bulldozer-price-project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "146eb58a",
   "metadata": {},
   "source": [
    "# 🚜 Predicting the Sale Price of Bulldozers using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652602b4",
   "metadata": {},
   "source": [
    "In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203e2137",
   "metadata": {},
   "source": [
    "# 1. Problem defition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e7ac38",
   "metadata": {},
   "source": [
    "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?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "050d7365",
   "metadata": {},
   "source": [
    "# 2. Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d46a109",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b54333",
   "metadata": {},
   "source": [
    "# 3. Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ac9696",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a148ce0f",
   "metadata": {},
   "source": [
    "# 4. Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aaffe319",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0a755743",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import training and validation sets\n",
    "df = pd.read_csv(\"bluebook-for-bulldozers/TrainAndValid.csv\",\n",
    "                 low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70f1ed94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7121d8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <td>66000.0</td>\n",
       "      <td>999089</td>\n",
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       "      <td>68.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1139248</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>117657</td>\n",
       "      <td>77</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>3/26/2004 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1139249</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>434808</td>\n",
       "      <td>7009</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2/26/2004 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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",
       "      <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>7/23/2009 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",
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       "<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  ...  \\\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]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc9e19f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "412698"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6881fd89",
   "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",
       "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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69d1e8ae",
   "metadata": {},
   "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'),\n",
       " 53)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns,len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9873e437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SalePrice.plot.hist(ylabel=\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "865da9d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df[\"saledate\"][:1000], df[\"SalePrice\"][:1000]);\n",
    "#plt.xlabel(\"Date\");\n",
    "plt.ylabel(\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "184a31ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7835c74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'11/16/2006 0:00'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "403cb89e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data again but this time parse dates\n",
    "df = pd.read_csv(\"bluebook-for-bulldozers/TrainAndValid.csv\",\n",
    "                 low_memory=False,\n",
    "                 parse_dates=[\"saledate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48dec414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "950114b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2006-11-16 00:00:00')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a3fc3181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df[\"saledate\"][:1000], df[\"SalePrice\"][:1000]);\n",
    "plt.xlabel(\"Year\");\n",
    "plt.ylabel(\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b74067b",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad2161b1",
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66a0ed4f",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "46ad323d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort datewise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "afe18fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by=[\"saledate\"],inplace=True,ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "446a6ade",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6adc4f6a",
   "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",
       "  </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",
       "\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",
       "\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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f8f2df88",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa4fea1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add date time parameter for saledate columns\n",
    "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": 26,
   "id": "f3d5c9f9",
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ed693364",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "958530cf",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>2.011161e+06</td>\n",
       "      <td>1.080068e+06</td>\n",
       "      <td>1139246.0</td>\n",
       "      <td>1421897.75</td>\n",
       "      <td>1645852.5</td>\n",
       "      <td>2261012.50</td>\n",
       "      <td>6333349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>3.121518e+04</td>\n",
       "      <td>2.314174e+04</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>14500.00</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>142000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>1.230061e+06</td>\n",
       "      <td>4.539533e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1088593.25</td>\n",
       "      <td>1284397.0</td>\n",
       "      <td>1478079.25</td>\n",
       "      <td>2486330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>6.947202e+03</td>\n",
       "      <td>6.280825e+03</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3261.00</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>8899.00</td>\n",
       "      <td>37198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>1.351694e+02</td>\n",
       "      <td>9.646749e+00</td>\n",
       "      <td>121.0</td>\n",
       "      <td>132.00</td>\n",
       "      <td>132.0</td>\n",
       "      <td>136.00</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>392562.0</td>\n",
       "      <td>6.585268e+00</td>\n",
       "      <td>1.715841e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>1.899050e+03</td>\n",
       "      <td>2.921902e+02</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1985.00</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>2001.00</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <td>147504.0</td>\n",
       "      <td>3.522988e+03</td>\n",
       "      <td>2.716993e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3209.00</td>\n",
       "      <td>2483300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleYear</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>2.004317e+03</td>\n",
       "      <td>5.821090e+00</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2009.00</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleMonth</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>6.300268e+00</td>\n",
       "      <td>3.437276e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDay</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>1.615289e+01</td>\n",
       "      <td>8.429510e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>2.594338e+00</td>\n",
       "      <td>1.417971e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfYear</th>\n",
       "      <td>412698.0</td>\n",
       "      <td>1.767841e+02</td>\n",
       "      <td>1.039107e+02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82.00</td>\n",
       "      <td>165.0</td>\n",
       "      <td>270.00</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count          mean           std        min  \\\n",
       "SalesID                   412698.0  2.011161e+06  1.080068e+06  1139246.0   \n",
       "SalePrice                 412698.0  3.121518e+04  2.314174e+04     4750.0   \n",
       "MachineID                 412698.0  1.230061e+06  4.539533e+05        0.0   \n",
       "ModelID                   412698.0  6.947202e+03  6.280825e+03       28.0   \n",
       "datasource                412698.0  1.351694e+02  9.646749e+00      121.0   \n",
       "auctioneerID              392562.0  6.585268e+00  1.715841e+01        0.0   \n",
       "YearMade                  412698.0  1.899050e+03  2.921902e+02     1000.0   \n",
       "MachineHoursCurrentMeter  147504.0  3.522988e+03  2.716993e+04        0.0   \n",
       "saleYear                  412698.0  2.004317e+03  5.821090e+00     1989.0   \n",
       "saleMonth                 412698.0  6.300268e+00  3.437276e+00        1.0   \n",
       "saleDay                   412698.0  1.615289e+01  8.429510e+00        1.0   \n",
       "saleDayOfWeek             412698.0  2.594338e+00  1.417971e+00        0.0   \n",
       "saleDayOfYear             412698.0  1.767841e+02  1.039107e+02        1.0   \n",
       "\n",
