model_training.ipynb

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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting category_encoders\n",
            "  Downloading category_encoders-2.6.1-py2.py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.9/81.9 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.10.1)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.13.5)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2022.7.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.9.0->category_encoders) (23.1)\n",
            "Installing collected packages: category_encoders\n",
            "Successfully installed category_encoders-2.6.1\n"
          ]
        }
      ],
      "source": [
        "# install only when you restart the session\n",
        "\n",
        "!pip install category_encoders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tDcsjGG_YPFd"
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      "source": [
        "# @title # Importing\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "import matplotlib\n",
        "import seaborn as sns\n",
        "import category_encoders as ce\n",
        "\n",
        "from scipy import stats\n",
        "from scipy.stats import skew\n",
        "import statsmodels.api as sm\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0iad3r16YRUF"
      },
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      "source": [
        "# @title # Functions\n",
        "\n",
        "# define a function where we pass the new width\n",
        "# do load me\n",
        "\n",
        "title_font = {'fontname': 'serif'}\n",
        "label_font = {'fontname': 'monospace'}\n",
        "\n",
        "def Width(ax, new_width: float):\n",
        "    \n",
        "    \"\"\" \n",
        "    sets the width for the bar or bar type plots.\n",
        "    ax: matplotlib axes\n",
        "    new_width: width_size\n",
        "\n",
        "    \"\"\" \n",
        "    for patch in ax.patches:\n",
        "        current_width = patch.get_width()\n",
        "        difference = current_width - new_width\n",
        "\n",
        "        # set new width\n",
        "        patch.set_width(new_width)\n",
        "\n",
        "        # Now Recenter the Bars\n",
        "        patch.set_x(patch.get_x() + difference * .5)\n",
        "\n",
        "def display_scores(scores):\n",
        "    print(f\"Scores: {scores}\")\n",
        "    print(f\"Scores, Mean: {scores.mean()}\")\n",
        "    print(f\"Standard Deviation: {scores.std()}\")\n",
        "\n",
        "\n",
        "# def find_outliers(df):\n",
        "#     q1 = df.quantile(0.25)\n",
        "#     q3 = df.quantile(0.75)\n",
        "\n",
        "#     iqr = q3 - q1\n",
        "\n",
        "#     outliers = df.loc[(df < (q1 - 1.5 * iqr)) | (df > (q3 + 1.5 * iqr))]\n",
        "\n",
        "#     return outliers\n",
        "\n",
        "def identify_outliers_iqr(group):\n",
        "    q1, q3 = np.percentile(group['Price'], [25, 75])\n",
        "    iqr = q3 - q1\n",
        "    lower_bound = q1 - (1.5 * iqr)\n",
        "    upper_bound = q3 + (1.5 * iqr)\n",
        "    outliers = group[(group['Price'] < lower_bound) | (group['Price'] > upper_bound)]\n",
        "    return outliers\n",
        "\n",
        "\n",
        "## Random state\n",
        "\n",
        "rs = 11"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fGwa7CC25M2-"
      },
      "outputs": [],
      "source": [
        "# def find_outliers(df):\n",
        "#     q1 = df.quantile(0.25)\n",
        "#     q3 = df.quantile(0.75)\n",
        "\n",
        "#     iqr = q3 - q1\n",
        "\n",
        "#     outliers = df.loc[(df < (q1 - 1.5 * iqr)) | (df > (q3 + 1.5 * iqr))]\n",
        "\n",
        "#     return outliers\n",
        "\n",
        "def identify_outliers_iqr(group):\n",
        "    q1, q3 = np.percentile(group['Price'], [25, 75])\n",
        "    iqr = q3 - q1\n",
        "    lower_bound = q1 - (1.5 * iqr)\n",
        "    upper_bound = q3 + (1.5 * iqr)\n",
        "    outliers = group[(group['Price'] < lower_bound) | (group['Price'] > upper_bound)]\n",
        "    return outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ezb_TItCYTo-"
      },
      "outputs": [],
      "source": [
        "prop_data = pd.read_csv(\"/content/drive/MyDrive/Dataset/bd_housing_data/property_listing_data_in_Bangladesh_new_mod1.csv\")"
      ]
    },
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              "   Beds  Baths  TotalRooms  Area       Type  \\\n",
              "0     3      4           7  2200  Apartment   \n",
              "1     3      4           7  1400  Apartment   \n",
              "2     3      4           7  1950  Apartment   \n",
              "3     3      3           6  2000  Apartment   \n",
              "4     3      4           7  1650  Apartment   \n",
              "\n",
              "                              Address  Sector_or_Block           Region  \\\n",
              "0     Block A, Bashundhara R-A, Dhaka          Block A  Bashundhara R-A   \n",
              "1  South Khulshi, Khulshi, Chattogram  No Sector/Block    South Khulshi   \n",
              "2     Block F, Bashundhara R-A, Dhaka          Block F  Bashundhara R-A   \n",
              "3             Sector 9, Uttara, Dhaka         Sector 9           Uttara   \n",
              "4     Block I, Bashundhara R-A, Dhaka          Block I  Bashundhara R-A   \n",
              "\n",
              "              Town        City    Price  \n",
              "0  Bashundhara R-A       Dhaka  50000.0  \n",
              "1          Khulshi  Chattogram  30000.0  \n",
              "2  Bashundhara R-A       Dhaka  30000.0  \n",
              "3           Uttara       Dhaka  35000.0  \n",
              "4  Bashundhara R-A       Dhaka  25000.0  "
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      "source": [
        "prop_data.drop(['Address', 'Sector_or_Block'], axis=1, inplace=True)"
      ]
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\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-314296f2-33b9-45d3-9a8f-694a3fffbc7b 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-314296f2-33b9-45d3-9a8f-694a3fffbc7b');\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",
              "  "
            ],
            "text/plain": [
              "   Beds  Baths  TotalRooms  Area       Type           Region             Town  \\\n",
              "0     3      4           7  2200  Apartment  Bashundhara R-A  Bashundhara R-A   \n",
              "1     3      4           7  1400  Apartment    South Khulshi          Khulshi   \n",
              "2     3      4           7  1950  Apartment  Bashundhara R-A  Bashundhara R-A   \n",
              "\n",
              "         City    Price  \n",
              "0       Dhaka  50000.0  \n",
              "1  Chattogram  30000.0  \n",
              "2       Dhaka  30000.0  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prop_data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UJ7QixJdPhH",
        "outputId": "d7e5164d-39f6-4e35-f091-f1246c0cee46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7557 entries, 0 to 7556\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Beds        7557 non-null   int64  \n",
            " 1   Baths       7557 non-null   int64  \n",
            " 2   TotalRooms  7557 non-null   int64  \n",
            " 3   Area        7557 non-null   int64  \n",
            " 4   Type        7557 non-null   object \n",
            " 5   Region      7557 non-null   object \n",
            " 6   Town        7557 non-null   object \n",
            " 7   City        7557 non-null   object \n",
            " 8   Price       7557 non-null   float64\n",
            "dtypes: float64(1), int64(4), object(4)\n",
            "memory usage: 531.5+ KB\n"
          ]
        }
      ],
      "source": [
        "prop_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "zxkqP5RmdRPH",
        "outputId": "27484945-d837-4e7d-a60d-3b8aeb1e6e1b"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Beds</th>\n",
              "      <th>Baths</th>\n",
              "      <th>TotalRooms</th>\n",
              "      <th>Area</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7557.00</td>\n",
              "      <td>7557.00</td>\n",
              "      <td>7557.00</td>\n",
              "      <td>7557.00</td>\n",
              "      <td>7557.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.75</td>\n",
              "      <td>2.69</td>\n",
              "      <td>5.45</td>\n",
              "      <td>1372.03</td>\n",
              "      <td>34079.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.98</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.74</td>\n",
              "      <td>956.76</td>\n",
              "      <td>64711.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>300.00</td>\n",
              "      <td>5500.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>900.00</td>\n",
              "      <td>15000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.00</td>\n",
