buldozer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🚜 Predicting the Sale Price of Bulldozers using Machine Learning\n",
    "\n",
    "In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers.\n",
    "\n",
    "## 1. Problem defition\n",
    "\n",
    "> How well can we predict the future sale price of a bulldozer, given its characteristics and previous examples of how much similar bulldozers have been sold for?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "The data is downloaded from the Kaggle Bluebook for Bulldozers competition: https://www.kaggle.com/c/bluebook-for-bulldozers/data\n",
    "\n",
    "There are 3 main datasets:\n",
    "\n",
    "* Train.csv is the training set, which contains data through the end of 2011.\n",
    "* Valid.csv is the validation set, which contains data from January 1, 2012 - April 30, 2012 You make predictions on this set throughout the majority of the competition. Your score on this set is used to create the public leaderboard.\n",
    "* Test.csv is the test set, which won't be released until the last week of the competition. It contains data from May 1, 2012 - November 2012. Your score on the test set determines your final rank for the competition.\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "The evaluation metric for this competition is the RMSLE (root mean squared log error) between the actual and predicted auction prices.\n",
    "\n",
    "For more on the evaluation of this project check: https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation\n",
    "\n",
    "**Note:** The goal for most regression evaluation metrics is to minimize the error. For example, our goal for this project will be to build a machine learning model which minimises RMSLE.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "Kaggle provides a data dictionary detailing all of the features of the dataset. You can view this data dictionary on Google Sheets: https://docs.google.com/spreadsheets/d/18ly-bLR8sbDJLITkWG7ozKm8l3RyieQ2Fpgix-beSYI/edit?usp=sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\project\\sample_project_1\\env\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (13,39,40,41) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# Import training and validation sets\n",
    "df = pd.read_csv('bluebook-for-bulldozers/TrainAndValid.csv')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb6db4a3c10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots()\n",
    "ax.scatter(df[\"saledate\"][:1000], df[\"SalePrice\"][:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb6e1997730>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAD4CAYAAADRuPC7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaoklEQVR4nO3df5RVZ33v8fdHUJKoJBAmEWdohxiWFrK0CSNi9famUgMmaYhdSe9k1RtqablNub3a3i4F02W8ulgLamuUtolSQwMYE5Cq4ZqbG5HUertWhEyMkR8JZRowGaFheuESag2R+L1/7OeUPSdnhsMwzzlnJp/XWnudvb97P3t/D8nMd579PGcfRQRmZmYj7VXNTsDMzMYmFxgzM8vCBcbMzLJwgTEzsyxcYMzMLIvxzU6gVUyZMiU6OzubnYaZ2ajy2GOP/UtEtNXa5wKTdHZ20tPT0+w0zMxGFUk/HGyfb5GZmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFv4k/wjpXPZAU657YOU1TbmumdnpuAdjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWRbYCI2mtpMOSdtXY98eSQtKUUmy5pF5JeyXNL8VnS9qZ9q2WpBSfIGljim+X1Flqs0jSvrQsyvUezcxscDl7MHcDC6qDkqYB7wWeKcVmAt3ArNTmDknj0u47gSXAjLRUzrkYOBoRlwK3A6vSuSYDtwHvAOYAt0maNMLvzczMTiNbgYmI7wBHauy6HfgIEKXYQuC+iDgREfuBXmCOpKnAxIh4JCICWA9cX2qzLq1vBual3s18YGtEHImIo8BWahQ6MzPLq6FjMJKuA34UEU9U7WoHni1t96VYe1qvjg9oExEngWPAhUOcq1Y+SyT1SOrp7+8f1nsyM7PaGlZgJJ0H3Ap8vNbuGrEYIj7cNgODEWsioisiutra2modYmZmw9TIHsybgOnAE5IOAB3A9yS9gaKXMa10bAdwMMU7asQpt5E0Hjif4pbcYOcyM7MGaliBiYidEXFRRHRGRCdFIbgiIv4Z2AJ0p5lh0ykG83dExCHguKS5aXzlZuD+dMotQGWG2A3Aw2mc5iHgKkmT0uD+VSlmZmYNlO0rkyXdC1wJTJHUB9wWEXfVOjYidkvaBOwBTgJLI+KltPsWihlp5wIPpgXgLmCDpF6Knkt3OtcRSZ8CHk3HfTIiak02MDOzjLIVmIi46TT7O6u2VwArahzXA1xWI/4CcOMg514LrD2DdM3MbIT5k/xmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZZGtwEhaK+mwpF2l2KclPSXpB5K+JumC0r7lknol7ZU0vxSfLWln2rdaklJ8gqSNKb5dUmepzSJJ+9KyKNd7NDOzweXswdwNLKiKbQUui4i3Av8ILAeQNBPoBmalNndIGpfa3AksAWakpXLOxcDRiLgUuB1Ylc41GbgNeAcwB7hN0qQM78/MzIaQrcBExHeAI1Wxb0bEybT5XaAjrS8E7ouIExGxH+gF5kiaCkyMiEciIoD1wPWlNuvS+mZgXurdzAe2RsSRiDhKUdSqC52ZmWXWzDGY3wYeTOvtwLOlfX0p1p7Wq+MD2qSidQy4cIhzvYykJZJ6JPX09/ef1ZsxM7OBmlJgJN0KnATuqYRqHBZDxIfbZmAwYk1EdEVEV1tb29BJm5nZGRnf6AumQfdrgXnpthcUvYxppcM6gIMp3lEjXm7TJ2k8cD7FLbk+4MqqNt8e0TfRQjqXPdC0ax9YeU3Trm1mra+hPRhJC4CPAtdFxL+Vdm0ButPMsOkUg/k7IuIQcFzS3DS+cjNwf6lNZYbYDcDDqWA9BFwlaVIa3L8qxczMrIGy9WAk3UvRk5giqY9iZtdyYAKwNc02/m5E/F5E7Ja0CdhDcetsaUS8lE51C8WMtHMpxmwq4zZ3ARsk9VL0XLoBIuKIpE8Bj6bjPhkRAyYbmJlZftkKTETcVCN81xDHrwBW1Ij3AJfViL8A3DjIudYCa+tO1szMRpw/yW9mZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWRbYCI2mtpMOSdpVikyVtlbQvvU4q7VsuqVfSXknzS/HZknamfaslKcUnSNqY4tsldZbaLErX2CdpUa73aGZmg8vZg7kbWFAVWwZsi4gZwLa0jaSZQDcwK7W5Q9K41OZOYAkwIy2Vcy4GjkbEpcDtwKp0rsnAbcA7gDnAbeVCZmZmjZGtwETEd4AjVeGFwLq0vg64vhS/LyJORMR+oBeYI2kqMDEiHomIANZXtamcazMwL/Vu5gNbI+JIRBwFtvLyQmdmZpk1egzm4og4BJBeL0rxduDZ0nF9Kdae1qvjA9pExEngGHDhEOd6GUlLJPVI6unv7z+Lt2VmZtVaZZBfNWIxRHy4bQYGI9ZERFdEdLW1tdWVqJmZ1afRBea5dNuL9Ho4xfuAaaXjOoCDKd5RIz6gjaTxwPkUt+QGO5eZmTVQowvMFqAyq2sRcH8p3p1mhk2nGMzfkW6jHZc0N42v3FzVpnKuG4CH0zjNQ8BVkialwf2rUszMzBpofK4TS7oXuBKYIqmPYmbXSmCTpMXAM8CNABGxW9ImYA9wElgaES+lU91CMSPtXODBtADcBWyQ1EvRc+lO5zoi6VPAo+m4T0ZE9WQDMzPLrK4CI+myiNh1+iNPiYibBtk1b5DjVwArasR7gMtqxF8gFaga+9YCa+tO1szMRly9t8g+L2mHpN+XdEHWjMzMbEyoq8BExLuB36QYPO+R9GVJ782amZmZjWp1D/JHxD7gT4CPAv8RWC3pKUm/nis5MzMbveoqMJLeKul24EngPcCvRcQvpPXbM+ZnZmajVL2zyP4S+GvgYxHxk0owIg5K+pMsmZmZ2ahWb4G5GvhJZeqwpFcB50TEv0XEhmzZmZnZqFXvGMy3KD6HUnFeipmZmdVUb4E5JyL+tbKR1s/Lk5KZmY0F9RaYH0u6orIhaTbwkyGONzOzV7h6x2A+DHxFUuWhkVOB/5QnJTMzGwvqKjAR8aiktwBvpngc/lMR8dOsmZmZ2ah2Jg+7fDvQmdpcLomIWJ8lKzMzG/XqfdjlBuBNwPeBylOOK19hbGZm9jL19mC6gJnp+1bMzMxOq95ZZLuAN+RMxMzMxpZ6ezBTgD2SdgAnKsGIuC5LVmZmNurVW2A+kTMJMzMbe+qdpvz3kn4emBER35J0HjAub2pmZjaa1fu4/t8FNgNfSKF24OvDvaikP5S0W9IuSfdKOkfSZElbJe1Lr5NKxy+X1Ctpr6T5pfhsSTvTvtWSlOITJG1M8e2SOoebq5mZDU+9g/xLgXcBz8O/f/nYRcO5oKR24L8BXRFxGUVPqBtYBmyLiBnAtrSNpJlp/yxgAXCHpErv6U5gCTAjLQtSfDFwNCIupfi+mlXDydXMzIav3gJzIiJerGxIGk/xOZjhGg+cm85zHnAQWAisS/vXAden9YXAfRFxIiL2A73AHElTgYkR8UiaPr2+qk3lXJuBeZXejZmZNUa9BebvJX2Moii8F/gK8D+Hc8GI+BHwZ8AzwCHgWER8E7g4Ig6lYw5xqofUDjxbOkVfirWn9er4gDYRcRI4BlxYnYukJZJ6JPX09/cP5+2Ymdkg6i0wy4B+YCfwX4D/BQzrmyzT2MpCYDrwRuC1kj4wVJMasRgiPlSbgYGINRHRFRFdbW1tQyduZmZnpN5ZZD+j+Mrkvx6Ba/4qsD8i+gEkfRX4JeA5SVMj4lC6/XU4Hd8HTCu176C4pdaX1qvj5TZ96Tbc+cCREcjdzMzqVO8ssv2Snq5ehnnNZ4C5ks5L4yLzgCeBLcCidMwi4P60vgXoTjPDplMM5u9It9GOS5qbznNzVZvKuW4AHvZjbszMGutMnkVWcQ5wIzB5OBeMiO2SNgPfA04CjwNrgNcBmyQtpihCN6bjd0vaBOxJxy+NiMoDN28B7qb4OucH0wJwF7BBUi9Fz6V7OLmamdnwabh/2Ev6h4h49wjn0zRdXV3R09Mz7Padyx4YwWxGhwMrr2l2CmbWZJIei4iuWvvqfVz/FaXNV1H0aF4/ArmZmdkYVe8tsj8vrZ8EDgC/MeLZmJnZmFHvLLJfyZ2ImZmNLfXeIvujofZHxGdGJh0zMxsrzmQW2dsppv8C/BrwHQZ+wt7MzOzfnckXjl0REccBJH0C+EpE/E6uxMzMbHSr91ExPwe8WNp+Eegc8WzMzGzMqLcHswHYIelrFM/0ej/F04vNzMxqqncW2QpJDwL/IYU+GBGP50vLzMxGu3pvkUHxvS3PR8TnKB4iOT1TTmZmNgbU+7DL24CPAstT6NXAl3IlZWZmo1+9PZj3A9cBPwaIiIP4UTFmZjaEegvMi+lx9wEg6bX5UjIzs7Gg3gKzSdIXgAsk/S7wLUbmy8fMzGyMOu0ssvRlXhuBtwDPA28GPh4RWzPnZmZmo9hpC0xEhKSvR8RswEXFzMzqUu8tsu9KenvWTMzMbEyp95P8vwL8nqQDFDPJRNG5eWuuxMzMbHQbsgcj6efS6vuAS4D3UDxJ+dr0OiySLpC0WdJTkp6U9E5JkyVtlbQvvU4qHb9cUq+kvZLml+KzJe1M+1an8SIkTZC0McW3S+ocbq5mZjY8p7tF9nWAiPgh8JmI+GF5OYvrfg743xHxFuBtwJPAMmBbRMwAtqVtJM0EuoFZwALgDknj0nnuBJYAM9KyIMUXA0cj4lLgdmDVWeRqZmbDcLoCo9L6JSNxQUkTgV8G7gKIiBcj4v8BC4F16bB1wPVpfSFwX0SciIj9QC8wR9JUYGJEPJI+o7O+qk3lXJuBeZXejZmZNcbpCkwMsn42LgH6gb+R9LikL6YPbl4cEYcA0utF6fh2Bn6xWV+Ktaf16viANhFxEjgGXDhC+ZuZWR1OV2DeJul5SceBt6b15yUdl/T8MK85HrgCuDMiLqeYNLBsiONr9TxiiPhQbQaeWFoiqUdST39//9BZm5nZGRmywETEuIiYGBGvj4jxab2yPXGY1+wD+iJie9reTFFwnku3vUivh0vHTyu17wAOpnhHjfiANpLGA+cDR2q8vzUR0RURXW1tbcN8O2ZmVku905RHTET8s6RnJb05IvYC84A9aVkErEyv96cmW4AvS/oM8EaKwfwdEfFS6knNBbYDNwN/UWqzCHgEuAF4OI3T2AjqXPZAU657YOU1TbmumZ2ZhheY5A+AeyS9Bnga+CBFb2qTpMXAM8CNABGxW9ImigJ0ElgaES+l89wC3A2cCzyYFigmEGyQ1EvRc+luxJsyM7NTmlJgIuL7QFeNXfMGOX4FsKJGvAe4rEb8BVKBMjOz5jiTb7Q0MzOrmwuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlm4wJiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFi4wZmaWhQuMmZll4QJjZmZZuMCYmVkWLjBmZpaFC4yZmWXhAmNmZlk0rcBIGifpcUnfSNuTJW2VtC+9Tiodu1xSr6S9kuaX4rMl7Uz7VktSik+QtDHFt0vqbPT7MzN7pWtmD+ZDwJOl7WXAtoiYAWxL20iaCXQDs4AFwB2SxqU2dwJLgBlpWZDii4GjEXEpcDuwKu9bMTOzak0pMJI6gGuAL5bCC4F1aX0dcH0pfl9EnIiI/UAvMEfSVGBiRDwSEQGsr2pTOddmYF6ld2NmZo3RrB7MZ4GPAD8rxS6OiEMA6fWiFG8Hni0d15di7Wm9Oj6gTUScBI4BF1YnIWmJpB5JPf39/Wf7nszMrKThBUbStcDhiHis3iY1YjFEfKg2AwMRayKiKyK62tra6kzHzMzqMb4J13wXcJ2kq4FzgImSvgQ8J2lqRBxKt78Op+P7gGml9h3AwRTvqBEvt+mTNB44HziS6w2ZmdnLNbwHExHLI6IjIjopBu8fjogPAFuARemwRcD9aX0L0J1mhk2nGMzfkW6jHZc0N42v3FzVpnKuG9I1XtaDMTOzfJrRgxnMSmCTpMXAM8CNABGxW9ImYA9wElgaES+lNrcAdwPnAg+mBeAuYIOkXoqeS3ej3oSZmRWaWmAi4tvAt9P6/wXmDXLcCmBFjXgPcFmN+AukAmVmZs3hT/KbmVkWLjBmZpaFC4yZmWXhAmNmZlm00iwys7p0LnugKdc9sPKaplzXbLRyD8bMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPLwgXGzMyycIExM7MsXGDMzCwLFxgzM8vCD7s0q1OzHrIJftCmjU4N78FImibp7yQ9KWm3pA+l+GRJWyXtS6+TSm2WS+qVtFfS/FJ8tqSdad9qSUrxCZI2pvh2SZ2Nfp9mZq90zbhFdhL47xHxC8BcYKmkmcAyYFtEzAC2pW3Svm5gFrAAuEPSuHSuO4ElwIy0LEjxxcDRiLgUuB1Y1Yg3ZmZmpzS8wETEoYj4Xlo/DjwJtAMLgXXpsHXA9Wl9IXBfRJyIiP1ALzBH0lRgYkQ8EhEBrK9qUznXZmBepXdjZmaN0dRB/nTr6nJgO3BxRByCoggBF6XD2oFnS836Uqw9rVfHB7SJiJPAMeDCGtdfIqlHUk9/f//IvCkzMwOaWGAkvQ74W+DDEfH8UIfWiMUQ8aHaDAxErImIrojoamtrO13KZmZ2BppSYCS9mqK43BMRX03h59JtL9Lr4RTvA6aVmncAB1O8o0Z8QBtJ44HzgSMj/07MzGwwzZhFJuAu4MmI+Exp1xZgUVpfBNxfinenmWHTKQbzd6TbaMclzU3nvLmqTeVcNwAPp3EaMzNrkGZ8DuZdwH8Gdkr6fop9DFgJbJK0GHgGuBEgInZL2gTsoZiBtjQiXkrtbgHuBs4FHkwLFAVsg6Reip5Ld+43ZWZmAzW8wETEP1B7jARg3iBtVgArasR7gMtqxF8gFSgzM2sOPyrGzMyycIExM7MsXGDMzCwLP+zSbBRo1oM2/ZBNOxvuwZiZWRYuMGZmloULjJmZZeECY2ZmWbjAmJlZFp5FZmaD8tdE29lwD8bMzLJwgTEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLDxN2cxakh/wOfq5B2NmZlm4wJiZWRZj+haZpAXA54BxwBcjYmWTUzKzFuenF4ycMduDkTQO+CvgfcBM4CZJM5ublZnZK8dY7sHMAXoj4mkASfcBC4E9Tc3KzGwQY21iw1guMO3As6XtPuAd5QMkLQGWpM1/lbS36hxTgH/JluHIG235wujLebTlC6MvZ+eb34CcteqszvXzg+0YywVGNWIxYCNiDbBm0BNIPRHRNdKJ5TLa8oXRl/NoyxdGX87ON79G5Txmx2AoeizTStsdwMEm5WJm9oozlgvMo8AMSdMlvQboBrY0OSczs1eMMXuLLCJOSvqvwEMU05TXRsTuMzzNoLfPWtRoyxdGX86jLV8YfTk73/wakrMi4vRHmZmZnaGxfIvMzMyayAXGzMyycIEZhKQFkvZK6pW0rIHXnSbp7yQ9KWm3pA+l+GRJWyXtS6+TSm2Wpzz3Sppfis+WtDPtWy1JKT5B0sYU3y6pcwTyHifpcUnfGCX5XiBps6Sn0r/1O1s5Z0l/mP5/2CXpXknntFq+ktZKOixpVynWkBwlLUrX2Cdp0Vnk++n0/8QPJH1N0gWtku9gOZf2/bGkkDSlZXKOCC9VC8WkgH8CLgFeAzwBzGzQtacCV6T11wP/SPGomz8FlqX4MmBVWp+Z8psATE95j0v7dgDvpPhM0IPA+1L894HPp/VuYOMI5P1HwJeBb6TtVs93HfA7af01wAWtmjPFh4b3A+em7U3Ab7VavsAvA1cAu0qx7DkCk4Gn0+uktD5pmPleBYxP66taKd/Bck7xaRQTmn4ITGmVnLP/whyNS/qHf6i0vRxY3qRc7gfeC+wFpqbYVGBvrdzS/2TvTMc8VYrfBHyhfExaH0/xiV6dRY4dwDbgPZwqMK2c70SKX9iqirdkzpx6KsXkdK5vUPwibLl8gU4G/sLOnmP5mLTvC8BNw8m3at/7gXtaKd/BcgY2A28DDnCqwDQ9Z98iq63WY2baG51E6p5eDmwHLo6IQwDp9aJ02GC5tqf16viANhFxEjgGXHgWqX4W+Ajws1KslfO9BOgH/kbFbb0vSnptq+YcET8C/gx4BjgEHIuIb7ZqvlUakWOun9ffpvjrvqXzlXQd8KOIeKJqV9NzdoGp7bSPmcmegPQ64G+BD0fE80MdWiMWQ8SHanPGJF0LHI6Ix+ptMsi1G5JvMp7iNsOdEXE58GOK2zeDafa/8SSKB7VOB94IvFbSB4ZqMsi1G/lvfDojmeOI5y7pVuAkcM9ZXDt7vpLOA24FPl5r9zCuP6I5u8DU1tTHzEh6NUVxuScivprCz0mamvZPBQ6fJte+tF4dH9BG0njgfODIMNN9F3CdpAPAfcB7JH2phfOtnK8vIran7c0UBadVc/5VYH9E9EfET4GvAr/UwvmWNSLHEf15TQPY1wK/Gel+UAvn+yaKPzyeSD+DHcD3JL2hJXIezj3hsb5Q/IX7dPoPVxnkn9WgawtYD3y2Kv5pBg6W/mlan8XAgbynOTWQ9ygwl1MDeVen+FIGDuRtGqHcr+TUGExL5wv8H+DNaf0TKd+WzJniKeC7gfPSddYBf9CK+fLyMZjsOVKMTe2nGHyelNYnDzPfBRRf6dFWdVxL5Fsr56p9Bzg1BtP0nLP/whytC3A1xQyufwJubeB1303R9fwB8P20XE1xH3QbsC+9Ti61uTXluZc0GyTFu4Bdad9fcurJDecAXwF6KWaTXDJCuV/JqQLT0vkCvwj0pH/nr6cfmpbNGfgfwFPpWhvSL42Wyhe4l2KM6KcUf/EublSOFOMlvWn54Fnk20sx1lD52ft8q+Q7WM5V+w+QCkwr5OxHxZiZWRYegzEzsyxcYMzMLAsXGDMzy8IFxszMsnCBMTOzLFxgzMwsCxcYMzPL4v8Dis5vTNcJw1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SalePrice.plot.hist()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing dates\n",
    "\n",
    "When we work with time series data, we want to enrich the time & date component as much as possible.\n",
    "\n",
    "We can do that by telling pandas which of our columns has dates in it using the `parse_dates` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2006-11-16\n",
       "1     2004-03-26\n",
       "2     2004-02-26\n",
       "3     2011-05-19\n",
       "4     2009-07-23\n",
       "         ...    \n",
       "995   2009-07-16\n",
       "996   2007-06-14\n",
       "997   2005-09-22\n",
       "998   2005-07-28\n",
       "999   2011-06-16\n",
       "Name: saledate, Length: 1000, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.saledate[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb6e328e400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(df[\"saledate\"][:1000], df[\"SalePrice\"][:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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</td>\n",
       "      <td>57000</td>\n",
       "      <td>10000</td>\n",
       "      <td>38500</td>\n",
       "      <td>11000</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</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</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</td>\n",
       "      <td>4640</td>\n",
       "      <td>2838</td>\n",
       "      <td>3486</td>\n",
       "      <td>722</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   \n",
       "MachineID                                                   999089   \n",
       "ModelID                                                       3157   \n",
       "datasource                                                     121   \n",
       "auctioneerID                                                     3   \n",
       "YearMade                                                      2004   \n",
       "MachineHoursCurrentMeter                                        68   \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   \n",
       "MachineID                                                   117657   \n",
       "ModelID                                                         77   \n",
       "datasource                                                     121   \n",
       "auctioneerID                                                     3   \n",
       "YearMade                                                      1996   \n",
       "MachineHoursCurrentMeter                                      4640   \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   \n",
       "MachineID                                                            434808   \n",
       "ModelID                                                                7009   \n",
       "datasource                                                              121   \n",
       "auctioneerID                                                              3   \n",
       "YearMade                                                               2001   \n",
       "MachineHoursCurrentMeter                                               2838   \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   \n",
       "MachineID                                                           1026470   \n",
       "ModelID                                                                 332   \n",
       "datasource                                                              121   \n",
       "auctioneerID                                                              3   \n",
       "YearMade                                                               2001   \n",
       "MachineHoursCurrentMeter                                               3486   \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  \n",
       "MachineID                                                           1057373  \n",
       "ModelID                                                               17311  \n",
       "datasource                                                              121  \n",
       "auctioneerID                                                              3  \n",
       "YearMade                                                               2007  \n",
       "MachineHoursCurrentMeter                                                722  \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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort DataFrame by saledate\n",
    "\n",
    "When working with time series data, it's a good idea to sort it by date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort dataframe in date order\n",
    "df.sort_values(by=[\"saledate\"], inplace=True, ascending=True) \n",
    "df.saledate.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a copy of the original DataFrame\n",
    "\n",
    "We make a copy of the original dataframe so when we manipulate the copy, we've still got our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add date time parameter for `saledate` column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp[\"saleYear\"]= df_tmp.saledate.dt.year\n",
    "df_tmp[\"saleMonth\"] = df_tmp.saledate.dt.month\n",
    "df_tmp[\"saleDay\"] = df_tmp.saledate.dt.day\n",
    "df_tmp[\"saleDayOfWeek\"] = df_tmp.saledate.dt.dayofweek\n",
    "df_tmp[\"saleDayOfYear\"] = df_tmp.saledate.dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>233186</th>\n",
       "      <th>142491</th>\n",
       "      <th>115536</th>\n",
       "      <th>92301</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>1646770</td>\n",
       "      <td>1728883</td>\n",
       "      <td>1508502</td>\n",
       "      <td>1452578</td>\n",
       "      <td>1403418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>9500</td>\n",
       "      <td>30000</td>\n",
       "      <td>21000</td>\n",
       "      <td>33000</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>1126363</td>\n",
       "      <td>1523610</td>\n",
       "      <td>1153157</td>\n",
       "      <td>1544443</td>\n",
       "      <td>1390168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>8434</td>\n",
       "      <td>9105</td>\n",
       "      <td>4138</td>\n",
       "      <td>3854</td>\n",
       "      <td>7110</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</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>1974</td>\n",
       "      <td>1986</td>\n",
       "      <td>1972</td>\n",
       "      <td>1974</td>\n",
       "      <td>1986</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>WA150</td>\n",
       "      <td>D7F</td>\n",
       "      <td>966C</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiBaseModel</th>\n",
       "      <td>TD20</td>\n",
       "      <td>WA150</td>\n",
       "      <td>D7</td>\n",
       "      <td>966</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</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>Medium</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 - 90.0 to 100.0 Horsepower</td>\n",
