHVKnKvbZSdLVYgVQcIEOuWwSDix2iUGkK//qpbiyoqpJq0t8iJipK5l5ZKReim3BPSOCiACCGENCv+YoMCaerpjntz07S0wCw33mIpPV1cT96iA/Ade5OfL4vFIqJHubvsdhE/tbXyumOHtLJQc0tNlfo+ZWViJerXTxdBSuCceKK41ppyT0jjYRaYD5gFRggh/mmsC6opmUkqwyw/39NyA/jOmvInlnbvliDm/v0lINkbh0NqFT33nMzv9tulyalqYxEXp8f9qPE7dkicz7Fjkh6v5qZS/ysrxTU2eHDdjDMgMrO1motgnt8UQD6gACKEEN80tdGpP/HUkKjKz5cigYmJnq0pFOXl4k5atkz29SeWysrEYhQf7ztAWB3n1lvFonP4sJ71ZTDodX769pXxO3eKNUjT9OrS7gKppEQyvsrLxcqTkKALHBUnVVQkmXLJyTKG2VqNh2nwhBBCQk6gLihfYgbwXDd2rP6QV6Lql19ETBiNQO/ekqU1erSMca8yrdxP0dF6Crl71pSvlhyapu8XGys/l5XJ/na73q+ruFiE0TvvyHX26SOVmu12OYfqHv/773Ksqio5XnW1zMdul+7uqlBiUpLEHf3+O3DjjcAZZ8j9+OorEWm+hCTFT8tAAUQIIaRBAm106nRKOrn7g11lQhUV1X3YAyKeDh6UbZWVcoyDB4EpU4B77gHmzBHR5HAAW7bIOE2TOcTESEq52SzbCwpkqawUAQToDUwrKmQ/p1PGbtsmoqeqSt47nXIt2dnAypWyv+rfpVxZZrPsU1Ymx46NleKIe/bo86mqkvfKClRVJZafM84QcRVsLBNpHugC8wFdYIQQ4kkgLqiDB0UQVFXpD/aiIomRAaT3VmqqHgeTmCjL9u0iKBwOERiqi/rx43Kud9+V/adMkfPExoqQUcJFPcVMJjl+TIxYgrp1kzls3y4WHnXsmho5NqBXZDYaZazFIiKookKsPyaTjHMXUQ6HWHzi48VSZbVKNWhlJXI65XwDB8r83eOTgOBimUhwBPP8btXbu3btWkyePBkZGRkwGAxYuXKla1tNTQ1uu+02DB48GHFxccjIyMD06dOxf//+eo+5YMECGAwGjyU7O7uZr4QQQto3DTU6tVhkjLIQxcXJA7yoSF6NRnEvmUyyLStLtm3aJILI4ZBjmEy6KImNFcGxYIFYl2JjZUxVlayvrfWcg9Eo8TuxsXpw8u+/yzh17NpaET+api8Wi8wnN1eytY4fFzGjRBKgu7IGDBBxZzKJ+ElK0qtBR0XJtSgrU1lZ3f5bvtxzCu8sNNK8tKoAKi8vR05ODpYsWVJnW0VFBTZu3Ih//vOf2LhxI1asWIFt27ZhypQpDR534MCBOHDggGv58ssvm2P6hBASMdTX6BSQIN6aGnmAqwe73S5CxWyWpbxcDyouLxfrT2WlCA3VUNQdlXn188/iHureXUSHO+4+jJgYER7FxSJInE4RRMqSUl2tu8EMBr1nV2WlZH7ZbPK+WzcZv2+f5/ENBrH6OBwisqLcgkiSksRiFB8vIsvplOPm5nq6tAIRktXVLVcVO5Jp1RigiRMnYuLEiT63Wa1WrF692mPdE088gWHDhmH37t3o7t1Qxo2oqCh06dIlpHMlhJBIZsgQiY3xV8xQtYhwTy2vrZVtRqP8XFUlVhmDQRazWbeW+HL3qHYSDoeICeVeMpn0isxVVZ6BzkajCKyyMl28HD+uixIVp1NdrZ8nOlrOoeJ2LBaJ2YmN9V2jJy1N3Hl794o1S50/KUl36Z1wgmSR5eZ6Xpu7kPTlSmQF6JajTXkYbTYbDAYDkrwbq3ixfft2ZGRk4IQTTsCll16K3bt3t8wECSGknaKKGVqtIgrKy0U0lJfrbp6UFBEkCvfUceV2MplEAERF6etUULI7miYiJSZGhIISIMrt5U8wKXdaTY28V9TUyGtcnKflBtA7t5eXi9WqslKEzN//DuTkiBVp7155VRade+/1fS927xaBtGgRcNJJdeephGRRkad1SV1zUZGkybMCdPPTZrLAKisrcdttt2HatGn1BjYNHz4cS5cuRb9+/XDgwAEsXLgQeXl52Lp1KxISEnzuU1VVhSq3v9rS0tKQz58QQto6vhqdms0iCm68UbK/3C1E8fHy87FjIkZUsLGyxqjaOU6niAf3GKDqatlmsUjFZE2TuBizWU8/B/Q4HpNJ1itR4Su9RwmjmBh9HiaTHuisRJcSOjNnyuKvPpG/e1FfEcNQVcUmTSdsssAMBgPefvttTJ06tc62mpoanH/++di7dy8+//zzoDKzSkpKkJWVhYcffhizZ8/2OWbBggVYuHBhnfXMAiOEkLr4K1qo0rttNv3BvmePFAIE9IKH7phMuhhSGI0inOLjpcmoqpg8a5aIBndrkXeMTiBPtNhYET+q9o/FIvvV1EgxwrS0wFPRW6MqNvFPuyqEWFNTgwsvvBCFhYX49NNPgxYkSUlJ6Nu3LwoKCvyOueOOOzBv3jzX+9LSUmRmZjZ6zoQQ0ho0tUt6U88xciRw/fXACy9IALHBICJDxdh4ix9AFzOqOWh1tYyrrpag4rvvlnicoiJ5VVYkZe0xGj0zurxRc3M/t2pNkZoqWWsVFXI8iwUYNizwqtbq+L4qSjdEXp5eCbo5f1/EP2EtgJT42b59Oz777DN0akRUmN1ux44dO3DZZZf5HRMTE4OYmJimTJUQQlqVpraoaOo5AM9tgBQIHD9e1qtgZYdDz75yt/wYDCJA+vUT0aRiZFRRxbIyWdehg1iFHA5xO1VX664tbwHkLo7c0TQRQSUlErekUuf//ndxebWUCGmseCKhoVUFkN1u97DM7Ny5E/n5+UhOTkZ6ejouuOACbNy4EatWrYLD4cDBgwcBAMnJyTCbzQCAcePG4dxzz8WcOXMAALfccgsmT56MrKws7N+/H/Pnz4fJZMK0adNa/gIJIaQFaInKwl98AVx1lbi3UlLkPOocs2bJmNpaz/Pv3w+89pqefeVw6DE+3sLEZBJLjMEgbqjqamD9evm5WzdxERUVyZjduyUlPidHYodUttnPP3vWBvJlcVI4HHK/7HbglFMkqJmup8iiVQXQhg0bMHbsWNd75YaaMWMGFixYgHf/r/xnrpdE/uyzzzBmzBgAwI4dO1BcXOzatnfvXkybNg1HjhxBamoqRo0ahW+//RapqhY7IYS0IwJtUTFyZOMtG198IVWgVSHD0lLg0CGptdO9uwgVQNxH7inpiYniClMCyF30uIsTVZNHVVBW2VC1teKmiosT4aWqPtfUSLuLI0dkDgaDXKc6VqBxQD17yjV16iT3h0QWrSqAxowZg/pisAOJz96louv+j2XLljV1WoQQ0mYIprJwY9wt69aJ5UdlOan6O6rpZ7duurBR6eB79sjPmibva2vFknPsWF1xomoCqeKEUVFy7PJy+dlsFhH1+++eosnpFFFUXi7va2okric5Wcb6w2DQawuZzTL/X39t/P0hbReGWxFCSBumOSsLu1uXoqJE/CgBERMjIuLAAV3UlJSIKCor04OaVXjlsWN6DZ7YWPnZZNKFj2pEGh8vYqa2Vv/599/lXN4CTxVXVMUDe/YUi1RGhu/rUWIrJkbOq1pksPJyZEIBRAghbZiGWlQ0pbKwsi6p9hbKAqOsOkaj3pkdkGao1dUilFSgs6r4bDTqtX1UHSA1RmVzde0qMT5FRXpj08JC3YXmC7VvZqbsc+yYVGRWtX4UyrrUoYN+XtW7i5WXI5OwzgIjhBBSPw21qCgqEteOd2XhQFLmlXUpNVWObbPVzdxSrSlUd3b3TK+YGN2SEx8vcUOq03pNjQiljh1lbHS0xBZpmlhyOneW8crFVV9EhMEg4mvPHs9CiKrIYXS0nO//cmdcndzj4iSg2tf9Ie0fCiBCCGnDNKaycKAp88q6VFzs2YZCocSGu3tKvapCg6py8+HD8moyieA480wJPE5NlS7r/+//6fWD9u+XY9pselC0r+sG9DYax4/Ldatg6qoq2VdZempq9HYYqiP97t2svBzJ8FdOCCFtHNWiwl/fKndRo1Lm8/MlS6tbNxEI334LXHGFZHwphgwRgbJjh1ht6sM9u0u5pVQhxMpKXYjEx0tMz2uv6a0jvvlGGofu2yfB3JmZQJcuMt6f5cc7jV4JH2V9UjFRKmtMueWU0FFZbKEoEUDaJrQAEUJIOyCQysLeKfM2m4iRigoRCkeOSLr7a69JHE1RkWRkuYsa92O5411x2WwWK4yK9amuFhGkgqbdz7V4cd00fmVB8oe3MKqq0s/hvgBSUdps1gWVKrSYnMz090iGAogQQtoJDVUWdk+Zt9mA7dt1N5WyjhQVAX/8o4gDh0Pem811g6zd6+0ooRETo4ue6mp9u9pXdV1X5ysulsrLTqdYfNzjl/bs0QOt6yto6H7tyiqkRJcqumg2S6yROzExTH+PdOgCI4SQCEEFNcfE6ALDvQO7CmA+flxS2ZOT9XWAiJfYWNnH3cKiMJkkyFk1OPVGtaBwOORYqqhicbGeLg/odYCUqGkIo1FqAKnijyrFXrnDoqPr7sP0d0IBRAghEYIKaj52TNxeZrNn5WaVxaUsOSq4Wbm3VBCzd8CzquWjsq7chZGyLKltSgQpK01cnBx3/359n5ISOb97ir1Czcf7HIBeqyg6Wn52OuXn+Pi694Lp74QCiBBCIgSVMl9cXDemx+HQe3WZzfLzvn16ELNyLal4IfVeCRH3IGRlMVLix91VpsYdP643NHU4gF27gC1b5Jz79vnv7q7Oq0SXEjtKsKnq00q8+QqkVuUB+vdn+nskQwFECCHNhNMp2VZr1shrILEszYnRCNx0k1h4qqv1eB2HQ+/grooD1tSISPEuKFhb6xnfk5AA9O4tGWWqMrOmiTA54QRdgLjXD1LnVCJMWW1KSiTjrKam4bR0Jabi4kTUxcfr56+tFVdYaqpeTFG16Sgvl/dMfycMgiaEkGYg0Fo7LT2nRx7RrSeVlSJmlGsK8Ax2Vhac2FgRQ+6VoFUgc2ysBBh36SKutQMHJH6oVy/ZpgKolWVGYTLJ9qoqEVHduokVyGaT7VFRnuN9YTBILI/VKllrdruIp6goie3JzQVuvFGu+ddf9X5mubkifpj+HtkYtEA6jkYYpaWlsFqtsNlsSExMbO3pEELaGKrWTkmJZFx5FyZsjdoz3nOqqgK2bRMBBOgiyFt0qGBn5e5S/cB69QKOHhWLiqpAHRMjtYiOHpVu7aWleiCysjYpLBY5X1SUpKknJcl+P/8sx8rMlEBtVbzQG6NRrE5paXINvgpAqvscSNVr0j4I5vnNjwAhhIQQ71o7KiMpLg7IyhILx8MPN94d1hi3mvucUlJEMJjNEgPj7qLy1W9LbQNE4MTG6kKoc2dxN6kWFoBsu/BCiRWqqtLT3pUrTZ1DtaNQ4gfQXWFqjBJj7llqBoPMISpKjnH11Q0XgFTlAcaNk1eKHwLQBUYIISHFvdaOt6AwGMRS8csv9def8WexaKxb7ccfgY0bxTV1+LDuAlOixGKRc5rNIlz8iaraWhEeBoO4xPbt04ONu3YVofL998APP8jP8fEiUlRLiqQkWXfggByvRw9xfyni42VOdrseBK1EkwqeVkHaqrVF167AihW08JDgoQAihJAQomrtqFYM3lgsEovir/6MP5Ezfjzw/PN13WqbN4trqz632uefS7NQQI/rMRjEfVVbKxYVp9N/R3mFyghLTJT5qzpC1dVieSkpEfGigqSTksSVpSw2Kh29vFzG+nJvKTHm3gTVPetL3VdVSyg5ueECkIT4ggKIEEJCiKq1U1kpbi9v6qs/4y92aPNm4LPPREhkZ+uWJVX4r7BQ3GojR+qWEmUR6dgRWLrUt9hQKepVVXrhQLW+PiuQyrSKjpZXh0PvxB4VpbunSkvltU8fT0tPSopsUxak5GQ9dqdzZ+lJ9tZbIhTd23Ao91tlpbzv1EksaoQ0BgogQggJIarWzubNenCwQtWfyc2tW3/GO3bIXeQ4nRLf4sut4+1WKyvztCDZbJKd5QvvrK7qas+Gob5EkEqTdzhEVHmPcRdaqiXGnj0SlGwwyFx275bjVFQAv/0mQqpTJ2DoUMnOAoDVq/WCjO7HVq616GgZzzo+pLFQABFCSAgxGiUmZ+5cscz4yk7yVX+mvtgh1ROrqkpcTO7WFEB3q33+uVh7lAWpslJSywNBdWxXwdDefb7Ue/cGpd5j3cepn2trRZSVlkrc0I4dsq1XL7EEHTsmc4+NlZR1QLeCZWWJWFKiymAQC5Fyr7GOD2kKFECEEBJi8vIkJkdZYgKpP1Nf7FB0tDzoHQ7fHdIrK2XMO+/oFiRArCuBoo4P6KnrSszUVywlkEIqNTXi7qqoEMtPv3565ldqqgihwkKp1wN4WsFiY8WCpGr8FBdLNldr1lMi7QMKIEIIaQby8iQmJ9DspPpih+LjRRjZ7SIg3FFutR49pJ+WsiCVlYnFJVCiovTYH/cGqO4CR/XaCua4KoNr8mTgk09kft7Xp9x4+fny3t0KZrXKYrfLNVVWAnfeCZx0UuBzIMQXjTYeVldXY9u2baj19XWEEEJIUPVnVOxQUZHv3lUWi8QUFRf7butwzjkSb6MsSOpfs6/aPr5QVh+HQ28i6k10dGAWH3dU09O+fT2zuLyxWPQGqL7GxMeLMDIa/cc0ERIMQQugiooKzJ49Gx06dMDAgQOxe/duAMDcuXNx3333hXyChBASCajYIavVd++qzp2Be+4RIVVaKm6hw4elDs711wOnnaZbkADdohOoAFLZXYAIEO+u7oAuUBpDZqa4AY8eFQFTVuYppiorZf7u1+ANO7iTUBK0ALrjjjuwefNmfP7557C4yfTx48fj9ddfD+nkCCEkklCxQ/4qG8+ZI0X/br0VyMiQffbtAx54AFi4UNxIyoIUH69XoW4I5aZS+DPsuzc0DQaTSQ+E3rZNlp9/BrZulXgf9+y43Fz/VjB2cCehJOgYoJUrV+L111/HqaeeCoPb14OBAwdihwrvJ4QQ0igaih366ivgscfq1gr68UcRGiaTnn2WnCzCIxBUOrvBoPcHU60nvMWICpAORAypQOb779e7xCu3mN0uYshqFQvXzTfLPsFm0BHSGIIWQEVFRUhLS6uzvry83EMQEUIIaRz+Khv7qhWkKiUnJopISEsTC86WLZI11