2_model_building.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction of Second Notebook"
      ],
      "metadata": {
        "id": "xPVxVkKyF4hs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook is the Second Notebook of the 2 part series of our CS3244 Project. This notebook mainly covers the Data Preprocessing steps that was used to prepare the data for model fitting and the testing of multiple Machine Learning Models to do our Resale Flat Price Predictions."
      ],
      "metadata": {
        "id": "9ENA0Q2PF6ou"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "D6QZsalpFyNK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bfmRPuaBPNVh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import datetime\n",
        "from scipy import stats\n",
        "\n",
        "# Preprocessing\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "\n",
        "# Models\n",
        "from sklearn.linear_model import LinearRegression, LassoCV, RidgeCV, ElasticNet, ElasticNetCV\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
        "from sklearn.feature_selection import SelectKBest, f_regression, RFE\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import ParameterGrid, train_test_split, cross_val_score, cross_val_predict\n",
        "from sklearn.linear_model import Ridge, Lasso\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from lightgbm import LGBMRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FrSRtJ6HQ0x7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1faa1b30-bf1e-438d-cb2c-c9b86b64d3e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZdQAPSB3RdN1"
      },
      "outputs": [],
      "source": [
        "df_train = pd.read_csv(\"df_train.csv\", index_col = 0)\n",
        "df_test = pd.read_csv(\"df_test.csv\", index_col = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KAKtjJsY9is",
        "outputId": "1ae820e0-daa0-40ca-bde1-8c97de8bc244"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 160858 entries, 0 to 160857\n",
            "Data columns (total 28 columns):\n",
            " #   Column                                 Non-Null Count   Dtype  \n",
            "---  ------                                 --------------   -----  \n",
            " 0   month                                  160858 non-null  object \n",
            " 1   town                                   160858 non-null  object \n",
            " 2   flat_type                              160858 non-null  object \n",
            " 3   block                                  160858 non-null  object \n",
            " 4   street_name                            160858 non-null  object \n",
            " 5   storey_range                           160858 non-null  object \n",
            " 6   floor_area_sqm                         160858 non-null  float64\n",
            " 7   flat_model                             160858 non-null  object \n",
            " 8   lease_commence_date                    160858 non-null  int64  \n",
            " 9   remaining_lease                        160858 non-null  object \n",
            " 10  resale_price                           160858 non-null  float64\n",
            " 11  latitude                               160858 non-null  float64\n",
            " 12  longitude                              160858 non-null  float64\n",
            " 13  shortest_distance_to_closest_mrt (km)  160858 non-null  float64\n",
            " 14  closest_mrt                            160858 non-null  object \n",
            " 15  max_floor_lvl                          160858 non-null  int64  \n",
            " 16  year_completed                         160858 non-null  int64  \n",
            " 17  residential                            160858 non-null  object \n",
            " 18  commercial                             160858 non-null  object \n",
            " 19  market_hawker                          160858 non-null  object \n",
            " 20  miscellaneous                          160858 non-null  object \n",
            " 21  multistorey_carpark                    160858 non-null  object \n",
            " 22  precinct_pavilion                      160858 non-null  object \n",
            " 23  bldg_contract_town                     160858 non-null  object \n",
            " 24  total_dwelling_units                   160858 non-null  int64  \n",
            " 25  is_elite_closest_pri_sch               160858 non-null  bool   \n",
            " 26  is_elite_closest_sec_sch               160858 non-null  bool   \n",
            " 27  is_elite_closest_mixed_sch             160858 non-null  bool   \n",
            "dtypes: bool(3), float64(5), int64(4), object(16)\n",
            "memory usage: 32.4+ MB\n"
          ]
        }
      ],
      "source": [
        "df_train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZFLHSGMGQv8G"
      },
      "outputs": [],
      "source": [
        "def drop_cols(df):\n",
        "    # Drop unnecessary columns\n",
        "    df.drop(['bldg_contract_town', 'latitude', 'longitude', 'lease_commence_date'], axis=1, inplace=True)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4dfacWe9PNVl"
      },
      "outputs": [],
      "source": [
        "df_train_filtered = drop_cols(df_train)\n",
        "df_test_filtered = drop_cols(df_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['flat_type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSVwpFAEyoI5",
        "outputId": "f946719f-29fc-4fa5-c008-af1c0d8757c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2 ROOM', '3 ROOM', '4 ROOM', '5 ROOM', 'EXECUTIVE', '1 ROOM',\n",
              "       'MULTI-GENERATION'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validating if Categorical Columns in Test Set are found in Train Set"
      ],
      "metadata": {
        "id": "HmQqnAt44aMx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a dictionary to store categories only in the test set along with their corresponding column names\n",
        "categories_only_in_test = {}\n",
        "\n",
        "# Loop through each categorical column\n",
        "for column_name in df_train_filtered.select_dtypes(include='object').columns:\n",
        "    # Get unique categories in the train set\n",
        "    train_categories = set(df_train_filtered[column_name])\n",
        "\n",
        "    # Get unique categories in the test set\n",
        "    test_categories = set(df_test_filtered[column_name])\n",
        "\n",
        "    # Find categories present in the test set but not in the train set\n",
        "    categories_only_in_test[column_name] = test_categories - train_categories\n",
        "\n",
        "# Print categories in the test set that do not belong to the train set along with their corresponding column names\n",
        "print(\"Categories in test set that do not belong to train set:\")\n",
        "for column_name, categories in categories_only_in_test.items():\n",
        "    print(\"Column:\", column_name)\n",
        "    print(\"Categories:\", categories)\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bbnW4wM4gFl",
        "outputId": "1cec5120-4763-41fe-fd31-fac668e4eaf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categories in test set that do not belong to train set:\n",
            "Column: month\n",
            "Categories: {'2024-02-01', '2023-10-01', '2023-11-01', '2024-03-01', '2024-01-01', '2023-12-01', '2023-09-01'}\n",
            "\n",
            "\n",
            "Column: town\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: flat_type\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: block\n",
            "Categories: {'182A', '996C', '606A', '462A', '182B', '460A', '438C', '606D', '605D', '461C', '996B', '605A', '462C', '605B', '323D', '605C', '995A', '461B', '991B', '860B', '999B', '437A', '995C', '462B', '995B', '606C', '462D', '998A', '606B', '996A'}\n",
            "\n",
            "\n",
            "Column: street_name\n",
            "Categories: {'YISHUN ST 43', 'TAMPINES ST 61'}\n",
            "\n",
            "\n",
            "Column: storey_range\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: flat_model\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: remaining_lease\n",
            "Categories: {'41 years 09 months', '42 years 02 months', '42 years 04 months', '41 years 11 months', '42 years 03 months', '42 years 01 month', '42 years'}\n",
            "\n",
            "\n",
            "Column: closest_mrt\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: residential\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: commercial\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: market_hawker\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: miscellaneous\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: multistorey_carpark\n",
            "Categories: set()\n",
            "\n",
            "\n",
            "Column: precinct_pavilion\n",
            "Categories: set()\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcJ7O79RUP-y"
      },
      "source": [
        "## Storey Range"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32qXbhZDUTez"
      },
      "source": [
        "We observe that the storey range values are categorical data. Converting them into numerical data by assigning the median value of each range can provide a more meaningful representation. For instance, for the range '10 TO 12', the median value would be 11. Similarly, '01 TO 03' would be represented as 2, '04 TO 06' as 5, and so forth. This transformation enables us to utilize the ordinal nature of the storey ranges in our analysis effectively"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qvckjn4xUMmo",
        "outputId": "905b8102-946d-4df9-f636-12cadfc0f30c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['10 TO 12', '01 TO 03', '04 TO 06', '07 TO 09', '13 TO 15',\n",
              "       '19 TO 21', '22 TO 24', '16 TO 18', '34 TO 36', '28 TO 30',\n",
              "       '37 TO 39', '49 TO 51', '25 TO 27', '40 TO 42', '31 TO 33',\n",
              "       '46 TO 48', '43 TO 45'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "df_train_filtered['storey_range'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IgOJfG62PNVl"
      },
      "outputs": [],
      "source": [
        "def get_median_storey_range(df):\n",
        "    df['storey_range'] = df['storey_range'].str.split(' TO ').apply(lambda x: (int(x[1]) + int(x[0])) / 2)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9FBY61OFaJHL"
      },
      "outputs": [],
      "source": [
        "df_train_preprocessed = get_median_storey_range(df_train_filtered)\n",
        "df_test_preprocessed = get_median_storey_range(df_test_filtered)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thCfB0kmUwkh"
      },
      "source": [
        "## Mapping Y/N columns to Boolean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GMfuevD5S3Yo"
      },
      "outputs": [],
      "source": [
        "# Encode columns with 'Y'/'N' values\n",
        "def map_yn_to_bool(df):\n",
        "    yn_cols = ['residential', 'commercial', 'market_hawker', 'miscellaneous', 'multistorey_carpark', 'precinct_pavilion']\n",
        "\n",
        "    # Map to True / False\n",
        "    for col in yn_cols:\n",
        "        df[col] = df[col].replace({'Y': True, 'N': False})\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BQYrbN2gaUuL"
      },
      "outputs": [],
      "source": [
        "df_train_preprocessed = map_yn_to_bool(df_train_preprocessed)\n",
        "df_test_preprocessed = map_yn_to_bool(df_test_preprocessed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Mg-3blLVMtO"
      },
      "source": [
        "## Converting the least commencement date to remaining lifespan of house"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XStp7d41Xliw"
      },
      "source": [
        "As the current remaining_lease column format is in categorical form, we will be converting it into a the remaining lifespan of the flat in months so that we can fit it as a numerical column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3KjH2OfXeN9",
        "outputId": "0d536b06-b3e8-4160-f1e9-5d57f0646811"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'61 years 04 months'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "df_train_filtered['remaining_lease'].unique()[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eLLJgtoEVG8m"
      },
      "outputs": [],
      "source": [
        "def convert_remaining_lease(df):\n",
        "    def convert(x):\n",
        "        parts = x.split()\n",
        "        years = int(parts[0])\n",
        "        months = int(parts[2]) if 'months' in x else 0\n",
        "        total_months = years * 12 + months\n",
        "        return total_months\n",
        "\n",
        "    df['remaining_lease'] = df['remaining_lease'].apply(convert)\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gbiKgmMuaVvF"
      },
      "outputs": [],
      "source": [
        "df_train_preprocessed = convert_remaining_lease(df_train_preprocessed)\n",
        "df_test_preprocessed = convert_remaining_lease(df_test_preprocessed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCVpVMibW71o"
      },
      "source": [
        "## Converting transaction date into Year and Month Column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wp8Dit8lYUD9"
      },
      "source": [
        "The current format of the date of transactions makes it difficult for us to process it in our model. Hence we will be splitting the transaction date into a Year column and Month column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAqUJvoKYPxX",
        "outputId": "78b70b25-1032-4138-f703-6ecbd31bc3f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2017-01-01', '2017-02-01', '2017-03-01', '2017-04-01',\n",
              "       '2017-05-01', '2017-06-01', '2017-07-01', '2017-08-01',\n",
              "       '2017-09-01', '2017-10-01', '2017-11-01', '2017-12-01',\n",
              "       '2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',\n",
              "       '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',\n",
              "       '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',\n",
              "       '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',\n",
              "       '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',\n",
              "       '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01',\n",