       "      <td>Track Type Tractor, Dozer - 190.0 to 260.0 Hor...</td>\n",
       "      <td>Wheel Loader - 150.0 to 175.0 Horsepower</td>\n",
       "      <td>Backhoe Loader - 14.0 to 15.0 Ft Standard Digg...</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>BL</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>Backhoe 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>Two Wheel Drive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosure</th>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>EROPS</td>\n",
       "      <td>EROPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</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>None or Unspecified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ride_Control</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>No</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>Extended</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>None or Unspecified</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>NaN</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>NaN</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>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>NaN</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coupler</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type</th>\n",
       "      <td>Straight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</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   \n",
       "MachineID                                                           1126363   \n",
       "ModelID                                                                8434   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             18   \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",
       "                                                           233186  \\\n",
       "SalesID                                                   1728883   \n",
       "SalePrice                                                   30000   \n",
       "MachineID                                                 1523610   \n",
       "ModelID                                                      9105   \n",
       "datasource                                                    132   \n",
       "auctioneerID                                                   99   \n",
       "YearMade                                                     1986   \n",
       "MachineHoursCurrentMeter                                      NaN   \n",
       "UsageBand                                                     NaN   \n",
       "saledate                                      1989-01-31 00:00:00   \n",
       "fiModelDesc                                                 WA150   \n",
       "fiBaseModel                                                 WA150   \n",
       "fiSecondaryDesc                                               NaN   \n",
       "fiModelSeries                                                 NaN   \n",
       "fiModelDescriptor                                             NaN   \n",
       "ProductSize                                                   NaN   \n",
       "fiProductClassDesc        Wheel Loader - 90.0 to 100.0 Horsepower   \n",
       "state                                                     Florida   \n",
       "ProductGroup                                                   WL   \n",
       "ProductGroupDesc                                     Wheel Loader   \n",
       "Drive_System                                                  NaN   \n",
       "Enclosure                                                   EROPS   \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                                                    NaN   \n",
       "Pushblock                                                     NaN   \n",
       "Ripper                                                        NaN   \n",
       "Scarifier                                                     NaN   \n",
       "Tip_Control                                                   NaN   \n",
       "Tire_Size                                                    26.5   \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                                              NaN   \n",
       "Blade_Type                                                    NaN   \n",
       "Travel_Controls                                               NaN   \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",
       "                                                                     142491  \\\n",
       "SalesID                                                             1508502   \n",
       "SalePrice                                                             21000   \n",
       "MachineID                                                           1153157   \n",
       "ModelID                                                                4138   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             99   \n",
       "YearMade                                                               1972   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                1989-01-31 00:00:00   \n",
       "fiModelDesc                                                             D7F   \n",
       "fiBaseModel                                                              D7   \n",
       "fiSecondaryDesc                                                           F   \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                                                            Angle   \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",
       "                                                            115536  \\\n",
       "SalesID                                                    1452578   \n",
       "SalePrice                                                    33000   \n",
       "MachineID                                                  1544443   \n",
       "ModelID                                                       3854   \n",
       "datasource                                                     132   \n",
       "auctioneerID                                                    99   \n",
       "YearMade                                                      1974   \n",
       "MachineHoursCurrentMeter                                       NaN   \n",
       "UsageBand                                                      NaN   \n",
       "saledate                                       1989-01-31 00:00:00   \n",
       "fiModelDesc                                                   966C   \n",
       "fiBaseModel                                                    966   \n",
       "fiSecondaryDesc                                                  C   \n",
       "fiModelSeries                                                  NaN   \n",
       "fiModelDescriptor                                              NaN   \n",
       "ProductSize                                                 Medium   \n",
       "fiProductClassDesc        Wheel Loader - 150.0 to 175.0 Horsepower   \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                                                     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",
       "saleYear                                                      1989   \n",
       "saleMonth                                                        1   \n",
       "saleDay                                                         31   \n",
       "saleDayOfWeek                                                    1   \n",
       "saleDayOfYear                                                   31   \n",
       "\n",
       "                                                                     92301   \n",
       "SalesID                                                             1403418  \n",
       "SalePrice                                                             24000  \n",
       "MachineID                                                           1390168  \n",
       "ModelID                                                                7110  \n",
       "datasource                                                              132  \n",
       "auctioneerID                                                             99  \n",
       "YearMade                                                               1986  \n",
       "MachineHoursCurrentMeter                                                NaN  \n",
       "UsageBand                                                               NaN  \n",
       "saledate                                                1989-01-31 00:00:00  \n",
       "fiModelDesc                                                             416  \n",
       "fiBaseModel                                                             416  \n",
       "fiSecondaryDesc                                                         NaN  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                             NaN  \n",
       "fiProductClassDesc        Backhoe Loader - 14.0 to 15.0 Ft Standard Digg...  \n",
       "state                                                               Florida  \n",
       "ProductGroup                                                             BL  \n",
       "ProductGroupDesc                                            Backhoe Loaders  \n",
       "Drive_System                                                Two Wheel Drive  \n",
