FastAi-ML1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro to Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About this course"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Teaching approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.imports import *\n",
    "from fastai.structured import *\n",
    "\n",
    "from pandas_summary import DataFrameSummary\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from IPython.display import display\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = '/Users/Vishy/Desktop/FastAi-ML/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FastAi-ML1.ipynb \u001b[31mTrain.csv\u001b[m\u001b[m        \u001b[34mtmp\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!ls {PATH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Blue Book for Bulldozers"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The dataset here is an old Kaggle Competition. Kaggle provides info about some of the fields of our dataset; on the Kaggle Data info page they say the following:\n",
    "\n",
    "For this competition, you are predicting the sale price of bulldozers sold at auctions. The data for this competition is split into three parts:\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",
    "The key fields are in train.csv are:\n",
    "\n",
    "SalesID: the uniue identifier of the sale\n",
    "MachineID: the unique identifier of a machine. A machine can be sold multiple times\n",
    "saleprice: what the machine sold for at auction (only provided in train.csv)\n",
    "saledate: the date of the sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv(f'{PATH}Train.csv', low_memory=False, \n",
    "                     parse_dates=[\"saledate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_all(df):\n",
    "    with pd.option_context(\"display.max_rows\", 1000, \"display.max_columns\", 1000): \n",
    "        display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>401120</th>\n",
       "      <th>401121</th>\n",
       "      <th>401122</th>\n",
       "      <th>401123</th>\n",
       "      <th>401124</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>6333336</td>\n",
       "      <td>6333337</td>\n",
       "      <td>6333338</td>\n",
       "      <td>6333341</td>\n",
       "      <td>6333342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>10500</td>\n",
       "      <td>11000</td>\n",
       "      <td>11500</td>\n",
       "      <td>9000</td>\n",
       "      <td>7750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>1840702</td>\n",
       "      <td>1830472</td>\n",
       "      <td>1887659</td>\n",
       "      <td>1903570</td>\n",
       "      <td>1926965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>21439</td>\n",
       "      <td>21439</td>\n",
       "      <td>21439</td>\n",
       "      <td>21435</td>\n",
       "      <td>21435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</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>2011-11-02 00:00:00</td>\n",
       "      <td>2011-11-02 00:00:00</td>\n",
       "      <td>2011-11-02 00:00:00</td>\n",
       "      <td>2011-10-25 00:00:00</td>\n",
       "      <td>2011-10-25 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelDesc</th>\n",
       "      <td>35NX2</td>\n",
       "      <td>35NX2</td>\n",
       "      <td>35NX2</td>\n",
       "      <td>30NX</td>\n",
       "      <td>30NX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiBaseModel</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiSecondaryDesc</th>\n",
       "      <td>NX</td>\n",
       "      <td>NX</td>\n",
       "      <td>NX</td>\n",
       "      <td>NX</td>\n",
       "      <td>NX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiModelSeries</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>Mini</td>\n",
       "      <td>Mini</td>\n",
       "      <td>Mini</td>\n",
       "      <td>Mini</td>\n",
       "      <td>Mini</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiProductClassDesc</th>\n",
       "      <td>Hydraulic Excavator, Track - 3.0 to 4.0 Metric...</td>\n",
       "      <td>Hydraulic Excavator, Track - 3.0 to 4.0 Metric...</td>\n",
       "      <td>Hydraulic Excavator, Track - 3.0 to 4.0 Metric...</td>\n",
       "      <td>Hydraulic Excavator, Track - 2.0 to 3.0 Metric...</td>\n",
       "      <td>Hydraulic Excavator, Track - 2.0 to 3.0 Metric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroup</th>\n",
       "      <td>TEX</td>\n",
       "      <td>TEX</td>\n",
       "      <td>TEX</td>\n",
       "      <td>TEX</td>\n",
       "      <td>TEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProductGroupDesc</th>\n",
       "      <td>Track Excavators</td>\n",
       "      <td>Track Excavators</td>\n",
       "      <td>Track Excavators</td>\n",
       "      <td>Track Excavators</td>\n",
       "      <td>Track Excavators</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</td>\n",
       "      <td>EROPS</td>\n",
       "      <td>EROPS</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>NaN</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>NaN</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>Auxiliary</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Auxiliary</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Standard</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>NaN</td>\n",
       "      <td>NaN</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>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>Steel</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Steel</td>\n",
       "      <td>Steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Undercarriage_Pad_Width</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>Stick_Length</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>Thumb</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>Pattern_Changer</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>Grouser_Type</th>\n",
       "      <td>Double</td>\n",
       "      <td>Double</td>\n",
       "      <td>Double</td>\n",
       "      <td>Double</td>\n",
       "      <td>Double</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                     401120  \\\n",
       "SalesID                                                             6333336   \n",
       "SalePrice                                                             10500   \n",
       "MachineID                                                           1840702   \n",
       "ModelID                                                               21439   \n",
       "datasource                                                              149   \n",
       "auctioneerID                                                              1   \n",
       "YearMade                                                               2005   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                2011-11-02 00:00:00   \n",
       "fiModelDesc                                                           35NX2   \n",
       "fiBaseModel                                                              35   \n",
       "fiSecondaryDesc                                                          NX   \n",
       "fiModelSeries                                                             2   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                            Mini   \n",
       "fiProductClassDesc        Hydraulic Excavator, Track - 3.0 to 4.0 Metric...   \n",
       "state                                                              Maryland   \n",
       "ProductGroup                                                            TEX   \n",
       "ProductGroupDesc                                           Track Excavators   \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                                                        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                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                            Steel   \n",
       "Undercarriage_Pad_Width                                 None or Unspecified   \n",
       "Stick_Length                                            None or Unspecified   \n",
       "Thumb                                                   None or Unspecified   \n",
       "Pattern_Changer                                         None or Unspecified   \n",
       "Grouser_Type                                                         Double   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "\n",
       "                                                                     401121  \\\n",
       "SalesID                                                             6333337   \n",
       "SalePrice                                                             11000   \n",
       "MachineID                                                           1830472   \n",
       "ModelID                                                               21439   \n",
       "datasource                                                              149   \n",
       "auctioneerID                                                              1   \n",
       "YearMade                                                               2005   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                2011-11-02 00:00:00   \n",
       "fiModelDesc                                                           35NX2   \n",
       "fiBaseModel                                                              35   \n",
       "fiSecondaryDesc                                                          NX   \n",
