end-to-end-bulldozer-price-regression.ipynb

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    "# 🚜 Predicting the Sale Price of Bulldozers using Machine Learning\n",
    "\n",
    "In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers\n",
    "\n",
    "## 1. Problem defination\n",
    "\n",
    "> How well can we predict the future sale price of a bulldozer, given its characteristics and previous examples of how much similar bulldozers have been sold for?\n",
    "## 2. Data\n",
    "\n",
    "The data is downloaded from the Kaggle Bluebook for Bulldozers competition: https://www.kaggle.com/c/bluebook-for-bulldozers/data\n",
    "\n",
    "There are 3 main datasets:\n",
    "\n",
    "* Train.csv is the training set, which contains data through the end of 2011.\n",
    "* Valid.csv is the validation set, which contains data from January 1, 2012 - April 30, 2012 You make predictions on this set throughout the majority of the competition. Your score on this set is used to create the public leaderboard.\n",
    "* Test.csv is the test set, which won't be released until the last week of the competition. It contains data from May 1, 2012 - November 2012. Your score on the test set determines your final rank for the competition.\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "The evaluation metric for this competition is the RMSLE (root mean squared log error) between the actual and predicted auction prices.\n",
    "\n",
    "For more on the evaluation of this project check: https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation\n",
    "\n",
    "**Note:** The goal for most regression evaluation metrics is to minimize the error. For example, our goal for this project will be to build a machine learning model which minimises RMSLE.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "Kaggle provides a data dictionary detailing all of the features of the dataset. You can view this data dictionary on Google Sheets: https://docs.google.com/spreadsheets/d/18ly-bLR8sbDJLITkWG7ozKm8l3RyieQ2Fpgix-beSYI/edit?usp=sharing\n"
   ]
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   "id": "2c1f4e34",
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALIENWARE\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d593a525",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import training and validation data sets\n",
    "\n",
    "df = pd.read_csv('data/bluebook-for-bulldozers/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d44f1539",
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>1139246</td>\n",
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       "      <td>999089</td>\n",
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       "      <td>2004</td>\n",
       "      <td>68.0</td>\n",
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       "      <td>11/16/2006 0:00</td>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "    <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",
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       "    <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",
       "    <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>5/19/2011 0:00</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>7/23/2009 0:00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 53 columns</p>\n",
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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",
       "\n",
       "  Undercarriage_Pad_Width Stick_Length Thumb Pattern_Changer Grouser_Type  \\\n",
       "0                     NaN          NaN   NaN             NaN          NaN   \n",
       "1                     NaN          NaN   NaN             NaN          NaN   \n",
       "2                     NaN          NaN   NaN             NaN          NaN   \n",
       "3                     NaN          NaN   NaN             NaN          NaN   \n",
       "4                     NaN          NaN   NaN             NaN          NaN   \n",
       "\n",
       "  Backhoe_Mounting Blade_Type Travel_Controls Differential_Type  \\\n",
       "0              NaN        NaN             NaN          Standard   \n",
       "1              NaN        NaN             NaN          Standard   \n",
       "2              NaN        NaN             NaN               NaN   \n",
       "3              NaN        NaN             NaN               NaN   \n",
       "4              NaN        NaN             NaN               NaN   \n",
       "\n",
       "  Steering_Controls  \n",
       "0      Conventional  \n",
       "1      Conventional  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "\n",
       "[5 rows x 53 columns]"
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    "df.head()"
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     "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",
      " 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: float64(3), int64(5), object(45)\n",
      "memory usage: 166.9+ MB\n"
     ]
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    {
     "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",
       "saledate                         0\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",
       "dtype: int64"
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   "source": [
    "df.isna().sum()"
   ]
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   "execution_count": 7,
   "id": "7de65386",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SalesID', 'SalePrice', 'MachineID', 'ModelID', 'datasource',\n",
       "       'auctioneerID', 'YearMade', 'MachineHoursCurrentMeter', 'UsageBand',\n",
       "       'saledate', 'fiModelDesc', 'fiBaseModel', 'fiSecondaryDesc',\n",
       "       'fiModelSeries', 'fiModelDescriptor', 'ProductSize',\n",
       "       'fiProductClassDesc', 'state', 'ProductGroup', 'ProductGroupDesc',\n",
       "       'Drive_System', 'Enclosure', 'Forks', 'Pad_Type', 'Ride_Control',\n",
       "       'Stick', 'Transmission', 'Turbocharged', 'Blade_Extension',\n",
       "       'Blade_Width', 'Enclosure_Type', 'Engine_Horsepower', 'Hydraulics',\n",
       "       'Pushblock', 'Ripper', 'Scarifier', 'Tip_Control', 'Tire_Size',\n",
       "       'Coupler', 'Coupler_System', 'Grouser_Tracks', 'Hydraulics_Flow',\n",
       "       'Track_Type', 'Undercarriage_Pad_Width', 'Stick_Length', 'Thumb',\n",
       "       'Pattern_Changer', 'Grouser_Type', 'Backhoe_Mounting', 'Blade_Type',\n",
       "       'Travel_Controls', 'Differential_Type', 'Steering_Controls'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
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   "source": [
    "df.columns"
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   "execution_count": 9,
   "id": "2a1bac83",
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['saledate'][:1000], df['SalePrice'][:1000]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5c14dd0f",
   "metadata": {},
   "outputs": [
    {
     "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",
       "995     7/16/2009 0:00\n",
       "996     6/14/2007 0:00\n",
       "997     9/22/2005 0:00\n",
       "998     7/28/2005 0:00\n",
       "999     6/16/2011 0:00\n",
       "Name: saledate, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74ff737a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c07dcfa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SalePrice.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac701e3",
   "metadata": {},
   "source": [
    "### Parsing dates\n",
    "\n",
    "When we work with time series data, we want to enrich the time & date component as much as possible.\n",
    "\n",
    "We can do that by telling pandas which of our columns has dates in it using the `parse_dates` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2de2d37f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data again but this time parse dates\n",
    "df = pd.read_csv('data/bluebook-for-bulldozers/bluebook-for-bulldozers/TrainAndValid.csv', low_memory=False, parse_dates=['saledate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "961545fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01353752",
   "metadata": {},
   "outputs": [
    {
     "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",
       "995   2009-07-16\n",
       "996   2007-06-14\n",
       "997   2005-09-22\n",
       "998   2005-07-28\n",
       "999   2011-06-16\n",
       "Name: saledate, Length: 1000, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a69ab62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>"
      ],
      "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]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d8cc5eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df['saledate'][:1000], df['SalePrice'][:1000]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "334038e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2006-11-16 00:00:00</td>\n",
       "      <td>2004-03-26 00:00:00</td>\n",
       "      <td>2004-02-26 00:00:00</td>\n",
       "      <td>2011-05-19 00:00:00</td>\n",
       "      <td>2009-07-23 00:00:00</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",
       "      <td>-6E</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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>"
      ],
      "text/plain": [
       "                                                                 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                                       2006-11-16 00:00:00   \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",
       "Tire_Size                                      None or Unspecified   \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",
       "                                                                 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                                       2004-03-26 00:00:00   \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",
       "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",
       "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                                                2004-02-26 00:00:00   \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                                                2011-05-19 00:00:00   \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                                                2009-07-23 00:00:00  \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  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T # can see all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "94d636a2",
   "metadata": {},
   "outputs": [
    {
     "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",
       "5    2008-12-18\n",
       "6    2004-08-26\n",
       "7    2005-11-17\n",
       "8    2009-08-27\n",
       "9    2007-08-09\n",
       "10   2008-08-21\n",
       "11   2006-08-24\n",
       "12   2005-10-20\n",
       "13   2006-01-26\n",
       "14   2006-01-03\n",
       "15   2006-11-16\n",
       "16   2007-06-14\n",
       "17   2010-01-28\n",
       "18   2006-03-09\n",
       "19   2005-11-17\n",
       "Name: saledate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6456fc14",
   "metadata": {},
   "source": [
    "### Sort DataFrame by saledate\n",
    "\n",
    "When working with time series data, it's a good idea to sort it by date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "70ef2651",
   "metadata": {},
   "outputs": [
    {
     "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]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort DataFrame in date order\n",
    "df.sort_values(by=['saledate'], inplace=True, ascending=True)\n",
    "df.saledate.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdbadf38",
   "metadata": {},
   "source": [
    "### Make a copy of the original DataFrame\n",
    "\n",
    "We make a copy of the original dataframe so when we manipulate the copy, we've still got our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "68f845e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy\n",
    "df_tmp = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "db859f47",
   "metadata": {},
   "outputs": [
    {
     "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]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.saledate.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af53e0d",
   "metadata": {},
   "source": [
    "### Add datetime parameters for `saledate` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7e8450a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615    1989\n",
       "Name: saledate, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp[:1].saledate.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8eedc95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615    17\n",
       "Name: saledate, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp[:1].saledate.dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0c5bd0f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615   1989-01-17\n",
       "Name: saledate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp[:1].saledate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "924af977",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp['saleYear'] = df_tmp.saledate.dt.year\n",
    "df_tmp['saleMonth'] = df_tmp.saledate.dt.month\n",
    "df_tmp['saleDay'] = df_tmp.saledate.dt.day\n",
    "df_tmp['saleDayofWeek'] = df_tmp.saledate.dt.dayofweek\n",
    "df_tmp['saleDayofYear'] = df_tmp.saledate.dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ec88220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>205615</th>\n",
       "      <th>274835</th>\n",
       "      <th>141296</th>\n",
       "      <th>212552</th>\n",
       "      <th>62755</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>1646770</td>\n",
       "      <td>1821514</td>\n",
       "      <td>1505138</td>\n",
       "      <td>1671174</td>\n",
       "      <td>1329056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>9500.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>22000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>1126363</td>\n",
       "      <td>1194089</td>\n",
       "      <td>1473654</td>\n",
       "      <td>1327630</td>\n",
       "      <td>1336053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>8434</td>\n",
       "      <td>10150</td>\n",
       "      <td>4139</td>\n",
       "      <td>8591</td>\n",
       "      <td>4089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>18.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>1974</td>\n",
       "      <td>1980</td>\n",
       "      <td>1978</td>\n",
       "      <td>1980</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineHoursCurrentMeter</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>UsageBand</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>saledate</th>\n",
       "      <td>1989-01-17 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "      <td>1989-01-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDesc</th>\n",
       "      <td>TD20</td>\n",
       "      <td>A66</td>\n",