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              "      <td>6.00</td>\n",
              "      <td>1200.00</td>\n",
              "      <td>19000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>1550.00</td>\n",
              "      <td>30000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>36.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>46.00</td>\n",
              "      <td>31500.00</td>\n",
              "      <td>2000000.00</td>\n",
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              "        \n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4bbf5c1d-7b2f-4dd3-ab7c-6445e6f34716 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4bbf5c1d-7b2f-4dd3-ab7c-6445e6f34716');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          Beds    Baths  TotalRooms      Area       Price\n",
              "count  7557.00  7557.00     7557.00   7557.00     7557.00\n",
              "mean      2.75     2.69        5.45   1372.03    34079.03\n",
              "std       0.98     0.90        1.74    956.76    64711.61\n",
              "min       1.00     1.00        2.00    300.00     5500.00\n",
              "25%       2.00     2.00        4.00    900.00    15000.00\n",
              "50%       3.00     3.00        6.00   1200.00    19000.00\n",
              "75%       3.00     3.00        6.00   1550.00    30000.00\n",
              "max      36.00    10.00       46.00  31500.00  2000000.00"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "round(prop_data.describe(), 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bb9Z8GM99NlI"
      },
      "outputs": [],
      "source": [
        "numericals = list(prop_data.select_dtypes(include=np.number))\n",
        "categoricals = list(prop_data.select_dtypes(exclude=np.number))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q47ExfKC9sSS",
        "outputId": "b22aed60-04f8-40bd-9029-f5b44cf53820"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Beds', 'Baths', 'TotalRooms', 'Area', 'Price']\n",
            "['Type', 'Region', 'Town', 'City']\n"
          ]
        }
      ],
      "source": [
        "print(numericals)\n",
        "print(categoricals)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "id": "HxboZLvcnHj9",
        "outputId": "f1f3d401-16d1-48ac-ad62-aab42437064d"
      },
      "outputs": [
        {
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",
            "text/plain": [
              "<Figure size 1500x1000 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "with sns.axes_style('darkgrid'):\n",
        "    figure, axes = plt.subplots(nrows=2, ncols=len(numericals)-1, figsize=(15, 10))\n",
        "\n",
        "    for i, num_cols in enumerate(numericals[:-1]):\n",
        "        fig = sm.qqplot(prop_data[num_cols], line='r', ax=axes[0, i])\n",
        "        axes[0, i].set_title(num_cols)\n",
        "        fig1 = sm.qqplot(np.log(prop_data[num_cols]), line='r', ax=axes[1, i])\n",
        "        axes[1, i].set_title(num_cols + \"(After log transform)\")\n",
        "    plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ioLZcm2kYZFk",
        "outputId": "a21dcd13-6db7-4749-b3d2-789d93fb99db"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Skewness: 11.546127\n",
            "Kurtosis: 225.387443\n"
          ]
        }
      ],
      "source": [
        "with sns.axes_style(\"darkgrid\"):\n",
        "    fig, axes = plt.subplot_mosaic([['upper left', 'upper right'],\n",
        "                               ['lower left', 'lower right']],\n",
        "                              figsize=(10, 10), layout=\"constrained\")\n",
        "    fig = sns.histplot(prop_data['Price'], bins=80, stat='density', kde=True, ax=axes['upper left'])\n",
        "    sns.kdeplot(data=prop_data, x=\"Price\", color='red', ax=axes['upper left'])\n",
        "    mu, std = stats.norm.fit(prop_data['Price'])\n",
        "    \n",
        "    xx = np.linspace(*axes['upper left'].get_xlim(), 100)\n",
        "    axes['upper left'].plot(xx, stats.norm.pdf(xx, mu, std));\n",
        "    axes['upper left'].legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, std)],\n",
        "            loc='best')\n",
        "    \n",
        "    # fig = plt.figure()\n",
        "    # res = stats.probplot(prop_data['Price'], plot=plt)\n",
        "    # plt.show()\n",
        "    fig2 = sm.qqplot(prop_data['Price'], line='r', ax=axes['lower left'])\n",
        "    # plt.show()\n",
        "    # plt.tight_layout()\n",
        "\n",
        "    fig3 = sns.boxplot(data=prop_data, y='Price', ax=axes['upper right'])\n",
        "    # plt.tight_layout()\n",
        "\n",
        "    fig4 = sns.boxplot(data=prop_data, x='Type', y='Price', ax=axes['lower right'])\n",
        "    plt.show()\n",
        "\n",
        "    print(\"\\nSkewness: %f\" % prop_data['Price'].skew())\n",
        "    print(\"Kurtosis: %f\" % prop_data['Price'].kurt())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "zLEX2IDEcnCG",
        "outputId": "15adeb6d-f481-4491-c80c-8728d07fc508"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-29b25f9c-4966-4878-9eb8-28527ee7a990\">\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>skewed_features</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Beds</th>\n",
              "      <td>13.542347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Area</th>\n",
              "      <td>10.026988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalRooms</th>\n",
              "      <td>5.556958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Baths</th>\n",
              "      <td>1.159457</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29b25f9c-4966-4878-9eb8-28527ee7a990')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-29b25f9c-4966-4878-9eb8-28527ee7a990 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-29b25f9c-4966-4878-9eb8-28527ee7a990');\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",
              "  "
            ],
            "text/plain": [
              "            skewed_features\n",
              "Beds              13.542347\n",
              "Area              10.026988\n",
              "TotalRooms         5.556958\n",
              "Baths              1.159457"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "skewed_feats = prop_data[numericals[:-1]].apply(lambda x: skew(x))\\\n",
        "                                                        .sort_values(ascending=False)\n",
        "\n",
        "skewness = pd.DataFrame({'skewed_features': skewed_feats})\n",
        "skewness"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "JBxjHP8xXirQ",
        "outputId": "1b483ffe-1997-4bad-cc4d-d9082a5e31ad"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "with sns.axes_style('darkgrid'):\n",
        "    figure, axes = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))\n",
        "\n",
        "    for i, types in enumerate(prop_data['Type'].unique()):\n",
        "        fig = sm.qqplot(prop_data.loc[prop_data['Type'] == types]['Price'], line='r', ax=axes[i])\n",
        "        axes[i].set_title(str(types))\n",
        "    \n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "7qosbbQ4MJe0",
        "outputId": "5c6a1bc1-cf49-434b-e651-b5c16a6b48bd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "prop_data_ = prop_data[numericals]\n",
        "corr = prop_data_.corr(method='pearson')\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
        "\n",
        "with sns.axes_style('darkgrid'):\n",
        "    figure, axes = plt.subplots(figsize=(8, 6))\n",
        "    fig = sns.heatmap(corr, mask=mask,\n",
        "                      vmax=1.0, vmin=-1.0,\n",
        "                      center=0, annot=True,\n",
        "                      square=False, \n",
        "                      linewidth=.5, \n",
        "                      cbar_kws={'shrink': 0.8}\n",
        "                      )\n",
        "    axes.set_title(\"Pearson's Correlation of Numerical Features\", fontsize=15, y=1.09)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "AsUEmsRP2Iim"
      },
      "source": [
        "# Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D52VDptfCz4D"
      },
      "outputs": [],
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder, OrdinalEncoder\n",
        "\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC, LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\n",
        "from sklearn.kernel_ridge import KernelRidge\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import RobustScaler, StandardScaler\n",
        "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "import joblib"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "kBwJwEgWCpJd"
      },
      "source": [
        "## Mean Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ksa7dciseGtR"
      },
      "outputs": [],
      "source": [
        "def mean_encode(df, columns, mean_with):\n",
        "\n",
        "    df_mean_enc = df.copy()\n",
        "\n",
        "    for col in columns:\n",
        "\n",