       "                                 25%        50%         75%        max  \n",
       "SalesID                   1421897.75  1645852.5  2261012.50  6333349.0  \n",
       "SalePrice                   14500.00    24000.0    40000.00   142000.0  \n",
       "MachineID                 1088593.25  1284397.0  1478079.25  2486330.0  \n",
       "ModelID                      3261.00     4605.0     8899.00    37198.0  \n",
       "datasource                    132.00      132.0      136.00      173.0  \n",
       "auctioneerID                    1.00        2.0        4.00       99.0  \n",
       "YearMade                     1985.00     1995.0     2001.00     2014.0  \n",
       "MachineHoursCurrentMeter        0.00        0.0     3209.00  2483300.0  \n",
       "saleYear                     2000.00     2006.0     2009.00     2012.0  \n",
       "saleMonth                       3.00        6.0        9.00       12.0  \n",
       "saleDay                         9.00       16.0       23.00       31.0  \n",
       "saleDayOfWeek                   2.00        3.0        3.00        6.0  \n",
       "saleDayOfYear                  82.00      165.0      270.00      365.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "de1a3b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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>SalesID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023119</td>\n",
       "      <td>0.310149</td>\n",
       "      <td>0.147883</td>\n",
       "      <td>0.768909</td>\n",
       "      <td>-0.003298</td>\n",
       "      <td>0.005316</td>\n",
       "      <td>0.007377</td>\n",
       "      <td>0.465565</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.029720</td>\n",
       "      <td>-0.109902</td>\n",
       "      <td>0.010487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>0.023119</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.216841</td>\n",
       "      <td>-0.038063</td>\n",
       "      <td>0.021708</td>\n",
       "      <td>-0.048350</td>\n",
       "      <td>0.154389</td>\n",
       "      <td>0.015660</td>\n",
       "      <td>0.043393</td>\n",
       "      <td>-0.033374</td>\n",
       "      <td>-0.000527</td>\n",
       "      <td>-0.056015</td>\n",
       "      <td>-0.033482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>0.310149</td>\n",
       "      <td>-0.216841</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.165140</td>\n",
       "      <td>0.375914</td>\n",
       "      <td>0.005946</td>\n",
       "      <td>-0.052118</td>\n",
       "      <td>-0.001149</td>\n",
       "      <td>0.083661</td>\n",
       "      <td>0.005383</td>\n",
       "      <td>0.003439</td>\n",
       "      <td>-0.026031</td>\n",
       "      <td>0.005633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>0.147883</td>\n",
       "      <td>-0.038063</td>\n",
       "      <td>0.165140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097704</td>\n",
       "      <td>-0.017852</td>\n",
       "      <td>-0.045511</td>\n",
       "      <td>-0.006736</td>\n",
       "      <td>0.144706</td>\n",
       "      <td>-0.002906</td>\n",
       "      <td>0.007134</td>\n",
       "      <td>-0.002707</td>\n",
       "      <td>-0.002308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>0.768909</td>\n",
       "      <td>0.021708</td>\n",
       "      <td>0.375914</td>\n",
       "      <td>0.097704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019021</td>\n",
       "      <td>0.070933</td>\n",
       "      <td>0.009747</td>\n",
       "      <td>0.354394</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>0.005641</td>\n",
       "      <td>-0.124246</td>\n",
       "      <td>0.002675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>-0.003298</td>\n",
       "      <td>-0.048350</td>\n",
       "      <td>0.005946</td>\n",
       "      <td>-0.017852</td>\n",
       "      <td>-0.019021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019037</td>\n",
       "      <td>0.030911</td>\n",
       "      <td>-0.060987</td>\n",
       "      <td>0.015074</td>\n",
       "      <td>-0.015651</td>\n",
       "      <td>0.101209</td>\n",
       "      <td>0.013506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>0.005316</td>\n",
       "      <td>0.154389</td>\n",
       "      <td>-0.052118</td>\n",
       "      <td>-0.045511</td>\n",
       "      <td>0.070933</td>\n",
       "      <td>-0.019037</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011598</td>\n",
       "      <td>-0.042783</td>\n",
       "      <td>0.017667</td>\n",
       "      <td>-0.011406</td>\n",
       "      <td>-0.045169</td>\n",
       "      <td>0.016959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <td>0.007377</td>\n",
       "      <td>0.015660</td>\n",
       "      <td>-0.001149</td>\n",
       "      <td>-0.006736</td>\n",
       "      <td>0.009747</td>\n",
       "      <td>0.030911</td>\n",
       "      <td>-0.011598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028243</td>\n",
       "      <td>-0.027272</td>\n",
       "      <td>-0.002772</td>\n",
       "      <td>0.031842</td>\n",
       "      <td>-0.028105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleYear</th>\n",
       "      <td>0.465565</td>\n",
       "      <td>0.043393</td>\n",
       "      <td>0.083661</td>\n",
       "      <td>0.144706</td>\n",
       "      <td>0.354394</td>\n",
       "      <td>-0.060987</td>\n",
       "      <td>-0.042783</td>\n",
       "      <td>0.028243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055154</td>\n",
       "      <td>0.037785</td>\n",
       "      <td>-0.077269</td>\n",
       "      <td>-0.052426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleMonth</th>\n",
       "      <td>0.008000</td>\n",
       "      <td>-0.033374</td>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.002906</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>0.015074</td>\n",
       "      <td>0.017667</td>\n",
       "      <td>-0.027272</td>\n",
       "      <td>-0.055154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.126323</td>\n",
       "      <td>0.039578</td>\n",
       "      <td>0.996805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDay</th>\n",
       "      <td>0.029720</td>\n",
       "      <td>-0.000527</td>\n",
       "      <td>0.003439</td>\n",
       "      <td>0.007134</td>\n",
       "      <td>0.005641</td>\n",
       "      <td>-0.015651</td>\n",
       "      <td>-0.011406</td>\n",
       "      <td>-0.002772</td>\n",
       "      <td>0.037785</td>\n",
       "      <td>-0.126323</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016801</td>\n",
       "      <td>-0.047048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <td>-0.109902</td>\n",
       "      <td>-0.056015</td>\n",
       "      <td>-0.026031</td>\n",
       "      <td>-0.002707</td>\n",
       "      <td>-0.124246</td>\n",
       "      <td>0.101209</td>\n",
       "      <td>-0.045169</td>\n",
       "      <td>0.031842</td>\n",
       "      <td>-0.077269</td>\n",
       "      <td>0.039578</td>\n",
       "      <td>0.016801</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saleDayOfYear</th>\n",
       "      <td>0.010487</td>\n",
       "      <td>-0.033482</td>\n",
       "      <td>0.005633</td>\n",
       "      <td>-0.002308</td>\n",
       "      <td>0.002675</td>\n",
       "      <td>0.013506</td>\n",
       "      <td>0.016959</td>\n",
       "      <td>-0.028105</td>\n",
       "      <td>-0.052426</td>\n",
       "      <td>0.996805</td>\n",
       "      <td>-0.047048</td>\n",
       "      <td>0.040731</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           SalesID  SalePrice  MachineID   ModelID  \\\n",
       "SalesID                   1.000000   0.023119   0.310149  0.147883   \n",
       "SalePrice                 0.023119   1.000000  -0.216841 -0.038063   \n",
       "MachineID                 0.310149  -0.216841   1.000000  0.165140   \n",
       "ModelID                   0.147883  -0.038063   0.165140  1.000000   \n",
       "datasource                0.768909   0.021708   0.375914  0.097704   \n",
       "auctioneerID             -0.003298  -0.048350   0.005946 -0.017852   \n",
       "YearMade                  0.005316   0.154389  -0.052118 -0.045511   \n",
       "MachineHoursCurrentMeter  0.007377   0.015660  -0.001149 -0.006736   \n",
       "saleYear                  0.465565   0.043393   0.083661  0.144706   \n",
       "saleMonth                 0.008000  -0.033374   0.005383 -0.002906   \n",
       "saleDay                   0.029720  -0.000527   0.003439  0.007134   \n",
       "saleDayOfWeek            -0.109902  -0.056015  -0.026031 -0.002707   \n",
       "saleDayOfYear             0.010487  -0.033482   0.005633 -0.002308   \n",
       "\n",
       "                          datasource  auctioneerID  YearMade  \\\n",
       "SalesID                     0.768909     -0.003298  0.005316   \n",
       "SalePrice                   0.021708     -0.048350  0.154389   \n",
       "MachineID                   0.375914      0.005946 -0.052118   \n",
       "ModelID                     0.097704     -0.017852 -0.045511   \n",
       "datasource                  1.000000     -0.019021  0.070933   \n",
       "auctioneerID               -0.019021      1.000000 -0.019037   \n",
       "YearMade                    0.070933     -0.019037  1.000000   \n",
       "MachineHoursCurrentMeter    0.009747      0.030911 -0.011598   \n",
       "saleYear                    0.354394     -0.060987 -0.042783   \n",
       "saleMonth                   0.002488      0.015074  0.017667   \n",
       "saleDay                     0.005641     -0.015651 -0.011406   \n",
