01_Linear_Regression.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "#1. Pandas Profiling Report\n",
        "\n",
        "First, we will use the Pandas Profiling method to create a profilig report, but we need to install the newer version of it.\n",
        "\n"
      ],
      "metadata": {
        "id": "PmMTFb5uX50B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running this command may or may not ask you to restart the runtime, but if it does, do restart it."
      ],
      "metadata": {
        "id": "Bj_MeJ7KaTvV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import necessary modules\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "WFDTvT3BZ588"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Read the csv file\n",
        "df = pd.read_csv(\"train.csv\")"
      ],
      "metadata": {
        "id": "lHjnbehXaCAK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now you can download that profiling report and view it on your browser**"
      ],
      "metadata": {
        "id": "NhL6bjFPbzZ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Description of entire dataset\n",
        "df.describe"
      ],
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        },
        "id": "Nc_rOEjoP7x3",
        "outputId": "02453df3-767a-4eba-ef4f-a6c196d48211"
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            "text/plain": [
              "<bound method NDFrame.describe of         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",
              "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
              "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
              "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
              "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
              "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
              "\n",
              "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
              "0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
              "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
              "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
              "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
              "\n",
              "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0         2   2008        WD         Normal     208500  \n",
              "1         5   2007        WD         Normal     181500  \n",
              "2         9   2008        WD         Normal     223500  \n",
              "3         2   2006        WD        Abnorml     140000  \n",
              "4        12   2008        WD         Normal     250000  \n",
              "...     ...    ...       ...            ...        ...  \n",
              "1455      8   2007        WD         Normal     175000  \n",
              "1456      2   2010        WD         Normal     210000  \n",
              "1457      5   2010        WD         Normal     266500  \n",
              "1458      4   2010        WD         Normal     142125  \n",
              "1459      6   2008        WD         Normal     147500  \n",
              "\n",
              "[1460 rows x 81 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
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          "data": {
            "text/plain": [
              "(1460, 81)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Top n rows of the dataset\n",
        "df.head(5)"
      ],
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          "height": 299
        },
        "id": "A7Kyv9sfQQ41",
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        {
          "output_type": "execute_result",
          "data": {
            "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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              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
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              "      <td>2</td>\n",
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              "      <th>2</th>\n",
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              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
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              "      <td>IR1</td>\n",
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              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
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              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
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              "<p>5 rows × 81 columns</p>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ozvsq6IZQdej",
        "outputId": "33f2e9aa-6d57-4941-924b-639361463a82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 int64\n",
              "MSSubClass         int64\n",
              "MSZoning          object\n",
              "LotFrontage      float64\n",
              "LotArea            int64\n",
              "                  ...   \n",
              "MoSold             int64\n",
              "YrSold             int64\n",
              "SaleType          object\n",
              "SaleCondition     object\n",
              "SalePrice          int64\n",
              "Length: 81, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dC1FmzITQfTI",
        "outputId": "37c09a87-92b9-4691-b6a4-68d74430f9ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([dtype('int64'), dtype('O'), dtype('float64')], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Feature Selection\n",
        "\n",
        "This is a method in Data Sciences in which we select the features which have the most impact on our dependant variable.\n",
        "\n",
        "Learn more in detail here : [Feature Selection with sklearn and Pandas](https://towardsdatascience.com/feature-selection-with-pandas-e3690ad8504b)"
      ],
      "metadata": {
        "id": "fGSLG0Mzf8sk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**We are targeting the \"SalePrice\" column which is the dependant variable in our case.**"
      ],
      "metadata": {
        "id": "bQLz6BaWhDsL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhNaZ_lVdo8r",
        "outputId": "f6587e23-0a18-4f25-ba0f-07f0bdb73b8c"
      },
      "execution_count": null,
      "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": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "These are the total null values in each of our 81 variables, you can check the top or bottom few by using the *head()* or *tail()*  methods."
      ],
      "metadata": {
        "id": "YfAcHTf5eFZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Get top 5 variables with highest number of null values\n",
        "df.isnull().sum().nlargest(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RiSujTUkd9vw",
        "outputId": "6fefbce2-85af-4ce7-e78e-d97ba741ee77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PoolQC         1453\n",
              "MiscFeature    1406\n",
              "Alley          1369\n",
              "Fence          1179\n",
              "FireplaceQu     690\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top 4 features have more than half of the values missing, means they don't affect our dependant variable as much as the others, so it's safe to drop them. It will increase our training speed and will also help our model learn better, but first we will look into a few other Feature Selection methods and techniques."
      ],
      "metadata": {
        "id": "T1JuXxwvemP8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1. Filter Method\n",
        "\n",
        "In this technique we filter features based on their correlation with the output/dependant variable. We will use the Pearson correlation built in Pandas.\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "The correlation coefficient has values between -1 to 1\n",
        "\n",
        "— A value closer to 0 implies weaker correlation (exact 0 implying no correlation)\n",
        "\n",
        "— A value closer to 1 implies stronger positive correlation\n",
        "\n",
        "— A value closer to -1 implies stronger negative correlation\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Here we will first plot the Pearson correlation heatmap using Seaborn library's heatmap and see the correlation of independent variables with the output variable *SalePrice*."
