end_to_end_bulldozer_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "999efbc0-f65d-4bed-aa44-3b8a8218b386",
   "metadata": {},
   "source": [
    "# 🚂Predicting the Sale Price of Bulldozers using Machine Learning\n",
    "* Press `WINDOW+PERIOD (.)` to access emojis\n",
    "\n",
    "## The Goal or Problem Description\n",
    "* **\"Predict the auction sale price for a piece of heavy equipment to create a \"blue book\" for bulldozers\".**\n",
    "\n",
    "The goal is to **predict sale prices of bulldozers given some attributes and historical sale prices** of similar bulldozers.\n",
    "tion\n",
    "## Dataset\n",
    "For Dataset Description see **Kaggle's** [Blue Book for Bulldozers](https://www.kaggle.com/c/bluebook-for-bulldozers/dataThe 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",
    "\n",
    "## Evaluation\n",
    "\"The evaluation metric for this competition is the **RMSLE (root mean squared log error)** between the actual and predicted auction prices.\" See [Evaluation](https://www.kaggle.com/c/bluebook-for-bulldozers/overview) on Kaggle.\n",
    "\n",
    "## Features\n",
    "The key fields 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.\n",
    "\n",
    "See also provide a [Data Dictionary](view-source:https://www.kaggle.com/c/bluebook-for-bulldozers/data?select=Data+Dictionary.xlsx) with all columns' descriptions in the dataset. sale) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9080dbe2-c5e3-4a63-9ecd-dd5b16c5f614",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60ff8f8-94f0-417a-a869-c972403f850d",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac7febd6-ecd8-4fde-a5ba-0474b486d21c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../resources/data/bulldozers/TrainAndValid.csv', low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "100ff9fe-7462-4cf4-bc49-78adf3f2a8eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(412698, 53)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1139246</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>999089</td>\n",
       "      <td>3157</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>11/16/2006 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1139248</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>117657</td>\n",
       "      <td>77</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>3/26/2004 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1139249</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>434808</td>\n",
       "      <td>7009</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2/26/2004 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1139251</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>1026470</td>\n",
       "      <td>332</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>High</td>\n",
       "      <td>5/19/2011 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1139253</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>1057373</td>\n",
       "      <td>17311</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>722.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>7/23/2009 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1139246    66000.0     999089     3157         121           3.0      2004   \n",
       "1  1139248    57000.0     117657       77         121           3.0      1996   \n",
       "2  1139249    10000.0     434808     7009         121           3.0      2001   \n",
       "3  1139251    38500.0    1026470      332         121           3.0      2001   \n",
       "4  1139253    11000.0    1057373    17311         121           3.0      2007   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand         saledate  ...  \\\n",
       "0                      68.0       Low  11/16/2006 0:00  ...   \n",
       "1                    4640.0       Low   3/26/2004 0:00  ...   \n",
       "2                    2838.0      High   2/26/2004 0:00  ...   \n",
       "3                    3486.0      High   5/19/2011 0:00  ...   \n",
       "4                     722.0    Medium   7/23/2009 0:00  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width Stick_Length Thumb Pattern_Changer Grouser_Type  \\\n",
       "0                     NaN          NaN   NaN             NaN          NaN   \n",
       "1                     NaN          NaN   NaN             NaN          NaN   \n",
       "2                     NaN          NaN   NaN             NaN          NaN   \n",
       "3                     NaN          NaN   NaN             NaN          NaN   \n",
       "4                     NaN          NaN   NaN             NaN          NaN   \n",
       "\n",
       "  Backhoe_Mounting Blade_Type Travel_Controls Differential_Type  \\\n",
       "0              NaN        NaN             NaN          Standard   \n",
       "1              NaN        NaN             NaN          Standard   \n",
       "2              NaN        NaN             NaN               NaN   \n",
       "3              NaN        NaN             NaN               NaN   \n",
       "4              NaN        NaN             NaN               NaN   \n",
       "\n",
       "  Steering_Controls  \n",
       "0      Conventional  \n",
       "1      Conventional  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9fdb1d97-332c-4f0a-b431-17bb2942a273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 412698 entries, 0 to 412697\n",
      "Data columns (total 53 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   SalesID                   412698 non-null  int64  \n",
      " 1   SalePrice                 412698 non-null  float64\n",
      " 2   MachineID                 412698 non-null  int64  \n",
      " 3   ModelID                   412698 non-null  int64  \n",
      " 4   datasource                412698 non-null  int64  \n",
      " 5   auctioneerID              392562 non-null  float64\n",
      " 6   YearMade                  412698 non-null  int64  \n",
      " 7   MachineHoursCurrentMeter  147504 non-null  float64\n",
      " 8   UsageBand                 73670 non-null   object \n",
      " 9   saledate                  412698 non-null  object \n",
      " 10  fiModelDesc               412698 non-null  object \n",
      " 11  fiBaseModel               412698 non-null  object \n",
      " 12  fiSecondaryDesc           271971 non-null  object \n",
      " 13  fiModelSeries             58667 non-null   object \n",
      " 14  fiModelDescriptor         74816 non-null   object \n",
      " 15  ProductSize               196093 non-null  object \n",
      " 16  fiProductClassDesc        412698 non-null  object \n",
      " 17  state                     412698 non-null  object \n",
      " 18  ProductGroup              412698 non-null  object \n",
      " 19  ProductGroupDesc          412698 non-null  object \n",
      " 20  Drive_System              107087 non-null  object \n",
      " 21  Enclosure                 412364 non-null  object \n",
      " 22  Forks                     197715 non-null  object \n",
      " 23  Pad_Type                  81096 non-null   object \n",
      " 24  Ride_Control              152728 non-null  object \n",
      " 25  Stick                     81096 non-null   object \n",
      " 26  Transmission              188007 non-null  object \n",
      " 27  Turbocharged              81096 non-null   object \n",
      " 28  Blade_Extension           25983 non-null   object \n",
      " 29  Blade_Width               25983 non-null   object \n",
      " 30  Enclosure_Type            25983 non-null   object \n",
      " 31  Engine_Horsepower         25983 non-null   object \n",
      " 32  Hydraulics                330133 non-null  object \n",
      " 33  Pushblock                 25983 non-null   object \n",
      " 34  Ripper                    106945 non-null  object \n",
      " 35  Scarifier                 25994 non-null   object \n",
      " 36  Tip_Control               25983 non-null   object \n",
      " 37  Tire_Size                 97638 non-null   object \n",
      " 38  Coupler                   220679 non-null  object \n",
      " 39  Coupler_System            44974 non-null   object \n",
      " 40  Grouser_Tracks            44875 non-null   object \n",
      " 41  Hydraulics_Flow           44875 non-null   object \n",
      " 42  Track_Type                102193 non-null  object \n",
      " 43  Undercarriage_Pad_Width   102916 non-null  object \n",
      " 44  Stick_Length              102261 non-null  object \n",
      " 45  Thumb                     102332 non-null  object \n",
      " 46  Pattern_Changer           102261 non-null  object \n",
      " 47  Grouser_Type              102193 non-null  object \n",
      " 48  Backhoe_Mounting          80712 non-null   object \n",
      " 49  Blade_Type                81875 non-null   object \n",
      " 50  Travel_Controls           81877 non-null   object \n",
      " 51  Differential_Type         71564 non-null   object \n",
      " 52  Steering_Controls         71522 non-null   object \n",
      "dtypes: float64(3), int64(5), object(45)\n",
      "memory usage: 166.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8dc683b1-0b12-4090-a123-fbcc8c8d844d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with missing values:\n",
      "auctioneerID                 20136\n",
      "MachineHoursCurrentMeter    265194\n",
      "UsageBand                   339028\n",
      "fiSecondaryDesc             140727\n",
      "fiModelSeries               354031\n",
      "fiModelDescriptor           337882\n",
      "ProductSize                 216605\n",
      "Drive_System                305611\n",
      "Enclosure                      334\n",
      "Forks                       214983\n",
      "Pad_Type                    331602\n",
      "Ride_Control                259970\n",
      "Stick                       331602\n",
      "Transmission                224691\n",
      "Turbocharged                331602\n",
      "Blade_Extension             386715\n",
      "Blade_Width                 386715\n",
      "Enclosure_Type              386715\n",
      "Engine_Horsepower           386715\n",
      "Hydraulics                   82565\n",
      "Pushblock                   386715\n",
      "Ripper                      305753\n",
      "Scarifier                   386704\n",
      "Tip_Control                 386715\n",
      "Tire_Size                   315060\n",
      "Coupler                     192019\n",
      "Coupler_System              367724\n",
      "Grouser_Tracks              367823\n",
      "Hydraulics_Flow             367823\n",
      "Track_Type                  310505\n",
      "Undercarriage_Pad_Width     309782\n",
      "Stick_Length                310437\n",
      "Thumb                       310366\n",
      "Pattern_Changer             310437\n",
      "Grouser_Type                310505\n",
      "Backhoe_Mounting            331986\n",
      "Blade_Type                  330823\n",
      "Travel_Controls             330821\n",
      "Differential_Type           341134\n",
      "Steering_Controls           341176\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "z = df.isnull().sum()\n",
    "print(f'Columns with missing values:\\n{z[z > 0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf82d9a-5e4c-4142-acca-d0c197e8da86",