       "Enclosure                                                             EROPS  \n",
       "Forks                                                   None or Unspecified  \n",
       "Pad_Type                                                None or Unspecified  \n",
       "Ride_Control                                                             No  \n",
       "Stick                                                              Extended  \n",
       "Transmission                                                       Standard  \n",
       "Turbocharged                                            None or Unspecified  \n",
       "Blade_Extension                                                         NaN  \n",
       "Blade_Width                                                             NaN  \n",
       "Enclosure_Type                                                          NaN  \n",
       "Engine_Horsepower                                                       NaN  \n",
       "Hydraulics                                                              NaN  \n",
       "Pushblock                                                               NaN  \n",
       "Ripper                                                                  NaN  \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                                                        NaN  \n",
       "Blade_Type                                                              NaN  \n",
       "Travel_Controls                                                         NaN  \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": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Low'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-cd4fbc98a101>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m                               random_state=42)\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_tmp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"SalePrice\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf_tmp\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"SalePrice\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    301\u001b[0m                 \u001b[1;34m\"sparse multilabel-indicator for y is not supported.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m             )\n\u001b[1;32m--> 303\u001b[1;33m         X, y = self._validate_data(X, y, multi_output=True,\n\u001b[0m\u001b[0;32m    304\u001b[0m                                    accept_sparse=\"csc\", dtype=DTYPE)\n\u001b[0;32m    305\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    794\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 796\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    797\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    798\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\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)\u001b[0m\n\u001b[0;32m    597\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    598\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 599\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    600\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    601\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'"
     ]
    }
   ],
   "source": [
    "# Let's build a machine learning model \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "model = RandomForestRegressor(n_jobs=-1,\n",
    "                              random_state=42)\n",
    "\n",
    "model.fit(df_tmp.drop(\"SalePrice\", axis=1), df_tmp[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert string to categories\n",
    "\n",
    "One way we can turn all of our data into numbers is by converting them into pandas catgories.\n",
    "\n",
    "We can check the different datatypes compatible with pandas here: https://pandas.pydata.org/pandas-docs/stable/reference/general_utility_functions.html#data-types-related-functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.api.types.is_string_dtype(df_tmp[\"UsageBand\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "# Finding string columns\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting string into cateogary 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.state.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.state.cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                     0.000000\n",
       "SalePrice                   0.000000\n",
       "MachineID                   0.000000\n",
       "ModelID                     0.000000\n",
       "datasource                  0.000000\n",
       "auctioneerID                0.048791\n",
       "YearMade                    0.000000\n",
       "MachineHoursCurrentMeter    0.642586\n",
       "UsageBand                   0.821492\n",
       "saledate                    0.000000\n",
       "fiModelDesc                 0.000000\n",
       "fiBaseModel                 0.000000\n",
       "fiSecondaryDesc             0.340993\n",
       "fiModelSeries               0.857845\n",
       "fiModelDescriptor           0.818715\n",
       "ProductSize                 0.524851\n",
       "fiProductClassDesc          0.000000\n",
       "state                       0.000000\n",
       "ProductGroup                0.000000\n",
       "ProductGroupDesc            0.000000\n",
       "Drive_System                0.740520\n",
       "Enclosure                   0.000809\n",
       "Forks                       0.520921\n",
       "Pad_Type                    0.803498\n",
       "Ride_Control                0.629928\n",
       "Stick                       0.803498\n",
       "Transmission                0.544444\n",
       "Turbocharged                0.803498\n",
       "Blade_Extension             0.937041\n",
       "Blade_Width                 0.937041\n",
       "Enclosure_Type              0.937041\n",
       "Engine_Horsepower           0.937041\n",
       "Hydraulics                  0.200062\n",
       "Pushblock                   0.937041\n",
       "Ripper                      0.740864\n",
       "Scarifier                   0.937014\n",
       "Tip_Control                 0.937041\n",
       "Tire_Size                   0.763415\n",
       "Coupler                     0.465277\n",
       "Coupler_System              0.891024\n",
       "Grouser_Tracks              0.891264\n",
       "Hydraulics_Flow             0.891264\n",
       "Track_Type                  0.752378\n",
       "Undercarriage_Pad_Width     0.750626\n",
       "Stick_Length                0.752213\n",
       "Thumb                       0.752041\n",
       "Pattern_Changer             0.752213\n",
       "Grouser_Type                0.752378\n",
       "Backhoe_Mounting            0.804428\n",
       "Blade_Type                  0.801610\n",
       "Travel_Controls             0.801606\n",
       "Differential_Type           0.826595\n",
       "Steering_Controls           0.826697\n",
       "saleYear                    0.000000\n",
       "saleMonth                   0.000000\n",
       "saleDay                     0.000000\n",
       "saleDayOfWeek               0.000000\n",
       "saleDayOfYear               0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHECK missing data\n",
    "df_tmp.isnull().sum()/len(df_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Save preproceed data\n",
    "df_tmp.to_csv(\"data/train_tmp.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\project\\sample_project_1\\env\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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</td>\n",
       "      <td>14000</td>\n",
       "      <td>50000</td>\n",
       "      <td>16000</td>\n",
       "      <td>22000</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</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>99</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   \n",
       "MachineID                                                           1126363   \n",
       "ModelID                                                                8434   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             18   \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   \n",
       "MachineID                                                  1194089   \n",
       "ModelID                                                      10150   \n",
       "datasource                                                     132   \n",
       "auctioneerID                                                    99   \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   \n",
       "MachineID                                                           1473654   \n",
       "ModelID                                                                4139   \n",
       "datasource                                                              132   \n",
       "auctioneerID                                                             99   \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   \n",
       "MachineID                                     1327630   \n",
       "ModelID                                          8591   \n",
       "datasource                                        132   \n",
       "auctioneerID                                       99   \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  \n",
       "MachineID                                                           1336053  \n",