      ],
      "metadata": {
        "id": "FUdHyhMhsisd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(40,40))\n",
        "cor = df.corr()\n",
        "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "06nsGWigsSNp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation of all features with the SalePrice Column\n",
        "df[df.columns[1:]].corr()['SalePrice'][:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rVcxj1HQvjk",
        "outputId": "bbd5a439-6f47-4214-ec62-0163b0aff382"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MSSubClass      -0.084284\n",
              "LotFrontage      0.351799\n",
              "LotArea          0.263843\n",
              "OverallQual      0.790982\n",
              "OverallCond     -0.077856\n",
              "YearBuilt        0.522897\n",
              "YearRemodAdd     0.507101\n",
              "MasVnrArea       0.477493\n",
              "BsmtFinSF1       0.386420\n",
              "BsmtFinSF2      -0.011378\n",
              "BsmtUnfSF        0.214479\n",
              "TotalBsmtSF      0.613581\n",
              "1stFlrSF         0.605852\n",
              "2ndFlrSF         0.319334\n",
              "LowQualFinSF    -0.025606\n",
              "GrLivArea        0.708624\n",
              "BsmtFullBath     0.227122\n",
              "BsmtHalfBath    -0.016844\n",
              "FullBath         0.560664\n",
              "HalfBath         0.284108\n",
              "BedroomAbvGr     0.168213\n",
              "KitchenAbvGr    -0.135907\n",
              "TotRmsAbvGrd     0.533723\n",
              "Fireplaces       0.466929\n",
              "GarageYrBlt      0.486362\n",
              "GarageCars       0.640409\n",
              "GarageArea       0.623431\n",
              "WoodDeckSF       0.324413\n",
              "OpenPorchSF      0.315856\n",
              "EnclosedPorch   -0.128578\n",
              "3SsnPorch        0.044584\n",
              "ScreenPorch      0.111447\n",
              "PoolArea         0.092404\n",
              "MiscVal         -0.021190\n",
              "MoSold           0.046432\n",
              "YrSold          -0.028923\n",
              "SalePrice        1.000000\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation with output variable\n",
        "cor_target = abs(cor[\"SalePrice\"])\n",
        "\n",
        "#Selecting highly correlated features, who have correlation coefficient of greater than or equal to 0\n",
        "relevant_features = cor_target[cor_target>=0]\n",
        "relevant_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nSTOw16UubC7",
        "outputId": "3f7c9a9f-0610-49c5-e1d3-d6f80ae40526"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               0.021917\n",
              "MSSubClass       0.084284\n",
              "LotFrontage      0.351799\n",
              "LotArea          0.263843\n",
              "OverallQual      0.790982\n",
              "OverallCond      0.077856\n",
              "YearBuilt        0.522897\n",
              "YearRemodAdd     0.507101\n",
              "MasVnrArea       0.477493\n",
              "BsmtFinSF1       0.386420\n",
              "BsmtFinSF2       0.011378\n",
              "BsmtUnfSF        0.214479\n",
              "TotalBsmtSF      0.613581\n",
              "1stFlrSF         0.605852\n",
              "2ndFlrSF         0.319334\n",
              "LowQualFinSF     0.025606\n",
              "GrLivArea        0.708624\n",
              "BsmtFullBath     0.227122\n",
              "BsmtHalfBath     0.016844\n",
              "FullBath         0.560664\n",
              "HalfBath         0.284108\n",
              "BedroomAbvGr     0.168213\n",
              "KitchenAbvGr     0.135907\n",
              "TotRmsAbvGrd     0.533723\n",
              "Fireplaces       0.466929\n",
              "GarageYrBlt      0.486362\n",
              "GarageCars       0.640409\n",
              "GarageArea       0.623431\n",
              "WoodDeckSF       0.324413\n",
              "OpenPorchSF      0.315856\n",
              "EnclosedPorch    0.128578\n",
              "3SsnPorch        0.044584\n",
              "ScreenPorch      0.111447\n",
              "PoolArea         0.092404\n",
              "MiscVal          0.021190\n",
              "MoSold           0.046432\n",
              "YrSold           0.028923\n",
              "SalePrice        1.000000\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Selecting lesser correlated features, with correlation coefficient of lesser than 0.3\n",
        "irrelevant_features = cor_target[cor_target<0.3]\n",
        "irrelevant_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hJOIUyBuyBH",
        "outputId": "75526a20-48bb-4c42-87fa-1e8893f2547f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               0.021917\n",
              "MSSubClass       0.084284\n",
              "LotArea          0.263843\n",
              "OverallCond      0.077856\n",
              "BsmtFinSF2       0.011378\n",
              "BsmtUnfSF        0.214479\n",
              "LowQualFinSF     0.025606\n",
              "BsmtFullBath     0.227122\n",
              "BsmtHalfBath     0.016844\n",
              "HalfBath         0.284108\n",
              "BedroomAbvGr     0.168213\n",
              "KitchenAbvGr     0.135907\n",
              "EnclosedPorch    0.128578\n",
              "3SsnPorch        0.044584\n",
              "ScreenPorch      0.111447\n",
              "PoolArea         0.092404\n",
              "MiscVal          0.021190\n",
              "MoSold           0.046432\n",
              "YrSold           0.028923\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See, correlation is telling us that there are many features who don't impact our target variable s0 much, so Feature Selection based on null values isn't enough, but there is another Feature Selection technique we can use."
      ],
      "metadata": {
        "id": "WemSA39gvKXM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2. Wrapper Method using Backward Elimination\n",
        "\n",
        "A wrapper method needs one machine learning algorithm and uses its performance as evaluation criteria. This means, you feed the features to the selected Machine Learning algorithm and based on the model performance you add/remove the features. This is an iterative and computationally expensive process but it is more accurate than the filter method.\n",
        "\n",
        "---\n",
        "\n",
        "In Backward Elimination, we feed all the possible features to the model at first. We check the performance of the model and then iteratively remove the worst performing features one by one till the overall performance of the model comes in acceptable range."
      ],
      "metadata": {
        "id": "2ln262-fvg1u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To be able to train our model we need to do the following things :\n",
        "\n",
        "1.   Drop the features with least correlation coefficients and null values.\n",
        "2.   Fill the missing values in selected features with mode(the most frequently occuring value) in categorical columns and mean(average of all values) in numerical columns.\n",
        "3.   Convert categorical columns into numerical ones using *.cat.codes* method from Pandas which simply returns the values with their indexes.\n",
        "\n"
      ],
      "metadata": {
        "id": "EaZm7_z1wQCp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping features with most null values and least correlation coefficients.\n",
        "final_df = df.drop(['Fence', 'MiscFeature','PoolQC','FireplaceQu','LotFrontage','Alley'], axis=1)"
      ],
      "metadata": {
        "id": "T3chaP6dwKCy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Filling null values in remaining features\n",
        "final_df['MSZoning']=final_df['MSZoning'].fillna(final_df['MSZoning'].mode()[0])\n",
        "final_df['Utilities']=final_df['Utilities'].fillna(final_df['Utilities'].mode()[0])\n",
        "final_df['Exterior1st']=final_df['Exterior1st'].fillna(final_df['Exterior1st'].mode()[0])\n",
        "final_df['Exterior2nd']=final_df['Exterior2nd'].fillna(final_df['Exterior2nd'].mode()[0])\n",
        "final_df['BsmtFinSF1']=final_df['BsmtFinSF1'].fillna(final_df['BsmtFinSF1'].mean())\n",
        "final_df['BsmtFinSF2']=final_df['BsmtFinSF2'].fillna(final_df['BsmtFinSF2'].mean())\n",
