new_self_Random_Forests.ipynb

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      "name": "new_Random Forests",
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      "authorship_tag": "ABX9TyMIB6OUvSPSy+WOk3a6bQIa",
      "include_colab_link": true
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hiro106/colab_notebooks/blob/main/new_Random_Forests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://www.kaggle.com/code/dansbecker/random-forests"
      ],
      "metadata": {
        "id": "ApqnFVCJarTp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "outputId": "2883959e-987c-4813-a07d-ea87b70281c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "MsUzXZ7uFB3F"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(pd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRyPUtZnVFI0",
        "outputId": "7e1fd851-e741-4a96-f441-7eb53ffd1567"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "module"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajoOqEVTVFLq",
        "outputId": "dce1444d-e06b-4e46-8c6f-f53a8101a280"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'pandas' from '/usr/local/lib/python3.7/dist-packages/pandas/__init__.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data\n",
        "melbourne_file_path = '/drive/My Drive/Colab Notebooks/melb_data.csv'"
      ],
      "metadata": {
        "id": "v-PBQGdjVFOL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data = pd.read_csv(melbourne_file_path)"
      ],
      "metadata": {
        "id": "ONUzz_8GVFQX"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data.head()"
      ],
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          "height": 421
        },
        "id": "jqwCvN1sFB5q",
        "outputId": "01876c72-9829-49bb-9033-8e291ea8d2b7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       Suburb           Address  Rooms Type      Price Method SellerG  \\\n",
              "0  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
              "1  Abbotsford   25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
              "2  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
              "3  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
              "4  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
              "\n",
              "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
              "0  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
              "1  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
              "2  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
              "3  4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
              "4  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
              "\n",
              "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
              "0        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
              "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
              "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
              "3        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
              "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
              "\n",
              "  Propertycount  \n",
              "0        4019.0  \n",
              "1        4019.0  \n",
              "2        4019.0  \n",
              "3        4019.0  \n",
              "4        4019.0  \n",
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              "[5 rows x 21 columns]"
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              "      <td>Abbotsford</td>\n",
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      "execution_count": 14,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Suburb        Address  Rooms Type      Price Method   SellerG  \\\n",
              "13575  Wheelers Hill   12 Strada Cr      4    h  1245000.0      S     Barry   \n",
              "13576   Williamstown  77 Merrett Dr      3    h  1031000.0     SP  Williams   \n",
              "13577   Williamstown    83 Power St      3    h  1170000.0      S     Raine   \n",
              "13578   Williamstown   96 Verdon St      4    h  2500000.0     PI   Sweeney   \n",
              "13579     Yarraville     6 Agnes St      4    h  1285000.0     SP   Village   \n",
              "\n",
              "             Date  Distance  Postcode  ...  Bathroom  Car  Landsize  \\\n",
              "13575  26/08/2017      16.7    3150.0  ...       2.0  2.0     652.0   \n",
              "13576  26/08/2017       6.8    3016.0  ...       2.0  2.0     333.0   \n",
              "13577  26/08/2017       6.8    3016.0  ...       2.0  4.0     436.0   \n",
              "13578  26/08/2017       6.8    3016.0  ...       1.0  5.0     866.0   \n",
              "13579  26/08/2017       6.3    3013.0  ...       1.0  1.0     362.0   \n",
              "\n",
              "       BuildingArea  YearBuilt  CouncilArea Lattitude  Longtitude  \\\n",
              "13575           NaN     1981.0          NaN -37.90562   145.16761   \n",
              "13576         133.0     1995.0          NaN -37.85927   144.87904   \n",
              "13577           NaN     1997.0          NaN -37.85274   144.88738   \n",
              "13578         157.0     1920.0          NaN -37.85908   144.89299   \n",