   "metadata": {},
   "source": [
    "### Some Visualizations..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6231038b-e6b4-47e3-aeb9-95bc89efc270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "ax.scatter(df.saledate[:1000], df.SalePrice[:1000])\n",
    "plt.xticks(rotation = 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dec83fc-ae1b-48c5-9509-a94d521bc1bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.SalePrice.plot(kind = 'hist');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2aa6a22-dba4-4bb9-b0bc-3fdaf97016aa",
   "metadata": {},
   "source": [
    "### Parsing Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4004e42f-d495-4d01-9e96-27950f9812d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "print(df.saledate.dtype)\n",
    "df = pd.read_csv('../resources/data/bulldozers/TrainAndValid.csv', low_memory = False, parse_dates = ['saledate'])\n",
    "print(df.saledate.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0080bf6d-0b28-4c0f-80ed-e0539c674fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "ax.scatter(df.saledate[:1000], df.SalePrice[:1000])\n",
    "plt.xticks(rotation = 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e4847a8-7d6a-4886-85ee-4412667d4d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1139246</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>999089</td>\n",
       "      <td>3157</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2006-11-16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1139248</td>\n",
       "      <td>57000.0</td>\n",
       "      <td>117657</td>\n",
       "      <td>77</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2004-03-26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1139249</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>434808</td>\n",
       "      <td>7009</td>\n",
       "      <td>121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2004-02-26</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1139246    66000.0     999089     3157         121           3.0      2004   \n",
       "1  1139248    57000.0     117657       77         121           3.0      1996   \n",
       "2  1139249    10000.0     434808     7009         121           3.0      2001   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate  ... Undercarriage_Pad_Width  \\\n",
       "0                      68.0       Low 2006-11-16  ...                     NaN   \n",
       "1                    4640.0       Low 2004-03-26  ...                     NaN   \n",
       "2                    2838.0      High 2004-02-26  ...                     NaN   \n",
       "\n",
       "  Stick_Length Thumb Pattern_Changer Grouser_Type Backhoe_Mounting Blade_Type  \\\n",
       "0          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "1          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "2          NaN   NaN             NaN          NaN              NaN        NaN   \n",
       "\n",
       "  Travel_Controls Differential_Type Steering_Controls  \n",
       "0             NaN          Standard      Conventional  \n",
       "1             NaN          Standard      Conventional  \n",
       "2             NaN               NaN               NaN  \n",
       "\n",
       "[3 rows x 53 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cb00c0-8d08-460a-bf87-0b9108452c5e",
   "metadata": {},
   "source": [
    "### Sort DataFrame by saledate\n",
    "It's a good approach to sort the DataFrame by date column when working with time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b13c78d8-8282-4a7a-ad37-a522e388e4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615   1989-01-17\n",
       "274835   1989-01-31\n",
       "141296   1989-01-31\n",
       "212552   1989-01-31\n",
       "62755    1989-01-31\n",
       "54653    1989-01-31\n",
       "81383    1989-01-31\n",
       "204924   1989-01-31\n",
       "135376   1989-01-31\n",
       "113390   1989-01-31\n",
       "113394   1989-01-31\n",
       "116419   1989-01-31\n",
       "32138    1989-01-31\n",
       "127610   1989-01-31\n",
       "76171    1989-01-31\n",
       "127000   1989-01-31\n",
       "128130   1989-01-31\n",
       "127626   1989-01-31\n",
       "55455    1989-01-31\n",
       "55454    1989-01-31\n",
       "Name: saledate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = ['saledate'], ascending = True, inplace = True)\n",
    "df.saledate.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1354125c-49f2-477f-87bd-ffc17225d3d3",
   "metadata": {},
   "source": [
    "### Make a copy of the DataFrame before manipulating it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9f195357-d82e-460b-9b56-0dd80fc13391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205615</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-17</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274835</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141296</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989-01-31</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  \\\n",
       "205615  1646770     9500.0    1126363     8434         132          18.0   \n",
       "274835  1821514    14000.0    1194089    10150         132          99.0   \n",
       "141296  1505138    50000.0    1473654     4139         132          99.0   \n",
       "\n",
       "        YearMade  MachineHoursCurrentMeter UsageBand   saledate  ...  \\\n",
       "205615      1974                       NaN       NaN 1989-01-17  ...   \n",
       "274835      1980                       NaN       NaN 1989-01-31  ...   \n",
       "141296      1978                       NaN       NaN 1989-01-31  ...   \n",
       "\n",
       "       Undercarriage_Pad_Width Stick_Length Thumb Pattern_Changer  \\\n",
       "205615                     NaN          NaN   NaN             NaN   \n",
       "274835                     NaN          NaN   NaN             NaN   \n",
       "141296                     NaN          NaN   NaN             NaN   \n",
       "\n",
       "       Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
       "205615          NaN  None or Unspecified   Straight  None or Unspecified   \n",
       "274835          NaN                  NaN        NaN                  NaN   \n",
       "141296          NaN  None or Unspecified   Straight  None or Unspecified   \n",
       "\n",
       "       Differential_Type Steering_Controls  \n",
       "205615               NaN               NaN  \n",
       "274835          Standard      Conventional  \n",
       "141296               NaN               NaN  \n",
       "\n",
       "[3 rows x 53 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy = df.copy()\n",
    "df_copy.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaaed2db-f1fc-485f-8399-b439ad9b4f66",
   "metadata": {},
   "source": [
    "### Feature Engineering: Add daytime columns in the DataFrame using `saledate`column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4785a80f-65d0-49df-85f0-83b1ae25bedb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy['saleYear'] = df_copy.saledate.dt.year\n",
    "df_copy['saleMonth'] = df_copy.saledate.dt.month\n",
    "df_copy['saleDay'] = df_copy.saledate.dt.day\n",
    "df_copy['saleDayOfWeek'] = df_copy.saledate.dt.dayofweek\n",
    "df_copy['saleDayOfYear'] = df_copy.saledate.dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2a341082-4f7c-4c3d-9778-8b5ab6eb7f34",
   "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>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <th>saleDayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205615</th>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274835</th>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141296</th>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212552</th>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62755</th>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        saleYear  saleMonth  saleDay  saleDayOfWeek  saleDayOfYear\n",
       "205615      1989          1       17              1             17\n",
       "274835      1989          1       31              1             31\n",
       "141296      1989          1       31              1             31\n",
       "212552      1989          1       31              1             31\n",
       "62755       1989          1       31              1             31"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy[['saleYear', 'saleMonth', 'saleDay', 'saleDayOfWeek', 'saleDayOfYear']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8be4686-8be5-473c-b433-7be101506414",
   "metadata": {},
   "source": [
    "### We can now drop the `saledate` from the copy of the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2bf8b42c-3509-4d70-b151-526bef931632",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.drop('saledate', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f0654b06-a876-4954-84ff-86bebb33472f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Florida           67320\n",
       "Texas             53110\n",
       "California        29761\n",
       "Washington        16222\n",
       "Georgia           14633\n",
       "Maryland          13322\n",
       "Mississippi       13240\n",
       "Ohio              12369\n",
       "Illinois          11540\n",
       "Colorado          11529\n",
       "New Jersey        11156\n",
       "North Carolina    10636\n",
       "Tennessee         10298\n",
       "Alabama           10292\n",
       "Pennsylvania      10234\n",
       "South Carolina     9951\n",
       "Arizona            9364\n",
       "New York           8639\n",
       "Connecticut        8276\n",
       "Minnesota          7885\n",
       "Missouri           7178\n",
       "Nevada             6932\n",
       "Louisiana          6627\n",
       "Kentucky           5351\n",
       "Maine              5096\n",
       "Indiana            4124\n",
       "Arkansas           3933\n",
       "New Mexico         3631\n",
       "Utah               3046\n",
       "Unspecified        2801\n",
       "Wisconsin          2745\n",
       "New Hampshire      2738\n",
       "Virginia           2353\n",
       "Idaho              2025\n",
       "Oregon             1911\n",
       "Michigan           1831\n",
       "Wyoming            1672\n",
       "Montana            1336\n",
       "Iowa               1336\n",
       "Oklahoma           1326\n",
       "Nebraska            866\n",
       "West Virginia       840\n",
       "Kansas              667\n",
       "Delaware            510\n",
       "North Dakota        480\n",
       "Alaska              430\n",
       "Massachusetts       347\n",
       "Vermont             300\n",
       "South Dakota        244\n",
       "Hawaii              118\n",
       "Rhode Island         83\n",
       "Puerto Rico          42\n",
       "Washington DC         2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.state.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "796a3a7a-ccd9-44ae-b974-b158776993cc",
   "metadata": {},
   "source": [