        "final_df['BsmtUnfSF']=final_df['BsmtUnfSF'].fillna(final_df['BsmtUnfSF'].mean())\n",
        "final_df['TotalBsmtSF']=final_df['TotalBsmtSF'].fillna(final_df['TotalBsmtSF'].mean())\n",
        "final_df['BsmtFullBath']=final_df['BsmtFullBath'].fillna(final_df['BsmtFullBath'].mean())\n",
        "final_df['BsmtHalfBath']=final_df['BsmtHalfBath'].fillna(final_df['BsmtHalfBath'].mean())\n",
        "final_df['KitchenQual']=final_df['KitchenQual'].fillna(final_df['KitchenQual'].mode()[0])\n",
        "final_df['Functional']=final_df['Functional'].fillna(final_df['Functional'].mode()[0])\n",
        "final_df['GarageCars']=final_df['GarageCars'].fillna(final_df['GarageCars'].mean())\n",
        "final_df['GarageArea']=final_df['GarageArea'].fillna(final_df['GarageArea'].mean())\n",
        "final_df['SaleType']=final_df['SaleType'].fillna(final_df['SaleType'].mode()[0])\n",
        "final_df['BsmtQual']=final_df['BsmtQual'].fillna(final_df['BsmtQual'].mode()[0])\n",
        "final_df['Electrical']=final_df['Electrical'].fillna(final_df['Electrical'].mode()[0])\n",
        "final_df['BsmtCond']=final_df['BsmtCond'].fillna(final_df['BsmtCond'].mode()[0])\n",
        "final_df['BsmtExposure']=final_df['BsmtExposure'].fillna(final_df['BsmtExposure'].mode()[0])\n",
        "final_df['BsmtFinType1']=final_df['BsmtFinType1'].fillna(final_df['BsmtFinType1'].mode()[0])\n",
        "final_df['BsmtFinType2']=final_df['BsmtFinType2'].fillna(final_df['BsmtFinType2'].mode()[0])\n",
        "final_df['MasVnrArea']=final_df['MasVnrArea'].fillna(final_df['MasVnrArea'].mean())\n",
        "final_df['MasVnrType']=final_df['MasVnrType'].fillna(final_df['MasVnrType'].mode()[0])\n",
        "final_df['GarageType']=final_df['GarageType'].fillna(final_df['GarageType'].mode()[0])\n",
        "final_df['GarageYrBlt']=final_df['GarageYrBlt'].fillna(final_df['GarageYrBlt'].mean())\n",
        "final_df['GarageFinish']=final_df['GarageFinish'].fillna(final_df['GarageFinish'].mode()[0])\n",
        "final_df['GarageQual']=final_df['GarageQual'].fillna(final_df['GarageQual'].mode()[0])\n",
        "final_df['GarageCond']=final_df['GarageCond'].fillna(final_df['GarageCond'].mode()[0])"
      ],
      "metadata": {
        "id": "NFaBLZv1x6ZM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gboJrKF7yWB6",
        "outputId": "1e9cff54-d3c6-4f9e-ce7b-fb558b311eda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 75)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Popping out the ID column as its not used for training the model\n",
        "popped_col = final_df.pop('Id')"
      ],
      "metadata": {
        "id": "4brS9SoeyZl9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting categorical columns to numerical\n",
        "for col_name in final_df.columns:\n",
        "    if(final_df[col_name].dtype == 'object'):\n",
        "        final_df[col_name]= final_df[col_name].astype('category')\n",
        "        final_df[col_name] = final_df[col_name].cat.codes\n",
        "\n",
        "for col_name in final_df.columns:\n",
        "    if(final_df[col_name].dtype == 'str'):\n",
        "        final_df[col_name]= final_df[col_name].astype('category')\n",
        "        final_df[col_name] = final_df[col_name].cat.codes  "
      ],
      "metadata": {
        "id": "72h2YXmLya-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperating our data set into dependant and independant variables\n",
        "\n",
        "X = final_df.drop(\"SalePrice\",1)   #Feature Matrix\n",
        "y = final_df[\"SalePrice\"]          #Target Variable"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBsUGYbEoph-",
        "outputId": "f02e23ef-4060-4cb3-df9e-51cce2f4dd19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tigO9Hjy14n",
        "outputId": "e9a96e0e-05bf-499d-98ed-f184052ce059"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 73)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Using a statsmodel to find out the pvalues\n",
        "import statsmodels.api as sm\n",
        "\n",
        "X_1 = sm.add_constant(X)\n",
        "#Fitting sm.OLS model\n",
        "model = sm.OLS(y,X_1.astype(float)).fit()\n",
        "\n",
        "#The lower the p value, the better\n",
        "model.pvalues"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqykx1Hvyied",
        "outputId": "6da52003-0536-4c25-d183-85f3e66ee58e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "const            0.139469\n",
              "MSSubClass       0.008894\n",
              "MSZoning         0.514386\n",
              "LotArea          0.000770\n",
              "Street           0.038084\n",
              "                   ...   \n",
              "MiscVal          0.756056\n",
              "MoSold           0.576590\n",
              "YrSold           0.070269\n",
              "SaleType         0.343033\n",
              "SaleCondition    0.002684\n",
              "Length: 74, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total number of pvalues\n",
        "model.pvalues.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtOC0lYayo3E",
        "outputId": "d016ca6e-e3e5-425b-d592-6326212b3370"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "74"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Top 20 features with largest pvalues\n",
        "model.pvalues.nlargest(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-mDcigvy5jG",
        "outputId": "766f2ed1-5c01-4204-cf8e-2f07240fef2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GarageFinish     0.998617\n",
              "HalfBath         0.960903\n",
              "GarageQual       0.949036\n",
              "GarageType       0.863644\n",
              "CentralAir       0.846317\n",
              "GarageYrBlt      0.801358\n",
              "LotConfig        0.793923\n",
              "MiscVal          0.756056\n",
              "BsmtHalfBath     0.737031\n",
              "EnclosedPorch    0.729414\n",
              "ExterCond        0.720414\n",
              "Electrical       0.658082\n",
              "PavedDrive       0.630084\n",
              "BsmtFinType2     0.613091\n",
              "LowQualFinSF     0.577799\n",
              "MoSold           0.576590\n",
              "GarageArea       0.564959\n",
              "YearRemodAdd     0.540060\n",
              "Condition1       0.536791\n",
              "Exterior2nd      0.534238\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bottom 10 features with smallest pvalues\n",
        "model.pvalues.nsmallest(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rEZDSL2Ay9IE",
        "outputId": "07bb7424-0044-4114-c486-260700065e4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OverallQual     1.048785e-20\n",
              "BsmtQual        8.831079e-10\n",
              "KitchenQual     1.347972e-09\n",
              "GrLivArea       2.236265e-07\n",
              "MasVnrArea      2.277047e-07\n",
              "ExterQual       4.080290e-06\n",
              "OverallCond     1.139581e-05\n",
              "Functional      1.620891e-05\n",
              "BsmtExposure    2.937483e-05\n",
              "GarageCars      9.723728e-05\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting them into numpy array so we can view them without scientific notation\n",
        "np.set_printoptions(suppress=True)\n",
        "model.pvalues.nsmallest(35).to_numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KMtflSOozcXZ",
        "outputId": "8fb429dd-a75d-4e0d-b62a-f78989c576e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.00000022, 0.00000023,\n",
              "       0.00000408, 0.0000114 , 0.00001621, 0.00002937, 0.00009724,\n",
              "       0.0007697 , 0.00081191, 0.00151557, 0.00265661, 0.00268434,\n",
              "       0.00281467, 0.00284813, 0.0030879 , 0.00522445, 0.00537494,\n",
              "       0.00591659, 0.00889435, 0.01239522, 0.01274283, 0.01424899,\n",
              "       0.01572483, 0.01785743, 0.01955068, 0.03808427, 0.04163463,\n",
              "       0.04583348, 0.06663602, 0.07026938, 0.09924521, 0.09947368])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usually the approach is to remove the feature with the highest pvalue and build the model again. This is an iterative process and is repeated untill all the features with pvalues higher than some specific value are removed. Usually that pvalue is set to 0.05. Let's implement this using a loop."