        "        mean_enc = df_mean_enc.groupby(col)[mean_with].mean()\n",
        "        # mean_enc_region = prop_data_mean_enc.groupby('Region')['Price'].mean()\n",
        "\n",
        "        df_mean_enc.loc[:, col + \"_mean_enc\"] = df_mean_enc[col].map(mean_enc)\n",
        "        # prop_data_mean_enc.loc[:, \"Region_mean_enc\"] = prop_data_mean_enc[\"Region\"].map(mean_enc_region)\n",
        "    return df_mean_enc\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fh6OxT8o9r4Z"
      },
      "outputs": [],
      "source": [
        "# from sklearn.base import BaseEstimator, TransformerMixin\n",
        "\n",
        "# class MeanEncoding():\n",
        "#     def __init__(self, columns, mean_with):\n",
        "#         self.mean_with = mean_with\n",
        "#         self.columns = columns\n",
        "\n",
        "#     def fit(self, X, y=None):\n",
        "#         return self     # Nothing to do in fit in this case\n",
        "\n",
        "#     def transform(self, X):\n",
        "#         for col in self.columns:\n",
        "#             mean_enc = X.groupby(col)[self.mean_with].mean()\n",
        "#             X.loc[:, col + \"_mean_enc\"] = X[col].map(mean_enc)\n",
        "        \n",
        "#         return X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5R2DPlhReGqm"
      },
      "outputs": [],
      "source": [
        "prop_data_mean_enc = mean_encode(prop_data, ['Town', 'Region'], 'Price')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztFzMXoleGoD",
        "outputId": "3c76f1d3-5e83-4b87-d946-d8a100c877ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7557 entries, 0 to 7556\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Beds             7557 non-null   int64  \n",
            " 1   Baths            7557 non-null   int64  \n",
            " 2   TotalRooms       7557 non-null   int64  \n",
            " 3   Area             7557 non-null   int64  \n",
            " 4   Type             7557 non-null   object \n",
            " 5   Region           7557 non-null   object \n",
            " 6   Town             7557 non-null   object \n",
            " 7   City             7557 non-null   object \n",
            " 8   Price            7557 non-null   float64\n",
            " 9   Town_mean_enc    7557 non-null   float64\n",
            " 10  Region_mean_enc  7557 non-null   float64\n",
            "dtypes: float64(3), int64(4), object(4)\n",
            "memory usage: 649.6+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "prop_data_mean_enc.head(3)\n",
        "print(prop_data_mean_enc.info())"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Pv0UnfYfrrl2"
      },
      "source": [
        "## Train test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eNwlEFKV9c16"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_set, test_val_set = train_test_split(prop_data_mean_enc, test_size=0.15,\n",
        "                                           random_state=rs, stratify=prop_data_mean_enc['Type'])\n",
        "\n",
        "\n",
        "val_set, test_set = train_test_split(test_val_set, test_size=0.5,\n",
        "                                     random_state=rs)\n",
        "\n",
        "train_X, train_y = train_set.drop('Price', axis=1), train_set.loc[:, 'Price']\n",
        "val_X, val_y = val_set.drop('Price', axis=1), val_set.loc[:, 'Price']\n",
        "test_X, test_y = test_set.drop('Price', axis=1), test_set.loc[:, 'Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-lCNTeRsmfi",
        "outputId": "8faf4e91-5ca3-4a41-b364-b0320b9057bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 6423 entries, 2749 to 2707\n",
            "Data columns (total 10 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Beds             6423 non-null   int64  \n",
            " 1   Baths            6423 non-null   int64  \n",
            " 2   TotalRooms       6423 non-null   int64  \n",
            " 3   Area             6423 non-null   int64  \n",
            " 4   Type             6423 non-null   object \n",
            " 5   Region           6423 non-null   object \n",
            " 6   Town             6423 non-null   object \n",
            " 7   City             6423 non-null   object \n",
            " 8   Town_mean_enc    6423 non-null   float64\n",
            " 9   Region_mean_enc  6423 non-null   float64\n",
            "dtypes: float64(2), int64(4), object(4)\n",
            "memory usage: 552.0+ KB\n"
          ]
        }
      ],
      "source": [
        "train_X.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PX98mCOLrvKj",
        "outputId": "fdc544cf-f281-4948-ddf9-1e73922539b8"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Beds</th>\n",
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              "      <td>14693.656598</td>\n",
              "      <td>14578.034682</td>\n",
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              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>2500</td>\n",
              "      <td>Apartment</td>\n",
              "      <td>Baridhara DOHS</td>\n",
              "      <td>Baridhara DOHS</td>\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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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Beds  Baths  TotalRooms  Area       Type              Region  \\\n",
              "2749     3      4           7  1700  Apartment            Banasree   \n",
              "3078     3      3           6  1500  Apartment              Uttara   \n",
              "4107     2      2           4  1000  Apartment  25 No. Rampur Ward   \n",
              "4947     3      3           6  1300  Apartment             Solmaid   \n",
              "5770     4      4           8  2500  Apartment      Baridhara DOHS   \n",
              "\n",
              "                Town        City  Town_mean_enc  Region_mean_enc  \n",
              "2749        Banasree       Dhaka   22270.491803     22989.130435  \n",
              "3078          Uttara       Dhaka   32927.421809     35972.178218  \n",
              "4107  Double Mooring  Chattogram   14693.656598     14578.034682  \n",
              "4947           Badda       Dhaka   21952.247191     27727.272727  \n",
              "5770  Baridhara DOHS       Dhaka   74485.714286     74485.714286  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NyKLYjIIeGlv"
      },
      "outputs": [],
      "source": [
        "transformer = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"bin_encoder\", OrdinalEncoder(dtype=np.int32), ['Type', 'City']),\n",
        "        ('numerical', StandardScaler(), numericals[:-1]),\n",
        "        ('_', 'drop', ['Region', 'Town'])],\n",
        "    remainder='passthrough',\n",
        "    verbose_feature_names_out=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oC-1f3P-eGiN"
      },
      "outputs": [],
      "source": [
        "X_train_transformed = transformer.fit_transform(train_X.copy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JadtI27SeGeC",
        "outputId": "b16026a9-3b41-4f39-a3ce-4f5d76993ae7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.00000000e+00,  1.00000000e+00,  2.47649917e-01, ...,\n",
              "         3.53886269e-01,  2.22704918e+04,  2.29891304e+04],\n",
              "       [ 0.00000000e+00,  1.00000000e+00,  2.47649917e-01, ...,\n",
              "         1.40946919e-01,  3.29274218e+04,  3.59721782e+04],\n",
              "       [ 0.00000000e+00,  0.00000000e+00, -7.56551735e-01, ...,\n",
              "        -3.91401455e-01,  1.46936566e+04,  1.45780347e+04],\n",
              "       ...,\n",
              "       [ 0.00000000e+00,  1.00000000e+00, -7.56551735e-01, ...,\n",
              "        -9.23749829e-01,  1.75019192e+04,  1.31642857e+04],\n",
              "       [ 0.00000000e+00,  1.00000000e+00,  2.47649917e-01, ...,\n",
              "        -1.78462105e-01,  1.75019192e+04,  1.48445122e+04],\n",
              "       [ 0.00000000e+00,  0.00000000e+00,  2.47649917e-01, ...,\n",
              "        -7.19924304e-02,  1.90000000e+04,  1.90000000e+04]])"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_transformed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7JBGNay2eGZ0",
        "outputId": "031434f9-d776-4e93-cb63-e87093ef9651"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Type', 'City', 'Beds', 'Baths', 'TotalRooms', 'Area',\n",
              "       'Town_mean_enc', 'Region_mean_enc'], dtype=object)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "transformer.get_feature_names_out()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e1Jeq8Qcr6eb"
      },
      "outputs": [],
      "source": [
        "# joblib.dump(transformer, '/content/drive/MyDrive/Dataset/bd_housing_data/transformer_obj.joblib')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wz8Dg-q2sVS5"
      },
      "outputs": [],
      "source": [
        "# joblib.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gDPS-N9ieGVs"
      },
      "outputs": [],
      "source": [
        "X_train = pd.DataFrame(X_train_transformed, columns=transformer.get_feature_names_out())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "ETkj7wEXLSe_",
