PyCaretModel.ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/naenumtou/statisticalModel/blob/main/PyCaretModel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWJVNswaEl-F"
      },
      "source": [
        "# Install PyCaret\n",
        "!pip install pycaret[full]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnglRRu6EQuD",
        "outputId": "0d6a648a-120c-4d7b-e594-0164399fd56e"
      },
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from pycaret.regression import *\n",
        "\n",
        "# Set display results\n",
        "pd.options.display.float_format = '{:,.4f}'.format\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "# PyCaret version\n",
        "print(f'PyCaret version: {pycaret.__version__}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/rvData\n",
            "PyCaret version: 2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 204
        },
        "id": "z50-Ll2MWisg",
        "outputId": "01952898-078d-4ee6-d029-e9cd634e345a"
      },
      "source": [
        "# Import data\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/naenumtou/statisticalModel/main/datasets/carData.csv')\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>carModel</th>\n",
              "      <th>carBrand</th>\n",
              "      <th>vehicleType</th>\n",
              "      <th>Year</th>\n",
              "      <th>sellingPrice</th>\n",
              "      <th>newPrice</th>\n",
              "      <th>kmDriven</th>\n",
              "      <th>fuelType</th>\n",
              "      <th>sellerType</th>\n",
              "      <th>gearType</th>\n",
              "      <th>owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3g</td>\n",
              "      <td>Honda</td>\n",
              "      <td>bike</td>\n",
              "      <td>2008</td>\n",
              "      <td>0.1700</td>\n",
              "      <td>0.5200</td>\n",
              "      <td>500000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>access</td>\n",
              "      <td>Suzuki</td>\n",
              "      <td>bike</td>\n",
              "      <td>2008</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.5800</td>\n",
              "      <td>1900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>activa</td>\n",
              "      <td>Honda</td>\n",
              "      <td>bike</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.4800</td>\n",
              "      <td>0.5100</td>\n",
              "      <td>4300</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>activa</td>\n",
              "      <td>Honda</td>\n",
              "      <td>bike</td>\n",
              "      <td>2017</td>\n",
              "      <td>0.4500</td>\n",
              "      <td>0.5100</td>\n",
              "      <td>4000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>activa</td>\n",
              "      <td>Honda</td>\n",
              "      <td>bike</td>\n",
              "      <td>2016</td>\n",
              "      <td>0.4500</td>\n",
              "      <td>0.5400</td>\n",
              "      <td>500</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  carModel carBrand vehicleType  Year  sellingPrice  newPrice  kmDriven  \\\n",
              "0       3g    Honda        bike  2008        0.1700    0.5200    500000   \n",
              "1   access   Suzuki        bike  2008        0.2500    0.5800      1900   \n",
              "2   activa    Honda        bike  2017        0.4800    0.5100      4300   \n",
              "3   activa    Honda        bike  2017        0.4500    0.5100      4000   \n",
              "4   activa    Honda        bike  2016        0.4500    0.5400       500   \n",
              "\n",
              "  fuelType  sellerType   gearType  owner  \n",
              "0   Petrol  Individual  Automatic      0  \n",
              "1   Petrol  Individual  Automatic      0  \n",
              "2   Petrol  Individual  Automatic      0  \n",
              "3   Petrol  Individual  Automatic      0  \n",
              "4   Petrol  Individual  Automatic      0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
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        },
        "id": "RrRBAh1mkddR",
        "outputId": "7fb76d63-0a4a-4ad1-b738-2c25c9374335"
      },
      "source": [
        "# Create 'age' variable\n",
        "df['age'] = 2018 - df['Year'] #Using 2018 as latest year \n",
        "df = df[df['age'] <= 10] #Drop data more than 10 years\n",
        "\n",
        "# Select only 'car' for model\n",
        "df = df[df['vehicleType'] == 'car']\n",
        "print(f'Total data avaliable: {df.shape[0]}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total data avaliable: 215\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      },
      "source": [
        "# Create 'RV' as target\n",
        "df['RV'] = df['sellingPrice'] / df['newPrice']\n",
        "df['logRV'] = np.log(df['RV'])\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>carModel</th>\n",
              "      <th>carBrand</th>\n",
              "      <th>vehicleType</th>\n",
              "      <th>Year</th>\n",
              "      <th>sellingPrice</th>\n",
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              "      <th>fuelType</th>\n",
              "      <th>sellerType</th>\n",
              "      <th>gearType</th>\n",
              "      <th>owner</th>\n",
              "      <th>age</th>\n",