              "       '2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',\n",
              "       '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01',\n",
              "       '2020-09-01', '2020-10-01', '2020-11-01', '2020-12-01',\n",
              "       '2021-01-01', '2021-02-01', '2021-03-01', '2021-04-01',\n",
              "       '2021-05-01', '2021-06-01', '2021-11-01', '2021-07-01',\n",
              "       '2021-08-01', '2021-09-01', '2021-10-01', '2021-12-01',\n",
              "       '2022-01-01', '2022-02-01', '2022-03-01', '2022-04-01',\n",
              "       '2022-05-01', '2022-06-01', '2022-07-01', '2022-08-01',\n",
              "       '2022-09-01', '2022-10-01', '2022-11-01', '2022-12-01',\n",
              "       '2023-01-01', '2023-02-01', '2023-03-01', '2023-04-01',\n",
              "       '2023-05-01', '2023-06-01', '2023-07-01', '2023-08-01'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "df_train_filtered['month'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "taApInnPXD-v"
      },
      "outputs": [],
      "source": [
        "def convert_transaction_date(df):\n",
        "    date_of_transaction = pd.to_datetime(df['month'])\n",
        "\n",
        "    # Create Year and Month Columns\n",
        "    df['year'] = date_of_transaction.dt.year\n",
        "    df['month_numeric'] = date_of_transaction.dt.month\n",
        "\n",
        "    # Drop original transaction date column\n",
        "    df.drop('month', axis = 1, inplace = True)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v4IG-AH5aW5w"
      },
      "outputs": [],
      "source": [
        "df_train_preprocessed = convert_transaction_date(df_train_preprocessed)\n",
        "df_test_preprocessed = convert_transaction_date(df_test_preprocessed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KipBaB56a1C4",
        "outputId": "20fac529-6bde-4caf-9136-af2bc86d704e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         town flat_type block        street_name  storey_range  \\\n",
              "0  ANG MO KIO    2 ROOM   406  ANG MO KIO AVE 10          11.0   \n",
              "1  ANG MO KIO    3 ROOM   108   ANG MO KIO AVE 4           2.0   \n",
              "2  ANG MO KIO    3 ROOM   602   ANG MO KIO AVE 5           2.0   \n",
              "3  ANG MO KIO    3 ROOM   465  ANG MO KIO AVE 10           5.0   \n",
              "4  ANG MO KIO    3 ROOM   601   ANG MO KIO AVE 5           2.0   \n",
              "\n",
              "   floor_area_sqm      flat_model  remaining_lease  resale_price  \\\n",
              "0            44.0        Improved              736      232000.0   \n",
              "1            67.0  New Generation              727      250000.0   \n",
              "2            67.0  New Generation              749      262000.0   \n",
              "3            68.0  New Generation              744      265000.0   \n",
              "4            67.0  New Generation              749      265000.0   \n",
              "\n",
              "   shortest_distance_to_closest_mrt (km)  ... market_hawker  miscellaneous  \\\n",
              "0                               1.007304  ...         False          False   \n",
              "1                               1.271398  ...         False          False   \n",
              "2                               1.069720  ...         False          False   \n",
              "3                               0.946884  ...         False          False   \n",
              "4                               1.092577  ...         False           True   \n",
              "\n",
              "   multistorey_carpark  precinct_pavilion  total_dwelling_units  \\\n",
              "0                False              False                   220   \n",
              "1                False              False                   122   \n",
              "2                False              False                   150   \n",
              "3                False              False                   206   \n",
              "4                False              False                   152   \n",
              "\n",
              "   is_elite_closest_pri_sch  is_elite_closest_sec_sch  \\\n",
              "0                     False                     False   \n",
              "1                     False                     False   \n",
              "2                     False                     False   \n",
              "3                     False                     False   \n",
              "4                     False                     False   \n",
              "\n",
              "   is_elite_closest_mixed_sch  year  month_numeric  \n",
              "0                        True  2017              1  \n",
              "1                        True  2017              1  \n",
              "2                        True  2017              1  \n",
              "3                        True  2017              1  \n",
              "4                        True  2017              1  \n",
              "\n",
              "[5 rows x 25 columns]"
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              "      <th></th>\n",
              "      <th>town</th>\n",
              "      <th>flat_type</th>\n",
              "      <th>block</th>\n",
              "      <th>street_name</th>\n",
              "      <th>storey_range</th>\n",
              "      <th>floor_area_sqm</th>\n",
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              "      <th>shortest_distance_to_closest_mrt (km)</th>\n",
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              "      <th>is_elite_closest_pri_sch</th>\n",
              "      <th>is_elite_closest_sec_sch</th>\n",
              "      <th>is_elite_closest_mixed_sch</th>\n",
              "      <th>year</th>\n",
              "      <th>month_numeric</th>\n",
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              "      <th>0</th>\n",
              "      <td>ANG MO KIO</td>\n",
              "      <td>2 ROOM</td>\n",
              "      <td>406</td>\n",
              "      <td>ANG MO KIO AVE 10</td>\n",
              "      <td>11.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>Improved</td>\n",
              "      <td>736</td>\n",
              "      <td>232000.0</td>\n",
              "      <td>1.007304</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>220</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANG MO KIO</td>\n",
              "      <td>3 ROOM</td>\n",
              "      <td>108</td>\n",
              "      <td>ANG MO KIO AVE 4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>New Generation</td>\n",
              "      <td>727</td>\n",
              "      <td>250000.0</td>\n",
              "      <td>1.271398</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>122</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANG MO KIO</td>\n",
              "      <td>3 ROOM</td>\n",
              "      <td>602</td>\n",
              "      <td>ANG MO KIO AVE 5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>New Generation</td>\n",
              "      <td>749</td>\n",
              "      <td>262000.0</td>\n",
              "      <td>1.069720</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>150</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ANG MO KIO</td>\n",
              "      <td>3 ROOM</td>\n",
              "      <td>465</td>\n",
              "      <td>ANG MO KIO AVE 10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>New Generation</td>\n",
              "      <td>744</td>\n",
              "      <td>265000.0</td>\n",
              "      <td>0.946884</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>206</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ANG MO KIO</td>\n",
              "      <td>3 ROOM</td>\n",
              "      <td>601</td>\n",
              "      <td>ANG MO KIO AVE 5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>New Generation</td>\n",
              "      <td>749</td>\n",
              "      <td>265000.0</td>\n",
              "      <td>1.092577</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>152</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 25 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_preprocessed"
            }
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "df_train_preprocessed.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6iE6L0cYbjle"
      },
      "source": [
        "## Dropping street and block columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8sVcTZL7bv5B"
      },
      "source": [
        "After applying the nunique() function, it is evident that both the 'block' and 'street_name' columns contain a large number of distinct values, indicating significant sparsity. Considering that the building information is already well-represented by other features in the dataset, maintaining these columns would likely introduce unnecessary complexity. Therefore, to streamline the dataset and reduce dimensionality, we have decided to drop the 'block' and 'street_name' columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXuOtLBcbqF9",
        "outputId": "4051399f-bcbc-4cc8-c3a6-cf6850c3fa46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "town                                       26\n",
              "flat_type                                   7\n",
              "block                                    2673\n",
              "street_name                               565\n",
              "storey_range                               17\n",
              "floor_area_sqm                            170\n",
              "flat_model                                 21\n",
              "remaining_lease                           608\n",
              "resale_price                             3574\n",
              "shortest_distance_to_closest_mrt (km)    9475\n",
              "closest_mrt                               125\n",
              "max_floor_lvl                              42\n",
              "year_completed                             60\n",
              "residential                                 1\n",
              "commercial                                  2\n",
              "market_hawker                               2\n",
              "miscellaneous                               2\n",
              "multistorey_carpark                         2\n",
              "precinct_pavilion                           2\n",
              "total_dwelling_units                      327\n",
              "is_elite_closest_pri_sch                    2\n",
              "is_elite_closest_sec_sch                    2\n",
              "is_elite_closest_mixed_sch                  2\n",
              "year                                        7\n",
              "month_numeric                              12\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "df_train_preprocessed.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PKGylQcfcIx-"
      },
      "outputs": [],
      "source": [
        "df_train_preprocessed = df_train_preprocessed.drop(['street_name', 'block'], axis = 1)\n",
        "df_test_preprocessed = df_test_preprocessed.drop(['street_name', 'block'], axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Flat Model"
      ],
      "metadata": {
        "id": "-lsP6PVu1Fgh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "During our categorical data encoding, we actually found some categories '2-room' and '3Gen' that were not originally in the train set. For these data points, we will be imputing their values with the most common flat model - Model A."
      ],
      "metadata": {
        "id": "TggtYbDz17nk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_categories = set(df_train_preprocessed['flat_model'])\n",
        "\n",
        "test_categories = set(df_test_preprocessed['flat_model'])\n",
        "\n",
        "categories_only_in_test = test_categories - train_categories\n",
        "\n",
        "print(df_train_preprocessed['flat_model'].value_counts())\n",
        "print(\"Categories in test set that do not belong to train set:\", categories_only_in_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nojcrz0Z1Hxl",
        "outputId": "bc9ab2cb-9c96-4791-d17c-74a54b908c79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "flat_model\n",
            "Model A                   54895\n",
            "Improved                  39878\n",
            "New Generation            20134\n",
            "Premium Apartment         18392\n",
            "Simplified                 6255\n",
            "Apartment                  6125\n",
            "Maisonette                 4645\n",
            "Standard                   4491\n",
            "DBSS                       2813\n",
            "Model A2                   1846\n",
            "Model A-Maisonette          293\n",
            "Adjoined flat               286\n",
            "Type S1                     285\n",
            "Type S2                     153\n",
            "Premium Apartment Loft       96\n",
            "Terrace                      90\n",
            "Multi Generation             73\n",
            "2-room                       55\n",
            "Improved-Maisonette          24\n",
            "3Gen                         15\n",
            "Premium Maisonette           14\n",
            "Name: count, dtype: int64\n",
            "Categories in test set that do not belong to train set: set()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter to '2-room' and '3Gen'\n",
        "filtered_rows = df_test_preprocessed[df_test_preprocessed['flat_model'].isin(['2-room', '3Gen'])]\n",
        "\n",
        "# Impute 'flat_model' of the filtered rows to be 'Model A'\n",
        "df_test_preprocessed.loc[filtered_rows.index, 'flat_model'] = 'Model A'"
      ],
      "metadata": {
        "id": "qQTj25Qy2kYn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## closest_mrt"
      ],
      "metadata": {
        "id": "4QkxM2QX5YSu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Earlier, we found that some of the closest mrt stations in the test were not actually found in the train set. For these stations, a quick google search shows that they are LRT stations that are found around the Punggol estate. For ease of ensuring that our encoding process runs smoothly, we will be imputing their values as \"Punggol\" MRT Station."