        "outputId": "c738ef7f-653a-4b1d-b44b-ca88868e160c"
      },
      "outputs": [
        {
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              "      <th>Type</th>\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",
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              "  "
            ],
            "text/plain": [
              "   Type  City      Beds     Baths  TotalRooms      Area  Town_mean_enc  \\\n",
              "0   0.0   1.0  0.247650  1.456045    0.887484  0.353886   22270.491803   \n",
              "1   0.0   1.0  0.247650  0.344675    0.317396  0.140947   32927.421809   \n",
              "2   0.0   0.0 -0.756552 -0.766694   -0.822780 -0.391401   14693.656598   \n",
              "\n",
              "   Region_mean_enc  \n",
              "0     22989.130435  \n",
              "1     35972.178218  \n",
              "2     14578.034682  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFshN5IWeGR7",
        "outputId": "7eaaaf54-2c97-4b9a-e292-54dcb73e0d56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6423 entries, 0 to 6422\n",
            "Data columns (total 8 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   Type             6423 non-null   float64\n",
            " 1   City             6423 non-null   float64\n",
            " 2   Beds             6423 non-null   float64\n",
            " 3   Baths            6423 non-null   float64\n",
            " 4   TotalRooms       6423 non-null   float64\n",
            " 5   Area             6423 non-null   float64\n",
            " 6   Town_mean_enc    6423 non-null   float64\n",
            " 7   Region_mean_enc  6423 non-null   float64\n",
            "dtypes: float64(8)\n",
            "memory usage: 401.6 KB\n"
          ]
        }
      ],
      "source": [
        "X_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k0wCL-hhj3n2"
      },
      "outputs": [],
      "source": [
        "rs = 11"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wgTXRt7Vg97k"
      },
      "outputs": [],
      "source": [
        "# from sklearn.model_selection import train_test_split\n",
        "\n",
        "# train_set, test_val_set = train_test_split(prop_data_transformed, test_size=0.15,\n",
        "#                                            random_state=rs, stratify=prop_data_transformed['Type'])\n",
        "\n",
        "\n",
        "# val_set, test_set = train_test_split(test_val_set, test_size=0.5,\n",
        "#                                      random_state=rs)\n",
        "\n",
        "# X_train, y_train = train_set.drop('Price', axis=1), train_set.loc[:, 'Price']\n",
        "# X_val, y_val = val_set.drop('Price', axis=1), val_set.loc[:, 'Price']\n",
        "# X_test, y_test = test_set.drop('Price', axis=1), test_set.loc[:, 'Price']"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPMjGpxunaTU"
      },
      "source": [
        "## Applying transformation on the Price, log1p"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6WpOcsmgmaVj"
      },
      "outputs": [],
      "source": [
        "# We use the numpy fuction log1p which  applies log(1+x) to all elements of the column\n",
        "y_train1p = np.log1p(train_y.astype(int))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "XH10dTFdmaSq",
        "outputId": "ba2769a6-8727-4f8d-f3dc-4d78c93b9a66"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "with sns.axes_style(\"darkgrid\"):\n",
        "    figure, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
        "\n",
        "    fig1 = sns.histplot(y_train1p, ax=axes[0], bins=80, kde=True)\n",
        "    axes[0].set_xlabel('Price (After Transformation)')\n",
        "    fig2 = sns.histplot(train_y, ax=axes[1], bins=80, kde=True)\n",
        "    axes[1].set_xlabel('Price (Before Transformation)')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J8gWbl92maPq"
      },
      "outputs": [],
      "source": [
        "def score_summary(regressors, k, train_X, train_y):\n",
        "    # cols = ['Regressor', 'val_rmse']\n",
        "    # data_table = pd.DataFrame(columns=cols)\n",
        "    kf = k\n",
        "\n",
        "    for reg in regressors:\n",
        "        rmse = np.sqrt(-cross_val_score(reg, train_X, train_y, scoring='neg_mean_squared_error', cv=kf))\n",
        "        print(f\"\\n{str(reg)}, 'Mean encoded, score: Mean ({rmse.mean(): .4f}) Std ({rmse.std(): .4f})\")\n",
        "\n",
        "# def model_score(regressors, train_X, train_y, val_X, val_y):\n",
        "#     cols = ['Regressor', 'val_rmse']\n",
        "#     # data_table=pd.DataFrame(columns=cols)\n",
        "\n",
        "#     for reg in regressors:\n",
        "#         reg.fit(train_X, train_y)\n",
        "#         val_predict = reg.predict(val_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sQMQfjoymaIM"
      },
      "outputs": [],
      "source": [
        "regs = [LinearRegression(),\n",
        "        DecisionTreeRegressor(random_state=rs),\n",
        "        RandomForestRegressor(random_state=rs),\n",
        "        SVR(),\n",
        "        Lasso(alpha=0.005, random_state=rs, max_iter=10000),\n",
        "        ElasticNet(alpha=0.005, l1_ratio=.9, random_state=rs, max_iter=10000),\n",
        "        GradientBoostingRegressor(random_state=rs),\n",
        "        xgb.XGBRegressor(random_state=rs),\n",
        "        lgb.LGBMRegressor(objective='regression')]\n",
        "\n",
        "names = ['svr', 'lasso', 'Enet', 'gbr', 'xgb', 'lgb']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-GiHGabmaDj",
        "outputId": "e7fabcfb-7d55-4e68-f121-a7788ace39b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "LinearRegression(), 'Mean encoded, score: Mean ( 0.3097) Std ( 0.0221)\n",
            "\n",
            "DecisionTreeRegressor(random_state=11), 'Mean encoded, score: Mean ( 0.2387) Std ( 0.0176)\n",
            "\n",
            "RandomForestRegressor(random_state=11), 'Mean encoded, score: Mean ( 0.2056) Std ( 0.0172)\n",
            "\n",
            "SVR(), 'Mean encoded, score: Mean ( 0.3737) Std ( 0.0050)\n",
            "\n",
            "Lasso(alpha=0.005, max_iter=10000, random_state=11), 'Mean encoded, score: Mean ( 0.3101) Std ( 0.0214)\n",
            "\n",
            "ElasticNet(alpha=0.005, l1_ratio=0.9, max_iter=10000, random_state=11), 'Mean encoded, score: Mean ( 0.3099) Std ( 0.0214)\n",
            "\n",
            "GradientBoostingRegressor(random_state=11), 'Mean encoded, score: Mean ( 0.2092) Std ( 0.0142)\n",
            "\n",
            "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
            "             colsample_bylevel=None, colsample_bynode=None,\n",
            "             colsample_bytree=None, early_stopping_rounds=None,\n",
            "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
            "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
            "             predictor=None, random_state=11, ...), 'Mean encoded, score: Mean ( 0.2032) Std ( 0.0168)\n",
            "\n",
            "LGBMRegressor(objective='regression'), 'Mean encoded, score: Mean ( 0.1997) Std ( 0.0149)\n"
          ]
        }
      ],
      "source": [
        "kf = 5\n",
        "score_summary(regs, kf, X_train, y_train1p)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "YwY7I1xXSueP"
      },
      "source": [
        "Model Tryouts! \n",
        "\n",
        "RandomForestRegressor,\n",
        "SVR, \n",
        "XGBregressor,\n",
        "LGMRegressor"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "hQmBRhXMubXw"
      },
      "source": [
        "## transforming the validation sets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z01GWYkfua3f"
      },
      "outputs": [],
      "source": [
        "X_val = transformer.transform(val_X)\n",
        "y_val = val_y.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Qx5Hv-acvA08",
        "outputId": "32943351-664f-46db-ad48-5622d49386cf"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-864661af-d84e-4579-bc2d-744fc131fdfd\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>City</th>\n",
              "      <th>Beds</th>\n",
              "      <th>Baths</th>\n",
              "      <th>TotalRooms</th>\n",
              "      <th>Area</th>\n",
              "      <th>Town_mean_enc</th>\n",
              "      <th>Region_mean_enc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.247650</td>\n",
              "      <td>0.344675</td>\n",
              "      <td>0.317396</td>\n",
              "      <td>0.140947</td>\n",
              "      <td>32927.421809</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.456045</td>\n",
              "      <td>0.887484</td>\n",
              "      <td>0.886235</td>\n",
              "      <td>58165.048544</td>\n",
              "      <td>72547.945205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>-0.756552</td>\n",
              "      <td>-0.766694</td>\n",
              "      <td>-0.822780</td>\n",
              "      <td>-0.231697</td>\n",