              "13579         112.0     1920.0          NaN -37.81188   144.88449   \n",
              "\n",
              "                       Regionname Propertycount  \n",
              "13575  South-Eastern Metropolitan        7392.0  \n",
              "13576        Western Metropolitan        6380.0  \n",
              "13577        Western Metropolitan        6380.0  \n",
              "13578        Western Metropolitan        6380.0  \n",
              "13579        Western Metropolitan        6543.0  \n",
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              "      <td>652.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.90562</td>\n",
              "      <td>145.16761</td>\n",
              "      <td>South-Eastern Metropolitan</td>\n",
              "      <td>7392.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13576</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>77 Merrett Dr</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1031000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Williams</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>1995.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.85927</td>\n",
              "      <td>144.87904</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13577</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>83 Power St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1170000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Raine</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>436.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.85274</td>\n",
              "      <td>144.88738</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13578</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>96 Verdon St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>2500000.0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Sweeney</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>866.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.85908</td>\n",
              "      <td>144.89299</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13579</th>\n",
              "      <td>Yarraville</td>\n",
              "      <td>6 Agnes St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1285000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Village</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>362.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.81188</td>\n",
              "      <td>144.88449</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6543.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKF-ww8kYXNh",
        "outputId": "b7cc7318-d80c-4895-9f2d-d96bc9535cb4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13580, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ywUB3TuQYgMd",
        "outputId": "7d439656-898e-43d9-aa23-7c25042a080e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13580 entries, 0 to 13579\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Suburb         13580 non-null  object \n",
            " 1   Address        13580 non-null  object \n",
            " 2   Rooms          13580 non-null  int64  \n",
            " 3   Type           13580 non-null  object \n",
            " 4   Price          13580 non-null  float64\n",
            " 5   Method         13580 non-null  object \n",
            " 6   SellerG        13580 non-null  object \n",
            " 7   Date           13580 non-null  object \n",
            " 8   Distance       13580 non-null  float64\n",
            " 9   Postcode       13580 non-null  float64\n",
            " 10  Bedroom2       13580 non-null  float64\n",
            " 11  Bathroom       13580 non-null  float64\n",
            " 12  Car            13518 non-null  float64\n",
            " 13  Landsize       13580 non-null  float64\n",
            " 14  BuildingArea   7130 non-null   float64\n",
            " 15  YearBuilt      8205 non-null   float64\n",
            " 16  CouncilArea    12211 non-null  object \n",
            " 17  Lattitude      13580 non-null  float64\n",
            " 18  Longtitude     13580 non-null  float64\n",
            " 19  Regionname     13580 non-null  object \n",
            " 20  Propertycount  13580 non-null  float64\n",
            "dtypes: float64(12), int64(1), object(8)\n",
            "memory usage: 2.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data = melbourne_data.dropna(axis=0)"
      ],
      "metadata": {
        "id": "HMf7CZWQFB8c"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "GelRAzoZFB_C",
        "outputId": "7f05cabe-3c4e-4ac1-c235-7244ca76326f"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Suburb          Address  Rooms Type      Price Method  \\\n",
              "1        Abbotsford  25 Bloomburg St      2    h  1035000.0      S   \n",
              "2        Abbotsford     5 Charles St      3    h  1465000.0     SP   \n",
              "4        Abbotsford      55a Park St      4    h  1600000.0     VB   \n",
              "6        Abbotsford     124 Yarra St      3    h  1876000.0      S   \n",
              "7        Abbotsford    98 Charles St      2    h  1636000.0      S   \n",
              "...             ...              ...    ...  ...        ...    ...   \n",