      ],
      "metadata": {
        "id": "ZI5Q9fCf5cOS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_preprocessed['closest_mrt'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "viLyMT3E5bdN",
        "outputId": "6ea4c32d-d19a-47a3-e122-54923bc78e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ang Mo Kio', 'Yio Chu Kang', 'Bedok Reservoir', 'Bedok',\n",
              "       'Tanah Merah', 'Kaki Bukit', 'Bedok North', 'Kembangan',\n",
              "       'Marymount', 'Bishan', 'Bukit Gombak', 'Bukit Batok',\n",
              "       'Tiong Bahru', 'Telok Blangah', 'HarbourFront', 'Queenstown',\n",
              "       'Redhill', 'Labrador Park', 'Pending', 'Bangkit', 'Segar',\n",
              "       'Jelapang', 'Petir', 'Fajar', 'Senja', 'Beauty World',\n",
              "       'Farrer Road', 'Chinatown', 'Jalan Besar', 'Bencoolen',\n",
              "       'Tanjong Pagar', 'Bugis', 'Bras Basah', 'Outram Park', 'Teck Whye',\n",
              "       'Keat Hong', 'South View', 'Yew Tee', 'Choa Chu Kang', 'Phoenix',\n",
              "       'Clementi', 'Mattar', 'MacPherson', 'Tai Seng', 'Eunos', 'Ubi',\n",
              "       'Paya Lebar', 'Dakota', 'Aljunied', 'Geylang Bahru', 'Mountbatten',\n",
              "       'Tongkang', 'Kovan', 'Hougang', 'Serangoon', 'Bartley', 'Buangkok',\n",
              "       'Jurong East', 'Chinese Garden', 'Pioneer', 'Boon Lay', 'Lakeside',\n",
              "       'King Albert Park', 'Boon Keng', 'Farrer Park', 'Hillview',\n",
              "       'Kallang', 'Lavender', 'Bendemeer', 'Toa Payoh', 'Changi Airport',\n",
              "       'Pasir Ris', 'Tampines East', 'Cove', 'Coral Edge', 'Meridian',\n",
              "       'Damai', 'Soo Teck', 'Punggol', 'Oasis', 'Kadaloor', 'Riviera',\n",
              "       'Commonwealth', 'Buona Vista', 'one-north', 'Dover',\n",
              "       'Holland Village', 'Sembawang', 'Fernvale', 'Kangkar', 'Ranggung',\n",
              "       'Bakau', 'Rumbia', 'Cheng Lim', 'Renjong', 'Sengkang',\n",
              "       'Compassvale', 'Lorong Chuan', 'Tampines', 'Tampines West',\n",
              "       'Simei', 'Braddell', 'Potong Pasir', 'Caldecott', 'Marsiling',\n",
              "       'Woodlands', 'Yishun', 'Khatib', 'Little India', 'Esplanade',\n",
              "       'Layar', 'Farmway', 'Upper Changi', 'Woodleigh', 'Bukit Panjang',\n",
              "       'Nicoll Highway', 'Newton', 'Novena', 'Pasir Panjang', 'Rochor',\n",
              "       'Botanic Gardens', 'Thanggam', 'Nibong', 'Kupang', 'Sumang'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter to '2-room' and '3Gen'\n",
        "filtered_rows = df_test_preprocessed[df_test_preprocessed['closest_mrt'].isin(['Kupang', 'Nibong', 'Thanggam', 'Sumang'])]\n",
        "\n",
        "# Impute 'flat_model' of the filtered rows to be 'Model A'\n",
        "df_test_preprocessed.loc[filtered_rows.index, 'closest_mrt'] = 'Punggol'"
      ],
      "metadata": {
        "id": "Kz1lxOj3531y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sTwm2hKbn8S"
      },
      "source": [
        "## Viewing the final preprocessed dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMSMgeCEbPa2",
        "outputId": "3e66e4cd-3fb8-4f9f-d554-72f70bb7f95e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 160858 entries, 0 to 160857\n",
            "Data columns (total 23 columns):\n",
            " #   Column                                 Non-Null Count   Dtype  \n",
            "---  ------                                 --------------   -----  \n",
            " 0   town                                   160858 non-null  object \n",
            " 1   flat_type                              160858 non-null  object \n",
            " 2   storey_range                           160858 non-null  float64\n",
            " 3   floor_area_sqm                         160858 non-null  float64\n",
            " 4   flat_model                             160858 non-null  object \n",
            " 5   remaining_lease                        160858 non-null  int64  \n",
            " 6   resale_price                           160858 non-null  float64\n",
            " 7   shortest_distance_to_closest_mrt (km)  160858 non-null  float64\n",
            " 8   closest_mrt                            160858 non-null  object \n",
            " 9   max_floor_lvl                          160858 non-null  int64  \n",
            " 10  year_completed                         160858 non-null  int64  \n",
            " 11  residential                            160858 non-null  bool   \n",
            " 12  commercial                             160858 non-null  bool   \n",
            " 13  market_hawker                          160858 non-null  bool   \n",
            " 14  miscellaneous                          160858 non-null  bool   \n",
            " 15  multistorey_carpark                    160858 non-null  bool   \n",
            " 16  precinct_pavilion                      160858 non-null  bool   \n",
            " 17  total_dwelling_units                   160858 non-null  int64  \n",
            " 18  is_elite_closest_pri_sch               160858 non-null  bool   \n",
            " 19  is_elite_closest_sec_sch               160858 non-null  bool   \n",
            " 20  is_elite_closest_mixed_sch             160858 non-null  bool   \n",
            " 21  year                                   160858 non-null  int32  \n",
            " 22  month_numeric                          160858 non-null  int32  \n",
            "dtypes: bool(9), float64(4), int32(2), int64(4), object(4)\n",
            "memory usage: 18.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df_train_preprocessed.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "h0iMwF_RbL4_",
        "outputId": "c9c516ad-10af-437b-acbf-6fa624b751bd"
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        {
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          "data": {
            "text/plain": [
              "              town flat_type  storey_range  floor_area_sqm      flat_model  \\\n",
              "160858  ANG MO KIO    2 ROOM           2.0            44.0        Improved   \n",
              "160859  ANG MO KIO    2 ROOM           5.0            45.0        Improved   \n",
              "160860  ANG MO KIO    3 ROOM           5.0            73.0  New Generation   \n",
              "160861  ANG MO KIO    3 ROOM           2.0            68.0  New Generation   \n",
              "160862  ANG MO KIO    3 ROOM           8.0            70.0         Model A   \n",
              "\n",
              "        remaining_lease  resale_price  shortest_distance_to_closest_mrt (km)  \\\n",
              "160858              640      282000.0                               0.423595   \n",
              "160859              737      300000.0                               1.223815   \n",
              "160860              620      370888.0                               0.994162   \n",
              "160861              679      360000.0                               0.854823   \n",
              "160862             1056      570000.0                               0.696736   \n",
              "\n",
              "         closest_mrt  max_floor_lvl  ...  market_hawker  miscellaneous  \\\n",
              "160858    Ang Mo Kio             12  ...          False           True   \n",
              "160859  Yio Chu Kang             10  ...          False           True   \n",
              "160860    Ang Mo Kio             10  ...          False          False   \n",
              "160861    Ang Mo Kio             12  ...          False          False   \n",
              "160862    Ang Mo Kio             21  ...          False          False   \n",
              "\n",
              "        multistorey_carpark  precinct_pavilion  total_dwelling_units  \\\n",
              "160858                False              False                   242   \n",
              "160859                False              False                   178   \n",
              "160860                False              False                   144   \n",
              "160861                False              False                   195   \n",
              "160862                False              False                   230   \n",
              "\n",
              "        is_elite_closest_pri_sch  is_elite_closest_sec_sch  \\\n",
              "160858                     False                     False   \n",
              "160859                     False                     False   \n",
              "160860                     False                     False   \n",
              "160861                     False                     False   \n",
              "160862                     False                     False   \n",
              "\n",
              "        is_elite_closest_mixed_sch  year  month_numeric  \n",
              "160858                        True  2023              9  \n",
              "160859                        True  2023              9  \n",
              "160860                        True  2023              9  \n",
              "160861                        True  2023              9  \n",
              "160862                        True  2023              9  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <td>ANG MO KIO</td>\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fd7aab11-b9e2-44b2-a66b-5ab1c59f887c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test_preprocessed"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "df_test_preprocessed.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rppb3kXObVzz",
        "outputId": "78bea6f1-6ff4-42fb-93ff-6a65cb445bd6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 14035 entries, 160858 to 174892\n",
            "Data columns (total 23 columns):\n",
            " #   Column                                 Non-Null Count  Dtype  \n",
            "---  ------                                 --------------  -----  \n",
            " 0   town                                   14035 non-null  object \n",
            " 1   flat_type                              14035 non-null  object \n",
            " 2   storey_range                           14035 non-null  float64\n",
            " 3   floor_area_sqm                         14035 non-null  float64\n",
            " 4   flat_model                             14035 non-null  object \n",
            " 5   remaining_lease                        14035 non-null  int64  \n",
            " 6   resale_price                           14035 non-null  float64\n",
            " 7   shortest_distance_to_closest_mrt (km)  14035 non-null  float64\n",
            " 8   closest_mrt                            14035 non-null  object \n",
            " 9   max_floor_lvl                          14035 non-null  int64  \n",
            " 10  year_completed                         14035 non-null  int64  \n",
            " 11  residential                            14035 non-null  bool   \n",
            " 12  commercial                             14035 non-null  bool   \n",
            " 13  market_hawker                          14035 non-null  bool   \n",
            " 14  miscellaneous                          14035 non-null  bool   \n",
            " 15  multistorey_carpark                    14035 non-null  bool   \n",
            " 16  precinct_pavilion                      14035 non-null  bool   \n",
            " 17  total_dwelling_units                   14035 non-null  int64  \n",
            " 18  is_elite_closest_pri_sch               14035 non-null  bool   \n",
            " 19  is_elite_closest_sec_sch               14035 non-null  bool   \n",
            " 20  is_elite_closest_mixed_sch             14035 non-null  bool   \n",
            " 21  year                                   14035 non-null  int32  \n",
            " 22  month_numeric                          14035 non-null  int32  \n",
            "dtypes: bool(9), float64(4), int32(2), int64(4), object(4)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ],
      "source": [
        "df_test_preprocessed.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRinIG_hWjaJ"
      },
      "source": [
        "# Model Fitting"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section, we will be attempting to fit our data into several models, ranging from basic models like Linear Regression to Gradient Boosting Models like Light GBM. We will be evaluating the results across all models and find out which is the best performing model."
      ],
      "metadata": {
        "id": "tIiPgowPqKI4"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fq1alGFbcfv0"
      },
      "source": [
        "## Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Gg8xlMRnYEZ"
      },
      "outputs": [],
      "source": [
        "# Train Test Split\n",
        "X_train = df_train_preprocessed.drop(['resale_price'], axis=1)\n",
        "y_train = df_train_preprocessed['resale_price']\n",
        "\n",
        "X_test = df_test_preprocessed.drop(['resale_price'], axis=1)\n",
        "y_test = df_test_preprocessed['resale_price']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iod4GBvBnYEa"
      },
      "source": [
        "## Feature encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fm3WTjZwnYEa"
      },
      "outputs": [],
      "source": [
        "# Define function to split column types\n",
        "def splitColumnTypes(df):\n",
        "    cate_cols = []\n",
        "    num_cols = []\n",
        "    dtypes = df.dtypes\n",
        "    for col, dtype in dtypes.items():\n",
        "        if dtype == 'object':\n",
        "            cate_cols.append(col)\n",
        "        else:\n",
        "            num_cols.append(col)\n",
        "    return cate_cols, num_cols\n",
        "\n",
        "# Split column types\n",
        "cate_cols, num_cols = splitColumnTypes(X_train)\n",
        "\n",
        "# Define transformer\n",
        "transformer = ColumnTransformer(transformers=[('num', StandardScaler(), num_cols),\\\n",
        "                                              ('cat', OneHotEncoder(), cate_cols)])\n",
        "\n",
        "pipeline = Pipeline(steps=[('preprocessor', transformer)])\n",
        "\n",
        "# Use the fitted Pipeline to transform Test Data - Prevent Data Leakage\n",
        "X_train_encoded = pipeline.fit_transform(X_train)\n",
        "X_test_encoded = pipeline.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2ZVxbU6nYEa",
        "outputId": "a6d11504-7405-430c-cb5d-ebfaffb908b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train_encoded: (160858, 197)\n",
            "Shape of X_test_encoded: (14035, 197)\n",
            "Shape of y_train: (160858,)\n",
            "Shape of y_test: (14035,)\n"
          ]
        }
      ],
      "source": [
        "print(\"Shape of X_train_encoded:\", X_train_encoded.shape)\n",
        "print(\"Shape of X_test_encoded:\", X_test_encoded.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We retrieve the encoded column names."