              "      <td>16297.872340</td>\n",
              "      <td>16264.705882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.247650</td>\n",
              "      <td>0.344675</td>\n",
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              "      <td>0.034477</td>\n",
              "      <td>32927.421809</td>\n",
              "      <td>35972.178218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>-0.756552</td>\n",
              "      <td>-0.766694</td>\n",
              "      <td>-0.822780</td>\n",
              "      <td>-0.764045</td>\n",
              "      <td>17501.919192</td>\n",
              "      <td>16269.101124</td>\n",
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              "</table>\n",
              "</div>\n",
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              "\n",
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              "          document.querySelector('#df-864661af-d84e-4579-bc2d-744fc131fdfd 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-864661af-d84e-4579-bc2d-744fc131fdfd');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Type  City      Beds     Baths  TotalRooms      Area  Town_mean_enc  \\\n",
              "0   0.0   1.0  0.247650  0.344675    0.317396  0.140947   32927.421809   \n",
              "1   0.0   1.0  0.247650  1.456045    0.887484  0.886235   58165.048544   \n",
              "2   0.0   0.0 -0.756552 -0.766694   -0.822780 -0.231697   16297.872340   \n",
              "3   0.0   1.0  0.247650  0.344675    0.317396  0.034477   32927.421809   \n",
              "4   0.0   1.0 -0.756552 -0.766694   -0.822780 -0.764045   17501.919192   \n",
              "\n",
              "   Region_mean_enc  \n",
              "0     35972.178218  \n",
              "1     72547.945205  \n",
              "2     16264.705882  \n",
              "3     35972.178218  \n",
              "4     16269.101124  "
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_val = pd.DataFrame(data=X_val.copy(), columns=transformer.get_feature_names_out())\n",
        "X_val.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "0hSkVAfnel4e",
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        {
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "   Type  City      Beds     Baths  TotalRooms      Area  Town_mean_enc  \\\n",
              "0   0.0   1.0  0.247650 -0.766694   -0.252692 -0.338167   23781.818182   \n",
              "1   0.0   1.0  0.247650  0.344675    0.317396 -0.125227   17501.919192   \n",
              "2   0.0   1.0 -0.756552 -0.766694   -0.822780 -0.604341   32927.421809   \n",
              "3   0.0   1.0  0.247650  1.456045    0.887484  0.460356   25642.857143   \n",
              "4   0.0   1.0  0.247650  1.456045    0.887484  1.418583   58165.048544   \n",
              "\n",
              "   Region_mean_enc  \n",
              "0     23058.823529  \n",
              "1     17153.846154  \n",
              "2     35972.178218  \n",
              "3     20666.666667  \n",
              "4     72547.945205  "
            ]
          },
          "execution_count": 43,
          "metadata": {},
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        }
      ],
      "source": [
        "y_test = test_y.copy()\n",
        "X_test = transformer.transform(test_X)\n",
        "X_test = pd.DataFrame(data=X_test.copy(),  columns=transformer.get_feature_names_out())\n",
        "X_test.head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "zzCHX4DhVSg4"
      },
      "source": [
        "## RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "14o8lYTUu4RD",
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        {
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-9a462a49-6085-409f-9c55-4e5a6f93d721 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-9a462a49-6085-409f-9c55-4e5a6f93d721');\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",
              "  "
            ],
            "text/plain": [
              "   Type  City      Beds     Baths  TotalRooms      Area  Town_mean_enc  \\\n",
              "0   0.0   1.0  0.247650  1.456045    0.887484  0.353886   22270.491803   \n",
              "1   0.0   1.0  0.247650  0.344675    0.317396  0.140947   32927.421809   \n",
              "2   0.0   0.0 -0.756552 -0.766694   -0.822780 -0.391401   14693.656598   \n",
              "3   0.0   1.0  0.247650  0.344675    0.317396 -0.071992   21952.247191   \n",
              "4   0.0   1.0  1.251852  1.456045    1.457572  1.205644   74485.714286   \n",
              "\n",
              "   Region_mean_enc  \n",
              "0     22989.130435  \n",
              "1     35972.178218  \n",
              "2     14578.034682  \n",
              "3     27727.272727  \n",
              "4     74485.714286  "
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "uekBrvXeSpGP",
        "outputId": "36227782-ef23-46e5-9cac-7c0231b40a6e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=11)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=11)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor(random_state=11)"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_reg = RandomForestRegressor(random_state=rs)\n",
        "rf_reg.fit(X_train, y_train1p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4lqyisIjV1LK"
      },
      "outputs": [],
      "source": [
        "rf_pred = np.expm1(rf_reg.predict(X_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhjhHhOkV1Fw",
        "outputId": "a23ab1bd-28fd-4d3e-b565-5302e063fc4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     5818.684440531006\n",
            "RMSE:    18625.744809379597\n"
          ]
        }
      ],
      "source": [
        "print(f\"MAE:     {mean_absolute_error(y_val, rf_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_val, rf_pred))}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "snjNuKDkO-VD",
        "outputId": "4b54841d-53cc-4dec-f72d-e1f1b7e1a4f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Actual Price \t Predicted Price\n",
            "65000.0 \t 29268.564632648693\n",
            "25000.0 \t 27407.750625868677\n",
            "11000.0 \t 10403.92878161275\n",
            "20000.0 \t 24780.038242865598\n",
            "25000.0 \t 32297.50293520428\n",
            "17000.0 \t 17004.2502140042\n",
            "15000.0 \t 14672.785751578882\n",
            "22000.0 \t 20595.03584226082\n",
            "10000.0 \t 10406.74370036511\n",
            "30000.0 \t 29999.99999999999\n",
            "28000.0 \t 22572.546043204755\n",
            "19000.0 \t 18803.46042659341\n",
            "15000.0 \t 12734.050150117917\n",
            "14000.0 \t 14033.154313876947\n",
            "15000.0 \t 14803.11070837561\n",
            "18000.0 \t 18744.001464473393\n",
            "21000.0 \t 28161.454827647987\n",
            "17000.0 \t 17077.18823537581\n",
            "22000.0 \t 21900.82555099333\n",
            "13500.0 \t 13831.232705942017\n",
            "20000.0 \t 20117.377107088767\n",
            "150000.0 \t 176100.80384256828\n",
            "16000.0 \t 16266.472924255062\n",
            "26000.0 \t 24242.314329522167\n",
            "16000.0 \t 15356.449467916227\n",
            "20000.0 \t 18744.848878114626\n",
            "18000.0 \t 20040.5925413267\n",
            "22000.0 \t 16521.02901992864\n",
            "12000.0 \t 13470.058205518373\n",
            "14000.0 \t 16288.97053857647\n"
          ]
        }
      ],
      "source": [
        "print(f\"Actual Price \\t Predicted Price\")\n",
        "for val, pred_val in zip(y_val[20:50], rf_pred[20:50]):\n",
        "    print(f\"{val} \\t {pred_val}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "lAFOhJI4QMVV"
      },
      "source": [
        ">>> So there are a few mistakes here and there (Well that should happen) but overall it's looks good. We can optimzed this even more by tweaking the parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3s5YMgdNyPAO"
      },
      "outputs": [],
      "source": [
        "scoring = 'neg_mean_absolute_error'\n",
        "param_grid = {\"n_estimators\": range(50, 300, 50), \n",
        "              \"criterion\": [\"squared_error\", \"absolute_error\"],\n",
        "              \"max_depth\": [None, 20, 30, 40, 50],\n",
        "              \"min_samples_split\": [2, 3, 4, 5],\n",
        "              }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DtPpiW2KV1A7"
      },
      "outputs": [],
      "source": [
        "gs = GridSearchCV(RandomForestRegressor(random_state=rs), param_grid, \n",
        "                  scoring=scoring, cv=3, n_jobs=-1, refit=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "k65nVWRhV08R",
        "outputId": "30f31b87-3053-4560-e6b0-70bbf59f2aa0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=11), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;squared_error&#x27;, &#x27;absolute_error&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [None, 20, 30, 40, 50],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 3, 4, 5],\n",
              "                         &#x27;n_estimators&#x27;: range(50, 300, 50)},\n",