    "## Build a machine learning Regressor Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "78682c78-2400-40aa-8355-f6a17c9704ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ab2b2c7-5955-4f36-8b23-cda85bea23c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This won't work since all columns are not numeric or date!\n",
    "# model = RandomForestRegressor(n_jobs = -1, random_state = 42)\n",
    "# model.fit(df_copy.drop('SalePrice', axis = 1), df_copy.SalePrice)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32cf3f7-8209-44a4-8937-6d1a8203f361",
   "metadata": {},
   "source": [
    "### Taking care of missing and categorical data\n",
    "#### Categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8021ee5a-26e2-4e0e-a0be-1e0d509b0ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                       int64\n",
       "SalePrice                   float64\n",
       "MachineID                     int64\n",
       "ModelID                       int64\n",
       "datasource                    int64\n",
       "auctioneerID                float64\n",
       "YearMade                      int64\n",
       "MachineHoursCurrentMeter    float64\n",
       "UsageBand                    object\n",
       "fiModelDesc                  object\n",
       "fiBaseModel                  object\n",
       "fiSecondaryDesc              object\n",
       "fiModelSeries                object\n",
       "fiModelDescriptor            object\n",
       "ProductSize                  object\n",
       "fiProductClassDesc           object\n",
       "state                        object\n",
       "ProductGroup                 object\n",
       "ProductGroupDesc             object\n",
       "Drive_System                 object\n",
       "Enclosure                    object\n",
       "Forks                        object\n",
       "Pad_Type                     object\n",
       "Ride_Control                 object\n",
       "Stick                        object\n",
       "Transmission                 object\n",
       "Turbocharged                 object\n",
       "Blade_Extension              object\n",
       "Blade_Width                  object\n",
       "Enclosure_Type               object\n",
       "Engine_Horsepower            object\n",
       "Hydraulics                   object\n",
       "Pushblock                    object\n",
       "Ripper                       object\n",
       "Scarifier                    object\n",
       "Tip_Control                  object\n",
       "Tire_Size                    object\n",
       "Coupler                      object\n",
       "Coupler_System               object\n",
       "Grouser_Tracks               object\n",
       "Hydraulics_Flow              object\n",
       "Track_Type                   object\n",
       "Undercarriage_Pad_Width      object\n",
       "Stick_Length                 object\n",
       "Thumb                        object\n",
       "Pattern_Changer              object\n",
       "Grouser_Type                 object\n",
       "Backhoe_Mounting             object\n",
       "Blade_Type                   object\n",
       "Travel_Controls              object\n",
       "Differential_Type            object\n",
       "Steering_Controls            object\n",
       "saleYear                      int32\n",
       "saleMonth                     int32\n",
       "saleDay                       int32\n",
       "saleDayOfWeek                 int32\n",
       "saleDayOfYear                 int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3f445f93-e075-451b-af1d-be1c8f461e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fiModelDesc',\n",
       " 'fiBaseModel',\n",
       " 'fiProductClassDesc',\n",
       " 'state',\n",
       " 'ProductGroup',\n",
       " 'ProductGroupDesc']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str_types = [col for col, values in df_copy.items() if pd.api.types.is_string_dtype(values)]\n",
    "str_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f9c9b923-9da1-4773-a6e1-50a97db67ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['UsageBand',\n",
       " 'fiModelDesc',\n",
       " 'fiBaseModel',\n",
       " 'fiSecondaryDesc',\n",
       " 'fiModelSeries',\n",
       " 'fiModelDescriptor',\n",
       " 'ProductSize',\n",
       " 'fiProductClassDesc',\n",
       " 'state',\n",
       " 'ProductGroup',\n",
       " 'ProductGroupDesc',\n",
       " 'Drive_System',\n",
       " 'Enclosure',\n",
       " 'Forks',\n",
       " 'Pad_Type',\n",
       " 'Ride_Control',\n",
       " 'Stick',\n",
       " 'Transmission',\n",
       " 'Turbocharged',\n",
       " 'Blade_Extension',\n",
       " 'Blade_Width',\n",
       " 'Enclosure_Type',\n",
       " 'Engine_Horsepower',\n",
       " 'Hydraulics',\n",
       " 'Pushblock',\n",
       " 'Ripper',\n",
       " 'Scarifier',\n",
       " 'Tip_Control',\n",
       " 'Tire_Size',\n",
       " 'Coupler',\n",
       " 'Coupler_System',\n",
       " 'Grouser_Tracks',\n",
       " 'Hydraulics_Flow',\n",
       " 'Track_Type',\n",
       " 'Undercarriage_Pad_Width',\n",
       " 'Stick_Length',\n",
       " 'Thumb',\n",
       " 'Pattern_Changer',\n",
       " 'Grouser_Type',\n",
       " 'Backhoe_Mounting',\n",
       " 'Blade_Type',\n",
       " 'Travel_Controls',\n",
       " 'Differential_Type',\n",
       " 'Steering_Controls']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_types = [col for col, values in df_copy.items() if pd.api.types.is_object_dtype(values)]\n",
    "obj_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ba48789c-f545-42eb-b75c-9aa7b2a4383a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for col in [col for col, values in df_copy.items() if pd.api.types.is_object_dtype(values)]:\n",
    "#     df_copy[col] = df_copy[col].astype('category')\n",
    "\n",
    "for col in [col for col, values in df_copy.items() if pd.api.types.is_object_dtype(values)]:\n",
    "    df_copy[col] = df_copy[col].astype('category').cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ecda164b-111f-4497-a63a-69cce1a1710b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                        int64\n",
       "SalePrice                    float64\n",
       "MachineID                      int64\n",
       "ModelID                        int64\n",
       "datasource                     int64\n",
       "auctioneerID                 float64\n",
       "YearMade                       int64\n",
       "MachineHoursCurrentMeter     float64\n",
       "UsageBand                   category\n",
       "fiModelDesc                 category\n",
       "fiBaseModel                 category\n",
       "fiSecondaryDesc             category\n",
       "fiModelSeries               category\n",
       "fiModelDescriptor           category\n",
       "ProductSize                 category\n",
       "fiProductClassDesc          category\n",
       "state                       category\n",
       "ProductGroup                category\n",
       "ProductGroupDesc            category\n",
       "Drive_System                category\n",
       "Enclosure                   category\n",
       "Forks                       category\n",
       "Pad_Type                    category\n",
       "Ride_Control                category\n",
       "Stick                       category\n",
       "Transmission                category\n",
       "Turbocharged                category\n",
       "Blade_Extension             category\n",
       "Blade_Width                 category\n",
       "Enclosure_Type              category\n",
       "Engine_Horsepower           category\n",
       "Hydraulics                  category\n",
       "Pushblock                   category\n",
       "Ripper                      category\n",
       "Scarifier                   category\n",
       "Tip_Control                 category\n",
       "Tire_Size                   category\n",
       "Coupler                     category\n",
       "Coupler_System              category\n",
       "Grouser_Tracks              category\n",
       "Hydraulics_Flow             category\n",
       "Track_Type                  category\n",
       "Undercarriage_Pad_Width     category\n",
       "Stick_Length                category\n",
       "Thumb                       category\n",
       "Pattern_Changer             category\n",
       "Grouser_Type                category\n",
       "Backhoe_Mounting            category\n",
       "Blade_Type                  category\n",
       "Travel_Controls             category\n",
       "Differential_Type           category\n",
       "Steering_Controls           category\n",
       "saleYear                       int32\n",
       "saleMonth                      int32\n",
       "saleDay                        int32\n",
       "saleDayOfWeek                  int32\n",
       "saleDayOfYear                  int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e74db344-70ea-429d-914d-67742f61fef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## This would equally work\n",
    "# for col in list(df_copy.select_dtypes(include = ['object'])):\n",
    "#     df_copy[col] = df_copy[col].astype('category').cat.as_ordered()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cc367bcb-a374-4062-8ce7-85ad7262b332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615         Texas\n",
       "274835       Florida\n",
       "141296       Florida\n",
       "212552       Florida\n",
       "62755        Florida\n",
       "             ...    \n",
       "410879    California\n",
       "412476    California\n",
       "411927    California\n",
       "407124    California\n",
       "409203    California\n",
       "Name: state, Length: 412698, dtype: category\n",
       "Categories (53, object): ['Alabama' < 'Alaska' < 'Arizona' < 'Arkansas' ... 'Washington DC' < 'West Virginia' < 'Wisconsin' < 'Wyoming']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3c3ad4-531c-4404-b6e7-6e8a0a27ee2f",
   "metadata": {},
   "source": [
    "### The State Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "48222182-7880-4272-a652-b94a19e1b31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado',\n",
       "       'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho',\n",
       "       'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana',\n",
       "       'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',\n",
       "       'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada',\n",
       "       'New Hampshire', 'New Jersey', 'New Mexico', 'New York',\n",
       "       'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon',\n",
       "       'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina',\n",
       "       'South Dakota', 'Tennessee', 'Texas', 'Unspecified', 'Utah', 'Vermont',\n",
       "       'Virginia', 'Washington', 'Washington DC', 'West Virginia', 'Wisconsin',\n",
       "       'Wyoming'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.state.cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5242e861-d5e3-4439-94d6-5b7c6c7cf315",
   "metadata": {},
   "source": [
    "### State Categories as numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1b8be672-64b3-4a48-b18f-af354725e6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205615    43\n",