       "fiModelSeries                                                             2   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                            Mini   \n",
       "fiProductClassDesc        Hydraulic Excavator, Track - 3.0 to 4.0 Metric...   \n",
       "state                                                              Maryland   \n",
       "ProductGroup                                                            TEX   \n",
       "ProductGroupDesc                                           Track Excavators   \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                                                         Standard   \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                                                            Steel   \n",
       "Undercarriage_Pad_Width                                 None or Unspecified   \n",
       "Stick_Length                                            None or Unspecified   \n",
       "Thumb                                                   None or Unspecified   \n",
       "Pattern_Changer                                         None or Unspecified   \n",
       "Grouser_Type                                                         Double   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "\n",
       "                                                                     401122  \\\n",
       "SalesID                                                             6333338   \n",
       "SalePrice                                                             11500   \n",
       "MachineID                                                           1887659   \n",
       "ModelID                                                               21439   \n",
       "datasource                                                              149   \n",
       "auctioneerID                                                              1   \n",
       "YearMade                                                               2005   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                2011-11-02 00:00:00   \n",
       "fiModelDesc                                                           35NX2   \n",
       "fiBaseModel                                                              35   \n",
       "fiSecondaryDesc                                                          NX   \n",
       "fiModelSeries                                                             2   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                            Mini   \n",
       "fiProductClassDesc        Hydraulic Excavator, Track - 3.0 to 4.0 Metric...   \n",
       "state                                                              Maryland   \n",
       "ProductGroup                                                            TEX   \n",
       "ProductGroupDesc                                           Track Excavators   \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                                                        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                                                          NaN   \n",
       "Grouser_Tracks                                                          NaN   \n",
       "Hydraulics_Flow                                                         NaN   \n",
       "Track_Type                                                            Steel   \n",
       "Undercarriage_Pad_Width                                 None or Unspecified   \n",
       "Stick_Length                                            None or Unspecified   \n",
       "Thumb                                                   None or Unspecified   \n",
       "Pattern_Changer                                         None or Unspecified   \n",
       "Grouser_Type                                                         Double   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "\n",
       "                                                                     401123  \\\n",
       "SalesID                                                             6333341   \n",
       "SalePrice                                                              9000   \n",
       "MachineID                                                           1903570   \n",
       "ModelID                                                               21435   \n",
       "datasource                                                              149   \n",
       "auctioneerID                                                              2   \n",
       "YearMade                                                               2005   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                               NaN   \n",
       "saledate                                                2011-10-25 00:00:00   \n",
       "fiModelDesc                                                            30NX   \n",
       "fiBaseModel                                                              30   \n",
       "fiSecondaryDesc                                                          NX   \n",
       "fiModelSeries                                                           NaN   \n",
       "fiModelDescriptor                                                       NaN   \n",
       "ProductSize                                                            Mini   \n",
       "fiProductClassDesc        Hydraulic Excavator, Track - 2.0 to 3.0 Metric...   \n",
       "state                                                               Florida   \n",
       "ProductGroup                                                            TEX   \n",
       "ProductGroupDesc                                           Track Excavators   \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                                                         Standard   \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                                                            Steel   \n",
       "Undercarriage_Pad_Width                                 None or Unspecified   \n",
       "Stick_Length                                            None or Unspecified   \n",
       "Thumb                                                   None or Unspecified   \n",
       "Pattern_Changer                                         None or Unspecified   \n",
       "Grouser_Type                                                         Double   \n",
       "Backhoe_Mounting                                                        NaN   \n",
       "Blade_Type                                                              NaN   \n",
       "Travel_Controls                                                         NaN   \n",
       "Differential_Type                                                       NaN   \n",
       "Steering_Controls                                                       NaN   \n",
       "\n",
       "                                                                     401124  \n",
       "SalesID                                                             6333342  \n",
       "SalePrice                                                              7750  \n",
       "MachineID                                                           1926965  \n",
       "ModelID                                                               21435  \n",
       "datasource                                                              149  \n",
       "auctioneerID                                                              2  \n",
       "YearMade                                                               2005  \n",
       "MachineHoursCurrentMeter                                                NaN  \n",
       "UsageBand                                                               NaN  \n",
       "saledate                                                2011-10-25 00:00:00  \n",
       "fiModelDesc                                                            30NX  \n",
       "fiBaseModel                                                              30  \n",
       "fiSecondaryDesc                                                          NX  \n",
       "fiModelSeries                                                           NaN  \n",
       "fiModelDescriptor                                                       NaN  \n",
       "ProductSize                                                            Mini  \n",
       "fiProductClassDesc        Hydraulic Excavator, Track - 2.0 to 3.0 Metric...  \n",
       "state                                                               Florida  \n",
       "ProductGroup                                                            TEX  \n",
       "ProductGroupDesc                                           Track Excavators  \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                                                         Standard  \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                                                            Steel  \n",
       "Undercarriage_Pad_Width                                 None or Unspecified  \n",
       "Stick_Length                                            None or Unspecified  \n",
       "Thumb                                                   None or Unspecified  \n",
       "Pattern_Changer                                         None or Unspecified  \n",
       "Grouser_Type                                                         Double  \n",
       "Backhoe_Mounting                                                        NaN  \n",
       "Blade_Type                                                              NaN  \n",
       "Travel_Controls                                                         NaN  \n",