              "12205    Whittlesea    30 Sherwin St      3    h   601000.0      S   \n",
              "12206  Williamstown      75 Cecil St      3    h  1050000.0     VB   \n",
              "12207  Williamstown    2/29 Dover Rd      1    u   385000.0     SP   \n",
              "12209       Windsor  201/152 Peel St      2    u   560000.0     PI   \n",
              "12212    Yarraville  54 Pentland Pde      6    h  2450000.0     VB   \n",
              "\n",
              "             SellerG        Date  Distance  Postcode  ...  Bathroom  Car  \\\n",
              "1             Biggin   4/02/2016       2.5    3067.0  ...       1.0  0.0   \n",
              "2             Biggin   4/03/2017       2.5    3067.0  ...       2.0  0.0   \n",
              "4             Nelson   4/06/2016       2.5    3067.0  ...       1.0  2.0   \n",
              "6             Nelson   7/05/2016       2.5    3067.0  ...       2.0  0.0   \n",
              "7             Nelson   8/10/2016       2.5    3067.0  ...       1.0  2.0   \n",
              "...              ...         ...       ...       ...  ...       ...  ...   \n",
              "12205            Ray  29/07/2017      35.5    3757.0  ...       2.0  1.0   \n",
              "12206       Williams  29/07/2017       6.8    3016.0  ...       1.0  0.0   \n",
              "12207       Williams  29/07/2017       6.8    3016.0  ...       1.0  1.0   \n",
              "12209  hockingstuart  29/07/2017       4.6    3181.0  ...       1.0  1.0   \n",
              "12212        Village  29/07/2017       6.3    3013.0  ...       3.0  2.0   \n",
              "\n",
              "       Landsize  BuildingArea  YearBuilt  CouncilArea Lattitude  Longtitude  \\\n",
              "1         156.0         79.00     1900.0        Yarra -37.80790   144.99340   \n",
              "2         134.0        150.00     1900.0        Yarra -37.80930   144.99440   \n",
              "4         120.0        142.00     2014.0        Yarra -37.80720   144.99410   \n",
              "6         245.0        210.00     1910.0        Yarra -37.80240   144.99930   \n",
              "7         256.0        107.00     1890.0        Yarra -37.80600   144.99540   \n",
              "...         ...           ...        ...          ...       ...         ...   \n",
              "12205     972.0        149.00     1996.0   Whittlesea -37.51232   145.13282   \n",
              "12206     179.0        115.00     1890.0  Hobsons Bay -37.86558   144.90474   \n",
              "12207       0.0         35.64     1967.0  Hobsons Bay -37.85588   144.89936   \n",
              "12209       0.0         61.60     2012.0  Stonnington -37.85581   144.99025   \n",
              "12212    1087.0        388.50     1920.0  Maribyrnong -37.81038   144.89389   \n",
              "\n",
              "                  Regionname Propertycount  \n",
              "1      Northern Metropolitan        4019.0  \n",
              "2      Northern Metropolitan        4019.0  \n",
              "4      Northern Metropolitan        4019.0  \n",
              "6      Northern Metropolitan        4019.0  \n",
              "7      Northern Metropolitan        4019.0  \n",
              "...                      ...           ...  \n",
              "12205      Northern Victoria        2170.0  \n",
              "12206   Western Metropolitan        6380.0  \n",
              "12207   Western Metropolitan        6380.0  \n",
              "12209  Southern Metropolitan        4380.0  \n",
              "12212   Western Metropolitan        6543.0  \n",
              "\n",
              "[6196 rows x 21 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Suburb</th>\n",
              "      <th>Address</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>25 Bloomburg St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1035000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/02/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.00</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80790</td>\n",
              "      <td>144.99340</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>5 Charles St</td>\n",
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              "      <td>h</td>\n",
              "      <td>1465000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.00</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80930</td>\n",
              "      <td>144.99440</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>55a Park St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1600000.0</td>\n",
              "      <td>VB</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>4/06/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.00</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80720</td>\n",
              "      <td>144.99410</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>124 Yarra St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1876000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Nelson</td>\n",
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              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>245.0</td>\n",
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              "      <td>1910.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80240</td>\n",