       "      <td>D7G</td>\n",
       "      <td>A62</td>\n",
       "      <td>D3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiBaseModel</th>\n",
       "      <td>TD20</td>\n",
       "      <td>A66</td>\n",
       "      <td>D7</td>\n",
       "      <td>A62</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelSeries</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>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>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Large</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiProductClassDesc</th>\n",
       "      <td>Track Type Tractor, Dozer - 105.0 to 130.0 Hor...</td>\n",
       "      <td>Wheel Loader - 120.0 to 135.0 Horsepower</td>\n",
       "      <td>Track Type Tractor, Dozer - 190.0 to 260.0 Hor...</td>\n",
       "      <td>Wheel Loader - Unidentified</td>\n",
       "      <td>Track Type Tractor, Dozer - 20.0 to 75.0 Horse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroup</th>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroupDesc</th>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure</th>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS</td>\n",
       "      <td>OROPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</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>Direct Drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</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>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</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>None or Unspecified</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>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>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler_System</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_Tracks</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_Flow</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>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>None or Unspecified</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>Blade_Type</th>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleYear</th>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleMonth</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDay</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayofWeek</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayofYear</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     205615  \\\n",
       "SalesID                                                             1646770   \n",
       "SalePrice                                                            9500.0   \n",
       "MachineID                                                           1126363   \n",
       "ModelID                                                                8434   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                           18.0   \n",
       "YearMade                                                               1974   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                1989-01-17 00:00:00   \n",
       "fiModelDesc                                                            TD20   \n",
       "fiBaseModel                                                            TD20   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                          Medium   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 105.0 to 130.0 Hor...   \n",
       "state                                                                 Texas   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                   Direct Drive   \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                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \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                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  17   \n",
       "saleDayofWeek                                                             1   \n",
       "saleDayofYear                                                            17   \n",
       "\n",
       "                                                            274835  \\\n",
       "SalesID                                                    1821514   \n",
       "SalePrice                                                  14000.0   \n",
       "MachineID                                                  1194089   \n",
       "ModelID                                                      10150   \n",
       "datasource                                                     132   \n",
       "auctioneerID                                                  99.0   \n",
       "YearMade                                                      1980   \n",
       "MachineHoursCurrentMeter                                       NaN   \n",
       "UsageBand                                                      NaN   \n",
       "saledate                                       1989-01-31 00:00:00   \n",
       "fiModelDesc                                                    A66   \n",
       "fiBaseModel                                                    A66   \n",
       "fiSecondaryDesc                                                NaN   \n",
       "fiModelSeries                                                  NaN   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                    NaN   \n",
       "fiProductClassDesc        Wheel Loader - 120.0 to 135.0 Horsepower   \n",
       "state                                                      Florida   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                                    OROPS   \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                                      None or Unspecified   \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",
       "saleYear                                                      1989   \n",
       "saleMonth                                                        1   \n",
       "saleDay                                                         31   \n",
       "saleDayofWeek                                                    1   \n",
       "saleDayofYear                                                   31   \n",
       "\n",
       "                                                                     141296  \\\n",
       "SalesID                                                             1505138   \n",
       "SalePrice                                                           50000.0   \n",
       "MachineID                                                           1473654   \n",
       "ModelID                                                                4139   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                           99.0   \n",
       "YearMade                                                               1978   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                1989-01-31 00:00:00   \n",
       "fiModelDesc                                                             D7G   \n",
       "fiBaseModel                                                              D7   \n",
       "fiSecondaryDesc                                                           G   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                           Large   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 190.0 to 260.0 Hor...   \n",
       "state                                                               Florida   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                       Standard   \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                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \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                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  31   \n",
       "saleDayofWeek                                                             1   \n",
       "saleDayofYear                                                            31   \n",
       "\n",
       "                                               212552  \\\n",
       "SalesID                                       1671174   \n",
       "SalePrice                                     16000.0   \n",
       "MachineID                                     1327630   \n",
       "ModelID                                          8591   \n",
       "datasource                                        132   \n",
       "auctioneerID                                     99.0   \n",
       "YearMade                                         1980   \n",
       "MachineHoursCurrentMeter                          NaN   \n",
       "UsageBand                                         NaN   \n",
       "saledate                          1989-01-31 00:00:00   \n",
       "fiModelDesc                                       A62   \n",
       "fiBaseModel                                       A62   \n",
       "fiSecondaryDesc                                   NaN   \n",
       "fiModelSeries                                     NaN   \n",
       "fiModelDescriptor                                 NaN   \n",
       "ProductSize                                       NaN   \n",
       "fiProductClassDesc        Wheel Loader - Unidentified   \n",
       "state                                         Florida   \n",
       "ProductGroup                                       WL   \n",
       "ProductGroupDesc                         Wheel Loader   \n",
       "Drive_System                                      NaN   \n",
       "Enclosure                                       EROPS   \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                         None or Unspecified   \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",
       "saleYear                                         1989   \n",
       "saleMonth                                           1   \n",
       "saleDay                                            31   \n",
       "saleDayofWeek                                       1   \n",
       "saleDayofYear                                      31   \n",
       "\n",
       "                                                                     62755   \n",
       "SalesID                                                             1329056  \n",
       "SalePrice                                                           22000.0  \n",
       "MachineID                                                           1336053  \n",
       "ModelID                                                                4089  \n",
       "datasource                                                              132  \n",
       "auctioneerID                                                           99.0  \n",
       "YearMade                                                               1984  \n",
       "MachineHoursCurrentMeter                                                NaN  \n",
       "UsageBand                                                               NaN  \n",
       "saledate                                                1989-01-31 00:00:00  \n",
       "fiModelDesc                                                             D3B  \n",
       "fiBaseModel                                                              D3  \n",
       "fiSecondaryDesc                                                           B  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                             NaN  \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 20.0 to 75.0 Horse...  \n",
       "state                                                               Florida  \n",
       "ProductGroup                                                            TTT  \n",
       "ProductGroupDesc                                        Track Type Tractors  \n",
       "Drive_System                                                            NaN  \n",
       "Enclosure                                                             OROPS  \n",
       "Forks                                                                   NaN  \n",
       "Pad_Type                                                                NaN  \n",
       "Ride_Control                                                            NaN  \n",
       "Stick                                                                   NaN  \n",
       "Transmission                                                       Standard  \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                                                  None or Unspecified  \n",
       "Scarifier                                                               NaN  \n",
       "Tip_Control                                                             NaN  \n",
       "Tire_Size                                                               NaN  \n",
       "Coupler                                                                 NaN  \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                                        None or Unspecified  \n",
       "Blade_Type                                                              PAT  \n",
       "Travel_Controls                                                       Lever  \n",
       "Differential_Type                                                       NaN  \n",
       "Steering_Controls                                                       NaN  \n",
       "saleYear                                                               1989  \n",
       "saleMonth                                                                 1  \n",
       "saleDay                                                                  31  \n",
       "saleDayofWeek                                                             1  \n",
       "saleDayofYear                                                            31  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "99f3e3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we've enriched our DataFrame with date time features, we can remove 'saledate'\n",
    "df_tmp.drop('saledate',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "42792765",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'saledate'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'saledate'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[1;32mIn [38]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf_tmp\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msaledate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3504\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3507\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3623\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3625\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3626\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3627\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3628\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'saledate'"
     ]
    }
   ],
   "source": [
    "df_tmp['saledate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "123d3388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Florida           67320\n",
       "Texas             53110\n",