VBHdcB/Grs/VC+wQNxhRqPe4X3wYN19pyo919TI8R59VM/qCjaDjpDGELQAOvnkk/Hee+9h7ty5AOASPc8//zxGjBgR2tkRQghx4V0rqKRExERFhZ4if/CgZz0fb3xZdBSqGKLC3zgVMB0V5d9dpjAaZUxFhQihpCQ9q6umRrbV1Mg6RbAZdIQ0hqAF0L333ouJEyfi559/Rm1tLR599FH8/PPP+Prrr/HFF180xxwJIYTAs1ZQSQmwfbsICJNJRIR33R5fAsafAPJOr28Is1mP7fEnlAwGPQZJ06QoY/fuYj2Kj9fT7ffurdsbjf29SHMTtJ4eNWoU8vPzUVtbi8GDB+Pjjz9GWloavvnmGwwdOrQ55kgIIRGJ0ym1cdaskdeOHSVL6vhxsfzU1nq2iwgkCsGXZUh1WPd3DCVk3C0wVVV65pa/Fh2qcrOyAtlsYsFyD4BmZhdpLRpVCLFXr1547rnnQj0XQggh/4e/rvCpqdJOoqJChIVKlw8Gb4uNwyGiyp91SAU8K/EUFSU/KwHWsaMEOh86pAdiGwxilVJuModDjx1SAdDbt0tbj1NPZWYXaXmCFkDvv/8+TCYTJkyY4LH+o48+gtPpxMSJE0M2OUIIiSRUF/fPPweeekqsI507e3aFVy6lqir/HdsbQ0NZXepcMTHSo6u0VESNamVht4sbKypK7yxvNMo69+7yqrO7qihtMkkPMMb3kJYm6I/c7bffDoePrxuapuH2228PyaQIISTSWLcOOO884KKLgL//Hfj9d7HuqA7ocXEiPBwOvUN8KHF3fcXE+I4JUn28qqqAYcPEcqOyueLjpVJ1dbXMraZGd8+p46kU+KoquY6EBL3VBSEtTdAWoO3bt2PAgAF11mdnZ6OgoCAkkyKEkEhi3Tq9A3p8vIgEs1l3E/XpI9lTBoOIjO3b9X3ry+oKBvdjVFUB3bpJCroKslZWnMOHJQvNV82erl1lbuXlMv/kZAlwVtfTp48IoZoaEUOxsb4DoAlpCYIWQFarFb///jt6qHbD/0dBQQHivDvcEUIIqRfv2j4lJbI+OlqWykoJeAb0ytA1Nfr+oRA/vjh8WCxOx46JoHE6ZTnhBGDRIt81e6qrxR2mBE5lpcyvQwe5NtUBXqGEEgOgSWsQtAA655xzcOONN+Ltt99Gr169AIj4ufnmmzFlypSQT5AQQtoDKr7Hu66Nd22f6Gi9jo/KzrLbZUyg1ZdDNd+jR4FBg0SoqE7sjz3m2YndV82eQYMky6uoCLj3XmDnzrpuLvf2FwyAJq1B0ALogQcewFlnnYXs7Gx069YNALB3717k5eXhwQcfDPkECSGkreMvo+vmm8Vq4t4BXfXwsttFIBmNehxQbKxYgFqC6GjJNLPbZU7FxcCJJ/quzeOrZo96b7GwtQUJTwyaFvz3CU3TsHr1amzevBmxsbEYMmQITjvttOaYX6tQWloKq9UKm82GxMTE1p4OIaQN4x7f4967SwmA66+XZqaJiSJ8AKmX89tvenp7VZUeQNxQ5eVQER8vwqtHDzm/1Srursa0onAXgNXVYtXq35+tLUjoCeb53SgB1N6hACKEhAKnUzK78vP13l0KTROrSE6O/Lx+vV4dOSFBxM+ePRKD496otCX+YxuNerp9RoZYfpoqVvy5AAkJJcE8vwNygT322GO46qqrYLFY8Nhjj9U79vrrrw98poQQ0o7xju9xx2AQl9DGjeISO3jQU9zExorVR6Fig9x7dTUHqpCh0SixPo8/Lu4sJVYaK2TY2oKEGwFZgHr27IkNGzagU6dO6Nmzp/+DGQz4/fffQzrB1oAWIEJIKFizBrjiCiAzU4r+qeyouDgJLD56VKxAqspyQzS3BchgEBedqtr85JPA5Zfr2+uLZaIri4QDIbcA7dy50+fPhBASyTRkDenUScTEli168T+VyaWCm4MRNKESP+6Vmd3XqeKHCQkSp5OVpW/3F8u0ebOsDyY+iO4wEg4ElQVWU1OD7OxsrFq1Cv3792+uORFCSKsSyAPanzXkppskYPjIEUn/ttkkk0qltodD1KXZrFdsNhgkALt3bwmwjoqSdWVlen0e71pFyp0XFyc1fgoLgYcflnT4hoQMrUgkXAhKAEVHR6OysrK55kIIIa1OIA9of9aQ774DpkwRAaT6YFVVBe7iaik0TRcxmgZ06SJWH/W+sNCzPk8gsUy//CLj6ovzCaUViZCmErTR8brrrsP999+P2pbKxSSEkBZCPaDz88Uqkpkpr+oBvW5dXWtIXJwEDdfUiNVEFQ1MThaLSm2t7vIKNaqpaExMXWFSH9XVMl7VGQLEPVdeLuLHuz6PEnKqVpE3Foscs76WFv7um+pxZrOJFSmchCJp3wRdCHH9+vVYs2YNPv74YwwePLhO+4sVK1aEbHKEENJSBOrmiYuraw3RNElZV0HOx4+L26u509eNRpmPKo6ourD7wmDQ3XDqvdEo43fvlmMkJooFxzvlvVMnEVmVlXqtIncqKxtuaREqKxIhoSJoAZSUlITzzz+/OeZCCCGtRqAP6PXrdWuIponQOXRI6vW4i5z9+z0Dnpuje7vRKK4rm03W1Sey3EWYpolY0jRd4MXGArfeCsycWddaNWSIuAE3b5ax3vWMAmlpEYgVqbiYjVFJyxG0AHrppZeaYx6EENKqHDkilowOHUTMREXphQkB/QENiDWkqEgWu10e7AqjUURBZaVujWkOt05UlBxXZW85nXWtP+61e9znoCxB0dHS3DQxUSpPv/iiCBn3uj/qODff3LSWFqGwIhESSgL2SjudTtx///0YOXIkTjnlFNx+++04fvx4c86NEEJajMJCEUG//AJs2wb8/LM09FTd2dUDeuhQsZYUFEisj3c4pMqsaq5sL5NJhEt8vHReV0RFSbf1E04QgaHmoAKeVWVnQK823bevvP/pJ6lJ9N13Urn6vPMk3smdvDwJUs7JEXeZ6kyfmxtY8LKyIhUV1b03yorUvz8bo5KWI+BWGHfffTcWLFiA8ePHIzY2Fh999BGmTZuGF198sbnn2OKwECIhkcW6dcCcOSJ8HA6xVBiNEtgbFSUp4jYb0LGjbNu40bMisz/B0xyuL9UTrFs34KWXxPoyd66k3PfpI+csKQG2b5eYJKdTBFHnziLaVH2f9HS951htrRw3kN5fTanho4LMbTbfViRmgZGm0iy9wPr06YNbbrkFV199NQDgk08+waRJk3D8+HEY21kFKwogQiIHpxMYPRr44QcRPzU1st5kErFQW6tbUGpq9GKGyjUWaAVnIHRWIZNJBMzQoeKaAuoKi6IisWo5nZKRZjaLaOnWTdLeAbFwlZXJeNVodcAAsS6pVPjly0ObwcbGqKQ5aRYBFBMTg4KCAmRmZrrWWSwWFBQUoFu3bk2bcZhBAURI5PDii8C114oAUEJAVW0GRGyoWBuDQc/yUiIoUFHjnnXVFGJigH799DgkZTkB6gqL7GypS5SVJdarhQvFepOVJSnvP/0kFi6jUSwx8fHAoEEy1/JycXEtWxb6rCxWgibNRTDP74A/crW1tbB4he9HR0ejRn1dagRr167F5MmTkZGRAYPBgJUrV7q21dTU4LbbbnOl2mdkZGD69OnYv39/g8ddsmQJevToAYvFguHDh+P7779v9BwJIe0Pp1Nq/axeDTz2mFg+YmP1+Jq4OFmionSBo/pkKSEUbJyPpuk9thqLySTWkuTkuvVzRo4EVqwQwfLcc/K6YoX08ho3Thqb3nKLCKbCQrH8qMrUlZVyrZmZnkHfDdX2aSyqMeq4cXUDrglpKQLOAtM0DTNnzkRMTIxrXWVlJf7yl7941AIKpg5QeXk5cnJycPnll+O8887z2FZRUYGNGzfin//8J3JycnDs2DHccMMNmDJlCjZs2OD3mK+//jrmzZuHp59+GsOHD8fixYsxYcIEbNu2DWlpaQHPjRASfgRqOahvnLsLpqwMOHxYREBtrVhNABEBKrOqokJvD6GOYTTWDX4OFE0TcZGRAQTbOzoqSu/XpebpXT+nPmuNCmR+6CERgMrtFR8v4icpSR/LrCzS3gnYBTZr1qyADtjYNHmDwYC3334bU6dO9Ttm/fr1GDZsGAoLC9G9e3efY4YPH45TTjkFTzzxBADJXsvMzMTcuXNx++23BzQXusAICT8C7SFV3zjAsxVDZaWMczj0YOHoaP1Y1dUigOLiZEx0tAiY6mrPIOhg6dRJLDYbN0o2lT8MBrFMVVTo1ql+/cSdpXA45BjPPScWlUBQFjDv4GmFezuMUMcAEdKchLwbPBAe9X9sNhsMBgOS3L+muFFdXY0ffvgBd9xxh2ud0WjE+PHj8c033/g9blVVFarcCnmUqrKqhJCwINAeUvWNmzNH6t24V3rWNN2qUlkpsT0q4Nnp1AOiU1OlNlBZmbxv6Gtjfe6xqCipNfTpp2J5qa96s8Uic1CxR+rVncZYaoxGcYndd5/cr927G1fbh5C2TJv5aFdWVuK2227DtGnT/Kq64uJiOBwOdO7c2WN9586dcfDgQb/HXrRoEaxWq2txD/QmhLQugfaQqq31Pa5DBxEHe/dKpldqqh7nEh8v45RbChDBUVWld0ZPThZhVFnpWVfHm5gYvdaOEj9Go6xTMUPq56IiOabVKkHHqnmqwmDQ3XEJCbLU1Mi1xMfr45paP6eptX0IacsEXQm6NaipqcGFF14ITdPw1FNPhfz4d9xxB+bNm+d6X1paShFESJgQaIuK5cvrjispkR5dFRXisnI4pMBhWpqIjuhoSQvfvl22G42yrUMHEQKpqcAVVwD/+IcIELNZr63jjtEoAqq2VrKnFL6sQAaDjDOZZOnYUZayMokJqq4WwWO1yjxMJhEm1dUy3/Ly0Fpq8vLEHcesLBJphL0AUuKnsLAQn376ab0+vZSUFJhMJhw6dMhj/aFDh9BFFb7wQUxMjEdwNyEkfAi0h9Tu3XrMT1mZiJ/9+/UUdkVFBbBrlx7orP70VRzQgQNyzBNPBO65R8RITIwID/eWF+44ndISw2z2dGkpi5HC4dC3GY0Sf3P4sAQgW60SGF1UBHTtql+D2QyceqrE93zyiYi84mJZ76txaWNQWVmERBJhLYCU+Nm+fTs+++wzdGrAyW02mzF06FCsWbPGFUztdDqxZs0azJkzpwVmTAgJNYH2kOreXcTF1q2yrrraU/x4W200Taw57oUPrVapmFxWpjcYfeEFvQdYfQUNHQ45r3J1NVTvR8Ue2e1iwYqJ0V1vZrNUnz7nHGDMGN0ic+21tNQQEipaVQDZ7XYUFBS43u/cuRP5+flITk5Geno6LrjgAmzcuBGrVq2Cw+FwxfEkJyfD/H8O8nHjxuHcc891CZx58+ZhxowZOPnkkzFs2DAsXrwY5eXlAWexEULCi0A6kefkiGBRDU3d09kDbUXhcEh8TUaG7LN9uwiOHTs84368xY9arwQMIBYk1SrR3/mVS8tk0l10Fouss1rFSrV0qVR7dk+/97bUsKggIY0jIAH07rvvBnzAKVOmBDx2w4YNGDt2rOu9isOZMWMGFixY4Dpvrtdf/GeffYYxY8YAAHbs2IFi9fUMwEUXXYSioiLceeedOHjwIHJzc/Hhhx/WCYwmhLQNGupEbjLJ+htuEAGiafprsEUHDxyQfY4dk+MfOKBv85fV5Z5J5n5+QA9+9ifEVNaZOk5trbjcUlJk+e034I47pFijr4KBgZYGIITUJaA6QIH2+jIYDHA0tc57GMA6QISEH+ph/8sv4jYyGqVGzp49ururtYiJEQGkAqBVvSD3zvBqfvWlyFssImAMBrmusjKxTHXtKgLIXdj4S/lnY1ESyYS8FYbT6QxoaQ/ihxASnuTlATfdJC4qo1FcXtu3y0O/NcWPsuCoWCKjUeZoMnm20VDU95VTpej/9puIH+Uis1j0mkfr1gVeGqA170s4owpBrlmjV8QmkUdYB0ETQohi3Tpxc5WUyIM+gLaALYLVCvTqJQJo2zYRQEqEBFMt2mCQWKDdu8UVphqzGo0Sm9Sli7j6Hn5Yrj+Q0gCqPQbRoduQKBolgMrLy/HFF19g9+7dqPb6C7/++utDMjFCSOsRboG1tbXAP/8pbqHkZBEJ4fCt3WCQ7DOjUeoGpaXJXLdvl2yy0lK9Aaq360v1FlOp8tHRYvVxL4JYXS3iJz7eU9isXx9YaYDmaGTalgm0ojiJDIIWQJs2bcLZZ5+NiooKlJeXIzk5GcXFxejQoQPS0tIogAhp44TbN+R164Drr5eHlKYBR4+2/Bz8ERWli5yaGj32x24XMRMfLz9rmogd7ygBJYAcDk9rkdEoAs9Xh3aV8xFIaQA2MtXxdhuqexoXJ9mFyro2ciSz6CKFoH/NN910EyZPnoxjx44hNjYW3377LQoLCzF06FA8+OCDzTFHQkgLob4h5+dL36zMTHl1jz9pTrxjM774Apg1C9iypeH+Wy2JwSAPzfR04PzzJRjbbBY3VZ8+IhhVNtcttwCnnSZixGgUkRQXJ9tUNejYWL2NBiACxmKRY/nq0H7KKXKOoqK696Wp7THaK4FWFP/xx9aZH2l5ghZA+fn5uPnmm2E0GmEymVBVVYXMzEw88MAD+Nvf/tYccySEtACtHVi7bh1w3nnAxRdL+4mLLpJlz57mOV9T6dVLBM0334ilrGdP/Z7FxwN9+8q9+u03qeB8770iZpxOcZMdPSpWn6goEUVRUbI9IUEvpmi16udzFzYqI8xqFctFeblYkcrL5T0bmdYlkIri1dV0G0YSQf95REdHu9Li09LSsHv3bgCA1WrFnnD9T0UIaRDvb8h2u9TDsdv9f0MOVTaNL8tTdLQ88FUPr3BC06SNxbFjEo/UkFXh//0/iS+JjhYLjtMp16RpUguookIEUI8eUgE6JkZca8XF/oUNG5kGh3tFcV/QbRh5BB0DdOKJJ2L9+vXo06cPRo8ejTvvvBPFxcV45ZVXMGjQoOaYIyGkBVDfkKur5eFeXq4XE4yLk1o07t+QQxUrVFsL3HmnFB3MzNSrPbd2entD2O3yarPJPPv2rTvGYhER98ILYlnLzhaRsmOHXLcKgDaZRPgod1ffvhJIbbOJOPLX94uNTAMnkIriubl0G0YSARVCdGfDhg0oKyvD2LFjcfjwYUyfPh1ff/01+vTpgxdffBE5OTnNNdcWg4UQSSSSnw9MmSJWB6dTHroqGLemRh4YKSnAu+9KgG8oivCtWyfi56uvdLdPhw7SoX3HDl1ktAW6dRMR4055uTQ7BeQ+qYDlsjLpWeYuVAYMEPeX2s9mA+66S+45hU1oUJZGm61uRXEWj2wfBPP8DloARQIUQCQSqa0VC0xRkTyo3R+2Tqc8lNPSpEfVhReKYHLPpgHkm3RhoXyTXr68/ge2ehgdPCiuJItF9q+ulmO2dnXnxnDaafo1q3vRtSuwb5/cW/dA561b9Q7y1dVAv35Ax47B3UMSPO6Wy+pquf/9+9e1rpG2STDPbxZCJIQAkAeyilGpqvK0AKkHRVQU8PbbdbNpNE2sGirle+NGEUgnnVT3PCpu6PbbxTrStau4hWpr5XjR0eHv/gJ8N0fdsQM44QRPq8LllwMPPFA3ZT0zUwKkKyvlPhuNIjLVfgxibh7oNiSKoAVQz549Yainw+Dvv//epAkRQlqHI0fEQtGnj1gsKir0GKD4eBEqZWUS9OueTVNSAvz+uy5+FDNmAE8+6fmtWn373rRJzqEKCHpbe8LFLq3in1Q8lPt6X5SVSTCye8zOyJHiNvSOPbFa5V5v2yb3/dgxiaPyFetDQovRyArZpBEC6MYbb/R4X1NTg02bNuHDDz/EX//611DNixDSwqgsmZgYYNAgETS1tWL1iY8XQVRVJZWPVTZNTY0eBA3o1Y41TdbPmgW89JI8zN1dXuXlInhUfFE44V6xefBguf5NmzzH+Os0P3OmxFF5WxX8dbMvKZGA5+uvl1IDtEYQ0nKELAZoyZIl2LBhA1566aVQHK5VYQwQiUScTqnDs3mzPIz9xfa8+SZwwQXixiork4e4qnSsxhqNepG/M84A3npL9vn2WxFWNTWyhIulxxcGg97U1GaTpT7MZrkfqo2FN4w9IaT5CXk3+ECYOHEili9fHqrDEUJaGKMxsOJ6UVEyLiZGRIG7NUQJGotFHvAOhwil5culHk5VlViVvCsfhyMmk4i0nj1liWrAXn7NNf7FDyAiZ8UKYNky4Lnn5HX5coofQlqLkAmgt956C8nJyaE6HCGkFQi0uF5enjzwlShQbi+jUYSReq9pInp27xbryPHjnn2uwpnaWnH72WxSn2fgQN8CJypKutQvXtzwMVXsybhx8urP1RWqApOEEP80qhCiexC0pmk4ePAgioqK8OSTT4Z0coSQlifQLJkxY6Tj+cGDujWnpkaPB1KWIZUhduSI3vDTaPQfSNwaeHdqV+uqq6UgYXq6BCl7d3Xv0EHEzPnnh24uoSow6XQy04mQ+gg6BmjBggUeAshoNCI1NRVjxoxBdnZ2yCfYGjAGiLRXQvlQdDqBc88FVq8Wa4lq7aA6nCurhcUiouH48dBdR0tgMklKvqrYbDTqPysRFBMjtXs6dw5NET0VKB6KApOhEFGEtDVYCLGJUACR9kioH4pOJ7B0KfCPf0gbC0C37CjxExWliwb1MG8LmEyeIg7QLVZK4KlA7/h4WU48sWmFC1UQeqgKTDZVRBHSFmnWIGiTyYTDqra7G0eOHIEp3KMaCYlQfDUbTUyUjK+5c2V7sMc77zwp8KfaZAB6arvRKG0dVDyQwSBiyF8n7nDAXXAYDLrlSuFu+QH0QO+KChFA3o1ig8W7Ga333Hw1o/XG6RSRW1IiIkp1p4+Lk8w+mw14+GHGFBECNEIA+TMYVVVVwVxfCgQhpFUI9UPRW0ylp4urSFWR7tpVatt07qzHAwHiIgunuB+Fdwabujdmswg4d5xOGR8TI4JOWYNUvJBqFNsYVDNafyLRYmn4HKEQUYRECgEHQT/22GMAAIPBgOeffx7x8fGubQ6HA2vXrm03MUCEtCeCeSg2VB3XW0wpi4iq+VNVBRw6JA1Va2tlAWRMZWX41f1RlqmaGnn1tvyodYA+d6dTT+dX2zVNBFOnTo2fiypE6d0yQ1FZ2fA5AhFRxcVNE2qEtBcCFkCPPPIIALEAPf300x7uLrPZjB49euDpp58O/QwJIU0ilA9FX2IqLk4e3Ha7HghtMsnP7oSb+ImOlr5dgAg6k0nEm0rfVz3Q3GOaAD0TrLZWhJPVKtd+4okSVN5YhgyRmCzvlhmAzKeoSARqfecIhYgiJFIIWADt3LkTADB27FisWLECHTt2bLZJEUJCRzAPxYayxLzFlM0G7NkjGV7K2gPo6e7hisEgIqNzZ6lRNGIEMGkScNNNuuXKYJA4puPHZXG3AKl7YjDI/UtKanrzUlWI0lfLjEAbpIZCRBESKQRdB+izzz5rjnkQQpqJQB+KNpsENvvKElN1gQoKdHdWba10M6+tFeuJe4CwipXxlU3VGri76mJiRERUVUmNn7Q0vWnp//4HrF8v4iM6Wvb55RcRIu7xTO6B3dHR0ssrFJlVqhClytYrLvZsrNrQOUIhogiJFIJOgz///PMxbNgw3HbbbR7rH3jgAaxfvx5vvvlmSCfYGjANnrQ3VOCyzeb7oXjFFcDzz/tOnTaZJNC5qEjWHTki7q3oaN0apKomA7rLSAUT19Q0f/yPu/jyVdQwKkrmEh3t2Yh12DBg0SJdWHjfp+PHReQpMZeZKQKqpkaOpbq4P/+8FEQMFU2t18S+YyRSadY6QKmpqfj0008xePBgj/VbtmzB+PHjcejQoeBnHGZQAJH2iL+H4o03Ao884rv+zNGjwM8/y8O3Tx8p+ldcLJag2loRPyqIuKpKb4Wh6v14F0VsDlTtoagoTyuNO3Fxkpmm4nXKymSOK1YAJ53kOdb9PpWVifCLi5N+YFar59jycmkVsmxZwwHkLQ0rQZNIJJjnd9AuMLvd7jPdPTo6GqWlpcEejhASJI19sPlrceErsNluF8tGYaEeD1NQIPVuMjMlfX7HDhER3i4192KIzZ36bjDInI4f18WPtwVIdXVX4iUuTkTciSf6Fi3u96moCLj3XmDXLkn5dyfcY2pU3zFCiG+CFkCDBw/G66+/jjvvvNNj/bJlyzBgwICQTYwQUpemVnP29VB0D2xWQc2lpWLVUUJCCRu7XeJm3Psem83iDnI4RBApV5iiPtdUUzEa5ZzuGWcGg8xHVaF2OESodOkSeCyM+32yWBhTQ0h7JGgB9M9//hPnnXceduzYgdNPPx0AsGbNGrz22mvtIv6HkHDFX4sDVc05mBYH7lYkFWhbVCTip6ZGT2f3Rp3z4EF9nckkYkMVBiwvl/Xe9XOaQ/yowOaaGnk1m+XaTjhB7pHNJtab8nKxWCUkBB5QrGhqYDIhJDxpVC+w9957D/feey/y8/MRGxuLIUOGYP78+Rg9enRzzLHFYQwQCTfc+0SlpIhlIzpa3D/B9IkC6lqRzGax+Bw9KttVewdVD0cRFSXnq64Wl5OysERH65lVTqeeEt+1q7ibCgr0GKBQWYGUm00VIKyq0lPba2uBfv0kXgmQ97//LrFOZ5zR+FgYxtQQEv40awwQAEyaNAmTJk2qs37r1q0YNGhQYw5JCKmHH38ENm6UoNzDh/U07Lg4ickJtJqzPyvSkSO6FUVZf7yFSm2tLjTUtoQEEUBKMKk52e26OHMPgG6q+DGZ9BR7hbulqaZGREmU23+2qiqZ5xlnNC0mhjE1hLQvGiWA3CkrK8Nrr72G559/Hj/88AMc3uVfCSFN5vPPpUox4GltsdslTbt374b7RPlqYwGIYOnaVYKeDQbP2B9vlOUHkDmccIKeWaVaQwDAtm3iKvKOB2osqvO6yjBTgdlGo1y3mq+yaKl/Q+EeqEwIaT0abcBdu3Ytpk+fjvT0dDz44IM4/fTT8e2334ZyboRENE6nuLxWrwb+8x95qJvNetFBJQgcDnGBRUfX3+Kgvp5gKpBZNfqsL3NLub2SkkT8qIrJHTuK1efIERFkx4/r6fBNJTpa3Fuq/g4g4sbhqBsA7XSKKNy1S67XbBb3F91VhBB3grIAHTx4EEuXLsULL7yA0tJSXHjhhaiqqsLKlSuZAUZICPFVi8ZgEAuHEkCAXv8mkF5U9fUEi4+XpaRE3huNdXt5uZOVJYLEOzNqzx6x+rinpTcVkwkYOlRvuVFTo6e+qzmq+B/VkPX4cZmbxSIC6JFH5Jp8lQGgMCIkMglYAE2ePBlr167FpEmTsHjxYpx11lkwmUxsgEpIiPGO0zGbxZ0E6BWXLRbdDVZTIwJgypT6H+YN9QRLTZVg6Npavd+VdxNQ1QO5shK44w7g3Xf1zCg1t9hYsQ6VlIg4U1lhjcFolHk//rj8/PnnwDvvSFDz/v1ybywWSXFPT5dzbtumi8TsbJnD5s3ArFl6RevGlBAghLQvAv7u88EHH2D27NlYuHAhJk2a5NENnhASGrzjdFSndeXuUjE2qvJyba0Iji5dgDFj6j+26glWVFQ3xkf19xo0SESFWqfibMxmOb+qr+N0ihVoxQqpgvzMM0CvXiJ8srPFQqMytYK1sERFybmSkoCBA+UeHDsmcTw33gisWQP87W8ijAYOFMtXerpYynbulLkpa5DTKfsnJUnT0x9+EHddZqYUNlQlBNatC26OhJC2T8D/mr788kuUlZVh6NChGD58OJ544gkUq6+lhJCQ4CtOJz5eHuK1tboI6tlTUr0HDJA4nKFDGw7yVY0yrVZxD5WXiwupvFzem83An/8sokIJLmVpUhlgKgDZaBSrj+qN3KkTcOCAdFdX1Zk7dPAMUG4IlUE2YIAIsZwc3YXlHttkNALDh4uoiYqSWj9btwI//SQiqKZGz0pT8UJ79+r7q9pBcXEi4mw24OGHW79hKyGkZQlYAJ166ql47rnncODAAVx99dVYtmwZMjIy4HQ6sXr1apSVlTXnPAmJCPzF6WRm6gX/HA696N+RIyIEAq1GrIr65eSIu2vvXilqePy4CIEHHpBYG0DmUVXlmVEFyPuDB4FbbpEmqhdfrDcQVfM2GGTOqlhhIKigZqNRrDSAWKv6968r7pQ1a88eqUxtt+vXbzCISFOL3S4iLyZGzlFbqx/HYPAsIUAIiRyCDv+Li4vD5Zdfji+//BJbtmzBzTffjPvuuw9paWmYMmVKc8yRkIjBPU7HHatVmnnGxspD/OhRETC5ucFVgAZkrHJdXX+9HK+0VARMSYn87N7EVAmYqCg9Bkil4HfrJq6knTvFTVVUpJ8nKQno3j3wXmDKZbVzp8T3/PabHNuXuDMagZtuEktPZaXemR3Q23aYTCLw3NP6VdC4OxZLwyUECCHtjyblP/Tr1w8PPPAA9u7di9deey1UcyIkYqkvTicxUUTFqFHA0qUiYJYvb1wAr9Eo51q+XOoLOZ0iIiwWPR1ejVNut+hovchghw56tpXqtG40Sup5aamIobIyEWxKvHgXKHQnKkovZGizSduKsrK6DUjdsVplSUgQy5EKBjcY5LwxMWL5qamR8dXVMu/4eM/jVFbWdbMRQto/TS6ECAAmkwlTp07F1KlTQ3E4QiIWFadTX/PNhQtDk7WUny+LwSDnUKJH9faqrpb3J5wgAqGmRqwzqueXcjEprFYRU5s3yzHUGFW5Wb33xmDw7D0WFSUB4B07igXHX5+zI0fkPIMH6+nxlZWyj6pV5N6RXtPEYuXdvZ6FEgmJTFgBg5Aww1ecTmPdXfWxfr1u/VACobZWt5ioooIOh4iR6GjdvaRETVSUuM02bRLxo9xm7jE4BoNUmvaHd9sNpzOwIOVOnWTuR4/KnKOiJBuuTx+x8ijxVVkJnHyyuONstrrB3+zoTkhkEhILECEktOTltWzRvtpaEQq++napV+UaU+4mJTK2b5d9Vdp8bKys1zQRJQaDiKRAY4GcTnGldegg4sRfnzObTYRhcbF+ng4dJPh64ECZ1wknAI89Jvt99RU7uhNCdCiACIlQTjlFXF8q00tZd7xRmWYqrqasTPbr1k0PMgZ0i5Bqq1Ferru81JhAqa6WYocnnijnKi72DFJetw644QY91V0VbrTbpRCi1Sop+YsWASedJPu0tKgkhIQ3FECEhCHurTCaq2rxkCHSsys/X9671zZVqe+qorLNJiIkIUEvNKgqP7vH9sTE6Pt7t9Jw7yJfHyobzG4XsaUKMaogZfdikdnZYgXas0evOK2CoR99tO69Ykd3QoiCAoiQMMO7FYYKglZVi0MRB6QE1oED+jpfgkWJnx49pEjimDHy/pFHpKpyTY2ICvdsMUDvA6bcXso9FmhNIBVnVFoq53APUvYuFqmywex2GavimKzWRt4cQkhEQOMvIWGEr1YYoa5arARWfr64ttxr6AB6xpbFIpYeo1FcXUuXikVm9GipI/TggxJ03Lu3HEe50ZT1xz2rzPsc9aGColW9I+8gZX/FIuPjJVg7OVkEEOv6EELqgwKIkDDCVysMRSiqFnsLrPh4sdrExsrP7u03VOVkk0nifZT4qq2V8ycni/ipqJDtUVF6qwynUxdp0dESmJyerqfQe6OsSGqOat9Bg+pavPwVi1Swrg8hJBDoAiMkjPBn3VD4CggOBm+BpXp22e163yyV/m4wSDByfLze3PSHH4AzzhDXmQqettlEdGRmSgVn9644mibj9u7Vj2s26y4xd1eZt1UrJsa3G0sVi9y8WebOuj6EkMbQqhagtWvXYvLkycjIyIDBYMDKlSs9tq9YsQJnnnkmOnXqBIPBgHwVrVkPS5cuhcFg8Fgs/p4mhIQZobZuOJ3i6lqzRl6LijwFlurZFRUl61UdH4dDzhUVJduVWDl4UCxQiYmyvksXESEVFeKuOn7c05oD6BWe1brKSj2oOyambiFEQNb37CmCzbtbe0NNXVnXhxASCK36L6K8vBw5OTlYsmSJ3+2jRo3C/fffH9RxExMTceDAAddSWFgYiukS0uzU1wpDWTeys0WkKFHjLx5o3TrgvPOkWalqWnrPPSJSDh8Wqw8g8Tt9+ujWFGWNiY+X9UlJcu7du/Vqyu6xSf36SeyNKpiYk+PZbkIJkZoaPQ6oqkosTv366Y1PARFcKSnSEb5LF/9xTy1VLJIQ0n5pVRfYxIkTMXHiRL/bL7vsMgDArl27gjquwWBAly5dmjI1QkKK0xlY/ZmGWmGYTHKMSy6pPz3eVyZZURHw/feyX1GRWJLi48WSk5QkYuK770TkDBwowkS5l+x2WeLjPQULoLvS9u+XnmAGg4idDh3EhaaEi8Mh1pnERLEWHT4s15uUJONSUqR2j7t48o57ck9hZ10fQkhTaJcxQHa7HVlZWXA6nTjppJNw7733YuDAgX7HV1VVoUoFIwAoLS1tiWmSCCHYmj7KuuFdtVjF2OzZU396vHegs0pl37NHRIjq9O5wiNVk2zZpE1FZKdYdQASKyaSfY+9e2S8ry/c1qmMaDHoVaFUQUbm3ampk/8REGX/77RJEXVAgBQu7d/edKVZf3BPr+hBCGku7+67Ur18/vPjii3jnnXfwn//8B06nE3/4wx+wd+9ev/ssWrQIVqvVtWRmZrbgjEl7xj3lXMXNJCbqosU9tsWdvDxJNV+2DHjuOeDVV8W9VFvbcHq8r0yyPXtk39hYWVRHd5NJrC9794oF6KWXZPF2LfXvL9YZs9n3fFXQtHv7C/d+YUajng6vYoCGDwfGjZNXJbR8wawuQkhz0O4sQCNGjMCIESNc7//whz+gf//+eOaZZ3D33Xf73OeOO+7AvHnzXO9LS0spgkiT8WWJAUS0dOggLq6HHxY3jnsKuC+XTn6+WGoCSY/3ziSz2yVAWDU9NRrlNStLBIndLiLjzjv1thHerqVBg4ALLvCfeVVeLuPsdnlVmWVqDqp3WFycXLd7lhazugghrUG7swB5Ex0djRNPPBEFBQV+x8TExCAxMdFjIaSpBFvTx1fQ8nnnyfpA0uOrq3XB4p5JVlPj2edLWWaioyWeJy1Nth07pmeNffaZjB07VsRHVFTDmVd//7vE8+zeLdcWFSUB1yozLCXFd5YWs7oIIa1Bu/+X4nA4sGXLFqSnp7f2VEiEEYxoachVVlgYeHq8dyaZ6uLudMr76mqxtKhgY7VvYaF/AQY0nHk1Z46+XdNk/mazLFarrPOXpcWsLkJIS9OqLjC73e5hmdm5cyfy8/ORnJyM7t274+jRo9i9ezf2798PANi2bRsAoEuXLq4sr+nTp6Nr165YtGgRAOCuu+7Cqaeeit69e6OkpAT//ve/UVhYiCuuuKKFr45EOu6WmLi4utuV8OjYEbjrrvpdZe++KynjP/4oMTyq03pUlIxzdxP5yiTr0EEKFKqYHFXbR7mYunWT5qFFRSJckpMlZsc7wLqhzCvv7R07yvpjxxrO0mJWFyGkJWlVAbRhwwaMHTvW9V7F4cyYMQNLly7Fu+++i1mzZrm2X3zxxQCA+fPnY8GCBQCA3bt3w+j2H/LYsWO48sorcfDgQXTs2BFDhw7F119/jQEDBrTAFRGiE2hsCxCYq2zaNGD9eklVd99uMAAZGZ5uIvdMsl9+0cWOwSBiJyFBRJQSPDabZGNpmogVg0GEVbduIszcY5UayrxqSmYWs7oIIS2FQdMC7c8cOZSWlsJqtcJmszEeiDQJ5dqy2erW9LFaRaRUV4vLKTPTdxr4sWPA9u0iSI4d0xuNmkx6dlV6umRveVtQSkqAhQtF3FRWSjyOwSACKDFRsrv69QMWLxYXmcWid2JXhQszM0UYLVtGcUIICW+CeX63uywwQsIJfzV9cnPFYpOXJ3E//lxlNhvw228ikkwmvTZPdbVel6dLF3F1/e1vInpUvSHVpys2VmrsuNf0iY0Fbr0VmD5durvX1sq5lQVJiStVNDEhofH9xwItAkkIIS0JBRAhzUxDsS31ucp279YbklZXi3hSQuj4cWlKGh8v4mb9eond6dZNBNXWrXqsUG2tXjeob18RTP/7nwixffskUNrbFqwyxVQF6MbU4Qm2CCQhhLQUFECEtAD1xbb4a39x9KhYcMxmSSHft0/G1taKmKitFTfVli16ZefUVBE5ZWUyJjZWxu3Zo3dWd48rWr9e1sXF6XV73AWYquzcrVvDdXi8LT02G3DDDZ7tOHxVriaEkNaAAoiQMMDdVZafr3dmj46WhqTR0dIGo6ZGRISq66Nq+lRXy3FURxfVjsJkku3l5bolB9DbS6ifY2PluCozTR27qkqyxi6/vH63lbelx2yWNHZNE4tPIEUgCSGkJeG/HkLCCOWGUgLHaNRdYB06iEBxOnXRoLLA1M8qTse9HYXq01VaKkHUqvKz2QyccooIlMpK6csVH+9pYTKZZMzMmb7n63QCL74o27//Xq9hFB0tAqu0VBZ3fBWBJISQloYWIBJRhGNArtMJLF0K3HOPxPV07SoWmePHxX20Y4dYaVJSxC2mUtqdThEoKissJsbT0qPaURiNYjkqLPQUSyefLG455X6z2YCePeV4FRUiXFJTgXvv9X2P1q0DHnwQ+PRTmWt0tJwnM1MEWFSUzNHd/aaor8EpIYS0BBRAJGIIx4BcNac1a0REmM1i8enWTV6TkoDDh2XOWVm6qHBvNBoXp1ttlKWnpkYEU3m5nvoeHS3nrK6Wcfv3A199VTdTTQVbDx+uZ6r5mvfcuTI31WTVYBDB9dtvMn/Vd8zb/QawwSkhpPVhHSAfsA5Q+0M9sL0Dct3r8bS0CHIXEUeP6i0rjh8XUQHo1h5A3Euqr5ayCCUlibCw2cSlVF2td18H9Ngg1QjVYBDLULdusk9uLrB8uR7zE4h1zOmUFhn5+XLvfvtNxKSaa1WVns5vt8trdrZeFVrT9Iao6tyEEBIKWAeIEDca05W9JeeUmioCCNBjb9xR4uT4cREulZUSGO0+V02T/VSBRE3ThVNUlFy3xSI/x8fLOLNZj8PJzQ28CrN7k1dliVLuOGVpqqiQ2kMVFWKNqq0V15q76GSDU0JIa0IBRNo9wXRlb6lKx+5zKi0VgaBcU+64Bzs7HDI2I0PvuG6xiDD67TcZO2CAiJzaWllfWCj7HzkCDBrkeezGxuG4N3k1GvVYI5VCr4KuY2L02J+aGinA6F0EkhBCWgsKINLuCaQre0sH5Ko5VVeLMHC32LjjHusTHS0CaM4ccT+pytLK+pKVpbuZAN0V5isNHmh8HI53k9fMTGnVoY6nrqWoCOjcWZqsWq3hFXjeVgjHoH1C2gsUQKTdE2hX9pYMyO3USc5ZWCjWmg4dRKR4iyDVwDQmRk+Jj4sDVqzQH4wFBcB994lFyB33TDBArDDux3XvIB8M3pWrk5LEJbdnj+7ysliAYcNY8bkphGPQPiHtCX6XIO0e9cAuKvItMIqKpClosEKgqXPKyBBxEh0t1hqz2ffYmBjdrWUwSGyNitcZN06ytZTAc8dgEOuM0SjuMxWHU14uwquxcTiqcrXVKscpL5deYT17ihjq3l2sPitW8EHdWFSAfH6+XlspMVGvor1uXWvPkJC2DwUQaff4emCHQgg0dU7nnCMipaZG5hMTU3eMWlRV5pQU4PzzPcfVJ/CsVllSUvQ4nNJSEU9NyXxTqfM5OXK8vXul/cbw4VLTqKHK0cQ/3kH7cXF6H7esLMnee/hhGUcIaTx0gZGIIJCu7N4EE3/RmFiNMWOkk7vq26VaXyhXlRIzKnsqOlo6vkd5/dX66yWmMq7S0mSbwyHjTzlFz/pqCg01eSWNIxyD9glpj1AAkYghmAd2MPEXwYx1F0odOwInnihujZQUcVFFRck3/927RQip7K+0NBE/c+b4vzZfAq9bN9n+4IPNE0cSaOo8CZxwDNonpD3CQog+YCHEyCaYoonBjvUWSqmpwIEDeid3tf+hQ7LPpEnA6NHi9vK2/PjCXWAVFkosjs0WPsUfScPk5wMXXywxP76C9svLxe24bBnFJyHesBAiIY0kmKKJQOBjv/rKt1Dau1fiO7p1E2GiLDcnndS4WjnKIqOqNdts4VP8kQSGd5aduxusKdl7hBBPKIAIcSOY+AsgsLH5+Q0LpU6dxFpz7FhoYmkYR9J2aSimi1W0CQkN/BMixI1A4i+qq2VcoGPXr29YjPz6qzzQxo0LTYByMNdBwg9fWXahyN4jhOjQAkSIG8EWTQxkLNDyQa3hWPyRBAez7AhpXiiACHEj2PiLQMaeckrLixHGkbQPmGVHSPPB7xKEuBFM0cRAx+bmtnwl6nAs/kgIIeEE//0R4kUw8ReBjG0tMcI4EkII8Q/rAPmAdYAIEPpK0O51gKqrxe3Vv3/j0t2b6zoIIaQtE8zzmwLIBxRApLmgGCGEkOaDhRAJCQHNIVYY1EoIIeEBBRAhPgimvxchhJC2B43vhHih+nvl50s/psxMed28WdavW9faMySEENJUKIAIccO7F1hcnPTqiosDsrKkt9bDD8s4QgghbRcKIELcCLYXGCGEkLYJBRAhbrCHFiGERAYUQIS44d5DyxfsoUUIIe0DCiDS7nE6JaB5zRp5rS9+R/XQasm2FYQQQloepsGTdk2w6eyqbcXcudKmIjVV3F6VlSJ+2EOLEELaB/w3TtotjU1nZw8tQghp/7AVhg/YCqNphEO7B6cTOO88ET89enhmdGmaWHdyc4Hly5vW36sp82vte0QIIe0NtsIgrUa4VFAOJp3dX2uK5mpbES73iBBCIhl+5yQhI5wqKIdrOns43SNCCIlkKIBISAi3CsrhmM4ebveIEEIiGQogEhLCrYJyOKazh9s9IoSQSIYCiISEcHM5qXR2q1UCnsvLAYdDXgsLWyedPdzuESGERDIUQCQkhKPLKdzS2cPxHhFCSKTCLDASEpTLafNmoEOHumnnRUUiPFq6gnJeHjByZHiknIfrPSKEkEiEFiASEsLR5eQ+t9xcYNw4eW2tejvhfI9ak2BalRBCSKhgIUQfsBBi43GvcVNdLS6d/v3lwc4aNwLvkQ5rIhFCQkkwz28KIB9QADUNVjluGN4jvSZSSYlkxnn3XGPbEUJIsLASNGlVmquCcnsi0u+Rd00kFQ8VFyfxUYWFUhNp5MjIE4aEkJaB/1oIIS0OayIRQlobWoBIi0P3T/uiMb/PQGoiFRezJhIhpPlo1cfO2rVrMXnyZGRkZMBgMGDlypUe21esWIEzzzwTnTp1gsFgQH5+fkDHffPNN5GdnQ2LxYLBgwfj/fffD/3kSaNYt066tF98MXDFFfJ63nnsgdVWaezvkzWRCCGtTasKoPLycuTk5GDJkiV+t48aNQr3339/wMf8+uuvMW3aNMyePRubNm3C1KlTMXXqVGzdujVU0yaNhI1A2xdN+X2GY6sSQkhkETZZYAaDAW+//TamTp1aZ9uuXbvQs2dPbNq0CbkNRI5edNFFKC8vx6pVq1zrTj31VOTm5uLpp58OaC7MAgs9TqdYBvLzPYNeAXngFRZKUPDy5XSHtQVC8ftUAspmk5gfZoERQppKMM/vdveo+eabbzB+/HiPdRMmTMA333zjd5+qqiqUlpZ6LCS0MOi1fRGK32e4tSohhEQW7S4I+uDBg+jcubPHus6dO+PgwYN+91m0aBEWLlzY3FOLaBj02r4I1e8znFqVEEIiC/6bAXDHHXfAZrO5lj179rT2lNodDHptX4Ty9xkurUoIIZFFu/tX06VLFxw6dMhj3aFDh9ClSxe/+8TExCAxMdFjIaGFQa/tC/4+CSFtnXYngEaMGIE1a9Z4rFu9ejVGjBjRSjMiABuBtjf4+ySEtHVaNQbIbrejoKDA9X7nzp3Iz89HcnIyunfvjqNHj2L37t3Yv38/AGDbtm0AxMqjLDrTp09H165dsWjRIgDADTfcgNGjR+Ohhx7CpEmTsGzZMmzYsAHPPvtsC18d8UYFvarml8XF4ibJzY3MRqBtDe+ChyNH8vdJCGm7tGoa/Oeff46xY8fWWT9jxgwsXboUS5cuxaxZs+psnz9/PhYsWAAAGDNmDHr06IGlS5e6tr/55pv4xz/+gV27dqFPnz544IEHcPbZZwc8L6bBNy+sBN32qK9rO4OYCSHhArvBNxEKIEJ02LWdENJWiOg6QISQ0OHdtT0uDjCZ5DUrS4oYPvywjCOEkLYEBRAhxC8sYEkIaa9QABFC/BJIwcPqahawJIS0PSiACCF+YQFLQkh7hQKIEOIXFjwkhLRXKIAIIX5hwUNCSHuF/7ZIu8bpBPLzgTVr5JXZSsHDru2EkPZIu+sGT4iivuJ9fGgHB7u2E0LaGxRApF3ir3jf5s2ynpaL4FFd2wkhpD3A72+k3cHifYQQQhqCAoi0O1i8jxBCSENQAJF2B4v3EUIIaQgKINLuYPE+QgghDUEBRNodLN5HCCGkISiASLuDxfsIIYQ0BB8BpF3C4n2EEELqg3WASLuFxfsIIYT4gwKItGtYvI8QQogv+F2YEEIIIREHBRAhhBBCIg4KIEIIIYREHBRAhBBCCIk4KIAIIYQQEnFQABFCCCEk4qAAIoQQQkjEQQFECCGEkIiDAogQQgghEQcFECGEEEIiDgogQgghhEQcFECEEEIIiTgogAghhBAScVAAEUIIISTioAAihBBCSMRBAUQIIYSQiIMCiBBCCCERBwUQIYQQQiIOCiBCCCGERBwUQIQQQgiJOKJaewKEEE+cTuDHH4EjR4BOnYAhQwAjv6oQQkhIoQAiJIxYtw546CHg11+BqiogJgbIzgZuvhnIy2vt2RFCSPuB3ytbEKcTyM8H1qyRV6eztWdEwol164C5c+WzkZgIZGbK6+bNsn7dutaeISGEtB9oAWoh+M2e1IfTKZ+PkhKgRw/AYJD1cXFAhw5AYSHw8MPAyJF0hxFCSCjgv9IWgN/sSUP8+KOI47Q0XfwoDAYgNRX45RcZRwghpOlQADUz3t/s4+IAk0les7IAm02+2dMdFtkcOSKWQYvF93aLBaiulnGEEEKaDgVQM8Nv9iQQOnUSt2hlpe/tlZWA2SzjCCGENB0KoGaG3+xJIAwZIjFhRUWApnlu0zRZ37+/jCOEENJ0KICaGX6zJ4FgNEpAvNUqAc/l5YDDIa+FhbJ+3jwGQBNCSKjgv9Nmht/sSaDk5QGPPw7k5AClpcDevfKamyvrmS1ICCGhg2nwzYz6Zj93rnyTT00Vt1dlpYgffrMn7uTlSao7K0ETQkjzYtA0b7sEKS0thdVqhc1mQ2JiYkiO6V4HqLpa3F79+4v44Td7QgghpOkE8/xu1e+Va9euxeTJk5GRkQGDwYCVK1d6bNc0DXfeeSfS09MRGxuL8ePHY/v27fUec8GCBTAYDB5LdnZ2M15FYOTlAStWAMuWAc89J6/Ll1P8EEIIIa1Bqwqg8vJy5OTkYMmSJT63P/DAA3jsscfw9NNP47vvvkNcXBwmTJiASn8Rxf/HwIEDceDAAdfy5ZdfNsf0g8ZolHiOcePklW4NQgghpHVo1RigiRMnYuLEiT63aZqGxYsX4x//+AfOOeccAMD/+3//D507d8bKlStx8cUX+z1uVFQUunTp0ixzJoQQQkjbJ2xtEDt37sTBgwcxfvx41zqr