       "saleDayOfWeek              -0.124246      0.101209 -0.045169   \n",
       "saleDayOfYear               0.002675      0.013506  0.016959   \n",
       "\n",
       "                          MachineHoursCurrentMeter  saleYear  saleMonth  \\\n",
       "SalesID                                   0.007377  0.465565   0.008000   \n",
       "SalePrice                                 0.015660  0.043393  -0.033374   \n",
       "MachineID                                -0.001149  0.083661   0.005383   \n",
       "ModelID                                  -0.006736  0.144706  -0.002906   \n",
       "datasource                                0.009747  0.354394   0.002488   \n",
       "auctioneerID                              0.030911 -0.060987   0.015074   \n",
       "YearMade                                 -0.011598 -0.042783   0.017667   \n",
       "MachineHoursCurrentMeter                  1.000000  0.028243  -0.027272   \n",
       "saleYear                                  0.028243  1.000000  -0.055154   \n",
       "saleMonth                                -0.027272 -0.055154   1.000000   \n",
       "saleDay                                  -0.002772  0.037785  -0.126323   \n",
       "saleDayOfWeek                             0.031842 -0.077269   0.039578   \n",
       "saleDayOfYear                            -0.028105 -0.052426   0.996805   \n",
       "\n",
       "                           saleDay  saleDayOfWeek  saleDayOfYear  \n",
       "SalesID                   0.029720      -0.109902       0.010487  \n",
       "SalePrice                -0.000527      -0.056015      -0.033482  \n",
       "MachineID                 0.003439      -0.026031       0.005633  \n",
       "ModelID                   0.007134      -0.002707      -0.002308  \n",
       "datasource                0.005641      -0.124246       0.002675  \n",
       "auctioneerID             -0.015651       0.101209       0.013506  \n",
       "YearMade                 -0.011406      -0.045169       0.016959  \n",
       "MachineHoursCurrentMeter -0.002772       0.031842      -0.028105  \n",
       "saleYear                  0.037785      -0.077269      -0.052426  \n",
       "saleMonth                -0.126323       0.039578       0.996805  \n",
       "saleDay                   1.000000       0.016801      -0.047048  \n",
       "saleDayOfWeek             0.016801       1.000000       0.040731  \n",
       "saleDayOfYear            -0.047048       0.040731       1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a correlation matarix\n",
    "df_tmp.corr(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2574fa4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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nt0e589qp54BWcinkrAYtq8mrRRX5zw8wlvGsVETV6njKpZCz8hV0Uou2teReJJ+OHz5npq1KGzYEHNGon5bIf93+Zyr/Ycf6unQlTJ+PmquTZ65q9qKtmrMkQP16NTOW+axHE23ecUw/TfLXqaCr+mmSv7bu+luf9Wj6qjYjzXqSdzr39FLt+p7yKJpXw75rqwdPyftLfGPzTqe4vNMpLu8sSZB35s8MUB5nO335bRuVisv7lasWVQHyvqTYcy2vFrF5p2AhZ/V4yrnW+rhzrR5x51oN4s61/JM51/IeMV9tOddKkcFg0CLfber2YQPVrV9WhYvm04jv2+v+/Ydavzrl33gW+W5TlWrF1LVnfbl7OKtrz/p6o2oxLfKNvyjo2JHzqlW3jN6sVVr5CzjIq2F5ValRXCf+5nzriVeZdyQpQ0ZrOTrZGl/2Djlf9SYBSIDBGfwrwcHBateunbp3764TJ04oICBArVu3lsFgUFRUlLp06aIdO3Zo7969Klq0qJo2baqoqKhkP+vu3buqW7eucubMqe3bt2vnzp3KmTOnGjdurIcPH0qS6tSpo969e6tLly5aunSplixZorlz5+rhw4dPXfdZ6zNixAi1bNlSx44dU/fuaWs6c0ZrK5V0sNGeYNOrh/YGR6ick22K67X0cJaLTVZNPXYhxTIJtSqSV+vP39D9R4//VX1fJ9HRMQo6eVnlqxY3WV6uanGdPHY+2XVO/nVB5RKVL1+1uIJOXFJMzCPjsqUzN8jWPqfqv131pdf7dRETHaPzpy6rTBXTWzl5vlFcp/86n+w6Z/6+IM83TOPvWaW4zv8TH/9inh46f+qSgo7H/t8IuRqmI3tPqFz1Ui9/I9KomOgYXT19SUUqmsaySMXiunji3/2gtmXBOmW3y6nKjav/q895XRliYnT/0gXlKFnaZHnOkqV172zQM33GvUsXdfdskLIXNf2/8/jBA53+6gudGva5Lv72s+5dSjqjANLVK+EKC41Slerx8cucOaMqVPLQscMpH1P/OnJBVWqYxrxqzeI6duS88f2OgL9VonRBfTlwnprWHqnOH0yQ/7LAl74NaVnwlXCFh0bpjUTxL1fZQ3+lEv+/j17QG9VM41+lenH9lSD+kjRh9O+q/lZJVa6W/G0C06vo6BgF/ZO0z1O+SnH9k1Kf59gFla9iWr5CteI6k6jP84TBYNCR/ad05cKNVG+VhqerWrGoNu8wnSmwadsRVSzroYwZM8SX2Z60TLVKRc1Wz7QipbxfvpKH/jqSSt4/esFkHUmqWsM07+zaFpv3vxo0T83rjFS3DyZo5XLyvhR/rpXcuVNKeefUXxeS5inOtV7I1cthCguNUtUa8fGM7e8U0bEjKff3jx05b7KOJFWrUVzHDp83vi9XoZD+DDyli+dDJEmnTl7RkYNnVeMtzreeeJV5R5IuXwhVy/rf6v0mozXiC19duZz8DHwArwbPnMG/EhwcrJiYGLVu3Vpubm6SJE/P2PtX1qtXz6Ts1KlTZW9vr23btql58+ZJPmvRokWytraWj4+PrKxin/I0a9Ys2dnZKSAgQA0bxt4Pf8yYMVq/fr3atm2rcePGqWTJkpo5c+ZT133W+rRv3/6pgzIPHjzQgwcPTJZlyZL6bcPMwT5LJmW0tlL4/Ycmy8PuR8spW/K3o3G1yao+5d3VbeNRPTI8/TvKOOZUUbscGrX31Muo8msj6uYdPX70WLkSXWVi55BTN8OSH5C8GRYpOwfTzmouh5x69Oixom7ekb2Trf45ck6bV+7TuHkDXlndXwdRt+Lib29jstzW3ka3wlOKf5Q8q5iWz2Vvo0ePHuv2zTuyc7JVtfoVFHnztr779FfJYNCjR49Vr1UNtejo9cq2Ja25G3lHjx8/Vk570wHgnPY2up1C7J/Fhb/P6sD6vfps0hf/toqvrZjbt6XHj5XR1jT2GWxsFRN5K9V1Tw37XI9uR8nw6JFyN3tb9jVrGf+WOW9e5e/UTVnzF9Sj+/cUvnWzzo/7QR5fjlCWPM6vZFvSqrDQ2Dbu4Gia+x0cbXQtOPnbrDxZz8HBNP84ONgYP0+Srl4O1+9L9qhtp1rq0rOejv91UeN/XKFMmTOo6ds8g0NKEP9Ex14Hh9TjHx4aJXtH0/jbO9ooPEH8N687rFP/XNG0+X1eYo1fD0/6PHaJ4p7LMaci9qbc58nlaNrnsYvr80TevCOHuIuI7ty+px7Nv1H0wxhZZ7DWR5+3TvLjKp6Pc247Xb9hekwICb2lTJkyysnBRtdCbso5t51CQpOWcc5tZ8aapg3hKeR9e0cbXb/67/LO1cvhWrFkj9p0qqXOPWLzvndc3m/SIn3n/RTzTirnWhFhkSrvkHreOXHknDat3KfxnGulKizsSbtP1HdxzPn0/k6SdWwUluBuHZ17eOn27fv64O0fZJ3BSo8fGfRxn6Zq1LTiS9yCtO1V5p1Snq766vu2cnHLrfCwKM2ZvlmfdP5V8/wGKZddjpe4FXjVrK2e4Qc9/Ccxcwb/Srly5eTl5SVPT0+9//77mj59uiIiYg8OISEh+vjjj1WsWDHlypVLuXLl0u3bt3XxYvJX3x44cEBnzpyRjY2NcubMqZw5c8rBwUH3799XUFD8FcDZsmXTwIEDlT17dvXt2/eZ133W+jzLs2vGjBlj/IwnrzFjxrxQDF+FxCnZSpIhmTxtbSWNqVlCvx27oItR957ps1sVzqvTN+/or7Db/7qer6Mng4NPGAyK3QHPWN6486yke3fua+LIBfpk6PuytWNq8TNJHE8ZksY4leKGBPGXpBOHzuiPeZvUZcC7+mbGAPX5vqsO7z6uFbM3vLQqvy6SRP4pbT81D+7e19Kx89Sqb1vlyEXbf7qk7T7p/wVT7v2/UKEvvlK+dp0UvnWTbv0Zf2Vu9kKFZVelurIWdFGOIsVUsMdHypzHWREBm19B3dOW9asPql7VYcbXkytvk+b+p58cJd1Fpvnq8WODipUsoE/6NlHxkgX0zvvV1fLdqvp9yZ5/uxlp1obVB9Wo+jDj69GTK5+Tif9T/gskk//j43/92k39PNZfX3/fTlmy8KylFCXTh0kt7lZKpo+U6GOyZc+iCfMG6v9m91OHj5to5sSVOnbgzEuqcPqV5NwgLugJc1XitGVlZfVMuex1t2H1QTWoNsz4ikkh78hgeGq/J2k3Nfm8/1GfJipWsoBavV9db7euqhXpOO8nlty5U6p5J7lzM8Wu8+RcqzfnWkmsW3VAdaoMNr5iop/0d5KWTe1cK7mVDAbTdTauO6R1qw7omx87au7igRr+fTvNn71Vq/33/evtSKvMmXeqv1lCdeJuVfdGtWL6v196SJLWrvzzX24F8N8xefJkFSpUSFmzZlWlSpW0Y0fKz7Ty8/NTgwYNlDt3btna2qp69epav379K60fM2fwr2TIkEEbN27U7t27tWHDBv3yyy8aNmyYAgMD9emnn+rGjRvy9vaWm5ubsmTJourVqxtvM5bY48ePValSJc2fPz/J33Lnzm3yPmPGjMqQIYPxoPIs63bt2vWZ6pMjx9OvDhg6dKgGDDC9uiZLlixascyyHYiIB9GKeWyQY9bMJssdsmZS2P3oJOVzZMyg0o42Km6fU0MqF5EUO2BjbWWlP9u9qU+2HNP+6/FX0WXNYK1Gbrn1W4IH+CGWjV0OWWewTnLl1q2I27JLdHX0E3aOtopIpnyGDNayyZVDl85eU0hwuMZ8PtP4d8Pj2DOK92t+rl8WD1begtyDWpJscsXG/1a46TOTIiNuy9Y++ZMtO8eks2oib0YpQwZr5cwVmweW+6xVjYaVVKdFNUmSS+H8enDvoWb931K93bm+rK25xiG7bQ5ZW1srKsI09nduRimnffJt/2nCgkMVcT1cviOmG5c9+YHo66b91c9nmBzz0/Yz5swpWVsnmSXzKCpKGW1SvpWlJGV2ij02Zi1QUDGRkbqxeqVyVU7+dh5W1tbK5uauBzdCXk7F07A365RSKU9X4/vohzGSYq8MdcodH/OI8NtJrhRNyNHJxngV6hPh4bdNroh0ym2jQh6mM5XcC+XR1k3H/tU2pGUpxT88LFH8I27LPoVjryQ5OJleNSpJN8Nvyz4u/qeOX1ZE+G192H6i8e+PHj3WkYPn9Pvi3dq0b4wyZEi/+T/FPk946n2e5PpIT/o8T1hbWytf3PM1PIoV0OXz17V8zmZ5Virykrci/bh+46by5jZ9blhuR1tFR8coLOK2sYxzMmUSz6ZJjxLnnYdP8s5z5v3k8k5EgrwjSY65beSeKO+7eeRRQDrO+088yTvJnTvlSiHv2D8l7zw51xqdzLnWezU/16/p+FzrrbqlVbrsION70/5OfK4ID7udZDZHQo5ONgpP9EzbiHDT2TS/jPtDnXt4qWGT2JkyRYrl17WrEZrjs1nNWlZ5KduT1pgz7ySWLXtmeRTNp8sXeb4wXg+LFy9Wv379NHnyZNWsWVNTp05VkyZNdPz4cbm6uiYpv337djVo0ECjR4+WnZ2dZs2apRYtWigwMFAVKlR4JXVkcAb/mpWVlWrWrKmaNWtq+PDhcnNz0++//64dO3Zo8uTJato09kGSly5dUmhoygm+YsWKWrx4sfLkySNb29R/VHqRdZ+3PqnJkiXLf+I2ZonFPDboRHiUquez09YE9wmtms9eAcncN/R29CO9u+qAybI2xfLpDWc7DdpxQldu3zf5W0M3J2XOYK3V5/iBLrFMmTKqcPGCOrLvlKrW8TQuP7rvlN6oVTrZdYqXcdOfO4+bLDsceFKFS7ooY8YMKuCWRxPmDzL5+4Kpa3X/7gN1799Kjs52L3070qqMmTLKvVhB/bX/lCrXKmtc/tf+U6r4ZvLxL1LaTYd2mcb/r32n5F7CxXgP9of3o2VtbXq5kXUG69iBAi4mlRQb+/xFXXTm0EmVrlnOuPzMoZMqWc0zlTVTltvFWX2mDDZZtnHOGj24d1/NP26tXNxmRZJklTGjsrq46c4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      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df_tmp.corr(numeric_only=True)\n",
    "fig, ax = plt.subplots(figsize=(20,15))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 linewidths=0.5,\n",
    "                 fmt=\".2f\",\n",
    "                 cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed8b966",
   "metadata": {},
   "source": [
    "# 5. Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12d370a",
   "metadata": {},
   "source": [
    "### Turning Data Into Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "200787a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 412698 entries, 205615 to 409203\n",
      "Data columns (total 58 columns):\n",
      " #   Column                    Non-Null Count   Dtype         \n",
      "---  ------                    --------------   -----         \n",
      " 0   SalesID                   412698 non-null  int64         \n",
      " 1   SalePrice                 412698 non-null  float64       \n",
      " 2   MachineID                 412698 non-null  int64         \n",
      " 3   ModelID                   412698 non-null  int64         \n",
      " 4   datasource                412698 non-null  int64         \n",
      " 5   auctioneerID              392562 non-null  float64       \n",
      " 6   YearMade                  412698 non-null  int64         \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64       \n",
      " 8   UsageBand                 73670 non-null   object        \n",
      " 9   saledate                  412698 non-null  datetime64[ns]\n",
      " 10  fiModelDesc               412698 non-null  object        \n",
      " 11  fiBaseModel               412698 non-null  object        \n",
      " 12  fiSecondaryDesc           271971 non-null  object        \n",
      " 13  fiModelSeries             58667 non-null   object        \n",
      " 14  fiModelDescriptor         74816 non-null   object        \n",