       "Differential_Type                                                       NaN  \n",
       "Steering_Controls                                                       NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_all(df_raw.tail(5).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SalesID</th>\n",
       "      <td>401125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.91971e+06</td>\n",
       "      <td>909021</td>\n",
       "      <td>1.13925e+06</td>\n",
       "      <td>1.41837e+06</td>\n",
       "      <td>1.63942e+06</td>\n",
       "      <td>2.24271e+06</td>\n",
       "      <td>6.33334e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>401125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31099.7</td>\n",
       "      <td>23036.9</td>\n",
       "      <td>4750</td>\n",
       "      <td>14500</td>\n",
       "      <td>24000</td>\n",
       "      <td>40000</td>\n",
       "      <td>142000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineID</th>\n",
       "      <td>401125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2179e+06</td>\n",
       "      <td>440992</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0887e+06</td>\n",
       "      <td>1.27949e+06</td>\n",
       "      <td>1.46807e+06</td>\n",
       "      <td>2.48633e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ModelID</th>\n",
       "      <td>401125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6889.7</td>\n",
       "      <td>6221.78</td>\n",
       "      <td>28</td>\n",
       "      <td>3259</td>\n",
       "      <td>4604</td>\n",
       "      <td>8724</td>\n",
       "      <td>37198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datasource</th>\n",
       "      <td>401125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134.666</td>\n",
       "      <td>8.96224</td>\n",
       "      <td>121</td>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>136</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>auctioneerID</th>\n",
       "      <td>380989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.55604</td>\n",
       "      <td>16.9768</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearMade</th>\n",
       "      <td>401125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1899.16</td>\n",
       "      <td>291.797</td>\n",
       "      <td>1000</td>\n",
       "      <td>1985</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <td>142765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3457.96</td>\n",
       "      <td>27590.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3025</td>\n",
       "      <td>2.4833e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UsageBand</th>\n",
       "      <td>69639</td>\n",
       "      <td>3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>33985</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>saledate</th>\n",
       "      <td>401125</td>\n",
       "      <td>3919</td>\n",
       "      <td>2009-02-16 00:00:00</td>\n",
       "      <td>1932</td>\n",
       "      <td>1989-01-17 00:00:00</td>\n",
       "      <td>2011-12-30 00:00:00</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>fiModelDesc</th>\n",
       "      <td>401125</td>\n",
       "      <td>4999</td>\n",
       "      <td>310G</td>\n",
       "      <td>5039</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>fiBaseModel</th>\n",
       "      <td>401125</td>\n",
       "      <td>1950</td>\n",
       "      <td>580</td>\n",
       "      <td>19798</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>fiSecondaryDesc</th>\n",
       "      <td>263934</td>\n",
       "      <td>175</td>\n",
       "      <td>C</td>\n",
       "      <td>43235</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>fiModelSeries</th>\n",
       "      <td>56908</td>\n",
       "      <td>122</td>\n",
       "      <td>II</td>\n",
       "      <td>13202</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>fiModelDescriptor</th>\n",
       "      <td>71919</td>\n",
       "      <td>139</td>\n",
       "      <td>L</td>\n",
       "      <td>15875</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>ProductSize</th>\n",
       "      <td>190350</td>\n",
       "      <td>6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>62274</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>fiProductClassDesc</th>\n",
       "      <td>401125</td>\n",
       "      <td>74</td>\n",
       "      <td>Backhoe Loader - 14.0 to 15.0 Ft Standard Digg...</td>\n",
       "      <td>56166</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>state</th>\n",
       "      <td>401125</td>\n",
       "      <td>53</td>\n",
       "      <td>Florida</td>\n",
       "      <td>63944</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>ProductGroup</th>\n",
       "      <td>401125</td>\n",
       "      <td>6</td>\n",
       "      <td>TEX</td>\n",
       "      <td>101167</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>ProductGroupDesc</th>\n",
       "      <td>401125</td>\n",
       "      <td>6</td>\n",
       "      <td>Track Excavators</td>\n",
       "      <td>101167</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>Drive_System</th>\n",
       "      <td>104361</td>\n",
       "      <td>4</td>\n",
       "      <td>Two Wheel Drive</td>\n",
       "      <td>46139</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>Enclosure</th>\n",
       "      <td>400800</td>\n",
       "      <td>6</td>\n",
       "      <td>OROPS</td>\n",
       "      <td>173932</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>Forks</th>\n",
       "      <td>192077</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>178300</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>Pad_Type</th>\n",
       "      <td>79134</td>\n",
       "      <td>4</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>70614</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>Ride_Control</th>\n",
       "      <td>148606</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>77685</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>Stick</th>\n",
       "      <td>79134</td>\n",
       "      <td>2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>48829</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>Transmission</th>\n",
       "      <td>183230</td>\n",
       "      <td>8</td>\n",
       "      <td>Standard</td>\n",
       "      <td>140328</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>Turbocharged</th>\n",
       "      <td>79134</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>75211</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>Blade_Extension</th>\n",
       "      <td>25219</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>24692</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>Blade_Width</th>\n",
       "      <td>25219</td>\n",
       "      <td>6</td>\n",
       "      <td>14'</td>\n",
       "      <td>9615</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>Enclosure_Type</th>\n",
       "      <td>25219</td>\n",
       "      <td>3</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>21923</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>Engine_Horsepower</th>\n",
       "      <td>25219</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>23937</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>Hydraulics</th>\n",
       "      <td>320570</td>\n",
       "      <td>12</td>\n",
       "      <td>2 Valve</td>\n",
       "      <td>141404</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>Pushblock</th>\n",
       "      <td>25219</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>19463</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>Ripper</th>\n",
       "      <td>104137</td>\n",
       "      <td>4</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>83452</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>Scarifier</th>\n",
       "      <td>25230</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>12719</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>Tip_Control</th>\n",
       "      <td>25219</td>\n",
       "      <td>3</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>16207</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>Tire_Size</th>\n",
       "      <td>94718</td>\n",
       "      <td>17</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>46339</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>Coupler</th>\n",
       "      <td>213952</td>\n",
       "      <td>3</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>184582</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>Coupler_System</th>\n",
       "      <td>43458</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>40430</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>Grouser_Tracks</th>\n",
       "      <td>43362</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>40515</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>Hydraulics_Flow</th>\n",
       "      <td>43362</td>\n",
       "      <td>3</td>\n",
       "      <td>Standard</td>\n",
       "      <td>42784</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>Track_Type</th>\n",
       "      <td>99153</td>\n",
       "      <td>2</td>\n",
       "      <td>Steel</td>\n",
       "      <td>84880</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>Undercarriage_Pad_Width</th>\n",
       "      <td>99872</td>\n",
       "      <td>19</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>79651</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>Stick_Length</th>\n",
       "      <td>99218</td>\n",
