Machine Learning Projects (Classic)/Blue_Book_for_Bulldozers_ML_Project.ipynb

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        "## Blue Book for Bulldozers Project \n",
        "#### Predict the auction sale price for a piece of heavy equipment to create a \"blue book\" for bulldozers.\n",
        "\n",
        ">*A blue book or bluebook is an almanac, buyer's guide or other compilation of statistics and information*"
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        "### Project Description \n",
        "The goal of the Project is to predict the sale price of a particular piece of heavy equiment at auction based on it's usage, equipment type, and configuaration.  The data is sourced from auction result postings and includes information on usage and equipment configurations.\n",
        "\n",
        "Fast Iron is creating a \"blue book for bull dozers,\" for customers to value what their heavy equipment fleet is worth at auction. \n",
        "\n",
        "### Project Evaluation Metrics\n",
        "The evaluation metric for this competition is the RMSLE (root mean squared log error) between the actual and predicted auction prices."
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        "### Data \n",
        "So, our data contains training, validation and test sets. Our target variable is `SalePrice` and this column is present only in training and validation set. Test set does not contain `SalePrice` as this what we are going to predict on unseen data in the test set. \n",
        "\n",
        "Training set contains close to 400,000 values and validation and test set contains 12000 values. "
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        "The key fields are in train.csv are:\n",
        "\n",
        "1. SalesID: the uniue identifier of the sale\n",
        "2. MachineID: the unique identifier of a machine.  A machine can be sold multiple times\n",
        "3. saleprice: what the machine sold for at auction (only provided in train.csv)\n",
        "4. saledate: the date of the sale\n",
        "\n",
        "____\n",
        "\n",
        "There are other files available as well but for this project we won't be keeping all the files here only the ones we are working with. This project is part of a Kaggle Competetion so feel free to get further details and download the files from here: https://www.kaggle.com/competitions/bluebook-for-bulldozers/\n",
        "\n",
        "____\n",
        "\n",
        "After downloading the files upload them in your Google drive if you are working on colab and mount your drive with your notebook. "
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        "# Let's first import the important tools \n",
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
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        "df = pd.read_csv(\"/content/drive/MyDrive/Data/Bluebook TrainAndValid.csv\")    # import data "
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            "<ipython-input-17-eb7a9552a14d>:1: DtypeWarning: Columns (13,39,40,41) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv(\"/content/drive/MyDrive/Data/Bluebook TrainAndValid.csv\")    # import data\n"
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        "### Understanding Our Dataset\n",
        "- Check for categorical datatypes \n",
        "- Check for null values \n",
        "- Check for important features/attributes that can potentially help us in further analysis\n",
        "- Check for basic statistics"
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              "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
              "0  1139246    66000.0     999089     3157         121           3.0      2004   \n",
              "1  1139248    57000.0     117657       77         121           3.0      1996   \n",
              "2  1139249    10000.0     434808     7009         121           3.0      2001   \n",
              "3  1139251    38500.0    1026470      332         121           3.0      2001   \n",
              "4  1139253    11000.0    1057373    17311         121           3.0      2007   \n",
              "\n",
              "   MachineHoursCurrentMeter UsageBand         saledate  ...  \\\n",
              "0                      68.0       Low  11/16/2006 0:00  ...   \n",
              "1                    4640.0       Low   3/26/2004 0:00  ...   \n",
              "2                    2838.0      High   2/26/2004 0:00  ...   \n",
              "3                    3486.0      High   5/19/2011 0:00  ...   \n",
              "4                     722.0    Medium   7/23/2009 0:00  ...   \n",
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              "                                                                 0  \\\n",
              "SalesID                                                    1139246   \n",
              "SalePrice                                                  66000.0   \n",
              "MachineID                                                   999089   \n",
              "ModelID                                                       3157   \n",
              "datasource                                                     121   \n",
              "auctioneerID                                                   3.0   \n",
              "YearMade                                                      2004   \n",
              "MachineHoursCurrentMeter                                      68.0   \n",
              "UsageBand                                                      Low   \n",
              "saledate                                           11/16/2006 0:00   \n",
              "fiModelDesc                                                   521D   \n",
              "fiBaseModel                                                    521   \n",
              "fiSecondaryDesc                                                  D   \n",
              "fiModelSeries                                                  NaN   \n",
              "fiModelDescriptor                                              NaN   \n",
              "ProductSize                                                    NaN   \n",
              "fiProductClassDesc        Wheel Loader - 110.0 to 120.0 Horsepower   \n",
              "state                                                      Alabama   \n",
              "ProductGroup                                                    WL   \n",
              "ProductGroupDesc                                      Wheel Loader   \n",
              "Drive_System                                                   NaN   \n",
              "Enclosure                                               EROPS w AC   \n",
              "Forks                                          None or Unspecified   \n",
              "Pad_Type                                                       NaN   \n",
              "Ride_Control                                   None or Unspecified   \n",
              "Stick                                                          NaN   \n",
              "Transmission                                                   NaN   \n",
              "Turbocharged                                                   NaN   \n",
              "Blade_Extension                                                NaN   \n",
              "Blade_Width                                                    NaN   \n",
              "Enclosure_Type                                                 NaN   \n",
              "Engine_Horsepower                                              NaN   \n",
              "Hydraulics                                                 2 Valve   \n",
              "Pushblock                                                      NaN   \n",
              "Ripper                                                         NaN   \n",
              "Scarifier                                                      NaN   \n",
              "Tip_Control                                                    NaN   \n",
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              "Coupler                                        None or Unspecified   \n",
              "Coupler_System                                                 NaN   \n",
              "Grouser_Tracks                                                 NaN   \n",
              "Hydraulics_Flow                                                NaN   \n",
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              "Blade_Type                                                     NaN   \n",
              "Travel_Controls                                                NaN   \n",
              "Differential_Type                                         Standard   \n",
              "Steering_Controls                                     Conventional   \n",
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              "                                                                 1  \\\n",
              "SalesID                                                    1139248   \n",
              "SalePrice                                                  57000.0   \n",
              "MachineID                                                   117657   \n",
              "ModelID                                                         77   \n",
              "datasource                                                     121   \n",
              "auctioneerID                                                   3.0   \n",
              "YearMade                                                      1996   \n",
              "MachineHoursCurrentMeter                                    4640.0   \n",
              "UsageBand                                                      Low   \n",
              "saledate                                            3/26/2004 0:00   \n",
              "fiModelDesc                                                 950FII   \n",
              "fiBaseModel                                                    950   \n",
              "fiSecondaryDesc                                                  F   \n",
              "fiModelSeries                                                   II   \n",
              "fiModelDescriptor                                              NaN   \n",
              "ProductSize                                                 Medium   \n",
              "fiProductClassDesc        Wheel Loader - 150.0 to 175.0 Horsepower   \n",
              "state                                               North Carolina   \n",
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              "ProductGroupDesc                                      Wheel Loader   \n",
              "Drive_System                                                   NaN   \n",
              "Enclosure                                               EROPS w AC   \n",
              "Forks                                          None or Unspecified   \n",
              "Pad_Type                                                       NaN   \n",
              "Ride_Control                                   None or Unspecified   \n",
              "Stick                                                          NaN   \n",
              "Transmission                                                   NaN   \n",
              "Turbocharged                                                   NaN   \n",
              "Blade_Extension                                                NaN   \n",
              "Blade_Width                                                    NaN   \n",
              "Enclosure_Type                                                 NaN   \n",
              "Engine_Horsepower                                              NaN   \n",
              "Hydraulics                                                 2 Valve   \n",
              "Pushblock                                                      NaN   \n",
              "Ripper                                                         NaN   \n",
              "Scarifier                                                      NaN   \n",
              "Tip_Control                                                    NaN   \n",
              "Tire_Size                                                     23.5   \n",
              "Coupler                                        None or Unspecified   \n",
              "Coupler_System                                                 NaN   \n",
              "Grouser_Tracks                                                 NaN   \n",
              "Hydraulics_Flow                                                NaN   \n",
              "Track_Type                                                     NaN   \n",
              "Undercarriage_Pad_Width                                        NaN   \n",
              "Stick_Length                                                   NaN   \n",
              "Thumb                                                          NaN   \n",
              "Pattern_Changer                                                NaN   \n",
              "Grouser_Type                                                   NaN   \n",
              "Backhoe_Mounting                                               NaN   \n",
              "Blade_Type                                                     NaN   \n",
              "Travel_Controls                                                NaN   \n",
              "Differential_Type                                         Standard   \n",
              "Steering_Controls                                     Conventional   \n",
              "\n",
              "                                                                          2  \\\n",
              "SalesID                                                             1139249   \n",
              "SalePrice                                                           10000.0   \n",
              "MachineID                                                            434808   \n",
              "ModelID                                                                7009   \n",
              "datasource                                                              121   \n",
              "auctioneerID                                                            3.0   \n",
              "YearMade                                                               2001   \n",
              "MachineHoursCurrentMeter                                             2838.0   \n",
              "UsageBand                                                              High   \n",
              "saledate                                                     2/26/2004 0:00   \n",
              "fiModelDesc                                                             226   \n",
              "fiBaseModel                                                             226   \n",
              "fiSecondaryDesc                                                         NaN   \n",
              "fiModelSeries                                                           NaN   \n",
              "fiModelDescriptor                                                       NaN   \n",
              "ProductSize                                                             NaN   \n",
              "fiProductClassDesc        Skid Steer Loader - 1351.0 to 1601.0 Lb Operat...   \n",
              "state                                                              New York   \n",
              "ProductGroup                                                            SSL   \n",
              "ProductGroupDesc                                         Skid Steer Loaders   \n",
              "Drive_System                                                            NaN   \n",
              "Enclosure                                                             OROPS   \n",
              "Forks                                                   None or Unspecified   \n",
              "Pad_Type                                                                NaN   \n",
              "Ride_Control                                                            NaN   \n",
              "Stick                                                                   NaN   \n",
              "Transmission                                                            NaN   \n",
              "Turbocharged                                                            NaN   \n",
              "Blade_Extension                                                         NaN   \n",
              "Blade_Width                                                             NaN   \n",
              "Enclosure_Type                                                          NaN   \n",
              "Engine_Horsepower                                                       NaN   \n",
              "Hydraulics                                                        Auxiliary   \n",
              "Pushblock                                                               NaN   \n",
              "Ripper                                                                  NaN   \n",
              "Scarifier                                                               NaN   \n",
              "Tip_Control                                                             NaN   \n",