       "California        29761\n",
       "Washington        16222\n",
       "Georgia           14633\n",
       "Maryland          13322\n",
       "Mississippi       13240\n",
       "Ohio              12369\n",
       "Illinois          11540\n",
       "Colorado          11529\n",
       "New Jersey        11156\n",
       "North Carolina    10636\n",
       "Tennessee         10298\n",
       "Alabama           10292\n",
       "Pennsylvania      10234\n",
       "South Carolina     9951\n",
       "Arizona            9364\n",
       "New York           8639\n",
       "Connecticut        8276\n",
       "Minnesota          7885\n",
       "Missouri           7178\n",
       "Nevada             6932\n",
       "Louisiana          6627\n",
       "Kentucky           5351\n",
       "Maine              5096\n",
       "Indiana            4124\n",
       "Arkansas           3933\n",
       "New Mexico         3631\n",
       "Utah               3046\n",
       "Unspecified        2801\n",
       "Wisconsin          2745\n",
       "New Hampshire      2738\n",
       "Virginia           2353\n",
       "Idaho              2025\n",
       "Oregon             1911\n",
       "Michigan           1831\n",
       "Wyoming            1672\n",
       "Montana            1336\n",
       "Iowa               1336\n",
       "Oklahoma           1326\n",
       "Nebraska            866\n",
       "West Virginia       840\n",
       "Kansas              667\n",
       "Delaware            510\n",
       "North Dakota        480\n",
       "Alaska              430\n",
       "Massachusetts       347\n",
       "Vermont             300\n",
       "South Dakota        244\n",
       "Hawaii              118\n",
       "Rhode Island         83\n",
       "Puerto Rico          42\n",
       "Washington DC         2\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the values of different columns\n",
    "df_tmp.state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bfa245ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "412698"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce63c6c3",
   "metadata": {},
   "source": [
    "## 5. Modelling\n",
    "\n",
    "We've done enough EDA (we could always do more) but let's start to do some model-driven EDA.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "eb0e1ab1",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Low'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [42]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mensemble\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RandomForestRegressor\n\u001b[0;32m      4\u001b[0m model \u001b[38;5;241m=\u001b[39m RandomForestRegressor(n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m) \u001b[38;5;66;03m# Use all of our computer memory; random_state = 42 is same as set np.random.seed(42)\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_tmp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSalePrice\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_tmp\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSalePrice\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:331\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    329\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(y):\n\u001b[0;32m    330\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse multilabel-indicator for y is not supported.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 331\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    332\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\n\u001b[0;32m    333\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    334\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    335\u001b[0m     sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(sample_weight, X)\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\base.py:596\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    594\u001b[0m         y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[0;32m    595\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 596\u001b[0m         X, y \u001b[38;5;241m=\u001b[39m check_X_y(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    597\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    599\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:1074\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m   1069\u001b[0m         estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[0;32m   1070\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1071\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1072\u001b[0m     )\n\u001b[1;32m-> 1074\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1075\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1076\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1077\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1078\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1079\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1080\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1081\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1082\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1083\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1084\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1085\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1086\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1087\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1088\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1090\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[0;32m   1092\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:856\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    854\u001b[0m         array \u001b[38;5;241m=\u001b[39m array\u001b[38;5;241m.\u001b[39mastype(dtype, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m\"\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 856\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    857\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m    858\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    859\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    860\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\pandas\\core\\generic.py:2064\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2063\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m-> 2064\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'"
     ]
    }
   ],
   "source": [
    "# Let's build a machine learning model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-1, random_state=42) # Use all of our computer memory; random_state = 42 is same as set np.random.seed(42)\n",
    "\n",
    "model.fit(df_tmp.drop('SalePrice', axis =1), df_tmp['SalePrice'] ) # Not all of colunms are numerical and non-null so there will be an error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "83a7d213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp['UsageBand'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b55fd768",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0d37a5",
   "metadata": {},
   "source": [
    "### Convert strings to categories\n",
    "\n",
    "One way we can turn all of our data into numbers is by converting them into pandas categories\n",
    "\n",
    "We can check the different datatypes compatible with pandas here: https://pandas.pydata.org/pandas-docs/stable/reference/general_utility_functions.html#data-types-related-functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0c7e9aaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>205615</th>\n",
       "      <th>274835</th>\n",
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       "      <th>62755</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>1646770</td>\n",
       "      <td>1821514</td>\n",
       "      <td>1505138</td>\n",
       "      <td>1671174</td>\n",
       "      <td>1329056</td>\n",
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       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>9500.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>22000.0</td>\n",
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       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>1126363</td>\n",
       "      <td>1194089</td>\n",
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       "      <td>8434</td>\n",
       "      <td>10150</td>\n",
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       "      <td>4089</td>\n",
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       "      <td>132</td>\n",
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       "      <td>132</td>\n",
       "      <td>132</td>\n",
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       "      <th>auctioneerID</th>\n",
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       "      <td>99.0</td>\n",
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       "      <td>1974</td>\n",
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       "      <td>A62</td>\n",
       "      <td>D3</td>\n",
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       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
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       "      <th>fiModelSeries</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>fiModelDescriptor</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>ProductSize</th>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Large</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiProductClassDesc</th>\n",
       "      <td>Track Type Tractor, Dozer - 105.0 to 130.0 Hor...</td>\n",
       "      <td>Wheel Loader - 120.0 to 135.0 Horsepower</td>\n",
       "      <td>Track Type Tractor, Dozer - 190.0 to 260.0 Hor...</td>\n",
       "      <td>Wheel Loader - Unidentified</td>\n",
       "      <td>Track Type Tractor, Dozer - 20.0 to 75.0 Horse...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroup</th>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "      <td>WL</td>\n",
       "      <td>TTT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroupDesc</th>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</td>\n",
       "      <td>Wheel Loader</td>\n",
       "      <td>Track Type Tractors</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure</th>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS</td>\n",
       "      <td>OROPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
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       "    <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>Direct Drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</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>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</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>None or Unspecified</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>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>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler</th>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler_System</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_Tracks</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_Flow</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>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>None or Unspecified</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>Blade_Type</th>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls</th>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleYear</th>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleMonth</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDay</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayofWeek</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayofYear</th>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     205615  \\\n",
       "SalesID                                                             1646770   \n",
       "SalePrice                                                            9500.0   \n",
       "MachineID                                                           1126363   \n",
       "ModelID                                                                8434   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                           18.0   \n",
       "YearMade                                                               1974   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "fiModelDesc                                                            TD20   \n",
       "fiBaseModel                                                            TD20   \n",
       "fiSecondaryDesc                                                         NaN   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                          Medium   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 105.0 to 130.0 Hor...   \n",
       "state                                                                 Texas   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                   Direct Drive   \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                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \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                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  17   \n",
       "saleDayofWeek                                                             1   \n",
       "saleDayofYear                                                            17   \n",
       "\n",
       "                                                            274835  \\\n",
       "SalesID                                                    1821514   \n",
       "SalePrice                                                  14000.0   \n",
       "MachineID                                                  1194089   \n",
       "ModelID                                                      10150   \n",
       "datasource                                                     132   \n",
       "auctioneerID                                                  99.0   \n",
       "YearMade                                                      1980   \n",
       "MachineHoursCurrentMeter                                       NaN   \n",
       "UsageBand                                                      NaN   \n",
       "fiModelDesc                                                    A66   \n",
       "fiBaseModel                                                    A66   \n",
       "fiSecondaryDesc                                                NaN   \n",
       "fiModelSeries                                                  NaN   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                    NaN   \n",
       "fiProductClassDesc        Wheel Loader - 120.0 to 135.0 Horsepower   \n",
       "state                                                      Florida   \n",
       "ProductGroup                                                    WL   \n",
       "ProductGroupDesc                                      Wheel Loader   \n",
       "Drive_System                                                   NaN   \n",