              "      <th>RV</th>\n",
              "      <th>logRV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>alto</td>\n",
              "      <td>Suzuki</td>\n",
              "      <td>car</td>\n",
              "      <td>2017</td>\n",
              "      <td>2.8500</td>\n",
              "      <td>3.6000</td>\n",
              "      <td>2135</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7917</td>\n",
              "      <td>-0.2336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>alto</td>\n",
              "      <td>Suzuki</td>\n",
              "      <td>car</td>\n",
              "      <td>2016</td>\n",
              "      <td>2.8500</td>\n",
              "      <td>3.9500</td>\n",
              "      <td>25000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7215</td>\n",
              "      <td>-0.3264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>alto</td>\n",
              "      <td>Suzuki</td>\n",
              "      <td>car</td>\n",
              "      <td>2014</td>\n",
              "      <td>2.5000</td>\n",
              "      <td>3.4600</td>\n",
              "      <td>45280</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.7225</td>\n",
              "      <td>-0.3250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>alto</td>\n",
              "      <td>Suzuki</td>\n",
              "      <td>car</td>\n",
              "      <td>2010</td>\n",
              "      <td>1.9500</td>\n",
              "      <td>3.9500</td>\n",
              "      <td>44542</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.4937</td>\n",
              "      <td>-0.7059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>alto</td>\n",
              "      <td>Suzuki</td>\n",
              "      <td>car</td>\n",
              "      <td>2016</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>3.7600</td>\n",
              "      <td>10079</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7979</td>\n",
              "      <td>-0.2258</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   carModel carBrand vehicleType  Year  sellingPrice  newPrice  kmDriven  \\\n",
              "7      alto   Suzuki         car  2017        2.8500    3.6000      2135   \n",
              "8      alto   Suzuki         car  2016        2.8500    3.9500     25000   \n",
              "9      alto   Suzuki         car  2014        2.5000    3.4600     45280   \n",
              "10     alto   Suzuki         car  2010        1.9500    3.9500     44542   \n",
              "12     alto   Suzuki         car  2016        3.0000    3.7600     10079   \n",
              "\n",
              "   fuelType sellerType gearType  owner  age     RV   logRV  \n",
              "7    Petrol     Dealer   Manual      0    1 0.7917 -0.2336  \n",
              "8    Petrol     Dealer   Manual      0    2 0.7215 -0.3264  \n",
              "9    Petrol     Dealer   Manual      0    4 0.7225 -0.3250  \n",
              "10   Petrol     Dealer   Manual      0    8 0.4937 -0.7059  \n",
              "12   Petrol     Dealer   Manual      0    2 0.7979 -0.2258  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUDvSt4WnKra",
        "outputId": "cd12a719-955e-422b-a312-82653674bae9"
      },
      "source": [
        "# Checking data before model devlopment\n",
        "print(df.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "carModel        0\n",
            "carBrand        0\n",
            "vehicleType     0\n",
            "Year            0\n",
            "sellingPrice    0\n",
            "newPrice        0\n",
            "kmDriven        0\n",
            "fuelType        0\n",
            "sellerType      0\n",
            "gearType        0\n",
            "owner           0\n",
            "age             0\n",
            "RV              0\n",
            "logRV           0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c68cf2938f70437dbf8820f5d01259cc",
            "8ce9fac6924b4d08aca9ee3f6b504c71",
            "6132f34fd1304f8db209f3c7cbe1dd94",
            "348a85d77e064f05852e771ff555b496",
            "cc5035f6d2494d83b7863039927956f1",
            "3c86ed3efe3e4ed48498703615133371"
          ]
        },
        "id": "AoO7WpcFFypv",
        "outputId": "19f2efb9-f384-400f-d894-ff3646d94e9e"
      },
      "source": [
        "# Set up model\n",
        "regression = setup(df,\n",
        "                   target = 'logRV',\n",
        "                   train_size = 0.8,\n",
        "                   categorical_features = ['carModel',\n",
        "                                           'carBrand',\n",
        "                                           'fuelType',\n",
        "                                           'gearType',\n",
        "                                           'owner'],\n",
        "                   categorical_imputation = 'constant',\n",
        "                   numeric_features = ['kmDriven',\n",
        "                                       'age'],\n",
        "                   numeric_imputation = 'mean', \n",
        "                   normalize = True,\n",
        "                   normalize_method = 'minmax',\n",
        "                   handle_unknown_categorical = True, \n",
        "                   unknown_categorical_method = 'least_frequent', \n",
        "                   polynomial_features = True,\n",
        "                   polynomial_degree = 2,\n",