      ],
      "metadata": {
        "id": "zSusxnQZ6em5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "33turTQvFKzw"
      },
      "outputs": [],
      "source": [
        "encoded_column_names = transformer.transformers_[1][1].get_feature_names_out(input_features=cate_cols)\n",
        "\n",
        "all_column_names = list(num_cols) + list(encoded_column_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtqcM0E6XMaS"
      },
      "source": [
        "## Evaluation Metrics"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "collection_of_model_results = {}"
      ],
      "metadata": {
        "id": "nY9Tl_X7l4de"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twTMdPGZXPgL"
      },
      "source": [
        "We define the following Evaluation Metrics for our Regression Task.\n",
        "\n",
        "Mean Absolute Error: Measures the average of the absolute differences between predicted and actual prices.\n",
        "\n",
        "R-Squared Score: Measures the proportion of the variance explained by the model.\n",
        "\n",
        "Root Mean Squared Error: Measures the average price distances (after punishing heavily mispredictions)\n",
        "\n",
        "Mean Absolute Percentage Error: Measures the average percentage difference between predicted and actual prices of flats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "THoU4qK3XNxn"
      },
      "outputs": [],
      "source": [
        "def evaluate_results(model_name, y_test, y_pred):\n",
        "    results = {}\n",
        "    results[model_name] = {\n",
        "        'Mean Absolute Error': mean_absolute_error(y_test, y_pred),\n",
        "        'R-squared score': r2_score(y_test, y_pred),\n",
        "        'Root Mean Squared Error': np.sqrt(mean_squared_error(y_test, y_pred)),\n",
        "        'Mean Absolute Percentage Error': mean_absolute_percentage_error(y_test, y_pred) * 100\n",
        "    }\n",
        "\n",
        "    print(f'{model_name}: Mean Absolute Error:', results[model_name]['Mean Absolute Error'])\n",
        "    print(f'{model_name}: R-squared score:', results[model_name]['R-squared score'])\n",
        "    print(f'{model_name}: Root Mean Squared Error:', results[model_name]['Root Mean Squared Error'])\n",
        "    print(f'{model_name}: Mean Absolute Percentage Error:', results[model_name]['Mean Absolute Percentage Error'], \"%\")\n",
        "\n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_prediction_results(y_test, y_pred):\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.scatter(y_test, y_pred, color='blue', alpha=0.5)\n",
        "    plt.title('Predicted Labels against Actual Labels')\n",
        "    plt.xlabel('True Labels')\n",
        "    plt.ylabel('Predicted Labels')"
      ],
      "metadata": {
        "id": "utbgCAxVA-Z7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4t3x74eRnYEc"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "qoOIGsbKZWLC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9NiHopFGnYEc"
      },
      "outputs": [],
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(X_train_encoded, y_train)\n",
        "y_pred_lr = lr.predict(X_test_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyviexBjZ_y8",
        "outputId": "7e866524-6eab-4b23-f024-5a7bc0dd7f04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression: Mean Absolute Error: 48455.090760864936\n",
            "Linear Regression: R-squared score: 0.8645750583529281\n",
            "Linear Regression: Root Mean Squared Error: 65433.19863576084\n",
            "Linear Regression: Mean Absolute Percentage Error: 8.253609793219747 %\n"
          ]
        }
      ],
      "source": [
        "lr_results = evaluate_results(\"Linear Regression\", y_test, y_pred_lr)\n",
        "collection_of_model_results['Linear Regression'] = lr_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "IoZLTtaPBhZe",
        "outputId": "8e8ebb43-af8e-4d19-88b3-49c5b8bff65a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CWuXLvBnYEc"
      },
      "source": [
        "## Linear Regression with Regularisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnzRd1VqP6ZI"
      },
      "source": [
        "In this section, we attempt to fit our encoded dataset into Linear Regression models with L2 and L1 Regression. Since our encoded dataset contains over 190 features, regularisation may have a big role in attaining higher test scores as compared to the baseline Regression Models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkgdKX7DP4hB"
      },
      "source": [
        "### L2 Regularisation (Ridge)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "35EA7N3unYEc",
        "outputId": "e46db2bd-37fa-442b-a17f-fef119a35790"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RidgeCV(cv=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeCV(cv=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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeCV</label><div class=\"sk-toggleable__content\"><pre>RidgeCV(cv=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "l2_lr = RidgeCV(cv = 5)\n",
        "l2_lr.fit(X_train_encoded, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yY5ez4SybZ6c"
      },
      "source": [
        "The best L2 Regularisation Parameter is 0.1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rfAMKxf8aiGf",
        "outputId": "548e650b-bdaf-4b82-de4c-bd8d26b4eb0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best alpha parameter: 0.1\n",
            "Best alpha cv score: 0.818291979982052\n"
          ]
        }
      ],
      "source": [
        "print(\"Best alpha parameter:\", l2_lr.alpha_)\n",
        "print(\"Best alpha cv score:\", l2_lr.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8kvNAHw4nYEd"
      },
      "outputs": [],
      "source": [
        "y_pred_ridge = l2_lr.predict(X_test_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l2_reg_results = evaluate_results(\"L2 Linear Regression (Ridge)\", y_test, y_pred_ridge)\n",
        "collection_of_model_results['L2 Linear Regression (Ridge)'] = l2_reg_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sza8R7IsljR2",
        "outputId": "b906c589-9241-4238-dae1-e78203d097b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L2 Linear Regression (Ridge): Mean Absolute Error: 48476.53629754607\n",
            "L2 Linear Regression (Ridge): R-squared score: 0.864473247747449\n",
            "L2 Linear Regression (Ridge): Root Mean Squared Error: 65457.78990343943\n",
            "L2 Linear Regression (Ridge): Mean Absolute Percentage Error: 8.252991480226274 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_ridge)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "2z7ZCiEmBt6g",
        "outputId": "22f00116-aa21-4f3b-c245-61dc0ab774ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybsMs8D0nYEd"
      },
      "source": [
        "### Lasso Model L1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2JSCFHIdRGt"
      },
      "source": [
        "It appears that the Lasso Regression model failed to converge during its cross validation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "Mgz2hRZenYEd",
        "outputId": "0991200e-2c07-4b88-ae9d-962faa0fa99f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39853496018526.09, tolerance: 373818057061.2144\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 105715652602153.16, tolerance: 373818057061.2144\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159408997751981.06, tolerance: 373818057061.2144\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164210316779973.9, tolerance: 373818057061.2144\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56823754221257.0, tolerance: 366816482827.3388\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112073609024509.42, tolerance: 366816482827.3388\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 184238227433445.2, tolerance: 366816482827.3388\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191906094777163.6, tolerance: 366816482827.3388\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65197067126177.5, tolerance: 364975173968.8339\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63933512631947.97, tolerance: 364975173968.8339\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171280198574470.38, tolerance: 364975173968.8339\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 178477916805653.9, tolerance: 364975173968.8339\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 25921268135232.188, tolerance: 370537892137.14514\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 116542268891796.45, tolerance: 370537892137.14514\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 187870996773092.22, tolerance: 370537892137.14514\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194779030198099.06, tolerance: 370537892137.14514\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36785596140594.31, tolerance: 336359560256.9839\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86393308094342.52, tolerance: 336359560256.9839\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155601316838537.94, tolerance: 336359560256.9839\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160899598693721.2, tolerance: 336359560256.9839\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:592: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234619921237327.1, tolerance: 455541750060.5352\n",
            "  model = cd_fast.sparse_enet_coordinate_descent(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LassoCV(alphas=[0.01, 0.1, 1.0, 10.0], max_iter=200)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoCV(alphas=[0.01, 0.1, 1.0, 10.0], max_iter=200)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LassoCV</label><div class=\"sk-toggleable__content\"><pre>LassoCV(alphas=[0.01, 0.1, 1.0, 10.0], max_iter=200)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ],
      "source": [
        "l1_cv = LassoCV(alphas=[0.01, 0.1, 1.0, 10.0], max_iter = 200)\n",
        "l1_cv.fit(X_train_encoded, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETmiBM6wbw-8",
        "outputId": "9e937e61-a17e-4142-da36-10bc151e0b48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best alpha parameter: 0.01\n"
          ]
        }
      ],
      "source": [
        "print(\"Best alpha parameter:\", l1_cv.alpha_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMpJB3wBnYEd",
        "outputId": "b3016dc4-679a-454b-9e22-1a922d34acc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L1 Linear Regression (Lasso): Mean Absolute Error: 48474.960338679964\n",
            "L1 Linear Regression (Lasso): R-squared score: 0.8645172054277863\n",
            "L1 Linear Regression (Lasso): Root Mean Squared Error: 65447.17352729639\n",
            "L1 Linear Regression (Lasso): Mean Absolute Percentage Error: 8.25331091243294 %\n"
          ]
        }
      ],
      "source": [
        "y_pred_lasso = l1_cv.predict(X_test_encoded)\n",
        "l1_reg_results = evaluate_results(\"L1 Linear Regression (Lasso)\", y_test, y_pred_lasso)\n",
        "collection_of_model_results['L1 Linear Regression (Lasso)'] = l1_reg_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_lasso)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "dqUrkWTNBvmh",
        "outputId": "7e02ce09-f30d-404f-d4a8-a0a549bb9b8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QI05AGn4nYEe"
      },
      "source": [
        "### ElasticNet Model (L1 + L2 Regularisation)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After fitting the data into the individual L1 and L2 Regression Models, we would want to see if using the ElasticNet Model (combined L1 and L2 Regularisation) would achieve better results. We also want to analyse what is the best L1-Ratio that achieves the highest CV score. The l1_ratio mixing parameter ranges from 0 to 1. When l1_ratio is 0, the penalty applied is an L2 penalty. Conversely, when l1_ratio is 1, it corresponds to an L1 penalty. For values of l1_ratio between 0 and 1 exclusive, the penalty combines elements of both L1 and L2 penalties.\n",
        "\n",
        "According to sklearn's documenatation on ElasticNet, a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge)."