       "Enclosure                                                    OROPS   \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                                      None or Unspecified   \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",
       "saleYear                                                      1989   \n",
       "saleMonth                                                        1   \n",
       "saleDay                                                         31   \n",
       "saleDayofWeek                                                    1   \n",
       "saleDayofYear                                                   31   \n",
       "\n",
       "                                                                     141296  \\\n",
       "SalesID                                                             1505138   \n",
       "SalePrice                                                           50000.0   \n",
       "MachineID                                                           1473654   \n",
       "ModelID                                                                4139   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                           99.0   \n",
       "YearMade                                                               1978   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "fiModelDesc                                                             D7G   \n",
       "fiBaseModel                                                              D7   \n",
       "fiSecondaryDesc                                                           G   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                           Large   \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 190.0 to 260.0 Hor...   \n",
       "state                                                               Florida   \n",
       "ProductGroup                                                            TTT   \n",
       "ProductGroupDesc                                        Track Type Tractors   \n",
       "Drive_System                                                            NaN   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                                   NaN   \n",
       "Pad_Type                                                                NaN   \n",
       "Ride_Control                                                            NaN   \n",
       "Stick                                                                   NaN   \n",
       "Transmission                                                       Standard   \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                                                  None or Unspecified   \n",
       "Scarifier                                                               NaN   \n",
       "Tip_Control                                                             NaN   \n",
       "Tire_Size                                                               NaN   \n",
       "Coupler                                                                 NaN   \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                                        None or Unspecified   \n",
       "Blade_Type                                                         Straight   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "saleYear                                                               1989   \n",
       "saleMonth                                                                 1   \n",
       "saleDay                                                                  31   \n",
       "saleDayofWeek                                                             1   \n",
       "saleDayofYear                                                            31   \n",
       "\n",
       "                                               212552  \\\n",
       "SalesID                                       1671174   \n",
       "SalePrice                                     16000.0   \n",
       "MachineID                                     1327630   \n",
       "ModelID                                          8591   \n",
       "datasource                                        132   \n",
       "auctioneerID                                     99.0   \n",
       "YearMade                                         1980   \n",
       "MachineHoursCurrentMeter                          NaN   \n",
       "UsageBand                                         NaN   \n",
       "fiModelDesc                                       A62   \n",
       "fiBaseModel                                       A62   \n",
       "fiSecondaryDesc                                   NaN   \n",
       "fiModelSeries                                     NaN   \n",
       "fiModelDescriptor                                 NaN   \n",
       "ProductSize                                       NaN   \n",
       "fiProductClassDesc        Wheel Loader - Unidentified   \n",
       "state                                         Florida   \n",
       "ProductGroup                                       WL   \n",
       "ProductGroupDesc                         Wheel Loader   \n",
       "Drive_System                                      NaN   \n",
       "Enclosure                                       EROPS   \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                         None or Unspecified   \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",
       "saleYear                                         1989   \n",
       "saleMonth                                           1   \n",
       "saleDay                                            31   \n",
       "saleDayofWeek                                       1   \n",
       "saleDayofYear                                      31   \n",
       "\n",
       "                                                                     62755   \n",
       "SalesID                                                             1329056  \n",
       "SalePrice                                                           22000.0  \n",
       "MachineID                                                           1336053  \n",
       "ModelID                                                                4089  \n",
       "datasource                                                              132  \n",
       "auctioneerID                                                           99.0  \n",
       "YearMade                                                               1984  \n",
       "MachineHoursCurrentMeter                                                NaN  \n",
       "UsageBand                                                               NaN  \n",
       "fiModelDesc                                                             D3B  \n",
       "fiBaseModel                                                              D3  \n",
       "fiSecondaryDesc                                                           B  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                             NaN  \n",
       "fiProductClassDesc        Track Type Tractor, Dozer - 20.0 to 75.0 Horse...  \n",
       "state                                                               Florida  \n",
       "ProductGroup                                                            TTT  \n",
       "ProductGroupDesc                                        Track Type Tractors  \n",
       "Drive_System                                                            NaN  \n",
       "Enclosure                                                             OROPS  \n",
       "Forks                                                                   NaN  \n",
       "Pad_Type                                                                NaN  \n",
       "Ride_Control                                                            NaN  \n",
       "Stick                                                                   NaN  \n",
       "Transmission                                                       Standard  \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                                                  None or Unspecified  \n",
       "Scarifier                                                               NaN  \n",
       "Tip_Control                                                             NaN  \n",
       "Tire_Size                                                               NaN  \n",
       "Coupler                                                                 NaN  \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                                        None or Unspecified  \n",
       "Blade_Type                                                              PAT  \n",
       "Travel_Controls                                                       Lever  \n",
       "Differential_Type                                                       NaN  \n",
       "Steering_Controls                                                       NaN  \n",
       "saleYear                                                               1989  \n",
       "saleMonth                                                                 1  \n",
       "saleDay                                                                  31  \n",
       "saleDayofWeek                                                             1  \n",
       "saleDayofYear                                                            31  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9048d28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.api.types.is_string_dtype(df_tmp['UsageBand'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c2d4adc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsageBand\n",
      "fiModelDesc\n",
      "fiBaseModel\n",
      "fiSecondaryDesc\n",
      "fiModelSeries\n",
      "fiModelDescriptor\n",
      "ProductSize\n",
      "fiProductClassDesc\n",
      "state\n",
      "ProductGroup\n",
      "ProductGroupDesc\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"
     ]
    }
   ],
   "source": [
    "# Find the columns which contain strings\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b382e5fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a key: key1, this is a value: hello\n",
      "this is a key: key2, this is a value: world!\n"
     ]
    }
   ],
   "source": [
    "# If you're wondering what df.items() does, here's and example\n",
    "\n",
    "random_dict = {'key1': 'hello',\n",
    "               'key2': 'world!'}\n",
    "\n",
    "for key, value in random_dict.items():\n",
    "    print(f'this is a key: {key}, this is a value: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ec067e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will turn all of the string value into category values\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_string_dtype(content):\n",
    "        df_tmp[label] = content.astype('category').cat.as_ordered() # pandas will treat categories as numbers by adding codes, and they will be ordered as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9cc6726d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\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: 60.1 MB\n"
     ]
    }
   ],
   "source": [
    "df_tmp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6e322898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado',\n",
       "       'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho',\n",
       "       'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana',\n",
       "       'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',\n",
       "       'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada',\n",
       "       'New Hampshire', 'New Jersey', 'New Mexico', 'New York',\n",
       "       'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon',\n",
       "       'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina',\n",
       "       'South Dakota', 'Tennessee', 'Texas', 'Unspecified', 'Utah', 'Vermont',\n",
       "       'Virginia', 'Washington', 'Washington DC', 'West Virginia', 'Wisconsin',\n",
       "       'Wyoming'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.state.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "80eb5a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615    43\n",
       "274835     8\n",
       "141296     8\n",
       "212552     8\n",
       "62755      8\n",
       "          ..\n",
       "410879     4\n",
       "412476     4\n",
       "411927     4\n",
       "407124     4\n",
       "409203     4\n",
       "Length: 412698, dtype: int8"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.state.cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83a9d507",
   "metadata": {},
   "source": [
    "Thanks to pandas Categories, we now have a way to access all of our data in the form of numbers.\n",
    "\n",
    "But we still have a bunch of missing data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5d276ab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                     0.000000\n",
       "SalePrice                   0.000000\n",
       "MachineID                   0.000000\n",
       "ModelID                     0.000000\n",
       "datasource                  0.000000\n",
       "auctioneerID                0.048791\n",
       "YearMade                    0.000000\n",
       "MachineHoursCurrentMeter    0.642586\n",
       "UsageBand                   0.821492\n",
       "fiModelDesc                 0.000000\n",
       "fiBaseModel                 0.000000\n",
       "fiSecondaryDesc             0.340993\n",
       "fiModelSeries               0.857845\n",
       "fiModelDescriptor           0.818715\n",
       "ProductSize                 0.524851\n",
       "fiProductClassDesc          0.000000\n",
       "state                       0.000000\n",
       "ProductGroup                0.000000\n",
       "ProductGroupDesc            0.000000\n",
       "Drive_System                0.740520\n",
       "Enclosure                   0.000809\n",
       "Forks                       0.520921\n",
       "Pad_Type                    0.803498\n",
       "Ride_Control                0.629928\n",
       "Stick                       0.803498\n",
       "Transmission                0.544444\n",
       "Turbocharged                0.803498\n",
       "Blade_Extension             0.937041\n",
       "Blade_Width                 0.937041\n",
       "Enclosure_Type              0.937041\n",
       "Engine_Horsepower           0.937041\n",
       "Hydraulics                  0.200062\n",
       "Pushblock                   0.937041\n",
       "Ripper                      0.740864\n",
       "Scarifier                   0.937014\n",
       "Tip_Control                 0.937041\n",
       "Tire_Size                   0.763415\n",
       "Coupler                     0.465277\n",
       "Coupler_System              0.891024\n",
       "Grouser_Tracks              0.891264\n",
       "Hydraulics_Flow             0.891264\n",
       "Track_Type                  0.752378\n",
       "Undercarriage_Pad_Width     0.750626\n",
       "Stick_Length                0.752213\n",