      " 15  ProductSize               196093 non-null  object        \n",
      " 16  fiProductClassDesc        412698 non-null  object        \n",
      " 17  state                     412698 non-null  object        \n",
      " 18  ProductGroup              412698 non-null  object        \n",
      " 19  ProductGroupDesc          412698 non-null  object        \n",
      " 20  Drive_System              107087 non-null  object        \n",
      " 21  Enclosure                 412364 non-null  object        \n",
      " 22  Forks                     197715 non-null  object        \n",
      " 23  Pad_Type                  81096 non-null   object        \n",
      " 24  Ride_Control              152728 non-null  object        \n",
      " 25  Stick                     81096 non-null   object        \n",
      " 26  Transmission              188007 non-null  object        \n",
      " 27  Turbocharged              81096 non-null   object        \n",
      " 28  Blade_Extension           25983 non-null   object        \n",
      " 29  Blade_Width               25983 non-null   object        \n",
      " 30  Enclosure_Type            25983 non-null   object        \n",
      " 31  Engine_Horsepower         25983 non-null   object        \n",
      " 32  Hydraulics                330133 non-null  object        \n",
      " 33  Pushblock                 25983 non-null   object        \n",
      " 34  Ripper                    106945 non-null  object        \n",
      " 35  Scarifier                 25994 non-null   object        \n",
      " 36  Tip_Control               25983 non-null   object        \n",
      " 37  Tire_Size                 97638 non-null   object        \n",
      " 38  Coupler                   220679 non-null  object        \n",
      " 39  Coupler_System            44974 non-null   object        \n",
      " 40  Grouser_Tracks            44875 non-null   object        \n",
      " 41  Hydraulics_Flow           44875 non-null   object        \n",
      " 42  Track_Type                102193 non-null  object        \n",
      " 43  Undercarriage_Pad_Width   102916 non-null  object        \n",
      " 44  Stick_Length              102261 non-null  object        \n",
      " 45  Thumb                     102332 non-null  object        \n",
      " 46  Pattern_Changer           102261 non-null  object        \n",
      " 47  Grouser_Type              102193 non-null  object        \n",
      " 48  Backhoe_Mounting          80712 non-null   object        \n",
      " 49  Blade_Type                81875 non-null   object        \n",
      " 50  Travel_Controls           81877 non-null   object        \n",
      " 51  Differential_Type         71564 non-null   object        \n",
      " 52  Steering_Controls         71522 non-null   object        \n",
      " 53  saleYear                  412698 non-null  int64         \n",
      " 54  saleMonth                 412698 non-null  int64         \n",
      " 55  saleDay                   412698 non-null  int64         \n",
      " 56  saleDayOfWeek             412698 non-null  int64         \n",
      " 57  saleDayOfYear             412698 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(3), int64(10), object(44)\n",
      "memory usage: 185.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_tmp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4cd74e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp[\"UsageBand\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f5c70219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(412698, 58)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ec8555d",
   "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",
       "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",
       "saleYear                         0\n",
       "saleMonth                        0\n",
       "saleDay                          0\n",
       "saleDayOfWeek                    0\n",
       "saleDayOfYear                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae02fd5c",
   "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": 35,
   "id": "eebf562e",
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "57f7cf68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 412698 entries, 205615 to 409203\n",
      "Data columns (total 58 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   saledate                  412698 non-null  datetime64[ns]\n",
      " 10  fiModelDesc               412698 non-null  category      \n",
      " 11  fiBaseModel               412698 non-null  category      \n",
      " 12  fiSecondaryDesc           271971 non-null  category      \n",
      " 13  fiModelSeries             58667 non-null   category      \n",
      " 14  fiModelDescriptor         74816 non-null   category      \n",
      " 15  ProductSize               196093 non-null  category      \n",
      " 16  fiProductClassDesc        412698 non-null  category      \n",
      " 17  state                     412698 non-null  category      \n",
      " 18  ProductGroup              412698 non-null  category      \n",
      " 19  ProductGroupDesc          412698 non-null  category      \n",
      " 20  Drive_System              107087 non-null  category      \n",
      " 21  Enclosure                 412364 non-null  category      \n",
      " 22  Forks                     197715 non-null  category      \n",
      " 23  Pad_Type                  81096 non-null   category      \n",
      " 24  Ride_Control              152728 non-null  category      \n",
      " 25  Stick                     81096 non-null   category      \n",
      " 26  Transmission              188007 non-null  category      \n",
      " 27  Turbocharged              81096 non-null   category      \n",
      " 28  Blade_Extension           25983 non-null   category      \n",
      " 29  Blade_Width               25983 non-null   category      \n",
      " 30  Enclosure_Type            25983 non-null   category      \n",
      " 31  Engine_Horsepower         25983 non-null   category      \n",
      " 32  Hydraulics                330133 non-null  category      \n",
      " 33  Pushblock                 25983 non-null   category      \n",
      " 34  Ripper                    106945 non-null  category      \n",
      " 35  Scarifier                 25994 non-null   category      \n",
      " 36  Tip_Control               25983 non-null   category      \n",
      " 37  Tire_Size                 97638 non-null   category      \n",
      " 38  Coupler                   220679 non-null  category      \n",
      " 39  Coupler_System            44974 non-null   category      \n",
      " 40  Grouser_Tracks            44875 non-null   category      \n",
      " 41  Hydraulics_Flow           44875 non-null   category      \n",
      " 42  Track_Type                102193 non-null  category      \n",
      " 43  Undercarriage_Pad_Width   102916 non-null  category      \n",
      " 44  Stick_Length              102261 non-null  category      \n",
      " 45  Thumb                     102332 non-null  category      \n",
      " 46  Pattern_Changer           102261 non-null  category      \n",
      " 47  Grouser_Type              102193 non-null  category      \n",
      " 48  Backhoe_Mounting          80712 non-null   category      \n",
      " 49  Blade_Type                81875 non-null   category      \n",
      " 50  Travel_Controls           81877 non-null   category      \n",
      " 51  Differential_Type         71564 non-null   category      \n",
      " 52  Steering_Controls         71522 non-null   category      \n",
      " 53  saleYear                  412698 non-null  int64         \n",
      " 54  saleMonth                 412698 non-null  int64         \n",
      " 55  saleDay                   412698 non-null  int64         \n",
      " 56  saleDayOfWeek             412698 non-null  int64         \n",
      " 57  saleDayOfYear             412698 non-null  int64         \n",
      "dtypes: category(44), datetime64[ns](1), float64(3), int64(10)\n",
      "memory usage: 66.4 MB\n"
     ]
    }
   ],
   "source": [
    "df_tmp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b971817d",
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now string into category\n",
    "df_tmp.state.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2f8b3bc6",
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.state.cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1f8ffd",
   "metadata": {},
   "source": [
    "### Fill Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "addf3945",
   "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                 4.879113\n",
       "YearMade                     0.000000\n",
       "MachineHoursCurrentMeter    64.258610\n",
       "UsageBand                   82.149174\n",
       "saledate                     0.000000\n",
       "fiModelDesc                  0.000000\n",
       "fiBaseModel                  0.000000\n",
       "fiSecondaryDesc             34.099269\n",
       "fiModelSeries               85.784520\n",
       "fiModelDescriptor           81.871490\n",
       "ProductSize                 52.485110\n",
       "fiProductClassDesc           0.000000\n",
       "state                        0.000000\n",
       "ProductGroup                 0.000000\n",
       "ProductGroupDesc             0.000000\n",
       "Drive_System                74.051970\n",
       "Enclosure                    0.080931\n",
       "Forks                       52.092087\n",
       "Pad_Type                    80.349796\n",
       "Ride_Control                62.992794\n",
       "Stick                       80.349796\n",
       "Transmission                54.444412\n",