1Yrhw4fjm2++qXff7du3IyMjAyeccAIuvfRS7N69u7mnSwghhJA2RNhmgR08eBAA0LlzZ4/1nTt3dm3zxfDhw7F06VL069cPBw4cwMKFC5GXl4etW7ciISHB5z5VVVWoqqpyvS8tLQ3BFRBCCCEkXAlbAdRY3F1qQ4YMwfDhw5GVlYU33ngDs2fP9rnPokWLsHDhwpaaIiGEEEJambB1gakYnkOHDnmsP3ToUFDxPUlJSejbty8KCgr8jrnjjjtgs9lcy549exo3aUIIIYS0CcJWAPXs2RNdunTBmjVrXOtKS0vx3XffYcSIEQEfx263Y8eOHUhPT/c7JiYmBomJiR4LIYQQQtovrSqA7HY78vPzkZ+fD0ACn/Pz87F7924YDAbceOON+Ne//oV3330XW7ZswfTp05GRkYGpU6e6jjFu3Dg88cQTrve33HILvvjiC+zatQtff/01zj33XJhMJkybNq2Fr44QQggh4UqrxgBt2LABY8eOdb2fN28eAGDGjBlYunQpbr31VpSXl+Oqq65CSUkJRo0ahQ8//BAWt9bqO3bsQHFxsev93r17MW3aNBw5cgSpqakYNWoUvv32W6SmprbchRFCCCEkrGErDB80RysMQgghhDQvwTy/210WWChQmpDp8IQQQkjbQT23A7HtUAD5oKysDACQmZnZyjMhhBBCSLCUlZXBarXWO4YuMB84nU7s378fCQkJMBgMrT2ddkNpaSkyMzOxZ88euhZbEN731oH3veXhPW8dwum+a5qGsrIyZGRkwNhAw01agHxgNBrRrVu31p5Gu4WlBloH3vfWgfe95eE9bx3C5b43ZPlRhG0dIEIIIYSQ5oICiBBCCCERBwUQaTFiYmIwf/58xMTEtPZUIgre99aB973l4T1vHdrqfWcQNCGEEEIiDlqACCGEEBJxUAARQgghJOKgACKEEEJIxEEBRAghhJCIgwKINIq1a9di8uTJyMjIgMFgwMqVKz22r1ixAmeeeSY6deoEg8GA/Pz8gI775ptvIjs7GxaLBYMHD8b7778f+sm3YZrjvi9duhQGg8FjsVgszXMBbZT67ntNTQ1uu+02DB48GHFxccjIyMD06dOxf//+Bo+7ZMkS9OjRAxaLBcOHD8f333/fjFfRtmiOe75gwYI6n/Xs7OxmvpK2RUP/YxYsWIDs7GzExcWhY8eOGD9+PL777rsGjxuOn3UKINIoysvLkZOTgyVLlvjdPmrUKNx///0BH/Prr7/GtGnTMHv2bGzatAlTp07F1KlTsXXr1lBNu83THPcdkAquBw4ccC2FhYWhmG67ob77XlFRgY0bN+Kf//wnNm7ciBUrVmDbtm2YMmVKvcd8/fXXMW/ePMyfPx8bN25ETk4OJkyYgMOHDzfXZbQpmuOeA8DAgQM9Putffvllc0y/zdLQ/5i+ffviiSeewJYtW/Dll1+iR48eOPPMM1FUVOT3mGH7WdcIaSIAtLffftvntp07d2oAtE2bNjV4nAsvvFCbNGmSx7rhw4drV199dQhm2f4I1X1/6aWXNKvVGtK5tWfqu++K77//XgOgFRYW+h0zbNgw7brrrnO9dzgcWkZGhrZo0aJQTbXdEKp7Pn/+fC0nJye0k2vHBHLfbTabBkD75JNP/I4J1886LUAkbPjmm28wfvx4j3UTJkzAN99800ozihzsdjuysrKQmZmJc845Bz/99FNrT6lNY7PZYDAYkJSU5HN7dXU1fvjhB4/Pu9FoxPjx4/l5byQN3XPF9u3bkZGRgRNOOAGXXnopdu/e3TITbIdUV1fj2WefhdVqRU5Ojt8x4fpZpwAiYcPBgwfRuXNnj3WdO3fGwYMHW2lGkUG/fv3w4osv4p133sF//vMfOJ1O/OEPf8DevXtbe2ptksrKStx2222YNm2a38aQxcXFcDgc/LyHiEDuOQAMHz4cS5cuxYcffoinnnoKO3fuRF5eHsrKylpwtm2fVatWIT4+HhaLBY888ghWr16NlJQUn2PD+bPObvCERDgjRozAiBEjXO//8Ic/oH///njmmWdw9913t+LM2h41NTW48MILoWkannrqqdaeTkQQzD2fOHGi6+chQ4Zg+PDhyMrKwhtvvIHZs2c391TbDWPHjkV+fj6Ki4vx3HPP4cILL8R3332HtLS01p5aUNACRMKGLl264NChQx7rDh06hC5durTSjCKT6OhonHjiiSgoKGjtqbQp1IO4sLAQq1evrtcSkZKSApPJxM97EwnmnvsiKSkJffv25Wc9SOLi4tC7d2+ceuqpeOGFFxAVFYUXXnjB59hw/qxTAJGwYcSIEVizZo3HutWrV3tYJ0jz43A4sGXLFqSnp7f2VNoM6kG8fft2fPLJJ+jUqVO9481mM4YOHerxeXc6nVizZg0/7wES7D33hd1ux44dO/hZbyJOpxNVVVU+t4XzZ50uMNIo7Ha7x7emnTt3Ij8/H8nJyejevTuOHj2K3bt3u+pybNu2DYBYeZTqnz59Orp27YpFixYBAG644QaMHj0aDz30ECZNmoRly5Zhw4YNePbZZ1v46sKX5rjvd911F0499VT07t0bJSUl+Pe//43CwkJcccUVLXx14Ut99z09PR0XXHABNm7ciFWrVsHhcLhiG5KTk2E2mwEA48aNw7nnnos5c+YAAObNm4cZM2bg5JNPxrBhw7B48WKUl5dj1qxZLX+BYUhz3PNbbrkFkydPRlZWFvbv34/58+fDZDJh2rRpLX+BYUp9971Tp0645557MGXKFKSnp6O4uBhLlizBvn378Kc//cm1T5v5rLdqDhpps3z22WcagDrLjBkzNE2T1Gpf2+fPn+86xujRo13jFW+88YbWt29fzWw2awMHDtTee++9lruoNkBz3Pcbb7xR6969u2Y2m7XOnTtrZ599trZx48aWvbAwp777rkoO+Fo+++wz1zGysrI8fg+apmmPP/64694PGzZM+/bbb1v2wsKY5rjnF110kZaenq6ZzWata9eu2kUXXaQVFBS0/MWFMfXd9+PHj2vnnnuulpGRoZnNZi09PV2bMmWK9v3333sco6181g2apmnNoKsIIYQQQsIWxgARQgghJOKgACKEEEJIxEEBRAghhJCIgwKIEEIIIREHBRAhhBBCIg4KIEIIIYREHBRAhBBCCIk4KIAIIWHHzJkzMXXqVNf7MWPG4MYbb2zxeXz++ecwGAwoKSkJ6XF37doFg8GA/Pz8kB6XEBI4FECEkICYOXMmDAYDDAYDzGYzevfujbvuugu1tbXNfu4VK1YE3Jm+uUSLP3r06OG6L3FxcTjppJPw5ptv1rtPZmYmDhw4gEGDBrXIHAkhdaEAIoQEzFlnnYUDBw5g+/btuPnmm7FgwQL8+9//9jm2uro6ZOdNTk5GQkJCyI4Xau666y4cOHAAmzZtwimnnIKLLroIX3/9tc+x1dXVMJlM6NKlC6Ki2I6RkNaCAogQEjAxMTHo0qULsrKycM0112D8+PF49913Aehuq3vuuQcZGRno168fAGDPnj248MILkZSUhOTkZJxzzjnYtWuX65gOhwPz5s1DUlISOnXqhFtvvRXeHXq8XWBVVVW47bbbkJmZiZiYGPTu3RsvvPACdu3ahbFjxwIAOnbsCIPBgJkzZwKQDtSLFi1Cz549ERsbi5ycHLz11lse53n//ffRt29fxMbGYuzYsR7zrI+EhAR06dIFffv2xZIlSxAbG4v//e9/AMRCdPfdd2P69OlITEzEVVdd5dMF9tNPP+GPf/wjEhMTkZCQgLy8POzYscO1/fnnn0f//v1hsViQnZ2NJ598MqC5EUJ8w68fhJBGExsbiyNHjrjer1mzBomJiVi9ejUAoKamBhMmTMCIESOwbt06REVF4V//+hfOOuss/PjjjzCbzXjooYewdOlSvPjii+jfvz8eeughvP322zj99NP9nnf69On45ptv8NhjjyEnJwc7d+5EcXExMjMzsXz5cpx//vnYtm0bEhMTERsbCwBYtGgR/vOf/+Dpp59Gnz59sHbtWvz5z39GamoqRo8ejT179uC8887Dddddh6uuugobNmzAzTffHPQ9iYqKQnR0tIcF7MEHH8Sdd96J+fPn+9xn3759OO200zBmzBh8+umnSExMxFdffeVyL/73v//FnXfeiSeeeAInnngiNm3ahCuvvBJxcXGYMWNG0HMkhIDd4AkhgTFjxgztnHPO0TRN05xOp7Z69WotJiZGu+WWW1zbO3furFVVVbn2eeWVV7R+/fppTqfTta6qqkqLjY3VPvroI03TNC09PV174IEHXNtramq0bt26uc6ladLB/oYbbtA0TdO2bdumAdBWr17tc56qm/WxY8dc6yorK7UOHTpoX3/9tcfY2bNna9OmTdM0TdPuuOMObcCAAR7bb7vttjrH8iYrK0t75JFHXNd27733agC0VatWubZPnTrVYx/VzXzTpk2uc/fs2VOrrq72eY5evXppr776qse6u+++WxsxYoTfeRFC6ocWIEJIwKxatQrx8fGoqamB0+nEJZdcggULFri2Dx48GGaz2fV+8+bNKCgoqBO/U1lZiR07dsBms+HAgQMYPny4a1tUVBROPvnkOm4wRX5+PkwmE0aPHh3wvAsKClBRUYEzzjjDY311dTVOPPFEAMAvv/ziMQ8AGDFiREDHv+222/CPf/wDlZWViI+Px3333YdJkya5tp988sn17p+fn4+8vDxER0fX2VZeXo4dO3Zg9uzZuPLKK13ra2trYbVaA5ofIaQuFECEkIAZO3YsnnrqKZjNZmRkZNQJ4o2Li/N4b7fbMXToUPz3v/+tc6zU1NRGzUG5tILBbrcDAN577z107drVY1tMTEyj5uHOX//6V8ycORPx8fHo3LkzDAaDx3bv++JNfdek5v7cc8/VEWgmk6mRMyaEUAARQgImLi4OvXv3Dnj8SSedhNdffx1paWlITEz0OSY9PR3fffcdTjvtNABi2fjhhx9w0kkn+Rw/ePBgOJ1OfPHFFxg/fnyd7coC5XA4XOsGDBiAmJgY7N6926/lqH///q6AbsW3337b8EUCSElJCeq+eDNkyBC8/PLLqKmpqWMF6ty5MzIyMvD777/j0ksvbfQ5CCGeMAuMENJsXHrppUhJScE555yDdevWYefOnfj8889x/fXXY+/evQCAG264Affddx9WrlyJX3/9Fddee229NXx69OiBGTNm4PLLL8fKlStdx3zjjTcAAFlZWTAYDFi1ahWKiopgt9uRkJCAW265BTfddBNefvll7NixAxs3bsTjjz+Ol19+GQDwl7/8Bdu3b8df//pXbNu2Da+++iqWLl3a3LcIADBnzhyUlpbi4osvxoYNG7B9+3a88sor2LZtGwBg4cKFWLRoER577DH89ttv2LJlC1566SU8/PDDLTI/QtojFECEkGajQ4cOWLt2Lbp3747zzjsP/fv3x+zZs1FZWemyCN1888247LLLMGPGDIwYMQIJCQk499xz6z3uU089hQsuuADXXnstsrOzceWVV6K8vBwA0LVrVyxcuBC33347OnfujDlz5gAA7r77bvzzn//EokWL0L9/f5x11ll477330LNnTwBA9+7dsXz5cqxcuRI5OTl4+umnce+99zbj3dHp1KkTPv30U9jtdowePRpDhw7Fc88957IGXXHFFXj++efx0ksvYfDgwRg9ejSWLl3qmjshJHgMmr9IQ0IIIYSQdgotQIQQQgiJOCiACCGEEBJxUAARQgghJOKgACKEEEJIxEEBRAghhJCIgwKIEEIIIREHBRAhhBBCIg4KIEIIIYREHBRAhBBCCIk4KIAIIYQQEnFQABFCCCEk4qAAIoQQQkjE8f8Bn+HjlWUtoZoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "actual_values = y_test\n",
        "plt.scatter(predictions, actual_values, alpha=.75,\n",
        "            color='b') #alpha helps to show overlapping data\n",
        "plt.xlabel('Predicted Price')\n",
        "plt.ylabel('Actual Price')\n",
        "plt.title('Gradient Boosting Regression Model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "collapsed": true,
        "id": "bMD3T3GHilBR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "9870f65f-8174-460e-ab07-03200b46c69c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id\n",
              "0     1461\n",
              "1     1462\n",
              "2     1463\n",
              "3     1464\n",
              "4     1465\n",
              "...    ...\n",
              "1454  2915\n",
              "1455  2916\n",
              "1456  2917\n",
              "1457  2918\n",
              "1458  2919\n",
              "\n",
              "[1459 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1454</th>\n",
              "      <td>2915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1455</th>\n",
              "      <td>2916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1456</th>\n",