              "Tire_Size                                                               NaN   \n",
              "Coupler                                                 None or Unspecified   \n",
              "Coupler_System                                          None or Unspecified   \n",
              "Grouser_Tracks                                          None or Unspecified   \n",
              "Hydraulics_Flow                                                    Standard   \n",
              "Track_Type                                                              NaN   \n",
              "Undercarriage_Pad_Width                                                 NaN   \n",
              "Stick_Length                                                            NaN   \n",
              "Thumb                                                                   NaN   \n",
              "Pattern_Changer                                                         NaN   \n",
              "Grouser_Type                                                            NaN   \n",
              "Backhoe_Mounting                                                        NaN   \n",
              "Blade_Type                                                              NaN   \n",
              "Travel_Controls                                                         NaN   \n",
              "Differential_Type                                                       NaN   \n",
              "Steering_Controls                                                       NaN   \n",
              "\n",
              "                                                                          3  \\\n",
              "SalesID                                                             1139251   \n",
              "SalePrice                                                           38500.0   \n",
              "MachineID                                                           1026470   \n",
              "ModelID                                                                 332   \n",
              "datasource                                                              121   \n",
              "auctioneerID                                                            3.0   \n",
              "YearMade                                                               2001   \n",
              "MachineHoursCurrentMeter                                             3486.0   \n",
              "UsageBand                                                              High   \n",
              "saledate                                                     5/19/2011 0:00   \n",
              "fiModelDesc                                                        PC120-6E   \n",
              "fiBaseModel                                                           PC120   \n",
              "fiSecondaryDesc                                                         NaN   \n",
              "fiModelSeries                                                           -6E   \n",
              "fiModelDescriptor                                                       NaN   \n",
              "ProductSize                                                           Small   \n",
              "fiProductClassDesc        Hydraulic Excavator, Track - 12.0 to 14.0 Metr...   \n",
              "state                                                                 Texas   \n",
              "ProductGroup                                                            TEX   \n",
              "ProductGroupDesc                                           Track Excavators   \n",
              "Drive_System                                                            NaN   \n",
              "Enclosure                                                        EROPS w AC   \n",
              "Forks                                                                   NaN   \n",
              "Pad_Type                                                                NaN   \n",
              "Ride_Control                                                            NaN   \n",
              "Stick                                                                   NaN   \n",
              "Transmission                                                            NaN   \n",
              "Turbocharged                                                            NaN   \n",
              "Blade_Extension                                                         NaN   \n",
              "Blade_Width                                                             NaN   \n",
              "Enclosure_Type                                                          NaN   \n",
              "Engine_Horsepower                                                       NaN   \n",
              "Hydraulics                                                          2 Valve   \n",
              "Pushblock                                                               NaN   \n",
              "Ripper                                                                  NaN   \n",
              "Scarifier                                                               NaN   \n",
              "Tip_Control                                                             NaN   \n",
              "Tire_Size                                                               NaN   \n",
              "Coupler                                                 None or Unspecified   \n",
              "Coupler_System                                                          NaN   \n",
              "Grouser_Tracks                                                          NaN   \n",
              "Hydraulics_Flow                                                         NaN   \n",
              "Track_Type                                                              NaN   \n",
              "Undercarriage_Pad_Width                                                 NaN   \n",
              "Stick_Length                                                            NaN   \n",
              "Thumb                                                                   NaN   \n",
              "Pattern_Changer                                                         NaN   \n",
              "Grouser_Type                                                            NaN   \n",
              "Backhoe_Mounting                                                        NaN   \n",
              "Blade_Type                                                              NaN   \n",
              "Travel_Controls                                                         NaN   \n",
              "Differential_Type                                                       NaN   \n",
              "Steering_Controls                                                       NaN   \n",
              "\n",
              "                                                                          4  \n",
              "SalesID                                                             1139253  \n",
              "SalePrice                                                           11000.0  \n",
              "MachineID                                                           1057373  \n",
              "ModelID                                                               17311  \n",
              "datasource                                                              121  \n",
              "auctioneerID                                                            3.0  \n",
              "YearMade                                                               2007  \n",
              "MachineHoursCurrentMeter                                              722.0  \n",
              "UsageBand                                                            Medium  \n",
              "saledate                                                     7/23/2009 0:00  \n",
              "fiModelDesc                                                            S175  \n",
              "fiBaseModel                                                            S175  \n",
              "fiSecondaryDesc                                                         NaN  \n",
              "fiModelSeries                                                           NaN  \n",
              "fiModelDescriptor                                                       NaN  \n",
              "ProductSize                                                             NaN  \n",
              "fiProductClassDesc        Skid Steer Loader - 1601.0 to 1751.0 Lb Operat...  \n",
              "state                                                              New York  \n",
              "ProductGroup                                                            SSL  \n",
              "ProductGroupDesc                                         Skid Steer Loaders  \n",
              "Drive_System                                                            NaN  \n",
              "Enclosure                                                             EROPS  \n",
              "Forks                                                   None or Unspecified  \n",
              "Pad_Type                                                                NaN  \n",
              "Ride_Control                                                            NaN  \n",
              "Stick                                                                   NaN  \n",
              "Transmission                                                            NaN  \n",
              "Turbocharged                                                            NaN  \n",
              "Blade_Extension                                                         NaN  \n",
              "Blade_Width                                                             NaN  \n",
              "Enclosure_Type                                                          NaN  \n",
              "Engine_Horsepower                                                       NaN  \n",
              "Hydraulics                                                        Auxiliary  \n",
              "Pushblock                                                               NaN  \n",
              "Ripper                                                                  NaN  \n",
              "Scarifier                                                               NaN  \n",
              "Tip_Control                                                             NaN  \n",
              "Tire_Size                                                               NaN  \n",
              "Coupler                                                 None or Unspecified  \n",
              "Coupler_System                                          None or Unspecified  \n",
              "Grouser_Tracks                                          None or Unspecified  \n",
              "Hydraulics_Flow                                                    Standard  \n",
              "Track_Type                                                              NaN  \n",
              "Undercarriage_Pad_Width                                                 NaN  \n",
              "Stick_Length                                                            NaN  \n",
              "Thumb                                                                   NaN  \n",
              "Pattern_Changer                                                         NaN  \n",
              "Grouser_Type                                                            NaN  \n",
              "Backhoe_Mounting                                                        NaN  \n",
              "Blade_Type                                                              NaN  \n",
              "Travel_Controls                                                         NaN  \n",
              "Differential_Type                                                       NaN  \n",
              "Steering_Controls                                                       NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-89c06d21-fb60-4a06-93cb-f581c7fdc5fc\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SalesID</th>\n",
              "      <td>1139246</td>\n",
              "      <td>1139248</td>\n",
              "      <td>1139249</td>\n",
              "      <td>1139251</td>\n",
              "      <td>1139253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SalePrice</th>\n",
              "      <td>66000.0</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>38500.0</td>\n",
              "      <td>11000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MachineID</th>\n",
              "      <td>999089</td>\n",
              "      <td>117657</td>\n",
              "      <td>434808</td>\n",
              "      <td>1026470</td>\n",
              "      <td>1057373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ModelID</th>\n",
              "      <td>3157</td>\n",
              "      <td>77</td>\n",
              "      <td>7009</td>\n",
              "      <td>332</td>\n",
              "      <td>17311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datasource</th>\n",
              "      <td>121</td>\n",
              "      <td>121</td>\n",
              "      <td>121</td>\n",
              "      <td>121</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>auctioneerID</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YearMade</th>\n",
              "      <td>2004</td>\n",
              "      <td>1996</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MachineHoursCurrentMeter</th>\n",
              "      <td>68.0</td>\n",
              "      <td>4640.0</td>\n",
              "      <td>2838.0</td>\n",
              "      <td>3486.0</td>\n",
              "      <td>722.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UsageBand</th>\n",
              "      <td>Low</td>\n",
              "      <td>Low</td>\n",
              "      <td>High</td>\n",
              "      <td>High</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>saledate</th>\n",
              "      <td>11/16/2006 0:00</td>\n",
              "      <td>3/26/2004 0:00</td>\n",
              "      <td>2/26/2004 0:00</td>\n",
              "      <td>5/19/2011 0:00</td>\n",
              "      <td>7/23/2009 0:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fiModelDesc</th>\n",
              "      <td>521D</td>\n",
              "      <td>950FII</td>\n",
              "      <td>226</td>\n",
              "      <td>PC120-6E</td>\n",
              "      <td>S175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fiBaseModel</th>\n",
              "      <td>521</td>\n",
              "      <td>950</td>\n",
              "      <td>226</td>\n",
              "      <td>PC120</td>\n",
              "      <td>S175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fiSecondaryDesc</th>\n",
              "      <td>D</td>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fiModelSeries</th>\n",
              "      <td>NaN</td>\n",
              "      <td>II</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>fiModelDescriptor</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ProductSize</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Medium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Small</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fiProductClassDesc</th>\n",
              "      <td>Wheel Loader - 110.0 to 120.0 Horsepower</td>\n",
              "      <td>Wheel Loader - 150.0 to 175.0 Horsepower</td>\n",
              "      <td>Skid Steer Loader - 1351.0 to 1601.0 Lb Operat...</td>\n",
              "      <td>Hydraulic Excavator, Track - 12.0 to 14.0 Metr...</td>\n",
              "      <td>Skid Steer Loader - 1601.0 to 1751.0 Lb Operat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>North Carolina</td>\n",
              "      <td>New York</td>\n",
              "      <td>Texas</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ProductGroup</th>\n",
              "      <td>WL</td>\n",
              "      <td>WL</td>\n",
              "      <td>SSL</td>\n",
              "      <td>TEX</td>\n",
              "      <td>SSL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ProductGroupDesc</th>\n",
              "      <td>Wheel Loader</td>\n",
              "      <td>Wheel Loader</td>\n",
              "      <td>Skid Steer Loaders</td>\n",
              "      <td>Track Excavators</td>\n",
              "      <td>Skid Steer Loaders</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Drive_System</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>Enclosure</th>\n",
              "      <td>EROPS w AC</td>\n",
              "      <td>EROPS w AC</td>\n",
              "      <td>OROPS</td>\n",
              "      <td>EROPS w AC</td>\n",
              "      <td>EROPS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Forks</th>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None or Unspecified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pad_Type</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ride_Control</th>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stick</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transmission</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turbocharged</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Blade_Extension</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Blade_Width</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Enclosure_Type</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engine_Horsepower</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydraulics</th>\n",