       "      <td>29</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>78820</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>Thumb</th>\n",
       "      <td>99288</td>\n",
       "      <td>3</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>83093</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>Pattern_Changer</th>\n",
       "      <td>99218</td>\n",
       "      <td>3</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>90255</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>Grouser_Type</th>\n",
       "      <td>99153</td>\n",
       "      <td>3</td>\n",
       "      <td>Double</td>\n",
       "      <td>84653</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>Backhoe_Mounting</th>\n",
       "      <td>78672</td>\n",
       "      <td>2</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>78652</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>Blade_Type</th>\n",
       "      <td>79833</td>\n",
       "      <td>10</td>\n",
       "      <td>PAT</td>\n",
       "      <td>38612</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>Travel_Controls</th>\n",
       "      <td>79834</td>\n",
       "      <td>7</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>69923</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>Differential_Type</th>\n",
       "      <td>69411</td>\n",
       "      <td>4</td>\n",
       "      <td>Standard</td>\n",
       "      <td>68073</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>Steering_Controls</th>\n",
       "      <td>69369</td>\n",
       "      <td>5</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>68679</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",
       "</div>"
      ],
      "text/plain": [
       "                           count unique  \\\n",
       "SalesID                   401125    NaN   \n",
       "SalePrice                 401125    NaN   \n",
       "MachineID                 401125    NaN   \n",
       "ModelID                   401125    NaN   \n",
       "datasource                401125    NaN   \n",
       "auctioneerID              380989    NaN   \n",
       "YearMade                  401125    NaN   \n",
       "MachineHoursCurrentMeter  142765    NaN   \n",
       "UsageBand                  69639      3   \n",
       "saledate                  401125   3919   \n",
       "fiModelDesc               401125   4999   \n",
       "fiBaseModel               401125   1950   \n",
       "fiSecondaryDesc           263934    175   \n",
       "fiModelSeries              56908    122   \n",
       "fiModelDescriptor          71919    139   \n",
       "ProductSize               190350      6   \n",
       "fiProductClassDesc        401125     74   \n",
       "state                     401125     53   \n",
       "ProductGroup              401125      6   \n",
       "ProductGroupDesc          401125      6   \n",
       "Drive_System              104361      4   \n",
       "Enclosure                 400800      6   \n",
       "Forks                     192077      2   \n",
       "Pad_Type                   79134      4   \n",
       "Ride_Control              148606      3   \n",
       "Stick                      79134      2   \n",
       "Transmission              183230      8   \n",
       "Turbocharged               79134      2   \n",
       "Blade_Extension            25219      2   \n",
       "Blade_Width                25219      6   \n",
       "Enclosure_Type             25219      3   \n",
       "Engine_Horsepower          25219      2   \n",
       "Hydraulics                320570     12   \n",
       "Pushblock                  25219      2   \n",
       "Ripper                    104137      4   \n",
       "Scarifier                  25230      2   \n",
       "Tip_Control                25219      3   \n",
       "Tire_Size                  94718     17   \n",
       "Coupler                   213952      3   \n",
       "Coupler_System             43458      2   \n",
       "Grouser_Tracks             43362      2   \n",
       "Hydraulics_Flow            43362      3   \n",
       "Track_Type                 99153      2   \n",
       "Undercarriage_Pad_Width    99872     19   \n",
       "Stick_Length               99218     29   \n",
       "Thumb                      99288      3   \n",
       "Pattern_Changer            99218      3   \n",
       "Grouser_Type               99153      3   \n",
       "Backhoe_Mounting           78672      2   \n",
       "Blade_Type                 79833     10   \n",
       "Travel_Controls            79834      7   \n",
       "Differential_Type          69411      4   \n",
       "Steering_Controls          69369      5   \n",
       "\n",
       "                                                                        top  \\\n",
       "SalesID                                                                 NaN   \n",
       "SalePrice                                                               NaN   \n",
       "MachineID                                                               NaN   \n",
       "ModelID                                                                 NaN   \n",
       "datasource                                                              NaN   \n",
       "auctioneerID                                                            NaN   \n",
       "YearMade                                                                NaN   \n",
       "MachineHoursCurrentMeter                                                NaN   \n",
       "UsageBand                                                            Medium   \n",
       "saledate                                                2009-02-16 00:00:00   \n",
       "fiModelDesc                                                            310G   \n",
       "fiBaseModel                                                             580   \n",
       "fiSecondaryDesc                                                           C   \n",
       "fiModelSeries                                                            II   \n",
       "fiModelDescriptor                                                         L   \n",
       "ProductSize                                                          Medium   \n",
       "fiProductClassDesc        Backhoe Loader - 14.0 to 15.0 Ft Standard Digg...   \n",
       "state                                                               Florida   \n",
       "ProductGroup                                                            TEX   \n",
       "ProductGroupDesc                                           Track Excavators   \n",
       "Drive_System                                                Two Wheel Drive   \n",
       "Enclosure                                                             OROPS   \n",
       "Forks                                                   None or Unspecified   \n",
       "Pad_Type                                                None or Unspecified   \n",
       "Ride_Control                                                             No   \n",
       "Stick                                                              Standard   \n",
       "Transmission                                                       Standard   \n",
       "Turbocharged                                            None or Unspecified   \n",
       "Blade_Extension                                         None or Unspecified   \n",
       "Blade_Width                                                             14'   \n",
       "Enclosure_Type                                          None or Unspecified   \n",
       "Engine_Horsepower                                                        No   \n",
       "Hydraulics                                                          2 Valve   \n",
       "Pushblock                                               None or Unspecified   \n",
       "Ripper                                                  None or Unspecified   \n",
       "Scarifier                                               None or Unspecified   \n",
       "Tip_Control                                             None or Unspecified   \n",
       "Tire_Size                                               None or Unspecified   \n",
       "Coupler                                                 None or Unspecified   \n",
       "Coupler_System                                          None or Unspecified   \n",
       "Grouser_Tracks                                          None or Unspecified   \n",
       "Hydraulics_Flow                                                    Standard   \n",
       "Track_Type                                                            Steel   \n",
       "Undercarriage_Pad_Width                                 None or Unspecified   \n",
       "Stick_Length                                            None or Unspecified   \n",
       "Thumb                                                   None or Unspecified   \n",
       "Pattern_Changer                                         None or Unspecified   \n",
       "Grouser_Type                                                         Double   \n",
       "Backhoe_Mounting                                        None or Unspecified   \n",
       "Blade_Type                                                              PAT   \n",
       "Travel_Controls                                         None or Unspecified   \n",
       "Differential_Type                                                  Standard   \n",
       "Steering_Controls                                              Conventional   \n",
       "\n",
       "                            freq                first                 last  \\\n",