      ],
      "metadata": {
        "id": "M6bIzCPn67gc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zvhx514-nYEe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "outputId": "2b0d8e3a-e246-4c06-fe0f-36f9a1275464"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Path: 000 out of 006\n",
            "Path: 001 out of 006\n",
            "Path: 002 out of 006\n",
            "Path: 003 out of 006\n",
            "Path: 004 out of 006\n",
            "Path: 005 out of 006\n",
            "Path: 000 out of 006\n",
            "Path: 001 out of 006\n",
            "Path: 002 out of 006\n",
            "Path: 003 out of 006\n",
            "Path: 004 out of 006\n",
            "Path: 005 out of 006\n",
            "Path: 000 out of 006\n",
            "Path: 001 out of 006\n",
            "Path: 002 out of 006\n",
            "Path: 003 out of 006\n",
            "Path: 004 out of 006\n",
            "Path: 005 out of 006\n",
            "Path: 000 out of 006\n",
            "Path: 001 out of 006\n",
            "Path: 002 out of 006\n",
            "Path: 003 out of 006\n",
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            "Path: 000 out of 006\n",
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            "Path: 002 out of 006\n",
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            "Path: 001 out of 006\n",
            "Path: 002 out of 006\n",
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            "Path: 004 out of 006\n",
            "Path: 005 out of 006\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ElasticNetCV(alphas=[0.1, 0.5, 1.0, 5.0, 10, 100], cv=2,\n",
              "             l1_ratio=[0.9, 0.95, 0.97], verbose=2)"
            ],
            "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>ElasticNetCV(alphas=[0.1, 0.5, 1.0, 5.0, 10, 100], cv=2,\n",
              "             l1_ratio=[0.9, 0.95, 0.97], verbose=2)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNetCV</label><div class=\"sk-toggleable__content\"><pre>ElasticNetCV(alphas=[0.1, 0.5, 1.0, 5.0, 10, 100], cv=2,\n",
              "             l1_ratio=[0.9, 0.95, 0.97], verbose=2)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "elastic_cv = ElasticNetCV(cv=2, alphas = [0.1, 0.5, 1.0, 5.0, 10, 100],\n",
        "                          l1_ratio = [0.9, 0.95, 0.97],\n",
        "                          verbose = 2)\n",
        "\n",
        "elastic_cv.fit(X_train_encoded, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uprj-_ptnYEe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "152f923f-9bf3-4273-83f0-ee9634610eec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best alpha parameter: 0.5\n",
            "l1_ratio: 0.9\n"
          ]
        }
      ],
      "source": [
        "alpha = elastic_cv.alpha_\n",
        "l1_ratio = elastic_cv.l1_ratio_\n",
        "\n",
        "print(\"Best alpha parameter:\", alpha)\n",
        "print(f'l1_ratio: {l1_ratio}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mKbABAepnYEe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "490aa5c5-63d4-4ca4-edf3-91a449c08f80"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ElasticNet(alpha=0.5, l1_ratio=0.9)"
            ],
            "text/html": [
              "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 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-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 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-20 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-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 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-20 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-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 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-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 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-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 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-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNet(alpha=0.5, l1_ratio=0.9)</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-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet(alpha=0.5, l1_ratio=0.9)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 277
        }
      ],
      "source": [
        "elastic_net = ElasticNet(alpha = alpha, l1_ratio=l1_ratio)\n",
        "\n",
        "elastic_net.fit(X_train_encoded, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hzmWSDG6nYEe"
      },
      "outputs": [],
      "source": [
        "y_pred_elastic = elastic_net.predict(X_test_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jMA8QKqjnYEe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56fcd7b2-93c8-426d-8a4f-a8987c35a5a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elastic Net Regression: Mean Absolute Error: 56420.77217768007\n",
            "Elastic Net Regression: R-squared score: 0.7825760589290705\n",
            "Elastic Net Regression: Root Mean Squared Error: 82909.15790247152\n",
            "Elastic Net Regression: Mean Absolute Percentage Error: 8.995242583923964 %\n"
          ]
        }
      ],
      "source": [
        "elastic_net_results = evaluate_results(\"Elastic Net Regression\", y_test, y_pred_elastic)\n",
        "collection_of_model_results['Elastic Net Regression'] = elastic_net_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_elastic)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "59ovQvjoByJD",
        "outputId": "e9e039c5-4bb2-45c0-fd3c-b20040768985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree Regressor"
      ],
      "metadata": {
        "id": "kcFQ7596oa9x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following the development of multiple features through feature engineering, our objective was to discern the significance of each feature. This prompted us to utilize the Decision Tree Regressor, acclaimed for its interpretability. Employing tree-based regression models allowed us to gain insights into feature importance. However, as our dataset contains more than 190 features and potential overfitting issue might arise, the fundamental Decision Tree algorithm did not yield more satisfactory outcomes as compared to the baseline linear regression model . Consequently, we expanded our exploration to include advanced ensemble techniques. We delved into Random Forest Regressor, LightGBM, and XGBoost to enhance model performance and derive a deeper understanding of feature influence."
      ],
      "metadata": {
        "id": "k2nSjMp0UsXA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt_reg = DecisionTreeRegressor(criterion = \"squared_error\",\n",
        "                               max_features = 'sqrt',\n",
        "                               min_samples_leaf = 5,\n",
        "                               min_samples_split = 3)"
      ],
      "metadata": {
        "id": "DUGOHVcqouww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt_reg.fit(X_train_encoded, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "qIckUFGDp0qS",
        "outputId": "31fd15c6-9d2e-4228-be40-4540cd41c452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_features='sqrt', min_samples_leaf=5,\n",
              "                      min_samples_split=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_features=&#x27;sqrt&#x27;, min_samples_leaf=5,\n",
              "                      min_samples_split=3)</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\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_features=&#x27;sqrt&#x27;, min_samples_leaf=5,\n",
              "                      min_samples_split=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_dt = dt_reg.predict(X_test_encoded)"
      ],
      "metadata": {
        "id": "UAMQDtWPp0rH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree_results = evaluate_results(\"Decision Tree Regressor\", y_test, y_pred_dt)\n",
        "collection_of_model_results['Decision Tree Regressor'] = decision_tree_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jZTzcWSp7dj",
        "outputId": "e1c9c68b-eb87-44f6-d84b-9be8e2036a3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Regressor: Mean Absolute Error: 54335.45822994394\n",
            "Decision Tree Regressor: R-squared score: 0.8078021858180239\n",
            "Decision Tree Regressor: Root Mean Squared Error: 77951.24293497844\n",
            "Decision Tree Regressor: Mean Absolute Percentage Error: 8.913309761552476 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XZUMLicnYEa"
      },
      "source": [
        "## Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GFmCi8V_nYEa"
      },
      "outputs": [],
      "source": [
        "# Hyper Parameter Finetuning on RandomForestRegressor\n",
        "# model = RandomForestRegressor(oob_score=True, random_state=3, n_jobs=-1)\n",
        "# params ={\n",
        "#     'n_estimators': [200, 300, 400, 500, 600, 700],\n",
        "#     'min_samples_leaf': [1, 2, 3, 5, 10, 25],\n",
        "#     'max_features': [None, 0.5, 'sqrt', 'log2'],\n",
        "#     'max_depth': [5, 6, 7, 8, 10, 15, 17, 20],\n",
        "#     'min_samples_split': [2, 3, 4]\n",
        "# }\n",
        "\n",
        "# best_score = 0\n",
        "# for g in ParameterGrid(params):\n",
        "#     model.set_params(**g)\n",
        "#     model.fit(X_train, y_train)\n",
        "#     if model.oob_score_ > best_score:\n",
        "#         best_score = model.oob_score_\n",
        "#         best_grid = g\n",
        "#         print('oob:', best_score, best_grid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8TQTn5hsnYEb",
        "outputId": "a0f09a2a-35c3-4ecf-baa2-db8d2afe8cba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.94821928549647"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "rf = RandomForestRegressor(n_jobs=-1, n_estimators=200, oob_score=True, max_depth=17, min_samples_leaf=4, min_samples_split=2, max_features=0.5)\n",
        "rf.fit(X_train_encoded, y_train)\n",
        "rf.oob_score_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPf0ZorMTKlW"
      },
      "source": [
        "As mentioned in Decision Tree Regressor, a benefit of RandomForestRegression is that we can view the feature importance of each variable. This gives us a sensing of what variables are more important.\n",
        "\n",
        "It is very interesting (and unsurprising) to see that the top variables of highest importance are floor_area_sqm, max_floor_lvl, year, flat_type_3_ROOM, year_completed...\n",
        "\n",
        "The most surprising insight was that `max_floor_lvl` and `shortest_distance_to_closest_mrt (km)` were not features that originally belong to the main HDB resale transaction dataset! It was actually derived from other datasets / API calls from our feature engineering that we decided to use to enhance the number of features of the original HDB resale transactions dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "eRqBjD_xnYEb",
        "outputId": "df91272f-19dd-4efe-9c33-fe919257a649"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Feature  Importance\n",
              "1                          floor_area_sqm    0.309710\n",
              "4                           max_floor_lvl    0.181987\n",
              "16                                   year    0.089611\n",
              "46                       flat_type_3 ROOM    0.078694\n",
              "5                          year_completed    0.047507\n",
              "49                    flat_type_EXECUTIVE    0.024997\n",
              "2                         remaining_lease    0.024882\n",
              "0                            storey_range    0.023684\n",
              "47                       flat_type_4 ROOM    0.022264\n",
              "3   shortest_distance_to_closest_mrt (km)    0.020957"
            ],
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              "\n",
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              "      <th>1</th>\n",
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              "      <td>0.309710</td>\n",
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              "      <th>16</th>\n",
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              "      <td>0.089611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>flat_type_3 ROOM</td>\n",
              "      <td>0.078694</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
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              "      <td>0.047507</td>\n",
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              "      <td>0.024997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>remaining_lease</td>\n",
              "      <td>0.024882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <th>47</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.020957</td>\n",
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              "        document.querySelector('#df-ffed9185-04b5-44be-ade0-3d34fa0be32f button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-ffed9185-04b5-44be-ade0-3d34fa0be32f');\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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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feat_imp')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5b64e83b-64b8-4616-9762-f6be83765885 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feat_imp');\n",
              "      }\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feat_imp",
              "summary": "{\n  \"name\": \"feat_imp\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"flat_type_4 ROOM\",\n          \"max_floor_lvl\",\n          \"flat_type_EXECUTIVE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09446506519142048,\n        \"min\": 0.02095735281011553,\n        \"max\": 0.30970954797327904,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.022264343438502424,\n          0.18198695497926903,\n          0.024997404164866118\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "def rf_feat_importance(m, df, columns):\n",
        "    return pd.DataFrame({'Feature': columns, 'Importance': m.feature_importances_}).sort_values('Importance', ascending=False)\n",
        "\n",
        "feat_imp = rf_feat_importance(rf, X_train_encoded, all_column_names).head(10)\n",
        "feat_imp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "evIQ5a7RIWtk"
      },
      "outputs": [],
      "source": [
        "y_pred_rfr = rf.predict(X_test_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LssEQgtqnYEc",
        "outputId": "65ce6ee3-221c-418f-dc88-024d53d8c485"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Regressor: Mean Absolute Error: 35874.32677461611\n",
            "Random Forest Regressor: R-squared score: 0.9126658380986281\n",
            "Random Forest Regressor: Root Mean Squared Error: 52546.14994171867\n",
            "Random Forest Regressor: Mean Absolute Percentage Error: 5.760127271097378 %\n"
          ]
        }
      ],
      "source": [
        "random_forest_results = evaluate_results(\"Random Forest Regressor\", y_test, y_pred_rfr)\n",
        "collection_of_model_results['Random Forest Regressor'] = random_forest_results"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_rfr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "YJMpxq8SAX7B",
        "outputId": "85f55a39-3a56-4643-d117-cbd07b17b287"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## MultiLayer Perceptron"
      ],
      "metadata": {
        "id": "GJaqADMH9dxi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section, we attempt to fit our data into a Simple Multi-Layer Neural Network with ReLU activations using the TensorFlow Library. The input shape will be equal to the total number of encoded features in the training set while the output shape will be a single neuron representing the predicted House Price. The main activation functions across all hidden layers will be ReLU. To also prevent overfitting on the training set, we will be adding Dropout layers with a probability of 0.5."