       "274835     8\n",
       "141296     8\n",
       "212552     8\n",
       "62755      8\n",
       "          ..\n",
       "410879     4\n",
       "412476     4\n",
       "411927     4\n",
       "407124     4\n",
       "409203     4\n",
       "Length: 412698, dtype: int8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.state.cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb19a6e6-19a8-4f96-919e-6d1aed6cb953",
   "metadata": {},
   "source": [
    "### Missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "55e1c3ef-7bcf-482b-83d5-265bb50736de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of missing values per column:\n",
      "auctioneerID                0.048791\n",
      "MachineHoursCurrentMeter    0.642586\n",
      "UsageBand                   0.821492\n",
      "fiSecondaryDesc             0.340993\n",
      "fiModelSeries               0.857845\n",
      "fiModelDescriptor           0.818715\n",
      "ProductSize                 0.524851\n",
      "Drive_System                0.740520\n",
      "Enclosure                   0.000809\n",
      "Forks                       0.520921\n",
      "Pad_Type                    0.803498\n",
      "Ride_Control                0.629928\n",
      "Stick                       0.803498\n",
      "Transmission                0.544444\n",
      "Turbocharged                0.803498\n",
      "Blade_Extension             0.937041\n",
      "Blade_Width                 0.937041\n",
      "Enclosure_Type              0.937041\n",
      "Engine_Horsepower           0.937041\n",
      "Hydraulics                  0.200062\n",
      "Pushblock                   0.937041\n",
      "Ripper                      0.740864\n",
      "Scarifier                   0.937014\n",
      "Tip_Control                 0.937041\n",
      "Tire_Size                   0.763415\n",
      "Coupler                     0.465277\n",
      "Coupler_System              0.891024\n",
      "Grouser_Tracks              0.891264\n",
      "Hydraulics_Flow             0.891264\n",
      "Track_Type                  0.752378\n",
      "Undercarriage_Pad_Width     0.750626\n",
      "Stick_Length                0.752213\n",
      "Thumb                       0.752041\n",
      "Pattern_Changer             0.752213\n",
      "Grouser_Type                0.752378\n",
      "Backhoe_Mounting            0.804428\n",
      "Blade_Type                  0.801610\n",
      "Travel_Controls             0.801606\n",
      "Differential_Type           0.826595\n",
      "Steering_Controls           0.826697\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "z = df_copy.isnull().sum() / len(df_copy)\n",
    "print(f'Proportion of missing values per column:\\n{z[z > 0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457d9480-8473-462e-b5e0-bb6712f3f368",
   "metadata": {},
   "source": [
    "### Save and load the new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c8791f66-3e67-42ef-b727-478fbc7e17a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy.to_csv('../resources/data/bulldozers/train_copy.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8b0d7cae-10cc-461c-a290-330b60fc576e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.read_csv('../resources/data/bulldozers/train_copy.csv', low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c60274c6-3af2-4fdf-83cc-b2e67b15ac95",
   "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>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "      <th>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <th>saleDayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TD20</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A66</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D7G</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Straight</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A62</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D3B</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>Lever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770     9500.0    1126363     8434         132          18.0      1974   \n",
       "1  1821514    14000.0    1194089    10150         132          99.0      1980   \n",
       "2  1505138    50000.0    1473654     4139         132          99.0      1978   \n",
       "3  1671174    16000.0    1327630     8591         132          99.0      1980   \n",
       "4  1329056    22000.0    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand fiModelDesc  ...     Backhoe_Mounting  \\\n",
       "0                       NaN       NaN        TD20  ...  None or Unspecified   \n",
       "1                       NaN       NaN         A66  ...                  NaN   \n",
       "2                       NaN       NaN         D7G  ...  None or Unspecified   \n",
       "3                       NaN       NaN         A62  ...                  NaN   \n",
       "4                       NaN       NaN         D3B  ...  None or Unspecified   \n",
       "\n",
       "  Blade_Type      Travel_Controls Differential_Type Steering_Controls  \\\n",
       "0   Straight  None or Unspecified               NaN               NaN   \n",
       "1        NaN                  NaN          Standard      Conventional   \n",
       "2   Straight  None or Unspecified               NaN               NaN   \n",
       "3        NaN                  NaN          Standard      Conventional   \n",
       "4        PAT                Lever               NaN               NaN   \n",
       "\n",
       "  saleYear saleMonth saleDay saleDayOfWeek saleDayOfYear  \n",
       "0     1989         1      17             1            17  \n",
       "1     1989         1      31             1            31  \n",
       "2     1989         1      31             1            31  \n",
       "3     1989         1      31             1            31  \n",
       "4     1989         1      31             1            31  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07402275-3aaa-46fb-a616-2b079756214c",
   "metadata": {},
   "source": [
    "### Filling the numeric missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c3978272-d632-4931-8d27-4ce205c15bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SalesID',\n",
       " 'SalePrice',\n",
       " 'MachineID',\n",
       " 'ModelID',\n",
       " 'datasource',\n",
       " 'auctioneerID',\n",
       " 'YearMade',\n",
       " 'MachineHoursCurrentMeter',\n",
       " 'saleYear',\n",
       " 'saleMonth',\n",
       " 'saleDay',\n",
       " 'saleDayOfWeek',\n",
       " 'saleDayOfYear']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_new.select_dtypes(exclude = ['object']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "813db166-2e91-4ea2-8045-32453c41a6cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                          0\n",
       "SalePrice                        0\n",
       "MachineID                        0\n",
       "ModelID                          0\n",
       "datasource                       0\n",
       "auctioneerID                 20136\n",
       "YearMade                         0\n",
       "MachineHoursCurrentMeter    265194\n",
       "saleYear                         0\n",
       "saleMonth                        0\n",
       "saleDay                          0\n",
       "saleDayOfWeek                    0\n",
       "saleDayOfYear                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.select_dtypes(exclude = ['object']).isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0eea9a7a-548a-4577-9364-6631a4b8ccfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auctioneerID                 20136\n",
      "MachineHoursCurrentMeter    265194\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "z = df_new.select_dtypes(include = ['number']).isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5156ec-546b-4924-b9a1-923af7d74e08",
   "metadata": {},
   "source": [
    "### Add a binary column which tells if the numeric data is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "df3dd9bf-19d8-4d24-9ce4-3b9eecb42a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = df_new.select_dtypes(exclude = ['object']).isnull().sum()\n",
    "for col in z[z > 0].index:\n",
    "    df_new[col + '_is_missing'] = df_new[col].isnull().astype(int)\n",
    "    df_new[col] = df_new[col].fillna(df_new[col].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "38f2bc85-8dba-423d-8a2d-9ef29a5e53b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>saleYear</th>\n",
       "      <th>saleMonth</th>\n",
       "      <th>saleDay</th>\n",
       "      <th>saleDayOfWeek</th>\n",
       "      <th>saleDayOfYear</th>\n",
       "      <th>auctioneerID_is_missing</th>\n",
       "      <th>MachineHoursCurrentMeter_is_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1646770</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>1126363</td>\n",
       "      <td>8434</td>\n",
       "      <td>132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1821514</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>1194089</td>\n",
       "      <td>10150</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1505138</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1473654</td>\n",
       "      <td>4139</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1671174</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1327630</td>\n",
       "      <td>8591</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1329056</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>1336053</td>\n",
       "      <td>4089</td>\n",
       "      <td>132</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1646770     9500.0    1126363     8434         132          18.0      1974   \n",
       "1  1821514    14000.0    1194089    10150         132          99.0      1980   \n",
       "2  1505138    50000.0    1473654     4139         132          99.0      1978   \n",
       "3  1671174    16000.0    1327630     8591         132          99.0      1980   \n",
       "4  1329056    22000.0    1336053     4089         132          99.0      1984   \n",
       "\n",
       "   MachineHoursCurrentMeter  saleYear  saleMonth  saleDay  saleDayOfWeek  \\\n",
       "0                       0.0      1989          1       17              1   \n",
       "1                       0.0      1989          1       31              1   \n",
       "2                       0.0      1989          1       31              1   \n",
       "3                       0.0      1989          1       31              1   \n",
       "4                       0.0      1989          1       31              1   \n",
       "\n",
       "   saleDayOfYear  auctioneerID_is_missing  MachineHoursCurrentMeter_is_missing  \n",
       "0             17                        0                                    1  \n",
       "1             31                        0                                    1  \n",
       "2             31                        0                                    1  \n",
       "3             31                        0                                    1  \n",