        "                   ignore_features = ['Year',\n",
        "                                      'vehicleType',\n",
        "                                      'sellingPrice',\n",
        "                                      'newPrice',\n",
        "                                      'sellerType',\n",
        "                                      'RV'],\n",
        "                   fold = 5,\n",
        "                   n_jobs = -1,\n",
        "                   use_gpu = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>3519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>logRV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(215, 14)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(172, 49)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(43, 49)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>USI</td>\n",
              "      <td>2e86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>minmax</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>0.1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Transform Target</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Transform Target Method</td>\n",
              "      <td>box-cox</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id              3519\n",
              "1                                   Target             logRV\n",
              "2                            Original Data         (215, 14)\n",
              "3                           Missing Values             False\n",
              "4                         Numeric Features                 2\n",
              "5                     Categorical Features                 5\n",
              "6                         Ordinal Features             False\n",
              "7                High Cardinality Features             False\n",
              "8                  High Cardinality Method              None\n",
              "9                    Transformed Train Set         (172, 49)\n",
              "10                    Transformed Test Set          (43, 49)\n",
              "11                      Shuffle Train-Test              True\n",
              "12                     Stratify Train-Test             False\n",
              "13                          Fold Generator             KFold\n",
              "14                             Fold Number                 5\n",
              "15                                CPU Jobs                -1\n",
              "16                                 Use GPU              True\n",
              "17                          Log Experiment             False\n",
              "18                         Experiment Name  reg-default-name\n",
              "19                                     USI              2e86\n",
              "20                         Imputation Type            simple\n",
              "21          Iterative Imputation Iteration              None\n",
              "22                         Numeric Imputer              mean\n",
              "23      Iterative Imputation Numeric Model              None\n",
              "24                     Categorical Imputer          constant\n",
              "25  Iterative Imputation Categorical Model              None\n",
              "26           Unknown Categoricals Handling    least_frequent\n",
              "27                               Normalize              True\n",
              "28                        Normalize Method            minmax\n",
              "29                          Transformation             False\n",
              "30                   Transformation Method              None\n",
              "31                                     PCA             False\n",
              "32                              PCA Method              None\n",
              "33                          PCA Components              None\n",
              "34                     Ignore Low Variance             False\n",
              "35                     Combine Rare Levels             False\n",
              "36                    Rare Level Threshold              None\n",
              "37                         Numeric Binning             False\n",
              "38                         Remove Outliers             False\n",
              "39                      Outliers Threshold              None\n",
              "40                Remove Multicollinearity             False\n",
              "41             Multicollinearity Threshold              None\n",
              "42                              Clustering             False\n",
              "43                    Clustering Iteration              None\n",
              "44                     Polynomial Features              True\n",
              "45                       Polynomial Degree                 2\n",
              "46                    Trignometry Features             False\n",
              "47                    Polynomial Threshold            0.1000\n",
              "48                          Group Features             False\n",
              "49                       Feature Selection             False\n",
              "50                Feature Selection Method           classic\n",
              "51            Features Selection Threshold              None\n",
              "52                     Feature Interaction             False\n",