       "SalesID                      NaN                  NaN                  NaN   \n",
       "SalePrice                    NaN                  NaN                  NaN   \n",
       "MachineID                    NaN                  NaN                  NaN   \n",
       "ModelID                      NaN                  NaN                  NaN   \n",
       "datasource                   NaN                  NaN                  NaN   \n",
       "auctioneerID                 NaN                  NaN                  NaN   \n",
       "YearMade                     NaN                  NaN                  NaN   \n",
       "MachineHoursCurrentMeter     NaN                  NaN                  NaN   \n",
       "UsageBand                  33985                  NaN                  NaN   \n",
       "saledate                    1932  1989-01-17 00:00:00  2011-12-30 00:00:00   \n",
       "fiModelDesc                 5039                  NaN                  NaN   \n",
       "fiBaseModel                19798                  NaN                  NaN   \n",
       "fiSecondaryDesc            43235                  NaN                  NaN   \n",
       "fiModelSeries              13202                  NaN                  NaN   \n",
       "fiModelDescriptor          15875                  NaN                  NaN   \n",
       "ProductSize                62274                  NaN                  NaN   \n",
       "fiProductClassDesc         56166                  NaN                  NaN   \n",
       "state                      63944                  NaN                  NaN   \n",
       "ProductGroup              101167                  NaN                  NaN   \n",
       "ProductGroupDesc          101167                  NaN                  NaN   \n",
       "Drive_System               46139                  NaN                  NaN   \n",
       "Enclosure                 173932                  NaN                  NaN   \n",
       "Forks                     178300                  NaN                  NaN   \n",
       "Pad_Type                   70614                  NaN                  NaN   \n",
       "Ride_Control               77685                  NaN                  NaN   \n",
       "Stick                      48829                  NaN                  NaN   \n",
       "Transmission              140328                  NaN                  NaN   \n",
       "Turbocharged               75211                  NaN                  NaN   \n",
       "Blade_Extension            24692                  NaN                  NaN   \n",
       "Blade_Width                 9615                  NaN                  NaN   \n",
       "Enclosure_Type             21923                  NaN                  NaN   \n",
       "Engine_Horsepower          23937                  NaN                  NaN   \n",
       "Hydraulics                141404                  NaN                  NaN   \n",
       "Pushblock                  19463                  NaN                  NaN   \n",
       "Ripper                     83452                  NaN                  NaN   \n",
       "Scarifier                  12719                  NaN                  NaN   \n",
       "Tip_Control                16207                  NaN                  NaN   \n",
       "Tire_Size                  46339                  NaN                  NaN   \n",
       "Coupler                   184582                  NaN                  NaN   \n",
       "Coupler_System             40430                  NaN                  NaN   \n",
       "Grouser_Tracks             40515                  NaN                  NaN   \n",
       "Hydraulics_Flow            42784                  NaN                  NaN   \n",
       "Track_Type                 84880                  NaN                  NaN   \n",
       "Undercarriage_Pad_Width    79651                  NaN                  NaN   \n",
       "Stick_Length               78820                  NaN                  NaN   \n",
       "Thumb                      83093                  NaN                  NaN   \n",
       "Pattern_Changer            90255                  NaN                  NaN   \n",
       "Grouser_Type               84653                  NaN                  NaN   \n",
       "Backhoe_Mounting           78652                  NaN                  NaN   \n",
       "Blade_Type                 38612                  NaN                  NaN   \n",
       "Travel_Controls            69923                  NaN                  NaN   \n",
       "Differential_Type          68073                  NaN                  NaN   \n",
       "Steering_Controls          68679                  NaN                  NaN   \n",
       "\n",
       "                                 mean      std          min          25%  \\\n",
       "SalesID                   1.91971e+06   909021  1.13925e+06  1.41837e+06   \n",
       "SalePrice                     31099.7  23036.9         4750        14500   \n",
       "MachineID                  1.2179e+06   440992            0   1.0887e+06   \n",
       "ModelID                        6889.7  6221.78           28         3259   \n",
       "datasource                    134.666  8.96224          121          132   \n",
       "auctioneerID                  6.55604  16.9768            0            1   \n",
       "YearMade                      1899.16  291.797         1000         1985   \n",
       "MachineHoursCurrentMeter      3457.96  27590.3            0            0   \n",
       "UsageBand                         NaN      NaN          NaN          NaN   \n",
       "saledate                          NaN      NaN          NaN          NaN   \n",
       "fiModelDesc                       NaN      NaN          NaN          NaN   \n",
       "fiBaseModel                       NaN      NaN          NaN          NaN   \n",
       "fiSecondaryDesc                   NaN      NaN          NaN          NaN   \n",
       "fiModelSeries                     NaN      NaN          NaN          NaN   \n",
       "fiModelDescriptor                 NaN      NaN          NaN          NaN   \n",
       "ProductSize                       NaN      NaN          NaN          NaN   \n",
       "fiProductClassDesc                NaN      NaN          NaN          NaN   \n",
       "state                             NaN      NaN          NaN          NaN   \n",
       "ProductGroup                      NaN      NaN          NaN          NaN   \n",
       "ProductGroupDesc                  NaN      NaN          NaN          NaN   \n",
       "Drive_System                      NaN      NaN          NaN          NaN   \n",
       "Enclosure                         NaN      NaN          NaN          NaN   \n",
       "Forks                             NaN      NaN          NaN          NaN   \n",
       "Pad_Type                          NaN      NaN          NaN          NaN   \n",
       "Ride_Control                      NaN      NaN          NaN          NaN   \n",
       "Stick                             NaN      NaN          NaN          NaN   \n",
       "Transmission                      NaN      NaN          NaN          NaN   \n",
       "Turbocharged                      NaN      NaN          NaN          NaN   \n",
       "Blade_Extension                   NaN      NaN          NaN          NaN   \n",
       "Blade_Width                       NaN      NaN          NaN          NaN   \n",
       "Enclosure_Type                    NaN      NaN          NaN          NaN   \n",
       "Engine_Horsepower                 NaN      NaN          NaN          NaN   \n",
       "Hydraulics                        NaN      NaN          NaN          NaN   \n",
       "Pushblock                         NaN      NaN          NaN          NaN   \n",
       "Ripper                            NaN      NaN          NaN          NaN   \n",
       "Scarifier                         NaN      NaN          NaN          NaN   \n",
       "Tip_Control                       NaN      NaN          NaN          NaN   \n",
       "Tire_Size                         NaN      NaN          NaN          NaN   \n",
       "Coupler                           NaN      NaN          NaN          NaN   \n",
       "Coupler_System                    NaN      NaN          NaN          NaN   \n",
       "Grouser_Tracks                    NaN      NaN          NaN          NaN   \n",
       "Hydraulics_Flow                   NaN      NaN          NaN          NaN   \n",
       "Track_Type                        NaN      NaN          NaN          NaN   \n",
       "Undercarriage_Pad_Width           NaN      NaN          NaN          NaN   \n",
       "Stick_Length                      NaN      NaN          NaN          NaN   \n",
       "Thumb                             NaN      NaN          NaN          NaN   \n",
       "Pattern_Changer                   NaN      NaN          NaN          NaN   \n",
       "Grouser_Type                      NaN      NaN          NaN          NaN   \n",
       "Backhoe_Mounting                  NaN      NaN          NaN          NaN   \n",
       "Blade_Type                        NaN      NaN          NaN          NaN   \n",
       "Travel_Controls                   NaN      NaN          NaN          NaN   \n",
       "Differential_Type                 NaN      NaN          NaN          NaN   \n",
       "Steering_Controls                 NaN      NaN          NaN          NaN   \n",
       "\n",
       "                                  50%          75%          max  \n",
       "SalesID                   1.63942e+06  2.24271e+06  6.33334e+06  \n",