      ],
      "metadata": {
        "id": "vPG4InxiC5kp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using TensorFlow's Sequential Neural Network Model Builder\n",
        "nn = Sequential()\n",
        "\n",
        "input_shape = (197,)\n",
        "\n",
        "nn.add(Dense(128, input_shape=input_shape, activation='relu'))\n",
        "nn.add(Dense(128, activation='relu'))\n",
        "nn.add(Dropout(0.5))\n",
        "nn.add(Dense(128, activation='relu'))\n",
        "nn.add(Dropout(0.5))\n",
        "nn.add(Dense(1, activation='linear'))\n",
        "\n",
        "\n",
        "nn.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])\n",
        "history = nn.fit(X_train_encoded.toarray(), y_train, batch_size=32, epochs=25, validation_split=0.3, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8_w7oCA9et7",
        "outputId": "dede3e3e-dbab-4c1d-b3d4-61951fb902cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "3519/3519 - 13s - loss: 22343936000.0000 - mae: 94872.7031 - val_loss: 10770232320.0000 - val_mae: 89708.6719 - 13s/epoch - 4ms/step\n",
            "Epoch 2/25\n",
            "3519/3519 - 14s - loss: 6686241280.0000 - mae: 61925.3320 - val_loss: 8983758848.0000 - val_mae: 82197.8672 - 14s/epoch - 4ms/step\n",
            "Epoch 3/25\n",
            "3519/3519 - 15s - loss: 6421055488.0000 - mae: 60790.7891 - val_loss: 8049482240.0000 - val_mae: 77551.2578 - 15s/epoch - 4ms/step\n",
            "Epoch 4/25\n",
            "3519/3519 - 15s - loss: 6192261120.0000 - mae: 59843.6758 - val_loss: 7803295232.0000 - val_mae: 76090.1016 - 15s/epoch - 4ms/step\n",
            "Epoch 5/25\n",
            "3519/3519 - 17s - loss: 5987533824.0000 - mae: 58767.2383 - val_loss: 4317015040.0000 - val_mae: 52895.1445 - 17s/epoch - 5ms/step\n",
            "Epoch 6/25\n",
            "3519/3519 - 11s - loss: 5933143552.0000 - mae: 58388.3633 - val_loss: 3206179328.0000 - val_mae: 43454.1055 - 11s/epoch - 3ms/step\n",
            "Epoch 7/25\n",
            "3519/3519 - 12s - loss: 5862645760.0000 - mae: 57984.7148 - val_loss: 2757310720.0000 - val_mae: 40163.6523 - 12s/epoch - 3ms/step\n",
            "Epoch 8/25\n",
            "3519/3519 - 17s - loss: 5833051648.0000 - mae: 57705.4219 - val_loss: 2430717440.0000 - val_mae: 36483.3047 - 17s/epoch - 5ms/step\n",
            "Epoch 9/25\n",
            "3519/3519 - 9s - loss: 5789085184.0000 - mae: 57655.8672 - val_loss: 2064619520.0000 - val_mae: 33343.5586 - 9s/epoch - 3ms/step\n",
            "Epoch 10/25\n",
            "3519/3519 - 9s - loss: 5759132672.0000 - mae: 57437.2617 - val_loss: 2288076800.0000 - val_mae: 35290.2969 - 9s/epoch - 3ms/step\n",
            "Epoch 11/25\n",
            "3519/3519 - 10s - loss: 5800523264.0000 - mae: 57617.2578 - val_loss: 1760966784.0000 - val_mae: 30415.7129 - 10s/epoch - 3ms/step\n",
            "Epoch 12/25\n",
            "3519/3519 - 9s - loss: 5712765440.0000 - mae: 57268.7812 - val_loss: 2184363520.0000 - val_mae: 33790.6328 - 9s/epoch - 3ms/step\n",
            "Epoch 13/25\n",
            "3519/3519 - 10s - loss: 5722919936.0000 - mae: 57146.7773 - val_loss: 1858364928.0000 - val_mae: 31300.7324 - 10s/epoch - 3ms/step\n",
            "Epoch 14/25\n",
            "3519/3519 - 11s - loss: 5676184576.0000 - mae: 57125.8398 - val_loss: 1880126080.0000 - val_mae: 31695.4766 - 11s/epoch - 3ms/step\n",
            "Epoch 15/25\n",
            "3519/3519 - 12s - loss: 5697412096.0000 - mae: 57132.1523 - val_loss: 1842778752.0000 - val_mae: 31322.3594 - 12s/epoch - 4ms/step\n",
            "Epoch 16/25\n",
            "3519/3519 - 9s - loss: 5707407872.0000 - mae: 57260.0547 - val_loss: 1702235776.0000 - val_mae: 30251.8633 - 9s/epoch - 3ms/step\n",
            "Epoch 17/25\n",
            "3519/3519 - 9s - loss: 5678865408.0000 - mae: 57048.3594 - val_loss: 1719352960.0000 - val_mae: 30224.9570 - 9s/epoch - 3ms/step\n",
            "Epoch 18/25\n",
            "3519/3519 - 11s - loss: 5662603264.0000 - mae: 57152.6914 - val_loss: 1687304064.0000 - val_mae: 29893.1914 - 11s/epoch - 3ms/step\n",
            "Epoch 19/25\n",
            "3519/3519 - 12s - loss: 5702389760.0000 - mae: 57245.6328 - val_loss: 1668122624.0000 - val_mae: 30254.0996 - 12s/epoch - 3ms/step\n",
            "Epoch 20/25\n",
            "3519/3519 - 9s - loss: 5639755776.0000 - mae: 56896.7227 - val_loss: 1813943168.0000 - val_mae: 31156.7520 - 9s/epoch - 2ms/step\n",
            "Epoch 21/25\n",
            "3519/3519 - 10s - loss: 5631116800.0000 - mae: 56824.0156 - val_loss: 1752309632.0000 - val_mae: 31046.4102 - 10s/epoch - 3ms/step\n",
            "Epoch 22/25\n",
            "3519/3519 - 10s - loss: 5645870080.0000 - mae: 56894.2812 - val_loss: 1645320704.0000 - val_mae: 29956.8086 - 10s/epoch - 3ms/step\n",
            "Epoch 23/25\n",
            "3519/3519 - 9s - loss: 5632729600.0000 - mae: 56827.6992 - val_loss: 1649674240.0000 - val_mae: 30470.0137 - 9s/epoch - 3ms/step\n",
            "Epoch 24/25\n",
            "3519/3519 - 10s - loss: 5578371072.0000 - mae: 56533.3203 - val_loss: 1663410688.0000 - val_mae: 30292.3223 - 10s/epoch - 3ms/step\n",
            "Epoch 25/25\n",
            "3519/3519 - 11s - loss: 5604164608.0000 - mae: 56755.6602 - val_loss: 1662023808.0000 - val_mae: 30041.6562 - 11s/epoch - 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets visualise the training loss and validation loss across the number of iterations. It appears that the validation loss converges rather quickly with a small number of iterations."
      ],
      "metadata": {
        "id": "Tx008whBS0-S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Train and Val Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kBoesFD4KJqe",
        "outputId": "2d446abb-e812-4e35-c7e3-c1439e6ef61e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nn = nn.predict(X_test_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzzH3kJ_ISW_",
        "outputId": "2ffeb6a8-0001-49ed-8564-68ef39976ddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "439/439 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nn_results = evaluate_results(\"Neural Network\", y_test, y_pred_nn)\n",
        "collection_of_model_results['Neural Network'] = nn_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ls1wv6bJ2ur",
        "outputId": "7917b8b5-ed32-4f32-a8aa-a80fa397e644"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Neural Network: Mean Absolute Error: 47712.13016458853\n",
            "Neural Network: R-squared score: 0.8962289569247369\n",
            "Neural Network: Root Mean Squared Error: 57277.877114695955\n",
            "Neural Network: Mean Absolute Percentage Error: 9.470080423320903 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_nn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "JisZBCyp0Pj0",
        "outputId": "1cb3be98-dd5a-4a3d-87dd-cb09f16dcdfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LightGBM"
      ],
      "metadata": {
        "id": "IPrCwK899pDI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drawing inspiration from the research paper \"Housing Price Prediction via Improved Machine Learning Techniques,\" we opted to implement the Light Gradient Boosting Machine (LightGBM), a sophisticated gradient boosting framework that harnesses tree-based learning algorithms. Notable for its efficacy and distributed computing capabilities, LightGBM offers several key advantages:\n",
        "\n",
        "Accelerated training speed paired with enhanced efficiency.\n",
        "\n",
        "Reduced memory consumption.\n",
        "Superior accuracy in predictive tasks.\n",
        "\n",
        "Comprehensive support for parallel, distributed, and GPU learning.\n",
        "\n",
        "Proficient at managing large-scale data, facilitating the handling of extensive datasets.\n",
        "\n",
        "Link to API: https://lightgbm.\n",
        "\n",
        "Link to research paper: readthedocs.io/en/latest/index.html#"
      ],
      "metadata": {
        "id": "SxMPHKOW13Y6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'learning_rate': [0.1, 0.15],\n",
        "    'n_estimators': [64, 128],\n",
        "    'min_child_weight' : [1, 2],\n",
        "    'reg_lambda': [0.4, 0.45, 0.5],\n",
        "    'num_leaves': [18, 36, 72],\n",
        "    'colsample_bytree': [0.7, 0.8]\n",
        "}\n",
        "\n",
        "model = LGBMRegressor()\n",
        "\n",
        "search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, verbose=2, n_jobs=-1, scoring='neg_mean_squared_error')\n",
        "search.fit(X_train_encoded, y_train)\n",
        "print(\"Best parameters:\", search.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkDOUZhwN_qC",
        "outputId": "66991c1c-ec50-4691-e872-33f497337fc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 144 candidates, totalling 432 fits\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.061422 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 1442\n",
            "[LightGBM] [Info] Number of data points in the train set: 160858, number of used features: 193\n",
            "[LightGBM] [Info] Start training from score 486502.012851\n",
            "Best parameters: {'colsample_bytree': 0.8, 'learning_rate': 0.15, 'min_child_weight': 1, 'n_estimators': 128, 'num_leaves': 72, 'reg_lambda': 0.5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm = LGBMRegressor(colsample_bytree = 0.8,\n",
        "                     learning_rate = 0.15,\n",
        "                     min_child_weight = 1,\n",
        "                     n_estimators= 128,\n",
        "                     num_leaves= 72,\n",
        "                     reg_lambda= 0.5)\n",
        "\n",
        "lgbm.fit(X_train_encoded, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "yiftRaV0b8zS",
        "outputId": "e6b73cb8-f3fb-455c-e770-26a8076ff03a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.152829 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 1442\n",
            "[LightGBM] [Info] Number of data points in the train set: 160858, number of used features: 193\n",
            "[LightGBM] [Info] Start training from score 486502.012851\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMRegressor(colsample_bytree=0.8, learning_rate=0.15, min_child_weight=1,\n",
              "              n_estimators=128, num_leaves=72, reg_lambda=0.5)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(colsample_bytree=0.8, learning_rate=0.15, min_child_weight=1,\n",
              "              n_estimators=128, num_leaves=72, reg_lambda=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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(colsample_bytree=0.8, learning_rate=0.15, min_child_weight=1,\n",
              "              n_estimators=128, num_leaves=72, reg_lambda=0.5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lgbm = lgbm.predict(X_test_encoded)"
      ],
      "metadata": {
        "id": "-6KMJM8ckQMK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_results = evaluate_results(\"LGBM\", y_test, y_pred_lgbm)\n",
        "collection_of_model_results['LGBM'] = lgbm_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ypFboP13kcRu",
        "outputId": "4e8081de-1cde-4a1d-cd74-d8bdb815240c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LGBM: Mean Absolute Error: 28049.784372841612\n",
            "LGBM: R-squared score: 0.950491728304414\n",
            "LGBM: Root Mean Squared Error: 39562.86235936871\n",
            "LGBM: Mean Absolute Percentage Error: 4.695477475121855 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_lgbm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "tjP21IUu0Mze",
        "outputId": "33e053cb-2190-44ab-8c0a-4b5c3700d0ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance = lgbm.feature_importances_\n",
        "\n",