       "4             31                        0                                    1  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.select_dtypes(exclude = ['object']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8deda62d-829e-4fae-a279-83d4803c468e",
   "metadata": {},
   "source": [
    "#### Check again for missing values in numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0c50800a-a543-40ee-a1c1-d76d3accc7ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "z = df_new.select_dtypes(exclude = ['object']).isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5a17995e-8322-4236-bf78-d90ad1a919da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "auctioneerID_is_missing\n",
       "0    392562\n",
       "1     20136\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.auctioneerID_is_missing.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90aca28-d98f-43da-b4d5-b4e08a006c60",
   "metadata": {},
   "source": [
    "### Imputing Missing Categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bf111f04-cffc-4632-9d07-f1ac28f41411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UsageBand                  339028\n",
      "fiSecondaryDesc            140727\n",
      "fiModelSeries              354031\n",
      "fiModelDescriptor          337882\n",
      "ProductSize                216605\n",
      "Drive_System               305611\n",
      "Enclosure                     334\n",
      "Forks                      214983\n",
      "Pad_Type                   331602\n",
      "Ride_Control               259970\n",
      "Stick                      331602\n",
      "Transmission               224691\n",
      "Turbocharged               331602\n",
      "Blade_Extension            386715\n",
      "Blade_Width                386715\n",
      "Enclosure_Type             386715\n",
      "Engine_Horsepower          386715\n",
      "Hydraulics                  82565\n",
      "Pushblock                  386715\n",
      "Ripper                     305753\n",
      "Scarifier                  386704\n",
      "Tip_Control                386715\n",
      "Tire_Size                  315060\n",
      "Coupler                    192019\n",
      "Coupler_System             367724\n",
      "Grouser_Tracks             367823\n",
      "Hydraulics_Flow            367823\n",
      "Track_Type                 310505\n",
      "Undercarriage_Pad_Width    309782\n",
      "Stick_Length               310437\n",
      "Thumb                      310366\n",
      "Pattern_Changer            310437\n",
      "Grouser_Type               310505\n",
      "Backhoe_Mounting           331986\n",
      "Blade_Type                 330823\n",
      "Travel_Controls            330821\n",
      "Differential_Type          341134\n",
      "Steering_Controls          341176\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "z = df_new.select_dtypes(exclude = ['number']).isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d51963f2-8e63-4aa9-be6d-16a8bb7a2a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalesID                                  int64\n",
       "SalePrice                              float64\n",
       "MachineID                                int64\n",
       "ModelID                                  int64\n",
       "datasource                               int64\n",
       "auctioneerID                           float64\n",
       "YearMade                                 int64\n",
       "MachineHoursCurrentMeter               float64\n",
       "UsageBand                               object\n",
       "fiModelDesc                             object\n",
       "fiBaseModel                             object\n",
       "fiSecondaryDesc                         object\n",
       "fiModelSeries                           object\n",
       "fiModelDescriptor                       object\n",
       "ProductSize                             object\n",
       "fiProductClassDesc                      object\n",
       "state                                   object\n",
       "ProductGroup                            object\n",
       "ProductGroupDesc                        object\n",
       "Drive_System                            object\n",
       "Enclosure                               object\n",
       "Forks                                   object\n",
       "Pad_Type                                object\n",
       "Ride_Control                            object\n",
       "Stick                                   object\n",
       "Transmission                            object\n",
       "Turbocharged                            object\n",
       "Blade_Extension                         object\n",
       "Blade_Width                             object\n",
       "Enclosure_Type                          object\n",
       "Engine_Horsepower                       object\n",
       "Hydraulics                              object\n",
       "Pushblock                               object\n",
       "Ripper                                  object\n",
       "Scarifier                               object\n",
       "Tip_Control                             object\n",
       "Tire_Size                               object\n",
       "Coupler                                 object\n",
       "Coupler_System                          object\n",
       "Grouser_Tracks                          object\n",
       "Hydraulics_Flow                         object\n",
       "Track_Type                              object\n",
       "Undercarriage_Pad_Width                 object\n",
       "Stick_Length                            object\n",
       "Thumb                                   object\n",
       "Pattern_Changer                         object\n",
       "Grouser_Type                            object\n",
       "Backhoe_Mounting                        object\n",
       "Blade_Type                              object\n",
       "Travel_Controls                         object\n",
       "Differential_Type                       object\n",
       "Steering_Controls                       object\n",
       "saleYear                                 int64\n",
       "saleMonth                                int64\n",
       "saleDay                                  int64\n",
       "saleDayOfWeek                            int64\n",
       "saleDayOfYear                            int64\n",
       "auctioneerID_is_missing                  int32\n",
       "MachineHoursCurrentMeter_is_missing      int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962a3dc1-4fa2-4154-ad0c-f26e9a2e4fa3",
   "metadata": {},
   "source": [
    "### Converting Categorical columns into Numeric using `pd.Categorical`\n",
    "`pd.Categorical(values).codes` converts missing values or `NaN` into `-1`. Adding 1 to the result will convert missing values to `0`.\n",
    "We are adding columns to **keep information about missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a52f02dd-f79d-480a-8a07-ffb10d5db000",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in list(df_new.select_dtypes(exclude = ['number'])):\n",
    "    df_new[col + '_is_missing'] = df_new[col].isnull().astype(int)\n",
    "    df_new[col] = pd.Categorical(df_new[col]).codes + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ab900c-3580-46bd-91fa-3379bfde3d8f",
   "metadata": {},
   "source": [
    "### Check the whole DataFrame for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f0f72f96-112d-4a20-a265-3179076913a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "z = df_new.isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5440a0ee-5055-4a2e-a1f9-0bcd1ed78c6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width_is_missing</th>\n",
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       "      <th>Differential_Type_is_missing</th>\n",
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       "      <td>0</td>\n",
       "      <td>2101</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412694</th>\n",
       "      <td>6324811</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>1919104</td>\n",
       "      <td>19330</td>\n",
       "      <td>149</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412695</th>\n",
       "      <td>6313029</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>1918416</td>\n",
       "      <td>17244</td>\n",
       "      <td>149</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>627</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412696</th>\n",
       "      <td>6266251</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>509560</td>\n",
       "      <td>3357</td>\n",
       "      <td>149</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412697</th>\n",
       "      <td>6283635</td>\n",
       "      <td>34000.0</td>\n",
       "      <td>1869284</td>\n",
       "      <td>4701</td>\n",
       "      <td>149</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>989</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SalesID  SalePrice  MachineID  ModelID  datasource  auctioneerID  \\\n",
       "412688  6283634    35000.0    1930870     4701         149          99.0   \n",
       "412689  6282337     7500.0    1928688    22072         149          99.0   \n",
       "412690  6311321    13000.0    1823846    17311         149          99.0   \n",
       "412691  6306476    53000.0    1278794     9114         149          99.0   \n",
       "412692  6281219    39000.0    1792049    13976         149          99.0   \n",
       "412693  6302984    16000.0    1915521     5266         149          99.0   \n",
       "412694  6324811     6000.0    1919104    19330         149          99.0   \n",
       "412695  6313029    16000.0    1918416    17244         149          99.0   \n",
       "412696  6266251    55000.0     509560     3357         149          99.0   \n",
       "412697  6283635    34000.0    1869284     4701         149          99.0   \n",
       "\n",
       "        YearMade  MachineHoursCurrentMeter  UsageBand  fiModelDesc  ...  \\\n",
       "412688      1998                       0.0          0          989  ...   \n",
       "412689      2005                       0.0          0          536  ...   \n",
       "412690      2005                       0.0          0         4261  ...   \n",
       "412691      2004                       0.0          0         4818  ...   \n",
       "412692      2000                       0.0          0         1319  ...   \n",
       "412693      2001                       0.0          0         2101  ...   \n",
       "412694      2004                       0.0          0          240  ...   \n",
       "412695      2004                       0.0          0          627  ...   \n",
       "412696      1993                       0.0          0           83  ...   \n",
       "412697      1000                       0.0          0          989  ...   \n",
       "\n",
       "        Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "412688                                   1                        1   \n",
       "412689                                   1                        1   \n",
       "412690                                   1                        1   \n",
       "412691                                   1                        1   \n",