       "SalePrice                       24000        40000       142000  \n",
       "MachineID                 1.27949e+06  1.46807e+06  2.48633e+06  \n",
       "ModelID                          4604         8724        37198  \n",
       "datasource                        132          136          172  \n",
       "auctioneerID                        2            4           99  \n",
       "YearMade                         1995         2000         2013  \n",
       "MachineHoursCurrentMeter            0         3025   2.4833e+06  \n",
       "UsageBand                         NaN          NaN          NaN  \n",
       "saledate                          NaN          NaN          NaN  \n",
       "fiModelDesc                       NaN          NaN          NaN  \n",
       "fiBaseModel                       NaN          NaN          NaN  \n",
       "fiSecondaryDesc                   NaN          NaN          NaN  \n",
       "fiModelSeries                     NaN          NaN          NaN  \n",
       "fiModelDescriptor                 NaN          NaN          NaN  \n",
       "ProductSize                       NaN          NaN          NaN  \n",
       "fiProductClassDesc                NaN          NaN          NaN  \n",
       "state                             NaN          NaN          NaN  \n",
       "ProductGroup                      NaN          NaN          NaN  \n",
       "ProductGroupDesc                  NaN          NaN          NaN  \n",
       "Drive_System                      NaN          NaN          NaN  \n",
       "Enclosure                         NaN          NaN          NaN  \n",
       "Forks                             NaN          NaN          NaN  \n",
       "Pad_Type                          NaN          NaN          NaN  \n",
       "Ride_Control                      NaN          NaN          NaN  \n",
       "Stick                             NaN          NaN          NaN  \n",
       "Transmission                      NaN          NaN          NaN  \n",
       "Turbocharged                      NaN          NaN          NaN  \n",
       "Blade_Extension                   NaN          NaN          NaN  \n",
       "Blade_Width                       NaN          NaN          NaN  \n",
       "Enclosure_Type                    NaN          NaN          NaN  \n",
       "Engine_Horsepower                 NaN          NaN          NaN  \n",
       "Hydraulics                        NaN          NaN          NaN  \n",
       "Pushblock                         NaN          NaN          NaN  \n",
       "Ripper                            NaN          NaN          NaN  \n",
       "Scarifier                         NaN          NaN          NaN  \n",
       "Tip_Control                       NaN          NaN          NaN  \n",
       "Tire_Size                         NaN          NaN          NaN  \n",
       "Coupler                           NaN          NaN          NaN  \n",
       "Coupler_System                    NaN          NaN          NaN  \n",
       "Grouser_Tracks                    NaN          NaN          NaN  \n",
       "Hydraulics_Flow                   NaN          NaN          NaN  \n",
       "Track_Type                        NaN          NaN          NaN  \n",
       "Undercarriage_Pad_Width           NaN          NaN          NaN  \n",
       "Stick_Length                      NaN          NaN          NaN  \n",
       "Thumb                             NaN          NaN          NaN  \n",
       "Pattern_Changer                   NaN          NaN          NaN  \n",
       "Grouser_Type                      NaN          NaN          NaN  \n",
       "Backhoe_Mounting                  NaN          NaN          NaN  \n",
       "Blade_Type                        NaN          NaN          NaN  \n",
       "Travel_Controls                   NaN          NaN          NaN  \n",
       "Differential_Type                 NaN          NaN          NaN  \n",
       "Steering_Controls                 NaN          NaN          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_all(df_raw.describe(include='all').T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw.SalePrice = np.log(df_raw.SalePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Conventional'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-08aa6c84eee0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_raw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'SalePrice'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_raw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSalePrice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \"\"\"\n\u001b[1;32m    246\u001b[0m         \u001b[0;31m# Validate or convert input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    431\u001b[0m                                       force_all_finite)\n\u001b[1;32m    432\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Conventional'"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_jobs=-1)\n",
    "m.fit(df_raw.drop('SalePrice', axis=1), df_raw.SalePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2006\n",
       "1    2004\n",
       "2    2004\n",
       "3    2011\n",
       "4    2009\n",
       "Name: saleYear, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FastAi Module\n",
    "add_datepart(df_raw, 'saledate')\n",
    "df_raw.saleYear.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The categorical variables are currently stored as strings, which is inefficient, and doesn't provide the numeric coding required for a random forest. Therefore we call train_cats to convert strings to pandas categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FastAi Module\n",
    "train_cats(df_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['High', 'Low', 'Medium'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can specify the order to use for categorical variables if we wish:\n",
    "df_raw.UsageBand.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw.UsageBand.cat.set_categories(['High', 'Medium', 'Low'], ordered=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Backhoe_Mounting            0.803872\n",
       "Blade_Extension             0.937129\n",
       "Blade_Type                  0.800977\n",
       "Blade_Width                 0.937129\n",
       "Coupler                     0.466620\n",
       "Coupler_System              0.891660\n",
       "Differential_Type           0.826959\n",
       "Drive_System                0.739829\n",
       "Enclosure                   0.000810\n",
       "Enclosure_Type              0.937129\n",
       "Engine_Horsepower           0.937129\n",
       "Forks                       0.521154\n",
       "Grouser_Tracks              0.891899\n",
       "Grouser_Type                0.752813\n",
       "Hydraulics                  0.200823\n",
       "Hydraulics_Flow             0.891899\n",
       "MachineHoursCurrentMeter    0.644089\n",
       "MachineID                   0.000000\n",
       "ModelID                     0.000000\n",
       "Pad_Type                    0.802720\n",
       "Pattern_Changer             0.752651\n",
       "ProductGroup                0.000000\n",
       "ProductGroupDesc            0.000000\n",
       "ProductSize                 0.525460\n",
       "Pushblock                   0.937129\n",
       "Ride_Control                0.629527\n",
       "Ripper                      0.740388\n",
       "SalePrice                   0.000000\n",
       "SalesID                     0.000000\n",
       "Scarifier                   0.937102\n",
       "Steering_Controls           0.827064\n",
       "Stick                       0.802720\n",
       "Stick_Length                0.752651\n",
       "Thumb                       0.752476\n",
       "Tip_Control                 0.937129\n",
       "Tire_Size                   0.763869\n",
       "Track_Type                  0.752813\n",
       "Transmission                0.543210\n",
       "Travel_Controls             0.800975\n",
       "Turbocharged                0.802720\n",
       "Undercarriage_Pad_Width     0.751020\n",
       "UsageBand                   0.826391\n",
       "YearMade                    0.000000\n",
       "auctioneerID                0.050199\n",
       "datasource                  0.000000\n",
       "fiBaseModel                 0.000000\n",
       "fiModelDesc                 0.000000\n",
       "fiModelDescriptor           0.820707\n",
       "fiModelSeries               0.858129\n",
       "fiProductClassDesc          0.000000\n",
       "fiSecondaryDesc             0.342016\n",
       "saleDay                     0.000000\n",
       "saleDayofweek               0.000000\n",
       "saleDayofyear               0.000000\n",
       "saleElapsed                 0.000000\n",
       "saleIs_month_end            0.000000\n",
       "saleIs_month_start          0.000000\n",
       "saleIs_quarter_end          0.000000\n",
       "saleIs_quarter_start        0.000000\n",
       "saleIs_year_end             0.000000\n",
       "saleIs_year_start           0.000000\n",
       "saleMonth                   0.000000\n",
       "saleWeek                    0.000000\n",
       "saleYear                    0.000000\n",
       "state                       0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# missing data\n",
    "display_all(df_raw.isnull().sum().sort_index()/len(df_raw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('tmp', exist_ok=True)\n",
    "df_raw.to_feather('tmp/bulldozers-raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the future we can simply read it from this fast format.\n",