              "      <td>2 Valve</td>\n",
              "      <td>2 Valve</td>\n",
              "      <td>Auxiliary</td>\n",
              "      <td>2 Valve</td>\n",
              "      <td>Auxiliary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pushblock</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ripper</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scarifier</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tip_Control</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tire_Size</th>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>23.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coupler</th>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>None or Unspecified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coupler_System</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None or Unspecified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grouser_Tracks</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None or Unspecified</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None or Unspecified</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydraulics_Flow</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Standard</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Standard</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Track_Type</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Undercarriage_Pad_Width</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stick_Length</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thumb</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pattern_Changer</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Grouser_Type</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Backhoe_Mounting</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Blade_Type</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Travel_Controls</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Differential_Type</th>\n",
              "      <td>Standard</td>\n",
              "      <td>Standard</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Steering_Controls</th>\n",
              "      <td>Conventional</td>\n",
              "      <td>Conventional</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89c06d21-fb60-4a06-93cb-f581c7fdc5fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-89c06d21-fb60-4a06-93cb-f581c7fdc5fc 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-89c06d21-fb60-4a06-93cb-f581c7fdc5fc');\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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "gRRMeWhuVCup",
        "outputId": "7cdd155a-cbe3-4864-8eaf-c17fa4265481"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            SalesID      SalePrice     MachineID        ModelID  \\\n",
              "count  4.126980e+05  412698.000000  4.126980e+05  412698.000000   \n",
              "mean   2.011161e+06   31215.181414  1.230061e+06    6947.201828   \n",
              "std    1.080068e+06   23141.743695  4.539533e+05    6280.824982   \n",
              "min    1.139246e+06    4750.000000  0.000000e+00      28.000000   \n",
              "25%    1.421898e+06   14500.000000  1.088593e+06    3261.000000   \n",
              "50%    1.645852e+06   24000.000000  1.284397e+06    4605.000000   \n",
              "75%    2.261012e+06   40000.000000  1.478079e+06    8899.000000   \n",
              "max    6.333349e+06  142000.000000  2.486330e+06   37198.000000   \n",
              "\n",
              "          datasource   auctioneerID       YearMade  MachineHoursCurrentMeter  \n",
              "count  412698.000000  392562.000000  412698.000000              1.475040e+05  \n",
              "mean      135.169361       6.585268    1899.049637              3.522988e+03  \n",
              "std         9.646749      17.158409     292.190243              2.716993e+04  \n",
              "min       121.000000       0.000000    1000.000000              0.000000e+00  \n",
              "25%       132.000000       1.000000    1985.000000              0.000000e+00  \n",
              "50%       132.000000       2.000000    1995.000000              0.000000e+00  \n",
              "75%       136.000000       4.000000    2001.000000              3.209000e+03  \n",
              "max       173.000000      99.000000    2014.000000              2.483300e+06  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8df6f6f9-0ba7-4c40-8205-2fe0679af7c6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>SalesID</th>\n",
              "      <th>SalePrice</th>\n",
              "      <th>MachineID</th>\n",
              "      <th>ModelID</th>\n",
              "      <th>datasource</th>\n",
              "      <th>auctioneerID</th>\n",
              "      <th>YearMade</th>\n",
              "      <th>MachineHoursCurrentMeter</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.126980e+05</td>\n",
              "      <td>412698.000000</td>\n",
              "      <td>4.126980e+05</td>\n",
              "      <td>412698.000000</td>\n",
              "      <td>412698.000000</td>\n",
              "      <td>392562.000000</td>\n",
              "      <td>412698.000000</td>\n",
              "      <td>1.475040e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.011161e+06</td>\n",
              "      <td>31215.181414</td>\n",
              "      <td>1.230061e+06</td>\n",
              "      <td>6947.201828</td>\n",
              "      <td>135.169361</td>\n",
              "      <td>6.585268</td>\n",
              "      <td>1899.049637</td>\n",
              "      <td>3.522988e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.080068e+06</td>\n",
              "      <td>23141.743695</td>\n",
              "      <td>4.539533e+05</td>\n",
              "      <td>6280.824982</td>\n",
              "      <td>9.646749</td>\n",
              "      <td>17.158409</td>\n",
              "      <td>292.190243</td>\n",
              "      <td>2.716993e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.139246e+06</td>\n",
              "      <td>4750.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.421898e+06</td>\n",
              "      <td>14500.000000</td>\n",
              "      <td>1.088593e+06</td>\n",
              "      <td>3261.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.645852e+06</td>\n",
              "      <td>24000.000000</td>\n",
              "      <td>1.284397e+06</td>\n",
              "      <td>4605.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1995.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.261012e+06</td>\n",
              "      <td>40000.000000</td>\n",
              "      <td>1.478079e+06</td>\n",
              "      <td>8899.000000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2001.000000</td>\n",
              "      <td>3.209000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.333349e+06</td>\n",
              "      <td>142000.000000</td>\n",
              "      <td>2.486330e+06</td>\n",
              "      <td>37198.000000</td>\n",
              "      <td>173.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>2014.000000</td>\n",
              "      <td>2.483300e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8df6f6f9-0ba7-4c40-8205-2fe0679af7c6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8df6f6f9-0ba7-4c40-8205-2fe0679af7c6 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-8df6f6f9-0ba7-4c40-8205-2fe0679af7c6');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgphVTtcVNby",
        "outputId": "7b01dd46-ad6a-4583-f4b5-17024f4c8159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 412698 entries, 0 to 412697\n",
            "Data columns (total 53 columns):\n",
            " #   Column                    Non-Null Count   Dtype  \n",
            "---  ------                    --------------   -----  \n",
            " 0   SalesID                   412698 non-null  int64  \n",
            " 1   SalePrice                 412698 non-null  float64\n",
            " 2   MachineID                 412698 non-null  int64  \n",
            " 3   ModelID                   412698 non-null  int64  \n",
            " 4   datasource                412698 non-null  int64  \n",
            " 5   auctioneerID              392562 non-null  float64\n",
            " 6   YearMade                  412698 non-null  int64  \n",
            " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
            " 8   UsageBand                 73670 non-null   object \n",
            " 9   saledate                  412698 non-null  object \n",
            " 10  fiModelDesc               412698 non-null  object \n",
            " 11  fiBaseModel               412698 non-null  object \n",
            " 12  fiSecondaryDesc           271971 non-null  object \n",
            " 13  fiModelSeries             58667 non-null   object \n",
            " 14  fiModelDescriptor         74816 non-null   object \n",
            " 15  ProductSize               196093 non-null  object \n",
            " 16  fiProductClassDesc        412698 non-null  object \n",
            " 17  state                     412698 non-null  object \n",
            " 18  ProductGroup              412698 non-null  object \n",
            " 19  ProductGroupDesc          412698 non-null  object \n",
            " 20  Drive_System              107087 non-null  object \n",
            " 21  Enclosure                 412364 non-null  object \n",
            " 22  Forks                     197715 non-null  object \n",
            " 23  Pad_Type                  81096 non-null   object \n",
            " 24  Ride_Control              152728 non-null  object \n",
            " 25  Stick                     81096 non-null   object \n",
            " 26  Transmission              188007 non-null  object \n",
            " 27  Turbocharged              81096 non-null   object \n",
            " 28  Blade_Extension           25983 non-null   object \n",
            " 29  Blade_Width               25983 non-null   object \n",
            " 30  Enclosure_Type            25983 non-null   object \n",
            " 31  Engine_Horsepower         25983 non-null   object \n",
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              "SalesID                       int64\n",
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              "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
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              "      <th>Travel_Controls</th>\n",
              "      <th>Differential_Type</th>\n",
              "      <th>Steering_Controls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1139246</td>\n",
              "      <td>66000.0</td>\n",
              "      <td>999089</td>\n",
              "      <td>3157</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2004</td>\n",
              "      <td>68.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>11/16/2006 0:00</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Standard</td>\n",
              "      <td>Conventional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1139248</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>117657</td>\n",
              "      <td>77</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1996</td>\n",
              "      <td>4640.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>3/26/2004 0:00</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Standard</td>\n",
              "      <td>Conventional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1139249</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>434808</td>\n",
              "      <td>7009</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2001</td>\n",
              "      <td>2838.0</td>\n",
              "      <td>High</td>\n",
              "      <td>2/26/2004 0:00</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 53 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba805ad7-206c-4d0f-adae-1b76da0242b5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba805ad7-206c-4d0f-adae-1b76da0242b5 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-ba805ad7-206c-4d0f-adae-1b76da0242b5');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"SalePrice\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VyxIL2dqXiK8",
        "outputId": "24fcda2e-418f-478c-f926-7ef8f2337437"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31215.181414012186"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For our problem, the important columns can be SalePrice and Saledate as we are predicting the price of the equipment on a future data. This is also a time series problem. "
      ],
      "metadata": {
        "id": "h8AYCykFYDJK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"saledate\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jDK4t7yYAJy",
        "outputId": "3cb54ebf-16d1-499e-e290-a423fb06601a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         11/16/2006 0:00\n",
              "1          3/26/2004 0:00\n",
              "2          2/26/2004 0:00\n",
              "3          5/19/2011 0:00\n",
              "4          7/23/2009 0:00\n",
              "               ...       \n",
              "412693      3/7/2012 0:00\n",
              "412694     1/28/2012 0:00\n",
              "412695     1/28/2012 0:00\n",
              "412696      3/7/2012 0:00\n",
              "412697     1/28/2012 0:00\n",
              "Name: saledate, Length: 412698, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see that we have date range from 2006 to 2012 here but notice that our date formating is not what Python expects so let's make changes to it. \n",
        "If you want to know how to parse date in pandas then read this: https://towardsdatascience.com/4-tricks-you-should-know-to-parse-date-columns-with-pandas-read-csv-27355bb2ad0e"
      ],
      "metadata": {
        "id": "t5N9IcMdYrBB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis \n",
        "Try to visulize the data using graphs and see if you can see any relationship between different features or attributes with our target varible. Details related to EDA is mentioned in our e-book. "
      ],
      "metadata": {
        "id": "v0uQWxnhNheB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.bar(df[\"saledate\"][:1000], df[\"SalePrice\"][:1000])\n",
        "plt.xlabel('Sale Date')\n",
        "plt.ylabel('Sale Price')\n",
        "plt.title(\"Sale Date Vs Sale Price\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "_yCDpL_qNhED",
        "outputId": "f78fd8aa-f614-4d8b-82cf-4795f0ea0d42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(df[\"saledate\"][:500], df[\"SalePrice\"][:500])     # we are just taking 500 rows as it it's not possible to clearly see things if we plot everything\n",
        "ax.set(title=\"Sale Price vs Sale Date\", xlabel=\"Date\", ylabel=\"Price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "f8RF5nY1NyFS",
        "outputId": "ce188908-79ca-4f44-e222-f98ef6e9bdcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Sale Price vs Sale Date'),\n",
              " Text(0.5, 0, 'Date'),\n",
              " Text(0, 0.5, 'Price')]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# try to check relationship between different variables\n",
        "df.hist(bins=50, figsize=(20, 15))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        },
        "id": "qQSuIBBEN8Mr",
        "outputId": "69289ba6-c25c-4fcf-bb0b-c2d873a2deae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_metrix = df.corr()\n",
        "corr_metrix[\"SalePrice\"].sort_values(ascending=False)    # you can use correlation to see if the target variable has any relation with other varibales if \"yes\" then how much"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G267NN_8OEUU",
        "outputId": "51d6ce4f-f676-41c5-e690-6d09abbb7464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalePrice                   1.000000\n",
              "YearMade                    0.154389\n",
              "SalesID                     0.023119\n",
              "datasource                  0.021708\n",
              "MachineHoursCurrentMeter    0.015660\n",
              "ModelID                    -0.038063\n",
              "auctioneerID               -0.048350\n",
              "MachineID                  -0.216841\n",
              "Name: SalePrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you can also use seaborn library to create correlation matrix \n",
        "import seaborn as sns \n",
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(df.corr(),annot=True,cmap=\"YlGnBu\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "id": "-tlyI1RjOQFI",
        "outputId": "b1425ea2-f778-4a18-f3a5-82f7be33e63e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can try to ask different questions here. How is Sale price distributed? Are there any outliers? How are graphs distributed? How they are skewed? Is there any relationship you see between the data and the variables?"