       "Thumb                       0.752041\n",
       "Pattern_Changer             0.752213\n",
       "Grouser_Type                0.752378\n",
       "Backhoe_Mounting            0.804428\n",
       "Blade_Type                  0.801610\n",
       "Travel_Controls             0.801606\n",
       "Differential_Type           0.826595\n",
       "Steering_Controls           0.826697\n",
       "saleYear                    0.000000\n",
       "saleMonth                   0.000000\n",
       "saleDay                     0.000000\n",
       "saleDayofWeek               0.000000\n",
       "saleDayofYear               0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking missing data\n",
    "df_tmp.isnull().sum()/len(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827a63ac",
   "metadata": {},
   "source": [
    "### Save preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4fe36465",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export current tmp dataframe\n",
    "df_tmp.to_csv('data/bluebook-for-bulldozers/bluebook-for-bulldozers/train_tmp.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d54b2daa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALIENWARE\\AppData\\Local\\Temp\\ipykernel_11720\\2565789777.py:2: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df_tmp = pd.read_csv('data/bluebook-for-bulldozers/bluebook-for-bulldozers/train_tmp.csv')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>fiModelDesc</th>\n",
       "      <th>...</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",
       "      <th>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayofWeek</th>\n",
       "      <th>saleDayofYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD20</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A66</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D7G</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A62</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D3B</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>Lever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770     9500.0    1126363     8434         132          18.0      1974   \n",
       "1  1821514    14000.0    1194089    10150         132          99.0      1980   \n",
       "2  1505138    50000.0    1473654     4139         132          99.0      1978   \n",
       "3  1671174    16000.0    1327630     8591         132          99.0      1980   \n",
       "4  1329056    22000.0    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand fiModelDesc  ...     Backhoe_Mounting  \\\n",
       "0                       NaN       NaN        TD20  ...  None or Unspecified   \n",
       "1                       NaN       NaN         A66  ...                  NaN   \n",
       "2                       NaN       NaN         D7G  ...  None or Unspecified   \n",
       "3                       NaN       NaN         A62  ...                  NaN   \n",
       "4                       NaN       NaN         D3B  ...  None or Unspecified   \n",
       "\n",
       "  Blade_Type      Travel_Controls Differential_Type Steering_Controls  \\\n",
       "0   Straight  None or Unspecified               NaN               NaN   \n",
       "1        NaN                  NaN          Standard      Conventional   \n",
       "2   Straight  None or Unspecified               NaN               NaN   \n",
       "3        NaN                  NaN          Standard      Conventional   \n",
       "4        PAT                Lever               NaN               NaN   \n",
       "\n",
       "  saleYear saleMonth saleDay saleDayofWeek saleDayofYear  \n",
       "0     1989         1      17             1            17  \n",
       "1     1989         1      31             1            31  \n",
       "2     1989         1      31             1            31  \n",
       "3     1989         1      31             1            31  \n",
       "4     1989         1      31             1            31  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import preprocessed data\n",
    "df_tmp = pd.read_csv('data/bluebook-for-bulldozers/bluebook-for-bulldozers/train_tmp.csv')\n",
    "df_tmp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fcdfa4a",
   "metadata": {},
   "source": [
    "## Fill missing values\n",
    "\n",
    "### Fill numerical missing value first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2cf84fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "eca97cf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          8434\n",
       "1         10150\n",
       "2          4139\n",
       "3          8591\n",
       "4          4089\n",
       "          ...  \n",
       "412693     5266\n",
       "412694    19330\n",
       "412695    17244\n",
       "412696     3357\n",
       "412697     4701\n",
       "Name: ModelID, Length: 412698, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.ModelID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5fc49863",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auctioneerID\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "# Check for which numerical columns have null values\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8b3d1d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill numeric rows with the median\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            # Add a binary column which tells us if the data was missing\n",
    "            df_tmp[label+'_is_missing'] = pd.isnull(content)\n",
    "            # Fill missing numeric values with median\n",
    "            df_tmp[label] = content.fillna(content.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7d73270d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,\n",
       "       100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Demonstrate how median is more robust to outliers than mean\n",
    "hundreds = np.full((1000,),100)\n",
    "hundreds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "00482738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100.0, 999100.8991008991, 100.0, 100.0)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Demonstrate how median is more robust to outliers than mean\n",
    "hundreds = np.full((1000,),100)\n",
    "hundreds_billion = np.append(hundreds, 1000000000)\n",
    "np.mean(hundreds), np.mean(hundreds_billion), np.median(hundreds), np.median(hundreds_billion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5ab42ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check if there's any null numeric values\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "84e76dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    392562\n",
       "True      20136\n",
       "Name: auctioneerID_is_missing, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check too see how many examples were missing\n",
    "df_tmp.auctioneerID_is_missing.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f2be5ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     265194\n",
       "False    147504\n",
       "Name: MachineHoursCurrentMeter_is_missing, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.MachineHoursCurrentMeter_is_missing.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2c7ea113",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df849f33",
   "metadata": {},
   "source": [
    "### Filling and turning categorical variables into numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9e6a5db5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsageBand\n",
      "fiModelDesc\n",
      "fiBaseModel\n",
      "fiSecondaryDesc\n",
      "fiModelSeries\n",
      "fiModelDescriptor\n",
      "ProductSize\n",
      "fiProductClassDesc\n",
      "state\n",
      "ProductGroup\n",
      "ProductGroupDesc\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"
     ]
    }
   ],
   "source": [
    "# Check for columns which aren't numeric\n",
    "for label, content in df_tmp.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ff3c49a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn categorical variables into numbers and fill missing\n",
    "for label, content in df_tmp.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        # Add binary column to indicate whether sample had missing value\n",
    "        df_tmp[label+'_is_missing'] = pd.isnull(content)\n",
    "        # Turn categorical into numbers and add +1\n",
    "        df_tmp[label] = pd.Categorical(content).codes+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "137adfb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43,  8,  8, ...,  4,  4,  4], dtype=int8)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_tmp['state']).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "cd1ebb1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, ..., -1, -1, -1], dtype=int8)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_tmp['UsageBand']).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "bc04603f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Columns: 103 entries, SalesID to Steering_Controls_is_missing\n",
      "dtypes: bool(46), float64(3), int16(4), int64(10), int8(40)\n",
      "memory usage: 77.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_tmp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5bad6b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>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>1646770</td>\n",
       "      <td>1821514</td>\n",
       "      <td>1505138</td>\n",
       "      <td>1671174</td>\n",
       "      <td>1329056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>9500.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>22000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>1126363</td>\n",
       "      <td>1194089</td>\n",
       "      <td>1473654</td>\n",
       "      <td>1327630</td>\n",
       "      <td>1336053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>8434</td>\n",
       "      <td>10150</td>\n",
       "      <td>4139</td>\n",
       "      <td>8591</td>\n",
       "      <td>4089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0        1        2        3        4\n",
       "SalesID                       1646770  1821514  1505138  1671174  1329056\n",
       "SalePrice                      9500.0  14000.0  50000.0  16000.0  22000.0\n",
       "MachineID                     1126363  1194089  1473654  1327630  1336053\n",
       "ModelID                          8434    10150     4139     8591     4089\n",
       "datasource                        132      132      132      132      132\n",
       "...                               ...      ...      ...      ...      ...\n",
       "Backhoe_Mounting_is_missing     False     True    False     True    False\n",
       "Blade_Type_is_missing           False     True    False     True    False\n",
       "Travel_Controls_is_missing      False     True    False     True    False\n",
       "Differential_Type_is_missing     True    False     True    False     True\n",
       "Steering_Controls_is_missing     True    False     True    False     True\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d8a5e841",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de1e9dfb",
   "metadata": {},
   "source": [
    "Now that all of data is numeric as well as our dataframe has no missing values, we should be able to build a machine learning model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "24c6a874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <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>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width_is_missing</th>\n",
       "      <th>Stick_Length_is_missing</th>\n",
       "      <th>Thumb_is_missing</th>\n",
       "      <th>Pattern_Changer_is_missing</th>\n",
       "      <th>Grouser_Type_is_missing</th>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
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       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2348</td>\n",
       "      <td>...</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770     9500.0    1126363     8434         132          18.0      1974   \n",
       "1  1821514    14000.0    1194089    10150         132          99.0      1980   \n",
       "2  1505138    50000.0    1473654     4139         132          99.0      1978   \n",
       "3  1671174    16000.0    1327630     8591         132          99.0      1980   \n",
       "4  1329056    22000.0    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  ...  \\\n",
       "0                       0.0          0         4593  ...   \n",
       "1                       0.0          0         1820  ...   \n",
       "2                       0.0          0         2348  ...   \n",
       "3                       0.0          0         1819  ...   \n",
       "4                       0.0          0         2119  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                True                     True   \n",
       "1                                True                     True   \n",
       "2                                True                     True   \n",
       "3                                True                     True   \n",
       "4                                True                     True   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0              True                        True                     True   \n",
       "1              True                        True                     True   \n",
       "2              True                        True                     True   \n",
       "3              True                        True                     True   \n",
       "4              True                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                        False                  False   \n",
       "1                         True                   True   \n",
       "2                        False                  False   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                       False                          True   \n",
       "1                        True                         False   \n",
       "2                       False                          True   \n",
       "3                        True                         False   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                          True  \n",
       "3                         False  \n",
       "4                          True  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "965b14dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "412698"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "cffe9753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 20min 41s\n",
      "Wall time: 2min 49s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</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(n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Instantiate model\n",
    "model = RandomForestRegressor(n_jobs=-1, random_state=42)\n",
    "\n",
    "# Fit the model\n",