              "53                           Feature Ratio             False\n",
              "54                   Interaction Threshold              None\n",
              "55                        Transform Target             False\n",
              "56                 Transform Target Method           box-cox"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483,
          "referenced_widgets": [
            "d833e3bf7601474b929ac08224edacc1",
            "2b1e9407777c49d5ad0239f972553300",
            "8d134213a4f9476ba52438e5ccfa1c36"
          ]
        },
        "id": "1Phntx6SLN0h",
        "outputId": "51d9c730-dc78-4dbb-928f-607109ddbef7"
      },
      "source": [
        "# Compare models\n",
        "estimatedModels = compare_models(include = ['ridge',\n",
        "                                            'catboost',\n",
        "                                            'gbr',\n",
        "                                            'rf',\n",
        "                                            'et',\n",
        "                                            'ada',\n",
        "                                            'knn',\n",
        "                                            'lightgbm',\n",
        "                                            'dt',\n",
        "                                            'lr',\n",
        "                                            'lasso',\n",
        "                                            'xgboost',\n",
        "                                            'en',\n",
        "                                            'svm'],\n",
        "                                 sort = 'R2',\n",
        "                                 n_select = 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>catboost</th>\n",
              "      <td>CatBoost Regressor</td>\n",
              "      <td>0.0990</td>\n",
              "      <td>0.0167</td>\n",
              "      <td>0.1281</td>\n",
              "      <td>0.8476</td>\n",
              "      <td>0.0835</td>\n",
              "      <td>0.4596</td>\n",
              "      <td>1.9940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>0.1023</td>\n",
              "      <td>0.0163</td>\n",
              "      <td>0.1273</td>\n",
              "      <td>0.8410</td>\n",
              "      <td>0.0852</td>\n",
              "      <td>0.4689</td>\n",
              "      <td>0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr</th>\n",
              "      <td>Gradient Boosting Regressor</td>\n",
              "      <td>0.1005</td>\n",
              "      <td>0.0175</td>\n",
              "      <td>0.1320</td>\n",
              "      <td>0.8340</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.4596</td>\n",
              "      <td>0.0600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>Support Vector Regression</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.0174</td>\n",
              "      <td>0.1316</td>\n",
              "      <td>0.8321</td>\n",
              "      <td>0.0888</td>\n",
              "      <td>0.5477</td>\n",
              "      <td>0.0320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>0.1134</td>\n",
              "      <td>0.0214</td>\n",
              "      <td>0.1457</td>\n",
              "      <td>0.7969</td>\n",
              "      <td>0.0941</td>\n",
              "      <td>0.5220</td>\n",
              "      <td>1.0980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Regressor</td>\n",
              "      <td>0.1111</td>\n",
              "      <td>0.0220</td>\n",
              "      <td>0.1481</td>\n",
              "      <td>0.7844</td>\n",
              "      <td>0.0977</td>\n",
              "      <td>0.4839</td>\n",
              "      <td>0.8660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>AdaBoost Regressor</td>\n",
              "      <td>0.1266</td>\n",
              "      <td>0.0268</td>\n",
              "      <td>0.1631</td>\n",
              "      <td>0.7482</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.6335</td>\n",
              "      <td>0.0900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.1338</td>\n",
              "      <td>0.0300</td>\n",
              "      <td>0.1717</td>\n",
              "      <td>0.7151</td>\n",
              "      <td>0.1098</td>\n",
              "      <td>0.5589</td>\n",
              "      <td>0.0280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>0.1414</td>\n",
              "      <td>0.0375</td>\n",
              "      <td>0.1903</td>\n",
              "      <td>0.6687</td>\n",
              "      <td>0.1216</td>\n",
              "      <td>0.5921</td>\n",
              "      <td>0.0100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Regressor</td>\n",
              "      <td>0.1666</td>\n",
              "      <td>0.0481</td>\n",
              "      <td>0.2184</td>\n",
              "      <td>0.5487</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.9621</td>\n",
              "      <td>0.0180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xgboost</th>\n",
              "      <td>Extreme Gradient Boosting</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>0.1167</td>\n",
              "      <td>0.3376</td>\n",
              "      <td>-0.0426</td>\n",
              "      <td>0.2129</td>\n",
              "      <td>1.4160</td>\n",