       "ModelID                                                                4089  \n",
       "datasource                                                              132  \n",
       "auctioneerID                                                             99  \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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import preproceed data\n",
    "df_tmp = pd.read_csv(\"data/train_tmp.csv\")   \n",
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fill missing values\n",
    "### Fill numerical values first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SalesID\n",
      "SalePrice\n",
      "MachineID\n",
      "ModelID\n",
      "datasource\n",
      "auctioneerID\n",
      "YearMade\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "for label, content in df.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "405675    26964\n",
       "268962     9549\n",
       "221519    22933\n",
       "283617     6054\n",
       "122970     4200\n",
       "          ...  \n",
       "19497     26433\n",
       "103424     4154\n",
       "19280     36016\n",
       "19335      4605\n",
       "22602      3539\n",
       "Name: ModelID, Length: 412698, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.ModelID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auctioneerID\n",
      "MachineHoursCurrentMeter\n"
     ]
    }
   ],
   "source": [
    "#Check for which numeric column have missing values\n",
    "for label, content in df.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling 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",
    "            df_tmp[label+\"_is_missing\"] = pd.isnull(content)\n",
    "            df_tmp[label] = content.fillna(content.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check if there's any null numeric values\n",
    "for label, content in df_tmp.items():\n",
    "    if pd.api.types.is_numeric_dtype(content):\n",
    "        if pd.isnull(content).sum():\n",
    "            print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    392562\n",
       "True      20136\n",
       "Name: auctioneerID_is_missing, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to see how many examples were missing\n",
    "df_tmp.auctioneerID_is_missing.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "auctioneerIDis missing                      0\n",
       "MachineHoursCurrentMeteris missing          0\n",
       "auctioneerID_is_missing                     0\n",
       "MachineHoursCurrentMeter_is_missing         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filling and turning cateogarical values into numerical \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsageBand\n",
      "saledate\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": [
    "for label, content in df.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label, content in df_tmp.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        df_tmp[label+\"_is_missing\"] = pd.isnull(content)\n",
    "        df_tmp[label] = pd.Categorical(content).codes +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 412698 entries, 405675 to 22602\n",
      "Columns: 102 entries, SalesID to Steering_Controls_is_missing\n",
      "dtypes: bool(49), float64(3), int16(6), int64(5), int8(39)\n",
      "memory usage: 67.7 MB\n"
     ]
    }
   ],
   "source": [
    "df_tmp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>405675</th>\n",
       "      <th>268962</th>\n",
       "      <th>221519</th>\n",
       "      <th>283617</th>\n",
       "      <th>122970</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>4449186</td>\n",
       "      <td>1806686</td>\n",
       "      <td>1691274</td>\n",
       "      <td>1895558</td>\n",
       "      <td>1467430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>46173.2</td>\n",
       "      <td>8000</td>\n",
       "      <td>20500</td>\n",
       "      <td>9000</td>\n",
       "      <td>17500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>2318824</td>\n",
       "      <td>1077085</td>\n",
       "      <td>1233251</td>\n",
       "      <td>1458959</td>\n",
       "      <td>1339914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>26964</td>\n",
       "      <td>9549</td>\n",
       "      <td>22933</td>\n",
       "      <td>6054</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>173</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               405675   268962   221519   283617   122970\n",
       "SalesID                       4449186  1806686  1691274  1895558  1467430\n",
       "SalePrice                     46173.2     8000    20500     9000    17500\n",
       "MachineID                     2318824  1077085  1233251  1458959  1339914\n",
       "ModelID                         26964     9549    22933     6054     4200\n",
       "datasource                        173      132      132      132      132\n",
       "...                               ...      ...      ...      ...      ...\n",
       "Backhoe_Mounting_is_missing     False     True     True     True     True\n",
       "Blade_Type_is_missing           False     True     True     True     True\n",
       "Travel_Controls_is_missing      False     True     True     True     True\n",
       "Differential_Type_is_missing     True     True     True     True     True\n",
       "Steering_Controls_is_missing     True     True     True     True     True\n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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_is_missing</th>\n",
       "      <th>Stick_Length_is_missing</th>\n",
       "      <th>Thumb_is_missing</th>\n",
       "      <th>Pattern_Changer_is_missing</th>\n",
       "      <th>Grouser_Type_is_missing</th>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405675</th>\n",
       "      <td>4449186</td>\n",
       "      <td>46173.2</td>\n",
       "      <td>2318824</td>\n",
       "      <td>26964</td>\n",
       "      <td>173</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268962</th>\n",
       "      <td>1806686</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>1077085</td>\n",
       "      <td>9549</td>\n",
       "      <td>132</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221519</th>\n",
       "      <td>1691274</td>\n",
       "      <td>20500.0</td>\n",
       "      <td>1233251</td>\n",
       "      <td>22933</td>\n",
       "      <td>132</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283617</th>\n",
       "      <td>1895558</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>1458959</td>\n",
       "      <td>6054</td>\n",
       "      <td>132</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122970</th>\n",
       "      <td>1467430</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>1339914</td>\n",
       "      <td>4200</td>\n",
       "      <td>132</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  \\\n",
       "405675  4449186    46173.2    2318824    26964         173          99.0   \n",
       "268962  1806686     8000.0    1077085     9549         132           2.0   \n",
       "221519  1691274    20500.0    1233251    22933         132           2.0   \n",
       "283617  1895558     9000.0    1458959     6054         132           2.0   \n",
       "122970  1467430    17500.0    1339914     4200         132           2.0   \n",
       "\n",
       "        YearMade  MachineHoursCurrentMeter  UsageBand  saledate  ...  \\\n",
       "405675      1996                       0.0          0         1  ...   \n",
       "268962      1998                       0.0          0         2  ...   \n",
       "221519      1994                       0.0          0         2  ...   \n",
       "283617      1994                       0.0          0         2  ...   \n",
       "122970      1988                       0.0          0         2  ...   \n",
       "\n",
       "        Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "405675                                True                     True   \n",
       "268962                                True                     True   \n",
       "221519                               False                    False   \n",
       "283617                               False                    False   \n",
       "122970                               False                    False   \n",
       "\n",
       "        Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "405675              True                        True                     True   \n",
       "268962              True                        True                     True   \n",
       "221519             False                       False                    False   \n",
       "283617             False                       False                    False   \n",
       "122970             False                       False                    False   \n",
       "\n",
       "        Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "405675                        False                  False   \n",
       "268962                         True                   True   \n",
       "221519                         True                   True   \n",
       "283617                         True                   True   \n",
       "122970                         True                   True   \n",