      ],
      "metadata": {
        "id": "ATHOFqD6OcUy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bullz = pd.read_csv(\"/content/drive/MyDrive/Data/Bluebook TrainAndValid.csv\", parse_dates=[\"saledate\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1nAEZgDYeKj",
        "outputId": "f897159b-e9db-4311-c808-b1daee996260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-f3b010073d19>:1: DtypeWarning: Columns (13,39,40,41) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  bullz = pd.read_csv(\"/content/drive/MyDrive/Data/Bluebook TrainAndValid.csv\", parse_dates=[\"saledate\"])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bullz.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "tbZ6NV2OazEy",
        "outputId": "1e284e3b-6e1b-45be-ac9d-c76d3471d995"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
              "0  1139246    66000.0     999089     3157         121           3.0      2004   \n",
              "1  1139248    57000.0     117657       77         121           3.0      1996   \n",
              "2  1139249    10000.0     434808     7009         121           3.0      2001   \n",
              "3  1139251    38500.0    1026470      332         121           3.0      2001   \n",
              "4  1139253    11000.0    1057373    17311         121           3.0      2007   \n",
              "\n",
              "   MachineHoursCurrentMeter UsageBand   saledate  ... Undercarriage_Pad_Width  \\\n",
              "0                      68.0       Low 2006-11-16  ...                     NaN   \n",
              "1                    4640.0       Low 2004-03-26  ...                     NaN   \n",
              "2                    2838.0      High 2004-02-26  ...                     NaN   \n",
              "3                    3486.0      High 2011-05-19  ...                     NaN   \n",
              "4                     722.0    Medium 2009-07-23  ...                     NaN   \n",
              "\n",
              "  Stick_Length Thumb Pattern_Changer Grouser_Type Backhoe_Mounting Blade_Type  \\\n",
              "0          NaN   NaN             NaN          NaN              NaN        NaN   \n",
              "1          NaN   NaN             NaN          NaN              NaN        NaN   \n",
              "2          NaN   NaN             NaN          NaN              NaN        NaN   \n",
              "3          NaN   NaN             NaN          NaN              NaN        NaN   \n",
              "4          NaN   NaN             NaN          NaN              NaN        NaN   \n",
              "\n",
              "  Travel_Controls Differential_Type Steering_Controls  \n",
              "0             NaN          Standard      Conventional  \n",
              "1             NaN          Standard      Conventional  \n",
              "2             NaN               NaN               NaN  \n",
              "3             NaN               NaN               NaN  \n",
              "4             NaN               NaN               NaN  \n",
              "\n",
              "[5 rows x 53 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a52e6df-d757-42d5-b748-e2b16ee6b9e0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SalesID</th>\n",
              "      <th>SalePrice</th>\n",
              "      <th>MachineID</th>\n",
              "      <th>ModelID</th>\n",
              "      <th>datasource</th>\n",
              "      <th>auctioneerID</th>\n",
              "      <th>YearMade</th>\n",
              "      <th>MachineHoursCurrentMeter</th>\n",
              "      <th>UsageBand</th>\n",
              "      <th>saledate</th>\n",
              "      <th>...</th>\n",
              "      <th>Undercarriage_Pad_Width</th>\n",
              "      <th>Stick_Length</th>\n",
              "      <th>Thumb</th>\n",
              "      <th>Pattern_Changer</th>\n",
              "      <th>Grouser_Type</th>\n",
              "      <th>Backhoe_Mounting</th>\n",
              "      <th>Blade_Type</th>\n",
              "      <th>Travel_Controls</th>\n",
              "      <th>Differential_Type</th>\n",
              "      <th>Steering_Controls</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1139246</td>\n",
              "      <td>66000.0</td>\n",
              "      <td>999089</td>\n",
              "      <td>3157</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2004</td>\n",
              "      <td>68.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>2006-11-16</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Standard</td>\n",
              "      <td>Conventional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1139248</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>117657</td>\n",
              "      <td>77</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1996</td>\n",
              "      <td>4640.0</td>\n",
              "      <td>Low</td>\n",
              "      <td>2004-03-26</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Standard</td>\n",
              "      <td>Conventional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1139249</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>434808</td>\n",
              "      <td>7009</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2001</td>\n",
              "      <td>2838.0</td>\n",
              "      <td>High</td>\n",
              "      <td>2004-02-26</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1139251</td>\n",
              "      <td>38500.0</td>\n",
              "      <td>1026470</td>\n",
              "      <td>332</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2001</td>\n",
              "      <td>3486.0</td>\n",
              "      <td>High</td>\n",
              "      <td>2011-05-19</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1139253</td>\n",
              "      <td>11000.0</td>\n",
              "      <td>1057373</td>\n",
              "      <td>17311</td>\n",
              "      <td>121</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2007</td>\n",
              "      <td>722.0</td>\n",
              "      <td>Medium</td>\n",
              "      <td>2009-07-23</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 53 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a52e6df-d757-42d5-b748-e2b16ee6b9e0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\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",
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              "          document.querySelector('#df-9a52e6df-d757-42d5-b748-e2b16ee6b9e0 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-9a52e6df-d757-42d5-b748-e2b16ee6b9e0');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bullz.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsU9eJEHa3xZ",
        "outputId": "09999c16-5237-4169-e66a-d7b678501405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 412698 entries, 0 to 412697\n",
            "Data columns (total 53 columns):\n",
            " #   Column                    Non-Null Count   Dtype         \n",
            "---  ------                    --------------   -----         \n",
            " 0   SalesID                   412698 non-null  int64         \n",
            " 1   SalePrice                 412698 non-null  float64       \n",
            " 2   MachineID                 412698 non-null  int64         \n",
            " 3   ModelID                   412698 non-null  int64         \n",
            " 4   datasource                412698 non-null  int64         \n",
            " 5   auctioneerID              392562 non-null  float64       \n",
            " 6   YearMade                  412698 non-null  int64         \n",
            " 7   MachineHoursCurrentMeter  147504 non-null  float64       \n",
            " 8   UsageBand                 73670 non-null   object        \n",
            " 9   saledate                  412698 non-null  datetime64[ns]\n",
            " 10  fiModelDesc               412698 non-null  object        \n",
            " 11  fiBaseModel               412698 non-null  object        \n",
            " 12  fiSecondaryDesc           271971 non-null  object        \n",
            " 13  fiModelSeries             58667 non-null   object        \n",
            " 14  fiModelDescriptor         74816 non-null   object        \n",
            " 15  ProductSize               196093 non-null  object        \n",
            " 16  fiProductClassDesc        412698 non-null  object        \n",
            " 17  state                     412698 non-null  object        \n",
            " 18  ProductGroup              412698 non-null  object        \n",
            " 19  ProductGroupDesc          412698 non-null  object        \n",
            " 20  Drive_System              107087 non-null  object        \n",
            " 21  Enclosure                 412364 non-null  object        \n",
            " 22  Forks                     197715 non-null  object        \n",
            " 23  Pad_Type                  81096 non-null   object        \n",
            " 24  Ride_Control              152728 non-null  object        \n",
            " 25  Stick                     81096 non-null   object        \n",
            " 26  Transmission              188007 non-null  object        \n",
            " 27  Turbocharged              81096 non-null   object        \n",
            " 28  Blade_Extension           25983 non-null   object        \n",
            " 29  Blade_Width               25983 non-null   object        \n",
            " 30  Enclosure_Type            25983 non-null   object        \n",
            " 31  Engine_Horsepower         25983 non-null   object        \n",
            " 32  Hydraulics                330133 non-null  object        \n",
            " 33  Pushblock                 25983 non-null   object        \n",
            " 34  Ripper                    106945 non-null  object        \n",
            " 35  Scarifier                 25994 non-null   object        \n",
            " 36  Tip_Control               25983 non-null   object        \n",
            " 37  Tire_Size                 97638 non-null   object        \n",
            " 38  Coupler                   220679 non-null  object        \n",
            " 39  Coupler_System            44974 non-null   object        \n",
            " 40  Grouser_Tracks            44875 non-null   object        \n",
            " 41  Hydraulics_Flow           44875 non-null   object        \n",
            " 42  Track_Type                102193 non-null  object        \n",
            " 43  Undercarriage_Pad_Width   102916 non-null  object        \n",
            " 44  Stick_Length              102261 non-null  object        \n",
            " 45  Thumb                     102332 non-null  object        \n",
            " 46  Pattern_Changer           102261 non-null  object        \n",
            " 47  Grouser_Type              102193 non-null  object        \n",
            " 48  Backhoe_Mounting          80712 non-null   object        \n",
            " 49  Blade_Type                81875 non-null   object        \n",
            " 50  Travel_Controls           81877 non-null   object        \n",
            " 51  Differential_Type         71564 non-null   object        \n",
            " 52  Steering_Controls         71522 non-null   object        \n",
            "dtypes: datetime64[ns](1), float64(3), int64(5), object(44)\n",
            "memory usage: 166.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bullz[\"saledate\"]   # see our date formatting has been changed "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0Zxr96Ya_pp",
        "outputId": "74f4e6e4-ee66-4b55-b57c-14c8bc8b7e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        2006-11-16\n",
              "1        2004-03-26\n",
              "2        2004-02-26\n",
              "3        2011-05-19\n",
              "4        2009-07-23\n",
              "            ...    \n",
              "412693   2012-03-07\n",
              "412694   2012-01-28\n",
              "412695   2012-01-28\n",
              "412696   2012-03-07\n",
              "412697   2012-01-28\n",
              "Name: saledate, Length: 412698, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort DataFrame in date order\n",
        "bullz.sort_values(by=[\"saledate\"], inplace=True, ascending=True)\n",
        "bullz.saledate.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YZfB-uKL72M",
        "outputId": "d89eb192-33d9-4a19-9a93-33bc02fc1994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "205615   1989-01-17\n",
              "274835   1989-01-31\n",
              "141296   1989-01-31\n",
              "212552   1989-01-31\n",
              "62755    1989-01-31\n",
              "54653    1989-01-31\n",
              "81383    1989-01-31\n",
              "204924   1989-01-31\n",
              "135376   1989-01-31\n",
              "113390   1989-01-31\n",
              "113394   1989-01-31\n",
              "116419   1989-01-31\n",
              "32138    1989-01-31\n",
              "127610   1989-01-31\n",
              "76171    1989-01-31\n",
              "127000   1989-01-31\n",
              "128130   1989-01-31\n",
              "127626   1989-01-31\n",
              "55455    1989-01-31\n",
              "55454    1989-01-31\n",
              "Name: saledate, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we will be adding new columns to work with date timne \n",
        "bullz[\"saleYear\"] = bullz.saledate.dt.year\n",
        "bullz[\"saleMonth\"] = bullz.saledate.dt.month\n",
        "bullz[\"saleDay\"] = bullz.saledate.dt.day\n",
        "bullz[\"saleDayofweek\"] = bullz.saledate.dt.dayofweek\n",
        "bullz[\"saleDayofyear\"] = bullz.saledate.dt.dayofyear\n",
        "\n",
        "# removing original saledate column \n",
        "bullz.drop(\"saledate\", axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "g257f5U7MHnc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# let's check for missing values in our data \n",
        "bullz.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AtupsW03fgVg",
        "outputId": "d5be7741-e36e-4f94-cfe8-50aacc3f9419"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalesID                          0\n",
              "SalePrice                        0\n",
              "MachineID                        0\n",
              "ModelID                          0\n",
              "datasource                       0\n",
              "auctioneerID                 20136\n",
              "YearMade                         0\n",
              "MachineHoursCurrentMeter    265194\n",
              "UsageBand                   339028\n",
              "fiModelDesc                      0\n",
              "fiBaseModel                      0\n",
              "fiSecondaryDesc             140727\n",
              "fiModelSeries               354031\n",
              "fiModelDescriptor           337882\n",
              "ProductSize                 216605\n",
              "fiProductClassDesc               0\n",
              "state                            0\n",
              "ProductGroup                     0\n",
              "ProductGroupDesc                 0\n",
              "Drive_System                305611\n",
              "Enclosure                      334\n",
              "Forks                       214983\n",
              "Pad_Type                    331602\n",
              "Ride_Control                259970\n",
              "Stick                       331602\n",
              "Transmission                224691\n",
              "Turbocharged                331602\n",
              "Blade_Extension             386715\n",
              "Blade_Width                 386715\n",
              "Enclosure_Type              386715\n",
              "Engine_Horsepower           386715\n",
              "Hydraulics                   82565\n",
              "Pushblock                   386715\n",
              "Ripper                      305753\n",
              "Scarifier                   386704\n",
              "Tip_Control                 386715\n",
              "Tire_Size                   315060\n",
              "Coupler                     192019\n",
              "Coupler_System              367724\n",
              "Grouser_Tracks              367823\n",
              "Hydraulics_Flow             367823\n",
              "Track_Type                  310505\n",
              "Undercarriage_Pad_Width     309782\n",
              "Stick_Length                310437\n",
              "Thumb                       310366\n",
              "Pattern_Changer             310437\n",
              "Grouser_Type                310505\n",
              "Backhoe_Mounting            331986\n",
              "Blade_Type                  330823\n",
              "Travel_Controls             330821\n",
              "Differential_Type           341134\n",
              "Steering_Controls           341176\n",
              "saleYear                         0\n",
              "saleMonth                        0\n",
              "saleDay                          0\n",
              "saleDayofweek                    0\n",
              "saleDayofyear                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see we do have a lot of missing values 😶. Addiotnally, notice most of the attributes are non numerical. We can first convert them into category dtype then it will easier to convert them into numbers. "
      ],
      "metadata": {