      ],
      "metadata": {
        "id": "wy96jQ0g0kKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Backward Elimination\n",
        "cols = list(X.columns)\n",
        "pmax = 1\n",
        "while (len(cols)>0):\n",
        "    p= []\n",
        "    X_1 = X[cols]\n",
        "    X_1 = sm.add_constant(X_1)\n",
        "    model = sm.OLS(y,X_1).fit()\n",
        "    p = pd.Series(model.pvalues.values[1:],index = cols)      \n",
        "    pmax = max(p)\n",
        "    feature_with_p_max = p.idxmax()\n",
        "    if(pmax>0.05):\n",
        "        cols.remove(feature_with_p_max)\n",
        "    else:\n",
        "        break\n",
        "selected_features_BE = cols\n",
        "print(selected_features_BE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jiqlUbvzzpo",
        "outputId": "d3a60d66-d40a-41b7-b420-8af6e5427c25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
            "  x = pd.concat(x[::order], 1)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['MSSubClass', 'LotArea', 'Street', 'LandContour', 'Neighborhood', 'Condition2', 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'RoofMatl', 'Exterior1st', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'BsmtQual', 'BsmtExposure', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'BsmtFullBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'GarageCars', 'WoodDeckSF', 'ScreenPorch', 'SaleCondition']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(selected_features_BE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyheQH4S1Moh",
        "outputId": "1da12107-8a0d-45c6-d576-ea34531e6dad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**So, out of the 74 p-values, only 32 are higher than the standard 0.05 value. If we drop the rest, there will be a significant data loss and our models might not be able to learn well. So it's upto us to decide the standard value, maybe lower it even further to the point where we dont lose a lot of features.**"
      ],
      "metadata": {
        "id": "0wMV40aw1UQC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3. Final Feature Selection\n",
        "\n",
        "Features PoolQC, Alley, MiscFeature and Fence have more than 1000 missing values so they neither give us much information no do they impact our dataset much. So we will be dropping these features without second thoughts.\n",
        "\n",
        "The results of our Backwards Elimination and Filter method based on correlation has shown us that there are other less relevant features as well but those features don't have any null values so we won't be dropping any other features."
      ],
      "metadata": {
        "id": "-BQSbmllSPWn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Fence', 'MiscFeature','PoolQC','Alley'], axis=1)"
      ],
      "metadata": {
        "id": "ZdZAh8GuUfxc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgSHhPQUVr0D",
        "outputId": "4378b216-bd0c-493f-f512-a376910e350b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 77)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4 features have been dropped, so our dataset's dimension has been reduced."
      ],
      "metadata": {
        "id": "kgHbVvwFVxfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "z6lNtQjvVwJL",
        "outputId": "3860ac54-4ebd-4da9-d98f-70e50ab1efa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
              "0   1          60       RL         65.0     8450   Pave      Reg         Lvl   \n",
              "1   2          20       RL         80.0     9600   Pave      Reg         Lvl   \n",
              "2   3          60       RL         68.0    11250   Pave      IR1         Lvl   \n",
              "3   4          70       RL         60.0     9550   Pave      IR1         Lvl   \n",
              "4   5          60       RL         84.0    14260   Pave      IR1         Lvl   \n",
              "\n",
              "  Utilities LotConfig  ... EnclosedPorch 3SsnPorch ScreenPorch PoolArea  \\\n",
              "0    AllPub    Inside  ...             0         0           0        0   \n",
              "1    AllPub       FR2  ...             0         0           0        0   \n",
              "2    AllPub    Inside  ...             0         0           0        0   \n",
              "3    AllPub    Corner  ...           272         0           0        0   \n",
              "4    AllPub       FR2  ...             0         0           0        0   \n",
              "\n",
              "  MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0       0      2    2008        WD         Normal     208500  \n",
              "1       0      5    2007        WD         Normal     181500  \n",
              "2       0      9    2008        WD         Normal     223500  \n",
              "3       0      2    2006        WD        Abnorml     140000  \n",
              "4       0     12    2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 77 columns]"
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              "\n",
              "  <div id=\"df-ebb9bd84-ec8b-45fb-bff7-a599380d99ae\">\n",
              "    <div 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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              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>...</th>\n",
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              "      <th>PoolArea</th>\n",
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              "      <th>MoSold</th>\n",
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              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
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              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
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              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>FR2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
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              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
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              "      <td>2</td>\n",
              "      <td>2006</td>\n",
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              "      <td>140000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
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              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
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              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 77 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As our ID column is not a features, instead just an identifier, so we will pop it out as well."
      ],
      "metadata": {
        "id": "42Z57E7SV_HC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Id_column = df.pop(\"Id\")"
      ],
      "metadata": {
        "id": "fbbqI9P3V6O2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muKO4-srWQK1",
        "outputId": "ccce91ac-9ca8-4171-a5a9-7608f5453f96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 76)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One more column has been popped out, reducing our dataset's dimensions again."
      ],
      "metadata": {
        "id": "yxC2Wy42WTih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "3s_z0VpYWRGV",
        "outputId": "4154b264-1f0a-4a5a-eac1-e3b1201ab1a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
              "0          60       RL         65.0     8450   Pave      Reg         Lvl   \n",
              "1          20       RL         80.0     9600   Pave      Reg         Lvl   \n",
              "2          60       RL         68.0    11250   Pave      IR1         Lvl   \n",
              "3          70       RL         60.0     9550   Pave      IR1         Lvl   \n",
              "4          60       RL         84.0    14260   Pave      IR1         Lvl   \n",
              "\n",
              "  Utilities LotConfig LandSlope  ... EnclosedPorch 3SsnPorch ScreenPorch  \\\n",
              "0    AllPub    Inside       Gtl  ...             0         0           0   \n",
              "1    AllPub       FR2       Gtl  ...             0         0           0   \n",
              "2    AllPub    Inside       Gtl  ...             0         0           0   \n",
              "3    AllPub    Corner       Gtl  ...           272         0           0   \n",
              "4    AllPub       FR2       Gtl  ...             0         0           0   \n",
              "\n",
              "  PoolArea MiscVal  MoSold  YrSold  SaleType  SaleCondition SalePrice  \n",
              "0        0       0       2    2008        WD         Normal    208500  \n",
              "1        0       0       5    2007        WD         Normal    181500  \n",
              "2        0       0       9    2008        WD         Normal    223500  \n",
              "3        0       0       2    2006        WD        Abnorml    140000  \n",
              "4        0       0      12    2008        WD         Normal    250000  \n",
              "\n",
              "[5 rows x 76 columns]"
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              "      <th></th>\n",
              "      <th>MSSubClass</th>\n",
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              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
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              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
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              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>60</td>\n",
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              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
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              "      <td>2008</td>\n",
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              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
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              "    <tr>\n",
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              "      <td>80.0</td>\n",
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              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>FR2</td>\n",
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              "      <td>...</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>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
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              "      <td>IR1</td>\n",
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              "      <td>Inside</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70</td>\n",
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              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
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              "      <td>272</td>\n",
              "      <td>0</td>\n",
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              "      <td>2006</td>\n",
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              "      <td>140000</td>\n",
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              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
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              "      <td>Lvl</td>\n",
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              "      <td>FR2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
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              "<p>5 rows × 76 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we are left with only our features and target variable, but notice there are categorical features as well and as a Machine Learning Engineer, you must know that ML models only take numbers as input, so we need to convert our Categorical Features into numerical ones and handle the missing values as well, that's what we will do in data preprocessing Module."