              "      <td>2917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>2918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>2919</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1459 rows × 1 columns</p>\n",
              "</div>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-877eceef-f346-4cbe-8f8d-bb1521744bf7')\"\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-877eceef-f346-4cbe-8f8d-bb1521744bf7 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-877eceef-f346-4cbe-8f8d-bb1521744bf7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e2ebdeef-57dc-439e-bd99-095b568db632\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e2ebdeef-57dc-439e-bd99-095b568db632')\"\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-e2ebdeef-57dc-439e-bd99-095b568db632 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "\n",
        "submission = pd.DataFrame()\n",
        "submission['Id'] = id.id\n",
        "submission"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "D1NK1xSjilBR"
      },
      "outputs": [],
      "source": [
        "predictions = model.predict(feats)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "collapsed": true,
        "id": "uFOr8lYsilBS"
      },
      "outputs": [],
      "source": [
        "final_predictions = np.exp(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFAkQxy0ilBS",
        "outputId": "ad484066-053d-465b-c828-8c4680fdf66b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original predictions are: \n",
            " [11.74134318 11.93198657 12.14026435 ... 12.07390437 11.68415823\n",
            " 12.44658797] \n",
            "\n",
            "Final predictions are: \n",
            " [125661.01233191 152053.32592871 187262.06818817 ... 175238.70806658\n",
            " 118676.69556184 254380.88414033]\n"
          ]
        }
      ],
      "source": [
        "print (\"Original predictions are: \\n\", predictions, \"\\n\")\n",
        "print (\"Final predictions are: \\n\", final_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "B2pAC53_ilBS",
        "outputId": "80165784-e8b5-46a6-e547-83dfa17ec234"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Id     SalePrice1\n",
              "0  1461  125661.012332\n",
              "1  1462  152053.325929\n",
              "2  1463  187262.068188\n",
              "3  1464  184706.016534\n",
              "4  1465  183601.195566"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d33cd69c-a0ce-46c3-a504-b9dd567faf8a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SalePrice1</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>125661.012332</td>\n",
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              "      <td>1462</td>\n",
              "      <td>152053.325929</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1463</td>\n",
              "      <td>187262.068188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1464</td>\n",
              "      <td>184706.016534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1465</td>\n",
              "      <td>183601.195566</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d33cd69c-a0ce-46c3-a504-b9dd567faf8a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\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-d33cd69c-a0ce-46c3-a504-b9dd567faf8a 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-d33cd69c-a0ce-46c3-a504-b9dd567faf8a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b93882ab-45b8-497f-8ea1-aad0b3948002\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b93882ab-45b8-497f-8ea1-aad0b3948002')\"\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-b93882ab-45b8-497f-8ea1-aad0b3948002 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "submission['SalePrice1'] = final_predictions\n",
        "submission.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final = pd.DataFrame()\n",
        "final.index = [\"RSquare\",\"RMSE\",\"MAE\"]"
      ],
      "metadata": {
        "id": "AEHbeAthJtfS"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list1 = [rsq1,rmse1,mae1]\n",
        "final[\"Gradient Boosting Regression\"] = list1\n",
        "final"
      ],
      "metadata": {
        "id": "6mTvHjdHMoz7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "a405d88f-1358-47a3-ef5b-d81aa74aa711"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Gradient Boosting Regression\n",
              "RSquare                      0.891279\n",
              "RMSE                         0.016801\n",
              "MAE                          0.086365"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gradient Boosting Regression</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RSquare</th>\n",
              "      <td>0.891279</td>\n",
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              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.016801</td>\n",
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              "    <tr>\n",
              "      <th>MAE</th>\n",
              "      <td>0.086365</td>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c074e736-7c27-41ee-b151-80d477138152 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-c074e736-7c27-41ee-b151-80d477138152');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "  }\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-f5636553-6b79-4652-b8f1-dfc8d736943c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---->>>>>  Algo 2   **Random Forest Regressor**"
      ],
      "metadata": {
        "id": "382HBEb9Yjer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = lr2.fit(X_train, y_train)\n",
        "rsq2 = model2.score(X_test, y_test)\n",
        "predictions2 = model2.predict(X_test)\n",
        "rmse2 = mean_squared_error(y_test, predictions2)\n",
        "mae2 = mean_absolute_error(y_test,predictions2)\n",
        "predictions2 = model2.predict(feats)\n",
        "final_predictions2 = np.exp(predictions2)\n",
        "submission['SalePrice2'] = final_predictions2\n",
        "list2 = [rsq2,rmse2,mae2]\n",
        "final[\"Random Forest Regression\"] = list2\n",
        "final\n"
      ],
      "metadata": {
        "id": "K_2nNzaafBGe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "e3937675-f28f-467c-cb96-aaead3aff8a6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Gradient Boosting Regression  Random Forest Regression\n",
              "RSquare                      0.891279                  0.759706\n",
              "RMSE                         0.016801                  0.037133\n",
              "MAE                          0.086365                  0.126793"
            ],
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gradient Boosting Regression</th>\n",
              "      <th>Random Forest Regression</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RSquare</th>\n",
              "      <td>0.891279</td>\n",
              "      <td>0.759706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.016801</td>\n",
              "      <td>0.037133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAE</th>\n",
              "      <td>0.086365</td>\n",