              "      <td>0.0100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>en</th>\n",
              "      <td>Elastic Net</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>0.1167</td>\n",
              "      <td>0.3376</td>\n",
              "      <td>-0.0426</td>\n",
              "      <td>0.2129</td>\n",
              "      <td>1.4160</td>\n",
              "      <td>0.0100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>14.1640</td>\n",
              "      <td>19,506.4069</td>\n",
              "      <td>66.0382</td>\n",
              "      <td>-147,909.0895</td>\n",
              "      <td>0.5927</td>\n",
              "      <td>42.8823</td>\n",
              "      <td>0.0140</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model     MAE         MSE    RMSE  \\\n",
              "catboost               CatBoost Regressor  0.0990      0.0167  0.1281   \n",
              "ridge                    Ridge Regression  0.1023      0.0163  0.1273   \n",
              "gbr           Gradient Boosting Regressor  0.1005      0.0175  0.1320   \n",
              "svm             Support Vector Regression  0.1057      0.0174  0.1316   \n",
              "rf                Random Forest Regressor  0.1134      0.0214  0.1457   \n",
              "et                  Extra Trees Regressor  0.1111      0.0220  0.1481   \n",
              "ada                    AdaBoost Regressor  0.1266      0.0268  0.1631   \n",
              "lightgbm  Light Gradient Boosting Machine  0.1338      0.0300  0.1717   \n",
              "dt                Decision Tree Regressor  0.1414      0.0375  0.1903   \n",
              "knn                 K Neighbors Regressor  0.1666      0.0481  0.2184   \n",
              "xgboost         Extreme Gradient Boosting  0.0000      0.0000  0.0000   \n",
              "lasso                    Lasso Regression  0.2655      0.1167  0.3376   \n",
              "en                            Elastic Net  0.2655      0.1167  0.3376   \n",
              "lr                      Linear Regression 14.1640 19,506.4069 66.0382   \n",
              "\n",
              "                    R2  RMSLE    MAPE  TT (Sec)  \n",
              "catboost        0.8476 0.0835  0.4596    1.9940  \n",
              "ridge           0.8410 0.0852  0.4689    0.0300  \n",
              "gbr             0.8340 0.0869  0.4596    0.0600  \n",
              "svm             0.8321 0.0888  0.5477    0.0320  \n",
              "rf              0.7969 0.0941  0.5220    1.0980  \n",
              "et              0.7844 0.0977  0.4839    0.8660  \n",
              "ada             0.7482 0.1060  0.6335    0.0900  \n",
              "lightgbm        0.7151 0.1098  0.5589    0.0280  \n",
              "dt              0.6687 0.1216  0.5921    0.0100  \n",
              "knn             0.5487 0.1403  0.9621    0.0180  \n",
              "xgboost         0.0000 0.0000  0.0000    0.0100  \n",
              "lasso          -0.0426 0.2129  1.4160    0.0100  \n",
              "en             -0.0426 0.2129  1.4160    0.0100  \n",
              "lr       -147,909.0895 0.5927 42.8823    0.0140  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266,
          "referenced_widgets": [
            "6a0146e70a1f4f1fb7de0eff5def3c38",
            "92533a0cf0bb40349350370567f797d8",
            "4daa583d07424776b01b0192a7b7b771"
          ]
        },
        "id": "92P4iXEzvAIb",
        "outputId": "d567c8aa-fa13-444a-d6e7-4a9062b3228a"
      },
      "source": [
        "# Select model\n",
        "model = create_model('catboost')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0920</td>\n",
              "      <td>0.0159</td>\n",
              "      <td>0.1262</td>\n",
              "      <td>0.8794</td>\n",
              "      <td>0.0825</td>\n",
              "      <td>0.6062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.1236</td>\n",
              "      <td>0.0237</td>\n",
              "      <td>0.1540</td>\n",
              "      <td>0.8452</td>\n",
              "      <td>0.0940</td>\n",
              "      <td>0.3855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0795</td>\n",
              "      <td>0.0103</td>\n",
              "      <td>0.1015</td>\n",
              "      <td>0.8239</td>\n",
              "      <td>0.0670</td>\n",
              "      <td>0.2148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>0.1367</td>\n",
              "      <td>0.8301</td>\n",
              "      <td>0.0883</td>\n",
              "      <td>0.6903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0938</td>\n",
              "      <td>0.0149</td>\n",
              "      <td>0.1221</td>\n",
              "      <td>0.8595</td>\n",
              "      <td>0.0856</td>\n",
              "      <td>0.4013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.0990</td>\n",
              "      <td>0.0167</td>\n",
              "      <td>0.1281</td>\n",
              "      <td>0.8476</td>\n",
              "      <td>0.0835</td>\n",
              "      <td>0.4596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0149</td>\n",
              "      <td>0.0044</td>\n",
              "      <td>0.0173</td>\n",
              "      <td>0.0201</td>\n",
              "      <td>0.0091</td>\n",
              "      <td>0.1694</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        MAE    MSE   RMSE     R2  RMSLE   MAPE\n",