              "             scoring=&#x27;neg_mean_absolute_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=11), n_jobs=-1,\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;squared_error&#x27;, &#x27;absolute_error&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [None, 20, 30, 40, 50],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 3, 4, 5],\n",
              "                         &#x27;n_estimators&#x27;: range(50, 300, 50)},\n",
              "             scoring=&#x27;neg_mean_absolute_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=11)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=11)</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=3, estimator=RandomForestRegressor(random_state=11), n_jobs=-1,\n",
              "             param_grid={'criterion': ['squared_error', 'absolute_error'],\n",
              "                         'max_depth': [None, 20, 30, 40, 50],\n",
              "                         'min_samples_split': [2, 3, 4, 5],\n",
              "                         'n_estimators': range(50, 300, 50)},\n",
              "             scoring='neg_mean_absolute_error')"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gs.fit(X_train, y_train1p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "N2iLW7SM4wWL",
        "outputId": "71dae53e-68da-4787-b390-794b043d4cf0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/Dataset/bd_housing_data/gridsearch_rf_0_1_0.joblib']"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "joblib.dump(gs, '/content/drive/MyDrive/Dataset/bd_housing_data/gridsearch_rf_0_1_0.joblib')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "IHPp1SISUZ9P",
        "outputId": "483e5256-af8a-4562-9ae3-c6d32f2d9176"
      },
      "outputs": [],
      "source": [
        "# joblib.load('/content/drive/MyDrive/Dataset/bd_housing_data/gridsearch_rf_0_1_0.joblib')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "GTieaCIwagzU",
        "outputId": "ab5e9189-c03f-4c65-a96c-04c24ca1620f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor(criterion='absolute_error', max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gs.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q379X0k3V04k",
        "outputId": "0a205bac-afa1-4de1-a2a3-ff9238247f64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "0.13304613909422613 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 3, 'n_estimators': 50}\n",
            "0.1324912416561338 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 3, 'n_estimators': 100}\n",
            "0.13237847534414623 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 3, 'n_estimators': 150}\n",
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            "0.13242469309345045 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 3, 'n_estimators': 250}\n",
            "0.13312733819430692 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 4, 'n_estimators': 50}\n",
            "0.13230035064150716 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 4, 'n_estimators': 100}\n",
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            "0.13215095009605457 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 4, 'n_estimators': 200}\n",
            "0.13211955579987142 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 4, 'n_estimators': 250}\n",
            "0.13273864437893257 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 5, 'n_estimators': 50}\n",
            "0.13210884406954476 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 5, 'n_estimators': 100}\n",
            "0.1320651806701302 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 5, 'n_estimators': 150}\n",
            "0.13211243899047329 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 5, 'n_estimators': 200}\n",
            "0.13204536610454576 {'criterion': 'absolute_error', 'max_depth': 40, 'min_samples_split': 5, 'n_estimators': 250}\n",
            "0.13325401263357514 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 2, 'n_estimators': 50}\n",
            "0.1326198792878574 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 2, 'n_estimators': 100}\n",
            "0.13253458859792586 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 2, 'n_estimators': 150}\n",
            "0.13255532474510248 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 2, 'n_estimators': 200}\n",
            "0.13251935472570434 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 2, 'n_estimators': 250}\n",
            "0.13304613909422613 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 3, 'n_estimators': 50}\n",
            "0.1324912416561338 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 3, 'n_estimators': 100}\n",
            "0.13237847534414623 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 3, 'n_estimators': 150}\n",
            "0.13248484466287777 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 3, 'n_estimators': 200}\n",
            "0.13242469309345045 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 3, 'n_estimators': 250}\n",
            "0.1331240478967448 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 4, 'n_estimators': 50}\n",
            "0.13230271694937304 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 4, 'n_estimators': 100}\n",
            "0.13212524948765178 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 4, 'n_estimators': 150}\n",
            "0.1321510360995615 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 4, 'n_estimators': 200}\n",
            "0.13212051055869103 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 4, 'n_estimators': 250}\n",
            "0.13273864437893257 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 5, 'n_estimators': 50}\n",
            "0.13210884406954476 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 5, 'n_estimators': 100}\n",
            "0.1320651806701302 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 5, 'n_estimators': 150}\n",
            "0.13211582385206674 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 5, 'n_estimators': 200}\n",
            "0.13204807399382051 {'criterion': 'absolute_error', 'max_depth': 50, 'min_samples_split': 5, 'n_estimators': 250}\n"
          ]
        }
      ],
      "source": [
        "results = gs.cv_results_\n",
        "for mean_score, params in zip(results['mean_test_score'], results['params']):\n",
        "    print(-mean_score, params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5_F4nYn-gZ_",
        "outputId": "c9536826-93ab-4ec5-a3bf-8d74d5513671"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RandomForestRegressor(criterion='absolute_error', max_depth=20,\n",
            "                      min_samples_split=5, n_estimators=250, random_state=11)\n",
            "{'criterion': 'absolute_error', 'max_depth': 20, 'min_samples_split': 5, 'n_estimators': 250}\n",
            "-0.131890334282456\n"
          ]
        }
      ],
      "source": [
        "print(gs.best_estimator_)\n",
        "print(gs.best_params_)\n",
        "print(gs.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfwbvwOug9gq",
        "outputId": "290b1c87-4f60-4963-d43f-a1ca9f2b122d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_criterion', 'param_max_depth', 'param_min_samples_split', 'param_n_estimators', 'params', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'mean_test_score', 'std_test_score', 'rank_test_score'])"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "fAL0mzuG7Drv",
        "outputId": "c6a278de-59f0-44a8-ceb9-871bbfb08cf4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor(criterion='absolute_error', max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_reg_gs = gs.best_estimator_\n",
        "rf_reg_gs.fit(X_train, y_train1p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zcd1VU7P7DoK"
      },
      "outputs": [],
      "source": [
        "rf_reg_gs_pred = np.expm1(rf_reg_gs.predict(X_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AR3dBrjF7Dks",
        "outputId": "677d82f9-92f6-4ccb-9e22-1a71aaea1ef2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     5669.882518557888\n",
            "RMSE:    17551.113252976218\n"
          ]
        }
      ],
      "source": [
        "print(f\"MAE:     {mean_absolute_error(y_val, rf_reg_gs_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_val, rf_reg_gs_pred))}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ek7a-z9T51Id",
        "outputId": "a3d75856-430f-4fed-b734-7afc29793ee2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Actual Price \t Predicted Price\n",
            "65000.0 \t 29624.195514287687\n",
            "25000.0 \t 28530.15906972022\n",
            "11000.0 \t 10278.198035327096\n",
            "20000.0 \t 24132.136951416793\n",
            "25000.0 \t 31949.332459622554\n",
            "17000.0 \t 17123.031631648642\n",
            "15000.0 \t 14392.6083675189\n",
            "22000.0 \t 20371.064865983426\n",
            "10000.0 \t 10374.413367677522\n",
            "30000.0 \t 29849.673650405417\n",
            "28000.0 \t 23632.178729744544\n",