       "\n",
       "        Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "405675                       False                          True   \n",
       "268962                        True                          True   \n",
       "221519                        True                          True   \n",
       "283617                        True                          True   \n",
       "122970                        True                          True   \n",
       "\n",
       "        Steering_Controls_is_missing  \n",
       "405675                          True  \n",
       "268962                          True  \n",
       "221519                          True  \n",
       "283617                          True  \n",
       "122970                          True  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Istantiate model\n",
    "model = RandomForestRegressor(n_jobs=-1, random_state=42)\n",
    "\n",
    "#Fitting the model\n",
    "model.fit(df_tmp.drop(\"SalePrice\", axis=1), df_tmp[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9785692596446042"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score the model\n",
    "model.score(df_tmp.drop(\"SalePrice\", axis=1), df_tmp[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This metric doesn't hold water \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Dara into train/valid sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.saleYear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.saleYear.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 401125)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spliting data into training and validations \n",
    "df_val  = df_tmp[df_tmp.saleYear==2012]\n",
    "df_train = df_tmp[df_tmp.saleYear!=2012]\n",
    "\n",
    "len(df_val), len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((401125, 104), (401125,), (11573, 104), (11573,))"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split the data into the x and y \n",
    "X_train, y_train = df_train.drop('SalePrice', axis=1), df_train.SalePrice\n",
    "X_valid, y_valid = df_val.drop('SalePrice', axis=1), df_val.SalePrice\n",
    "\n",
    "X_train.shape, y_train.shape, X_valid.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          9500.0\n",
       "1         14000.0\n",
       "2         50000.0\n",
       "3         16000.0\n",
       "4         22000.0\n",
       "           ...   \n",
       "401120    29000.0\n",
       "401121    11000.0\n",
       "401122    11000.0\n",
       "401123    18000.0\n",
       "401124    13500.0\n",
       "Name: SalePrice, Length: 401125, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building an evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RMLSE\n",
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error, r2_score\n",
    "\n",
    "def rmlse(y_test, y_preds):\n",
    "        \"\"\"\n",
    "        Calculates root mean square log error between prediction and true \n",
    "        labels\n",
    "        \"\"\"\n",
    "        return np.sqrt(mean_squared_log_error(y_test, y_preds))\n",
    "    \n",
    "# function to evaluate model on different levels\n",
    "def show_scores(model):\n",
    "    train_preds = model.predict(X_train)\n",
    "    val_preds   = model.predict(X_valid)\n",
    "    scores = {\n",
    "            \"Training MAE\" : mean_absolute_error(y_train, train_preds),\n",
    "            \"Valid MAE\" : mean_absolute_error(y_valid, val_preds),\n",
    "            \"Training RMLSE\" : rmlse(y_train, train_preds),\n",
    "            \"Valid RMLSE\" : rmlse(y_valid, val_preds),\n",
    "            \"Training R2\" : r2_score(y_train, train_preds),\n",
    "            \"Valid R2\" : r2_score(y_valid, val_preds)\n",
    "            }\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing our model on subsets to tune the hyperparams\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This takes far too long... for experimenting\n",
    "\n",
    "# %%time\n",
    "# model = RandomForestRegressor(n_jobs=-1, \n",
    "#                               random_state=42)\n",
    "\n",
    "# model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401125"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change max_sample values\n",
    "model = RandomForestRegressor(n_jobs=-1,\n",
    "                              random_state=42,\n",
    "                              max_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 31.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "#Cutting down samples\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.1125"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train.shape[0] * 100) / 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5488.134193605484,\n",
       " 'Valid MAE': 7269.714188196665,\n",
       " 'Training RMLSE': 0.2560829314175677,\n",
       " 'Valid RMLSE': 0.2985795740584381,\n",
       " 'Training R2': 0.8634563880532485,\n",
       " 'Valid R2': 0.8306146646608248}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning with randomized CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 31s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=RandomForestRegressor(n_jobs=-1, random_state=42),\n",
       "                   n_iter=2,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'max_features': [0.5, 1, 'sqrt',\n",
       "                                                         'auto'],\n",
       "                                        'max_samples': [100000],\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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "#hyerparams\n",
    "rf_grid = {\n",
    "            \"n_estimators\":np.arange(10,100,10),\n",
    "            \"max_depth\":[None, 3,5,10],\n",
    "            \"min_samples_split\": np.arange(2,20,2),\n",
    "            \"min_samples_leaf\": np.arange(1,20,2),\n",
    "            \"max_features\":[0.5,1,\"sqrt\",\"auto\"],\n",
    "            \"max_samples\":[100000]\n",
    "}\n",
    "# Instantiating model\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(n_jobs=-1, random_state=42),\n",
    "                              param_distributions=rf_grid,\n",
    "                              n_iter=2, cv=5, verbose=True)\n",
    "#fit the model\n",
    "rs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 60,\n",
       " 'min_samples_split': 12,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_samples': 100000,\n",
       " 'max_features': 1,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#best params\n",
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 10806.287410406278,\n",
       " 'Valid MAE': 12914.973911003193,\n",
       " 'Training RMLSE': 0.47750420385603626,\n",
       " 'Valid RMLSE': 0.502693324713736,\n",
       " 'Training R2': 0.5638387205973852,\n",
       " 'Valid R2': 0.531700400347298}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluating rs_model\n",
    "show_scores(rs_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a model with the best hyperparamters\n",
    "\n",
    "**Note:** These were found after 100 iterations of `RandomizedSearchCV`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 31s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=40,\n",
       "                      n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Most ideal hyperparamters\n",
    "ideal_model = RandomForestRegressor(n_estimators=40,\n",
    "                                    min_samples_leaf=1,\n",
    "                                    min_samples_split=14,\n",
    "                                    max_features=0.5,\n",
    "                                    n_jobs=-1,\n",
    "                                    max_samples=None,\n",
    "                                    random_state=42) # random state so our results are reproducible\n",
    "\n",
    "# Fit the ideal model\n",
    "ideal_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 2875.5973328072514,\n",
       " 'Valid MAE': 6071.310021919277,\n",
       " 'Training RMLSE': 0.14161386215161598,\n",
       " 'Valid RMLSE': 0.25155936974791676,\n",
       " 'Training R2': 0.9608407761286749,\n",
       " 'Valid R2': 0.8804608407331429}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "        # Scores for ideal_model (trained on all the data)\n",
    "show_scores(ideal_model)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 10806.287410406278,\n",
       " 'Valid MAE': 12914.973911003193,\n",
       " 'Training RMLSE': 0.47750420385603626,\n",
       " 'Valid RMLSE': 0.502693324713736,\n",
       " 'Training R2': 0.5638387205973852,\n",
       " 'Valid R2': 0.531700400347298}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    # Scores on rs_model (only trained on ~10,000 examples)\n",
    "show_scores(rs_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>1006309</td>\n",
       "      <td>3168</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-03</td>\n",
       "      <td>580G</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>936</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>EC210BLC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>9' 6\"</td>\n",
       "      <td>Manual</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>330CL</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Triple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>650K</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate fiModelDesc  ...  \\\n",