       "412692                                   1                        1   \n",
       "412693                                   1                        1   \n",
       "412694                                   1                        1   \n",
       "412695                                   1                        1   \n",
       "412696                                   1                        1   \n",
       "412697                                   1                        1   \n",
       "\n",
       "        Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "412688                 1                           1                        1   \n",
       "412689                 1                           1                        1   \n",
       "412690                 1                           1                        1   \n",
       "412691                 1                           1                        1   \n",
       "412692                 1                           1                        1   \n",
       "412693                 1                           1                        1   \n",
       "412694                 1                           1                        1   \n",
       "412695                 1                           1                        1   \n",
       "412696                 1                           1                        1   \n",
       "412697                 1                           1                        1   \n",
       "\n",
       "        Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "412688                            1                      1   \n",
       "412689                            1                      1   \n",
       "412690                            1                      1   \n",
       "412691                            1                      1   \n",
       "412692                            1                      1   \n",
       "412693                            0                      0   \n",
       "412694                            1                      1   \n",
       "412695                            1                      1   \n",
       "412696                            1                      1   \n",
       "412697                            1                      1   \n",
       "\n",
       "        Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "412688                           1                             0   \n",
       "412689                           1                             1   \n",
       "412690                           1                             1   \n",
       "412691                           1                             0   \n",
       "412692                           1                             1   \n",
       "412693                           0                             1   \n",
       "412694                           1                             1   \n",
       "412695                           1                             1   \n",
       "412696                           1                             1   \n",
       "412697                           1                             0   \n",
       "\n",
       "        Steering_Controls_is_missing  \n",
       "412688                             0  \n",
       "412689                             1  \n",
       "412690                             1  \n",
       "412691                             0  \n",
       "412692                             1  \n",
       "412693                             1  \n",
       "412694                             1  \n",
       "412695                             1  \n",
       "412696                             1  \n",
       "412697                             0  \n",
       "\n",
       "[10 rows x 103 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de2c206-549b-46a8-a641-30863008b8b6",
   "metadata": {},
   "source": [
    "### Fitting a Machine Learning Model\n",
    "Now that our dataset is all numeric and without any missing values, we can use it to train a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "82a4754f-5049-4922-b66f-ab1042beb1a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 38min 38s\n",
      "Wall time: 5min 15s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model = RandomForestRegressor(n_jobs = -1, random_state = 42)\n",
    "model.fit(df_new.drop('SalePrice', axis = 1), df_new.SalePrice)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d0fc85-b8ac-493d-9253-a0a2fa2ccc19",
   "metadata": {},
   "source": [
    "### Score the model using same data it was trained on\n",
    "The default metric for a regression model is the **Coefficient of Determination (R^2)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "71427732-85ba-497f-aea0-6409cf2968a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 44 s\n",
      "Wall time: 7.18 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9875468079970562"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.score(df_new.drop('SalePrice', axis = 1), df_new.SalePrice)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693e5a8d-e645-4c8d-b1ad-2f662726b68e",
   "metadata": {},
   "source": [
    "### Train - Validation Split\n",
    "The **validation set** includes all data in 2012. According to [Dataset Description](https://www.kaggle.com/c/bluebook-for-bulldozers/data?select=Data+Dictionary.xlsx):\n",
    "\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "017b60c2-2e2e-4cf6-b8d8-10c6c6bb7034",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_val = df_new[df_new.saleYear == 2012]\n",
    "df_train = df_new[df_new.saleYear != 2012]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c6884327-1dfa-4cbe-9897-2c9c06fc540f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11573, 401125)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_val), len(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0d3c62-9051-41d4-9277-e1fe96f095b8",
   "metadata": {},
   "source": [
    "### Create Features X and Target y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "97986cca-e0cd-4598-b4b2-210b6b91d924",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = df_train.drop('SalePrice', axis = 1), df_train.SalePrice\n",
    "X_val, y_val = df_val.drop('SalePrice', axis = 1), df_val.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8dadd633-401d-4b25-ac96-0fedc12285dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape train features: (401125, 102)\n",
      "Shape train target: (401125,)\n",
      "Shape validation features: (11573, 102)\n",
      "Shape validation target: (11573,)\n"
     ]
    }
   ],
   "source": [
    "print(f'Shape train features: {X_train.shape}')\n",
    "print(f'Shape train target: {y_train.shape}')\n",
    "print(f'Shape validation features: {X_val.shape}')\n",
    "print(f'Shape validation target: {y_val.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f523ddee-ff2f-4abc-926b-1d40c0bc036b",
   "metadata": {},
   "source": [
    "### Root Mean Squared Log Error (RMSLE)\n",
    "**RMSLE** is the evaluation metric for this project as stated in the Dataset Description on Kaggle.\n",
    "* **RMSLE** is non-negative floating point value (the best value is 0.0),  \n",
    "* or an array of floating point values, one for each individual target.\n",
    "\n",
    "#### Helper Function to evaluate RMSLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cecf8b64-b07e-4a5a-881c-970b87bd22fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import root_mean_squared_log_error  # requires sklearn v1.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b7be248c-6dc5-48fe-854b-814c4bf500c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_log_error, mean_absolute_error, r2_score\n",
    "\n",
    "def rmsle(y_test, y_preds):\n",
    "    '''\n",
    "    Computes Root Mean Squared Log Error of a regression model\n",
    "    y_test: Ground truth\n",
    "    y_preds: model predictions\n",
    "    return: RMSLE\n",
    "    '''\n",
    "    return np.sqrt(mean_squared_log_error(y_test, y_preds))\n",
    "\n",
    "def regression_scores_from_model(model, X_train, y_train, X_val, y_val):\n",
    "    '''\n",
    "    Computes evaluation matrics for a regression model\n",
    "    model: The trained model\n",
    "    X_train: Training features\n",
    "    y_train: Training targets\n",
    "    X_val: Validation features\n",
    "    y_val: Validation targets\n",
    "    return: dictionary of scores\n",
    "    '''\n",
    "    y_preds_train = model.predict(X_train)\n",
    "    y_preds_val = model.predict(X_val)\n",
    "    scores = {'Training MAE': mean_absolute_error(y_train, y_preds_train),\n",
    "              'Validation MAE': mean_absolute_error(y_val, y_preds_val),\n",
    "              'Training RMSLE': rmsle(y_train, y_preds_train),\n",
    "              'Validation RMSLE': rmsle(y_val, y_preds_val),\n",
    "              'Training R^2': r2_score(y_train, y_preds_train),\n",
    "              'Validation R^2': r2_score(y_val, y_preds_val),\n",
    "             }\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a13259f-e721-4824-8b40-63cf0cf728a5",
   "metadata": {},
   "source": [
    "### Testing our model on a subset of our data to reduce the processing time\n",
    "Since we are using `RandomForestRegressor` it has a parameter called `max_samples` which we can adjust or set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "bf196e50-7cb8-466d-8401-32efabad11ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 28s\n",
      "Wall time: 14.8 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_samples=10000, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model = RandomForestRegressor(n_jobs = -1, max_samples = 10_000, random_state = 42)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c7b79d-1ec3-4e02-a29d-40c2d5bcd49f",
   "metadata": {},
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e99c29c0-9927-4e5e-aac9-5086b828e674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 5561.2988092240585,\n",
       " 'Validation MAE': 7177.26365505919,\n",
       " 'Training RMSLE': 0.257745378256977,\n",
       " 'Validation RMSLE': 0.29362638671089003,\n",
       " 'Training R^2': 0.8606658995199189,\n",
       " 'Validation R^2': 0.8320374995090507}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = regression_scores_from_model(model, X_train, y_train, X_val, y_val)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8963a0d8-e854-4b6a-b776-68bc62a8cd94",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a5eaebf8-5cdb-4f98-9629-8bc65a8817c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "CPU times: total: 18min 57s\n",
      "Wall time: 19min 21s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=100,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;max_features&#x27;: [0.5, 1, &#x27;sqrt&#x27;,\n",
       "                                                         &#x27;log2&#x27;],\n",
       "                                        &#x27;max_samples&#x27;: [10000],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=100,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;max_features&#x27;: [0.5, 1, &#x27;sqrt&#x27;,\n",