      ],
      "metadata": {
        "id": "FsPCtJdcWeG6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. Data Preprocessing\n",
        "\n",
        "Data preprocessing is final stage before modeling, in this, you prepare your selected dataset for training and evaluation."
      ],
      "metadata": {
        "id": "WvqVGfDcXZQD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1. Seperating Numerical and Categorical columns."
      ],
      "metadata": {
        "id": "Byu1dmJQX4lf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Popping out the Target Variable so we are only left with features.\n",
        "SalePrice_column = df.pop(\"SalePrice\")"
      ],
      "metadata": {
        "id": "rDzlqczMWcs2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "Nvl_MXCNYSh2",
        "outputId": "d13bbfb7-fc11-432f-caea-cb42ad9442f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 75)"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One more column has been popped out, Good.....!!!!!"
      ],
      "metadata": {
        "id": "xMo5NL6pYUdZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save all categorical columns in list\n",
        "categorical_columns = [col for col in df.columns.values if df[col].dtype == 'object']\n",
        "\n",
        "# Dataframe with categorical features\n",
        "data_cat = df[categorical_columns]\n",
        "# Dataframe with numerical features\n",
        "data_num = df.drop(categorical_columns, axis=1)"
      ],
      "metadata": {
        "id": "C4n-uIi2YTtg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cat.shape, data_num.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jR9XZpDIYkWz",
        "outputId": "b4282e5c-9bd7-460d-e5f8-9f436a40546a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1460, 39), (1460, 36))"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 39 categorical and 36 numerical columns."
      ],
      "metadata": {
        "id": "C_dKzL72YrQJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_cat.head()"
      ],
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          "height": 299
        },
        "id": "4DElLawxYp8K",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
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              "  MSZoning Street LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0       RL   Pave      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1       RL   Pave      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2       RL   Pave      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3       RL   Pave      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4       RL   Pave      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "\n",
              "  Neighborhood Condition1 Condition2  ... KitchenQual Functional FireplaceQu  \\\n",
              "0      CollgCr       Norm       Norm  ...          Gd        Typ         NaN   \n",
              "1      Veenker      Feedr       Norm  ...          TA        Typ          TA   \n",
              "2      CollgCr       Norm       Norm  ...          Gd        Typ          TA   \n",
              "3      Crawfor       Norm       Norm  ...          Gd        Typ          Gd   \n",
              "4      NoRidge       Norm       Norm  ...          Gd        Typ          TA   \n",
              "\n",
              "  GarageType GarageFinish GarageQual GarageCond PavedDrive SaleType  \\\n",
              "0     Attchd          RFn         TA         TA          Y       WD   \n",
              "1     Attchd          RFn         TA         TA          Y       WD   \n",
              "2     Attchd          RFn         TA         TA          Y       WD   \n",
              "3     Detchd          Unf         TA         TA          Y       WD   \n",
              "4     Attchd          RFn         TA         TA          Y       WD   \n",
              "\n",
              "  SaleCondition  \n",
              "0        Normal  \n",
              "1        Normal  \n",
              "2        Normal  \n",
              "3       Abnorml  \n",
              "4        Normal  \n",
              "\n",
              "[5 rows x 39 columns]"
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              "      <th>4</th>\n",
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              "      <td>...</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Typ</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 39 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-078ddf4b-f91b-4808-9075-96f87417f78b')\"\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_num.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "hX8R8mdfY2H1",
        "outputId": "2c96a9ab-4870-43e0-8899-8dd970888803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
              "0          60         65.0     8450            7            5       2003   \n",
              "1          20         80.0     9600            6            8       1976   \n",
              "2          60         68.0    11250            7            5       2001   \n",
              "3          70         60.0     9550            7            5       1915   \n",
              "4          60         84.0    14260            8            5       2000   \n",
              "\n",
              "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  GarageArea  \\\n",
              "0          2003       196.0         706           0  ...         548   \n",
              "1          1976         0.0         978           0  ...         460   \n",
              "2          2002       162.0         486           0  ...         608   \n",
              "3          1970         0.0         216           0  ...         642   \n",
              "4          2000       350.0         655           0  ...         836   \n",
              "\n",
              "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
              "0           0           61              0          0            0         0   \n",
              "1         298            0              0          0            0         0   \n",
              "2           0           42              0          0            0         0   \n",
              "3           0           35            272          0            0         0   \n",
              "4         192           84              0          0            0         0   \n",
              "\n",
              "   MiscVal  MoSold  YrSold  \n",
              "0        0       2    2008  \n",
              "1        0       5    2007  \n",
              "2        0       9    2008  \n",
              "3        0       2    2006  \n",
              "4        0      12    2008  \n",
              "\n",
              "[5 rows x 36 columns]"
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              "      <td>162.0</td>\n",
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              "      <td>2008</td>\n",
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              "<p>5 rows × 36 columns</p>\n",
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              "          const element = document.querySelector('#df-060a6f44-1a0d-4833-be11-f47419c5021e');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2. Handling missing values."
      ],
      "metadata": {
        "id": "z2dLVc5VZMF7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_cat.isnull().sum().nlargest(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsyJsIzgY4TG",
        "outputId": "ca1b4848-686a-4454-f6c4-7125098b3c81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FireplaceQu     690\n",
              "GarageType       81\n",
              "GarageFinish     81\n",
              "GarageQual       81\n",
              "GarageCond       81\n",
              "BsmtExposure     38\n",
              "BsmtFinType2     38\n",
              "BsmtQual         37\n",
              "BsmtCond         37\n",
              "BsmtFinType1     37\n",
              "MasVnrType        8\n",
              "Electrical        1\n",
              "MSZoning          0\n",
              "Street            0\n",
              "LotShape          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_num.isnull().sum().nlargest(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pEK1qNNOZVJE",
        "outputId": "630c6f69-6f75-4265-ab6d-0a96438a2566"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LotFrontage    259\n",
              "GarageYrBlt     81\n",
              "MasVnrArea       8\n",
              "MSSubClass       0\n",
              "LotArea          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Categorical Features have more missing values than numerical ones.\n",
        "\n",
        "Let's first handle missing values in numerical features, every numerical column has an average or in statistical terms, a mean value."
      ],
      "metadata": {
        "id": "CclJRQGFZhld"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_num['LotFrontage'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_ao1t-_Zeyk",
        "outputId": "c9562875-60e1-4c97-a4ad-420a2cbc7234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "70.04995836802665"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can fill the missing values with the mean in numerical columns. For the stat model we did it for every column, but let's write a loop and save ourselves some time doing it in far lesser lines of code."