    "model.fit(df_tmp.drop('SalePrice', axis=1), df_tmp['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "2f4fa572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9875468079970562"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score the model\n",
    "model.score(df_tmp.drop('SalePrice', axis=1), df_tmp['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76da1119",
   "metadata": {},
   "source": [
    "**Question:** Why doesn't the above metric hold water? (Why isn't the metric reliablem)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2284d714",
   "metadata": {},
   "source": [
    "### Splitting data into train/validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "df37a604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width_is_missing</th>\n",
       "      <th>Stick_Length_is_missing</th>\n",
       "      <th>Thumb_is_missing</th>\n",
       "      <th>Pattern_Changer_is_missing</th>\n",
       "      <th>Grouser_Type_is_missing</th>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4593</td>\n",
       "      <td>...</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1820</td>\n",
       "      <td>...</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2348</td>\n",
       "      <td>...</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1819</td>\n",
       "      <td>...</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2119</td>\n",
       "      <td>...</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770     9500.0    1126363     8434         132          18.0      1974   \n",
       "1  1821514    14000.0    1194089    10150         132          99.0      1980   \n",
       "2  1505138    50000.0    1473654     4139         132          99.0      1978   \n",
       "3  1671174    16000.0    1327630     8591         132          99.0      1980   \n",
       "4  1329056    22000.0    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  ...  \\\n",
       "0                       0.0          0         4593  ...   \n",
       "1                       0.0          0         1820  ...   \n",
       "2                       0.0          0         2348  ...   \n",
       "3                       0.0          0         1819  ...   \n",
       "4                       0.0          0         2119  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                True                     True   \n",
       "1                                True                     True   \n",
       "2                                True                     True   \n",
       "3                                True                     True   \n",
       "4                                True                     True   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0              True                        True                     True   \n",
       "1              True                        True                     True   \n",
       "2              True                        True                     True   \n",
       "3              True                        True                     True   \n",
       "4              True                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                        False                  False   \n",
       "1                         True                   True   \n",
       "2                        False                  False   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                       False                          True   \n",
       "1                        True                         False   \n",
       "2                       False                          True   \n",
       "3                        True                         False   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                          True  \n",
       "3                         False  \n",
       "4                          True  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1750b95e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1989\n",
       "1         1989\n",
       "2         1989\n",
       "3         1989\n",
       "4         1989\n",
       "          ... \n",
       "412693    2012\n",
       "412694    2012\n",
       "412695    2012\n",
       "412696    2012\n",
       "412697    2012\n",
       "Name: saleYear, Length: 412698, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.saleYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "67b4fdb6",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.saleYear.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "fc2e0f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 401125)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into training and validation\n",
    "df_val = df_tmp[df_tmp.saleYear == 2012]\n",
    "df_train = df_tmp[df_tmp.saleYear != 2012]\n",
    "len(df_val), len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ee62a78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((401125, 102), (401125,), (11573, 102), (11573,))"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into X and y\n",
    "X_train, y_train = df_train.drop('SalePrice', axis =1), df_train.SalePrice\n",
    "X_valid, y_valid = df_val.drop('SalePrice', axis =1), df_val.SalePrice\n",
    "X_train.shape, y_train.shape, X_valid.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "a1c2f00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          9500.0\n",
       "1         14000.0\n",
       "2         50000.0\n",
       "3         16000.0\n",
       "4         22000.0\n",
       "           ...   \n",
       "401120    29000.0\n",
       "401121    11000.0\n",
       "401122    11000.0\n",
       "401123    18000.0\n",
       "401124    13500.0\n",
       "Name: SalePrice, Length: 401125, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5e17e4",
   "metadata": {},
   "source": [
    "### Building an evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "e1524d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create evaluation function (the competition use RMSLE)\n",
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error, r2_score\n",
    "\n",
    "def rmsle(y_test, y_preds):\n",
    "    '''\n",
    "    Calculate root mean square log error between predictions and true labels.\n",
    "    '''\n",
    "    return np.sqrt(mean_squared_log_error(y_test, y_preds))\n",
    "\n",
    "# Create function to evaluate model on a few different levels\n",
    "def show_scores(model):\n",
    "    train_preds = model.predict(X_train)\n",
    "    val_preds = model.predict(X_valid)\n",
    "    scores = {'Training MAE': mean_absolute_error(y_train, train_preds),\n",
    "              'Valid MAE': mean_absolute_error(y_valid, val_preds),\n",
    "              'Training RMSLE': rmsle(y_train, train_preds),\n",
    "              'Valid RMSLE': rmsle(y_valid, val_preds),\n",
    "              'Training R^2': r2_score(y_train, train_preds),\n",
    "              'Valid R^2': r2_score(y_valid, val_preds)}\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a38bcae2",
   "metadata": {},
   "source": [
    "## Testing our model on a subset (to tune the hyperparameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "c817963f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 0 ns\n",
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# This takes far too long...for experimenting \n",
    "\n",
    "# model = RandomForestRegressor(n_jobs=-1, random_state=42)\n",
    "# model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e99b5ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change max_samples value\n",
    "model = RandomForestRegressor(n_jobs=-1, random_state=42, max_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "b6990d59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 40.3 s\n",
      "Wall time: 5.64 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Cutting down on the max number of samples each estimator can see improves training time\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "501ca974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5561.2988092240585,\n",
       " 'Valid MAE': 7177.26365505919,\n",
       " 'Training RMSLE': 0.257745378256977,\n",
       " 'Valid RMSLE': 0.29362638671089003,\n",
       " 'Training R^2': 0.8606658995199189,\n",
       " 'Valid R^2': 0.8320374995090507}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270c416e",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "6db23d12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n",
      "CPU times: total: 17.9 s\n",
      "Wall time: 23.8 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "                   n_iter=2,\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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "                   n_iter=2,\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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "                   n_iter=2,\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)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Different RandomForestRegressor hyperparameters\n",
    "rf_grid = {'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",
    "# Instantiate RandomizedSearchCV model\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(n_jobs=-1, random_state=42), param_distributions=rf_grid, n_iter=2, cv=5, verbose=True)\n",
    "\n",
    "# Fit the RandomizedSearchCV model\n",
    "rs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f3f7136a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 80,\n",
       " 'min_samples_split': 16,\n",
       " 'min_samples_leaf': 11,\n",
       " 'max_samples': 10000,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best model hyperparameters\n",
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "01886427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 8347.524995802843,\n",
       " 'Valid MAE': 9915.885374990145,\n",
       " 'Training RMSLE': 0.36685398434860705,\n",
       " 'Valid RMSLE': 0.3956498668750463,\n",
       " 'Training R^2': 0.7158410256551295,\n",
       " 'Valid R^2': 0.7103168326132097}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the RandomizedSearch model\n",
    "show_scores(rs_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb0d50c",
   "metadata": {},
   "source": [
    "### Train a model with the best hyperparameters\n",
    "**Note:** These were found after 100 iterations of `RandomizedSearchCV`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "8cb02288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3min 50s\n",
      "Wall time: 32.5 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=40,\n",
       "                      n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=40,\n",
       "                      n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=40,\n",
       "                      n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Most ideal hyperparameters\n",
    "ideal_model = RandomForestRegressor(n_estimators=40, min_samples_leaf=1, min_samples_split=14, max_features=0.5, n_jobs=-1, max_samples=None, random_state=42)\n",
    "\n",
    "# Fit the model\n",
    "ideal_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "ec6feb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 2953.8161137163484,\n",
       " 'Valid MAE': 5951.247761444453,\n",
       " 'Training RMSLE': 0.14469006962371855,\n",
       " 'Valid RMSLE': 0.24524163989538328,\n",
       " 'Training R^2': 0.9588145522577225,\n",
       " 'Valid R^2': 0.8818019502450094}"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scores for ideal model (trained on all data)\n",
    "show_scores(ideal_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "446dec82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 8347.524995802843,\n",
       " 'Valid MAE': 9915.885374990145,\n",
       " 'Training RMSLE': 0.36685398434860705,\n",
       " 'Valid RMSLE': 0.3956498668750463,\n",
       " 'Training R^2': 0.7158410256551295,\n",
       " 'Valid R^2': 0.7103168326132098}"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scores on rs_model (only trained on ~10,000 examples)\n",
    "show_scores(rs_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36630de1",
   "metadata": {},
   "source": [
    "## Make preidictions on test data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "5f3fe4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</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>fiModelDesc</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>1227829</td>\n",
       "      <td>1006309</td>\n",
       "      <td>3168</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-03</td>\n",
       "      <td>580G</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>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>936</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>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>EC210BLC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>9' 6\"</td>\n",
       "      <td>Manual</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</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>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>330CL</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Triple</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>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>650K</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>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "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]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the test data\n",
    "df_test = pd.read_csv('data/bluebook-for-bulldozers/bluebook-for-bulldozers/Test.csv', low_memory=False, parse_dates=['saledate'])\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ad48b1d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALIENWARE\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
      "Feature names unseen at fit time:\n",
      "- saledate\n",
      "Feature names seen at fit time, yet now missing:\n",
      "- Backhoe_Mounting_is_missing\n",
      "- Blade_Extension_is_missing\n",
      "- Blade_Type_is_missing\n",
      "- Blade_Width_is_missing\n",
      "- Coupler_System_is_missing\n",
      "- ...\n",