       "                                                         &#x27;log2&#x27;],\n",
       "                                        &#x27;max_samples&#x27;: [10000],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_iter=100,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'max_features': [0.5, 1, 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'max_samples': [10000],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': np.arange(10, 100, 10),\n",
    "    'max_depth': [None, 3, 5, 10],\n",
    "    'min_samples_split': np.arange(2, 20, 2),\n",
    "    'min_samples_leaf': np.arange(1, 20, 2),\n",
    "    'max_features': [0.5, 1, 'sqrt', 'log2'],\n",
    "    'max_samples': [10000]\n",
    "}\n",
    "\n",
    "rs_model = RandomizedSearchCV(RandomForestRegressor(),\n",
    "                              param_distributions = param_grid,\n",
    "                              cv = 5,\n",
    "                              n_iter = 100,\n",
    "                              verbose = True\n",
    "                             )\n",
    "\n",
    "rs_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c124bcee-a1cd-4133-827f-683184031b1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 30,\n",
       " 'min_samples_split': 16,\n",
       " 'min_samples_leaf': 3,\n",
       " 'max_samples': 10000,\n",
       " 'max_features': 0.5,\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda95595-f8f4-4f14-af17-f2d1dc324b28",
   "metadata": {},
   "source": [
    "### Evaluate the randomized model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a72bad26-4e77-4f7e-86d6-491fd2e1e180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 6127.8168475875555,\n",
       " 'Validation MAE': 7470.280734247674,\n",
       " 'Training RMSLE': 0.27769391978432667,\n",
       " 'Validation RMSLE': 0.30351561966479695,\n",
       " 'Training R^2': 0.8329303814145863,\n",
       " 'Validation R^2': 0.8197024790490098}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = regression_scores_from_model(rs_model, X_train, y_train, X_val, y_val)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22578f0e-b5c8-4713-8839-13e40d2a284d",
   "metadata": {},
   "source": [
    "### Training a model using best hyperparameters (from instructor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "cc9b90cd-2331-431d-ac71-47e05f2edea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=40,\n",
       "                      n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=40,\n",
       "                      n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=0.5, min_samples_split=14, n_estimators=40,\n",
       "                      n_jobs=-1)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_reg_model = RandomForestRegressor(n_estimators = 40,\n",
    "                                     min_samples_leaf = 1,\n",
    "                                     min_samples_split = 14,\n",
    "                                     max_features = 0.5,\n",
    "                                     n_jobs = -1\n",
    "                                    )\n",
    "\n",
    "rf_reg_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ef87c13a-26e8-4e63-90fc-36c37355d823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Training MAE': 2946.833670641681,\n",
       " 'Validation MAE': 5961.094179172086,\n",
       " 'Training RMSLE': 0.14425004297528,\n",
       " 'Validation RMSLE': 0.24582834474656592,\n",
       " 'Training R^2': 0.9590974568183458,\n",
       " 'Validation R^2': 0.8816127282949133}"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = regression_scores_from_model(rf_reg_model, X_train, y_train, X_val, y_val)\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fd3d14-8976-4651-9c53-b0e913463a32",
   "metadata": {},
   "source": [
    "### Testing the model (`rf_reg_model`) using the test dataset provided\n",
    "* **Test.csv** is the test set, which won't be released until the last week of the competition. It contains data from May 1, 2012 - November 2012. Your score on the test set determines your final rank for the competition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "205932cf-11be-49c5-8e83-ef500919032a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12457, 52)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>saledate</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width</th>\n",
       "      <th>Stick_Length</th>\n",
       "      <th>Thumb</th>\n",
       "      <th>Pattern_Changer</th>\n",
       "      <th>Grouser_Type</th>\n",
       "      <th>Backhoe_Mounting</th>\n",
       "      <th>Blade_Type</th>\n",
       "      <th>Travel_Controls</th>\n",
       "      <th>Differential_Type</th>\n",
       "      <th>Steering_Controls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>1006309</td>\n",
       "      <td>3168</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-03</td>\n",
       "      <td>580G</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>936</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>EC210BLC</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>9' 6\"</td>\n",
       "      <td>Manual</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>High</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>330CL</td>\n",
       "      <td>...</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Triple</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>650K</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>PAT</td>\n",
       "      <td>None or Unspecified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter UsageBand   saledate fiModelDesc  ...  \\\n",
       "0                    3688.0       Low 2012-05-03        580G  ...   \n",
       "1                   28555.0      High 2012-05-10         936  ...   \n",
       "2                    6038.0    Medium 2012-05-10    EC210BLC  ...   \n",
       "3                    8940.0      High 2012-05-10       330CL  ...   \n",
       "4                    2286.0       Low 2012-05-10        650K  ...   \n",
       "\n",
       "  Undercarriage_Pad_Width         Stick_Length   Thumb      Pattern_Changer  \\\n",
       "0                     NaN                  NaN     NaN                  NaN   \n",
       "1                     NaN                  NaN     NaN                  NaN   \n",
       "2     None or Unspecified                9' 6\"  Manual  None or Unspecified   \n",
       "3     None or Unspecified  None or Unspecified  Manual                  Yes   \n",
       "4                     NaN                  NaN     NaN                  NaN   \n",
       "\n",
       "  Grouser_Type     Backhoe_Mounting Blade_Type      Travel_Controls  \\\n",
       "0          NaN                  NaN        NaN                  NaN   \n",
       "1          NaN                  NaN        NaN                  NaN   \n",
       "2       Double                  NaN        NaN                  NaN   \n",
       "3       Triple                  NaN        NaN                  NaN   \n",
       "4          NaN  None or Unspecified        PAT  None or Unspecified   \n",
       "\n",
       "  Differential_Type Steering_Controls  \n",
       "0               NaN               NaN  \n",
       "1          Standard      Conventional  \n",
       "2               NaN               NaN  \n",
       "3               NaN               NaN  \n",
       "4               NaN               NaN  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('../resources/data/bulldozers/Test.csv', parse_dates = ['saledate'], low_memory = False)\n",
    "print(df_test.shape)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f5a74591-aab2-4640-9bfe-44b27477c857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MachineHoursCurrentMeter    10328\n",
      "UsageBand                   10623\n",
      "fiSecondaryDesc              3975\n",
      "fiModelSeries               10451\n",
      "fiModelDescriptor            9433\n",
      "ProductSize                  6409\n",
      "Drive_System                 9698\n",
      "Enclosure                       2\n",
      "Forks                        6149\n",
      "Pad_Type                    10349\n",
      "Ride_Control                 8216\n",
      "Stick                       10349\n",
      "Transmission                 7639\n",
      "Turbocharged                10349\n",
      "Blade_Extension             11806\n",
      "Blade_Width                 11806\n",
      "Enclosure_Type              11806\n",
      "Engine_Horsepower           11806\n",
      "Hydraulics                   2142\n",
      "Pushblock                   11806\n",
      "Ripper                       9753\n",
      "Scarifier                   11806\n",
      "Tip_Control                 11806\n",
      "Tire_Size                    9679\n",
      "Coupler                      4856\n",
      "Coupler_System              10391\n",
      "Grouser_Tracks              10391\n",
      "Hydraulics_Flow             10391\n",
      "Track_Type                   9063\n",
      "Undercarriage_Pad_Width      9059\n",
      "Stick_Length                 9063\n",
      "Thumb                        9062\n",
      "Pattern_Changer              9063\n",
      "Grouser_Type                 9063\n",
      "Backhoe_Mounting            10406\n",
      "Blade_Type                  10399\n",
      "Travel_Controls             10399\n",
      "Differential_Type           10328\n",
      "Steering_Controls           10328\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "z = df_test.isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "072dd12a-c28a-4060-9a81-8ff126215501",
   "metadata": {},
   "source": [
    "### Preprocessing the test data (similar to the training dataset)\n",
    "#### Helper function for preprocessing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8c9ce3bd-d6a6-4f6b-bb4b-37adfd707e2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(test_df):\n",
    "    df = test_df.copy()\n",
    "    df['saleYear'] = df.saledate.dt.year\n",
    "    df['saleMonth'] = df.saledate.dt.month\n",
    "    df['saleDay'] = df.saledate.dt.day\n",
    "    df['saleDayOfWeek'] = df.saledate.dt.dayofweek\n",
    "    df['saleDayOfYear'] = df.saledate.dt.dayofyear\n",
    "    \n",
    "    df.drop('saledate', axis = 1, inplace = True)\n",
    "\n",
    "    # Handling numerical columns\n",
    "    z = df.select_dtypes(exclude = ['object']).isnull().sum()\n",
    "    for col in z[z > 0].index:\n",
    "        df[col + '_is_missing'] = df[col].isnull().astype(int)\n",
    "        df[col] = df[col].fillna(df[col].median())\n",
    "        \n",
    "    # Handling categorical columns\n",
    "    for col in list(df.select_dtypes(exclude = ['number'])):\n",
    "        df[col + '_is_missing'] = df[col].isnull().astype(int)\n",
    "        df[col] = pd.Categorical(df[col]).codes + 1\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b3bc40a-d00b-456b-98d9-0df7f24ba174",
   "metadata": {},
   "source": [
    "#### Process Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "17680ca4-6122-4e26-b553-f5ecdf3b16b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_new = preprocess_data(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "34e61f60-662a-419b-b118-cc973f9b10c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12457, 101)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2d8edd23-6538-4eb5-9511-dc4328d12dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>MachineID</th>\n",