      ],
      "metadata": {
        "id": "PmLqXQHiaFp8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_len = data_num.shape[0]\n",
        "\n",
        "# Check what is percentage of missing values in categorical dataframe\n",
        "for col in data_num.columns.values:\n",
        "    missing_values = data_num[col].isnull().sum()\n",
        "    print(\"{} - missing values: {} ({:0.2f}%)\".format(col, missing_values, missing_values/data_len*100))\n",
        "    print(\"Filling missing values with mean in column: {}\".format(col))\n",
        "    data_num[col] = data_num[col].fillna(data_num[col].mean())\n",
        "    print(\"------------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jvvRpmTaDaH",
        "outputId": "79c9959a-6dad-4ec8-81a6-1ec43580688a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSSubClass - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: MSSubClass\n",
            "------------------------\n",
            "LotFrontage - missing values: 259 (17.74%)\n",
            "Filling missing values with mean in column: LotFrontage\n",
            "------------------------\n",
            "LotArea - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: LotArea\n",
            "------------------------\n",
            "OverallQual - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: OverallQual\n",
            "------------------------\n",
            "OverallCond - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: OverallCond\n",
            "------------------------\n",
            "YearBuilt - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: YearBuilt\n",
            "------------------------\n",
            "YearRemodAdd - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: YearRemodAdd\n",
            "------------------------\n",
            "MasVnrArea - missing values: 8 (0.55%)\n",
            "Filling missing values with mean in column: MasVnrArea\n",
            "------------------------\n",
            "BsmtFinSF1 - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: BsmtFinSF1\n",
            "------------------------\n",
            "BsmtFinSF2 - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: BsmtFinSF2\n",
            "------------------------\n",
            "BsmtUnfSF - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: BsmtUnfSF\n",
            "------------------------\n",
            "TotalBsmtSF - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: TotalBsmtSF\n",
            "------------------------\n",
            "1stFlrSF - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: 1stFlrSF\n",
            "------------------------\n",
            "2ndFlrSF - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: 2ndFlrSF\n",
            "------------------------\n",
            "LowQualFinSF - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: LowQualFinSF\n",
            "------------------------\n",
            "GrLivArea - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: GrLivArea\n",
            "------------------------\n",
            "BsmtFullBath - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: BsmtFullBath\n",
            "------------------------\n",
            "BsmtHalfBath - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: BsmtHalfBath\n",
            "------------------------\n",
            "FullBath - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: FullBath\n",
            "------------------------\n",
            "HalfBath - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: HalfBath\n",
            "------------------------\n",
            "BedroomAbvGr - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: BedroomAbvGr\n",
            "------------------------\n",
            "KitchenAbvGr - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: KitchenAbvGr\n",
            "------------------------\n",
            "TotRmsAbvGrd - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: TotRmsAbvGrd\n",
            "------------------------\n",
            "Fireplaces - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: Fireplaces\n",
            "------------------------\n",
            "GarageYrBlt - missing values: 81 (5.55%)\n",
            "Filling missing values with mean in column: GarageYrBlt\n",
            "------------------------\n",
            "GarageCars - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: GarageCars\n",
            "------------------------\n",
            "GarageArea - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: GarageArea\n",
            "------------------------\n",
            "WoodDeckSF - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: WoodDeckSF\n",
            "------------------------\n",
            "OpenPorchSF - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: OpenPorchSF\n",
            "------------------------\n",
            "EnclosedPorch - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: EnclosedPorch\n",
            "------------------------\n",
            "3SsnPorch - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: 3SsnPorch\n",
            "------------------------\n",
            "ScreenPorch - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: ScreenPorch\n",
            "------------------------\n",
            "PoolArea - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: PoolArea\n",
            "------------------------\n",
            "MiscVal - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: MiscVal\n",
            "------------------------\n",
            "MoSold - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: MoSold\n",
            "------------------------\n",
            "YrSold - missing values: 0 (0.00%)\n",
            "Filling missing values with mean in column: YrSold\n",
            "------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check if there are any remaining missing values in our numerical dataset."
      ],
      "metadata": {
        "id": "YOV8Pbb6bX2l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_num.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1gok0nFaiPq",
        "outputId": "9132f0f1-4e2a-430e-aa90-e47ef5e8f951"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MSSubClass       0\n",
              "LotFrontage      0\n",
              "LotArea          0\n",
              "OverallQual      0\n",
              "OverallCond      0\n",
              "YearBuilt        0\n",
              "YearRemodAdd     0\n",
              "MasVnrArea       0\n",
              "BsmtFinSF1       0\n",
              "BsmtFinSF2       0\n",
              "BsmtUnfSF        0\n",
              "TotalBsmtSF      0\n",
              "1stFlrSF         0\n",
              "2ndFlrSF         0\n",
              "LowQualFinSF     0\n",
              "GrLivArea        0\n",
              "BsmtFullBath     0\n",
              "BsmtHalfBath     0\n",
              "FullBath         0\n",
              "HalfBath         0\n",
              "BedroomAbvGr     0\n",
              "KitchenAbvGr     0\n",
              "TotRmsAbvGrd     0\n",
              "Fireplaces       0\n",
              "GarageYrBlt      0\n",
              "GarageCars       0\n",
              "GarageArea       0\n",
              "WoodDeckSF       0\n",
              "OpenPorchSF      0\n",
              "EnclosedPorch    0\n",
              "3SsnPorch        0\n",
              "ScreenPorch      0\n",
              "PoolArea         0\n",
              "MiscVal          0\n",
              "MoSold           0\n",
              "YrSold           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So we have taken care of the missing values in our numerical dataset using mean, what about the categorical dataset.\n",
        "\n",
        "Every column has one or more most frequently occuring value, which is called mode in stats. We can fill missing values in categorical dataset using mode but it will be better if we just assign them a \"None\" value for the time being."
      ],
      "metadata": {
        "id": "5tGpBsCQbgjc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_cat = data_cat.fillna(\"None\")"
      ],
      "metadata": {
        "id": "sl13IGimbe9r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cat.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QIjx9y_b-tS",
        "outputId": "fd8797df-cc78-468b-d67d-d07edc960ba7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MSZoning         0\n",
              "Street           0\n",
              "LotShape         0\n",
              "LandContour      0\n",
              "Utilities        0\n",
              "LotConfig        0\n",
              "LandSlope        0\n",
              "Neighborhood     0\n",
              "Condition1       0\n",
              "Condition2       0\n",
              "BldgType         0\n",
              "HouseStyle       0\n",
              "RoofStyle        0\n",
              "RoofMatl         0\n",
              "Exterior1st      0\n",
              "Exterior2nd      0\n",
              "MasVnrType       0\n",
              "ExterQual        0\n",
              "ExterCond        0\n",
              "Foundation       0\n",
              "BsmtQual         0\n",
              "BsmtCond         0\n",
              "BsmtExposure     0\n",
              "BsmtFinType1     0\n",
              "BsmtFinType2     0\n",
              "Heating          0\n",
              "HeatingQC        0\n",
              "CentralAir       0\n",
              "Electrical       0\n",
              "KitchenQual      0\n",
              "Functional       0\n",
              "FireplaceQu      0\n",
              "GarageType       0\n",
              "GarageFinish     0\n",
              "GarageQual       0\n",
              "GarageCond       0\n",
              "PavedDrive       0\n",
              "SaleType         0\n",
              "SaleCondition    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Good, now there aren't any missing values in our numerical dataset as well, but remember our models need numerical data to train, so we need to convert categorical columns into numerical ones as well."
      ],
      "metadata": {
        "id": "W4DHJehPcCC-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3. Catcoding categorical dataset.\n",
        "\n",
        "Pandas catcoding is basically indexing in essence. It assigns one index to all the unique values in a categorical column so it can be trained.\n",
        "\n",
        "Let's look at all unique values in our columns."