        "id": "I0_g3CFhf3ry"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#converting string objects to category\n",
        "for label, cols in bullz.items():\n",
        "  if pd.api.types.is_string_dtype(cols):\n",
        "    bullz[label] = cols.astype(\"category\").cat.as_ordered()"
      ],
      "metadata": {
        "id": "11JjMVjEs4fM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "in case you want to know what cat.as_ordered is doing: https://stackoverflow.com/questions/55232786/what-does-pd-series-cat-as-ordered-do-in-pandas"
      ],
      "metadata": {
        "id": "8F4NqgP6uG8C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let's check \n",
        "bullz.info()   # see our object dtype is gone and now we have categories dtype "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7CJRLJzt8Sl",
        "outputId": "d6bb4df5-6bc6-48e6-fa5b-644115a16b17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 412698 entries, 205615 to 409203\n",
            "Data columns (total 57 columns):\n",
            " #   Column                    Non-Null Count   Dtype   \n",
            "---  ------                    --------------   -----   \n",
            " 0   SalesID                   412698 non-null  int64   \n",
            " 1   SalePrice                 412698 non-null  float64 \n",
            " 2   MachineID                 412698 non-null  int64   \n",
            " 3   ModelID                   412698 non-null  int64   \n",
            " 4   datasource                412698 non-null  int64   \n",
            " 5   auctioneerID              392562 non-null  float64 \n",
            " 6   YearMade                  412698 non-null  int64   \n",
            " 7   MachineHoursCurrentMeter  147504 non-null  float64 \n",
            " 8   UsageBand                 73670 non-null   category\n",
            " 9   fiModelDesc               412698 non-null  category\n",
            " 10  fiBaseModel               412698 non-null  category\n",
            " 11  fiSecondaryDesc           271971 non-null  category\n",
            " 12  fiModelSeries             58667 non-null   category\n",
            " 13  fiModelDescriptor         74816 non-null   category\n",
            " 14  ProductSize               196093 non-null  category\n",
            " 15  fiProductClassDesc        412698 non-null  category\n",
            " 16  state                     412698 non-null  category\n",
            " 17  ProductGroup              412698 non-null  category\n",
            " 18  ProductGroupDesc          412698 non-null  category\n",
            " 19  Drive_System              107087 non-null  category\n",
            " 20  Enclosure                 412364 non-null  category\n",
            " 21  Forks                     197715 non-null  category\n",
            " 22  Pad_Type                  81096 non-null   category\n",
            " 23  Ride_Control              152728 non-null  category\n",
            " 24  Stick                     81096 non-null   category\n",
            " 25  Transmission              188007 non-null  category\n",
            " 26  Turbocharged              81096 non-null   category\n",
            " 27  Blade_Extension           25983 non-null   category\n",
            " 28  Blade_Width               25983 non-null   category\n",
            " 29  Enclosure_Type            25983 non-null   category\n",
            " 30  Engine_Horsepower         25983 non-null   category\n",
            " 31  Hydraulics                330133 non-null  category\n",
            " 32  Pushblock                 25983 non-null   category\n",
            " 33  Ripper                    106945 non-null  category\n",
            " 34  Scarifier                 25994 non-null   category\n",
            " 35  Tip_Control               25983 non-null   category\n",
            " 36  Tire_Size                 97638 non-null   category\n",
            " 37  Coupler                   220679 non-null  category\n",
            " 38  Coupler_System            44974 non-null   category\n",
            " 39  Grouser_Tracks            44875 non-null   category\n",
            " 40  Hydraulics_Flow           44875 non-null   category\n",
            " 41  Track_Type                102193 non-null  category\n",
            " 42  Undercarriage_Pad_Width   102916 non-null  category\n",
            " 43  Stick_Length              102261 non-null  category\n",
            " 44  Thumb                     102332 non-null  category\n",
            " 45  Pattern_Changer           102261 non-null  category\n",
            " 46  Grouser_Type              102193 non-null  category\n",
            " 47  Backhoe_Mounting          80712 non-null   category\n",
            " 48  Blade_Type                81875 non-null   category\n",
            " 49  Travel_Controls           81877 non-null   category\n",
            " 50  Differential_Type         71564 non-null   category\n",
            " 51  Steering_Controls         71522 non-null   category\n",
            " 52  saleYear                  412698 non-null  int64   \n",
            " 53  saleMonth                 412698 non-null  int64   \n",
            " 54  saleDay                   412698 non-null  int64   \n",
            " 55  saleDayofweek             412698 non-null  int64   \n",
            " 56  saleDayofyear             412698 non-null  int64   \n",
            "dtypes: category(44), float64(3), int64(10)\n",
            "memory usage: 63.6 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing Data For Machine Learning Models \n",
        "Converting non-numerics to number as ml models only accept numbers"
      ],
      "metadata": {
        "id": "uY5sRq26jbL_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for numeric columns \n",
        "for label, content in bullz.items():\n",
        "    if pd.api.types.is_numeric_dtype(content):\n",
        "        print(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWKk8IaVjRGw",
        "outputId": "12ff70cb-8afd-4e10-b7be-83cf56fa4652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SalesID\n",
            "SalePrice\n",
            "MachineID\n",
            "ModelID\n",
            "datasource\n",
            "auctioneerID\n",
            "YearMade\n",
            "MachineHoursCurrentMeter\n",
            "saleYear\n",
            "saleMonth\n",
            "saleDay\n",
            "saleDayofweek\n",
            "saleDayofyear\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for missing values in numeric columns \n",
        "for label, content in bullz.items():\n",
        "  if pd.api.types.is_numeric_dtype(content):\n",
        "    if pd.isnull(content).sum():\n",
        "      print(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAR2Y81Q6ZJD",
        "outputId": "25c07544-ddf2-4dc9-a89c-188da79493dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auctioneerID\n",
            "MachineHoursCurrentMeter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now let's fill missing values in those two columns \n",
        "for label, content in bullz.items():\n",
        "  if pd.api.types.is_numeric_dtype(content):\n",
        "    if pd.isnull(content).sum():\n",
        "      bullz[label+\"_is_missing\"] = pd.isnull(content) # adding a new column that tells if the data was missing or not\n",
        "      bullz[label] = content.fillna(content.median()) # replacing the missing values using median as median is robust measure of location unaffected by outliers"
      ],
      "metadata": {
        "id": "T4PsPv4S6yPL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check again for the missing values \n",
        "for label, content in bullz.items():\n",
        "  if pd.api.types.is_numeric_dtype(content):\n",
        "    if pd.isnull(content).sum():\n",
        "      print(label)  # there is not output so no missing values"
      ],
      "metadata": {
        "id": "hu6nYFG676ck"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bullz.isnull().sum()   # now only categorical values are left to filled. "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGVATP0Y8dIN",
        "outputId": "1d3a8db0-e033-4e3b-8d8c-54649add2956"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalesID                                     0\n",
              "SalePrice                                   0\n",
              "MachineID                                   0\n",
              "ModelID                                     0\n",
              "datasource                                  0\n",
              "auctioneerID                                0\n",
              "YearMade                                    0\n",
              "MachineHoursCurrentMeter                    0\n",
              "UsageBand                              339028\n",
              "fiModelDesc                                 0\n",
              "fiBaseModel                                 0\n",
              "fiSecondaryDesc                        140727\n",
              "fiModelSeries                          354031\n",
              "fiModelDescriptor                      337882\n",
              "ProductSize                            216605\n",
              "fiProductClassDesc                          0\n",
              "state                                       0\n",
              "ProductGroup                                0\n",
              "ProductGroupDesc                            0\n",
              "Drive_System                           305611\n",
              "Enclosure                                 334\n",
              "Forks                                  214983\n",
              "Pad_Type                               331602\n",
              "Ride_Control                           259970\n",
              "Stick                                  331602\n",
              "Transmission                           224691\n",
              "Turbocharged                           331602\n",
              "Blade_Extension                        386715\n",
              "Blade_Width                            386715\n",
              "Enclosure_Type                         386715\n",
              "Engine_Horsepower                      386715\n",
              "Hydraulics                              82565\n",
              "Pushblock                              386715\n",
              "Ripper                                 305753\n",
              "Scarifier                              386704\n",
              "Tip_Control                            386715\n",
              "Tire_Size                              315060\n",
              "Coupler                                192019\n",
              "Coupler_System                         367724\n",
              "Grouser_Tracks                         367823\n",
              "Hydraulics_Flow                        367823\n",
              "Track_Type                             310505\n",
              "Undercarriage_Pad_Width                309782\n",
              "Stick_Length                           310437\n",
              "Thumb                                  310366\n",
              "Pattern_Changer                        310437\n",
              "Grouser_Type                           310505\n",
              "Backhoe_Mounting                       331986\n",
              "Blade_Type                             330823\n",
              "Travel_Controls                        330821\n",
              "Differential_Type                      341134\n",
              "Steering_Controls                      341176\n",
              "saleYear                                    0\n",
              "saleMonth                                   0\n",
              "saleDay                                     0\n",
              "saleDayofweek                               0\n",
              "saleDayofyear                               0\n",
              "auctioneerID_is_missing                     0\n",
              "MachineHoursCurrentMeter_is_missing         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check columns with missing values that are categorical \n",
        "for label, content in bullz.items():\n",
        "  if not pd.api.types.is_numeric_dtype(content):    # see we have used 'not' to check non-numeric missing values \n",
        "    if pd.isnull(content).sum():\n",
        "      print(label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Y64coFJ8q7j",
        "outputId": "48d2fa12-2460-49b4-f0b2-0e5cd508ddcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UsageBand\n",
            "fiSecondaryDesc\n",
            "fiModelSeries\n",
            "fiModelDescriptor\n",
            "ProductSize\n",
            "Drive_System\n",
            "Enclosure\n",
            "Forks\n",
            "Pad_Type\n",
            "Ride_Control\n",
            "Stick\n",
            "Transmission\n",
            "Turbocharged\n",
            "Blade_Extension\n",
            "Blade_Width\n",
            "Enclosure_Type\n",
            "Engine_Horsepower\n",
            "Hydraulics\n",
            "Pushblock\n",
            "Ripper\n",
            "Scarifier\n",
            "Tip_Control\n",
            "Tire_Size\n",
            "Coupler\n",
            "Coupler_System\n",
            "Grouser_Tracks\n",
            "Hydraulics_Flow\n",
            "Track_Type\n",
            "Undercarriage_Pad_Width\n",
            "Stick_Length\n",
            "Thumb\n",
            "Pattern_Changer\n",
            "Grouser_Type\n",
            "Backhoe_Mounting\n",
            "Blade_Type\n",
            "Travel_Controls\n",
            "Differential_Type\n",
            "Steering_Controls\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert categorical variables to numbers \n",
        "for label, content in bullz.items():\n",
        "  if not pd.api.types.is_numeric_dtype(content):\n",
        "    bullz[label+\"_is_missing\"] = pd.isnull(content)   # creating a boolean column \n",
        "    bullz[label] = pd.Categorical(content).codes+1    # converting and replacing missing values "
      ],
      "metadata": {
        "id": "je1Vlktj9czL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bullz.isna().sum()   # we don't have any missing values now"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKODLuvU_mCy",
        "outputId": "24f2eec7-8560-49ca-d3dc-96cacd5e65fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalesID                         0\n",
              "SalePrice                       0\n",
              "MachineID                       0\n",
              "ModelID                         0\n",
              "datasource                      0\n",
              "                               ..\n",
              "Backhoe_Mounting_is_missing     0\n",
              "Blade_Type_is_missing           0\n",
              "Travel_Controls_is_missing      0\n",
              "Differential_Type_is_missing    0\n",
              "Steering_Controls_is_missing    0\n",
              "Length: 103, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bullz.dtypes    # check our categorical values are numeric now"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCLAdKnY_vJ5",
        "outputId": "3b69c1e7-683c-4e9c-9a0f-23266ebf2d02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalesID                           int64\n",
              "SalePrice                       float64\n",
              "MachineID                         int64\n",
              "ModelID                           int64\n",
              "datasource                        int64\n",
              "                                 ...   \n",
              "Backhoe_Mounting_is_missing        bool\n",
              "Blade_Type_is_missing              bool\n",
              "Travel_Controls_is_missing         bool\n",
              "Differential_Type_is_missing       bool\n",
              "Steering_Controls_is_missing       bool\n",
              "Length: 103, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bullz.tail().T  # notice the missing boolean colunns at the end"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "fTEChEux_3Lo",
        "outputId": "43251dd0-d791-40a6-b821-f71a62c436d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               410879   412476   411927   407124   409203\n",
              "SalesID                       6302984  6324811  6313029  6266251  6283635\n",
              "SalePrice                     16000.0   6000.0  16000.0  55000.0  34000.0\n",
              "MachineID                     1915521  1919104  1918416   509560  1869284\n",
              "ModelID                          5266    19330    17244     3357     4701\n",
              "datasource                        149      149      149      149      149\n",
              "...                               ...      ...      ...      ...      ...\n",
              "Backhoe_Mounting_is_missing     False     True     True     True     True\n",
              "Blade_Type_is_missing           False     True     True     True     True\n",
              "Travel_Controls_is_missing      False     True     True     True     True\n",
              "Differential_Type_is_missing     True     True     True     True    False\n",
              "Steering_Controls_is_missing     True     True     True     True    False\n",
              "\n",
              "[103 rows x 5 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>410879</th>\n",
              "      <th>412476</th>\n",
              "      <th>411927</th>\n",
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              "      <th>409203</th>\n",