              "      <td>0.126793</td>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      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-b927f2f8-0b86-4466-94ad-4090e382d499 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-b927f2f8-0b86-4466-94ad-4090e382d499');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "<div id=\"df-d5c11d74-9c2d-4eb9-baa1-ed0d97783bcc\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "\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-d5c11d74-9c2d-4eb9-baa1-ed0d97783bcc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---->>>>> Algo 3 **Linear Regression**\n",
        "\n"
      ],
      "metadata": {
        "id": "hZh0IsHthSAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = lr3.fit(X_train, y_train)\n",
        "rsq3 = model3.score(X_test, y_test)\n",
        "predictions3 = model3.predict(X_test)\n",
        "rmse3 = mean_squared_error(y_test, predictions3)\n",
        "mae3 = mean_absolute_error(y_test,predictions3)\n",
        "predictions3 = model3.predict(feats)\n",
        "final_predictions3 = np.exp(predictions3)\n",
        "submission['SalePrice3'] = final_predictions3\n",
        "list3 = [rsq3,rmse3,mae3]\n",
        "final[\"Linear Regression\"] = list3\n",
        "final"
      ],
      "metadata": {
        "id": "LkQ8AWlqixmc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "654d0cf8-a432-4ccc-9926-189aece5eff6"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Gradient Boosting Regression  Random Forest Regression  \\\n",
              "RSquare                      0.891279                  0.759706   \n",
              "RMSE                         0.016801                  0.037133   \n",
              "MAE                          0.086365                  0.126793   \n",
              "\n",
              "         Linear Regression  \n",
              "RSquare           0.885357  \n",
              "RMSE              0.017716  \n",
              "MAE               0.090308  "
            ],
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              "      <th></th>\n",
              "      <th>Gradient Boosting Regression</th>\n",
              "      <th>Random Forest Regression</th>\n",
              "      <th>Linear Regression</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>RSquare</th>\n",
              "      <td>0.891279</td>\n",
              "      <td>0.759706</td>\n",
              "      <td>0.885357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.016801</td>\n",
              "      <td>0.037133</td>\n",
              "      <td>0.017716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAE</th>\n",
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              "        document.querySelector('#df-28daadf5-2479-431b-a57b-6eb79591b573 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-28daadf5-2479-431b-a57b-6eb79591b573');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\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-8a73c03b-dd14-40c8-a82c-46edb067ed47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pdata = pd.DataFrame()\n",
        "# pdata = y_test\n",
        "# pdata\n",
        "# pred = model3.predict(X_test)\n",
        "# pred"
      ],
      "metadata": {
        "id": "zivlim9SONC-"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------->>>>>>>    Algo 6 **Lasso Regression**"
      ],
      "metadata": {
        "id": "ZxbTSUIsmBh2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model6 = lr6.fit(X_train, y_train)\n",
        "rsq6 = model6.score(X_test, y_test)\n",
        "predictions6 = model6.predict(X_test)\n",
        "rmse6 = mean_squared_error(y_test, predictions6)\n",
        "mae6 = mean_absolute_error(y_test,predictions6)\n",
        "predictions6 = model6.predict(feats)\n",
        "final_predictions6 = np.exp(predictions6)\n",
        "submission['SalePrice6'] = final_predictions6\n",
        "list6 = [rsq6,rmse6,mae6]\n",
        "final[\"Lasso Regression\"] = list6\n",
        "final"
      ],
      "metadata": {
        "id": "qi7ljfEkmIGH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "4a0e165a-6259-4a85-ef6d-3fac9303bf3d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Gradient Boosting Regression  Random Forest Regression  \\\n",
              "RSquare                      0.891279                  0.759706   \n",
              "RMSE                         0.016801                  0.037133   \n",
              "MAE                          0.086365                  0.126793   \n",
              "\n",
              "         Linear Regression  Lasso Regression  \n",
              "RSquare           0.885357          0.797330  \n",
              "RMSE              0.017716          0.031319  \n",
              "MAE               0.090308          0.120196  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Gradient Boosting Regression</th>\n",
              "      <th>Random Forest Regression</th>\n",
              "      <th>Linear Regression</th>\n",
              "      <th>Lasso Regression</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RSquare</th>\n",
              "      <td>0.891279</td>\n",
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              "      <td>0.885357</td>\n",
              "      <td>0.797330</td>\n",
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              "      <th>RMSE</th>\n",
              "      <td>0.016801</td>\n",
              "      <td>0.037133</td>\n",
              "      <td>0.017716</td>\n",
              "      <td>0.031319</td>\n",
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              "      <th>MAE</th>\n",
              "      <td>0.086365</td>\n",
              "      <td>0.126793</td>\n",
              "      <td>0.090308</td>\n",
              "      <td>0.120196</td>\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b737bdf9-b9ad-49a6-8c02-cf18da1f9bca 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-b737bdf9-b9ad-49a6-8c02-cf18da1f9bca');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8c731a61-6a84-4799-9b88-4304b7f9474f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8c731a61-6a84-4799-9b88-4304b7f9474f')\"\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-8c731a61-6a84-4799-9b88-4304b7f9474f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---->>>>  Algo 7 **Decision Tree**"
      ],
      "metadata": {
        "id": "OhQWm7oPnA9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "tr_regressor = DecisionTreeRegressor(random_state=0)\n",
        "model7 = tr_regressor.fit(X_train, y_train)\n",
        "rsq7 = model7.score(X_test, y_test)\n",
        "predictions7 = model7.predict(X_test)\n",
        "rmse7 = mean_squared_error(y_test, predictions7)\n",
        "mae7 = mean_absolute_error(y_test,predictions7)\n",
        "predictions7 = model7.predict(feats)\n",
        "final_predictions7 = np.exp(predictions7)\n",
        "submission['SalePrice7'] = final_predictions7\n",
        "list7 = [rsq7,rmse7,mae7]\n",
        "final[\"Decision Tree\"] = list7\n",
        "final"
      ],
      "metadata": {
        "id": "JaDRcCIFnI29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "6f9bb725-0fa4-4acd-cbbd-6257eb772509"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Gradient Boosting Regression  Random Forest Regression  \\\n",