              "      <td>144.99930</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>98 Charles St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1636000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>8/10/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>107.00</td>\n",
              "      <td>1890.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80600</td>\n",
              "      <td>144.99540</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12205</th>\n",
              "      <td>Whittlesea</td>\n",
              "      <td>30 Sherwin St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>601000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Ray</td>\n",
              "      <td>29/07/2017</td>\n",
              "      <td>35.5</td>\n",
              "      <td>3757.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>972.0</td>\n",
              "      <td>149.00</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>Whittlesea</td>\n",
              "      <td>-37.51232</td>\n",
              "      <td>145.13282</td>\n",
              "      <td>Northern Victoria</td>\n",
              "      <td>2170.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12206</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>75 Cecil St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1050000.0</td>\n",
              "      <td>VB</td>\n",
              "      <td>Williams</td>\n",
              "      <td>29/07/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>115.00</td>\n",
              "      <td>1890.0</td>\n",
              "      <td>Hobsons Bay</td>\n",
              "      <td>-37.86558</td>\n",
              "      <td>144.90474</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12207</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>2/29 Dover Rd</td>\n",
              "      <td>1</td>\n",
              "      <td>u</td>\n",
              "      <td>385000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Williams</td>\n",
              "      <td>29/07/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.64</td>\n",
              "      <td>1967.0</td>\n",
              "      <td>Hobsons Bay</td>\n",
              "      <td>-37.85588</td>\n",
              "      <td>144.89936</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12209</th>\n",
              "      <td>Windsor</td>\n",
              "      <td>201/152 Peel St</td>\n",
              "      <td>2</td>\n",
              "      <td>u</td>\n",
              "      <td>560000.0</td>\n",
              "      <td>PI</td>\n",
              "      <td>hockingstuart</td>\n",
              "      <td>29/07/2017</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3181.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.60</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>Stonnington</td>\n",
              "      <td>-37.85581</td>\n",
              "      <td>144.99025</td>\n",
              "      <td>Southern Metropolitan</td>\n",
              "      <td>4380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12212</th>\n",
              "      <td>Yarraville</td>\n",
              "      <td>54 Pentland Pde</td>\n",
              "      <td>6</td>\n",
              "      <td>h</td>\n",
              "      <td>2450000.0</td>\n",
              "      <td>VB</td>\n",
              "      <td>Village</td>\n",
              "      <td>29/07/2017</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1087.0</td>\n",
              "      <td>388.50</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>Maribyrnong</td>\n",
              "      <td>-37.81038</td>\n",
              "      <td>144.89389</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6543.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6196 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a6907b1-bf92-4b15-b8e0-7ed778e457c3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-8a6907b1-bf92-4b15-b8e0-7ed778e457c3 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-8a6907b1-bf92-4b15-b8e0-7ed778e457c3');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = melbourne_data.Price"
      ],
      "metadata": {
        "id": "djvQOOTxFCBq"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HG0jj5MYz_Q",
        "outputId": "6fa398dc-13a9-46d1-d92d-1f09fafbc0cb"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BSZdJT6qY0CJ",
        "outputId": "a0152298-db6f-4614-d309-20e27926c46b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1        1035000.0\n",
              "2        1465000.0\n",
              "4        1600000.0\n",
              "6        1876000.0\n",
              "7        1636000.0\n",
              "           ...    \n",
              "12205     601000.0\n",
              "12206    1050000.0\n",
              "12207     385000.0\n",
              "12209     560000.0\n",
              "12212    2450000.0\n",
              "Name: Price, Length: 6196, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5PLh2qQY39w",
        "outputId": "c9267631-644d-41f6-871a-13f7acbc49fe"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1        1035000.0\n",
            "2        1465000.0\n",
            "4        1600000.0\n",
            "6        1876000.0\n",
            "7        1636000.0\n",
            "           ...    \n",