       "      <th>ModelID</th>\n",
       "      <th>datasource</th>\n",
       "      <th>auctioneerID</th>\n",
       "      <th>YearMade</th>\n",
       "      <th>MachineHoursCurrentMeter</th>\n",
       "      <th>UsageBand</th>\n",
       "      <th>fiModelDesc</th>\n",
       "      <th>fiBaseModel</th>\n",
       "      <th>...</th>\n",
       "      <th>Undercarriage_Pad_Width_is_missing</th>\n",
       "      <th>Stick_Length_is_missing</th>\n",
       "      <th>Thumb_is_missing</th>\n",
       "      <th>Pattern_Changer_is_missing</th>\n",
       "      <th>Grouser_Type_is_missing</th>\n",
       "      <th>Backhoe_Mounting_is_missing</th>\n",
       "      <th>Blade_Type_is_missing</th>\n",
       "      <th>Travel_Controls_is_missing</th>\n",
       "      <th>Differential_Type_is_missing</th>\n",
       "      <th>Steering_Controls_is_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>1006309</td>\n",
       "      <td>3168</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1999</td>\n",
       "      <td>3688.0</td>\n",
       "      <td>2</td>\n",
       "      <td>499</td>\n",
       "      <td>180</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>1022817</td>\n",
       "      <td>7271</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>28555.0</td>\n",
       "      <td>1</td>\n",
       "      <td>831</td>\n",
       "      <td>292</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>1031560</td>\n",
       "      <td>22805</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2004</td>\n",
       "      <td>6038.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1177</td>\n",
       "      <td>404</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>56204</td>\n",
       "      <td>1269</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2006</td>\n",
       "      <td>8940.0</td>\n",
       "      <td>1</td>\n",
       "      <td>287</td>\n",
       "      <td>113</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>1053887</td>\n",
       "      <td>22312</td>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>2286.0</td>\n",
       "      <td>2</td>\n",
       "      <td>566</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID  MachineID  ModelID  datasource  auctioneerID  YearMade  \\\n",
       "0  1227829    1006309     3168         121             3      1999   \n",
       "1  1227844    1022817     7271         121             3      1000   \n",
       "2  1227847    1031560    22805         121             3      2004   \n",
       "3  1227848      56204     1269         121             3      2006   \n",
       "4  1227863    1053887    22312         121             3      2005   \n",
       "\n",
       "   MachineHoursCurrentMeter  UsageBand  fiModelDesc  fiBaseModel  ...  \\\n",
       "0                    3688.0          2          499          180  ...   \n",
       "1                   28555.0          1          831          292  ...   \n",
       "2                    6038.0          3         1177          404  ...   \n",
       "3                    8940.0          1          287          113  ...   \n",
       "4                    2286.0          2          566          196  ...   \n",
       "\n",
       "   Undercarriage_Pad_Width_is_missing  Stick_Length_is_missing  \\\n",
       "0                                   1                        1   \n",
       "1                                   1                        1   \n",
       "2                                   0                        0   \n",
       "3                                   0                        0   \n",
       "4                                   1                        1   \n",
       "\n",
       "   Thumb_is_missing  Pattern_Changer_is_missing  Grouser_Type_is_missing  \\\n",
       "0                 1                           1                        1   \n",
       "1                 1                           1                        1   \n",
       "2                 0                           0                        0   \n",
       "3                 0                           0                        0   \n",
       "4                 1                           1                        1   \n",
       "\n",
       "   Backhoe_Mounting_is_missing  Blade_Type_is_missing  \\\n",
       "0                            1                      1   \n",
       "1                            1                      1   \n",
       "2                            1                      1   \n",
       "3                            1                      1   \n",
       "4                            0                      0   \n",
       "\n",
       "   Travel_Controls_is_missing  Differential_Type_is_missing  \\\n",
       "0                           1                             1   \n",
       "1                           1                             0   \n",
       "2                           1                             1   \n",
       "3                           1                             1   \n",
       "4                           0                             1   \n",
       "\n",
       "   Steering_Controls_is_missing  \n",
       "0                             1  \n",
       "1                             0  \n",
       "2                             1  \n",
       "3                             1  \n",
       "4                             1  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "842dba58-dec5-42ba-992b-e0b64242c2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "z = df_test_new.isnull().sum()\n",
    "print(z[z > 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68eee66b-3d89-497f-bd8c-38d970874013",
   "metadata": {},
   "source": [
    "### Evaluating the model using preprocessed test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8d885aa3-7752-432c-a5bd-8deecb04ad24",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_preds = rf_reg_model.predict(df_test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4a9458-630d-4ecf-a0a1-eee86770bde7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set(list(X_train)) - set(list(df_test_new))      # find the missing column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a7ce0b-4989-432a-974e-d3b067443a1b",
   "metadata": {},
   "source": [
    "**The column `auctioneerID` has NO missing! As a result the preprocessed test dataset is missing `auctioneerID_is_missing` column. We can add this with all values `0`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "147638e3-8a3b-4366-b5e5-4b012fbc756b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_test_new).index('MachineHoursCurrentMeter_is_missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "616ae2dc-c7de-4c1d-b3f4-ea64c036ce2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12457, 102)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = list(df_test_new).index('MachineHoursCurrentMeter_is_missing')\n",
    "df_test_new.insert(idx, 'auctioneerID_is_missing', [0]*len(df_test_new))\n",
    "df_test_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "27e826a0-953c-47e2-878d-bd81568c3fef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 500 ms\n",
      "Wall time: 248 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "y_preds = rf_reg_model.predict(df_test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "cb498b0a-b861-4b96-8a7a-79964a67143b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15990.26821082, 16733.87498421, 47007.915008  , ...,\n",
       "       16473.8933564 , 18733.6421833 , 28903.67559084])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4b768ba9-30c3-4aa0-90d3-780cb3c21e32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12457"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d9c2e5c-ea7b-4d41-a0c8-ac630306fba2",
   "metadata": {},
   "source": [
    "### Formatting the results as indicated by Kaggle\n",
    "https://www.kaggle.com/c/bluebook-for-bulldozers/overview \n",
    "\n",
    "* Have a header: \"SalesID,SalePrice\"\n",
    "* Contain two columns\n",
    "    * **SalesID**: SalesID for the validation set in sorted order\n",
    "    * **SalePrice**: Your predicted price of the sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "252822c5-301b-48d3-8bbd-6ec73cb0445f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SalesID</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1227829</td>\n",
       "      <td>15990.268211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1227844</td>\n",
       "      <td>16733.874984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1227847</td>\n",
       "      <td>47007.915008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1227848</td>\n",
       "      <td>70404.188816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1227863</td>\n",
       "      <td>58220.485165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalesID     SalePrice\n",
       "0  1227829  15990.268211\n",
       "1  1227844  16733.874984\n",
       "2  1227847  47007.915008\n",
       "3  1227848  70404.188816\n",
       "4  1227863  58220.485165"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = pd.DataFrame()\n",
    "df_results['SalesID'] = df_test_new.SalesID\n",
    "df_results['SalePrice'] = y_preds\n",
    "df_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f547e0b8-e914-4ad5-a996-5b94d34fec19",
   "metadata": {},
   "source": [
    "### Save results as `.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "93ad1041-5335-4b56-acd6-73f0b8ae870b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results.to_csv('./model_preds/bulldozers_price_predictions.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c95b52-bedb-4262-a238-d6a6145fc464",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "cf915bd0-5685-437b-9916-62cd2556f53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance(model, X_train, n = 2):\n",
    "    '''\n",
    "    Computes and plots feature importance from model.\n",
    "    model: Trained model\n",
    "    X_train: Training features\n",
    "    n: top n features for plotting\n",
    "    return fig\n",
    "    '''\n",
    "    fig, ax = plt.subplots()\n",
    "    features = pd.DataFrame({'Importance': model.feature_importances_*100}, index = list(X_train))\n",
    "    features.sort_values('Importance', axis = 0).tail(n).plot(kind = 'barh', color = 'b', ax = ax)\n",
    "    ax.set_xlabel('Feature Importance (x100)')\n",
    "    ax.set_ylabel('Features')\n",
    "    ax.get_legend().remove()\n",
    "    plt.show()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "e48144c3-3988-472b-b4d6-768cdc22a671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_feature_importance(rf_reg_model, X_train, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "3b8c3adb-1fd2-4108-b245-de425f24da8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75f29c94-a131-45b3-be16-616bb52e1674",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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