            "19000.0 \t 18859.604474145523\n",
            "15000.0 \t 12598.556821892329\n",
            "14000.0 \t 14000.000000000342\n",
            "15000.0 \t 14999.99999999975\n",
            "18000.0 \t 18109.63732054481\n",
            "21000.0 \t 27413.158926639957\n",
            "17000.0 \t 17325.18689592122\n",
            "22000.0 \t 21374.045521725384\n",
            "13500.0 \t 13418.535149158884\n",
            "20000.0 \t 20778.26742785924\n",
            "150000.0 \t 183365.58969947003\n",
            "16000.0 \t 16156.043455086088\n",
            "26000.0 \t 24036.950670507314\n",
            "16000.0 \t 15432.793344597218\n",
            "20000.0 \t 18351.89707201159\n",
            "18000.0 \t 19258.132975118195\n",
            "22000.0 \t 17366.260016056454\n",
            "12000.0 \t 13469.512730675637\n",
            "14000.0 \t 15939.002655097876\n"
          ]
        }
      ],
      "source": [
        "print(f\"Actual Price \\t Predicted Price\")\n",
        "for val, pred_val in zip(y_val[20:50], rf_reg_gs_pred[20:50]):\n",
        "    print(f\"{val} \\t {pred_val}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "DoTk3fYDlNx0"
      },
      "source": [
        "### Test set eval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5Nh0vePlMMy",
        "outputId": "0b9da2f7-14d7-4949-c646-941f46f66b14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     7846.014279481042\n",
            "RMSE:    30511.87940322815\n"
          ]
        }
      ],
      "source": [
        "rf_reg_gs_test_pred = np.expm1(rf_reg_gs.predict(X_test))\n",
        "print(f\"MAE:     {mean_absolute_error(y_test, rf_reg_gs_test_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_test, rf_reg_gs_test_pred))}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNX4Y6K7lZgA",
        "outputId": "a91a351c-7163-44ad-ffd1-14e9f0ed4bd5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Actual Price \t Predicted Price\n",
            "16000.0 \t 17081.0605192772\n",
            "45000.0 \t 50904.24390286416\n",
            "30000.0 \t 27461.65397266431\n",
            "20000.0 \t 16421.169456257103\n",
            "20000.0 \t 21325.735735121194\n",
            "14000.0 \t 14999.99999999975\n",
            "27000.0 \t 29600.40636637733\n",
            "30000.0 \t 32980.8429336164\n",
            "21000.0 \t 21308.69472856371\n",
            "15000.0 \t 15009.715174926449\n",
            "140000.0 \t 162464.23126512932\n",
            "10000.0 \t 13222.831565633714\n",
            "50000.0 \t 42746.3177255044\n",
            "16000.0 \t 16081.570725772775\n",
            "13000.0 \t 16506.282449240203\n",
            "80000.0 \t 45193.380612051886\n",
            "25000.0 \t 16595.528764223793\n",
            "50000.0 \t 63449.091589828546\n",
            "20000.0 \t 23373.42446488156\n",
            "16000.0 \t 14307.579256386874\n",
            "25000.0 \t 26590.567326524088\n",
            "50000.0 \t 51468.73112251383\n",
            "10000.0 \t 12000.000000000127\n",
            "40000.0 \t 42844.31634607477\n",
            "13000.0 \t 17992.568014883378\n",
            "80000.0 \t 84424.07909060239\n",
            "11000.0 \t 11560.029252345525\n",
            "25000.0 \t 28655.077434733787\n",
            "16000.0 \t 21300.95003442361\n",
            "13000.0 \t 12722.598563666235\n"
          ]
        }
      ],
      "source": [
        "print(f\"Actual Price \\t Predicted Price\")\n",
        "for val, pred_val in zip(y_test[100:130], rf_reg_gs_test_pred[100:130]):\n",
        "    print(f\"{val} \\t {pred_val}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "gpBFKcKm7Ddi",
        "outputId": "ca28203c-ac36-4a11-ee0c-47eb7fb85499"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "feat_importances = pd.Series(rf_reg_gs.feature_importances_, index=rf_reg_gs.feature_names_in_)\n",
        "feat_importances.nlargest(8).plot(kind='barh')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm-RisXAlLkr",
        "outputId": "1f2f4a6d-6a68-429e-c6b2-aa0c81d848b1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.00073074, 0.00657277, 0.01514826, 0.01493377, 0.04064562,\n",
              "       0.55482472, 0.05475993, 0.31238419])"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "feature_importances = gs.best_estimator_.feature_importances_\n",
        "feature_importances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VY4lRmfrBfWb",
        "outputId": "7e17ee9f-67c4-43ee-f97f-107bfee4af1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     5629.194451655086\n",
            "RMSE:    17241.20254981877\n"
          ]
        }
      ],
      "source": [
        "rf_reg_gs_feats = gs.best_estimator_\n",
        "rf_reg_gs_feats.fit(X_train[['Area', 'Region_mean_enc', 'Town_mean_enc', 'TotalRooms']], y_train1p)\n",
        "rf_reg_gs_feats_pred = np.expm1(rf_reg_gs_feats.predict(X_val[['Area', 'Region_mean_enc', 'Town_mean_enc', 'TotalRooms']]))\n",
        "print(f\"MAE:     {mean_absolute_error(y_val, rf_reg_gs_feats_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_val, rf_reg_gs_feats_pred))}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "bgH02V25mMtw"
      },
      "source": [
        "### Test set eval with Feat. importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHMz3T6ImBl-",
        "outputId": "1a7696ca-cbc6-450f-c557-428a16c078cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     7732.584749558438\n",
            "RMSE:    29877.39777583574\n"
          ]
        }
      ],
      "source": [
        "rf_reg_gs_feats_test_pred = np.expm1(rf_reg_gs_feats.predict(X_test[['Area', 'Region_mean_enc', 'Town_mean_enc', 'TotalRooms']]))\n",
        "print(f\"MAE:     {mean_absolute_error(y_test, rf_reg_gs_feats_test_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_test, rf_reg_gs_feats_test_pred))}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCPiVM2KmVoT",
        "outputId": "df83a036-5b45-4966-d6eb-f6634cc44ef6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Actual Price \t Predicted Price\n",
            "16000.0 \t 16925.224644343976\n",
            "45000.0 \t 49477.731303590954\n",
            "30000.0 \t 26242.77626627146\n",
            "20000.0 \t 16361.809582499287\n",
            "20000.0 \t 21813.06415950203\n",
            "14000.0 \t 14999.99999999975\n",
            "27000.0 \t 29558.573578164713\n",
            "30000.0 \t 33532.20482718118\n",
            "21000.0 \t 21203.246855499914\n",
            "15000.0 \t 15003.872819259854\n",
            "140000.0 \t 163247.18429564743\n",
            "10000.0 \t 13187.750621057443\n",
            "50000.0 \t 42855.550453166186\n",
            "16000.0 \t 16208.786339981698\n",
            "13000.0 \t 16505.289243516912\n",
            "80000.0 \t 45607.312398829636\n",
            "25000.0 \t 16382.02672902228\n",
            "50000.0 \t 59895.78069753124\n",
            "20000.0 \t 23176.049871596057\n",
            "16000.0 \t 14546.19551523261\n",
            "25000.0 \t 27002.242542526776\n",
            "50000.0 \t 48522.364705295506\n",
            "10000.0 \t 12000.000000000127\n",
            "40000.0 \t 42913.37522265047\n",
            "13000.0 \t 17919.444266746632\n",
            "80000.0 \t 82413.4961377683\n",
            "11000.0 \t 11657.659287476981\n",
            "25000.0 \t 28541.573475093304\n",
            "16000.0 \t 21098.23854592129\n",
            "13000.0 \t 12696.674978209563\n"
          ]
        }
      ],
      "source": [
        "print(f\"Actual Price \\t Predicted Price\")\n",
        "for val, pred_val in zip(y_test[100:130], rf_reg_gs_feats_test_pred[100:130]):\n",
        "    print(f\"{val} \\t {pred_val}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XSzh7hfqdX6M"
      },
      "outputs": [],
      "source": [
        "gs_joblib = joblib.load('/content/drive/MyDrive/Dataset/bd_housing_data/gridsearch_rf_0_1_0.joblib')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "mdunVXttdoOo",
        "outputId": "24a161d3-0835-41a1-f273-4b2005993484"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(criterion=&#x27;absolute_error&#x27;, max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor(criterion='absolute_error', max_depth=20,\n",
              "                      min_samples_split=5, n_estimators=250, random_state=11)"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gs_joblib.best_estimator_"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "OZ17h2G3wCpz"
      },
      "source": [
        "## Gradient Boost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqkOzJgXwBuz",
        "outputId": "5f9f2c2f-967e-4ed0-9c49-8691ac12103c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     5667.94391015353\n",
            "RMSE:    15507.061380853722\n"
          ]
        }
      ],
      "source": [
        "gbr_reg = GradientBoostingRegressor(random_state=rs)\n",
        "gbr_reg.fit(X_train, y_train1p)\n",
        "\n",
        "gbr_reg_pred = np.expm1(gbr_reg.predict(X_val))\n",
        "\n",