       "Turbocharged                80.349796\n",
       "Blade_Extension             93.704113\n",
       "Blade_Width                 93.704113\n",
       "Enclosure_Type              93.704113\n",
       "Engine_Horsepower           93.704113\n",
       "Hydraulics                  20.006155\n",
       "Pushblock                   93.704113\n",
       "Ripper                      74.086378\n",
       "Scarifier                   93.701448\n",
       "Tip_Control                 93.704113\n",
       "Tire_Size                   76.341538\n",
       "Coupler                     46.527727\n",
       "Coupler_System              89.102443\n",
       "Grouser_Tracks              89.126431\n",
       "Hydraulics_Flow             89.126431\n",
       "Track_Type                  75.237825\n",
       "Undercarriage_Pad_Width     75.062637\n",
       "Stick_Length                75.221348\n",
       "Thumb                       75.204144\n",
       "Pattern_Changer             75.221348\n",
       "Grouser_Type                75.237825\n",
       "Backhoe_Mounting            80.442842\n",
       "Blade_Type                  80.161038\n",
       "Travel_Controls             80.160553\n",
       "Differential_Type           82.659475\n",
       "Steering_Controls           82.669652\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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum() * 100/len(df_tmp.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e1c200f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(412698, 58)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c1ffc93f",
   "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>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>205615</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>1989-01-17</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>274835</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>1989-01-31</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>141296</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>1989-01-31</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>212552</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>1989-01-31</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>62755</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>1989-01-31</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 × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  \\\n",
       "205615  1646770     9500.0    1126363     8434         132          18.0   \n",
       "274835  1821514    14000.0    1194089    10150         132          99.0   \n",
       "141296  1505138    50000.0    1473654     4139         132          99.0   \n",
       "212552  1671174    16000.0    1327630     8591         132          99.0   \n",
       "62755   1329056    22000.0    1336053     4089         132          99.0   \n",
       "\n",
       "        YearMade  MachineHoursCurrentMeter UsageBand   saledate  ...  \\\n",
       "205615      1974                       NaN       NaN 1989-01-17  ...   \n",
       "274835      1980                       NaN       NaN 1989-01-31  ...   \n",
       "141296      1978                       NaN       NaN 1989-01-31  ...   \n",
       "212552      1980                       NaN       NaN 1989-01-31  ...   \n",
       "62755       1984                       NaN       NaN 1989-01-31  ...   \n",
       "\n",
       "           Backhoe_Mounting Blade_Type      Travel_Controls Differential_Type  \\\n",
       "205615  None or Unspecified   Straight  None or Unspecified               NaN   \n",
       "274835                  NaN        NaN                  NaN          Standard   \n",
       "141296  None or Unspecified   Straight  None or Unspecified               NaN   \n",
       "212552                  NaN        NaN                  NaN          Standard   \n",
       "62755   None or Unspecified        PAT                Lever               NaN   \n",
       "\n",
       "       Steering_Controls saleYear saleMonth saleDay saleDayOfWeek  \\\n",
       "205615               NaN     1989         1      17             1   \n",
       "274835      Conventional     1989         1      31             1   \n",
       "141296               NaN     1989         1      31             1   \n",
       "212552      Conventional     1989         1      31             1   \n",
       "62755                NaN     1989         1      31             1   \n",
       "\n",
       "       saleDayOfYear  \n",
       "205615            17  \n",
       "274835            31  \n",
       "141296            31  \n",
       "212552            31  \n",
       "62755             31  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bd11e309",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export current tmp dataframe\n",
    "df_tmp.to_csv(\"data-dataset/bluebook-for-bulldozers_numeric_tmp.csv\",\n",
    "              index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7fe6cb7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import preprocessed data\n",
    "df_tmp = pd.read_csv(\"data-dataset/bluebook-for-bulldozers_numeric_tmp.csv\",\n",
    "                     low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "94f51e84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>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</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>1989-01-31</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": [
       "                                                                          0  \\\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   \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",
       "                                                                 1  \\\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   \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",
       "                                                                          2  \\\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   \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",
       "                                                    3  \\\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   \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",
       "                                                                          4  \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  \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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0592f6c2",
   "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": [
    "## fill numerical missing values\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c9ba4e73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auctioneerID\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "# return numeric null values label name\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": 47,
   "id": "6c7d80de",
   "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 or not\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": 48,
   "id": "48aaa013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.auctioneerID.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "69ac6857",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check any numeric null columns presents?\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": 50,
   "id": "9e594a33",
   "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",
       "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",
       "saleYear                         0\n",
       "saleMonth                        0\n",
       "saleDay                          0\n",
       "saleDayOfWeek                    0\n",
       "saleDayOfYear                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0fbc5998",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill categotical missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "57907242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsageBand\n",
      "fiSecondaryDesc\n",
      "fiModelSeries\n",
      "fiModelDescriptor\n",
      "ProductSize\n",
      "Drive_System\n",
      "Enclosure\n",
      "Forks\n",
      "Pad_Type\n",
      "Ride_Control\n",
      "Stick\n",
      "Transmission\n",
      "Turbocharged\n",
      "Blade_Extension\n",
      "Blade_Width\n",
      "Enclosure_Type\n",
      "Engine_Horsepower\n",
      "Hydraulics\n",
      "Pushblock\n",
      "Ripper\n",
      "Scarifier\n",
      "Tip_Control\n",
      "Tire_Size\n",
      "Coupler\n",
      "Coupler_System\n",
      "Grouser_Tracks\n",
      "Hydraulics_Flow\n",
      "Track_Type\n",
      "Undercarriage_Pad_Width\n",
      "Stick_Length\n",
      "Thumb\n",
      "Pattern_Changer\n",
      "Grouser_Type\n",
      "Backhoe_Mounting\n",
      "Blade_Type\n",
      "Travel_Controls\n",
      "Differential_Type\n",
      "Steering_Controls\n"
     ]
    }
   ],
   "source": [
    "# return non numeric null values label name\n",
    "for label, content in df_tmp.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "53f7d30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, ..., -1, -1, -1], dtype=int8)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_tmp[\"UsageBand\"]).codes\n",
    "# if categotical value are null then it's value are {-1};"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d1230cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43,  8,  8, ...,  4,  4,  4], dtype=int8)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_tmp[\"state\"]).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d1899923",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if we app {1} all non numerical columns than over problem solve\n",
    "# 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 categories into numbers and add +1\n",