            "12205     601000.0\n",
            "12206    1050000.0\n",
            "12207     385000.0\n",
            "12209     560000.0\n",
            "12212    2450000.0\n",
            "Name: Price, Length: 6196, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWmbPd1WY_M_",
        "outputId": "51b76a9c-d94f-46c6-8195-d16927d6fd3a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6196,)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea',\n",
        "                      'YearBuilt', 'Lattitude', 'Longtitude']"
      ],
      "metadata": {
        "id": "KTv4rqP6Y_Pt"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = melbourne_data[melbourne_features]"
      ],
      "metadata": {
        "id": "Lvhqfsl0Y_SH"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "h8KTAL1SY_Uj",
        "outputId": "ef74e8f0-d262-41ec-f461-c1628c30f5a4"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rooms  Bathroom  Landsize  BuildingArea  YearBuilt  Lattitude  Longtitude\n",
              "1      2       1.0     156.0          79.0     1900.0   -37.8079    144.9934\n",
              "2      3       2.0     134.0         150.0     1900.0   -37.8093    144.9944\n",
              "4      4       1.0     120.0         142.0     2014.0   -37.8072    144.9941\n",
              "6      3       2.0     245.0         210.0     1910.0   -37.8024    144.9993\n",
              "7      2       1.0     256.0         107.0     1890.0   -37.8060    144.9954"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c07572ce-44c1-4b80-ad0a-34429faf275b\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>-37.8079</td>\n",
              "      <td>144.9934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>-37.8093</td>\n",
              "      <td>144.9944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>-37.8072</td>\n",
              "      <td>144.9941</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>-37.8060</td>\n",
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              "      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-c07572ce-44c1-4b80-ad0a-34429faf275b 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-c07572ce-44c1-4b80-ad0a-34429faf275b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNAH62eoY_XC",
        "outputId": "f6d797a6-59b1-41cc-8a26-02bd66806cad"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 6196 entries, 1 to 12212\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Rooms         6196 non-null   int64  \n",
            " 1   Bathroom      6196 non-null   float64\n",
            " 2   Landsize      6196 non-null   float64\n",
            " 3   BuildingArea  6196 non-null   float64\n",
            " 4   YearBuilt     6196 non-null   float64\n",
            " 5   Lattitude     6196 non-null   float64\n",
            " 6   Longtitude    6196 non-null   float64\n",
            "dtypes: float64(6), int64(1)\n",
            "memory usage: 387.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "N8nad7zuZ-G6"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_test_split"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0oE1ZyEaGg7",
        "outputId": "39b8d1af-1526-4012-9c27-d6552d4d81c0"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function sklearn.model_selection._split.train_test_split>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)"
      ],
      "metadata": {
        "id": "DNnyBy4IaGmA"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRzPa5fZZ-Jf",
        "outputId": "59ad0686-429e-4eea-a3ef-79f47ee35eef"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4647 entries, 10385 to 4996\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Rooms         4647 non-null   int64  \n",
            " 1   Bathroom      4647 non-null   float64\n",
            " 2   Landsize      4647 non-null   float64\n",
            " 3   BuildingArea  4647 non-null   float64\n",
            " 4   YearBuilt     4647 non-null   float64\n",
            " 5   Lattitude     4647 non-null   float64\n",
            " 6   Longtitude    4647 non-null   float64\n",
            "dtypes: float64(6), int64(1)\n",
            "memory usage: 290.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_X.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeglBKvvZ-Lw",
        "outputId": "adf8b354-a02e-49f9-e6c4-26d5cb207077"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1549 entries, 4850 to 8375\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Rooms         1549 non-null   int64  \n",
            " 1   Bathroom      1549 non-null   float64\n",
            " 2   Landsize      1549 non-null   float64\n",
            " 3   BuildingArea  1549 non-null   float64\n",
            " 4   YearBuilt     1549 non-null   float64\n",
            " 5   Lattitude     1549 non-null   float64\n",
            " 6   Longtitude    1549 non-null   float64\n",
            "dtypes: float64(6), int64(1)\n",
            "memory usage: 96.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pioUCLI4Z-OZ",
        "outputId": "8169dc7e-267f-42fc-8181-73b6995b0d1b"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10385    1060000.0\n",
              "5805      390000.0\n",
              "8488      502000.0\n",
              "6672     1055000.0\n",
              "776      1900000.0\n",