       "0                    3688.0       Low 2012-05-03        580G  ...   \n",
       "1                   28555.0      High 2012-05-10         936  ...   \n",
       "2                    6038.0    Medium 2012-05-10    EC210BLC  ...   \n",
       "3                    8940.0      High 2012-05-10       330CL  ...   \n",
       "4                    2286.0       Low 2012-05-10        650K  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width         Stick_Length   Thumb      Pattern_Changer  \\\n",
       "0                     NaN                  NaN     NaN                  NaN   \n",
       "1                     NaN                  NaN     NaN                  NaN   \n",
       "2     None or Unspecified                9' 6\"  Manual  None or Unspecified   \n",
       "3     None or Unspecified  None or Unspecified  Manual                  Yes   \n",
       "4                     NaN                  NaN     NaN                  NaN   \n",
       "\n",
       "  Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
       "0          NaN                  NaN        NaN                  NaN   \n",
       "1          NaN                  NaN        NaN                  NaN   \n",
       "2       Double                  NaN        NaN                  NaN   \n",
       "3       Triple                  NaN        NaN                  NaN   \n",
       "4          NaN  None or Unspecified        PAT  None or Unspecified   \n",
       "\n",
       "  Differential_Type Steering_Controls  \n",
       "0               NaN               NaN  \n",
       "1          Standard      Conventional  \n",
       "2               NaN               NaN  \n",
       "3               NaN               NaN  \n",
       "4               NaN               NaN  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 200,
     "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": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2012-05-03\n",
       "1       2012-05-10\n",
       "2       2012-05-10\n",
       "3       2012-05-10\n",
       "4       2012-05-10\n",
       "           ...    \n",
       "12452   2012-10-24\n",
       "12453   2012-10-24\n",
       "12454   2012-10-24\n",
       "12455   2012-10-24\n",
       "12456   2012-09-19\n",
       "Name: saledate, Length: 12457, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.saledate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Low'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-202-dcaddf54aa59>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Make predictions on the test dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtest_preds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mideal_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    781\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m         \u001b[1;31m# Check data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 783\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    784\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    785\u001b[0m         \u001b[1;31m# Assign chunk of trees to jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    419\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 421\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    422\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    386\u001b[0m         \u001b[1;34m\"\"\"Validate X whenever one tries to predict, apply, predict_proba\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    387\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csr\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    389\u001b[0m             if issparse(X) and (X.indices.dtype != np.intc or\n\u001b[0;32m    390\u001b[0m                                 X.indptr.dtype != np.intc):\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     71\u001b[0m                           FutureWarning)\n\u001b[0;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     74\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\utils\\validation.py\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)\u001b[0m\n\u001b[0;32m    597\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    598\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 599\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    600\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    601\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \"\"\"\n\u001b[1;32m---> 85\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Low'"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test dataset\n",
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing the data (getting the test dataset in the same format as our training dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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",
    "            #print('done')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>fiBaseModel</th>\n",
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       "      <th>Undercarriage_Pad_Width_is_missing</th>\n",
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       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                True                     True   \n",
       "1                                True                     True   \n",
       "2                               False                    False   \n",
       "3                               False                    False   \n",
       "4                                True                     True   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0              True                        True                     True   \n",
       "1              True                        True                     True   \n",
       "2             False                       False                    False   \n",
       "3             False                       False                    False   \n",
       "4              True                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                         True                   True   \n",
       "1                         True                   True   \n",
       "2                         True                   True   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                        True                          True   \n",
       "1                        True                         False   \n",
       "2                        True                          True   \n",
       "3                        True                          True   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                          True  \n",
       "1                         False  \n",
       "2                          True  \n",
       "3                          True  \n",
       "4                          True  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 204,
     "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": 206,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Number of features of the model must match the input. Model n_features is 104 and input n_features is 101 ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-206-816969861579>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Make predictions on updated test data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtest_preds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mideal_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    781\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m         \u001b[1;31m# Check data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 783\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    784\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    785\u001b[0m         \u001b[1;31m# Assign chunk of trees to jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    419\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 421\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    422\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32me:\\project\\sample_project_1\\env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    394\u001b[0m         \u001b[0mn_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_features_\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mn_features\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 396\u001b[1;33m             raise ValueError(\"Number of features of the model must \"\n\u001b[0m\u001b[0;32m    397\u001b[0m                              \u001b[1;34m\"match the input. Model n_features is %s and \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m                              \u001b[1;34m\"input n_features is %s \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Number of features of the model must match the input. Model n_features is 104 and input n_features is 101 "
     ]
    }
   ],
   "source": [
    "# Make predictions on updated test data\n",
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </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>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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    0        1        2        3        4\n",
       "SalesID                       1646770  1821514  1505138  1671174  1329056\n",
       "MachineID                     1126363  1194089  1473654  1327630  1336053\n",