    "        df_tmp[label] = pd.Categorical(content).codes+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b91a248b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int8)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_tmp[\"UsageBand\"]).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "156685da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43,  8,  8, ...,  4,  4,  4], dtype=int8)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(df_tmp[\"state\"]).codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6e8dfd02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(412698, 58)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bdce33b3",
   "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                   0\n",
       "saledate                    0\n",
       "fiModelDesc                 0\n",
       "fiBaseModel                 0\n",
       "fiSecondaryDesc             0\n",
       "fiModelSeries               0\n",
       "fiModelDescriptor           0\n",
       "ProductSize                 0\n",
       "fiProductClassDesc          0\n",
       "state                       0\n",
       "ProductGroup                0\n",
       "ProductGroupDesc            0\n",
       "Drive_System                0\n",
       "Enclosure                   0\n",
       "Forks                       0\n",
       "Pad_Type                    0\n",
       "Ride_Control                0\n",
       "Stick                       0\n",
       "Transmission                0\n",
       "Turbocharged                0\n",
       "Blade_Extension             0\n",
       "Blade_Width                 0\n",
       "Enclosure_Type              0\n",
       "Engine_Horsepower           0\n",
       "Hydraulics                  0\n",
       "Pushblock                   0\n",
       "Ripper                      0\n",
       "Scarifier                   0\n",
       "Tip_Control                 0\n",
       "Tire_Size                   0\n",
       "Coupler                     0\n",
       "Coupler_System              0\n",
       "Grouser_Tracks              0\n",
       "Hydraulics_Flow             0\n",
       "Track_Type                  0\n",
       "Undercarriage_Pad_Width     0\n",
       "Stick_Length                0\n",
       "Thumb                       0\n",
       "Pattern_Changer             0\n",
       "Grouser_Type                0\n",
       "Backhoe_Mounting            0\n",
       "Blade_Type                  0\n",
       "Travel_Controls             0\n",
       "Differential_Type           0\n",
       "Steering_Controls           0\n",
       "saleYear                    0\n",
       "saleMonth                   0\n",
       "saleDay                     0\n",
       "saleDayOfWeek               0\n",
       "saleDayOfYear               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1894f7c",
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "095dbf4e",
   "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>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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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 × 58 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  saledate  ...  Backhoe_Mounting  \\\n",
       "0                       0.0          0         1  ...                 1   \n",
       "1                       0.0          0         2  ...                 0   \n",
       "2                       0.0          0         2  ...                 1   \n",
       "3                       0.0          0         2  ...                 0   \n",
       "4                       0.0          0         2  ...                 1   \n",
       "\n",
       "   Blade_Type  Travel_Controls  Differential_Type  Steering_Controls  \\\n",
       "0           8                6                  0                  0   \n",
       "1           0                0                  4                  2   \n",
       "2           8                6                  0                  0   \n",
       "3           0                0                  4                  2   \n",
       "4           6                5                  0                  0   \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 58 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b740cf0",
   "metadata": {},
   "source": [
    "## Let's try 7 diffrent ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9a89ae07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear Regression\n",
    "# Stochastic Gradient Descent Regression\n",
    "# Kernel Ridge Regression\n",
    "# Elastic Net Regression\n",
    "# Bayesian Ridge Regression\n",
    "# Gradient Boosting Regression\n",
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4e6e2e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regression Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import BayesianRidge\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f4524f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model evaluations\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix,\n",
    "                            classification_report,\n",
    "                            precision_score,recall_score,\n",
    "                            f1_score,\n",
    "                            plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f63918ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "models_dic = {\"LR\":LinearRegression(),\n",
    "              \"SGDR\":SGDRegressor(),\n",
    "              \"KRR\":KernelRidge(),\n",
    "              \"ENR\":ElasticNet(),\n",
    "              \"BRR\":BayesianRidge(),\n",
    "              \"GBR\":GradientBoostingRegressor(),\n",
    "              \"SVR\":SVR(),\n",
    "              \"RFR\":RandomForestRegressor()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2ed91845",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "x = df_tmp.drop(\"SalePrice\",axis=1)\n",
    "y = df_tmp[\"SalePrice\"]\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0ab46977",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_score(models_dic,x_train,x_test,y_train,y_test):\n",
    "    np.random.seed(42)\n",
    "    models_score = {}\n",
    "    for name,model in models_dic.items():\n",
    "        model.fit(x_train,y_train)\n",
    "        models_score[name]=round(model.score(x_test,y_test)*100,2)\n",
    "    return models_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "795a590f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# models_score = fit_and_score(models_dic,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22d10ca",
   "metadata": {},
   "source": [
    "We can see here more space and more time requried, Now we create a small dataframe and than try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "183914ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "df_1000 = df_tmp.head(10000)\n",
    "\n",
    "x = df_1000.drop(\"SalePrice\",axis=1)\n",
    "y = df_1000[\"SalePrice\"]\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "97cfeac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pradip Jaliya\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\linear_model\\_ridge.py:249: LinAlgWarning: Ill-conditioned matrix (rcond=1.32882e-18): result may not be accurate.\n",
      "  dual_coef = linalg.solve(K, y, assume_a=\"pos\", overwrite_a=False)\n",
      "C:\\Users\\Pradip Jaliya\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+10, tolerance: 3.809e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "models_score = fit_and_score(models_dic,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e491f596",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LR': 43.58,\n",
       " 'SGDR': -5.769784305622237e+34,\n",
       " 'KRR': 40.86,\n",
       " 'ENR': 40.68,\n",
       " 'BRR': 43.47,\n",
       " 'GBR': 84.5,\n",
       " 'SVR': -11.83,\n",
       " 'RFR': 89.26}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "64613ed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pradip Jaliya\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\linear_model\\_ridge.py:249: LinAlgWarning: Ill-conditioned matrix (rcond=1.32882e-18): result may not be accurate.\n",
      "  dual_coef = linalg.solve(K, y, assume_a=\"pos\", overwrite_a=False)\n",
      "C:\\Users\\Pradip Jaliya\\AppData\\Roaming\\Python\\Python310\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+10, tolerance: 3.809e+08\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 34.9 s\n",
      "Wall time: 19.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "models_score = fit_and_score(models_dic,x_train,x_test,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "3b18972d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's try only one model at a tim\n",
    "\n",
    "\n",
    "e\n",
    "# fit the test - train split data \n",
    "\n",
    "np.random.seed(42)\n",
    "model1 = RandomForestRegressor()\n",
    "model1.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "130357ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8922265037378236"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test data score\n",
    "model1.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "444c4c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9846860438539036"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train data score\n",
    "model1.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "e1e9a8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LR': 43.58,\n",
       " 'SGDR': -5.769784305622237e+34,\n",
       " 'KRR': 40.86,\n",
       " 'ENR': 40.68,\n",
       " 'BRR': 43.47,\n",
       " 'GBR': 84.5,\n",
       " 'SVR': -11.83,\n",