        "lgbm_fi = pd.DataFrame({'Feature': all_column_names, 'Importance': feature_importance})\n",
        "lgbm_fi = lgbm_fi.sort_values(by='Importance', ascending=False).iloc[:10,] # Filter top 10 features\n",
        "lgbm_fi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "oPD9lM46-KfZ",
        "outputId": "ba7058f2-1102-439b-8b72-dc42b610121a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Feature  Importance\n",
              "1                          floor_area_sqm        1060\n",
              "3   shortest_distance_to_closest_mrt (km)         811\n",
              "5                          year_completed         690\n",
              "2                         remaining_lease         541\n",
              "4                           max_floor_lvl         501\n",
              "12                   total_dwelling_units         463\n",
              "16                                   year         461\n",
              "0                            storey_range         241\n",
              "20                            town_BISHAN         106\n",
              "13               is_elite_closest_pri_sch         106"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d60c8d21-8360-4e22-ab92-c6e888615563\" 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>Feature</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>floor_area_sqm</td>\n",
              "      <td>1060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>shortest_distance_to_closest_mrt (km)</td>\n",
              "      <td>811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>year_completed</td>\n",
              "      <td>690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>remaining_lease</td>\n",
              "      <td>541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max_floor_lvl</td>\n",
              "      <td>501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>total_dwelling_units</td>\n",
              "      <td>463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>year</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>storey_range</td>\n",
              "      <td>241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>town_BISHAN</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>is_elite_closest_pri_sch</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d60c8d21-8360-4e22-ab92-c6e888615563')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d60c8d21-8360-4e22-ab92-c6e888615563 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-d60c8d21-8360-4e22-ab92-c6e888615563');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ab14711e-fec3-4857-b730-198e430376df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ab14711e-fec3-4857-b730-198e430376df')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab14711e-fec3-4857-b730-198e430376df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_941b7518-8f3c-4cbc-ad1c-0bcd33bb1616\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('lgbm_fi')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_941b7518-8f3c-4cbc-ad1c-0bcd33bb1616 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('lgbm_fi');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lgbm_fi",
              "summary": "{\n  \"name\": \"lgbm_fi\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"town_BISHAN\",\n          \"shortest_distance_to_closest_mrt (km)\",\n          \"total_dwelling_units\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          241,\n          811,\n          463\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## XGBoost"
      ],
      "metadata": {
        "id": "xBAEVJucWnj_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Motivated by the insights from the research paper titled \"Housing Price Prediction via Improved Machine Learning Techniques,\" we elected to incorporate Extreme Gradient Boosting (XGBoost). XGBoost is an optimized distributed gradient boosting library, renowned for its efficiency, flexibility, and portability. Employing a level-wise growth strategy, XGBoost methodically processes all possible splits at a current depth before progressing to the next, ensuring a balanced tree structure.\n",
        "\n",
        "In the course of our project, XGBoost has not only outperformed LightGBM in terms of evaluation metrics, potentially due to LightGBM's overfitting issues with a substantial number of encoded features, but it has also demonstrated greater speed relative to Random Forest thanks to its innovative approach to handling sparse data. Nevertheless, consistent with the observations in the research paper, XGBoost's level-wise expansion does result in slower training times when compared to LightGBM.\n",
        "\n",
        "Link to algorithm:https://creativecommons.org/licenses/by-nc-nd/4.0/\n",
        "\n",
        "Link to research paper:https://xgboost.readthedocs.io/en/stable/index.html\n"
      ],
      "metadata": {
        "id": "oQxvJ8eS2HET"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb\n",
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "metadata": {
        "id": "NJ3qesflWoRW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'learning_rate': [0.1, 0.2],\n",
        "    'min_child_weight': [1, 2],\n",
        "    'subsample': [1],\n",
        "    'colsample_bytree' : [0.6, 0.8],\n",
        "    'reg_lambda': [0.45],\n",
        "    'gamma': [0.3, 0.4, 0.5]\n",
        "}\n",
        "\n",
        "xgbr = xgb.XGBRegressor()\n",
        "\n",
        "search = GridSearchCV(estimator=xgbr, param_grid=param_grid, cv=3, verbose=2, n_jobs=-1)\n",
        "search.fit(X_train_encoded, y_train)\n",
        "print(\"Best parameters:\", search.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLQ6nLGzYrTc",
        "outputId": "88fc1fad-69d9-49c6-d755-43db81b949f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 72 candidates, totalling 216 fits\n",
            "Best parameters: {'colsample_bytree': 0.8, 'gamma': 0.3, 'learning_rate': 0.2, 'min_child_weight': 2, 'n_estimators': 300, 'reg_lambda': 0.45, 'subsample': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgbr = xgb.XGBRegressor(\n",
        "    colsample_bytree=0.8,\n",
        "    gamma=0.3,\n",
        "    learning_rate=0.2,\n",
        "    min_child_weight=2,\n",
        "    n_estimators=300,\n",
        "    reg_lambda=0.45,\n",
        "    subsample=1\n",
        ")\n",
        "\n",
        "xgbr.fit(X_train_encoded, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "xW1rOK2fZixk",
        "outputId": "10803799-a006-4e43-e09b-ce880cd8e44a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=0.3, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.2, 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=2, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=300, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 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-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 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-10 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-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 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-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 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-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 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-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=0.3, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.2, 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=2, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=300, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=0.3, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.2, 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=2, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=300, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_xgb = xgbr.predict(X_test_encoded)"
      ],
      "metadata": {
        "id": "Ng6rK7mXk0MM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgbr_results = evaluate_results(\"XGBR\", y_test, y_pred_xgb)\n",
        "collection_of_model_results['XGBR'] = xgbr_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGHIkz6Xk3Jh",
        "outputId": "10c0807c-aff2-40e4-a587-d248b592c822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBR: Mean Absolute Error: 27568.409085500534\n",
            "XGBR: R-squared score: 0.9536627662968679\n",
            "XGBR: Root Mean Squared Error: 38274.883183916645\n",
            "XGBR: Mean Absolute Percentage Error: 4.6591122275074035 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_prediction_results(y_test, y_pred_xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "juiNlgbh0VJx",
        "outputId": "19ae3fb9-d909-4c0f-a37a-7f093f357905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is very interesting to see that the XGBR Model did not even have the \"floor_area_sqm\" as a top 10 feature, in terms of importance. This is unlike the Random Forest Regressor and Light GBM Models."
      ],
      "metadata": {
        "id": "BRqDxD6-ACQf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance = xgbr.feature_importances_\n",
        "\n",
        "xgbr_fi = pd.DataFrame({'Feature': all_column_names, 'Importance': feature_importance})\n",
        "xgbr_fi = xgbr_fi.sort_values(by='Importance', ascending=False).iloc[:10,] # Filter top 10\n",
        "xgbr_fi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "J69MF-GI9vhO",
        "outputId": "ac44bfba-f851-4d52-df42-b61458ab1068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Feature  Importance\n",
              "49  flat_type_EXECUTIVE    0.082093\n",
              "46     flat_type_3 ROOM    0.080903\n",
              "20          town_BISHAN    0.043640\n",
              "4         max_floor_lvl    0.033104\n",
              "38        town_SENGKANG    0.031149\n",
              "45     flat_type_2 ROOM    0.030712\n",
              "23   town_BUKIT PANJANG    0.028180\n",
              "22     town_BUKIT MERAH    0.027295\n",
              "69   flat_model_Terrace    0.025170\n",
              "48     flat_type_5 ROOM    0.024912"
            ],
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              "      <th>49</th>\n",
              "      <td>flat_type_EXECUTIVE</td>\n",
              "      <td>0.082093</td>\n",
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              "      <td>max_floor_lvl</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>flat_type_5 ROOM</td>\n",
              "      <td>0.024912</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "xgbr_fi",
              "summary": "{\n  \"name\": \"xgbr_fi\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"flat_model_Terrace\",\n          \"flat_type_3 ROOM\",\n          \"flat_type_2 ROOM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.02516978234052658,\n          0.08090253919363022,\n          0.030712036415934563\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysing all Model Results"
      ],
      "metadata": {
        "id": "ZpeP5-ltwVut"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Top 2 best performing madels are the Gradient Boosting Models LGBM and XGBR, with an average Mean Absolute Error of 28049 and 27568 respectively. When compared to the average resale price of the HDB Flats, this error makes up a small proportion of the price of each Flat. This can be compared to the mean value of each resale flat across all the transactions in the test dataset - which is $582082. We can also look at the Mean Absolute Percentage Error to see that the error only makes up a small percentage (4.7%) of the actual value.\n",
        "\n",
        "It is surprising to see that The Neural Network model have approximately the same performance as compared to the baseline regression models. This metric could benefit from further optimisation."