              "0    0.0920 0.0159 0.1262 0.8794 0.0825 0.6062\n",
              "1    0.1236 0.0237 0.1540 0.8452 0.0940 0.3855\n",
              "2    0.0795 0.0103 0.1015 0.8239 0.0670 0.2148\n",
              "3    0.1060 0.0187 0.1367 0.8301 0.0883 0.6903\n",
              "4    0.0938 0.0149 0.1221 0.8595 0.0856 0.4013\n",
              "Mean 0.0990 0.0167 0.1281 0.8476 0.0835 0.4596\n",
              "SD   0.0149 0.0044 0.0173 0.0201 0.0091 0.1694"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofMApZcWvTqe"
      },
      "source": [
        "# Final model\n",
        "finalModel = finalize_model(model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYZBFSNdwouU"
      },
      "source": [
        "# Model prediction\n",
        "prediction = predict_model(finalModel,\n",
        "                           data = df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWGbDmsfzMPW"
      },
      "source": [
        "# Prediction results\n",
        "prediction = prediction[['age', 'RV', 'Label' ]] #Return as dataframe\n",
        "\n",
        "# Inverse target varialbe\n",
        "prediction['predictedRV'] = np.exp(prediction['Label'])\n",
        "\n",
        "# Average RV by year\n",
        "averageRV = prediction.groupby(['age'], as_index = False)['RV'].mean()\n",
        "averageRV['predictedRV'] = list(prediction.groupby(['age'])['predictedRV'].mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "x6wLUvEbzi-j",
        "outputId": "293ed326-86d7-4c69-85b2-b40a75b9f866"
      },
      "source": [
        "#Plot results\n",
        "plt.figure(figsize = (10, 6))\n",
        "with plt.style.context('seaborn'):\n",
        "  plt.title('Residual value')\n",
        "  plt.xlabel('Year')\n",
        "  plt.ylabel('%RV')\n",
        "  plt.plot(range(1, averageRV.shape[0] + 1), averageRV['RV'], c = 'royalblue', linewidth = 2.5, label = 'Actual')\n",
        "  plt.plot(range(1, averageRV.shape[0] + 1), averageRV['predictedRV'], c = 'firebrick', linestyle = '--', marker = 'o', label = 'Predicted')\n",
        "  plt.xticks(list(range(1, averageRV.shape[0] + 1)), averageRV['age'])\n",
        "  for i, rv in enumerate(averageRV['predictedRV']):\n",
        "    rvLabel = f'{rv * 100:.2f}%'\n",
        "    plt.annotate(rvLabel, (i + 1, rv), textcoords = 'offset points', xytext = (10, 10), ha = 'center')\n",
        "  plt.legend(frameon = True, facecolor = 'white')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 609,
              "height": 386
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ekNnh70J72Un",
        "outputId": "c9e59f40-3d99-4579-e99e-dbf511593cdd"
      },
      "source": [
        "# Save model\n",
        "save_model(finalModel, 'RVModel')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Succesfully Saved\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Pipeline(memory=None,\n",
              "          steps=[('dtypes',\n",
              "                  DataTypes_Auto_infer(categorical_features=['carModel',\n",
              "                                                             'carBrand',\n",
              "                                                             'fuelType',\n",
              "                                                             'gearType',\n",
              "                                                             'owner'],\n",
              "                                       display_types=True,\n",
              "                                       features_todrop=['Year', 'vehicleType',\n",
              "                                                        'sellingPrice',\n",
              "                                                        'newPrice', 'sellerType',\n",
              "                                                        'RV'],\n",
              "                                       id_columns=[], ml_usecase='regression',\n",
              "                                       numerical_features=['kmDriven', 'age'],\n",
              "                                       target='logRV', time_features=[])),\n",
              "                 ('im...\n",
              "                 ('rem_outliers', 'passthrough'), ('cluster_all', 'passthrough'),\n",
              "                 ('dummy', Dummify(target='logRV')),\n",
              "                 ('fix_perfect', Remove_100(target='logRV')),\n",
              "                 ('clean_names', Clean_Colum_Names()),\n",
              "                 ('feature_select', 'passthrough'), ('fix_multi', 'passthrough'),\n",
              "                 ('dfs', 'passthrough'), ('pca', 'passthrough'),\n",
              "                 ['trained_model',\n",
              "                  <catboost.core.CatBoostRegressor object at 0x7fb3601397f0>]],\n",
              "          verbose=False), 'RVModel.pkl')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NQAse1KN-k4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9262671e-f37e-452c-8e9c-840aeaa0a8f5"
      },
      "source": [
        "# Load model (Only can use PyCaret to load pickle model)\n",
        "loadModel = load_model('RVModel') \n",
        "load_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformation Pipeline and Model Successfully Loaded\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function pycaret.regression.load_model>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    }
  ]
}