              "RSquare                      0.891279                  0.759706   \n",
              "RMSE                         0.016801                  0.037133   \n",
              "MAE                          0.086365                  0.126793   \n",
              "\n",
              "         Linear Regression  Lasso Regression  Decision Tree  \n",
              "RSquare           0.885357          0.797330       0.638444  \n",
              "RMSE              0.017716          0.031319       0.055872  \n",
              "MAE               0.090308          0.120196       0.160634  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gradient Boosting Regression</th>\n",
              "      <th>Random Forest Regression</th>\n",
              "      <th>Linear Regression</th>\n",
              "      <th>Lasso Regression</th>\n",
              "      <th>Decision Tree</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RSquare</th>\n",
              "      <td>0.891279</td>\n",
              "      <td>0.759706</td>\n",
              "      <td>0.885357</td>\n",
              "      <td>0.797330</td>\n",
              "      <td>0.638444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.016801</td>\n",
              "      <td>0.037133</td>\n",
              "      <td>0.017716</td>\n",
              "      <td>0.031319</td>\n",
              "      <td>0.055872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAE</th>\n",
              "      <td>0.086365</td>\n",
              "      <td>0.126793</td>\n",
              "      <td>0.090308</td>\n",
              "      <td>0.120196</td>\n",
              "      <td>0.160634</td>\n",
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              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-754ea83a-62f1-4b34-b90e-31754f858b6c 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-754ea83a-62f1-4b34-b90e-31754f858b6c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --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",
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              "\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-2646ffb7-3554-4e91-9fa0-458ae1cff03e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "----->>>> Algo 8 **Support Vector Regression**"
      ],
      "metadata": {
        "id": "c3TIf6QhrC5p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR\n",
        "lr8 = SVR(kernel = 'rbf')\n",
        "model8 = lr8.fit(X_train, y_train)\n",
        "rsq8 = model8.score(X_test, y_test)\n",
        "predictions8 = model8.predict(X_test)\n",
        "rmse8 = mean_squared_error(y_test, predictions8)\n",
        "mae8 = mean_absolute_error(y_test,predictions8)\n",
        "predictions8 = model8.predict(feats)\n",
        "final_predictions8 = np.exp(predictions8)\n",
        "submission['SalePrice8'] = final_predictions8\n",
        "list8 = [rsq8,rmse8,mae8]\n",
        "final[\"Support Vector Regression\"] = list8\n",
        "final"
      ],
      "metadata": {
        "id": "CEO7tXD-rM1x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "3898b7e2-0d1c-410d-f573-adb52f6e58c2"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Gradient Boosting Regression  Random Forest Regression  \\\n",
              "RSquare                      0.891279                  0.759706   \n",
              "RMSE                         0.016801                  0.037133   \n",
              "MAE                          0.086365                  0.126793   \n",
              "\n",
              "         Linear Regression  Lasso Regression  Decision Tree  \\\n",
              "RSquare           0.885357          0.797330       0.638444   \n",
              "RMSE              0.017716          0.031319       0.055872   \n",
              "MAE               0.090308          0.120196       0.160634   \n",
              "\n",
              "         Support Vector Regression  \n",
              "RSquare                   0.731221  \n",
              "RMSE                      0.041535  \n",
              "MAE                       0.142916  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gradient Boosting Regression</th>\n",
              "      <th>Random Forest Regression</th>\n",
              "      <th>Linear Regression</th>\n",
              "      <th>Lasso Regression</th>\n",
              "      <th>Decision Tree</th>\n",
              "      <th>Support Vector Regression</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RSquare</th>\n",
              "      <td>0.891279</td>\n",
              "      <td>0.759706</td>\n",
              "      <td>0.885357</td>\n",
              "      <td>0.797330</td>\n",
              "      <td>0.638444</td>\n",
              "      <td>0.731221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMSE</th>\n",
              "      <td>0.016801</td>\n",
              "      <td>0.037133</td>\n",
              "      <td>0.017716</td>\n",
              "      <td>0.031319</td>\n",
              "      <td>0.055872</td>\n",
              "      <td>0.041535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAE</th>\n",
              "      <td>0.086365</td>\n",
              "      <td>0.126793</td>\n",
              "      <td>0.090308</td>\n",
              "      <td>0.120196</td>\n",
              "      <td>0.160634</td>\n",
              "      <td>0.142916</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-8b5003a7-e7be-4546-8182-7e3d92163746')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8b5003a7-e7be-4546-8182-7e3d92163746 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-8b5003a7-e7be-4546-8182-7e3d92163746');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5712420c-f8ae-44f7-b662-eab0ec978320\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5712420c-f8ae-44f7-b662-eab0ec978320')\"\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-5712420c-f8ae-44f7-b662-eab0ec978320 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final.to_csv('final_submission.csv', index=False)"
      ],
      "metadata": {
        "id": "U9v8Bi2vizLa"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "collapsed": true,
        "id": "vJ8DXzchilBS"
      },
      "outputs": [],
      "source": [
        "submission.to_csv('Anand_houseprice_submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Saving the model:-**"
      ],
      "metadata": {
        "id": "PGTOgGZA6rvp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "filename = 'finalized_model.sav'\n",
        "pickle.dump(model, open(filename, 'wb'))\n",
        "# some time later...\n",
        "# load the model from disk\n",
        "loaded_model = pickle.load(open('/content/finalized_model.sav', 'rb'))\n",
        "result = loaded_model.score(X_test, y_test)\n",
        "print(result)"
      ],
      "metadata": {
        "id": "QkocHevy6ssN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdea04b0-e390-4115-bfb3-ba69ebe4ac79"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8912793943119743\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.5.2"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}