              "           ...    \n",
              "9510     1875000.0\n",
              "6023      605000.0\n",
              "2960      709000.0\n",
              "4729     1000000.0\n",
              "4996      890000.0\n",
              "Name: Price, Length: 4647, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6q7VCauZ-Q5",
        "outputId": "a60ba694-082a-4306-c7cf-f79d605ef7a3"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4850      815000.0\n",
              "2307      655000.0\n",
              "10090     957500.0\n",
              "3645     1330000.0\n",
              "4930      722000.0\n",
              "           ...    \n",
              "8223      520000.0\n",
              "11190     870000.0\n",
              "8563      200000.0\n",
              "1867     1002000.0\n",
              "8375     1710000.0\n",
              "Name: Price, Length: 1549, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "metadata": {
        "id": "BxR-XiZSZ-Tz"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RandomForestRegressor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7sQyk1DZ-Vz",
        "outputId": "f2e1607d-4f1d-48cb-c94c-e3ab8a7a0b0a"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.ensemble._forest.RandomForestRegressor"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(RandomForestRegressor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rL4zZbdVY_Zk",
        "outputId": "554fadbe-06e9-4129-96b3-1b1b1442eade"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "abc.ABCMeta"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "Rd6V_eewY_b-"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWSUTWvmY_ea",
        "outputId": "80e948d7-0b93-4b92-ac4b-40e8b3122216"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function sklearn.metrics._regression.mean_absolute_error>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forest_model = RandomForestRegressor(random_state=1)"
      ],
      "metadata": {
        "id": "XG0ve78qY_gt"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forest_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkUUvVU6Y_jT",
        "outputId": "fbdf29f4-39de-4f5b-8498-681f67a8e1cd"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(forest_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjGNrMIWcaAd",
        "outputId": "f51834d4-b5f1-4be4-c4ae-0ef9874f2db4"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.ensemble._forest.RandomForestRegressor"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forest_model.fit(train_X, train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJ5ff4Z6ccib",
        "outputId": "66ba7389-d030-4bee-cce6-b991905b1a1f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(forest_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SV0XYV61cclN",
        "outputId": "7c2afe85-08b6-447d-e96a-bb6100ec640c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.ensemble._forest.RandomForestRegressor"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melb_preds = forest_model.predict(val_X)"
      ],
      "metadata": {
        "id": "Vc6fl4sXdQrq"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "melb_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TKqrfKCdQuu",
        "outputId": "f34ed2c5-4328-440b-e4a5-055cefaf6496"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 947155.,  540290.,  976405., ...,  250015., 1224435., 1802110.])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(melb_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_Dnrre0dQxT",
        "outputId": "a39e11f5-fc2d-4fd8-e2ef-9b2676f6ae7c"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1549"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(mean_absolute_error(val_y, melb_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmyRH9C1dQz7",
        "outputId": "8510abbe-efc6-402d-cb86-f8742efc8001"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "191669.7536453626\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jFghAE4BdQ2c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "AbFWwrIIdQ8w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yj8iQjKtdQ_e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xuD_MYlqdRCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "hNRXZX1Qccn1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "DQvKV7h2ccqg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "GBRlkP-XcaDN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3RvePvGwcaFj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IGb67TVvcaII"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "9L3horBvcaKy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "UCJ5ZBAyY_lu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0I5Ao-SuY4Aq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "T7pQS4sVY4Ct"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "f2LSf8uYY4E8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NL61fSgyY4Hf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "YiSzjMzBY0Eb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "d3qyEtxfY0Gz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "GIYVnkMKFCEI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "dVUrSmyAFCGu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "grLwlGQQFCJM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}