    "df_raw = pd.read_feather('tmp/bulldozers-raw')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We'll replace categories with their numeric codes, handle missing continuous values, and split the dependent variable into a separate variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FastAi Module\n",
    "df, y, nas = proc_df(df_raw, 'SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SalesID', 'SalePrice', 'MachineID', 'ModelID', 'datasource',\n",
       "       'auctioneerID', 'YearMade', 'MachineHoursCurrentMeter', 'UsageBand',\n",
       "       'fiModelDesc', 'fiBaseModel', 'fiSecondaryDesc', 'fiModelSeries',\n",
       "       'fiModelDescriptor', 'ProductSize', 'fiProductClassDesc', 'state',\n",
       "       'ProductGroup', 'ProductGroupDesc', 'Drive_System', 'Enclosure',\n",
       "       'Forks', 'Pad_Type', 'Ride_Control', 'Stick', 'Transmission',\n",
       "       'Turbocharged', 'Blade_Extension', 'Blade_Width', 'Enclosure_Type',\n",
       "       'Engine_Horsepower', 'Hydraulics', 'Pushblock', 'Ripper', 'Scarifier',\n",
       "       'Tip_Control', 'Tire_Size', 'Coupler', 'Coupler_System',\n",
       "       'Grouser_Tracks', 'Hydraulics_Flow', 'Track_Type',\n",
       "       'Undercarriage_Pad_Width', 'Stick_Length', 'Thumb', 'Pattern_Changer',\n",
       "       'Grouser_Type', 'Backhoe_Mounting', 'Blade_Type', 'Travel_Controls',\n",
       "       'Differential_Type', 'Steering_Controls', 'saleYear', 'saleMonth',\n",
       "       'saleWeek', 'saleDay', 'saleDayofweek', 'saleDayofyear',\n",
       "       'saleIs_month_end', 'saleIs_month_start', 'saleIs_quarter_end',\n",
       "       'saleIs_quarter_start', 'saleIs_year_end', 'saleIs_year_start',\n",
       "       'saleElapsed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayofweek</th>\n",
       "      <th>saleDayofyear</th>\n",
       "      <th>saleIs_month_end</th>\n",
       "      <th>saleIs_month_start</th>\n",
       "      <th>saleIs_quarter_end</th>\n",
       "      <th>saleIs_quarter_start</th>\n",
       "      <th>saleIs_year_end</th>\n",
       "      <th>saleIs_year_start</th>\n",
       "      <th>saleElapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1139246</td>\n",
       "      <td>11.097410</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>521D</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>320</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1163635200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1139248</td>\n",
       "      <td>10.950807</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>950FII</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>86</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1080259200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1139249</td>\n",
       "      <td>9.210340</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>226</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1077753600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1139251</td>\n",
       "      <td>10.558414</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>PC120-6E</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>139</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1305763200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1139253</td>\n",
       "      <td>9.305651</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>S175</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>204</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1248307200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1139246  11.097410     999089     3157         121           3.0      2004   \n",
       "1  1139248  10.950807     117657       77         121           3.0      1996   \n",
       "2  1139249   9.210340     434808     7009         121           3.0      2001   \n",
       "3  1139251  10.558414    1026470      332         121           3.0      2001   \n",
       "4  1139253   9.305651    1057373    17311         121           3.0      2007   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand fiModelDesc     ...     saleDay  \\\n",
       "0                      68.0       Low        521D     ...          16   \n",
       "1                    4640.0       Low      950FII     ...          26   \n",
       "2                    2838.0      High         226     ...          26   \n",
       "3                    3486.0      High    PC120-6E     ...          19   \n",
       "4                     722.0    Medium        S175     ...          23   \n",
       "\n",
       "  saleDayofweek saleDayofyear saleIs_month_end saleIs_month_start  \\\n",
       "0             3           320            False              False   \n",
       "1             4            86            False              False   \n",
       "2             3            57            False              False   \n",
       "3             3           139            False              False   \n",
       "4             3           204            False              False   \n",
       "\n",
       "  saleIs_quarter_end saleIs_quarter_start saleIs_year_end saleIs_year_start  \\\n",
       "0              False                False           False             False   \n",
       "1              False                False           False             False   \n",
       "2              False                False           False             False   \n",
       "3              False                False           False             False   \n",
       "4              False                False           False             False   \n",
       "\n",
       "  saleElapsed  \n",
       "0  1163635200  \n",
       "1  1080259200  \n",
       "2  1077753600  \n",
       "3  1305763200  \n",
       "4  1248307200  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9830589514342102"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_jobs=-1)\n",
    "m.fit(df, y)\n",
    "m.score(df,y)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "todo define r^2\n",
    "\n",
    "Wow, an r^2 of 0.98 - that's great, right? Well, perhaps not...\n",
    "\n",
    "Possibly the most important idea in machine learning is that of having separate training & validation data sets. As motivation, suppose you don't divide up your data, but instead use all of it. And suppose you have lots of parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"https://github.com/fastai/fastai/raw/33e6eea6fe22210b7dbab594d7a51f023f2ee88d/courses/ml1/images/overfitting2.png\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The error for the pictured data points is lowest for the model on the far right (the blue curve passes through the red points almost perfectly), yet it's not the best choice. Why is that? If you were to gather some new data points, they most likely would not be on that curve in the graph on the right, but would be closer to the curve in the middle graph.\n",
    "\n",
    "This illustrates how using all our data can lead to overfitting. A validation set helps diagnose this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((389125, 66), (389125,), (12000, 66), (12000,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def split_vals(a,n): return a[:n].copy(), a[n:].copy()\n",
    "\n",
    "n_valid = 12000  # same as Kaggle's test set size\n",
    "n_trn = len(df)-n_valid\n",
    "raw_train, raw_valid = split_vals(df_raw, n_trn)\n",
    "X_train, X_valid = split_vals(df, n_trn)\n",
    "y_train, y_valid = split_vals(y, n_trn)\n",
    "\n",
    "X_train.shape, y_train.shape, X_valid.shape, y_valid.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base Model - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(x,y):\n",
    "    return math.sqrt(((x-y)**2).mean())\n",
    "\n",
    "def print_score(m):\n",
    "    res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid),\n",
    "                m.score(X_train, y_train), m.score(X_valid, y_valid)]\n",
    "    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 49s, sys: 2.1 s, total: 1min 51s\n",
      "Wall time: 49.8 s\n",
      "[0.09044387427246701, 0.247325233290708, 0.9829040538460588, 0.890759210853356]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_jobs=-1)\n",
    "%time m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An r^2 in the high-80's isn't bad at all (and the RMSLE puts us around rank 100 of 470 on the Kaggle leaderboard), but we can see from the validation set score that we're over-fitting badly. To understand this issue, let's simplify things down to a single small tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Speeding things up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trn, y_trn, nas = proc_df(df_raw, 'SalePrice', subset=30000, na_dict=nas)\n",
    "X_train, _ = split_vals(df_trn, 20000)\n",
    "y_train, _ = split_vals(y_trn, 20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 66), (20000,), (12000, 66), (12000,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_valid.shape, y_valid.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.95 s, sys: 61.5 ms, total: 4.01 s\n",
      "Wall time: 1.45 s\n",
      "[0.11159937265127794, 0.376587043284135, 0.9732353096135146, 0.7467329326907574]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_jobs=-1)\n",
    "%time m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5223702530360365, 0.5831479569535625, 0.4135977979406753, 0.3926969100209765]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, max_depth=3, bootstrap=False, n_jobs=-1)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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       "<g id=\"graph0\" class=\"graph\" transform=\"scale(.8136 .8136) rotate(0) translate(4 530)\">\n",