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              "    <tr>\n",
              "      <th>SalesID</th>\n",
              "      <td>6302984</td>\n",
              "      <td>6324811</td>\n",
              "      <td>6313029</td>\n",
              "      <td>6266251</td>\n",
              "      <td>6283635</td>\n",
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              "    <tr>\n",
              "      <th>SalePrice</th>\n",
              "      <td>16000.0</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>34000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MachineID</th>\n",
              "      <td>1915521</td>\n",
              "      <td>1919104</td>\n",
              "      <td>1918416</td>\n",
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              "      <td>5266</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Backhoe_Mounting_is_missing</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Blade_Type_is_missing</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Travel_Controls_is_missing</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Differential_Type_is_missing</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Steering_Controls_is_missing</th>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>103 rows × 5 columns</p>\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's save our data so that we can start working on the pre-processed data without having to start working on everything once again. \n",
        "bullz.to_csv(\"/content/drive/MyDrive/Data/bullz.csv\")"
      ],
      "metadata": {
        "id": "jUc6PjPcJ-zJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# re-initiate our pre-processed data \n",
        "bullz_df_1 = pd.read_csv(\"/content/drive/MyDrive/Data/bullz.csv\")\n",
        "bullz_df_1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PekngH7lBtBM",
        "outputId": "246b7cfa-db14-469d-b097-2f4b6cebee9e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 412698 entries, 0 to 412697\n",
            "Columns: 104 entries, Unnamed: 0 to Steering_Controls_is_missing\n",
            "dtypes: bool(46), float64(3), int64(55)\n",
            "memory usage: 200.7 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bullz_df_1.head()  # notice the unnamed column. It is created automatically so we need to remove it before training the model "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "OCBNzZ4dnj4G",
        "outputId": "e7338dca-3cd0-4876-fe73-78b9c607b53d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  SalesID  SalePrice  MachineID  ModelID  datasource  \\\n",
              "0      205615  1646770     9500.0    1126363     8434         132   \n",
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          "metadata": {},
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    {
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      "source": [
        "bullz_df = bullz_df_1.drop(\"Unnamed: 0\", axis = 1)\n",
        "bullz_df.head()                                      # now we have removed the unnamed column"
      ],
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        "id": "SyJNhx2xnwka",
        "outputId": "d69a781d-5fc5-41d1-cd45-2f5d537ab9bc"
      },
      "execution_count": 6,
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bullz_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnuL6aRWCVup",
        "outputId": "7d963af8-6e1c-4fea-9975-021ccada702c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SalesID                         0\n",
              "SalePrice                       0\n",
              "MachineID                       0\n",
              "ModelID                         0\n",
              "datasource                      0\n",
              "                               ..\n",
              "Backhoe_Mounting_is_missing     0\n",
              "Blade_Type_is_missing           0\n",
              "Travel_Controls_is_missing      0\n",
              "Differential_Type_is_missing    0\n",
              "Steering_Controls_is_missing    0\n",
              "Length: 103, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training And Validation Set\n",
        "According to the requirement of this project we have to divide the training and set based on dates as we are predicting the future price of the bulldozers"
      ],
      "metadata": {
        "id": "bSNC2ECxPXgS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "According to Kaggle: \n",
        "1. Training Set:data through the end of 2011.\n",
        "2. Validation Set:data from January 1, 2012 - April 30, 2012. "
      ],
      "metadata": {
        "id": "9QmmAn_bQYJj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bullz_df.saleYear.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xs2Y0MkrPQjS",
        "outputId": "4b4ce2b6-19dd-4303-d102-521ce89cb3aa"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2009    43849\n",
              "2008    39767\n",
              "2011    35197\n",
              "2010    33390\n",
              "2007    32208\n",
              "2006    21685\n",
              "2005    20463\n",
              "2004    19879\n",
              "2001    17594\n",
              "2000    17415\n",
              "2002    17246\n",
              "2003    15254\n",
              "1998    13046\n",
              "1999    12793\n",
              "2012    11573\n",
              "1997     9785\n",
              "1996     8829\n",
              "1995     8530\n",
              "1994     7929\n",
              "1993     6303\n",
              "1992     5519\n",
              "1991     5109\n",
              "1989     4806\n",
              "1990     4529\n",
              "Name: saleYear, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# spliting data into training and valiation set \n",
        "training_set = bullz_df[bullz_df.saleYear != 2012]  # as we have to take data until 2011\n",
        "validation_set = bullz_df[bullz_df.saleYear == 2012]"
      ],
      "metadata": {
        "id": "PCg1SzT9Q8JK"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(training_set)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xgT3qw8jRyat",
        "outputId": "cd94e337-b615-48ad-a5fc-a0a6f12bc0b1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "401125"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
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    {
      "cell_type": "code",
      "source": [
        "len(validation_set)"
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      "metadata": {
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        },
        "id": "ltGWoqI8R09J",
        "outputId": "df941f6e-7e7d-4d5f-e1a4-53936c4aadd1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11573"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# once we have the train and val set we can now seperate them into dependent and independent variable for training that is X and Y\n",
        "\n",
        "# for training set \n",
        "X_train = training_set.drop(\"SalePrice\", axis = 1)  # removing SalePrice as it is our dependednt variable\n",
        "y_train = training_set.SalePrice                    # y has dependent variable\n",
        "\n",
        "# same for validation set \n",
        "\n",
        "X_val = validation_set.drop(\"SalePrice\", axis = 1)\n",
        "y_val = validation_set.SalePrice "
      ],
      "metadata": {
        "id": "fG5UNRNCR204"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now let's check their shapes so that we don't have any issue when calculation is performed whil training our model \n",
        "X_train.shape, y_train.shape, X_val.shape, y_val.shape"
      ],
      "metadata": {
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      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((401125, 102), (401125,), (11573, 102), (11573,))"
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          "execution_count": 14
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      "cell_type": "code",
      "source": [
        "X_train.head()"
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        "outputId": "10b92225-da13-4094-d801-e0c365663588"
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      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
              "0  1646770    1126363     8434         132          18.0      1974   \n",
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              "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
              "0                       0.0          0         4593         1744  ...   \n",
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              "4                       0.0          0         2119          683  ...   \n",
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              "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
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              "0                       False                          True   \n",
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              "3                        True                         False   \n",
              "4                       False                          True   \n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08aa332e-28ee-4977-9fa1-8185c9e7647c 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-08aa332e-28ee-4977-9fa1-8185c9e7647c');\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": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For our project, we have chosen RMSLE as the evaluation metric but the library we are going to use does not have RMSLE inbuilt so we can create a function of our own. \n",
        "\n",
        "What is RMSLE: https://hrngok.github.io/posts/metrics/ \n",
        "- RMSLE is used when y has long tail distribution, or we are interested in the ratio of true value and predicted value. \n",
        "- 0≤R²≤1 (commonly but some worst cases you will see negative values) and higher R² is better, which means the prediction is closer to the data."
      ],
      "metadata": {
        "id": "36beWA06UlEx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation Function \n",
        "\n",
        "from sklearn.metrics import mean_squared_log_error, mean_absolute_error     # notice we are using SK Learn Library for the modeling purpose \n",
        "def rmsle(y_test, y_preds):\n",
        "  return np.sqrt(mean_squared_log_error(y_test, y_preds))"
      ],
      "metadata": {
        "id": "hJ7Bc0tjTUEi"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a function to show model performance \n",
        "\n",
        "def model_scores(model):\n",
        "  train_predictions = model.predict(X_train)\n",
        "  val_predictions = model.predict(X_val)\n",
        "  scores = {\"Training Set MAE\": mean_absolute_error(y_train, train_predictions),\n",
        "            \"Validation Set MAE\": mean_absolute_error(y_val, val_predictions), \n",
        "            \"Training RMSLE\":  rmsle(y_train, train_predictions),\n",
        "            \"Validation RMSLE\": rmsle(y_val, val_predictions),\n",
        "            \"Training R2 Score\": model.score(X_train, y_train),\n",
        "            \"Validation R2 Score\": model.score(X_val, y_val)\n",
        "  }\n",
        "  return scores"
      ],
      "metadata": {
        "id": "14niDJDfVpU0"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check the number of samples in training set\n",
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1l3LpxTYJ_p",
        "outputId": "97bf45ee-16c6-4072-c8e3-791852c35ed0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "401125"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For our machine learning model, we will be using Random Forest Regressor. The model explaining can be found in our video or the e-book"
      ],
      "metadata": {
        "id": "Mk0Gbth-Y3Vo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# for training we will not be using the entire data but a small sample of it to see how our model performs \n",
        "from sklearn.ensemble import RandomForestRegressor    # import the model from sklearn library \n",
        "\n",
        "# name our model and fit the data (10K samples for now)\n",
        "model = RandomForestRegressor(n_jobs=-1,\n",
        "                              max_samples=10000)\n"
      ],
      "metadata": {
        "id": "w9Mhx1jgYyL4"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use time magic function to check the timing \n",
        "%%time \n",
        "model.fit(X_train, y_train)   # fit the model and train "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "gvfEoWfaZxV_",
        "outputId": "daa2fc57-d0eb-4628-8a8e-f2569c4ffaad"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 42.4 s, sys: 200 ms, total: 42.6 s\n",
            "Wall time: 30.8 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_samples=10000, n_jobs=-1)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_scores(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3n-oNwLZ3Sg",
        "outputId": "37c84c35-5e32-40c9-da13-dd37c80741b2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Training Set MAE': 5566.40332966033,\n",
              " 'Validation Set MAE': 7162.18661971831,\n",
              " 'Training RMSLE': 0.2577965846951573,\n",
              " 'Validation RMSLE': 0.29351702797256146,\n",
              " 'Training R2 Score': 0.8603341278085728,\n",
              " 'Validation R2 Score': 0.8333591868521966}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have some scores. Let's hypertune some parameters and see what we can achieve. \n",
        "\n",
        "## Hyperparameter Tuning With RandomizedSearchCV\n",
        "\n",
        "Don't worry we will learn about RandomizedSearchCv in our book but for your refrence visit this: https://machinelearningmastery.com/hyperparameter-optimization-with-random-search-and-grid-search/ "
      ],
      "metadata": {
        "id": "ZlzdKP8XaQe7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "# these are different hyperparameters that we can modify and see if we can improve the model performance or not \n",
        "grid_rmf = {\"n_estimators\": np.arange(10, 100, 10),\n",
        "            \"max_depth\": [None, 3, 5, 10],\n",
        "            \"min_samples_split\": np.arange(2, 20, 2),\n",
        "            \"min_samples_leaf\": np.arange(1, 20, 2), \n",
        "            \"max_features\": [0.5, 1, 'sqrt', 'auto'],\n",
        "            \"max_samples\": [10000]\n",
        "            }\n",
        "\n",
        "rscv_model = RandomizedSearchCV(RandomForestRegressor(),\n",
        "                                param_distributions = grid_rmf,\n",
        "                                n_iter = 20,\n",
        "                                cv = 5,\n",
        "                                verbose = True)\n",
        "\n",
        "rscv_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6roG2hNWanAw",
        "outputId": "da66d5b9-78c9-4dd3-a1d7-0193b20340c3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
              "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
              "                                        'max_features': [0.5, 1, 'sqrt',\n",
              "                                                         'auto'],\n",
              "                                        'max_samples': [10000],\n",
              "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
              "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
              "                                        'n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
              "                   verbose=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
              "                                        &#x27;max_features&#x27;: [0.5, 1, &#x27;sqrt&#x27;,\n",
              "                                                         &#x27;auto&#x27;],\n",
              "                                        &#x27;max_samples&#x27;: [10000],\n",
              "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
              "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
              "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
              "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=20,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
              "                                        &#x27;max_features&#x27;: [0.5, 1, &#x27;sqrt&#x27;,\n",
              "                                                         &#x27;auto&#x27;],\n",
              "                                        &#x27;max_samples&#x27;: [10000],\n",
              "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
              "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
              "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
              "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# best parameters from the RandomizedSearch \n",
        "rscv_model.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usL4-PBmc2vw",
        "outputId": "a7dbe485-4239-43b1-a0e5-5430dc311b5c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 20,\n",
              " 'min_samples_split': 8,\n",
              " 'min_samples_leaf': 1,\n",
              " 'max_samples': 10000,\n",
              " 'max_features': 'auto',\n",
              " 'max_depth': None}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we the have best parameters here. We can use these to train our model that is kind of ideal and best fit for the project. So let's do that!"