      ],
      "metadata": {
        "id": "PYNSkyIB8k5b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame.from_dict({(j): collection_of_model_results[i][j] for i in collection_of_model_results.keys() for j in collection_of_model_results[i].keys()}, orient='index')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "HQ2VUybpwYbr",
        "outputId": "cb37715d-1a02-466f-a20b-326f1d7d52fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Mean Absolute Error  R-squared score  \\\n",
              "Linear Regression                    48455.090761         0.864575   \n",
              "Random Forest Regressor              35982.440512         0.911841   \n",
              "L2 Linear Regression (Ridge)         48476.536298         0.864473   \n",
              "L1 Linear Regression (Lasso)         48474.960339         0.864517   \n",
              "Neural Network                       47712.130165         0.896229   \n",
              "LGBM                                 28049.784373         0.950492   \n",
              "XGBR                                 27568.409086         0.953663   \n",
              "Elastic Net Regression               56420.772178         0.782576   \n",
              "Decision Tree Regressor              54335.458230         0.807802   \n",
              "\n",
              "                              Root Mean Squared Error  \\\n",
              "Linear Regression                        65433.198636   \n",
              "Random Forest Regressor                  52793.726633   \n",
              "L2 Linear Regression (Ridge)             65457.789903   \n",
              "L1 Linear Regression (Lasso)             65447.173527   \n",
              "Neural Network                           57277.877115   \n",
              "LGBM                                     39562.862359   \n",
              "XGBR                                     38274.883184   \n",
              "Elastic Net Regression                   82909.157902   \n",
              "Decision Tree Regressor                  77951.242935   \n",
              "\n",
              "                              Mean Absolute Percentage Error  \n",
              "Linear Regression                                   8.253610  \n",
              "Random Forest Regressor                             5.772779  \n",
              "L2 Linear Regression (Ridge)                        8.252991  \n",
              "L1 Linear Regression (Lasso)                        8.253311  \n",
              "Neural Network                                      9.470080  \n",
              "LGBM                                                4.695477  \n",
              "XGBR                                                4.659112  \n",
              "Elastic Net Regression                              8.995243  \n",
              "Decision Tree Regressor                             8.913310  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f66ecc4-bb8a-4a00-809b-69fc919d3846\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean Absolute Error</th>\n",
              "      <th>R-squared score</th>\n",
              "      <th>Root Mean Squared Error</th>\n",
              "      <th>Mean Absolute Percentage Error</th>\n",
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              "      <th>Linear Regression</th>\n",
              "      <td>48455.090761</td>\n",
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              "      <th>Random Forest Regressor</th>\n",
              "      <td>35982.440512</td>\n",
              "      <td>0.911841</td>\n",
              "      <td>52793.726633</td>\n",
              "      <td>5.772779</td>\n",
              "    </tr>\n",
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              "      <th>L2 Linear Regression (Ridge)</th>\n",
              "      <td>48476.536298</td>\n",
              "      <td>0.864473</td>\n",
              "      <td>65457.789903</td>\n",
              "      <td>8.252991</td>\n",
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              "      <th>L1 Linear Regression (Lasso)</th>\n",
              "      <td>48474.960339</td>\n",
              "      <td>0.864517</td>\n",
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              "      <td>8.253311</td>\n",
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              "      <th>Neural Network</th>\n",
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              "      <td>0.896229</td>\n",
              "      <td>57277.877115</td>\n",
              "      <td>9.470080</td>\n",
              "    </tr>\n",
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              "      <th>LGBM</th>\n",
              "      <td>28049.784373</td>\n",
              "      <td>0.950492</td>\n",
              "      <td>39562.862359</td>\n",
              "      <td>4.695477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBR</th>\n",
              "      <td>27568.409086</td>\n",
              "      <td>0.953663</td>\n",
              "      <td>38274.883184</td>\n",
              "      <td>4.659112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Elastic Net Regression</th>\n",
              "      <td>56420.772178</td>\n",
              "      <td>0.782576</td>\n",
              "      <td>82909.157902</td>\n",
              "      <td>8.995243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree Regressor</th>\n",
              "      <td>54335.458230</td>\n",
              "      <td>0.807802</td>\n",
              "      <td>77951.242935</td>\n",
              "      <td>8.913310</td>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9f8a04e-8066-4a74-a9cf-81096c1276d8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9f8a04e-8066-4a74-a9cf-81096c1276d8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9f8a04e-8066-4a74-a9cf-81096c1276d8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Mean Absolute Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10743.875762223319,\n        \"min\": 27568.409085500534,\n        \"max\": 56420.77217768007,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          56420.77217768007,\n          35982.44051172902,\n          28049.784372841612\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R-squared score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05812925134760145,\n        \"min\": 0.7825760589290705,\n        \"max\": 0.9536627662968679,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.7825760589290705,\n          0.9118409312402136,\n          0.950491728304414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Root Mean Squared Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15338.489603552925,\n        \"min\": 38274.883183916645,\n        \"max\": 82909.15790247152,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          82909.15790247152,\n          52793.72663260498,\n          39562.86235936871\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean Absolute Percentage Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8950595537819008,\n        \"min\": 4.6591122275074035,\n        \"max\": 9.470080423320903,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8.995242583923964,\n          5.772779318405761,\n          4.695477475121855\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['resale_price'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zvlk995-9ArU",
        "outputId": "4cb1d066-f82d-4bbf-f1ec-31e37680d678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "582082.2950936944"
            ]
          },
          "metadata": {},
          "execution_count": 343
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparing Feature Importance across the Tree Models"
      ],
      "metadata": {
        "id": "DeULqwpGAeNa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The 3 different 3 models (XGBR, Light GBM, and Random Forest Regressor) have placed different importance on different Features. It is interesting to note that the important features of the LGBM Model are very similar to that of the Random Forest Model. The similar top feature importance variables across these 2 models are floor_area_sqm, max_floor_lvl, storey_range, year and remaining_lease. On the other hand, XGBR tends to place a lot more emphasis on the categorical data, which has been one-hot-encoded. For instance, flat type and town were one hot encoded features that dominated in the feature importance list for XGBR. The variation in important features may be attributed to the high correlation between flat type and floor area. For example, a four-room flat typically has a larger floor area compared to a one-room flat. Thus, we still consider the floor area to be a significant factor in the context of this project."
      ],
      "metadata": {
        "id": "6cigVMPvBHom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display\n",
        "\n",
        "display(feat_imp, lgbm_fi, xgbr_fi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NMm2-j8VAh8m",
        "outputId": "83b001a2-b7d1-4101-e814-ded553ccf996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                  Feature  Importance\n",
              "1                          floor_area_sqm    0.296129\n",
              "4                           max_floor_lvl    0.180538\n",
              "16                                   year    0.089778\n",
              "46                       flat_type_3 ROOM    0.084105\n",
              "5                          year_completed    0.047846\n",
              "47                       flat_type_4 ROOM    0.027011\n",
              "49                    flat_type_EXECUTIVE    0.026267\n",
              "2                         remaining_lease    0.025912\n",
              "0                            storey_range    0.025539\n",
              "3   shortest_distance_to_closest_mrt (km)    0.020494"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-837a9116-8b77-418b-b088-9dba3f9812fe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>floor_area_sqm</td>\n",
              "      <td>0.296129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max_floor_lvl</td>\n",
              "      <td>0.180538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>year</td>\n",
              "      <td>0.089778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>flat_type_3 ROOM</td>\n",
              "      <td>0.084105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>year_completed</td>\n",
              "      <td>0.047846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>flat_type_4 ROOM</td>\n",
              "      <td>0.027011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>flat_type_EXECUTIVE</td>\n",
              "      <td>0.026267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>remaining_lease</td>\n",
              "      <td>0.025912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>storey_range</td>\n",
              "      <td>0.025539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>shortest_distance_to_closest_mrt (km)</td>\n",
              "      <td>0.020494</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        document.querySelector('#df-837a9116-8b77-418b-b088-9dba3f9812fe 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-837a9116-8b77-418b-b088-9dba3f9812fe');\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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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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            "text/plain": [
              "                                  Feature  Importance\n",
              "1                          floor_area_sqm        1060\n",
              "3   shortest_distance_to_closest_mrt (km)         811\n",
              "5                          year_completed         690\n",
              "2                         remaining_lease         541\n",
              "4                           max_floor_lvl         501\n",
              "12                   total_dwelling_units         463\n",
              "16                                   year         461\n",
              "0                            storey_range         241\n",
              "20                            town_BISHAN         106\n",
              "13               is_elite_closest_pri_sch         106"
            ],
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              "\n",
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              "      <th>Importance</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>floor_area_sqm</td>\n",
              "      <td>1060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>shortest_distance_to_closest_mrt (km)</td>\n",
              "      <td>811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>year_completed</td>\n",
              "      <td>690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>remaining_lease</td>\n",
              "      <td>541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max_floor_lvl</td>\n",
              "      <td>501</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>total_dwelling_units</td>\n",
              "      <td>463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>year</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>storey_range</td>\n",
              "      <td>241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>town_BISHAN</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>is_elite_closest_pri_sch</td>\n",
              "      <td>106</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c5c8a156-b3fc-4c30-842e-3c3de5c441fc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_3fde79be-4d20-419f-b8dc-520a02faa293\">\n",
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              "type": "dataframe",
              "variable_name": "lgbm_fi",
              "summary": "{\n  \"name\": \"lgbm_fi\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"town_BISHAN\",\n          \"shortest_distance_to_closest_mrt (km)\",\n          \"total_dwelling_units\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          241,\n          811,\n          463\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "                Feature  Importance\n",
              "49  flat_type_EXECUTIVE    0.082093\n",
              "46     flat_type_3 ROOM    0.080903\n",
              "20          town_BISHAN    0.043640\n",
              "4         max_floor_lvl    0.033104\n",
              "38        town_SENGKANG    0.031149\n",
              "45     flat_type_2 ROOM    0.030712\n",
              "23   town_BUKIT PANJANG    0.028180\n",
              "22     town_BUKIT MERAH    0.027295\n",
              "69   flat_model_Terrace    0.025170\n",
              "48     flat_type_5 ROOM    0.024912"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>flat_type_EXECUTIVE</td>\n",
              "      <td>0.082093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>flat_type_3 ROOM</td>\n",
              "      <td>0.080903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>town_BISHAN</td>\n",
              "      <td>0.043640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>max_floor_lvl</td>\n",
              "      <td>0.033104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>town_SENGKANG</td>\n",
              "      <td>0.031149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>flat_type_2 ROOM</td>\n",
              "      <td>0.030712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>town_BUKIT PANJANG</td>\n",
              "      <td>0.028180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>town_BUKIT MERAH</td>\n",
              "      <td>0.027295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>flat_model_Terrace</td>\n",
              "      <td>0.025170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>flat_type_5 ROOM</td>\n",
              "      <td>0.024912</td>\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hybridise Regression"
      ],
      "metadata": {
        "id": "erxdWNQ04ms3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets attempt to aggregate the regression predictions from the top 2 models (XGBR and LGBM) and analyse whether the ensemble of these 2 models results in a better performance."
      ],
      "metadata": {
        "id": "He6idcrj4ty4"
      }
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    {
      "cell_type": "code",
      "source": [
        "y_pred_hybrid = (np.array(y_pred_xgb) + np.array(y_pred_lgbm)) / 2"
      ],
      "metadata": {
        "id": "2TlqtQUV3GoX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hybrid_results = evaluate_results(\"LGBM + XGBRS\", y_test, y_pred_hybrid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItfIEXTK7skJ",
        "outputId": "6b3c1f68-0418-40a4-db12-6076f976335c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LGBM + XGBRS: Mean Absolute Error: 27144.774272233914\n",
            "LGBM + XGBRS: R-squared score: 0.9543239335450038\n",
            "LGBM + XGBRS: Root Mean Squared Error: 38000.837696084134\n",
            "LGBM + XGBRS: Mean Absolute Percentage Error: 4.55458475653832 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is very interesting to note that the average of the predictions from LGBM and XGBR resulted in a slightly better MAE as compared to the individual models. The LGBM individual model had a MAE of 28049 and XGBR had a MAE of 27568."
      ],
      "metadata": {
        "id": "cJ7EPTsb9XSA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(hybrid_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "wK9IJfN28K-3",
        "outputId": "852b5fef-dd9f-4dd2-d167-002eb1327922"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                LGBM + XGBRS\n",
              "Mean Absolute Error             27144.774272\n",
              "Mean Absolute Percentage Error      4.554585\n",
              "R-squared score                     0.954324\n",
              "Root Mean Squared Error         38000.837696"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-85e2b95a-986c-49f0-9f44-2119e540e2bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"LGBM + XGBRS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19319.552441908916,\n        \"min\": 0.9543239335450038,\n        \"max\": 38000.837696084134,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4.55458475653832,\n          38000.837696084134,\n          27144.774272233914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 345
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusions\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "LPyH4QydbxL8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This project placed a lot of emphasis in creating a robust feature engineering strategy to generate meaningful features to improve our model performances. This is evident from the feature importance analysis which had displayed some features which were not originally present in the Transactions dataset. Importance features like shortest_distance_to_closest_mrt (km), is_elite_closest_pri_sch, year_completed were either generated from feature engineering or found from external datasets.  \n",
        "\n",
        "\n",
        "When initially exploring the use of baseline models, these models perform sufficiently well with a acceptable MAE of approximately 50000. We also noted that the performance of our Neural Network was almost similar to the performance of the baseline regression models (Linear Regression, Decision Tree Regressor). This was a shock to us as we had even attempted to reduce overfitting using Dropout Layers.\n",
        "\n",
        "\n",
        "Given that our preprocessed dataset comprised over 190 features, this high dimensionality likely introduced additional noise and complexity, exacerbating the non-ensemble models' tendencies to overfit. Recognising these challenges, we dedicated further efforts to investigate and implement more complex techniques such as Random Forest Regressor, LightGBM and XGBoost to make much more accurate predictions.\n",
        "\n",
        "We also explored the use of further ensembling the predictions of both of our top models and found that the Hybridised model had a better performance as compared to the individual models. This was a quick and simple way for us to make better predictions by ensembling the best models together.\n"
      ],
      "metadata": {
        "id": "FgOK28DdLERY"
      }
    }
  ],
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