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       "<text text-anchor=\"start\" x=\"272.5393\" y=\"-340.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">YearMade ≤ 1985.5</text>\n",
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       "<text text-anchor=\"start\" x=\"761.9724\" y=\"-292.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 8760</text>\n",
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       "<text text-anchor=\"start\" x=\"500.5491\" y=\"-218.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">fiProductClassDesc ≤ 39.5</text>\n",
       "<text text-anchor=\"start\" x=\"540.4983\" y=\"-204.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.081</text>\n",
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       "<text text-anchor=\"start\" x=\"500.5491\" y=\"-109.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">fiProductClassDesc ≤ 40.5</text>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x1c38fbd908>"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "draw_tree(m.estimators_[0], df_trn, precision=3)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Let's see what happens if we create a bigger tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.7682219008410604e-17, 0.5335567197192034, 1.0, 0.4915957624748482]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=1, bootstrap=False, n_jobs=-1)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set result looks great! But the validation set is worse than our original model. This is why we need to use bagging of multiple trees to get more generalizable results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro to Bagging"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "To learn about bagging in random forests, let's start with our basic model again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.11091111970735361, 0.37168480830233797, 0.9735644167423935, 0.7532838407915435]\n"
     ]
    }
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   "source": [
    "m = RandomForestRegressor(n_jobs=-1)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We'll grab the predictions for each individual tree, and look at one example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([10.12663, 10.12663,  9.21034, 10.22194,  9.07681,  9.68034,  9.07681,  9.21034,  9.76996,  9.76996]),\n",
       " 9.626975851505614,\n",
       " 9.104979856318357)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "preds = np.stack([t.predict(X_valid) for t in m.estimators_])\n",
    "preds[:,0], np.mean(preds[:,0]), y_valid[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 12000)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([metrics.r2_score(y_valid, np.mean(preds[:i+1], axis=0)) for i in range(10)]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape of this curve suggests that adding more trees isn't going to help us much. Let's check. (Compare this to our original model on a sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10113864375705268, 0.36162364019433935, 0.9780177082039376, 0.7664598250729396]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=20, n_jobs=-1)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.09634052297664142, 0.34910582005590785, 0.9800539585887876, 0.7823482555784067]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=40, n_jobs=-1)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.09336430912190956, 0.35354471222902056, 0.9812672950391845, 0.7767781689557703]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=80, n_jobs=-1)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-bag (OOB) score¶"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Is our validation set worse than our training set because we're over-fitting, or because the validation set is for a different time period, or a bit of both? With the existing information we've shown, we can't tell. However, random forests have a very clever trick called out-of-bag (OOB) error which can handle this (and more!)\n",
    "\n",
    "The idea is to calculate error on the training set, but only include the trees in the calculation of a row's error where that row was not included in training that tree. This allows us to see whether the model is over-fitting, without needing a separate validation set.\n",
    "\n",
    "This also has the benefit of allowing us to see whether our model generalizes, even if we only have a small amount of data so want to avoid separating some out to create a validation set.\n",
    "\n",
    "This is as simple as adding one more parameter to our model constructor. We print the OOB error last in our print_score function below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.09613802035614658, 0.34960837852689886, 0.980137721485577, 0.7817211594198845, 0.8582335046943224]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=40, n_jobs=-1, oob_score=True)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This shows that our validation set time difference is making an impact, as is model over-fitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing over-fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subsampling"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "It turns out that one of the easiest ways to avoid over-fitting is also one of the best ways to speed up analysis: subsampling. Let's return to using our full dataset, so that we can demonstrate the impact of this technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trn, y_trn, nas = proc_df(df_raw, 'SalePrice')\n",
    "X_train, X_valid = split_vals(df_trn, n_trn)\n",
    "y_train, y_valid = split_vals(y_trn, n_trn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "389125"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The basic idea is this: rather than limit the total amount of data that our model can access, let's instead limit it to a different random subset per tree. That way, given enough trees, the model can still see all the data, but for each individual tree it'll be just as fast as if we had cut down our dataset as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_rf_samples(20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.2 s, sys: 1.14 s, total: 14.3 s\n",
      "Wall time: 10.7 s\n",
      "[0.24044842714127154, 0.28561290327037964, 0.8791689061144566, 0.8543187625701018, 0.8666428308640319]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_jobs=-1, oob_score=True)\n",
    "%time m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since each additional tree allows the model to see more data, this approach can make additional trees more useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22741592932682606, 0.26265299576013784, 0.8919122219580267, 0.8767994373447925, 0.8803782341396877]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=40, n_jobs=-1, oob_score=True)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree building parameters"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We revert to using a full bootstrap sample in order to show the impact of other over-fitting avoidance methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_rf_samples()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Let's get a baseline for this full set to compare to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.07828660300660112, 0.23523122766005236, 0.9871911622804048, 0.901181575775732, 0.9087156825916087]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=40, n_jobs=-1, oob_score=True)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Another way to reduce over-fitting is to grow our trees less deeply. We do this by specifying (with min_samples_leaf) that we require some minimum number of rows in every leaf node. This has two benefits:\n",
    "\n",
    "There are less decision rules for each leaf node; simpler models should generalize better\n",
    "The predictions are made by averaging more rows in the leaf node, resulting in less volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1151527590391674, 0.23543325651632246, 0.9722870012504123, 0.9010117620434303, 0.9082409369394742]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=40, min_samples_leaf=3, n_jobs=-1, oob_score=True)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can also increase the amount of variation amongst the trees by not only use a sample of rows for each tree, but to also using a sample of columns for each split. We do this by specifying max_features, which is the proportion of features to randomly select from at each split.\n",
    "\n",
    "None\n",
    "0.5\n",
    "'sqrt'\n",
    "1, 3, 5, 10, 25, 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1189534196034478, 0.22699314449156965, 0.9704274558472126, 0.9079818587861528, 0.9119604403940322]\n"
     ]
    }
   ],
   "source": [
    "m = RandomForestRegressor(n_estimators=40, min_samples_leaf=3, max_features=0.5, n_jobs=-1, oob_score=True)\n",
    "m.fit(X_train, y_train)\n",
    "print_score(m)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can't compare our results directly with the Kaggle competition, since it used a different validation set (and we can no longer to submit to this competition) - but we can at least see that we're getting similar results to the winners based on the dataset we have.\n",
    "\n",
    "The sklearn docs show an example of different max_features methods with increasing numbers of trees - as you see, using a subset of features on each split requires using more trees, but results in better models:\n",
    "\n",
    "http://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"http://scikit-learn.org/stable/_images/sphx_glr_plot_ensemble_oob_001.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}