      ],
      "metadata": {
        "id": "otyXlS2DebDE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the model score \n",
        "model_scores(rscv_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5s6fiXjtdBnX",
        "outputId": "c54d5c4e-33c9-4b2a-9b4c-52c62426b588"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Training Set MAE': 5790.377747679841,\n",
              " 'Validation Set MAE': 7385.560428102013,\n",
              " 'Training RMSLE': 0.26633642154957893,\n",
              " 'Validation RMSLE': 0.30023609911887383,\n",
              " 'Training R2 Score': 0.848940987015854,\n",
              " 'Validation R2 Score': 0.8236271852105099}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time \n",
        "# now let's create our ideal model by using the best parameters from previous model training\n",
        "selected_model = RandomForestRegressor(n_estimators = 20,\n",
        "                                       min_samples_split = 8,\n",
        "                                       min_samples_leaf = 1,\n",
        "                                       max_samples = None,     #notice here we are going with entire sample\n",
        "                                       max_features = 'auto',\n",
        "                                       n_jobs=-1\n",
        "                                       )\n",
        "\n",
        "selected_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "id": "cFcZSGrieaBf",
        "outputId": "e32f3e0e-5e36-43c1-f61f-c76f6d438213"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
            "  warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3min 53s, sys: 470 ms, total: 3min 53s\n",
            "Wall time: 2min 21s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_features='auto', min_samples_split=8, n_estimators=20,\n",
              "                      n_jobs=-1)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=&#x27;auto&#x27;, min_samples_split=8, n_estimators=20,\n",
              "                      n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=&#x27;auto&#x27;, min_samples_split=8, n_estimators=20,\n",
              "                      n_jobs=-1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_scores(selected_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TyhpDLCvfaIq",
        "outputId": "59fa968f-8192-4ae3-b501-d35aba8a2ed1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Training Set MAE': 2331.9213092060636,\n",
              " 'Validation Set MAE': 6253.556143624393,\n",
              " 'Training RMSLE': 0.11969781967966082,\n",
              " 'Validation RMSLE': 0.25859997890101594,\n",
              " 'Training R2 Score': 0.9724742501071193,\n",
              " 'Validation R2 Score': 0.8672400774962052}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can further modify and experiment with the model by hypertuning its parameters and see what changes for the sake this project, We are going with the selected model as our RMSLE and R^2 score results have improved significantly after choosing to train the entire samples using the best params. "
      ],
      "metadata": {
        "id": "ILe4GhLvgU--"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have created and selected our model. Let's now check its performance on real world unknown data that is Test Dataset and see its performance. \n",
        "\n",
        "Please note we will have to create the same data preprocessing for the test data as the data on which we trained our model was modified. So, to do that we can write a preprocessing function and all the new data will go through this process: "
      ],
      "metadata": {
        "id": "n6bc4siKg66i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let's first save the model \n",
        "import pickle\n",
        "filename = 'finalized_model.sav'\n",
        "pickle.dump(selected_model, open(\"/content/drive/MyDrive/Colab Notebooks/model_1\", 'wb'))"
      ],
      "metadata": {
        "id": "tClJeg-cxcNO"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a preprocessing function to make our test data set same as training set\n",
        "\n",
        "def preprocessing_data(df):\n",
        "   # Adding datetime parameters for saledate\n",
        "    df[\"saleYear\"] = df.saledate.dt.year\n",
        "    df[\"saleMonth\"] = df.saledate.dt.month\n",
        "    df[\"saleDay\"] = df.saledate.dt.day\n",
        "    df[\"saleDayofweek\"] = df.saledate.dt.dayofweek\n",
        "    df[\"saleDayofyear\"] = df.saledate.dt.dayofyear\n",
        "\n",
        "    # removing original saledate column\n",
        "    df.drop(\"saledate\", axis=1, inplace=True)\n",
        "\n",
        "    # Filling numerical missing values with the median\n",
        "    for label, content in df.items():\n",
        "        if pd.api.types.is_numeric_dtype(content):\n",
        "            if pd.isnull(content).sum():\n",
        "                df[label+\"_is_missing\"] = pd.isnull(content)\n",
        "                df[label] = content.fillna(content.median())\n",
        "\n",
        "         # Turning categorical variables into numbers\n",
        "        if not pd.api.types.is_numeric_dtype(content):\n",
        "            df[label+\"_is_missing\"] = pd.isnull(content)\n",
        "            # add the +1 because pandas encodes missing categories as -1\n",
        "            df[label] = pd.Categorical(content).codes+1        \n",
        "    \n",
        "    return df      "
      ],
      "metadata": {
        "id": "6pGVUksQfhvP"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Just know that here we are able to create the same preprocessing pipeline but in real world the test set may have different missing values so try to analyze the test set in the same manner as training set"
      ],
      "metadata": {
        "id": "VfQQMJahiKym"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset = pd.read_csv(\"/content/drive/MyDrive/Data/Test.csv\", parse_dates=[\"saledate\"]) # don't forget to use parse date otherwise preprocessing function may create error"
      ],
      "metadata": {
        "id": "WRN25bVJieip"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "qtUitBSZiqaQ",
        "outputId": "a780e7c2-349c-4ba9-975f-155cae618037"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
              "0  1227829    1006309     3168         121             3      1999   \n",
              "1  1227844    1022817     7271         121             3      1000   \n",
              "2  1227847    1031560    22805         121             3      2004   \n",
              "3  1227848      56204     1269         121             3      2006   \n",
              "4  1227863    1053887    22312         121             3      2005   \n",
              "\n",
              "   MachineHoursCurrentMeter UsageBand   saledate fiModelDesc  ...  \\\n",
              "0                    3688.0       Low 2012-05-03        580G  ...   \n",
              "1                   28555.0      High 2012-05-10         936  ...   \n",
              "2                    6038.0    Medium 2012-05-10    EC210BLC  ...   \n",
              "3                    8940.0      High 2012-05-10       330CL  ...   \n",
              "4                    2286.0       Low 2012-05-10        650K  ...   \n",
              "\n",
              "  Undercarriage_Pad_Width         Stick_Length   Thumb      Pattern_Changer  \\\n",
              "0                     NaN                  NaN     NaN                  NaN   \n",
              "1                     NaN                  NaN     NaN                  NaN   \n",
              "2     None or Unspecified                9' 6\"  Manual  None or Unspecified   \n",
              "3     None or Unspecified  None or Unspecified  Manual                  Yes   \n",
              "4                     NaN                  NaN     NaN                  NaN   \n",
              "\n",
              "  Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
              "0          NaN                  NaN        NaN                  NaN   \n",
              "1          NaN                  NaN        NaN                  NaN   \n",
              "2       Double                  NaN        NaN                  NaN   \n",
              "3       Triple                  NaN        NaN                  NaN   \n",
              "4          NaN  None or Unspecified        PAT  None or Unspecified   \n",
              "\n",
              "  Differential_Type Steering_Controls  \n",
              "0               NaN               NaN  \n",
              "1          Standard      Conventional  \n",
              "2               NaN               NaN  \n",
              "3               NaN               NaN  \n",
              "4               NaN               NaN  \n",
              "\n",
              "[5 rows x 52 columns]"
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              "      <td>2012-05-10</td>\n",
              "      <td>EC210BLC</td>\n",
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              "      <td>None or Unspecified</td>\n",
              "      <td>9' 6\"</td>\n",
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              "      <td>8940.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort DataFrame in date order\n",
        "test_dataset.sort_values(by=[\"saledate\"], inplace=True, ascending=True)\n",
        "test_dataset.saledate.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoDG0k11tycU",
        "outputId": "51d26663-87a2-432a-d391-7b1ed6f90f39"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1408   2012-05-01\n",
              "1023   2012-05-01\n",
              "1728   2012-05-01\n",
              "1167   2012-05-01\n",
              "1594   2012-05-01\n",
              "1732   2012-05-01\n",
              "1445   2012-05-01\n",
              "1242   2012-05-01\n",
              "1446   2012-05-01\n",
              "1179   2012-05-01\n",
              "1240   2012-05-01\n",
              "1004   2012-05-01\n",
              "1451   2012-05-01\n",
              "1487   2012-05-01\n",
              "1363   2012-05-01\n",
              "1006   2012-05-01\n",
              "1459   2012-05-01\n",
              "1312   2012-05-01\n",
              "1227   2012-05-01\n",
              "1414   2012-05-01\n",
              "Name: saledate, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set = preprocessing_data(test_dataset)  # preprocessing test data to make it compatible for model"
      ],
      "metadata": {
        "id": "vh8RxtUkiIMA"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = selected_model.predict(test_set)   # making prediction "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "jTCGwPJmjQGu",
        "outputId": "0c5dbc60-e504-4a13-d29b-3e2198b50971"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-ac31301ef465>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselected_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# making prediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    979\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    600\u001b[0m         Validate X whenever one tries to predict, apply, predict_proba.\"\"\"\n\u001b[1;32m    601\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindptr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No support for np.int64 index based sparse matrices\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    546\u001b[0m             \u001b[0mvalidated\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m         \"\"\"\n\u001b[0;32m--> 548\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_feature_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"requires_y\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_check_feature_names\u001b[0;34m(self, X, reset)\u001b[0m\n\u001b[1;32m    479\u001b[0m                 )\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 481\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    483\u001b[0m     def _validate_data(\n",
            "\u001b[0;31mValueError\u001b[0m: The feature names should match those that were passed during fit.\nFeature names seen at fit time, yet now missing:\n- auctioneerID_is_missing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are getting error here because a column seems to be missing from the test set. "
      ],
      "metadata": {
        "id": "WzOTdA5Djomw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_set"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "VV24KZOe1S0p",
        "outputId": "c472ec53-ae91-4713-d8cd-0af95274460a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
              "1408   4317459    2295526     8132         172             1      1998   \n",
              "1023   4249784    1745722     3178         172             1      1995   \n",
              "1728   4392603    2283116    18111         172             1      2004   \n",
              "1167   4274831    2288988     3783         172             1      2001   \n",
              "1594   4362801    2300591    16490         172             1      2006   \n",
              "...        ...        ...      ...         ...           ...       ...   \n",
              "9941   6620191    2272921     9518         172             1      2005   \n",
              "9936   6620142    2297127     9579         172             1      2008   \n",
              "11620  6635116    1412453     2160         149             1      2002   \n",
              "9995   6621460    2554811    13316         149             1      2003   \n",
              "9803   6469532    2522919     5864         121             3      1998   \n",
              "\n",
              "       MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
              "1408                     4359.0          3          397          148  ...   \n",
              "1023                     4489.0          2          509          180  ...   \n",
              "1728                     2126.0          2          906          320  ...   \n",
              "1167                     9115.0          3          824          290  ...   \n",
              "1594                      885.0          2         1513          610  ...   \n",
              "...                         ...        ...          ...          ...  ...   \n",
              "9941                     3525.0          0          423          154  ...   \n",
              "9936                     3525.0          0         1675          676  ...   \n",
              "11620                    3525.0          0         1722          702  ...   \n",
              "9995                     3525.0          0          936          341  ...   \n",
              "9803                     3525.0          0         1377          534  ...   \n",
              "\n",
              "       Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
              "1408                                 True                     True   \n",
              "1023                                 True                     True   \n",
              "1728                                 True                     True   \n",
              "1167                                 True                     True   \n",
              "1594                                False                    False   \n",
              "...                                   ...                      ...   \n",
              "9941                                 True                     True   \n",
              "9936                                 True                     True   \n",
              "11620                               False                    False   \n",
              "9995                                False                    False   \n",
              "9803                                 True                     True   \n",
              "\n",