       " 'RFR': 89.26}"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32e27a7",
   "metadata": {},
   "source": [
    "We can see here `RandomForestRegressor()` model are more accurate\n",
    "\n",
    "So, now we only work with `RandomForestRegressor()` model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e586cf75",
   "metadata": {},
   "source": [
    "### RFR : `RandomForestRegressor()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "26a365ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit all data in a model\n",
    "\n",
    "np.random.seed(42)\n",
    "model = RandomForestRegressor()\n",
    "model.fit(df_tmp.drop(\"SalePrice\", axis=1), df_tmp[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "a2f0f555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9878615201476373"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score of the model\n",
    "model.score(df_tmp.drop(\"SalePrice\", axis=1), df_tmp[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "c0b14ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This also take lot's of time, we requried to improv over model\n",
    "# around 6-min that's not a good"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3074259c",
   "metadata": {},
   "source": [
    "### Split the data into Train & Validation group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "94366055",
   "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": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.saleYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "990e5ca2",
   "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": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.saleYear.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "f14c1768",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Year-2012 recored in validation and other in train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "63b44de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_val = df_tmp[df_tmp.saleYear==2012]\n",
    "df_train = df_tmp[df_tmp.saleYear!=2012]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "4a8dd7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 401125)"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_val),len(df_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "ad30c085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(11573 + 401125)==len(df_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "ed610683",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into X & Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "dab1ca52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((401125, 57), (401125,), (11573, 57), (11573,))"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\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",
    "\n",
    "X_train.shape, y_train.shape, X_valid.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b923c56c",
   "metadata": {},
   "source": [
    "### Building an evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "b441e53e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create evaluation function (the competition uses 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",
    "    Caculates root mean squared log error between predictions and\n",
    "    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": "143bee58",
   "metadata": {},
   "source": [
    "### Testing over models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2050707a",
   "metadata": {},
   "source": [
    "Now change `max_sample = 10000` because less time requried to fit the model defulte = len(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "d14f2de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor(n_jobs=-1,\n",
    "                              random_state=8,\n",
    "                              max_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "522427a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 52.7 s\n",
      "Wall time: 7.23 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_samples=10000, n_jobs=-1, random_state=8)</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-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" 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=8)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=8)"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "ee4372ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5465.935232408849,\n",
       " 'Valid MAE': 7214.067027564159,\n",
       " 'Training RMSLE': 0.2554319520242904,\n",
       " 'Valid RMSLE': 0.2989391867281827,\n",
       " 'Training R^2': 0.8645249839547323,\n",
       " 'Valid R^2': 0.8329083803385005}"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_scores(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "2f8cf86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.29083803385005"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_valid,y_valid)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "075334c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# max_sample = 100000\n",
    "model2 = RandomForestRegressor(n_jobs=-1,\n",
    "                              random_state=8,\n",
    "                              max_samples=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "29599e28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 9min 42s\n",
      "Wall time: 1min 22s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_samples=100000, n_jobs=-1, random_state=8)</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_samples=100000, n_jobs=-1, random_state=8)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_samples=100000, n_jobs=-1, random_state=8)"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "1fc41340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 3532.539780267997,\n",
       " 'Valid MAE': 6200.301235634667,\n",
       " 'Training RMSLE': 0.1737670665792947,\n",
       " 'Valid RMSLE': 0.2591441726022528,\n",
       " 'Training R^2': 0.9405625469659071,\n",
       " 'Valid R^2': 0.8733950736532192}"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_scores(model2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "9cb12689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.33950736532192"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(X_valid,y_valid)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac60769",
   "metadata": {},
   "source": [
    "### Hyerparameter tuning with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "ac244ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n",
      "CPU times: total: 44.6 s\n",
      "Wall time: 58.8 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" 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_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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" 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_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-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" 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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" 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_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": 347,
     "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_samples\": [10000]}\n",
    "\n",
    "# Instantiate RandomizedSearchCV model\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(n_jobs=-1,\n",
    "                                                    random_state=42),\n",
    "                              param_distributions=rf_grid,\n",
    "                              n_iter=2,\n",
    "                              cv=5,\n",
    "                              verbose=True)\n",
    "\n",
    "# Fit the RandomizedSearchCV model\n",
    "rs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "fa13137f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 70,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 13,\n",
       " 'max_samples': 10000,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best model hyperparameters\n",
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "16338d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 6180.190341498504,\n",
       " 'Valid MAE': 7855.219539520904,\n",
       " 'Training RMSLE': 0.2809238439223785,\n",
       " 'Valid RMSLE': 0.3124929728944024,\n",
       " 'Training R^2': 0.825414537416495,\n",
       " 'Valid R^2': 0.7900359584311969}"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the RandomizedSearch model\n",
    "show_scores(rs_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30cc88f4",
   "metadata": {},
   "source": [
    "### Make predictions on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "71615c3a",
   "metadata": {},
   "outputs": [
    {
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       "      <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": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the test data\n",
    "df_test = pd.read_csv(\"bluebook-for-bulldozers/Test.csv\",\n",
    "                      low_memory=False,\n",
    "                      parse_dates=[\"saledate\"])\n",
    "\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "54bde833",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Pradip Jaliya\\AppData\\Roaming\\Python\\Python310\\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",
      "- saleDay\n",
      "- saleDayOfWeek\n",
      "- saleDayOfYear\n",
      "- saleMonth\n",