      "\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Low'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [158]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Make predictions on the test dataset\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m test_preds \u001b[38;5;241m=\u001b[39m \u001b[43mideal_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:991\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    989\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    990\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m--> 991\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    993\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m    994\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:605\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    602\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    603\u001b[0m \u001b[38;5;124;03mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[0;32m    604\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 605\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m    607\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\base.py:577\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    575\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation should be done on X, y or both.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    576\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[1;32m--> 577\u001b[0m     X \u001b[38;5;241m=\u001b[39m check_array(X, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n\u001b[0;32m    578\u001b[0m     out \u001b[38;5;241m=\u001b[39m X\n\u001b[0;32m    579\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\utils\\validation.py:856\u001b[0m, in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[0;32m    854\u001b[0m         array \u001b[38;5;241m=\u001b[39m array\u001b[38;5;241m.\u001b[39mastype(dtype, casting\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munsafe\u001b[39m\u001b[38;5;124m\"\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    855\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 856\u001b[0m         array \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    857\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[0;32m    858\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    859\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[0;32m    860\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\pandas\\core\\generic.py:2064\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2063\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array__\u001b[39m(\u001b[38;5;28mself\u001b[39m, dtype: npt\u001b[38;5;241m.\u001b[39mDTypeLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray:\n\u001b[1;32m-> 2064\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test dataset\n",
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "ab7fa31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                         0\n",
       "MachineID                       0\n",
       "ModelID                         0\n",
       "datasource                      0\n",
       "auctioneerID                    0\n",
       "YearMade                        0\n",
       "MachineHoursCurrentMeter    10328\n",
       "UsageBand                   10623\n",
       "saledate                        0\n",
       "fiModelDesc                     0\n",
       "fiBaseModel                     0\n",
       "fiSecondaryDesc              3975\n",
       "fiModelSeries               10451\n",
       "fiModelDescriptor            9433\n",
       "ProductSize                  6409\n",
       "fiProductClassDesc              0\n",
       "state                           0\n",
       "ProductGroup                    0\n",
       "ProductGroupDesc                0\n",
       "Drive_System                 9698\n",
       "Enclosure                       2\n",
       "Forks                        6149\n",
       "Pad_Type                    10349\n",
       "Ride_Control                 8216\n",
       "Stick                       10349\n",
       "Transmission                 7639\n",
       "Turbocharged                10349\n",
       "Blade_Extension             11806\n",
       "Blade_Width                 11806\n",
       "Enclosure_Type              11806\n",
       "Engine_Horsepower           11806\n",
       "Hydraulics                   2142\n",
       "Pushblock                   11806\n",
       "Ripper                       9753\n",
       "Scarifier                   11806\n",
       "Tip_Control                 11806\n",
       "Tire_Size                    9679\n",
       "Coupler                      4856\n",
       "Coupler_System              10391\n",
       "Grouser_Tracks              10391\n",
       "Hydraulics_Flow             10391\n",
       "Track_Type                   9063\n",
       "Undercarriage_Pad_Width      9059\n",
       "Stick_Length                 9063\n",
       "Thumb                        9062\n",
       "Pattern_Changer              9063\n",
       "Grouser_Type                 9063\n",
       "Backhoe_Mounting            10406\n",
       "Blade_Type                  10399\n",
       "Travel_Controls             10399\n",
       "Differential_Type           10328\n",
       "Steering_Controls           10328\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "aa0ce31e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12457 entries, 0 to 12456\n",
      "Data columns (total 52 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   SalesID                   12457 non-null  int64         \n",
      " 1   MachineID                 12457 non-null  int64         \n",
      " 2   ModelID                   12457 non-null  int64         \n",
      " 3   datasource                12457 non-null  int64         \n",
      " 4   auctioneerID              12457 non-null  int64         \n",
      " 5   YearMade                  12457 non-null  int64         \n",
      " 6   MachineHoursCurrentMeter  2129 non-null   float64       \n",
      " 7   UsageBand                 1834 non-null   object        \n",
      " 8   saledate                  12457 non-null  datetime64[ns]\n",
      " 9   fiModelDesc               12457 non-null  object        \n",
      " 10  fiBaseModel               12457 non-null  object        \n",
      " 11  fiSecondaryDesc           8482 non-null   object        \n",
      " 12  fiModelSeries             2006 non-null   object        \n",
      " 13  fiModelDescriptor         3024 non-null   object        \n",
      " 14  ProductSize               6048 non-null   object        \n",
      " 15  fiProductClassDesc        12457 non-null  object        \n",
      " 16  state                     12457 non-null  object        \n",
      " 17  ProductGroup              12457 non-null  object        \n",
      " 18  ProductGroupDesc          12457 non-null  object        \n",
      " 19  Drive_System              2759 non-null   object        \n",
      " 20  Enclosure                 12455 non-null  object        \n",
      " 21  Forks                     6308 non-null   object        \n",
      " 22  Pad_Type                  2108 non-null   object        \n",
      " 23  Ride_Control              4241 non-null   object        \n",
      " 24  Stick                     2108 non-null   object        \n",
      " 25  Transmission              4818 non-null   object        \n",
      " 26  Turbocharged              2108 non-null   object        \n",
      " 27  Blade_Extension           651 non-null    object        \n",
      " 28  Blade_Width               651 non-null    object        \n",
      " 29  Enclosure_Type            651 non-null    object        \n",
      " 30  Engine_Horsepower         651 non-null    object        \n",
      " 31  Hydraulics                10315 non-null  object        \n",
      " 32  Pushblock                 651 non-null    object        \n",
      " 33  Ripper                    2704 non-null   object        \n",
      " 34  Scarifier                 651 non-null    object        \n",
      " 35  Tip_Control               651 non-null    object        \n",
      " 36  Tire_Size                 2778 non-null   object        \n",
      " 37  Coupler                   7601 non-null   object        \n",
      " 38  Coupler_System            2066 non-null   object        \n",
      " 39  Grouser_Tracks            2066 non-null   object        \n",
      " 40  Hydraulics_Flow           2066 non-null   object        \n",
      " 41  Track_Type                3394 non-null   object        \n",
      " 42  Undercarriage_Pad_Width   3398 non-null   object        \n",
      " 43  Stick_Length              3394 non-null   object        \n",
      " 44  Thumb                     3395 non-null   object        \n",
      " 45  Pattern_Changer           3394 non-null   object        \n",
      " 46  Grouser_Type              3394 non-null   object        \n",
      " 47  Backhoe_Mounting          2051 non-null   object        \n",
      " 48  Blade_Type                2058 non-null   object        \n",
      " 49  Travel_Controls           2058 non-null   object        \n",
      " 50  Differential_Type         2129 non-null   object        \n",
      " 51  Steering_Controls         2129 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(6), object(44)\n",
      "memory usage: 4.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0650d371",
   "metadata": {},
   "source": [
    "### Preprocessing the data (getting the test dataset in the same format as our training dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "5583b02c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(df):\n",
    "    '''\n",
    "    Performs transformations on df and returns transformed df.\n",
    "    '''\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",
    "    df.drop('saledate', axis=1, inplace=True)\n",
    "    \n",
    "    # Fill the numeric rows with median\n",
    "    for label, content in df.items():\n",
    "        if pd.api.types.is_numeric_dtype(content):\n",
    "            if pd.isnull(content).sum():\n",
    "                # Add a binary column which tells us if the data was missing\n",
    "                df[label+'_is_missing'] = pd.isnull(content)\n",
    "                # Fill missing numeric values with median\n",
    "                df[label] = content.fillna(content.median())\n",
    "       \n",
    "            # Fill categorical missing data and turn categorical into numbers\n",
    "        if not pd.api.types.is_numeric_dtype(content):\n",
    "            \n",
    "            # Add a binary column which tells us if the data was missing\n",
    "            df[label+'_is_missing'] = pd.isnull(content)\n",
    "            # We add +1 to the category code because pandas encodes missing categories as -1\n",
    "            df[label] = pd.Categorical(content).codes+1\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "dac3e886",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>1</td>\n",
       "      <td>831</td>\n",
       "      <td>292</td>\n",
       "      <td>...</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>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1177</td>\n",
       "      <td>404</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>1</td>\n",
       "      <td>287</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</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",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "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  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                True                     True   \n",
       "1                                True                     True   \n",
       "2                               False                    False   \n",
       "3                               False                    False   \n",
       "4                                True                     True   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0              True                        True                     True   \n",
       "1              True                        True                     True   \n",
       "2             False                       False                    False   \n",
       "3             False                       False                    False   \n",
       "4              True                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                         True                   True   \n",
       "1                         True                   True   \n",
       "2                         True                   True   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                        True                          True   \n",
       "1                        True                         False   \n",
       "2                        True                          True   \n",
       "3                        True                          True   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                          True  \n",
       "3                          True  \n",
       "4                          True  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Process test data\n",
    "df_test = preprocess_data(df_test)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "3ea6d4a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALIENWARE\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
      "Feature names seen at fit time, yet now missing:\n",
      "- auctioneerID_is_missing\n",
      "\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "X has 101 features, but RandomForestRegressor is expecting 102 features as input.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [222]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Make predictions on updated test data\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m test_preds \u001b[38;5;241m=\u001b[39m \u001b[43mideal_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:991\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    989\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    990\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m--> 991\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    993\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m    994\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:605\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    602\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    603\u001b[0m \u001b[38;5;124;03mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[0;32m    604\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 605\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m    607\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\base.py:600\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    597\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    599\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 600\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_n_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    602\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "File \u001b[1;32m~\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\base.py:400\u001b[0m, in \u001b[0;36mBaseEstimator._check_n_features\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_features \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_:\n\u001b[1;32m--> 400\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    401\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_features\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features, but \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    402\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis expecting \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features as input.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    403\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: X has 101 features, but RandomForestRegressor is expecting 102 features as input."