       "ModelID                          8434    10150     4139     8591     4089\n",
       "datasource                        132      132      132      132      132\n",
       "auctioneerID                        3        3        3        3        3\n",
       "...                               ...      ...      ...      ...      ...\n",
       "Backhoe_Mounting_is_missing     False     True    False     True    False\n",
       "Blade_Type_is_missing           False     True    False     True    False\n",
       "Travel_Controls_is_missing      False     True    False     True    False\n",
       "Differential_Type_is_missing     True    False     True    False     True\n",
       "Steering_Controls_is_missing     True    False     True    False     True\n",
       "\n",
       "[104 rows x 5 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'auctioneerID_is_missing', 'saledate', 'saledate_is_missing'}"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can find how the columns differ using sets\n",
    "set(X_train.columns) - set(df_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>287</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0                        True                     True   \n",
       "1                        True                     True   \n",
       "2                       False                    False   \n",
       "3                       False                    False   \n",
       "4                        True                     True   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                         True                   True   \n",
       "1                         True                   True   \n",
       "2                         True                   True   \n",
       "3                         True                   True   \n",
       "4                        False                  False   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                        True                          True   \n",
       "1                        True                         False   \n",
       "2                        True                          True   \n",
       "3                        True                          True   \n",
       "4                       False                          True   \n",
       "\n",
       "   Steering_Controls_is_missing  auctioneerID_is_missing  saldate_is_missing  \\\n",
       "0                          True                    False                True   \n",
       "1                         False                    False                True   \n",
       "2                          True                    False                True   \n",
       "3                          True                    False                True   \n",
       "4                          True                    False                True   \n",
       "\n",
       "   saledate  \n",
       "0         1  \n",
       "1         2  \n",
       "2         2  \n",
       "3         2  \n",
       "4         2  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manually adjust df_test to have auctioneerID_is_missing column\n",
    "df_test[\"auctioneerID_is_missing\"] = False\n",
    "df_test[\"saldate_is_missing\"]=True\n",
    "df_test[\"saledate\"]= X_train[\"saledate\"]\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          1\n",
       "1          2\n",
       "2          2\n",
       "3          2\n",
       "4          2\n",
       "        ... \n",
       "12452    239\n",
       "12453    239\n",
       "12454    239\n",
       "12455    239\n",
       "12456    239\n",
       "Name: saledate, Length: 12457, dtype: int16"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.saledate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test data\n",
    "test_preds = ideal_model.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([59784.14960448, 27956.24457179, 45499.40856464, ...,\n",
       "       27184.13723274, 57835.6711984 , 61369.76682908])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've made some predictions but they're not in the same format Kaggle is asking for: https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>59784.149604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>27956.244572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>45499.408565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>47180.931306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>56481.912766</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>63731.010307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12453</th>\n",
       "      <td>6643173</td>\n",
       "      <td>59871.765601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>6643184</td>\n",
       "      <td>27184.137233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12455</th>\n",
       "      <td>6643186</td>\n",
       "      <td>57835.671198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12456</th>\n",
       "      <td>6643196</td>\n",
       "      <td>61369.766829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12457 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SalesID     SalePrice\n",
       "0      1227829  59784.149604\n",
       "1      1227844  27956.244572\n",
       "2      1227847  45499.408565\n",
       "3      1227848  47180.931306\n",
       "4      1227863  56481.912766\n",
       "...        ...           ...\n",
       "12452  6643171  63731.010307\n",
       "12453  6643173  59871.765601\n",
       "12454  6643184  27184.137233\n",
       "12455  6643186  57835.671198\n",
       "12456  6643196  61369.766829\n",
       "\n",
       "[12457 rows x 2 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preds = pd.DataFrame()\n",
    "df_preds[\"SalesID\"] = df_test[\"SalesID\"]\n",
    "df_preds[\"SalePrice\"] = test_preds\n",
    "df_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export prediction data\n",
    "df_preds.to_csv(\"data/test_predicitons.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature importance seeks to figure out which different attributes of the data were most importance when it comes to predicting the **target variable** (SalePrice)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.89061624e-02, 1.68951020e-02, 4.04606707e-02, 8.48999796e-04,\n",
       "       2.12185790e-03, 1.96943029e-01, 1.52807133e-03, 1.20623613e-03,\n",
       "       6.09646996e-02, 4.20342903e-02, 3.93478948e-02, 7.07582924e-02,\n",
       "       4.17370223e-03, 2.02489558e-02, 1.40984963e-01, 4.43528907e-02,\n",
       "       5.20993934e-03, 3.92861261e-03, 5.21979892e-03, 3.44473063e-03,\n",
       "       9.62096252e-02, 5.52491865e-04, 3.88835944e-05, 9.57308310e-04,\n",
       "       2.11481324e-04, 1.08449945e-03, 5.61563611e-05, 2.07393448e-03,\n",
       "       9.94240945e-03, 2.57519275e-03, 6.97338973e-04, 4.17152631e-03,\n",
       "       2.02148050e-03, 4.52402673e-03, 1.58234264e-04, 3.38736800e-03,\n",
       "       8.26986139e-03, 9.17613798e-04, 1.48222223e-02, 4.13090497e-03,\n",
       "       8.41151457e-04, 8.27490612e-04, 8.62338046e-04, 1.02607707e-03,\n",
       "       5.58008606e-04, 5.01932430e-04, 3.31974511e-04, 3.50062376e-04,\n",
       "       1.61874923e-03, 1.18426628e-03, 3.39949515e-04, 2.68644067e-04,\n",
       "       2.32126140e-02, 2.49151577e-03, 4.86069026e-03, 2.51317675e-03,\n",
       "       6.69617767e-03, 1.71860925e-04, 3.48309190e-03, 4.57400713e-04,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.20615676e-03,\n",
       "       1.38021067e-03, 2.20627555e-03, 2.16690904e-02, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.71388080e-05,\n",
       "       3.36393232e-06, 3.16751705e-04, 7.38077870e-06, 2.18067125e-04,\n",
       "       2.75277721e-04, 3.15334775e-04, 5.94422249e-06, 1.71656337e-04,\n",
       "       1.99876986e-03, 3.78723482e-04, 2.12590894e-03, 5.19090728e-04,\n",
       "       1.28833268e-04, 2.26909982e-03, 7.46828390e-04, 2.74765457e-03,\n",
       "       2.22348413e-03, 2.27782173e-04, 1.36054332e-02, 9.96924516e-04,\n",
       "       2.03569517e-03, 4.60283435e-05, 1.08829309e-04, 5.44240657e-05,\n",
       "       2.51882079e-04, 4.06896938e-05, 4.27407911e-05, 9.48656354e-04,\n",
       "       3.03723221e-04, 9.20223108e-05, 1.61794881e-04, 1.33699868e-04])"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find feature importance of our best model\n",
    "ideal_model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function for plotting feature importance\n",
    "def plot_features(columns, importances, n=20):\n",
    "    df = (pd.DataFrame({\"features\": columns,\n",
    "                        \"feature_importances\": importances})\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\"][:n], df[\"feature_importances\"][:20])\n",
    "    ax.set_ylabel(\"Features\")\n",
    "    ax.set_xlabel(\"Feature importance\")\n",
    "    ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_features(X_train.columns, ideal_model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    177971\n",
       "1    141769\n",
       "3     92601\n",
       "0       334\n",
       "2        18\n",
       "4         3\n",
       "5         2\n",
       "Name: Enclosure, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Enclosure\"].value_counts()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}