      "- saleYear\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 [356]\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[43mmodel\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~\\AppData\\Roaming\\Python\\Python310\\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~\\AppData\\Roaming\\Python\\Python310\\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~\\AppData\\Roaming\\Python\\Python310\\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~\\AppData\\Roaming\\Python\\Python310\\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~\\AppData\\Roaming\\Python\\Python310\\site-packages\\pandas\\core\\generic.py:2069\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   2068\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-> 2069\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 = model.predict(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb710c6",
   "metadata": {},
   "source": [
    "#### Preprocessing the data (getting the test dataset in the same format as our training dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "7f7c2f07",
   "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 or not\n",
    "                #df[label+\"_is_missing\"] = pd.isnull(content)\n",
    "                # Fill missing numeric values with median\n",
    "                df[label] = content.fillna(content.median())\n",
    "    \n",
    "        # Filled categorical missing data and turn categories into numbers\n",
    "        if not pd.api.types.is_numeric_dtype(content):\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",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "5891e28c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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",
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       "      <td>1000</td>\n",
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       "      <td>22805</td>\n",
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       "      <td>6038.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>131</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>9</td>\n",
       "      <td>287</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>131</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>9</td>\n",
       "      <td>566</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 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          2         3          499  ...   \n",
       "1                   28555.0          1         9          831  ...   \n",
       "2                    6038.0          3         9         1177  ...   \n",
       "3                    8940.0          1         9          287  ...   \n",
       "4                    2286.0          2         9          566  ...   \n",
       "\n",
       "   Backhoe_Mounting  Blade_Type  Travel_Controls  Differential_Type  \\\n",
       "0                 0           0                0                  0   \n",
       "1                 0           0                0                  3   \n",
       "2                 0           0                0                  0   \n",
       "3                 0           0                0                  0   \n",
       "4                 1           4                5                  0   \n",
       "\n",
       "   Steering_Controls  saleYear  saleMonth  saleDay  saleDayOfWeek  \\\n",
       "0                  0      2012          5        3              3   \n",
       "1                  2      2012          5       10              3   \n",
       "2                  0      2012          5       10              3   \n",
       "3                  0      2012          5       10              3   \n",
       "4                  0      2012          5       10              3   \n",
       "\n",
       "   saleDayOfYear  \n",
       "0            124  \n",
       "1            131  \n",
       "2            131  \n",
       "3            131  \n",
       "4            131  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Process the test data \n",
    "df_test = preprocess_data(df_test)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "cab8b384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12457, 57)"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "5c74d893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(412698, 58)"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "584df426",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "d2234347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31036.25, 28676.  , 83925.  , ..., 13597.5 , 19625.  , 30372.5 ])"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "5416fd46",
   "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>SalesPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>31036.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>28676.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>83925.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>89715.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>59235.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12452</th>\n",
       "      <td>6643171</td>\n",
       "      <td>69625.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12453</th>\n",
       "      <td>6643173</td>\n",
       "      <td>15677.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>6643184</td>\n",
       "      <td>13597.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12455</th>\n",
       "      <td>6643186</td>\n",
       "      <td>19625.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12456</th>\n",
       "      <td>6643196</td>\n",
       "      <td>30372.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SalesID  SalesPrice\n",
       "0      1227829    31036.25\n",
       "1      1227844    28676.00\n",
       "2      1227847    83925.00\n",
       "3      1227848    89715.00\n",
       "4      1227863    59235.00\n",
       "...        ...         ...\n",
       "12452  6643171    69625.00\n",
       "12453  6643173    15677.50\n",
       "12454  6643184    13597.50\n",
       "12455  6643186    19625.00\n",
       "12456  6643196    30372.50\n",
       "\n",
       "[12457 rows x 2 columns]"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert into format \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": 365,
   "id": "5e9104b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export prediction data\n",
    "df_preds.to_csv(\"data-dataset/bluebook-for-bulldozers_test_predictions.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d56f6c8",
   "metadata": {},
   "source": [
    "## Feature Importance\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": 367,
   "id": "972c3d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.38782289e-02, 2.11260120e-02, 3.21443977e-02, 1.09710158e-03,\n",
       "       5.42504773e-03, 2.50066482e-01, 3.80186197e-03, 1.53525514e-03,\n",
       "       7.44500404e-02, 2.15508320e-02, 5.15495012e-02, 5.58886370e-02,\n",
       "       4.62202196e-03, 7.56896064e-03, 2.14999503e-01, 3.34244731e-02,\n",
       "       1.02889716e-02, 1.29540230e-03, 1.57354657e-03, 1.51471743e-03,\n",
       "       2.78530539e-02, 8.75251561e-04, 8.78248122e-05, 1.36923449e-03,\n",
       "       1.95920407e-04, 1.18885580e-03, 6.84447699e-05, 1.19720099e-03,\n",
       "       1.95984197e-02, 3.01945216e-03, 2.89426908e-03, 6.89740780e-03,\n",
       "       3.65411090e-03, 3.65347482e-03, 7.36161229e-04, 2.87308705e-03,\n",
       "       4.77981968e-03, 1.53395538e-03, 2.64453824e-02, 1.99190445e-03,\n",
       "       7.19559907e-04, 2.66853760e-04, 1.30963901e-03, 1.65055615e-03,\n",
       "       9.50141398e-04, 7.48173353e-04, 8.34156185e-04, 3.64194266e-04,\n",
       "       3.22571908e-03, 1.47972993e-03, 3.23383862e-04, 2.61302862e-04,\n",
       "       6.79468290e-03, 3.93773167e-03, 1.15471557e-02, 5.71184236e-03,\n",
       "       1.11609522e-02])"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find feature importance of our best model\n",
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "67a6ea3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57, (412698, 58))"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.feature_importances_),df_tmp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "9eee9398",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function for plotting feature importance\n",
    "def plot_features(columns, importances,n=15):\n",
    "    df = (pd.DataFrame({\"features\": columns,\n",
    "                        \"feature_importances\": importances})\n",
    "          .sort_values(\"feature_importances\", ascending=False)\n",
    "          .reset_index(drop=True))\n",
    "    \n",
    "    # Plot the dataframe\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.barh(df[\"features\"][:15], df[\"feature_importances\"][:15])\n",
    "    ax.set_ylabel(\"Features\")\n",
    "    ax.set_xlabel(\"Feature importance\")\n",
    "    ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "59988a7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_features(X_train.columns,model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "3a35981a",
   "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": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ProductSize\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "76eecad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000    39391\n",
       "2005    22096\n",
       "1998    21751\n",
       "2004    20914\n",
       "1999    19274\n",
       "        ...  \n",
       "2012        1\n",
       "1949        1\n",
       "1942        1\n",
       "2013        1\n",
       "1937        1\n",
       "Name: YearMade, Length: 73, dtype: int64"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"YearMade\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "ee11f1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"ProductSize\"].value_counts().plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e528744",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}