      ],
      "metadata": {
        "id": "D5JjI6y9cjJF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#All unique values in MSZoning column.\n",
        "data_cat.MSZoning.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04-AsF7fcAtn",
        "outputId": "290066f4-3bc9-4f70-c6bc-78d902672e0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['RL', 'RM', 'C (all)', 'FV', 'RH'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Number of unique values in MSZoning column.\n",
        "len(data_cat.MSZoning.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLCYhqp0dJi-",
        "outputId": "9f977a08-6d1f-448b-88a1-8722f5c9a801"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#All unique values in FireplaceQu column.\n",
        "data_cat.FireplaceQu.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nhf_9Hz2drAo",
        "outputId": "e26daae4-4e11-4ad1-cd34-bc08b3a1dec6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['None', 'TA', 'Gd', 'Fa', 'Ex', 'Po'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See, there are also None values in this column, because we filled the missing values here with None."
      ],
      "metadata": {
        "id": "eVb_MlMeeEqD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Number of unique values in FireplaceQu column.\n",
        "len(data_cat.FireplaceQu.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tc5tHg_LeDGY",
        "outputId": "ed3ba6cd-796f-4724-8600-e0d11348225b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So Pandas categorical coding will use 5 indexes in MSZoning column, 6 in FireplaceQu and so on.\n",
        "\n",
        "There is another way of converting Categorical features to numerical ones called \"One Hot Encoding\", which creates new columns for every unique categorical feature. It can also be done with Pandas but we have a seperate Test dataset as well so we will stick to this approach.\n",
        "\n",
        "Bear in mind that both Catcoding and one hot encoding yield same results, there isn't much difference but as one hot encoding creates new columns, it might be computationally expensive."
      ],
      "metadata": {
        "id": "E4Rs0uJieTwR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Viewing our categorical dataset again.\n",
        "data_cat.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "QC4cwby9gQxm",
        "outputId": "35790ede-2d2f-4939-99a5-d2a110deaa63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  MSZoning Street LotShape LandContour Utilities LotConfig LandSlope  \\\n",
              "0       RL   Pave      Reg         Lvl    AllPub    Inside       Gtl   \n",
              "1       RL   Pave      Reg         Lvl    AllPub       FR2       Gtl   \n",
              "2       RL   Pave      IR1         Lvl    AllPub    Inside       Gtl   \n",
              "3       RL   Pave      IR1         Lvl    AllPub    Corner       Gtl   \n",
              "4       RL   Pave      IR1         Lvl    AllPub       FR2       Gtl   \n",
              "\n",
              "  Neighborhood Condition1 Condition2  ... KitchenQual Functional FireplaceQu  \\\n",
              "0      CollgCr       Norm       Norm  ...          Gd        Typ        None   \n",
              "1      Veenker      Feedr       Norm  ...          TA        Typ          TA   \n",
              "2      CollgCr       Norm       Norm  ...          Gd        Typ          TA   \n",
              "3      Crawfor       Norm       Norm  ...          Gd        Typ          Gd   \n",
              "4      NoRidge       Norm       Norm  ...          Gd        Typ          TA   \n",
              "\n",
              "  GarageType GarageFinish GarageQual GarageCond PavedDrive SaleType  \\\n",
              "0     Attchd          RFn         TA         TA          Y       WD   \n",
              "1     Attchd          RFn         TA         TA          Y       WD   \n",
              "2     Attchd          RFn         TA         TA          Y       WD   \n",
              "3     Detchd          Unf         TA         TA          Y       WD   \n",
              "4     Attchd          RFn         TA         TA          Y       WD   \n",
              "\n",
              "  SaleCondition  \n",
              "0        Normal  \n",
              "1        Normal  \n",
              "2        Normal  \n",
              "3       Abnorml  \n",
              "4        Normal  \n",
              "\n",
              "[5 rows x 39 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e37632e2-88b9-4a80-a39b-02f13e432539\">\n",
              "    <div 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>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>...</th>\n",
              "      <th>KitchenQual</th>\n",
              "      <th>Functional</th>\n",
              "      <th>FireplaceQu</th>\n",
              "      <th>GarageType</th>\n",
              "      <th>GarageFinish</th>\n",
              "      <th>GarageQual</th>\n",
              "      <th>GarageCond</th>\n",
              "      <th>PavedDrive</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Typ</td>\n",
              "      <td>None</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Veenker</td>\n",
              "      <td>Feedr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>TA</td>\n",
              "      <td>Typ</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>CollgCr</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Typ</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Crawfor</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Typ</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Detchd</td>\n",
              "      <td>Unf</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RL</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>NoRidge</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>...</td>\n",
              "      <td>Gd</td>\n",
              "      <td>Typ</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>RFn</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 39 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e37632e2-88b9-4a80-a39b-02f13e432539')\"\n",
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              "                                                     [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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting categorical columns to numerical\n",
        "for col_name in data_cat.columns:\n",
        "    if(data_cat[col_name].dtype == 'object'):\n",
        "        data_cat[col_name]= data_cat[col_name].astype('category')\n",
        "        data_cat[col_name] = data_cat[col_name].cat.codes\n",
        "\n",
        "for col_name in data_cat.columns:\n",
        "    if(data_cat[col_name].dtype == 'str'):\n",
        "        data_cat[col_name]= data_cat[col_name].astype('category')\n",
        "        data_cat[col_name] = data_cat[col_name].cat.codes  "
      ],
      "metadata": {
        "id": "nBzXx6_YeRhE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_cat.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "GVqWFXU6gW7U",
        "outputId": "1f297152-39e8-4587-dd8f-5e151f04b0d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   MSZoning  Street  LotShape  LandContour  Utilities  LotConfig  LandSlope  \\\n",
              "0         3       1         3            3          0          4          0   \n",
              "1         3       1         3            3          0          2          0   \n",
              "2         3       1         0            3          0          4          0   \n",
              "3         3       1         0            3          0          0          0   \n",
              "4         3       1         0            3          0          2          0   \n",
              "\n",
              "   Neighborhood  Condition1  Condition2  ...  KitchenQual  Functional  \\\n",
              "0             5           2           2  ...            2           6   \n",
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              "4            15           2           2  ...            2           6   \n",
              "\n",
              "   FireplaceQu  GarageType  GarageFinish  GarageQual  GarageCond  PavedDrive  \\\n",
              "0            3           1             2           5           5           2   \n",
              "1            5           1             2           5           5           2   \n",
              "2            5           1             2           5           5           2   \n",
              "3            2           5             3           5           5           2   \n",
              "4            5           1             2           5           5           2   \n",
              "\n",
              "   SaleType  SaleCondition  \n",
              "0         8              4  \n",
              "1         8              4  \n",
              "2         8              4  \n",
              "3         8              0  \n",
              "4         8              4  \n",
              "\n",
              "[5 rows x 39 columns]"
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c83f6ce1-b4e5-4c68-961c-84ee05082785 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
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              "          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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And Boooommmmm, our categorical dataset has been converted into Numerical one."