     ]
    }
   ],
   "source": [
    "# Make predictions on updated test data\n",
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "70b2999a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Grouser_Type_is_missing</th>\n",
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       "      <td>1821514</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
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       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>1671174</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1329056</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2119</td>\n",
       "      <td>683</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770    1126363     8434         132          18.0      1974   \n",
       "1  1821514    1194089    10150         132          99.0      1980   \n",
       "2  1505138    1473654     4139         132          99.0      1978   \n",
       "3  1671174    1327630     8591         132          99.0      1980   \n",
       "4  1329056    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                       0.0          0         4593         1744  ...   \n",
       "1                       0.0          0         1820          559  ...   \n",
       "2                       0.0          0         2348          713  ...   \n",
       "3                       0.0          0         1819          558  ...   \n",
       "4                       0.0          0         2119          683  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                True                     True   \n",
       "1                                True                     True   \n",
       "2                                True                     True   \n",
       "3                                True                     True   \n",
       "4                                True                     True   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0              True                        True                     True   \n",
       "1              True                        True                     True   \n",
       "2              True                        True                     True   \n",
       "3              True                        True                     True   \n",
       "4              True                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                        False                  False   \n",
       "1                         True                   True   \n",
       "2                        False                  False   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                       False                          True   \n",
       "1                        True                         False   \n",
       "2                       False                          True   \n",
       "3                        True                         False   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                          True  \n",
       "3                         False  \n",
       "4                          True  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "b740c8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'auctioneerID_is_missing'}"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can find how the columns differ using sets\n",
    "set(X_train.columns) - set(df_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "a5f95af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   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  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Stick_Length_is_missing  Thumb_is_missing  Pattern_Changer_is_missing  \\\n",
       "0                     True              True                        True   \n",
       "1                     True              True                        True   \n",
       "2                    False             False                       False   \n",
       "3                    False             False                       False   \n",
       "4                     True              True                        True   \n",
       "\n",
       "   Grouser_Type_is_missing  Backhoe_Mounting_is_missing  \\\n",
       "0                     True                         True   \n",
       "1                     True                         True   \n",
       "2                    False                         True   \n",
       "3                    False                         True   \n",
       "4                     True                        False   \n",
       "\n",
       "   Blade_Type_is_missing  Travel_Controls_is_missing  \\\n",
       "0                   True                        True   \n",
       "1                   True                        True   \n",
       "2                   True                        True   \n",
       "3                   True                        True   \n",
       "4                  False                       False   \n",
       "\n",
       "   Differential_Type_is_missing  Steering_Controls_is_missing  \\\n",
       "0                          True                          True   \n",
       "1                         False                         False   \n",
       "2                          True                          True   \n",
       "3                          True                          True   \n",
       "4                          True                          True   \n",
       "\n",
       "   auctioneerID_is_missing  \n",
       "0                    False  \n",
       "1                    False  \n",
       "2                    False  \n",
       "3                    False  \n",
       "4                    False  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manually adjust df_test to have auctioneerID_is_missing column\n",
    "df_test['auctioneerID_is_missing'] = False\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5d4232",
   "metadata": {},
   "source": [
    "Finally now our test dataframe has the same features as our training dataframe, we can make predictions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "e34c789f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ALIENWARE\\Desktop\\bulldozer-price-predictions\\env\\lib\\site-packages\\sklearn\\base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
      "Feature names must be in the same order as they were in fit.\n",
      "\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test data\n",
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "621ea168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20614.36780887, 19897.80170658, 44852.21959446, ...,\n",
       "       14296.98620472, 22164.85757662, 31683.80063427])"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ee3901",
   "metadata": {},
   "source": [
    "We've made some predictions but they're not in the same format Kaggle is asking for: https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "e53b2882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>12453</th>\n",
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       "</table>\n",
       "<p>12457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SalesID    SalesPrice\n",
       "0      1227829  20614.367809\n",
       "1      1227844  19897.801707\n",
       "2      1227847  44852.219594\n",
       "3      1227848  68346.325323\n",
       "4      1227863  39487.349708\n",
       "...        ...           ...\n",
       "12452  6643171  46466.092910\n",
       "12453  6643173  17500.493352\n",
       "12454  6643184  14296.986205\n",
       "12455  6643186  22164.857577\n",
       "12456  6643196  31683.800634\n",
       "\n",
       "[12457 rows x 2 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format predictions into the same format Kaggle is after\n",
    "df_preds = pd.DataFrame()\n",
    "df_preds['SalesID'] = df_test['SalesID']\n",
    "df_preds['SalesPrice'] = test_preds\n",
    "df_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "bd4e73c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export prediction data to CSV\n",
    "df_preds.to_csv('data/bluebook-for-bulldozers/bluebook-for-bulldozers/test_predictions.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68d2603",
   "metadata": {},
   "source": [
    "### Feature importance\n",
    "\n",
    "Feature importance seeks to figure out which different attributes of the data were most importance when it comes to predicting the **target variable** (SalePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "eb278d45",
   "metadata": {},
   "outputs": [
    {
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       "       5.29163902e-06, 1.11952381e-04, 4.78452431e-06, 3.43582863e-04,\n",
       "       5.57068428e-06, 1.07167376e-03, 3.99179008e-03, 4.07753410e-03,\n",
       "       1.05749617e-04, 2.76528927e-03, 2.59244312e-05, 3.51888176e-04,\n",
       "       2.31519337e-03, 1.99211177e-03, 4.02034629e-03, 2.03778082e-04,\n",
       "       1.13483313e-02, 9.02551628e-04, 1.58182497e-03, 4.63243398e-05,\n",
       "       2.92071004e-04, 3.11923094e-05, 1.56873538e-04, 2.87205987e-05,\n",
       "       3.80543083e-05, 2.55045807e-04, 1.66878572e-04, 2.10341792e-04,\n",
       "       1.26024842e-04, 9.40663015e-05])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find feature importance of our best model\n",
    "ideal_model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "06b79923",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function for plotting feature importance\n",
    "def plot_features(columns, importances, n=20):\n",
    "    df = (pd.DataFrame({'features': columns,\n",
    "                        'feature_importances': importances}).sort_values('feature_importances', ascending=False).reset_index(drop=True))\n",
    "    # Plot the dataframe\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.barh(df['features'][:n], df['feature_importances'][:20])\n",
    "    ax.set_ylabel('Features')\n",
    "    ax.set_xlabel('Feature importance')\n",
    "    ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "5862ccd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_features(X_train.columns, ideal_model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "af1e7305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium            64342\n",
       "Large / Medium    51297\n",
       "Small             27057\n",
       "Mini              25721\n",
       "Large             21396\n",
       "Compact            6280\n",
       "Name: ProductSize, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ProductSize'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8639aac7",
   "metadata": {},
   "source": [
    "**Question to finish:** Why might knowing the feature importance of a trained machine learning model be helpful?\n",
    "\n",
    "**Final chalenge:** What other machine learning models could you try on our dataset? Hint: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html check out the regression section of this map, or try to look at something like CatBoost.ai or XGBooost.ai."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33da8091",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}