              "       Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
              "1408               True                        True                     True   \n",
              "1023               True                        True                     True   \n",
              "1728               True                        True                     True   \n",
              "1167               True                        True                     True   \n",
              "1594              False                       False                    False   \n",
              "...                 ...                         ...                      ...   \n",
              "9941               True                        True                     True   \n",
              "9936               True                        True                     True   \n",
              "11620             False                       False                    False   \n",
              "9995              False                       False                    False   \n",
              "9803               True                        True                     True   \n",
              "\n",
              "       Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
              "1408                          True                   True   \n",
              "1023                          True                   True   \n",
              "1728                          True                   True   \n",
              "1167                          True                   True   \n",
              "1594                          True                   True   \n",
              "...                            ...                    ...   \n",
              "9941                          True                   True   \n",
              "9936                          True                   True   \n",
              "11620                         True                   True   \n",
              "9995                          True                   True   \n",
              "9803                          True                   True   \n",
              "\n",
              "       Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
              "1408                         True                          True   \n",
              "1023                         True                          True   \n",
              "1728                         True                          True   \n",
              "1167                         True                         False   \n",
              "1594                         True                          True   \n",
              "...                           ...                           ...   \n",
              "9941                         True                          True   \n",
              "9936                         True                          True   \n",
              "11620                        True                          True   \n",
              "9995                         True                          True   \n",
              "9803                         True                         False   \n",
              "\n",
              "       Steering_Controls_is_missing  \n",
              "1408                           True  \n",
              "1023                           True  \n",
              "1728                           True  \n",
              "1167                          False  \n",
              "1594                           True  \n",
              "...                             ...  \n",
              "9941                           True  \n",
              "9936                           True  \n",
              "11620                          True  \n",
              "9995                           True  \n",
              "9803                          False  \n",
              "\n",
              "[12457 rows x 101 columns]"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SalesID</th>\n",
              "      <th>MachineID</th>\n",
              "      <th>ModelID</th>\n",
              "      <th>datasource</th>\n",
              "      <th>auctioneerID</th>\n",
              "      <th>YearMade</th>\n",
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              "      <th>fiModelDesc</th>\n",
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              "      <th>Blade_Type_is_missing</th>\n",
              "      <th>Travel_Controls_is_missing</th>\n",
              "      <th>Differential_Type_is_missing</th>\n",
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              "    </tr>\n",
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              "      <td>1995</td>\n",
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              "      <td>2004</td>\n",
              "      <td>2126.0</td>\n",
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              "      <td>906</td>\n",
              "      <td>320</td>\n",
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              "      <td>True</td>\n",
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              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
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              "      <th>9941</th>\n",
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              "      <td>True</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>9936</th>\n",
              "      <td>6620142</td>\n",
              "      <td>2297127</td>\n",
              "      <td>9579</td>\n",
              "      <td>172</td>\n",
              "      <td>1</td>\n",
              "      <td>2008</td>\n",
              "      <td>3525.0</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "test_set.insert(loc=56, column=\"auctioneerID_is_missing\", value=False)  # inserting the column at the right location in test dataset"
      ],
      "metadata": {
        "id": "sz5s3tT-06YC"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.T"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
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        "id": "QxpiVldNrVat",
        "outputId": "33a07c69-e2fa-4a74-e8ad-bfcfb67087ac"
      },
      "execution_count": 38,
      "outputs": [
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              "                               0        1        2        3        4       \\\n",
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              "      <td>...</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Differential_Type_is_missing</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Steering_Controls_is_missing</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>...</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>102 rows × 401125 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8649408-8b13-49aa-b0b9-106777dbcb8a')\"\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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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-f8649408-8b13-49aa-b0b9-106777dbcb8a button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8649408-8b13-49aa-b0b9-106777dbcb8a');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test dataset using the best model\n",
        "test_preds = selected_model.predict(test_set)"
      ],
      "metadata": {
        "id": "IZ9EGJ-_q2Nh"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a DataFrame to visualize things \n",
        "df_preds = pd.DataFrame()\n",
        "df_preds[\"SalesID\"] = test_set[\"SalesID\"]\n",
        "df_preds[\"SalePrice\"] = test_preds\n",
        "df_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "XFr3GBsS1utq",
        "outputId": "f168f5e3-79c7-4156-a870-7f58a8fb4fd8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SalesID     SalePrice\n",
              "1408   4317459  10879.234848\n",
              "1023   4249784  16885.965639\n",
              "1728   4392603  22099.661797\n",
              "1167   4274831  27293.932497\n",
              "1594   4362801  10693.161977\n",
              "...        ...           ...\n",
              "9941   6620191   7364.447421\n",
              "9936   6620142  13384.337662\n",
              "11620  6635116  45238.446970\n",
              "9995   6621460  45356.116696\n",
              "9803   6469532  40148.970543\n",
              "\n",
              "[12457 rows x 2 columns]"
            ],
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              "\n",
              "  <div id=\"df-cddd4cca-ceb2-4ac2-9c6a-e03019c29c98\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
              "      <th>SalesID</th>\n",
              "      <th>SalePrice</th>\n",
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              "      <th>1408</th>\n",
              "      <td>4317459</td>\n",
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              "      <th>1023</th>\n",
              "      <td>4249784</td>\n",
              "      <td>16885.965639</td>\n",
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              "      <th>1728</th>\n",
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              "      <td>22099.661797</td>\n",
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              "      <th>1167</th>\n",
              "      <td>4274831</td>\n",
              "      <td>27293.932497</td>\n",
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              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>4362801</td>\n",
              "      <td>10693.161977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>9941</th>\n",
              "      <td>6620191</td>\n",
              "      <td>7364.447421</td>\n",
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              "      <th>9936</th>\n",
              "      <td>6620142</td>\n",
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              "      <th>9995</th>\n",
              "      <td>6621460</td>\n",
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              "    <tr>\n",
              "      <th>9803</th>\n",
              "      <td>6469532</td>\n",
              "      <td>40148.970543</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12457 rows × 2 columns</p>\n",
              "</div>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cddd4cca-ceb2-4ac2-9c6a-e03019c29c98 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-cddd4cca-ceb2-4ac2-9c6a-e03019c29c98');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Export to csv...\n",
        "df_preds.to_csv(\"/content/drive/MyDrive/Data/predictions.csv\")"
      ],
      "metadata": {
        "id": "R8M4SAjM2V08"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Find feature importance of our best model - which features were most important when it comes to predicting the target variable\n",
        "selected_model.feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sU-gWjhf2kIi",
        "outputId": "b4a63f65-44b0-4dc7-b1bd-08c6e464a15e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.86297999e-02, 1.42734082e-02, 3.31125477e-02, 2.54235324e-04,\n",
              "       3.78125101e-03, 2.66290697e-01, 2.73726470e-03, 9.55153156e-04,\n",
              "       2.01427945e-02, 8.02689414e-02, 5.24904783e-02, 4.37330850e-03,\n",
              "       3.37265879e-03, 2.32737589e-01, 3.63703226e-02, 7.61701035e-03,\n",
              "       2.06286892e-04, 1.88346769e-04, 1.78003126e-03, 8.15084397e-03,\n",
              "       3.42370080e-04, 2.87854605e-05, 4.92013605e-04, 1.64010326e-04,\n",
              "       1.39493701e-03, 2.09708285e-05, 7.70517025e-04, 7.88101473e-03,\n",
              "       1.10305238e-04, 4.86527256e-05, 3.32489926e-03, 1.83414685e-03,\n",
              "       2.20928990e-03, 5.63138403e-03, 1.85527270e-04, 1.67888471e-03,\n",
              "       8.72241564e-04, 1.53679900e-02, 3.97621174e-06, 3.31754324e-06,\n",
              "       1.55028892e-04, 8.97296158e-04, 1.18916573e-03, 6.31161735e-04,\n",
              "       1.66246608e-04, 4.10819263e-04, 1.74681117e-04, 1.46565265e-03,\n",
              "       8.65035898e-04, 1.46882616e-04, 2.13240605e-04, 7.67758053e-02,\n",
              "       2.42032458e-03, 7.54496800e-03, 3.70554868e-03, 1.34456455e-02,\n",
              "       2.41002244e-04, 3.13961928e-04, 1.61437345e-04, 0.00000000e+00,\n",
              "       0.00000000e+00, 7.78524066e-04, 5.94821739e-04, 8.30983786e-04,\n",
              "       2.68219766e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
              "       0.00000000e+00, 3.32431438e-06, 3.22623459e-06, 4.93713251e-05,\n",
              "       8.38373583e-07, 5.04554913e-05, 1.48415025e-06, 7.73834755e-05,\n",
              "       2.00049621e-06, 7.82210501e-04, 5.83626659e-06, 3.06821904e-06,\n",
              "       7.69518796e-04, 1.13063427e-03, 4.48589752e-06, 4.20063383e-05,\n",
              "       3.86956236e-03, 7.74178694e-04, 3.88481002e-04, 8.89902522e-05,\n",
              "       1.52280062e-02, 1.29063669e-05, 1.17345363e-06, 2.62704866e-05,\n",
              "       1.64871071e-05, 2.23831262e-05, 1.72512712e-05, 3.45405170e-05,\n",
              "       2.70681488e-05, 3.31077354e-04, 1.09577150e-04, 8.36274444e-05,\n",
              "       5.89478608e-05, 7.49913460e-05])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# this above is same as \n",
        "X_train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbebedemSelc",
        "outputId": "452fc422-ef0f-4c18-ac3d-81d117ff615a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['SalesID', 'MachineID', 'ModelID', 'datasource', 'auctioneerID',\n",
              "       'YearMade', 'MachineHoursCurrentMeter', 'UsageBand', 'fiModelDesc',\n",
              "       'fiBaseModel',\n",
              "       ...\n",
              "       'Undercarriage_Pad_Width_is_missing', 'Stick_Length_is_missing',\n",
              "       'Thumb_is_missing', 'Pattern_Changer_is_missing',\n",
              "       'Grouser_Type_is_missing', 'Backhoe_Mounting_is_missing',\n",
              "       'Blade_Type_is_missing', 'Travel_Controls_is_missing',\n",
              "       'Differential_Type_is_missing', 'Steering_Controls_is_missing'],\n",
              "      dtype='object', length=102)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we can use the graph to show our important features \n",
        "\n",
        "import seaborn as sns\n",
        "def plot_features(columns, importances, n=20):    # n = 20 means we are only taking into account 20 values \n",
        "    df = (pd.DataFrame({\"features\": columns,      # creating data frame\n",
        "                        \"feature_importance\": importances})   \n",
        "          .sort_values(\"feature_importance\", ascending=False)  # sorting the values \n",
        "          .reset_index(drop=True))\n",
        "    \n",
        "    # plotting \n",
        "    sns.barplot(x=\"feature_importance\",\n",
        "                y=\"features\",\n",
        "                data=df[:n],\n",
        "                orient=\"h\")"
      ],
      "metadata": {
        "id": "OMWyRiIG2w49"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_features(X_train.columns, selected_model.feature_importances_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "ESbBSizY209T",
        "outputId": "2a96741a-9d14-458b-b03c-a9bd433a3921"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(selected_model.feature_importances_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ejrYIRT26CH",
        "outputId": "9fdde82f-e98d-4a7a-c1ea-0a57de19e6a4"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999999999999998"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have created our machine learning project. You can also try to use different machine learning models to check which one performs best. "
      ],
      "metadata": {
        "id": "Zfod0NdbU1XP"
      }
    }
  ]
}