      ],
      "metadata": {
        "id": "u8YieosSgbyZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Data Visualization.\n",
        "\n",
        "Well, how about some visuals???"
      ],
      "metadata": {
        "id": "Dlt3w7l8gn0y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizing the range of categorical columns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "data_cat.hist(layout=(13,3), figsize=(35, 35))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9th5-cJpgufH",
        "outputId": "d9277727-85ee-4af1-e448-256b6a760bd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2520x2520 with 39 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizing the range of categorical columns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "data_num.hist(layout=(12,3), figsize=(33, 33))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mWYB3dwmg7H4",
        "outputId": "440ce9a2-a8a8-4155-bd0e-73fbc2fbb8cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2376x2376 with 36 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Baseline Scikit Learn Linear Regression model."
      ],
      "metadata": {
        "id": "14hwjTBElCuz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([data_cat, data_num, SalePrice_column], axis=1)"
      ],
      "metadata": {
        "id": "xkjYcSDPkf9-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2CQOLWHolg1_",
        "outputId": "41517fa8-1c13-4161-b53d-09eb5f3ceb23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 76)"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "aH4URGutliM5",
        "outputId": "9f542605-4502-4f58-c6bb-3fc40c0ef213"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MSZoning  Street  LotShape  LandContour  Utilities  LotConfig  LandSlope  \\\n",
              "0         3       1         3            3          0          4          0   \n",
              "1         3       1         3            3          0          2          0   \n",
              "2         3       1         0            3          0          4          0   \n",
              "3         3       1         0            3          0          0          0   \n",
              "4         3       1         0            3          0          2          0   \n",
              "\n",
              "   Neighborhood  Condition1  Condition2  ...  WoodDeckSF  OpenPorchSF  \\\n",
              "0             5           2           2  ...           0           61   \n",
              "1            24           1           2  ...         298            0   \n",
              "2             5           2           2  ...           0           42   \n",
              "3             6           2           2  ...           0           35   \n",
              "4            15           2           2  ...         192           84   \n",
              "\n",
              "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
              "0              0          0            0         0        0       2    2008   \n",
              "1              0          0            0         0        0       5    2007   \n",
              "2              0          0            0         0        0       9    2008   \n",
              "3            272          0            0         0        0       2    2006   \n",
              "4              0          0            0         0        0      12    2008   \n",
              "\n",
              "   SalePrice  \n",
              "0     208500  \n",
              "1     181500  \n",
              "2     223500  \n",
              "3     140000  \n",
              "4     250000  \n",
              "\n",
              "[5 rows x 76 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-90ed79ec-8008-499c-894d-d335f612883e\">\n",
              "    <div 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>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>LandSlope</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Condition1</th>\n",
              "      <th>Condition2</th>\n",
              "      <th>...</th>\n",
              "      <th>WoodDeckSF</th>\n",
              "      <th>OpenPorchSF</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>298</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>192</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 76 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90ed79ec-8008-499c-894d-d335f612883e')\"\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have the complete dataset again."
      ],
      "metadata": {
        "id": "vfhgV32hmWWY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Seperating features and labels\n",
        "X = df.iloc[:, :75].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "h06v87YEmHL0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Splittilng the data set into training and test \n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, shuffle=False)"
      ],
      "metadata": {
        "id": "vS7TFkoKmhkl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fitting the set for linear regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqDYWrPQmp8y",
        "outputId": "7993a8ed-1c5d-406d-8b21-68c1e7f82985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting the results for the test set\n",
        "y_pred = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "MFLgkDnvmskK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "GMzP9PPjnFAe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "absolute_error = mean_absolute_error(y_test, y_pred)\n",
        "absolute_error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLeMNmzCnsow",
        "outputId": "69a2e49c-f96c-490d-e8fa-510f738dd7f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21704.78721857384"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "squared_error = mean_squared_error(y_test, y_pred)\n",
        "squared_error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMXchYX2n1vZ",
        "outputId": "6a0355e3-a5e7-4c1e-a7ce-5ca59606befd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2288808695.074739"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['SalePrice'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rcg7bHXsokvn",
        "outputId": "03141039-a8d5-4d87-a719-32b9b852787f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "180921.19589041095"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Average SalePrice of houses is around 180k while our baseline linear regression model has a mean absolute error of around 21k, means every prediction it made is off by an average of 21k.\n",
        "\n",
        "This might seem a lot but if we make deep learning models with fully connected deep layers we will realize this score is very hard to come close to let alone beating it."
      ],
      "metadata": {
        "id": "Gu_mUIYLovjO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you use linear regression model, you can tweak the dataset, maybe reduce skewness or drop another few less relevant features to obtain better results.\n",
        "\n",
        "But we will finish this notebook here and download this dataset."
      ],
      "metadata": {
        "id": "_qcB99q3qQ4c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([Id_column, df], axis=1)"
      ],
      "metadata": {
        "id": "wd_NIDbborur"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wk_N-W50qzgG",
        "outputId": "7df5383f-8ae1-4c4a-e843-e4e41a2dc367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 77)"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "p7wZ33Kgq06-",
        "outputId": "b2b37dca-efab-425a-92ab-600c2a50d5b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSZoning  Street  LotShape  LandContour  Utilities  LotConfig  \\\n",
              "0   1         3       1         3            3          0          4   \n",
              "1   2         3       1         3            3          0          2   \n",
              "2   3         3       1         0            3          0          4   \n",
              "3   4         3       1         0            3          0          0   \n",
              "4   5         3       1         0            3          0          2   \n",
              "\n",
              "   LandSlope  Neighborhood  Condition1  ...  WoodDeckSF  OpenPorchSF  \\\n",
              "0          0             5           2  ...           0           61   \n",
              "1          0            24           1  ...         298            0   \n",
              "2          0             5           2  ...           0           42   \n",
              "3          0             6           2  ...           0           35   \n",
              "4          0            15           2  ...         192           84   \n",
              "\n",
              "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
              "0              0          0            0         0        0       2    2008   \n",
              "1              0          0            0         0        0       5    2007   \n",
              "2              0          0            0         0        0       9    2008   \n",
              "3            272          0            0         0        0       2    2006   \n",
              "4              0          0            0         0        0      12    2008   \n",
              "\n",
              "   SalePrice  \n",
              "0     208500  \n",
              "1     181500  \n",
              "2     223500  \n",
              "3     140000  \n",
              "4     250000  \n",
              "\n",
              "[5 rows x 77 columns]"
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              "    .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",
              "    [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-dad8a16a-cc80-4b6b-a879-5ee39fb3cba7 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-dad8a16a-cc80-4b6b-a879-5ee39fb3cba7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('PreprocessedDataset.csv', index=False)"
      ],
      "metadata": {
        "id": "GZ7x4R2Rq2Ty"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "files.download(\"PreprocessedDataset.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "m0mM4qMvrfwf",
        "outputId": "1724f5b8-9ec6-4ea9-ed33-43b8c2bfcd15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e8ecf480-f11a-482b-9068-0a98c423ef35\", \"PreprocessedDataset.csv\", 303209)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yrgPRjAXrwvT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}