        "print(f\"MAE:     {mean_absolute_error(y_val, gbr_reg_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_val, gbr_reg_pred))}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Hyper parameter tuning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wNTNMDVHyOBa"
      },
      "outputs": [],
      "source": [
        "scoring = 'neg_mean_absolute_error'\n",
        "param_grid = dict(loss=['squared_error', 'absolute_error',],\n",
        "                  learning_rate=[0.003, 0.03, 0.001, 0.01],\n",
        "                  n_estimators=[150, 200, 300, 500],\n",
        "                  subsample=[0.2, 0.5, 0.1],\n",
        "                  max_depth=[None, 30, 50, 60, 80],\n",
        "                  min_samples_split=[2, 4, 6]  \n",
        "                  )\n",
        "\n",
        "\n",
        "### Hyper-param turning using the RandomizedSearchCV ###\n",
        "\n",
        "rs_gbr = RandomizedSearchCV(GradientBoostingRegressor(random_state=rs), param_grid, scoring=scoring, cv=3, \n",
        "                            refit=True, n_jobs=-1, n_iter=200, random_state=rs)\n",
        "\n",
        "### Hyper-param turning using the GridSearchCV ###\n",
        "\n",
        "# gs_gbr = GridSearchCV(GradientBoostingRegressor(random_state=rs, verbose=2), param_grid, scoring=scoring, cv=3, \n",
        "#                       refit=True, n_jobs=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "H54uXb_C1h1S",
        "outputId": "8c73b3cb-aee0-406b-b5c4-8e717935efb2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=GradientBoostingRegressor(random_state=11),\n",
              "                   n_iter=200, n_jobs=-1,\n",
              "                   param_distributions={&#x27;learning_rate&#x27;: [0.003, 0.03, 0.001,\n",
              "                                                          0.01],\n",
              "                                        &#x27;loss&#x27;: [&#x27;squared_error&#x27;,\n",
              "                                                 &#x27;absolute_error&#x27;],\n",
              "                                        &#x27;max_depth&#x27;: [None, 30, 50, 60, 80],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
              "                                        &#x27;n_estimators&#x27;: [150, 200, 300, 500],\n",
              "                                        &#x27;subsample&#x27;: [0.2, 0.5, 0.1]},\n",
              "                   random_state=11, scoring=&#x27;neg_mean_absolute_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=GradientBoostingRegressor(random_state=11),\n",
              "                   n_iter=200, n_jobs=-1,\n",
              "                   param_distributions={&#x27;learning_rate&#x27;: [0.003, 0.03, 0.001,\n",
              "                                                          0.01],\n",
              "                                        &#x27;loss&#x27;: [&#x27;squared_error&#x27;,\n",
              "                                                 &#x27;absolute_error&#x27;],\n",
              "                                        &#x27;max_depth&#x27;: [None, 30, 50, 60, 80],\n",
              "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
              "                                        &#x27;n_estimators&#x27;: [150, 200, 300, 500],\n",
              "                                        &#x27;subsample&#x27;: [0.2, 0.5, 0.1]},\n",
              "                   random_state=11, scoring=&#x27;neg_mean_absolute_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=11)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(random_state=11)</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomizedSearchCV(cv=3, estimator=GradientBoostingRegressor(random_state=11),\n",
              "                   n_iter=200, n_jobs=-1,\n",
              "                   param_distributions={'learning_rate': [0.003, 0.03, 0.001,\n",
              "                                                          0.01],\n",
              "                                        'loss': ['squared_error',\n",
              "                                                 'absolute_error'],\n",
              "                                        'max_depth': [None, 30, 50, 60, 80],\n",
              "                                        'min_samples_split': [2, 4, 6],\n",
              "                                        'n_estimators': [150, 200, 300, 500],\n",
              "                                        'subsample': [0.2, 0.5, 0.1]},\n",
              "                   random_state=11, scoring='neg_mean_absolute_error')"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# gs_gbr.fit(X_train, y_train1p)\n",
        "\n",
        "rs_gbr.fit(X_train, y_train1p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "OZFO5afTUD-k",
        "outputId": "4fd55799-3c2e-4595-a5c5-a394223d46a3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=0.03, loss=&#x27;absolute_error&#x27;,\n",
              "                          max_depth=None, min_samples_split=6, n_estimators=200,\n",
              "                          random_state=11, subsample=0.5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.03, loss=&#x27;absolute_error&#x27;,\n",
              "                          max_depth=None, min_samples_split=6, n_estimators=200,\n",
              "                          random_state=11, subsample=0.5)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "GradientBoostingRegressor(learning_rate=0.03, loss='absolute_error',\n",
              "                          max_depth=None, min_samples_split=6, n_estimators=200,\n",
              "                          random_state=11, subsample=0.5)"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rs_gbr.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3zUt6Ld1w74",
        "outputId": "36b3db4b-321f-4fe4-fcf3-30d9edbc680a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/Dataset/bd_housing_data/randomsearch_gbr_0_1_0.joblib']"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# joblib.dump(gs_gbr, '/content/drive/MyDrive/Dataset/bd_housing_data/gridsearch_gbr_0_1_0.joblib')\n",
        "\n",
        "joblib.dump(rs_gbr, '/content/drive/MyDrive/Dataset/bd_housing_data/randomsearch_gbr_0_1_0.joblib')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9KBPG5PcUePz",
        "outputId": "9badc533-50ad-47d2-de7b-844e9196c51e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     5604.104796188496\n",
            "RMSE:    18368.696498546513\n"
          ]
        }
      ],
      "source": [
        "rs_gbr_reg = rs_gbr.best_estimator_\n",
        "\n",
        "rs_gbr_reg.fit(X_train, y_train1p)\n",
        "\n",
        "rs_gbr_reg_pred = np.expm1(rs_gbr_reg.predict(X_val))\n",
        "\n",
        "print(f\"MAE:     {mean_absolute_error(y_val, rs_gbr_reg_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_val, rs_gbr_reg_pred))}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ejyp4v-DWttu",
        "outputId": "1a2fe224-acd9-4145-8df2-2d26ea0f661b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     8173.3485697024\n",
            "RMSE:    32245.72367172902\n"
          ]
        }
      ],
      "source": [
        "rs_gbr_reg_test_pred = np.expm1(rs_gbr_reg.predict(X_test))\n",
        "\n",
        "print(f\"MAE:     {mean_absolute_error(y_test, rs_gbr_reg_test_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_test, rs_gbr_reg_test_pred))}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## LGBM Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-zSwUpv73hj",
        "outputId": "1a83461b-6e67-4130-c77b-2f689891f250"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     5779.4461213272425\n",
            "RMSE:    17418.169109258564\n"
          ]
        }
      ],
      "source": [
        "lgb_reg = lgb.LGBMRegressor(objective='regression')\n",
        "lgb_reg.fit(X_train, y_train1p)\n",
        "lgb_reg_pred = np.expm1(lgb_reg.predict(X_val))\n",
        "\n",
        "print(f\"MAE:     {mean_absolute_error(y_val, lgb_reg_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_val, lgb_reg_pred))}\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test set eval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwobTuuUInku",
        "outputId": "8e7bafc9-92f4-466c-e123-6afa601876f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE:     33346.04114698163\n",
            "RMSE:    80606.68152681115\n"
          ]
        }
      ],
      "source": [
        "lgb_reg_test_pred = np.expm1(lgb_reg.predict(X_test))\n",
        "\n",
        "print(f\"MAE:     {mean_absolute_error(y_val, lgb_reg_test_pred)}\")\n",
        "print(f\"RMSE:    {np.sqrt(mean_squared_error(y_val, lgb_reg_test_pred))}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7XEvCfMP13Y0"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mw05UFK_13TF"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x_s8MLE413OE"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gxwmXKhl13GY"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6uG2G9WbjXzl"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
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